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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030103SynopsisNeuroscienceCatHow the Brain Signals a Sound Source Synopsis3 2005 22 2 2005 22 2 2005 3 3 e103Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Location Coding by Opponent Neural Populations in the Auditory Cortex
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Navigating one's environment requires sensory filters to distinguish friend from foe, zero in on prey, and sense impending danger. For a barn owl, this boils down mostly to homing in on a field mouse scurrying in the night. For a human—no longer faced with the reputedly fearsome saber-toothed Megantereon—it might mean deciding whether to fear rapidly approaching footsteps from behind on a dark, desolate street.
How does the brain encode auditory space? The long-standing model, based on the work of Lloyd Jeffress, proposes that the brain creates a topographic map of sounds in space and that individual neurons are tuned to particular interaural time differences (difference in the time it takes for a sound to reach both ears). Another key aspect of this model is that the location of a sound source is encoded by the identity of responding neurons.
Discriminating sound locations from neural data
Evidence for local coding of auditory space has been shown in the brains of owls and in a subcortical region of small mammals, but no such map has been found in the higher centers of the mammalian auditory cortex. What's more, electrophysiological recordings in mammals indicate that most neurons show the highest response to sounds emanating from the far left or right and that few neurons show that kind of response to sounds approaching head-on—even though subjects are best at localizing sounds originating in front of them.
Faced with such contrary evidence, other investigators have suggested that sound localization may rely on a different kind of code—one based on the activity distributed over large populations of neurons. In a new study, Christopher Stecker, Ian Harrington, and John Middlebrooks find evidence to support such a population code. In their alternative model, groups of neurons that are broadly responsive to sounds from the left or right can still provide accurate information about sounds coming from a central location. Although such broadly tuned neurons, by definition, cannot individually encode locations with high precision, it is clear from the authors' model that the most accurate aural discrimination occurs where neuron activity changes abruptly, that is, at the midpoint between both ears—a transition zone between neurons tuned to sounds coming from the left and those tuned to sounds coming from the right. These patterns of neuronal activity were found in the three areas of the cat auditory cortex that the authors studied.
These findings suggest that the auditory cortex has two spatial channels (the neuron subpopulations) tuned to different sound emanations and that their differential responses effect localization. Neurons within each subpopulation are found on each side of the brain. That sound localization emerges from this opponent-channel mechanism, Stecker et al. argue, allows the brain to identify where a sound is coming from even if the sound's level increases, because it is not the absolute response of a neuron (which also changes with loudness) that matters, but the difference of activity across neurons.
How this opponent-channel code allows an animal to orient itself to sound sources is unclear. However auditory cues translate to physical response, the authors argue that the fundamental encoding of auditory space in the cortex does not follow the topographic map model. How neurons contribute to solving other sound-related tasks also remains to be seen.
| 15736983 | PMC1044837 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 22; 3(3):e103 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030103 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030104SynopsisBioinformatics/Computational BiologyDevelopmentSystems BiologyDrosophilaEngineering Gene Networks to Probe Embryonic Pattern Formation in Flies Synopsis3 2005 22 2 2005 22 2 2005 3 3 e104Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Engineering Gene Networks to Emulate Drosophila Embryonic Pattern Formation
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When it comes to making the perfect body, it's all about expressing the right genes in the right place at the right time. This process begins even before the sperm and egg combine to form a zygote, because maternal factors are laid down in the egg that help establish the key axes of the body. After fertilization, precisely coordinated interactions between proteins called morphogens and a network of gene regulators establish a fly's anterior–posterior axis and its pattern of segments in just three hours.
In a new study, Mark Isalan, Caroline Lemerle, and Luis Serrano simulated segmentation patterning by creating a synthetic embryo and engineering an artificial version of the gap gene network, the first patterning genes expressed in the zygote. This simple system, combined with computer simulations to test network parameters, identifies significant features of the complex embryo and could do the same for other complex biological systems.
An artificial network to study patterning in development
One of the first molecules to act is the Bicoid protein. This morphogen is present in a concentration gradient—highest at the future head end. Different gap genes (so-called because their mutations create gaps in the segmentation pattern) respond to different levels of Bicoid, and are therefore switched on in different parts of the embryo. Expressed gap genes in turn modulate each other's activity. In the fruitfly, all of this action takes place while the embryo is a syncytium—having many nuclei but no cell membranes to separate them.
Isalan et al. created a model of segmentation patterning by using a tiny plastic chamber containing various purified genes, proteins, metabolites, and cell extracts to mimic the gap gene network. Some of the genes were attached to magnetic microbeads, so that their location could be controlled by magnets anchored to the bottom of the chamber.
The authors investigated a number of open questions about pattern formation, including how a morphogen diffusing from a local source generates an expression pattern along a gradient and how transcriptional repression sets pattern boundaries. After testing the system to mimic a simple network of sequential gene transcription and repression, the authors increased the components and connectivity of the network, starting with systems that had no repression interactions and moving on to systems that had different levels of cross-repression. Patterns generated by networks involving repression were much different from those generated by networks lacking repression, fitting with observations that patterning boundaries in living flies require cross-repression.
But even the unrepressed system generated reproducible patterns, possibly caused by simple competition between the proteins. While such a situation likely bears little resemblance to that inside a fly egg, the authors suggest that any such competition effects would have to be tested in flies. In any case, this simplified approach can test hypotheses of how simple networks might evolve inside a cell. And since many aspects of Drosophila embryonic patterning remain obscure, these synthetic chambers will provide a powerful resource for testing different hypotheses.
| 15736984 | PMC1044838 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 22; 3(3):e104 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030104 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030108SynopsisEcologyEvolutionMicrobiologyZoologyAnimalsArchaeaTubeworm May Live Longer by Cycling Its Sulfur Downward Synopsis3 2005 22 2 2005 22 2 2005 3 3 e108Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Modeling the Mutualistic Interactions between Tubeworms and Microbial Consortia
Microfauna-Macrofauna Interaction in the Seafloor: Lessons from the Tubeworm
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Any organism may be limited by some essential nutrient in short supply—nitrogen for a plant on poor soil, for instance, or iron for phytoplankton in the open ocean. For the long-lived tubeworm Lamellibrachia luymesi, what it needs most and has least is sulfide. L. luymesi lives clustered around hydrocarbon-releasing ocean floor seeps in the Gulf of Mexico, and it—or rather, its menagerie of internal bacterial symbionts—uses the high-energy sulfide the way plants use sunlight, extracting energy and releasing the waste products, in this case sulfate.
With a lifespan of up to 250 years, L. luymesi is among the longest-lived of all animals, but how it obtains sufficient sulfide to keep going for this long has been a mystery. In this issue, Erik Cordes and colleagues propose a model in which, by releasing its waste sulfate not up into the ocean but down into the sediments, L. luymesi stimulates the growth of sulfide-producing microbes, and ensures its own long-term survival.
An aggregation of Lamellibrachia luymesi in the Gulf of Mexico (Photo: Ian MacDonald)
The sulfide L. luymesi needs is created by a consortium of bacteria and archaea that live in the sediments surrounding the vent. These chemoautotrophs use energy from hydrocarbons to reduce sulfate to sulfide, which L. luymesi absorbs through its unique “roots,” extensions of its body that it tunnels into the sediments. Measurements of sulfide and sulfate fluxes in the water near the vents are inconsistent with the observed tubeworm colony size and individual longevity, leading Cordes et al. to propose that L. luymesi also uses its roots to release sulfate back to the microbial consortia from which it draws its sulfide.
Without this return of sulfate, the model predicts an average lifespan of only 39 years in a colony of 1,000 individuals; with it, survival increases to over 250 years, matching the longevity of actual living tubeworms. The model, which was based largely on empirical data, is relatively unperturbed by changes in hydrocarbon seep rate, or in the growth and recruitment rates for the colony. The authors note that the proposed return of sulfate into deep sediments would, in theory, increase the local rate of carbonate rock formation, creating a barrier to fluid circulation into the sediments. Their model predicts this to occur after about 50 years, in line with observed reductions in tubeworm recruitment in colonies of this age. They propose that carbonate precipitation may be inhibited if roots can also release hydrogen ions, a possibility open to further testing. Their model also explains several biogeochemical anomalies observed near tubeworm colonies, including elevated levels of highly degraded hydrocarbons and higher than predicted rates of sulfur cycling.
To date, the proposed return of sulfate to the sediments through the roots is only a hypothesis—albeit one with much to support it—that still awaits direct confirmation. By providing a model in which this hypothetical interaction provides real benefits and explains real observations, the authors hope to stimulate further research into the biology of L. luymesi. For more on tubeworms, see “Microfauna–Macrofauna Interaction in the Seafloor: Lessons from the Tubeworm” (DOI: 10.1371/journal.pbio.0030102), also in this issue.
| 15736985 | PMC1044839 | CC BY | 2021-01-05 08:28:11 | no | PLoS Biol. 2005 Mar 22; 3(3):e108 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030108 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030109SynopsisNeuroscienceHomo (Human)Predicting the Future: Mirror Neurons Reflect the Intentions of Others Synopsis3 2005 22 2 2005 22 2 2005 3 3 e109Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Grasping the Intentions of Others with One's Own Mirror Neuron System
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One of the more intriguing recent discoveries in brain science is the existence of “mirror neurons,” a set of neurons in the premotor area of the brain that are activated not only when performing an action oneself, but also while observing someone else perform that action. It is believed mirror neurons increase an individual's ability to understand the behaviors of others, an important skill in social species such as humans. A critical aspect of understanding the behavior of another person is recognizing the intent of his actions—is he coming to praise me or to bury me? In this issue, Marco Iacoboni and colleagues use functional magnetic resonance imaging (fMRI) to show that the mirror neuron system tracks not only the actions, but also the intentions, of others.
The researchers presented subjects with one of three types of movie clips, “context,” “action,” and “intention.” The “context” clip came in two versions. In the first, a mug of tea, a teapot, a pitcher of cream, and a plate of cookies sit neatly on a nondescript surface. In the second, the mug is empty, the pitcher is on its side, and a napkin lies crumpled beside scattered cookie crumbs—the dregs of an apparently well-enjoyed snack. The “action” clip shows only an empty mug, being grasped either by the whole hand around the rim (called whole-hand prehension), or with the fingers on the handle (precision grip). The “intention” clip puts it all together, providing the context needed to understand the intent of the action. In the first context scene, the mug is grasped in the context of a well-laid spread, signaling intent to drink the tea. In the second, the mug is grasped against the backdrop of the rest of the tea-time dregs, signaling the intent to clean up. In both cases, the type of grasp used is alternated, to avoid implying intent merely based on grip type.
The essential interpretive technique in fMRI is to subtract the activation patterns from two different stimuli, thereby highlighting brain regions that are activated differentially in response to the difference in stimulus. In this study, “intention minus context” showed areas involved in recognizing both action and intention, while “intention minus action” showed areas for both context and intention. Comparing these two results, Iacoboni and colleagues found that an area in the right inferior frontal cortex, an area known to be involved in the mirror system, was activated only by scenes in which intention could be inferred. In the authors' words, “it appears that there are sets of neurons in human inferior frontal cortex that specifically code the ‘why’ of the action and respond differently to different intentions.”
They note that these neurons differ in function from previously defined mirror neurons in that they apparently code not current actions, but some aspect of future ones. In this interpretation, an action observed within a familiar context activates mirror neurons for “logically related” actions, those that most likely will follow the observed one. This suggests the mirror neuron system is intimately involved not only with understanding the behavior of others, but predicting it as well.
| 15736986 | PMC1044840 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 22; 3(3):e109 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030109 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030116SynopsisEcologyEvolutionGenetics/Genomics/Gene TherapyHomo (Human)An Evolutionary Road Less Traveled: From Farming to Hunting and Gathering Synopsis3 2005 22 2 2005 22 2 2005 3 3 e116Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Recent Origin and Cultural Reversion of a Hunter-Gatherer Group
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Invested with the arguably unique capacity for self-reflection, humans may well have asked the question, “Where did we come from?” ever since the dawn of self-awareness. From this universal question come origin stories as diverse as the cultures who tell them. In some cases, little is known about a population's evolutionary history aside from these stories—such is the case for the Mlabri people of Southeast Asia.
Until expanding agricultural development and modernization encroached on their forest homelands, the Mlabri lived mostly as nomadic hunter–gatherers in the forests of northeastern Thailand and western Laos. This lifestyle is unique among the other so-called hill tribes of Thailand—who all farm—raising the possibility that the Mlabri descended from the ancient Hoabinhian hunting–gathering culture of Southeast Asia and practice a way of life that predates agriculture.
The Mlabri of Thailand: Their history holds a lesson for anthropologists (Photo: Takafumi Ishida)
Scant historical information exists on Mlabri language, culture, and origin, but Mlabri traditions speak to a long history as hunter–gatherers. The oral traditions of a neighboring hill tribe, the Tin Prai, paint a slightly different picture: several hundred years ago, legend has it, Tin Prai villagers sent two banished children downriver on a raft; the children, who survived by foraging in the forest, became the first Mlabri. In a new study, Mark Stoneking and colleagues use the tools of molecular anthropology to investigate the agricultural versus hunting–gathering origin of the Mlabri and reveal a scenario remarkably similar to the traditional origin stories.
The notion that genetic analyses can shed light on this question, the authors explain, comes from a body of research indicating that hunting–gathering groups have a lower level of genetic diversity and a higher frequency of unique mitochondrial (mtDNA) sequence types than neighboring agricultural groups. In this study, Stoneking and colleagues compared the genetic diversity of the Mlabri with that of six other agriculture-based hill tribes by analyzing specific regions of each population's mtDNA, Y chromosomes, and autosomes (non-sex chromosomes).
mtDNA and Y chromosomes can help uncover clues to evolutionary origins because both are in effect haploid systems (i.e., there is only one copy of the Y chromosome and a lot of identical copies of mtDNA present in each cell), and so do not undergo recombination. This in turn means that observed genetic variations likely result from random mutation—which is assumed to occur at a predictable rate—allowing scientists to estimate the age of the genetic variation found in a population.
The mtDNA analysis revealed something remarkable: all the Mlabri mtDNA sequences were identical. Not only did all of the other hill tribes show “significantly higher” variation, but this lack of variation hasn't been found in any other human population. The Y-chromosome and autosome analyses revealed the same reduced diversity, indicating a “severe reduction in population size” for the Mlabri. This reduction likely happened 500 to 800 years ago, Stoneking and colleagues conclude, and at most 1,000 years ago. But how? Since genetic analyses can't distinguish between a population bottleneck and a founding event, the authors used simulations to calculate the amount of population reduction required to completely eliminate mtDNA diversity, arriving at “not more than two unrelated females” and “perhaps even only one.”
But were the first Mlabri farmers or hunter–gatherers? Unlike other hunting–gathering groups, the Mlabri share closely related mtDNA, autosomal, and Y-chromosome sequences with both the agriculture-practicing hill tribes and other agricultural groups in Southeast Asia. Linguistic studies suggest that the Mlabri language arose after speakers of a related language, probably Tin, split off and came into contact with another, as yet unknown language, an event that likely happened less than 1,000 years ago.
The genetic and linguistic evidence indicates that the Mlabri were “founded” between 500 to 1,000 years ago by a single maternal lineage and one to four paternal lineages from an agricultural culture. With too few hands to farm, this tiny group likely turned to hunting and gathering. Altogether, Stoneking and colleagues conclude, these findings caution against automatically assuming that contemporary hunter–gatherer groups “represent the pre-agricultural lifestyle of human populations, descended unchanged from the Stone Age.” Interestingly, the authors' scenario of Mlabri origins is not so different from the story told by the Tin.
| 15736987 | PMC1044841 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 22; 3(3):e116 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030116 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573702010.1371/journal.pbio.0030067Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaCell-by-Cell Dissection of Gene Expression and Chromosomal Interactions Reveals Consequences of Nuclear Reorganization Consequences of Nuclear ReorganizationHarmon Brian
1
Sedat John [email protected]
1
1University of California, San FranciscoCaliforniaUnited States of AmericaHawley R. Scott Academic EditorStowers Institute for Medical ResearchUnited States of America3 2005 1 3 2005 1 3 2005 3 3 e6718 5 2004 17 12 2004 Copyright: © 2005 Harmon and Sedat.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Location, Location, Location: How Position Affects Gene Expression in the Nucleus
The functional consequences of long-range nuclear reorganization were studied in a cell-by-cell analysis of gene expression and long-range chromosomal interactions in the Drosophila eye and eye imaginal disk. Position-effect variegation was used to stochastically perturb gene expression and probe nuclear reorganization. Variegating genes on rearrangements of Chromosomes X, 2, and 3 were probed for long-range interactions with heterochromatin. Studies were conducted only in tissues known to express the variegating genes. Nuclear structure was revealed by fluorescence in situ hybridization with probes to the variegating gene and heterochromatin. Gene expression was determined alternately by immunofluorescence against specific proteins and by eye pigment autofluorescence. This allowed cell-by-cell comparisons of nuclear architecture between cells in which the variegating gene was either expressed or silenced. Very strong correlations between heterochromatic association and silencing were found. Expressing cells showed a broad distribution of distances between variegating genes and their own centromeric heterochromatin, while silenced cells showed a very tight distribution centered around very short distances, consistent with interaction between the silenced genes and heterochromatin. Spatial and temporal analysis of interactions with heterochromatin indicated that variegating genes primarily associate with heterochromatin in cells that have exited the cell cycle. Differentiation was not a requirement for association, and no differences in association were observed between cell types. Thus, long-range interactions between distal chromosome regions and their own heterochromatin have functional consequences for the organism.
The authors have devised a way to compare the expression of a gene and its association with heterochromatin in a single cell - such association tightly correlates with gene silencing
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Introduction
From the broad level of the whole chromosome down to the individual gene, interphase chromosomes in every organism studied adhere to common organizational principles (reviewed in [1]). An aspect of chromosome structure important for organism function is long-range chromosomal interactions (LRCIs) between distant loci. LRCIs have been linked with gene silencing by insulators in Drosophila [2,3] and with Polycomb silencing of homeotic genes [4,5]. LRCIs between euchromatic loci and heterochromatin can silence genes (reviewed in [6,7]). LRCIs are not static, for example, the polar organization of Drosophila embryonic chromosomes changes as homologous loci pair and LRCIs within the nucleus form [8,9,10,11]. Mouse immune cells adopt unique contacts between silenced genes and heterochromatin during differentiation and cell fate specification [12,13,14]. These changes appear to be functional rather than merely structural, such that altering LRCIs appears to have profound biological consequences.
Live studies of green-fluorescent-protein-tagged chromosomal loci reveal how LRCIs can change. Individual loci exhibit Brownian motion constrained to a defined volume, as observed in yeast [15,16,17], mammalian cells [15,18], and Drosophila [17,19]. Constraints are under developmental and cell cycle control, as evidenced by the observation that individual loci in male Drosophila pre-meiotic spermatocyte nuclei are more tightly confined in late G2 than in early G2 [19]. Relaxing constraints to allow considerable motion permits new LRCIs to form, while constraining loci more tightly can stabilize them. Developmental control of locus confinement could reconfigure a basic polar chromosomal organization into relatively stable developmental and cell-fate-specific architectures.
Drosophila position-effect variegation (PEV) is an ideal system to study the functional consequences of altered LRCIs (reviewed in [20,21]). PEV occurs when chromosome rearrangements juxtapose euchromatic genes and heterochromatin, producing a variegated expression pattern such that the gene is silenced in some but not all cells. These rearrangements also cause the affected genes to form long-range interactions with heterochromatin in a subset of cells [9,22]. Genetic evidence suggests that PEV may utilize these long-range interactions to silence genes. PEV can skip over one gene to silence another [23] or silence a wild-type locus on a homologous chromosome [9,22,24]. In the case of bwD variegation, chromosome rearrangements that alleviate PEV move the affected gene farther away from heterochromatin, while rearrangements that move the locus closer to heterochromatin enhance PEV [25,26]. This suggests that juxtaposition between a gene and heterochromatin allows for gene-to-heterochromatin interactions that can cause silencing. Once formed, these contacts may cause a gene to be silenced either by repackaging the gene into heterochromatin or by a specific silencing activity sequestered within heterochromatin itself.
Interaction with heterochromatin does seem to correlate with the silencing of specific genes, but the connection between association with heterochromatin and silencing has not been directly verified. Fluorescence in situ hybridization (FISH) techniques to identify chromosomal and heterochromatic loci are not generally compatible with the detection of gene expression. Furthermore, studies that have examined the connection between LRCIs and silencing by modulating the amount of PEV-induced repression have given conflicting results: one study found a correlation between relaxed silencing and relaxed association [22], whereas another did not [24]. Because the affected gene's expression was not compared to its association with heterochromatin on a cell-by-cell basis, it remains unclear whether, in a given cell, a heterochromatin-associated locus was silenced or expressed.
For the first time, to our knowledge, we present an experimental system that compares the expression of a variegating gene and its association with heterochromatin on a cell-by-cell basis for three different variegating genes in Drosophila whole-mount tissues. Multiple lines were chosen to ensure that results could be generally applied to PEV, rather than being limited to a specific rearrangement. The positions of variegating chromosomal loci and regions of heterochromatin were probed by FISH while fluorescent detection of eye pigments or variegating gene proteins marked gene expression. The affected gene in each line is quite far (>10 MB) from the centromere, with a block of heterochromatin placed nearby either through insertion or inversion. This provided an easy assay for long-range interactions with centromeric heterochromatin, as an interacting gene would relocate a significant distance across the nucleus to interact with centromeric regions. The variegating genes used are expressed in the Drosophila eye or eye imaginal disk; therefore, our examinations were limited to these tissues. Cell types, developmental stages, and cell cycle states were identified to determine what effects these variables might have on LRCIs with heterochromatin.
Our results show that interactions between variegating loci and heterochromatin are tightly correlated with gene silencing. Cells in which the variegating gene has been silenced exhibit a tight distribution of distances, suggesting direct interaction between heterochromatin and the distally located gene. Expressing cells, however, show a much broader distribution, with far fewer interactions between variegating loci and heterochromatin. Furthermore, these interactions with heterochromatin are primarily found in non-dividing cells in the eye disk. Differentiation is not required for LRCIs to occur, and interactions did not differ between cell types.
Results
LRCIs Are Strongly Correlated with Gene Silencing
Using gene expression as a functional assay, the consequences of LRCIs between chromosomal loci and heterochromatin were probed on a cell-by-cell basis. The first variegating gene examined was the brown gene on the bwD rearrangement. Expression of brown RNA has only been detected in recently eclosed adult heads; therefore, gene expression experiments were confined to this tissue [27]. Our experiments were greatly simplified by working in a scarlet background as in [26] as this eliminated the brown pigments from the eye. Therefore brown-expressing cells contained red pteridine pigment while non-expressors contained none (Figure 1).
Figure 1 Gene Expression and LRCIs
Color images show combined FISH–gene expression results for each of the three lines studied.
(A and B) bwD adult eye tissue. brown-expressing cells with pigment are blue, DAPI is red, single-copy FISH marking the brown locus is white, and heterochromatic single is green. (A) Wider view of eye. (B) Close-up view showing two expressing cells at right and lower left, and one silenced cell at the lower right.
(C and D) In(3L)BL1 eye disk tissue. Blue is anti-beta-galactosidase staining, green is anti-lamin staining, white is single copy FISH, red is heterochromatic FISH signal. (C) Wide-angle view of disk. (D) Close-up view showing silenced cells and expressing cells (blue in nucleus).
(E and F) In(1)rst3 eye disk tissue. Blue is anti-White staining, green is anti-lamin, red marks heterochromatic FISH signals, and white is single-copy FISH to the white gene. (E) Wide-angle view of disk. (F) Close-up showing expressing cell ringed by White protein and silenced cell nearby with no staining.
Gene-to-heterochromatin distance distributions from brown-expressing cells were quite different from those of silenced cells (Figure 2). The distribution of distances from expressing cells was broad, centered around 1 μm and extending to about 2 μm (Figure 2A). Looking at the cumulative percentage plots, few nuclei (10%) had variegating-gene-to-heterochromatin distances shorter than 0.5μm, and barely 25% had distances shorter than 0.75 μm (Figure 2G, blue line). Silenced cells, however, had a tight distribution of distances centered around 0.5 μm, with virtually all variegating-gene-to-heterochromatin distances being under 1 μm (Figure 2D). Cumulative percentage plots show nearly 40% of distances were shorter than 0.5 μm, and nearly 80% of nuclei had distances shorter than 0.75 μm (Figure 2G, red line). The Mann–Whitney U test revealed that these differences were highly significant. (p < 0.0001; Table 1).
Figure 2 Silenced Genes Associate Tightly with Heterochromatin
Physical distances in microns between heterochromatic sequences near each chromosome's centromere and the variegating gene were measured and sorted based on expressing or non-expressing cells.
(A–C) Expressing cells.
(D–F) Silenced cells.
(G–I) percentile plots of (A–F), in which blue represents expressing cells, red represents silenced cells, and green represents wild-type nuclei. bwD nuclei are Su(bwD)208 cells in adult eyes; In(1)rst3 and In(3L)BL1 data are from imaginal eye disks.
Table 1 Summary of Reported Results
The second variegating gene examined was the white gene in the In(1)rst3. Antisera developed against the unique N-terminus of the White protein (see Figure S1 and part 1 of Protocol S1) stained white-expressing cells in In(1)rst3 disks as expected from their eye pigmentation (Figure 1E and 1F). LRCIs between white and the 1.688 satellite correlated strongly with PEV-mediated silencing of white (Figure 2B, 2E, and 2H). As before, cells expressing the variegating gene had a broad distribution of distances, in this case centered at 1.5 μm and continuing to 4 μm (Figure 2B). Cumulative percentage plots reveal that only 20% of nuclei expressing white had variegating-gene-to-heterochromatin distances under 1 μm (Figure 2H, blue line). The distance distribution for silenced cells was much narrower than that for expressing cells, with distances centered around 0.8 μm and extending to only 2 μm in total distance (Figure 2E). Cumulative percentage plots show that over 60% of nuclei from silenced cells had variegating-gene-to-heterochromatin distances of under 1 μm (Figure 2H, red line) compared to 20% for expressing cells. Differences between expressing and silenced populations of cells were highly significant (p < 0.0001; Table 1). Despite the marked differences in association levels between expressing and silenced cells, the distributions of distances overlapped considerably between expressing and non-expressing cells (Figure 2B and 2E).
The third variegating gene examined was the heat-shock inducible beta-galacosidase transgene located on the In(3L)BL1 rearrangement. The expression of beta-galactosidase in In(3L)BL1 showed no preference for cell type, and was only found behind the morphogenic furrow (Figure 1C and 1D) [28,29]. In(3L)BL1 nuclei showed patterns of interaction and expression similar to those seen in the brown and white variegating lines. Expressing cells showed a broad distance distribution centered around 1.5 μm and extending out to nearly 4 μm (Figure 2C). Cumulative percentage plots show barely 15% of expressing cells had variegating-gene-to-heterochromatin distances 1 μm or less (Figure 2I, blue line).
In(3L)BL1 silenced nuclei displayed a rather narrow distribution, with a median distance of under 1 μm (Figure 2F). As in In(1)rst3 disks, cumulative percentage plots also showed more than 60% of silenced nuclei had distances between the silenced gene and heterochromatin under 1 μm (Figure 2I, red line). The narrow distribution of distances meant that silenced nuclei rarely had variegating-gene-to-heterochromatin distances greater than 2 μm, while expressing cells had greater distances in at least 50% of cells. Also similar to In(1)rst3, variegating genes in silenced cells were not 100% associated with heterochromatin (Figure 2C and 2F), with 40% of silenced nuclei having variegating-gene-to-heterochromatin distances greater than 1 μm. Additionally, some expressing cells had variegating-gene-to-heterochromatin distances less than 1 μm, in this case roughly 10%.
Interaction between Variegating Genes and Heterochromatin Primarily Occurs in Non-Dividing Cells
While it was clear that silencing of a variegating gene was tightly correlated with its interaction with heterochromatin, it was not known what other factors might affect this interaction. A study of how cell cycle progression and differentiation might affect interactions between a variegating gene and heterochromatin was undertaken. The cell cycle had previously been implicated as a force that periodically disrupts LRCIs [9,30]. The third-instar eye imaginal disk is an excellent tissue to explore this possibility because it contains well-separated populations of dividing and non-dividing cells. The anterior portion of the eye imaginal disk contains dividing cells (Figure 3A [left of dotted line] and 3B). The morphogenic furrow contains cells arrested in G1. Posterior to the morphogenic furrow, many cells cease dividing and differentiate (Figure 3A [right of dotted line] and 3C). Because of our sample preservation methods (see Materials and Methods), it was also possible to distinguish each individual photoreceptor cell and cone cell by their unique three-dimensional position and nuclear shape (Figure 3D; [31]). Our results therefore contain three groups of nuclei: nuclei from dividing cells anterior to the morphogenic furrow and differentiated cone cell nuclei and photoreceptor cell nuclei from behind the morphogenic furrow.
Figure 3 Preservation of Tissue Architecture and Cell-by-Cell Identification in the Drosophila Eye Disk
bwD eye disks were processed for FISH and stained for nuclear lamin as described in Materials and Methods. Red, fluorescein lamin stain; blue,
AACAC-cy5 heterochromatic probe; green, rhodamine-labeled P1 probe covering brown.
(A) Low-magnification view of eye disk showing anterior (left) and posterior (right) portions of the disk. Morphogenic furrow is marked with a dotted line.
(B) View of cells anterior to morphogenic furrow. These cells are still dividing and have not yet undergone differentiation.
(C) Cells posterior to morphogenic furrow. These cells have ceased dividing and are differentiating into adult eye structures.
(D) Four cone cell clusters from the two boxes in (C), with each photoreceptor and cone cell identified. Apical cells are at the top, basal at the bottom.
bwD
LRCIs were dramatically different between dividing and non-dividing cells in bwD disks (Figure 4). Cells anterior to the morphogenic furrow (Figure 4A) had a roughly Gaussian distribution of distances. The cumulative percentage plots show that few nuclei had less than 1 μm distance between the variegating gene and heterochromatin (<10%) (Figure 4J, red line).
Figure 4 Association Primarily Occurs in Non-Dividing Cells
Physical distances in microns between heterochromatic probe signals and P1 probe signals were measured for each line and sorted based upon their position relative to the morphogenic furrow and cell fate.
(A–C) Cells anterior to morphogenic furrow.
(D–F) Differentiated cone cells behind the morphogenic furrow.
(G–I) Differentiated photoreceptor cells behind the morphogenic furrow.
(J–L) Percentile plots for histograms in (A–I). Blue, wild-type cells; red, anterior cells; green, cone cells; pink, photoreceptor cells.
Entirely different results were found for cone and photoreceptor cells behind the morphogenic furrow. The distributions in both cases were substantially skewed, with a long tail of distances representing cells in which brown was greater than 1 μm from heterochromatin (Figure 4D and 4G). Cumulative percentage plots reveal that interactions were far more common in differentiated cells behind the morphogenic furrow than in the dividing anterior cells (Figure 4J, purple and green lines). The majority of nuclei in both cone cells and photoreceptor cells had their variegating gene closer than 1 μm to the centromeric heterochromatin on 2R. Statistical tests showed that the distributions of cone cells and photoreceptor cells were significantly different from dividing anterior cells (p < 0.0001; Table 1) but were not significantly different from one another (p = 0.15). This argues that differences between cell types do not have a significant effect upon brown-to-centeromeric-heterochromatin LRCIs. Cone cells and photoreceptor cells are also different in shape and nuclear volume. The fact that no difference in heterochromatic association was found suggests that in these cell types nuclear shape and volume do not play a meaningful role in LRCIs with heterochromatin.
In(1)rst3
The inverted X chromosome In(1)rst3 displayed similar results to that of bwD. Gene-to-heterochromatin distance distributions in anterior cells were nearly Gaussian (Figure 4B), such that few nuclei had variegating genes closer than 1 μm to the centromeric heterochromatin (<10%; Figure 4K, red line). Nuclei posterior to the morphogenic furrow displayed a skewed distribution similar to bwD (Figure 4E and 4H). Cone cells and photoreceptor cells displayed nearly identical distributions and were not statistically distinct (p = 0.0319). These distributions are markedly different from those of anterior cells (Figure 4B), with most nuclei exhibiting a shorter variegating-gene-to-heterochromatin distance (Figure 4E and 4H). The cumulative percentage plots confirm this (Figure 4K, purple and green lines), in that posterior cells routinely showed association levels 40% higher than anterior cells.
In(3L)BL1
The line In(3L)BL1 was a notable exception to previous results as it had an almost bimodal distribution of distances in cells anterior to the morphogenic furrow (Figure 4C). The cumulative percentage plots show dividing nuclei with variegating-gene-to-heterochromatin distances less than 1 μm (25%; Figure 4L, red line). This may be due to confounding effects of the multiply inverted balancer TM3. Gene-to-heterochromatin distance distributions from differentiated cells were not substantially skewed towards shorter distances relative to anterior cells (Figure 4F and 4I). There were few noticeable differences, as shown in cumulative percentage plots (Figure 4L, purple and green lines relative to blue). These differences were not statistically significant between cone and anterior cells (p = 0.0567; see Table 1) nor between photoreceptor and anterior cells (p = 0.6600; Table 1). Distributions from cone and photoreceptor cells were not significantly different (p = 0.0184; Table 1; Figure 4F and 4I), nor were there any noticeable differences between the cell types in the cumulative percentage plots (Figure 4L, purple and green lines).
Data from each variegating locus were compared with the behavior of loci on wild-type chromosomes; variegating loci behaved quite differently from those on wild-type chromosomes (see Table S1 and part 2 of Protocol S1).
Differentiation Is Not Necessary for Association
Because LRCIs were found in differentiated nuclei, this suggested that differentiation was important for interaction with heterochromatin. Alternatively, cessation of the cell cycle may allow loci to interact with heterochromatin because the periodic anaphases are eliminated [9]. The unique cell cycle profile of the eye imaginal disk presented an elegant way to distinguish between these two possibilities. Eye disk cells anterior to the morphogenic furrow divide in a band (I in Figure 5A and 5B) and arrest in the furrow at G1, as cartooned in Figure 5A and 5B. Posterior to the furrow, cells either differentiate into ommatidia or replicate their DNA. Some cells in this undifferentiated pool divide in the second mitotic wave (II in Figure 5A and 5B) and differentiate immediately. The remaining cells pause in G2 and await a signal to divide [32]. These G2-arrested cells behind the morphogenic furrow have been in interphase since the first mitotic wave ([32]; Figure 5A, shaded nuclei). If interaction requires differentiation, these G2-arrested cells should show less association than differentiated cells. If these G2 cells show equivalent levels of association, then a sufficiently long cell cycle permits variegating genes to interact with heterochromatin. G2 cells are easy to identify in third-instar eye disks as their nuclei react to anti–cyclin A antisera and are basal to differentiated and dividing nuclei (Figure 5D and 5E; [33,34]). G1-arrested cells are also clearly identified as a band of unstained nuclei between the anterior and posterior portions of the eye disk (Figure 5C).
Figure 5 Identification of Cell Cycle Stages in Drosophila Eye Disks
A combination of immunofluorescence to cyclins and tissue architecture was used to identify cells in specific cell cycle states.
(A) Cartoon of eye disk that shows populations of dividing and cell-cycle-arrested cells. I, first mitotic wave; II, second mitotic wave. G2-arrested cells are filled nuclei located basally posterior to the furrow.
(B) Cartoon of same disk viewed from above apical side of disk. Anterior is to the left.
(C–E) Immunofluorescence against G2 cyclins identifies cells in G1 and G2 arrest. Red is anti–cyclin A, green is single-copy FISH signal, and blue is heterochromatic FISH signal. (C) G1-arrested cells can be identified as those near and in the morphogenic furrow that do not show G2 cyclin staining. (D) G2-arrested cells posterior to the morphogenic furrow show dense anti–cyclin A staining. (E) Close-up of cells shown in (D).
G2-arrested cells in the bwD line showed variegating-gene-to-heterochromatin distances similar to those of differentiated cells (Figure 6). As in differentiated cells, the majority of G2-arrested nuclei had distances between the variegating gene and heterochromatin less than 1 μm. As shown in the cumulative percentage plots, G2-arrested nuclei were nearly indistinguishable from differentiated nuclei (Figure 6M, yellow and purple lines). These two distributions were also similar by statistical tests (p > 0.2; Table 1). Similar results were seen with In(1)rst3 and In(3L)BL1 G2-arrested nuclei, such that the gene-to-heterochromatin distance distributions in arrested nuclei were indistinguishable from those of differentiated cells (Figure 4H and 4I compared to Figure 6K and 6L). Cumulative percentage plots likewise do not show any differences between arrested nuclei and differentiated ones (Figure 6N and 6O), and these distributions were not statistically distinct (p > 0.1; Table 1).
Figure 6 Differentiation Is Not Required for Association
Physical distances in microns between heterochromatic probe signals and P1 probe signals were measured for each line and sorted based upon staining for G2 cyclins.
(A–C) Cells anterior to morphogenic furrow.
(D–F) G1-arrested cells (in furrow, no cyclin staining).
(G–I) G2-arrested cells, basal and stained for cyclins.
(J–L) Differentiated cells behind the morphogenic furrow.
(M–O) Percentile plots for (A–L). Blue, wild-type cells; red, anterior cells; green, G1-arrested cells; pink, G2-arrested cells; yellow, differentiated cells.
bw-to-heterochromatin distances in bwD G1-arrested nuclei displayed a bimodal distribution (Figure 6D), unlike the unimodal distributions seen for anterior cells (Figure 6A) and differentiated cells (Figure 4J). One peak shows a distance distribution similar to that seen for G2-arrested and differentiated cells (Figure 6G and 6J), while a second is intermediate between that of differentiated and anterior nuclei. Cumulative percentage plots show a marked difference between G1-arrested nuclei and all other populations, including an inflection in the curve between the two peaks in the G1 distribution (Figure 6M, red line). It appears that the morphogenic furrow is a transition state, whereby variegating genes that were once quite far from heterochromatin (Figure 6A) begin to associate with centromeric regions on the same chromosome. Similar behavior is also seen in In(1)rst3 and In(3L)BL1 nuclei (Figure 6E and 6F). Distributions in these two lines are not bimodal, but they are intermediate between anterior and differentiated nuclear distributions. Cumulative percentage plots also show distinctions between these nuclei and the other populations (Figure 6N and 6O, red lines). Statistical tests showed that all three distributions of G1 nuclei were distinct from anterior cells (p < 0.0001; Table 1), but that only bwD G1 nuclei were different from differentiated cells (p < 0.0001; Table 1).
Variegating Genes Associate with Heterochromatin at Their Own and Other Centromeres
An appreciable percentage of loci were found far from centromeric cis-heterochromatin, even in PEV-silenced nuclei. These silenced yet far loci may form LRCIs with heterochromatin on other chromosomes (trans-heterochromatin). While other authors found that variegating rearrangements involving bw preferentially associated with cis-heterochromatin [9,35], we felt this issue merited reexamination. In two sets of experiments per line, probes marking variegating loci were compared with two different heterochromatic probes: one marked cis-heterochromatin while another identified trans-heterochromatin. Each variegating gene was examined for promiscuous interaction with centromeric heterochromatin on each large chromosome. Data were sorted based on whether the variegating gene was within 1 μm of heterochromatin on its own chromosome (Figure 7A), on a different chromosome (Figure 7B), or on both chromosomes (Figure 7C).
Figure 7 Interaction of Variegating Genes with Heterochromatin on Other Chromosomes
All three lines were examined to see how often variegating genes interacted with heterochromatin on their own and other chromosomes.
(A) Close-up of In(1)rst3 nucleus showing variegating gene interaction with heterochromatin on its own chromosome.
(B) Close-up of In(1)rst3 nucleus showing variegating gene interaction with heterochromatin on a different chromosome.
(C) In(1)rst3 nucleus showing variegating gene interaction with heterochromatin on both its own and another chromosome.
(D–F) Bar plots of interaction frequencies of variegating genes with their own heterochromatin and that found on other chromosomes. A distance of 1.0 μm was chosen as the maximum distance for interaction to occur.
As in Dernburg et al. [9], the brown locus on the bwD chromosome interacted primarily with heterochromatin on the same chromosome (Figure 7D). Interactions with trans-heterochromatin on Chromosomes X and 3 where the brown gene was not associating with cis-heterochromatin were generally limited to 5%–10% of examined nuclei. Interactions where the brown gene was close to heterochromatin on multiple chromosomes were also infrequent, between 2%–5% of examined nuclei.
The In(3L)BL1 chromosome differed from the bwD chromosome in that comparable levels of association were observed between cis- and trans-heterochromatin (Figure 7E). The In(3L)BL1 chromosome's transgene interacted similarly with heterochromatin on Chromosomes 2 and 3 (20% of nuclei examined), but only 10% with the X chromosome. Interactions of the variegating gene with heterochromatin on multiple chromosomes were less common, at around 5%–10% of nuclei examined. The variegating locus on In(3L)BL1 seemed to show a preference for associating either with heterochromatin on its own chromosome or that of Chromosome 2.
The In(1)rst3 line showed similar levels of association with heterochromatic blocks on all three chromosomes, with around 10% of nuclei exhibiting distances less than 1 μm (Figure 7F). Unlike in the other lines examined, the variegating gene was rarely close to heterochromatin on multiple chromosomes, suggesting that the white locus interacts with only one block of heterochromatin at a time.
While the bwD chromosome exhibited a marked preference for associating with heterochromatin on its own chromosome, the other two lines did not. This suggests that the tails of the distances seen in the silenced cells of In(1)rst3 and In(3L)BL1 could well be explained by interaction with heterochromatin on other chromosomes. bwD's infrequent association with blocks of heterochromatin on other chromosomes may explain the smaller tails seen in the distributions of bwD disk nuclei.
Discussion
The variegating rearrangements studied here cause a nuclear reorganization with clear consequences for the organism. The rearrangements alter the behavior of affected loci relative to wild-type (see Table S1 and part 2 of Protocol S1), changing their nuclear position. This in turn increases the likelihood that nearby genes will contact and form persistent interactions with heterochromatin. Those loci that do interact with heterochromatin may be silenced. Supporting this conclusion is the observation that variegating-gene-to-heterochromatin distance distributions in silenced cells are vastly different from those of expressing cells (Figure 2D–2F). Silenced nuclei show a tight unimodal distribution with a peak centered around short distances, 0.5 μm in the case of bwD, and 0.8 μm in the case of In(3L)BL1 and In(1)rst3. We posit that the lower variability of these silenced cell distributions means that variegating genes are forming persistent contacts with heterochromatin. Because the same level of association was seen between recently differentiated nuclei and those that had differentiated as many as 12–18 h before (see Figure S2 and part 5 of Protocol S1), this suggests that the interactions are quite persistent and do not appear and disappear periodically. Distances of nearly a micron may seem inordinately high. However, since the analysis scheme measured between FISH signal centers, distances will never be zero even when FISH signals overlap considerably. This is compounded by the relatively large volume occupied by heterochromatic FISH signals. However, FISH signals that we classified as interacting based on distance were often touching or partially overlapping (Figure 1). Unlike silenced cells, expressing cells have a broader, nearly Gaussian distribution centered at 1.0–1.5 μm and extending to nearly 4 μm (Figure 2A–2C). We interpret the more variable distribution to mean that expressing loci are not interacting with heterochromatin and are therefore less restricted.
Despite clear differences between expressing and silenced loci behavior, we do not see absolute distinctions between them. Appreciable numbers of silenced loci are far from cis-heterochromatin, while some expressing loci are quite close. Some of these results may be explained by limitations of our experimental procedures. Not all centromeric heterochromatin is labeled by our FISH methods. It remains possible that a “far silenced” gene may be interacting with heterochromatin not labeled by our oligonucleotide probes in either cis- or trans-heterochromatin. Two lines exhibited significant association with trans-heterochromatin, suggesting that this could explain cases where silenced genes are not associated with labeled cis-heterochromatin. Some of the expressing yet heterochromatin-close cases may have expressed the gene for some time before contact with heterochromatin silenced the variegating locus. Our use of accumulated gene product to mark gene expression would mask these examples. While FISH methods do not drastically perturb nuclear structure, these methods may impact interloci distances somewhat [36,37]. Heat shock utilized for the In(3L)BL1 line may explain its unique behavior relative to other chromosomes, as heat shock has been shown to affect nuclear structure [2]. A live chromosomal imaging system [15] that tracks the long-range interactions of a variegating gene with heterochromatin should provide the best control for sample manipulations discussed here.
Specific biological interactions may also explain the “far silenced” and “close expressing” loci. Interaction with heterochromatin may be required for the establishment but not the maintenance of silencing. Once a gene is silenced, chromosomal motion may pull it away from centromeric heterochromatin without changing the affected gene's expression. A second possibility is that the proximal block of heterochromatin near the variegating locus can occasionally silence the gene by itself. For example, the block of heterochromatin near the white gene in In(1)rst3 is known to be a weak silencer [38]. The observation that variegating loci in some expressing cells seem to interact with heterochromatin (upper limit of 20%) may mean that interaction does not guarantee silencing. Early transcriptional activation allows a gene to escape PEV, arguing that once a gene is expressed, future interactions with heterochromatin might not silence it [39]. The timing of interaction relative to the normal expression pattern of the gene may therefore be critical. Some genes vary in their sensitivity, such that interaction with heterochromatin may not necessarily result in silencing [40,41].
Based on Figure 2 and the results in Table 1, we can determine what the differences between expressing and silenced cells mean in terms of chromosomal and nuclear dimensions. In silenced cells, the median distance between the variegating locus and heterochromatin is nearly halved relative to expressing cells. For example in In(1)rst3 the distance falls from around 1.5 μm in expressing cells to 0.85 μm in silenced ones. Based upon calculations of linear base pair distance, this is what we would expect if the silenced locus is located 9 MB closer to heterochromatin than in the expressing case, or about half of a chromosome arm. Given the complex folding of an interphase chromosome arm, this reduction is likely more pronounced than a linear calculation would suggest. Additionally, the behavior of a silenced locus is different in ways far more dramatic than merely “closeness” to heterochromatin. The distribution of distances for silenced loci are also less variable than for expressing loci, roughly half that of an expressing locus. This becomes particularly clear when we imagine the variegating-gene-to-heterochromatin distance in statistical terms, using two standard deviations as a 95% confidence interval. Using the In(1)rst3 case as an example, the expressing locus might be anywhere from 0 to 3 μm from heterochromatin, while the silenced locus will not be found farther than 1.7 μm away. Thinking about this in terms of three-dimensional nuclear volume, this means than an expressing locus will inhabit a 95% confidence volume five times larger than that of a silenced locus. We posit that the shorter median distance and reduced variability in position relative to heterochromatin suggests a persistent interaction.
It is not known to what extent the association of a variegating gene with heterochromatin is a cause or an effect of gene silencing. Even though our results argue that the two events are connected, a gene could be silenced by local factors and associate with heterochromatin independently. Future examinations of this problem would require a live study to pinpoint precisely when interactions occur. If interaction occurs before the gene is silenced, that suggests that the interaction could be a cause of silencing. If interactions occur after silencing, however, this would suggest that the interaction is merely a side effect. Studying the persistence of such associations will reveal whether interaction with heterochromatin causes loci to be more constrained than non-interacting loci, which could explain why gene-to-heterochromatin distance distributions from expressing cells are so much broader than those of silenced cells.
Now that specific interactions with heterochromatin have been shown to be closely correlated with silencing, one can ask how such interactions form. Time-lapse studies have shown that individual loci can move substantial distances within the nucleus, eventually bringing the variegating locus into contact with heterochromatin [19]. Additional live studies found that chromosomal loci can be held in place after contacting certain structures such as the nuclear envelope or the nucleolus [42]. Once contact is made, the proximal and centromeric blocks of heterochromatin may mediate a persistent interaction through multimerization of proteins such as HP1. Loci on wild-type chromosomes will not form persistent interactions because they lack a proximal block of heterochromatin. Interestingly, studies have found that as interphase progresses, nuclear motion decreases such that contacts between euchromatic loci and heterochromatin may stabilize [19,43,44].
Recent insights into the molecular behavior of heterochromatin proteins suggest how persistent interaction with heterochromatin may silence genes (reviewed in [45]). We hypothesize that the associated loci, because of intimate (possibly molecular) contact, acquire the molecular features of heterochromatin. The chromatin of PEV-silenced genes acquire heterochromatic features such as a regular nucleosome array, insensitivity to nucleases [46], and binding of heterochromatin proteins [47,48]. This altered chromatin structure seems to occlude the affected gene's promoter, preventing the loading of RNA polymerase and transcriptional activators, thereby preventing gene expression [49].
A model of how LRCIs with heterochromatin occur in the eye disk is outlined in Figure 8. In dividing cells anterior to the morphogenic furrow, few loci interact with heterochromatin. As cells pass into the morphogenic furrow, the G1 cell cycle arrest allows loci to explore their chromosome territory and make contacts with regions of heterochromatin on their own and possibly other chromosomes. Loci that contact heterochromatin may form persistent interactions that cause silencing. The normal expression pattern of the white gene, for example, is now altered by association with heterochromatin. Instead of being expressed in every r8 cell behind the morphogenic furrow, as in a wild-type chromosome, its expression pattern is periodically interrupted by silenced cells. Loci that contact but do not form persistent interactions remain relatively unconstrained.
Figure 8 Model of Variegating Gene Association with Heterochromatin and Its Consequences
Cartoon of eye disk showing when and where association occurs and its effects on gene silencing. Anterior is to the left. Long-range interactions are not observed anterior to the morphogenic furrow in regularly dividing cells. In the morphogenic furrow, G1 arrest allows variegating genes the time to begin to find and associate with blocks of heterochromatin. Once behind the morphogenic furrow, variegating loci in more cells associate with heterochromatin, and once formed these interactions persist. When a developmental signal is sent for a gene to be expressed, its association with heterochromatin greatly affects its chances for being expressed, such that a gene interacting with heterochromatin is far less likely to be activated than one that is not.
This is the first study to examine the expression state of a chromosomal locus and its interaction with heterochromatin on a cell-by-cell basis within intact tissue. In each of the three cases examined, relocalization of a gene to heterochromatin was tightly correlated with gene silencing. These results strongly suggest that spatial organization of the Drosophila genome is an integral part of organism function.
Materials and Methods
Drosophila strains and chromosomes
Three classes of rearrangements were used, each representing one of Drosophila's three largest chromosomes: X, 2, and 3. Each variegates for a different gene (Table 2) and has a proximal block of heterochromatin placed into or nearby the variegating locus. It was expected that the small block of heterochromatin near the variegating gene would mediate interaction between the variegating gene and centromeric heterochromatin without silencing the variegating gene directly.
Table 2 List of Rearranged Chromosomes Studied
Satellite sequences are taken from [66]
Bloomington, Bloomington Stock Center, University of Indiana, Bloomington, Indiana, United States; Eissenberg, Joel Eissenberg, St. Louis University, St. Louis, Missouri, United States; Hawley, Scott Hawley, Stower's Institute, Kansas City, Missouri, United States; Henikoff, Stephen Henikoff, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States; ND, not determined; NR, not relevant
The X chromosome was represented by the line In(1)rst3 y1car1. Variegation for white was produced by mating In(1)rst3 males with virgin females of the genotype C(1)RM v, v; C(4) ci evR × X^Y, In(1)EN, v f B; C(4) ci evR to produce normal phenotype XX/Y females and variegating X/O males [50]. X/O males were distinguished from XX/Y females by the expression pattern of white in third-instar eye disks (see Results).
The second chromosome was represented by two different lines containing the bwD insertion [51]. Experiments studying the bwD rearrangement in imaginal disk tissue were performed with larvae homozygous for the bwD chromosome. Control experiments with bwD/bw+ tissues produced identical results (data not shown). Experiments studying the long-range interactions with heterochromatin and silencing in adult eyes used the line Su(bwD)208/SM1. The In(2LR)SM1, Cy balancer carries a wild-type brown gene to allow bw variegation to be observed. The Su(bwD)208 rearrangement increased the number of expressing cells per eye from the normal bwD/bw+ expression level (<5%) to about 20%–30% [26]. Experiments were carried out in a scarlet (st) background to eliminate brown ommochrome pigment; brown-expressing cells appeared red against a field of white non-expressors, as opposed to red-brown expressors against a field of brown non-expressors. All gene expression experiments in adult eyes were in flies with the genotype In(2LR)SM1 Cy/Su(bwD)208; st/st.
The third chromosome was represented by the beta-galactosidase variegating line In(3L)BL1 [28]. The precise genotype for the In(3L)BL1 line is Df(1)y w, In(3L)BL1/Tm3 hb-lacZ.
Diagrams of all variegating chromosomes including FISH staining of polytene chromosomes are shown in Figure 9.
Figure 9 Variegating Lines and Probes Used in This Study
Variegating lines were used for three different chromosomes, each variegating for a different gene. For full details see Materials and Methods.
(A) Diagram of each line showing approximate breakpoints and locations of variegating genes. FISH probes were made from P1 clones covering each gene and from heterochromatic sequences unique to each chromosome (see Materials and Methods).
(B–D) FISH probes for each chromosome. FISH probes were hybridized to polytene squashes to show cytological location of each probe. Regions of proximal heterochromatin from inversion and insertion are marked with an arrow. Each chromosome spread is taken from individual experiments. (B) Probe used in experiments for the line In(1)rst3. (C) Probe used in experiments for the line In(3L)BL1. (D) Probe used in experiments for the line bwD.
FISH probes
FISH probes marked the three-dimensional locations of chromosomal loci. Repetitive heterochromatin sequences were hybridized with hapten-conjugated DNA oligonucleotide probes. Each probe's location is presented in Figure 9A. Chromosome 3 heterochromatin was hybridized to a 46-bp DNA oligonucleotide containing the sequence (
CCCGTACTGGT)4 corresponding to the dodeca satellite sequence. Chromosome 2 heterochromatin was hybridized to a 35-bp DNA oligonucleotide containing the sequence (
AACAC)7. X heterochromatin was hybridized to a PCR product of 359 bp comprising the bulk of the 1.688 satellite sequence. Satellite sequences and probe construction are discussed in more detail in [52]. DNA oligonucleotides were synthesized by Invitrogen (Carlsbad, California, United States) using sequences as published in [52]. Heterochromatic probes made from these oligonucleotides were labeled with digoxygenin-conjugated nucleotides by terminal transferase. Digoxygenin-labeled probes were detected by sheep anti-dig F(ab)2 fragments coupled to Cy5 (Amersham Biosciences, Piscataway, New Jersey, United States).
Euchromatic probes were made from P1 phage clones provided by the Berkeley Drosophila Genome Project and Genome Systems (St. Louis, Missouri, United States) [53,54]. Experiments with bwD lines used P1 clone DS003480, which hybridizes to 59E (Figure 9B) [9]. Experiments with the In(1)rst3 line used P1 clone DS006812, corresponding to 3C2–3. For the line In(3L)BL1, the P1 clone DS000374 was utilized, which hybridizes to 65D2–3. P1 DNA was amplified in bacteria and purified via alkaline lysis and silica gel chromatography (Qiagen Midi Tips, Qiagen, Valencia, California, United States). The purified DNA was digested with restriction endonucleases and labeled with Rhodamine 4-dUTP (FluoroRed, Amersham Biosciences) by terminal transferase as described previously [52].
Sample preparation
Eye imaginal disks were dissected from climbing third-instar larvae raised at 16 °C and fed on a yeast paste/glucose/agar/instant Drosophila food (Carolina Biological Supply, Burlington, North Carolina, United States) recipe. Eye disks were dissected into modified M3 medium [55] or Grace's and fixed for 15 min in 3.7% formaldehyde and Buffer A+ [56]. After fixation, disks were permeablized for 30 min in Buffer A+/0.1% Triton X-100 (Pierce Chemical, Rockford, Illinois, United States) and transferred to PBS (pH 7.4) with 0.1% Tween 20, unless specified otherwise.
Adult eyes from recently eclosed (0–3 h) adult heads were dissected into a drop of Grace's medium and fixed for 15 min in Buffer A+/3.7% formaldehyde. Heads were transferred back into Grace's medium and the eyes cut away with a 15° microsurgical knife (Surgical Specialties Corporation, Reading, Pennsylvania, United States). These eyes were fixed for an additional 15 min in A+/3.7% formaldehyde and then pried away from the compound lens with forceps and a scalpel modified into a scoop (made by Daron Brown, Fine Science Tools, Foster City, California, United States). Bovine serum albumin (5%) prevented eyes from sticking together.
Detection of gene expression
Detection of gene expression in In(3L)BL1 relied on immunofluorescence of the beta-galactosidase gene product after heat shock at 37 °C. Eppendorf tubes containing a 1-ml agarose plug were preheated for one hour at 37 °C in a circulating water bath. The plug was removed, larvae were placed inside the eppendorf tube, and the pre-warmed plug was loosely placed over the larvae and the tube sealed. Larvae were incubated in the water bath for 5 min, followed by immersion in a room temperature water bath and a 1 h recovery period. Eye disks were dissected, followed by blocking for 4 h in PBS +0.1% Tween 20 (PBT) plus 5% dry milk (Nestle, Solon, Ohio, United States). Disks were incubated with a mouse monoclonal antibody in PBT at a dilution of 1:1,000 (Promega, Madison, Wisconsin, United States) for 2 h. After three 30-min washes in PBT, disks were incubated with horseradish-peroxidase-conjugated anti-mouse secondary antibodies (Jackson ImmunoResearch, West Grove, Pennsylvania, United States) at a 1:500 dilution in PBT for 1 h. After three more washes in PBT, the horseradish-peroxidase-coupled antibody was treated with fluorescent coumarin-coupled tyramide signal amplification reagents (TSA-direct, PerkinElmer Life Sciences, Wellesly, Massachusetts, United States) to develop a stable fluorescent stain that would survive FISH.
Detection of gene expression in the line In(1)rst3 relied on immunofluorescence to the white gene product. Details of anti-White antibodies are presented in Figure S1 and part 1 of Protocol S1. Anti-White antibody staining proceeded as anti-beta-galactosidase staining, except that anti-White primary antibody was used at a concentration of 0.5 μg/ml in PBT and stained overnight. The primary antibody was detected by anti-rabbit horseradish-peroxidase-conjugated secondary antibodies (Jackson ImmunoResearch) at dilution of 1:500, followed by treatment with tyramide as described above.
For bwD, adult eyes were embedded in polyacrylamide immediately after dissection (see below), stained with 0.5 μg/ml DAPI in PBS (pH 7.4), and imaged at low magnification with an Olympus (Tokyo, Japan) 20 × 0.8 NA lens to identify gross structural features for later realignment. Select regions of each eye were imaged under a 1.4 NA 100x lens to acquire high-resolution three-dimensional images of each nucleus. The precise positions of both high and low magnification datasets were recorded for realignment after FISH. Expressing cells were identified by their bright autofluoresence.
Detection of cell cycle arrest
Experiments to determine the role of differentiation and the cell cycle were carried out in third-instar eye disks. A rabbit anti–cyclin A antibody detected the presence of G2 cyclins [34]. G1-arrested cells were identified by their location within the morphogenic furrow and the absence of cyclin A.
FISH
Whole-mount disks were processed for in situ hybridization as described elsewhere [52]. After FISH, tissues were stained in 2XSSCT with anti-Lamin antibodies [57] and an FITC-labeled goat anti-mouse secondary antibody to mark the nuclear periphery (Jackson ImmunoResearch). Before imaging, all tissues were washed three times in 50 mM Tris (pH 8.0) and embedded in polyacrylamide pads as in [58] to support three-dimensional structure.
Adult eyes were processed for FISH similar to [58] with modifications described below. Adult eyes were placed within a nail polish ring on a #1.5 20 × 30 mm coverslip in 13.5 ml of Tris (pH 8.0). Then 6.5 ml of 3X activated acrylamide buffer (Buffer A+, 15% acrylamide, and 0.333% bis-acrylamide) was added. Twenty-five microliters of 20% ammonium persulfate and 25 μl of 20% sodium sulfite were added to 500 μl of acrylamide buffer immediately before addition to tissue. The acrylamide buffer drop was mixed by repeated pipetting, and sealed with a clean 20 × 30 mm coverslip. After 30 min of polymerization, the top coverslip was removed and pads washed four times in Buffer A+, followed by DAPI staining and pigment imaging.
After pigment imaging, pads were stepped into 2XSSCT/50% formamide and incubated with probe and hybridization solution overnight at 37 °C before denaturation. Pre- and post-FISH washes proceeded as described for eye disks in PCR tubes, except that washes were extended to 1 h apiece in humid chambers with agitation. Eyes were denatured as described elsewhere [52,58], except that denaturation was extended to 10 min. After FISH, eyes were washed five times with 50% formamide/2xSSCT, stepped into PBT, and stained with DAPI.
Three-dimensional imaging
Before mounting, all tissues were washed three times in 50 mM Tris (pH 8.0) to remove any detergent. Disks were mounted in Vectashield (Vector Labs, Burlingame, California, United States) while adult eyes were mounted with ProLong (Molecular Probes, Eugene, Oregon, United States). Three-dimensional imaging was carried out on a computer-controlled epifluorescence microscope [59,60]. All images were collected on a Peltier-cooled CCD camera (Photometrics, Tucson, Arizona) connected to an SGI Indigo2 workstation (SGI, Mountain View, California, United States). Imaginal disks were imaged with a 60x Olympus 1.4 NA oil lens while adult eyes were imaged on an Olympus 20 × 0.8 NA oil lens followed by a Nikon (Tokyo, Japan) 100 × 1.4 NA oil lens. For adult eyes, the same tissues examined for pigmentation were re-imaged and carefully aligned with pre-FISH images. Data were processed after collection by a constrained iterative deconvolution software program [61].
Image analysis
Image analysis was performed with the Image Visualization Environment software package PRIISM ([62]; D. Diggs and E. Branlund, unpublished data). The Water algorithm automatically identified nuclei [63] and segmented them into three-dimensional objects. After segmentation the center point of each object was the starting point to find the nuclear periphery. The nuclear lamin signal was found by searching for pixels above an intensity threshold a specified radial distance from the center point. Pixels above the threshold were marked and refined by a second- and third-order surface harmonic expansion [10]. Individual FISH signals within each modeled nucleus were identified based on their intensity peak above background (M. Lowenstein and D. Diggs, unpublished data). FISH signal distances were measured and sorted by nucleus membership. Distances were measured from the intensity-weighted center of mass rather than from the edge of each FISH signal. This means that even overlapping FISH signals will return a measured distance. Differences between different groups of nuclear distances were tested for statistical significance by the Mann–Whitney U test (see Table 1), which is a two-sample rank-sum test for position that makes no assumptions regarding the distributions of sample data [64].
Plots and statistical comparisons were performed by the Statview 4.5 (Abacus Concepts, Berkeley, California, United States) analysis program. Results are summarized in Table 2.
Data presentation
Groups of distances mentioned previously were tested for statistical significance relative to one another (see Table 1). Variegating chromosomal loci close to heterochromatin were considered to be interacting, while loci farther away were considered to be not interacting. Data were presented in two different ways. First, standard histograms presented the full distribution of the data including any subpopulations. Second, cumulative percentile plots with percent association between variegating genes and their own centromeric heterochromatin plotted as a function of gene-to-heterochromatin distance more clearly distinguished varying interaction levels between different cell populations. This plotting method also revealed that the exact placement of a cutoff for interaction between loci was unimportant, as clear differences could be seen at distances less, equal to, or greater than 1 μm. As a general rule 1 μm in distance was used as a cutoff for interaction. This cutoff was not intended to be mechanistically meaningful, but to serve as a rule of thumb for thinking about the results.
Monte Carlo analysis and correlation between nuclear distances and nuclear dimensions
Distance distributions in eye disk nuclei were compared to pairwise distances measured from 50 points randomly placed within the nucleus. Also, distances between chromosomal loci were examined for their sensitivity to nuclear shape and volume. Data and results are presented in Table S2 and parts 3 and 4 of Protocol S1.
Supporting Information
Protocol S1 Anti-White Antibody Generation, and the Effects of Gene Expression, Nuclear Dimensions, and Time on Gene to Heterochromatin Distances
(31 KB DOC).
Click here for additional data file.
Figure S1 Production and Characterization of Anti-White Antisera
Full details available in Materials and Methods.
(A) Western blots showing specificity of antibody for a single polypeptide of approximately 70 kDa in larval (L) and adult (A) extracts. Preimmune sera show no reactivity for that polypeptide.
(B) Immunofluorescence of eye disks with affinity-purified anti-White antisera. Anti-White staining surrounded each nucleus, possibly because the protein is enriched in the endoplasmic reticulum. Expression was seen only in four cone cell ommatidial clusters several cell rows behind the morphogenic furrow.
(C) Immunofluoresence in eye disks of the white variegating line In(1)wm51b. Gaps in the white expression pattern are marked with dotted polygons.
(D) Vertical series of images through a single four cone cell cluster in the eye disk stained for White protein (cyan) and the nuclear lamin (green). In every cell cluster examined, expression was only seen in the eighth photoreceptor. Each panel represents a step of about 2.5 μm from the most apical to basal parts of the cluster. Total distance from basal edge of cell cluster to cone cells is about 10 μm.
(2.9 MB TIF).
Click here for additional data file.
Figure S2 Comparison of Different Cell Populations Based upon Time after Differentiation or Cell Cycle Arrest
Individual rows of differentiated nuclei were compared to one another to see whether differences in interaction levels existed between younger and older cells.
(A–C) Row-by-row analysis method for lines bwD, In(1)rst3, and In(3L)BL1 respectively. Each color represents a different row, rows 4–8 respectively.
(D–F) Scattergrams for each row based on pooling of data from three different disks per line. Y-axis is distance in microns; X-axis is row number.
(G–I) Percentile plots of each row.
(2.6 MB TIF).
Click here for additional data file.
Table S1 Variegating Locus-to-Heterochromatin Distances in Wild-Type Cells and Random Distributions
(53 KB DOC).
Click here for additional data file.
Table S2 Effects of Nuclear Dimensions on Variegating Gene-to-Heterochromatin Distances
(40 KB DOC).
Click here for additional data file.
The authors would like to thank Orion Weiner, Wallace Marshall, Abby Dernburg, Julio Vazquez, and Jennifer Fung for critical comments on the manuscript, and to thank Steven Henikoff and Joel Eissenberg for generously providing the various bwD and In(3L)BL1 lines, respectively. This work was supported by National Institutes of Health grant GM-2501–25 (JWS) and by a National Science Foundation Predoctoral Fellowship (BPH).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. BH and JS conceived and designed the experiments. BH performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, and wrote the paper.
Citation: Harmon B, Sedat J (2005) Cell-by-cell dissection of gene expression and chromosomal interactions reveals consequences of nuclear reorganization. PLoS Biol 3(3): e67.
Abbreviations
FISHfluorescence in situ hybridization
LRCIlong-range chromsomal interaction
PEVposition-effect variegation
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| 15737020 | PMC1054879 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 1; 3(3):e67 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030067 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706210.1371/journal.pbio.0030068Research ArticleNeuroscienceRattus (Rat)Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits Nonrandom Connectivity in CortexSong Sen
1
Sjöström Per Jesper
2
3
Reigl Markus
1
Nelson Sacha
2
Chklovskii Dmitri B [email protected]
1
1Cold Spring Harbor Laboratory, Cold Spring HarborNew YorkUnited States of America2Department of Biology and Volen National Center for Complex Systems, Brandeis UniversityWaltham, MassachusettsUnited States of America3Wolfson Institute for Biomedical Research and Department of Physiology, University CollegeLondonUnited KingdomFriston Karl J. Academic EditorUniversity College LondonUnited Kingdom3 2005 1 3 2005 1 3 2005 3 3 e686 7 2004 17 12 2004 Copyright: © 2005 Song et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
The Few, the Strong: Rat Cortex Features Small Numbers of Powerful Connections
How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.
A dataset of hundreds of recordings in which four neurons were simultaneously monitored reveals clustered connectivity patterns among cortical neurons
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Introduction
Understanding cortical function requires unraveling synaptic connectivity in cortical circuits, that is, establishing the wiring diagrams. Although smaller wiring diagrams can be reconstructed using electron microscopy [1], reconstruction on the scale of a cortical column is impossible with current technology (but see [2] for a promising approach). Even if such a possibility were within reach, synaptic connectivity likely varies from animal to animal and within one animal over time. Therefore, a reasonable approach is to describe synaptic connectivity statistically, or probabilistically. Such statistical description may be based on random sampling of connections with multineuron recordings [3,4,5]. For example, electrophysiological recordings from neuronal pairs showed that the probability of connection is often low [3,5,6,7,8,9,10,11], suggesting that the network is sparse. Such statistical approaches may create the impression that synaptic connectivity in local cortical circuits is random. This view is consistent with previous suggestions [12,13,14], but hard to reconcile with cortical functionality, which must rely on specificity of connections [15,16,17,18].
In general, statistical sampling of connections does not imply that the underlying network has random connectivity. Indeed, statistical sampling has already revealed several nonrandom features in cortical connectivity. In particular, specific connectivity patterns exist between different classes in the local circuit [3,19,20]. Within one putatively homogeneous class, the number of reciprocally connected pairs is greater than expected in a random network [5,6,11]. Distribution of the number of synapses per connection is non-Poisson and has low variance [6,21]. At the same time, the distribution of the connection strength is broad [5,6,10,11]. But many questions remain unanswered: Are there nonrandom features in patterns involving more than two neurons? What is the distribution of synaptic connection strength? Are synaptic connection strengths correlated?
Recently, new approaches for network connectivity analysis have been developed and various nonrandom features were discovered in natural and man-made networks. In particular, many networks are scale-free, that is, the number of connections per node (node degree) often follows a power-law distribution [22]. Also, many networks exhibit the small-world property, that is, high local clustering of connections in combination with a short path between any two nodes [23,24]. In addition, probability of connection between nodes depends on how many connections they have [25]. Although local cortical networks may possess these properties, existing connectivity data are not sufficient for such analyses. These data are obtained by random sampling of connections and call for other approaches. One such approach is to explore local structures in network connectivity by studying the distribution of few-node connectivity patterns, or motifs [26,27]. Another such approach is analyzing the utilization (or, in this case, the strength) of connections [28,29,30].
In this paper, we apply a combination of statistical methods to a large dataset from hundreds of simultaneous quadruple whole-cell recordings from visual cortex in developing rats. Our results confirm previous indications of nonrandomness and point out several new ones. In particular, we show that the distribution of connection strengths between pyramidal neurons is non-Poisson and find correlations in the strength of the connections sharing pre- or postsynaptic neurons. Also, we find several overrepresented three-neuron connectivity patterns, or motifs. Surprisingly, we find that some few-neuron motifs can play an important role in the dynamics of layer 5 local cortical networks because they are composed of exceptionally strong connections. This suggests a novel view of the local cortical network, in which a skeleton of stronger connections is immersed in a sea of weaker ones.
Results
We studied connectivity among thick tufted layer 5 neurons in rat visual cortex with quadruple whole-cell recordings (Figure 1A and 1B). Thick tufted layer 5 pyramidal neurons are important projection neurons from the cerebral cortex to subcortical areas [9,31,32]. Synaptic connection strengths were assessed by evoking action potentials in each of the four cells and measuring the averaged peak excitatory postsynaptic potential (EPSP) amplitudes in the other three cells (see Figure 1C and Materials and Methods). Results of these measurements for a sample quadruple recording are shown in Figure 1D. Each arrow indicates a detected connection with the corresponding connection strengths. The dataset contained a total of 816 quadruple recording attempts (some of these attempts contained data for only two or three neurons, if whole-cell configuration was not successfully established with all four cells). As previously reported [5], the rate of connectivity was p = 11.6% (931 connections out of 8,050 possible connections), which is similar to that reported for rat somatosensory cortex layers 5 [6,9] and 2/3 [11], as well as those previously reported for rat visual cortical layers 5 [3,10] and 2/3 [11].
Figure 1 Illustration of a Quadruple Whole-Cell Recording
(A) Dodt contrast image showing four thick-tufted L5 neurons before patching on.
(B) Fluorescent image of the same four cells in whole-cell configuration.
(C) Average EPSP waveform measured in the postsynaptic neuron (bottom) while evoking action potentials in the presynaptic neuron (top).
(D) Diagram of detected synaptic connections and their strengths for this quadruple recording.
Two-Neuron Patterns
We started by assessing how well a randomly connected network [33] describes our dataset. In this model, the existence of a connection between any two neurons is independently chosen with a uniform probability p (Figure 2A). We test the predictions of this model by classifying all simultaneously recorded pairs of neurons into three classes: unconnected, unidirectionally connected, and bidirectionally connected. Given connection probability p and total number of pairs N, the expected number of unconnected pairs should be N(1 − p)
2. The expected number of unidirectionally connected pairs should be 2Np(1 − p), and the expected number of bidirectionally connected pairs should be Np2. We find that the actual number of bidirectionally connected pairs is four times that of the expected numbers (p < 0.0001) (Figure 2B). The observed overrepresentation of reciprocally connected layer 5 neurons confirms previous reports [5,6]. Such overrepresentation has also been observed in layer 2/3 of the rat visual cortex [11]. However, whether projections between layers observe this pattern remains an open question.
Figure 2 Two-Neuron Connectivity Patterns Are Nonrandom
(A) Null hypothesis is generated by assuming independent probabilities of connection.
(B) Reciprocal connections are four times more likely than predicted by the null hypothesis (p < 0.0001, Monte Carlo simulation to test for overrepresentation). Numbers on top of bars are actual counts. Error bars are standard deviations estimated by bootstrap method.
Can the overrepresentation of reciprocal connections reflect an experimental artifact? Indeed, such overrepresentation could arise from a nonuniform probability of connection for different neurons. For example, connection probability may depend on interneuron distance. To control for this artifact, we measured distances between neurons in recordings where labeling was performed successfully. We found that the probability of connection does not depend systematically on the interneuron distance (p = 0.21, chi square test) (Figures 3, S1, and S2). This is not surprising because most neurons were located closer than the span of their dendritic (and especially axonal) arbors. Our result is consistent with Holmgren et al.'s study [11], which found only a small (22%) decrease in connection probability up to 50 μm, with a more significant drop (44%) up to 100 μm for layer 2/3 pyramidal neurons.
Figure 3 Probability of Connection among Adjacent Neurons Does Not Depend Strongly on the Interneuron Distance
(A) Relative location of labeled neurons in the plane of the section. Positive direction of y-axis is aligned with apical dendrite. Potentially presynaptic neuron is located at the origin. Red—bidirectionally connected pairs; blue—unidirectionally connected pairs; green—unconnected pairs.
(B) Histogram showing the numbers of pairs in the three classes as a function of distance between neurons (Euclidian distance was calculated from relative X, Y, Z coordinates).
(C) Probability of connection versus interneuron distance. Error bars are 95% confidence intervals estimated from binomial distribution.
Another source of nonuniformity in connection probability may be axonal arbors being cut off differentially, depending on the depth of the recorded neuron from the slice surface. (The recording depths were from 10 to 100 μm.) To explore such a possibility we measured the dependence of the connection probability on the recording depth. Neither connection probability, nor strength of connection was found to depend systematically on the recording depth (see Figure S3). In addition, for every successfully labeled tissue we measured the distance from the average cell position to the nearest axonal cut point (see Figure S3). Again no strong trends in connection probability or connection strength were found. These results show that the cutting artifact is unlikely to explain observed nonrandom features.
We also considered the possible artifact of connection probability varying with age. We found a weak decline in connection probability and EPSP amplitude (consistent with Reyes and Sakmann [34]) within the range used in experiments (P12–P20; see Figure S4). Yet, such a decline is insufficient to account for the observed nonrandomness. To demonstrate this, we repeated most of the analysis on a subset of the data from 14 to 16-d-old animals when the majority of measurements were performed (see Figure S5). We found that bidirectional connections are also overrepresented in this subset of data. Results of other analyses that will be described later in the paper are also confirmed on this subset (Figure S5).
Finally, it is possible that some extreme degree of inhomogeneity in connections probability is able to explain the observed overrepresentation of reciprocal pairs, but this would probably reflect large local inhomogeneity in cortical connectivity patterns—possibly differences between subclasses [6,35], rather than experimental artifacts—and is in line with the main conclusions of this paper.
Three-Neuron Patterns
We extended our analysis by comparing the statistics of three-neuron patterns to those expected by chance [26,27]. We classify all triplets into 16 classes and count the number of triplets in each class. In order to avoid reporting overrepresented three-neuron patterns just because they contain popular two-neuron patterns, we have revised the null hypothesis[26,27]. The control distribution was obtained numerically by constructing random triplets where constituent pairs are chosen independently, but with the same probability of bidirectional and unidirectional connections as in data (Figure 4A). For example, the actual probability of a unidirectional connection is (according to Figure 2B) 495/(3312 + 495 + 218) = 0.123. Then the probability of unidirectional connection from A to B is 0.123/2 = 0.0615, the same as from B to A (see Figure 4A). The probability of bidirectional connection is (according to Figure 2B) 218/(3312 + 495 + 218) = 0.0542. The probability of finding the particular triplet class in Figure 4A by chance is the product of the probabilities of finding the three constituent pairs and a factor to account for permutations of the three neurons. The ratio of the observed counts and the expected counts for each class are plotted in Figure 4B. The actual counts are given as numbers on top of the bars. Although triplets from several of these patterns have been reported previously [9,10], small numbers of recordings have precluded statistical analysis.
Figure 4 Several Three-Neuron Patterns Are Overrepresented as Compared to the Random Network
(A) Null hypothesis for three-neuron patterns assumes independent combinations of connection probabilities of two kinds of two-neuron patterns.
(B) Ratio of actual counts (numbers above bars) to that predicted by the null hypothesis. Error bars are standard deviations estimated by bootstrap method.
(C) Raw (open bars) and multiple-hypothesis testing corrected (filled bars) p-values. p-values above 0.5 are not shown.
According to Figure 4B, several triplet patterns are overrepresented. Is this overrepresentation statistically significant? Because we look for overrepresentation in many pattern classes at the same time, the raw p-values (Figure 4C) overestimate the true significance (underestimate the true p-value). To correct the raw p-values, one has to apply a multiple-hypothesis testing procedure. We chose to correct the p-values by applying a step-down min-P-based multiple-hypothesis testing correction procedure (see Materials and Methods). The corrected p-values (Figure 4C) give the probability of mistakenly reporting at least one of the patterns as overrepresented when it is not.
Two-neuron correlations are summarized by saying that if neuron A synapses onto neuron B, then the probability of B synapsing onto A is several times greater than chance. Three-neuron correlations are summarized roughly by saying that if A connects with B and B connects with C (regardless of direction), the probability of connection between A and C is several times greater than chance. Interestingly, similar results have been obtained in the analysis of the Caenorhabditis elegans wiring diagram [36], which was reconstructed from serial section electron microscopy [1]. Because different techniques have different biases, the similarity of results suggests that correlations in synaptic connectivity represent a general property of neuronal circuits. Such property may represent evolutionary conservation from invertebrates to mammals or convergence driven by similar computational constraints.
Although individual connectivity patterns containing more than three neurons could not be analyzed statistically for the existing dataset (Table S1), we found a 70% overrepresentation of “chain” quadruplets (patterns number 21 23 24 26 28 29 31 32 33 34 35 38 39 41 43 as defined in Figure S6, p = 0.004) relative to the null hypothesis requiring that a random matrix has the actual proportion of triplet classes.
Distribution of Synaptic Connection Strengths
Next, we turned our attention to the distribution of synaptic connection strengths as characterized by EPSP amplitude (Figure 5A). We estimated the probability density function by binning connection strengths and dividing the number of occurrences in each bin by the bin size. Since there are many more weak connections than strong ones, we chose bins whose sizes increase linearly with the connection strength at the bin center. In other words, bin sizes are uniform on the log scale. The estimated density function is independent of the chosen bin size since the bin size is divided out.
Figure 5 Distribution of Synaptic Connection Strength Has a Heavy Tail
(A) Estimated probability density function in log–log space, with both lognormal fit (p[w] = 0.426exp[−(ln[w] + 0.702)2/(2 × 0.9355)2]/w) and exponential fit (p[w] = 1.82exp[−1.683w]). Notice that the lognormal fit has a heavier tail than the exponential distribution. Error bars are standard deviations estimated by bootstrap method (not shown when narrower than the dot). The numbers on top on the dots are the actual counts (not shown when more than 50).
(B) Estimated probability density distribution in semilog space, with the lognormal fit. The lognormal function shows up as a normal function in the semilog space.
(C) Empirical cumulative density function for both the probability distribution of synaptic strengths and the synaptic contribution (normalized product of probability and connection strength). They are generated directly from the data rather than the fits. The vertical line illustrates the fact that 17% of the synaptic connections contribute to half of the total synaptic strengths.
(D) Probability density function of synaptic connection strengths p(w) fitted by a lognormal function and the synaptic contribution defined as the product of the strength, w, and p(w). The total areas under both curves are normalized to 1.
The obtained distribution has a mean of 0.77 mV and a heavy tail, that is, a greater number of strong synaptic connections than expected for either the exponential distribution (Figure 5A) or the normal distribution (not shown). There are significantly more connections with strengths above 1 mV than expected by best exponential or normal fit (p < 0.0001; see Materials and Methods). We find that the dataset is best fit by a lognormal distribution, which has a bell shape when plotted on a semilog scale (Figure 5B). Although our measurements were performed in developing animals, experiments in mature animals have also revealed large single EPSPs (>5 mV) [37].
The overrepresentation of strong synaptic connections is likely to have important implications for the cortical network dynamics. This is because strong connections are few but powerful. For example, although synaptic connections with strength above 1.2 mV constitute only 17% of all connections, they contribute about half of the total synaptic weight (Figure 5C and 5D). This estimate was obtained by multiplying the number of synaptic connections by the connection strengths (assuming equal presynaptic firing rates).
Correlation of Connection Strengths in Two-Neuron Patterns
Next, we analyzed the correlations between the strengths of the synaptic connections in two-neuron patterns. We find that the synaptic strengths of the bidirectional connections are on average stronger than the unidirectional synaptic connections (mean 0.95 mV versus 0.61 mV, p = 3.1 × 10−7, Student's t-test) in agreement with [6]. The distribution of connection strengths for the bidirectional connections is expanded toward stronger connections compared to that of unidirectional connections (Figure 6A; note the semilog scale). Furthermore, the strengths of the two connections in a bidirectional pair are moderately but significantly correlated with each other (Figure 6B). To control for possible systematic variations between different quadruplets, we looked at correlations in the strength of synaptic connections that shared no pre- and postsynaptic neurons and found no significant correlation (Figure 6C). Could the correlation in connection strength result from nearby neurons having stronger connections? We do not think so because the strengths of bidirectional connections do not depend strongly on the distance between neurons (Figure 6D).
Figure 6 Bidirectionally Connected Pairs Contain Connections That Are Stronger and Correlated
(A) Synaptic connections in bidirectionally connected pairs are on average stronger than those in unidirectionally connected pairs. The probability density distribution for both the reciprocal (red solid, p(w) = 0.41exp(−(ln w + 0.60)2/(2 × 0.9762)/w) and nonreciprocal (blue dashed, p(w) = 0.47exp(−(ln w + 0.81)2/(2 × 0.8342)/w) connections are shown.
(B) In bidirectionally connected pairs synaptic connection strengths are moderately but significantly correlated (R = 0.36, p < 0.0001).
(C) Scatter plot of the strength of synaptic connections that shared no pre- and postsynaptic neurons in the same quadruple recording. There might be other connections in the quadruplet besides these two connections. No significant correlation is observed (R = 0.068, p = 0.48). All correlations calculated using Pearson's R method in log space.
(D) Average connection strength for bidirectional connections does not vary systemically with interneuron distance (one-way ANOVA, p = 0.068). Numbers on top of data points are the number of connections. Error bars are standard errors of the mean.
Another way to characterize correlations in connection strengths is by analyzing the overrepresentation of the bidirectional motif for different synaptic-strength thresholds. For every threshold value, we modify the dataset by keeping only those synaptic connections that exceed this threshold. In a unidirectionally connected pair, connection is kept only if its strength exceeds the threshold. In a bidirectionally connected pair, if both connections exceed the threshold, they are both kept. If only one of the connections exceeds the threshold, the pair becomes unidirectionally connected. Then we predict the numbers of bidirectional synaptic connections that exceed threshold by using the null model assuming independent probability, as was done for two-neuron patterns. The actual number of bidirectional connections exceeding the threshold is compared with the predicted. We find that, as the threshold is raised, the ratio of actual to expected number of bidirectional connections monotonically increases (Figure 7). This shows that reciprocity of connections is greater for stronger connections.
Figure 7 Stronger Connections Are More Likely Reciprocal than Weaker Ones
Overrepresentation of bidirectionally connected motifs gets more dramatic for higher threshold of connection strength (counts differ from random with p < 0.001 for all thresholds, Monte Carlo simulation). Significance of monotonicity is assessed by applying the Kolmogorov-Smirnov test (p < 3.5 × 10−10 for all successive pairs). Numbers on top of dots show the counts of actual pairs.
Three-Neuron Patterns with Strong Connections
We also analyzed the overrepresentation of three-neuron patterns as a function of threshold. Because of small numbers of patterns in some classes, we have grouped highly connected patterns (boxed patterns in Figure 8) together and calculated the measured counts relative to random for different thresholds. Similar to the two-neuron motifs, overrepresentation of the highly connected motifs gets more dramatic as the threshold is raised (Figure 8). Although the numbers of overrepresented three-neuron patterns are small, they may contribute to the neuronal dynamics in nontrivial ways, for example, by supporting recurrent activity. Furthermore, the contribution of three-neuron patterns depends on the chosen connection strength threshold. The relative fraction of overrepresented patterns in the network of stronger connections is much greater than that in the network of weaker connections.
Figure 8 Stronger Connections Are More Clustered than Weaker Ones
Relative overrepresentation of highly connected three-neuron motifs monotonically increases as the threshold is raised (counts differ from random p < 0.001 for all thresholds, Monte Carlo simulation). Significance of monotonicity is assessed by applying the Kolmogorov-Smirnov test (p <3.5 × 10−10 for all successive pairs). Numbers show the actual triplet counts. For the second to highest threshold, two instances of pattern 12 and one instance of pattern 16 survive. For the highest threshold, one instance each of pattern 12 and 15 survive (one of the connections in pattern 16 drops out and it becomes pattern 15).
In addition, we analyzed the strengths of synaptic connections made onto the same neuron, synaptic connections coming out of the same neuron, and synaptic connections onto and out of the same neuron (Figure S7). These strengths are weakly correlated. Correlations in the strength of incoming or outgoing connections may suggest, although not conclusively prove, the presence of neurons with particularly strong connections. Such neurons may be analogous to “network hubs,” or nodes with particularly large numbers of connections (degrees), which are known to exist in other networks [22,38].
Discussion
We showed that synaptic connectivity in the local network of layer 5 pyramidal neurons is highly nonrandom. The network consists of sparse synaptic connections that tend to cluster together in the form of overrepresented patterns, or motifs. The distribution of connection strengths has a significant tail; strong connections are few but powerful and even more clustered than the weak ones. These results suggest that the network may be viewed as a skeleton of stronger connections in a sea of weaker ones (Figure 9). Interestingly, the existence of few but powerful synaptic connections makes analyzing the network with few-neuron connectivity patterns a reasonable first step. Indeed one could have thought that, since each neuron receives inputs from thousands of others collectively determining its dynamics, analysis of few-neuron motifs is akin to “searching under the street light.” Yet, the finding of a heavy tail in the connection strength distribution suggests that a lot of power is due to a few connections. Therefore, our analysis has illuminated a significant part of the local cortical architecture, especially if the stronger connections are distributed uniformly among neurons. Naturally, this description is not complete, and future studies should investigate whether stronger synaptic connections are distributed among neurons uniformly or belong preferentially to “hub” neurons. Also, studies involving larger networks of neurons will be needed to provide a more complete understanding of the network structure and function.
Figure 9 Statistically Reconstructed Network of 50 Layer 5 Pyramidal Neurons Illustrates That Stronger Connections form a Skeleton Immersed in a Sea of the Weaker Ones
Details of statistical reconstruction are given in Materials and Methods. For illustrative purposes, neurons are arranged so that strongly interconnected nodes are close by. Dotted arrows are weak (<1 mV) unidirectional connections; solid arrows are weak bidirectional connections. Red arrows are strong (>1 mV) unidirectional connections with arrow size indicating the strength. Red arrows with double lines are strong bidirectional connections.
Although broad distribution of synaptic connections strength has been seen in the cortex [6,11] and in the cerebellum [39], heavy-tailed distributions have not been suggested as suitable fits previously. For example, in the feed-forward projection from granule to Purkinje cells in the cerebellum, the distribution was fitted by a truncated Gaussian distribution, argued to be optimal for information storage [40]. It would be interesting to see if analogous theory could be developed to explain the lognormal distribution seen among the layer 5 pyramid recurrent connections. Another relevant observation is that of mini-EPSC amplitudes [41], which were fitted by a Poisson distribution based on a binomial model of the data. In this case, however, we are looking at direct unitary connections between pairs of neurons rather than individual synapses, and such direct connections between nearby cortical neurons are typically comprised of multiple individual synapses [6,21,34,42]. Evoked and spontaneous release may also produce different synaptic strength distributions because the underlying molecular mechanisms are different. Alternatively, the lognormal distribution could depend on network activity patterns not present in dissociated cultures.
To illustrate the possible impact of the skeleton of strong connections on the network dynamics, let us consider a local network of layer 5 neurons occupying the 300 μm × 300 μm area. According to Peters et al. [43], there are 4,480 thick tufted layer 5 neurons under 1 mm2 of cortex, and therefore 400 thick tufted neurons in the considered network. With a connection probability of 0.11, each neuron receives inputs from 44 others. If distribution of connection strength is uniform among neurons, then each neuron has only about 2–3 connections in the greater than 2-mV range. If the corresponding 2–3 presynaptic neurons fire simultaneously, they may drive the postsynaptic neuron to fire. This suggests that a sparse skeleton of strong connections may drive the dynamics of the network. An exceptionally strong connection (>10 mV) may alone drive a postsynaptic neuron to fire. Suprathreshold EPSPs have been observed previously with paired recordings [37,44,45] and with calcium imaging [46]. However, such connections occur with a very low probability (about 1/1000, estimated from lognormal distribution), meaning that there are only about 20 of such connections in the considered network and that therefore most neurons do not have them. Finally, inhibitory neurons may make it more difficult to drive a postsynaptic neuron to fire and need to be investigated.
Because the highly influential, strong, and reliable (Figure S8) synaptic connections in the network are few in number, their exact connectivity pattern and properties might therefore be important and make firing patterns of the involved cortical neurons highly reproducible. This may be manifested in the simultaneous activation of several neurons in organized patterns during spontaneous and evoked activity that has been observed in cortical slices [47,48,49,50] and elsewhere [51,52,53,54]. Unfortunately, most current experimental studies rely on random sampling of neurons appropriate for studying the properties of average connections rather than the particularly strong connections. It might be important in the future to devise methods to selectively study particularly strong connections, because of their anticipated large influence on network dynamics [35,49].
Although stronger connections are likely to be important for network dynamics, weaker connections need to be considered as well. Collectively, they could affect the dynamics of the network significantly and might carry out computations with a population code. The weaker connections may be a potent driving force if firing is correlated between neurons. In addition, weaker connections may serve as a potential reserve for cortical plasticity. Indeed, weaker connections could be strengthened easily through a variety of activity-dependent learning rules (see below for an example).
In neurobiological literature, synaptic connections have been classified previously by their impact into “drivers” and “modulators” [55]. Drivers are less numerous and produce stronger impact than modulators. We stopped short of calling stronger connections among layer 5 pyramidal neurons drivers, and weaker connections modulators, for two reasons. First, previously drivers and modulators have been used to describe inputs arising from a priori different subsets of neurons, such as different pathways. Second, we do not find a clearly bimodal distribution of connections strength, suggesting that the distinction between stronger and weaker connections is not clearly defined enough to warrant two separate classes.
Next, we consider how observed distributions of synaptic strength and correlations between them might have arisen. Although it is possible that the neurons bound by stronger connections form a distinct subclass defined by perhaps distinct long-range projection patterns, or different channel densities and/or gene expression patterns, it is also possible that the distributions arise as a natural consequence of activity-dependent plasticity rules.
The lognormal distribution of synaptic connection strength may be explained by a random multiplicative process, which has been extensively studied previously [56,57,58,59]. The idea behind this is demonstrated below. Suppose a synaptic connection changes its strength multiplicatively after ith plasticity episode. This can be expressed as
wi = Fiwi-1 ,
where wi is the synaptic connection strength after ith plasticity episode, and F
i is the fractional change in synaptic strength induced by that episode. Then it is easy to see that
where wn is the current synaptic connection strength, w
0 is the initial synaptic connection strength, and Fis are the fractional changes in synaptic connection strength. If we assume all Fis to be independent and identically distributed with finite mean and variance, then by applying central limit theorem, log wn should obey a Gaussian distribution, which implies that wn obeys a lognormal distribution. For a more rigorous treatment, a decay term has to be added to make the distribution stationary, which is analogous to the Gompertz stochastic growth model in ecology [56,59] (also see Appendix S1). In a network, the fractional change in synaptic connection strengths due to long-term potentiation (LTP) would have complex dependencies both on current synaptic connection strength and on correlated activity in the network. In a previous study of layer 5 pyramidal cells, we found only a weak—although statistically significant—dependence of the percentage amount of LTP on the pre-LTP EPSP amplitude so that fractional synaptic change due to LTP was in effect approximately constant for most synaptic strengths [5]. Studies in other brain areas have found a more marked negative dependency [60,61,62]. However, this negative dependency could be counterbalanced by the stronger correlation between presynaptic and postsynaptic firing patterns introduced by a stronger synaptic connection. Regardless, it is curious that a simple independency assumption, together with synaptic decay, reproduces the observed distribution, despite the complex interactions in the network. How this is achieved warrants further investigation.
Can the overrepresentation of bidirectional connections and the correlation in the reciprocal connection strength arise from known learning rules? For example, the synaptic connections studied in this paper are known to obey a temporally asymmetric spike-timing-dependent plasticity rule [5,8], in which the strength of a synaptic connection changes according to the timing of pre- and postsynaptic spikes. If a presynaptic spike shortly precedes a postsynaptic spike, the synaptic connection is strengthened. Conversely, if a presynaptic spike follows a postsynaptic spike, the synaptic connection is weakened. Simulations have shown that in a recurrent network, if effects of spike pairs are assumed to sum linearly, this rule leads to underrepresentation of bidirectional motifs, instead of the overrepresentation observed here [63], because the firing statistics are exactly reversed for the reciprocal synaptic connections. However, for highly correlated firing of pre- and postsynaptic cells, depending on the relative durations and amplitudes of the long-term potentiation and long-term depression temporal windows, more potentiation than depression may be triggered for connections going both ways [64,65]. Furthermore, nonlinear spike interactions are known to operate at those synapses. In particular, the spike-timing-dependent plasticity rule becomes temporally symmetric if the pre- and postsynaptic neurons fire at higher than 50 Hz [5]. Whether these factors can explain this discrepancy, or additional factors need to be considered, remains to be studied.
In a wide range of networks, there is a power-law relationship between the numbers of connections a particular node has (its degree) and the abundance of such nodes [66]. These networks have been termed scale-free networks [22]. In particular, such a power-law distribution of the number of connections a neuron makes has been reported in C. elegans [22]. Here, we have not studied the degree distribution because of the lack of adequate data (such as, for example, the full connectivity diagram for the cortical network). We instead analyzed the strengths of the connections and found a lognormal distribution of synaptic connection strengths, which has a heavy tail, similar to the power-law distribution. Similar distributions have been observed in many nonbiological networks [67,68]. In the biological setting, using an in silico model of metabolic flow in yeast, Almaas et al. [28] found that network use is highly uneven and dominated by several “hot links” that represent high-activity interactions that are embedded into a web of less active interactions. Such heavy-tailed distribution for connection strengths has also been suggested based on experimental data for metabolic flow and gene regulation networks [29,30]. Therefore, a heavy-tailed distribution for connection strengths along with clustering of stronger connections into a backbone might represent a novel universal feature of many networks, in addition to the power-law distribution of number of connections commonly discussed. Such an arrangement would give the stronger links a larger role in the network and might represent a hierarchal organizational scheme of the network structure [38].
In conclusion, the statistics of connectivity in a local network of layer 5 tufted pyramidal neurons are highly nonrandom and bear similarities to other biological networks. The cortical network is best visualized as a skeleton of stronger connections in a sea of weaker connections. These findings are likely to have important implications for cortical dynamics.
Materials and Methods
Electrophysiology
The dataset used for this study was originally used for the study of long-term plasticity, and the methods were previously described in detail [5,69]. Briefly, acute visual cortical slices were cut from rats aged P12–P20. Rats were anesthetized with isoflurane, decapitated, and the brain was rapidly removed to ice-cold artificial cerebrospinal fluid (in mM: NaCl, 126; KCl, 3; MgCl2, 1; NaH2PO4, 1; CaCl2, 2.5; NaHCO3, 25; dextrose, 25; osmolality 320 mOsm, bubbled with 95% O2/5% CO2 [pH 7.4]). Slices were used after at least 1 h of incubation, and up to 11 h after slicing. Recordings were done at 32–34 °C.
Whole-cell recording pipettes (5–10 MΩ, 1–2 μm diameter) were filled with (in mM): KCl, 20; (K)Gluconate, 100; (K)HEPES, 10; (Mg)ATP, 4; (Na)GTP, 0.3; (Na)Phosphocreatine, 10; and 0.1% w/v biocytin, adjusted with KOH to pH 7.4, and with sucrose to 290–300 mOsm. Thick tufted L5 neurons were identified at 400X magnification using IR-DIC optics (Olympus BX-50; Olympus, Melville, New York, United States). To ensure that arborizations of recorded L5 neurons were minimally damaged during dissection, slices were used only if L5 apical dendrites were approximately parallel with the slice surface and could be traced most or all of the way to the pial surface. Gigaohm seals were then established on four neurons, after which breakthroughs were performed in quick succession. In some cases, one or two breakthroughs failed, thus yielding triple or double recordings; connections found in these cases were included in the dataset. Signals were amplified with AxoPatch 200B, AxoPatch-1B, and AxoClamp 2B amplifiers (Axon Instruments, Foster City, California, United States) and filtered at 5 kHz. Acquisition was done at 10 kHz using MIO-16E boards (National Instruments, Austin, Texas, United States) and custom software running on Igor Pro (WaveMetrics, Lake Oswego, Oregon, United States) on Macintosh computers (Apple Computer, Cupertino, California, United States). Recordings were terminated if membrane potential changed more than 8 mV or input resistance (measured from 250-ms-long 25 pA hyperpolarizing pulses preceding each trace) changed more than 30% from the baseline.
Measurement of synaptic connection strengths
We assessed connectivity by averaging ten or more traces. Synaptic connection strength was calculated by averaging the peak EPSP amplitudes (measured using a 1-ms-long window centered on the peak of the averaged EPSP trace) from 45 to 60 responses obtained during a 10- to 15-min-long baseline period just after breakthrough. In some cases (less than 5%), the EPSP amplitude was determined from fewer than 45 responses (although never fewer than 10 responses), typically because the recordings failed. The standard deviation of EPSP amplitude is within 0.04–1.4 mV and depends weakly on the mean EPSP amplitude (see Figure S8). As the averaged EPSP waveforms were time-locked to the presynaptic spike, the signal-to-noise ratio was good enough to allow for detection of synaptic connections with strengths as low as 0.01 mV. However, as only ten traces were averaged to determine connectivity, we might have missed connections with very low release probability.
Analysis and statistics
To evaluate correlations in synaptic connection strength, Pearson's R is calculated using the following standard formula:
where X and Y are vectors of paired samples and N is the total number of pairs. The p-value score of significance is calculated based on Fisher's z-score calculated from R. For synaptic connection strengths of reciprocally connected pairs, assignment of connection strengths as X and Y would be arbitrary. Therefore, the R value calculated should not depend on the assignment. We use each pair of X, Y values twice when calculating the R score. For each pair Xi, Yi, the pair constructed by flipping the order also entered in the formula; therefore, N is twice the total number of pairs. When calculating the p-value, the number N is taken to be the total number of pairs instead of twice the total number, so as not to overestimate the significance. The correlation scores and p-values calculated this way agree with those calculated from a reshuffling procedure by randomly assigning each neuron as either X or Y and using each pair only once.
To analyze the distribution of synaptic connection strength, we generated fits to the data, by using a mean-square error-based procedure from the MATLAB curve-fitting toolbox in the log–log space. To test whether the experimental distribution has a longer tail than the exponential or Gaussian distribution, we chose a threshold T, and counted the number of experimental observations with higher value than T, and denoted it by n, out of a total of N observations. We then calculated the p-value as the probability of generating more than n observations with values larger than T out of N observations from the null distributions.
To assess the monotonicity in Figures 7 and 8, we used the Kolmogorov-Smirnov test. For each threshold of synaptic connection strength, we generated an ensemble of 1,000 random matrix sets with matched connection statistics as described in the Motif finding section below. We then computed the distribution of ratios between occurrence counts in the random ensemble and the observed occurrence counts for motif(s) of interest. These ratios are the inverses of the ratios plotted in Figures 2 and 4 in order to avoid division by zero. We then tested for monotonicity between successive pairs of distributions with the Kolmogorov-Smirnov test.
To generate bootstrap distributions for a dataset with N observations, we drew an ensemble of 1,000 trials of N samples each from the dataset with replacement and computed the appropriate statistic on each trial. The statistics from these trials formed the bootstrap distribution. Mean and standard deviations were then computed on the bootstrap distribution of the chosen statistic.
Motif finding
To find overrepresented motifs, we used a statistic based on how the observed counts compare with the expected counts from the null hypothesis. For the null hypothesis, we generated B = 1,000 sets of random connection matrices. Each set contained as many matrices as the number of quadruplets in experimental data. The randomization procedure is as follows: In the two-neuron case, the probability that neuron A is connected with neuron B is the same as experimentally measured, and the connection from B to A is treated independently. In the three-neuron case, each neuron pair is treated as one unit, and the probabilities of having one-way and bidirectional connections within the pair are the same as measured. But how the three pairs form a triplet is random. In the four-neuron case, a 90 × 90 matrix of connections was generated. The fractional counts for each triplet motif to total triplet counts from this big matrix were matched to experiment data using a simulated annealing procedure (see [36]). To generate each random connection matrix in each of the B = 1,000 sets of matrices, we randomly picked four neurons from this 90 × 90 random connection matrix to form a 4 × 4 random connection matrix. This procedure matches the probability of observing a triplet motif to experimental data while randomizing how triplets combine to form quadruplets.
For each motif, we counted the number of its occurrences in measured data and in each set of random matrices. The p-value for this motif is the fraction of random matrices with occurrence counts above or below the observed occurrence counts. This tests for significant deviation from random, including both overrepresentation and underrepresentation.
Since we are testing for many motifs simultaneously, we applied the step-down min-P-based algorithm for multiple-hypothesis correction [70,71,72]. This procedure ensures weak control for the familywise error rate, which is defined as the probability of at least one type I error (stating that a pattern is overrepresented when it is not) among the family of hypotheses (all motifs). Weak control refers to the fact that type I error is controlled under the complete null hypothesis when all the null hypotheses are assumed to be false. Strong control, which is not used here, would control type I error rate under any combination of true and false null hypotheses, but is harder to achieve. The idea behind the step-down procedures is to order hypotheses according to the raw p-values in ascending order. Then for a chosen cutoff p-value, the hypotheses are considered successively. For each hypothesis, we test for the possibility of committing at least one type I error for the subset of hypotheses with lower or equal raw p-values. Further tests depend on the outcomes of earlier ones. As soon as one fails to reject a null hypothesis, no further hypotheses are rejected. The real procedure combines the testing for all cutoff p-values into one procedure, as described in more detail below.
First, we test for M motifs with an ensemble R of B random matrix sets, R = {Rb,b ∈ {1,…,B}} generated as described above. For each motif i ∈ {1,…,M}, we calculate the mean occurrence counts over the ensemble and denote it with c¯Ri
(step 1). Second, we calculate the raw p-values p*i
for each motif i for the two-sided statistic T, defined as the absolute value of the difference between the observed counts c and the mean ensemble counts, T = |c − c¯Ri|
. We calculate the proportion of sets of random matrices in the ensemble with a larger or equal value for statistic T than observed.
for i = 1,…, M (step 2). Third, we then order the raw p-values such that p*k1
>p*k2
> … >p*kM
(step 3). Fourth, for each Rb,b ∈ {1,…,B} and each motif ki,i ∈ {1,…,M}, we repeat step 4: count its number of occurrences cki,b
in Rb, calculate T = |cki,b − c¯Ri|
and the p-value, pki,b,
as in step 2, and then compute qki,B = minl=k1,…,kipl,b,
, the successive minima of the raw p-values (step4). Fifth, the corrected p-values p˜k1
are estimated by calculating the proportion of sets of random matrices in the ensemble in which qki,b
is smaller than or equal to the observed p-value p*k1
.
for i = 1,…,M (step 5). Finally, we enforce the monotonicity constraints by successively setting p˜ki
to max(p˜ki−1,p˜ki
for i = 2,…,M (step 6).
Statistical reconstruction of the network
To generate Figure 9, links were assigned randomly among 50 nodes with the experimentally measured probability of unidirectional and bidirectional connections. Strengths of connections were drawn from the experimentally measured distribution. Then we manually adjusted the connections to have roughly similar probability of occurrence of three-neuron motifs. In constructing this diagram, we assumed that each individual cell has the same distribution of strong and weak synaptic connections. This assumption could be violated if some cells have many stronger synaptic connections while others have few or none. Whether this is the case should be investigated in future studies. This figure is for illustration purposes only.
Positions of recorded neurons
To investigate the dependence of connectivity on pairwise distances, we measured the relative coordinates of the recorded cells from slices prepared by biocytin histochemistry after recordings. Distances were not corrected for tissue shrinkage. Since we were most interested in the relationship between connectivity and distance, an equal amount of shrinkage for all slices would not affect our results. Some inhomogeneities of shrinkage were likely, but we did not expect the shrinkage factor to vary greatly across slices.
During recording sessions, the approximate relative positions of cells and the positions of recorded quadruplets in the slice were kept in notes. In most cases, these drawings allowed unambiguous identification of recorded cells, and cell positions were then measured on those cases after identification of the recorded cells. If a quadruplet was totally unconnected, drawings were not provided. However, totally unconnected cells did not have to be identified, and the assignment was made randomly. In some cases, some of the cells in the quadruplet were not well stained. If positions of at least three cells out of the quadruplet could be recovered, the positions of those cells were recorded.
We defined the position of each cell as the three-dimensional coordinate of the axonal initial segment and measured it using the Neurolucida system (MicroBrightField, Williston, Vermont, United States). We estimate the measurement error to be less than 2 μm in X and Y positions and less than 3 μm in Z positions, based on repeated measurements of the same quadruplets. In about 10% of the cases, the initial segment of the axon was obscured by other cells and could not be positively identified, and the cell position was instead measured from the middle of the base of the cell body. In these cases, the measurement error could be as large as 3 μm in X and Y and 5 μm in Z. For each slice, we also estimated the average position of the main apical dendrites of the quadruplet around 300 μm away. The positive direction for the vector from the mean positions of the cells to the estimated average position of the main apical dendrites was defined to be the pia direction in the slice. We rotated the original relative coordinates of pairs in the X, Y plane so this vector pointed in the positive Y direction. We normalized the vector and defined it as the original coordinates of the new unit Y vector. The new unit X vector is the normal direction to the unit Y vector. To calculate the relative X, Y coordinates of two cells in the new coordinates, we took the dot product of the relative vector calculated in the original coordinates and the unit X and Y vectors, also defined in the original coordinates. The relative Z coordinates were not subject to rotation. Notice that rotation was done on the relative positions of any two cells and not on the positions of each individual cell.
A total of 817 cells in 83 triplets and 142 quadruplets were measured, resulting in a total of 2,202 possible connections. For each possible connection, the relative position of the target neuron to the originating neuron was plotted in Figure 3A. If a connection was present and was involved in a bidirectional connection, the position was indicated with red. If a connection was present, and the reciprocal connection was not present, the position was indicated with blue. If a connection was not present, regardless of the status of the reciprocal connection, the position was indicated with green. Most of the cells included in the dataset came from nearby positions (<50 μm, 82% of pairs), with the remaining 18% of the connections in the 50 μm–110 μm range (Figure 3B). The densities of red, green, and blue connections are proportional to each other regardless of the distance from the soma. The connection probability is mostly uniform within the range of three-dimensional distances recorded in the experiments (Figure 3C). However, the connection probability for all distances (0.013) is slightly higher than that calculated for the entire dataset (0.0116). This is likely due to less efficient recovery of unconnected quadruplets, as less care was taken to preserve them. The connection strengths do not vary with distance systematically (data not shown), although the distance does seem to have an effect (p = 0.02, one-way analysis of variance).
To control for cutting artifacts, we have measured the closest cut ending of the main axons out of the four cells in the quadruplet to the mean position of the cell bodies. The main axons go toward white matter to innervate subcortical structures. When the distance is small, then we might have cut off more portions of the axonal arbor, and cutting artifact might be a concern. However, since the main axons start branching approximately 100 μm from the cell bodies [73], the local connectivity might not be greatly affected (see Figure S3).
We also measured the Z coordinate of the slice surface. From this coordinate and the coordinate of the cells, we can deduce the depth of each cell from the slice surface. Cells closer to the surface might have had larger portions of their axonal arbors cut off, which would reduce their connectivity. However, the connectivity seems to be fairly uniform regardless of the depth of either the originating cell or the target cell (see Figure S3). The caveat is that the measurements we have taken from the fixed slices are uncorrected for shrinkage, and differential shrinkage in the Z direction might have randomized a trend that might otherwise be present.
Supporting Information
Appendix S1 Properties of the Lognormal Distribution
(39 KB DOC).
Click here for additional data file.
Figure S1 Connectivity and Mean Synaptic Strengths of the Connections Are Uniform for All Distances between a Pair of Neurons
For the x-axis, positive means the receiving cell is to the right of the sending cell in the slice. For the y-axis, positive means the receiving cell is above the sending cell. For the z-axis, positive means the receiving cell is on top of the sending cell.
(A, D, G, and J) Connection probability for X, Y, and Z and angle between the vector connecting two neurons and the x-axis separately (no significant variation; all chi square tests, p > 0.05). Error bars are 95% confidence intervals estimated from binomial distributions.
(B, E ,H , and K) Mean synaptic strengths for X, Y, and Z and angle separately (no significant variation; all one-way ANOVA tests, p > 0.05). Error bars are standard errors of the mean.
(C, F, I, and L) Histogram of connections for X, Y, and Z and angle separately.
(363 KB DOC).
Click here for additional data file.
Figure S2 The Overrepresentation of Bidirectionally Connected Pairs Is Not Due to Inhomogeneous Connection Probabilities for Neurons of Different Distances
Counts relative to random are shown for neurons of different distances. The red line indicates the value calculated for pooled data for neurons of all distances. Notice that the values calculated for certain distances are very similar to that calculated for pooled data. However, the ratio calculated for all distances (3.0) is a bit lower than that calculated for the entire dataset (4.0), probably owing to increased connection probability (0.013 versus 0.0116) caused possibly by inefficient recovery of totally unconnected quadruplets. Error bars are standard deviations from bootstrap.
(119 KB DOC).
Click here for additional data file.
Figure S3 Connection Probability and Mean Synaptic Connection Strengths Are Not Greatly Modified by Cutting
(A) Connection probability is uniform with regard to the distance to the closest main axon cut ending (p = 0.077, chi square test). Notable exception is distances more than 600 μm away, where the connection probability seems to be slightly increased. However, since there are relatively few neurons with axon cut distance of more than 600 μm and the increase in connection probability is not statistically significant, we do not expect this to fully explain our results.
(B) Mean synaptic connection strength does not vary systematically with regard to the distance to the closest main axon cut ending (however, mean strength depends on distance; p = 0.02 by one-way ANOVA).
(C) Histogram of neurons with certain axon cut distances.
(D) Connection probability is uniform with regard to the depth of both the neuron sending the connection and the neuron receiving the connection (p = 0.99, chi square test).
(E) Mean synaptic connection is uniform with regard to the depth of both the neuron sending the connection and the neuron receiving the connection (p = 0.2, one-way ANOVA).
(F) Histogram of recorded neurons with certain depth.
Error bars in (A) and (D) are 95% confidence intervals estimated from binomial distribution. Error bars in (B) and (E) are standard errors of the mean.
(244 KB DOC).
Click here for additional data file.
Figure S4 EPSP Size and Rate of Connectivity Does Not Significantly Depend on Animal Age
(A and B) We found no statistically significant difference among EPSP amplitudes for animals of different ages (one-way ANOVA in log space, p = 0.36). We note, however, that there is a weak downward trend, in agreement with the observation of [34] that L5-to-L5 synaptic strength is significantly weaker in P28 animals than in P14 animals. Error bars are standard errors of the mean.
(C) The connectivity rate does not depend on animal age (chi square test, p = 0.92). Error bars are 95% confidence intervals calculated from binomial distribution.
(218 KB DOC).
Click here for additional data file.
Figure S5 Main Results of This Paper Are Still Valid for the Subset of Data from P14–P16 Animals
(A and B) Overrepresentation of bidirectional connections and highly connected triplets. Numbers on top of bars are actual counts.
(C) Synaptic connection strengths are well fit by the lognormal distribution. Number on top of dots are actual counts (not shown when greater than 50).
(D) Probability of significant deviation from random for a given triplet motif.
(E and F) Increase in overrepresentation of bidirectional connections and highly connected triplets for increasingly higher connection strength thresholds.
(847 KB DOC).
Click here for additional data file.
Figure S6 Quadruplet Catalogue
(1.2 MB DOC).
Click here for additional data file.
Figure S7 Synaptic Strengths of Incoming and Outgoing Connections Are Weakly Correlated
(A) Scatter plot of incoming synaptic connection strength. A weak correlation of 0.2 is observed (p = 0.029).
(B) Scatter plot of outgoing synaptic connection strength. A weak correlation of 0.17 is observed (p = 0.054).
(C) Scatter plot of outgoing and incoming synaptic connection strength. A weak correlation of 0.13 is observed (p = 0.039). All correlations calculated using Pearson's R method in log space.
(231 KB DOC).
Click here for additional data file.
Figure S8 EPSP Standard Deviation Depends Weakly on EPSP Amplitude
(A) EPSP standard deviation depends weakly on EPSP amplitude.
(B) Coefficient of variation is inversely proportional to the EPSP amplitude. Note the log–log scale.
(128 KB DOC).
Click here for additional data file.
Table S1 Quadruplet Counts
Quadruplets are numbered according to the catalogue in Figure S1.
(124 KB DOC).
Click here for additional data file.
We thank Yuri Zilberter, Anthony Zador, Armen Stepanyants, Michael Häusser, Kazuo Kitamura, David Hansel, Nicolas Brunel, and Alanna Watt for help and useful discussions. We thank Dongyu Zhao for help with Figure 9 and Ju Lu for proofreading the manuscript. This work was supported by a Marie Curie Intra-European Fellowship (PJS), the David and Lucille Packard Foundation and the National Institutes of Health (NIH)/National Institute of Mental Health grant number 69838 (DBC), NIH/National Eye Institute grant EY015273 (SN), and an NIH–National Research Service Award postdoctoral fellowship (SS). PJS also thanks the Wellcome Trust and Gatsby Foundation for support.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. PJS and SN conceived and designed experiments. PJS performed the experiments. SS and DBC conceived, designed, and performed the analysis using software developed by MR. SS measured cell positions.
Citation: Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol 3(3): e68.
Abbreviations
EPSPexcitatory postsynaptic potential
LTPlong-term potentiation
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| 15737062 | PMC1054880 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 1; 3(3):e68 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030068 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706310.1371/journal.pbio.0030069Research ArticleCell BiologyMolecular Biology/Structural BiologyBiochemistryHomo (Human)Dissociation of Cohesin from Chromosome Arms and Loss of Arm Cohesion during Early Mitosis Depends on Phosphorylation of SA2 Regulation of Cohesin by PhosphorylationHauf Silke
1
¤aRoitinger Elisabeth
1
Koch Birgit
1
Dittrich Christina M
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¤bMechtler Karl
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Peters Jan-Michael [email protected]
1
1Research Institute of Molecular PathologyViennaAustriaHawley R. Scott Academic EditorStowers Institute for Medical ResearchUnited States of America3 2005 1 3 2005 1 3 2005 3 3 e697 9 2004 14 12 2004 Copyright: © 2005 Hauf et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Shugoshin Prevents Dissociation of Cohesin from Centromeres During Mitosis in Vertebrate Cells
Chromosome Cohesion: A Cycle of Holding Together and Falling Apart
Separating Sisters: Shugoshin Protects SA2 at Centromeres but Not at Chromosome Arms
Cohesin is a protein complex that is required to hold sister chromatids together. Cleavage of the Scc1 subunit of cohesin by the protease separase releases the complex from chromosomes and thereby enables the separation of sister chromatids in anaphase. In vertebrate cells, the bulk of cohesin dissociates from chromosome arms already during prophase and prometaphase without cleavage of Scc1. Polo-like kinase 1 (Plk1) and Aurora-B are required for this dissociation process, and Plk1 can phosphorylate the cohesin subunits Scc1 and SA2 in vitro, consistent with the possibility that cohesin phosphorylation by Plk1 triggers the dissociation of cohesin from chromosome arms. However, this hypothesis has not been tested yet, and in budding yeast it has been found that phosphorylation of Scc1 by the Polo-like kinase Cdc5 enhances the cleavability of cohesin, but does not lead to separase-independent dissociation of cohesin from chromosomes. To address the functional significance of cohesin phosphorylation in human cells, we have searched for phosphorylation sites on all four subunits of cohesin by mass spectrometry. We have identified numerous mitosis-specific sites on Scc1 and SA2, mutated them, and expressed nonphosphorylatable forms of both proteins stably at physiological levels in human cells. The analysis of these cells lines, in conjunction with biochemical experiments in vitro, indicate that Scc1 phosphorylation is dispensable for cohesin dissociation from chromosomes in early mitosis but enhances the cleavability of Scc1 by separase. In contrast, our data reveal that phosphorylation of SA2 is essential for cohesin dissociation during prophase and prometaphase, but is not required for cohesin cleavage by separase. The similarity of the phenotype obtained after expression of nonphosphorylatable SA2 in human cells to that seen after the depletion of Plk1 suggests that SA2 is the critical target of Plk1 in the cohesin dissociation pathway.
Cohesin holds newly replicated chromosomes together until a cell is ready to divide. These authors show how phosphorylation regulates cohesin function
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Introduction
Faithful inheritance of the genome depends on its accurate replication and correct distribution to the two daughter cells. In eukaryotes, the two copies of a chromosome that are generated in S-phase (sister chromatids) remain connected until they are separated in anaphase of mitosis. This physical association (cohesion) allows the mitotic segregation machinery to handle sister chromatids as entities that have to be distributed to opposite poles. Sister chromatid cohesion depends on cohesin, a protein complex that is highly conserved in evolution and consists of at least four subunits: two “structural maintenance of chromosomes” proteins, Smc1 and Smc3, the so-called “kleisin” subunit Scc1 (also called Rad21 or Mcd1), and Scc3 (reviewed in [1]). Cells of humans, Xenopus, and other higher eukaryotes contain two mitotic orthologs of Scc3, called SA1 and SA2. Cohesin complexes in these cells contain either SA1 or SA2, but not both [2,3].
In order to segregate sister chromatids to opposite poles in anaphase, cohesin has to be removed from chromosomes. In budding yeast, the prevalent mode of cohesin removal is by proteolytic cleavage of the Scc1 subunit at the onset of anaphase by the endopeptidase separase [4,5]. Prior to anaphase, separase is kept inactive by its inhibitor securin [5,6,7,8,9,10], and in vertebrate cells also by inhibitory phosphorylation mediated by Cdk1 [11]. Both securin and Cdk1's activating subunit cyclin B are ubiquitinated at the onset of anaphase by the anaphase-promoting complex/cyclosome, leading to their proteasome-dependent degradation and to separase activation (reviewed in [12]).
In higher eukaryotes, removal of cohesin from chromosomes occurs in at least two steps. During prophase and prometaphase, the bulk of cohesin dissociates from chromosomes without Scc1 cleavage [3,13]. Only minor amounts of cohesin (an estimated 10%) remain on chromosomes up to metaphase, preferentially at centromeres [10,14]. A similarly minor amount of cohesin, presumably the chromosome-bound fraction, is cleaved by separase at the onset of anaphase [10]. As in budding yeast, the cleavage of Scc1 is essential for anaphase to occur [15]. Two mitosis-specific protein kinases are required for the cleavage-independent removal of cohesin from chromosome arms: Plk1, called Plx1 in Xenopus, and Aurora-B [16,17,18]. Plk1/Plx1, but not Aurora-B, can phosphorylate the cohesin subunits Scc1 and SA2 in vitro [16,17], and is required for their phosphorylation in Xenopus egg extracts [16]. In these extracts, the ability of cohesin to bind to chromatin correlates inversely with its phosphorylation state [16]. This observation, and the finding that Plk1 is required for cohesin dissociation from chromosomes, raise the possibility that phosphorylation of cohesin by Plk1 leads to its cleavage-independent dissociation from chromosomes. However, it is unknown whether Plk1's critical target in the cohesin dissociation process is cohesin itself, and whether cohesin phosphorylation is required for dissociation of the complex from chromosomes. So far, the functional relevance of cohesin phosphorylation has been studied only in budding yeast. In this organism, Scc1 is also phosphorylated by a Polo-like kinase, called Cdc5, but this modification does not seem to result in cohesin's dissociation from chromatin; rather, it renders Scc1 more susceptible to cleavage by separase [19,20].
To test whether cohesin phosphorylation is required for its dissociation from chromosome arms during prophase and prometaphase, and to address whether this modification could explain the requirement for Plk1 in this process, we have searched for phosphorylation sites on all four subunits of the human cohesin complex by mass spectrometry. We have identified numerous mitosis-specific sites on Scc1 and SA2, mutated them, and expressed wild-type and nonphosphorylatable forms of both proteins stably at physiological levels in cultured human cells. The analysis of these cell lines, in conjunction with biochemical experiments in vitro, imply that Scc1 phosphorylation is dispensable for cohesin dissociation from chromosomes in early mitosis, but enhances the cleavability of Scc1 by separase. In contrast, our data reveal that phosphorylation of SA2 is essential for cohesin dissociation during prophase and prometaphase, but is not required for cohesin cleavage by separase. The similarity of the phenotype obtained after expression of nonphosphorylatable SA2 (this study) to that seen after the depletion of Plk1 [18] strongly suggests that SA2 is the critical target of Plk1 in the cohesin dissociation pathway.
Results
Identification of Mitosis-Specific Phosphorylation Sites on Human Cohesin
The SA1, SA2, and Scc1 subunits of vertebrate cohesin complexes are phosphorylated specifically in mitosis [2,14,16]. To be able to analyze the functional relevance of these modifications, we mapped mitosis-specific phosphorylation sites on human cohesin by mass spectrometry. We prepared lysates from HeLa cells that had been arrested either at the G1/S transition (interphase) by hydroxyurea or in mitosis by the spindle poison nocodazole, and immunoprecipitated SA1- and SA2-containing cohesin complexes with antibody 447, which recognizes the C termini of both SA1 and SA2 [3]. Separation of the isolated proteins by SDS-PAGE and staining with silver (Figure 1A) or cohesin-specific antibodies (Figure 1B) demonstrated reduced electrophoretic mobility of both SA1 and SA2 when the complexes had been isolated from mitotic cells, indicating that the mitosis-specific phosphorylation of these subunits was preserved during the isolation procedure. This notion was confirmed by immunoblotting with phosphothreonine-specific antibodies. This assay yielded a pattern that was consistent with the presence of phosphothreonine residues on SA1, SA2, and Scc1 in mitosis, whereas no phosphothreonine signal was detected on cohesin subunits isolated from interphase cells (Figure 1B).
Figure 1 Identification of Mitosis-Specific Phosphorylation Sites on Human Cohesin
(A and B) Cohesin was immunoprecipitated by antibody 447 (which recognizes SA1 and SA2) from extracts prepared from HeLa cells that were either arrested in S-phase by hydroxyurea (HU) or in mitosis by nocodazole (Noc). Cohesin was eluted by buffer of low pH and analyzed by (A) silver staining and (B) immunoblotting with antibodies to cohesin subunits and phosphorylated threonine (P-Thr).
(C) Schematic representation of the phosphorylation sites on Scc1 and SA2 that were identified by mass spectrometry, and of the mutant versions of the proteins that have been generated. The star indicates a phosphorylation site that was found in both interphase and mitotic Scc1. All SA2 constructs used for in vitro experiments lack the 69 N-terminal amino acids. SA2-WT-myc and SA2–12xA-myc cell lines contain the entire open reading frame of 1,231 amino acids.
To identify phosphorylation sites, purified cohesin complexes were digested with various proteases in solution and analyzed by high performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS). In total, 28 phosphorylated serine or threonine residues were identified. Of these, 18 could be assigned unequivocally to specific residues, whereas in ten cases it could not be determined which of several serine and threonine residues in a given peptide was phosphorylated (Figure 1C and Table 1). Two sites each were found in Smc1 and Smc3, ten in Scc1, and 14 in SA2. We were unable to identify phosphorylated peptides derived from SA1, presumably because SA1-containing cohesin complexes are much less abundant than SA2-containing complexes [2,3]. By analyzing cohesin peptides obtained by digestion with various proteases, we were able to obtain a sequence coverage of approximately 80% for each of the subunits (Figure S1 and unpublished data). This indicates that our analysis was theoretically able to identify the majority of in vivo phosphorylation sites on cohesin subunits, although we cannot exclude the possibility that some sites have gone undetected.
Table 1 Phosphorylation Sites Identified in Cohesin Subunits
Where two or more residues are marked, the phosphorylation site could not be unequivocally assigned to any one of the adjacent residues
+, phosphorylated; ++, highly phosphorylated
DOI: 10.1371/journal.pbio.0030069.t001
The phosphorylation sites we identified on Smc1 and Smc3 were present in complexes from both interphase and mitotic cells, and we therefore did not analyze them any further. Two of these sites (Ser966 and Ser957 in Smc1) have been shown to be phosphorylated by the ATM kinase in response to DNA damage [21,22]. Only one of the sites in Scc1 (Ser153), and none in SA2, was found phosphorylated in interphase. All other sites were only identified in cohesin samples from mitotic cells. These results confirm that Scc1 and SA1/2 are specifically phosphorylated in mitosis.
Since Plk1/Plx1 can phosphorylate Scc1 and SA1/SA2 in vitro and is required for the phosphorylation of these subunits in Xenopus egg extracts [16], we compared the sequence surrounding the identified sites to the consensus sequences that have been reported as binding and phosphorylation sites for Plk1. The C-terminal Polo box domain of Plk1 binds to phosphorylation sites for which the consensus S-[pT/pS]-[P/x] has been proposed [23]. These phosphorylation sites are thought to be generated by proline-directed kinases, such as Cdk1, that “prime” the substrate for subsequent recognition by Plk1. Once bound to the substrate, Plk1 is thought to phosphorylate sites that are distinct from the one that is recognized by the Polo box. Based on in vitro phosphorylation experiments using peptides derived from the phosphatase Cdc25C, Nakajima et al. [24] have proposed the consensus (E/D)-x-(S/T)Φ-x-(E/D) for Plk1 phosphorylation-sites, where Φ signifies a hydrophobic amino acid, whereas Barr et al. [25] have suggested the consensus (E/D/Q)-x-(S/T)Φ. One putative phosphorylation site in Scc1 (Thr186) and three putative sites in SA2 (Ser1065, Thr1124, and Ser1178) match the Polo box-binding consensus sequence, although none of these sites contains a proline residue at the +1 position (these are putative sites because they are present in peptides in which the exact identity of the residue carrying the phosphomoiety could not be determined; Table 1). We also note that, although Cdk1 can phosphorylate SA1/2 in vitro [2], it does not appear to be essential for cohesin dissociation in human cells and Xenopus egg extracts [3,16]. Two phosphorylation sites in Scc1 (Thr144 and Thr312) match the consensus proposed by Nakajima et al. [24]. These two sites, in addition to one in Scc1 (Ser454) and three in SA2 (Thr1109, Ser1137, and Ser1224) conform with the consensus proposed by Barr et al. [25]. These findings are consistent with the possibility that at least some of the sites in Scc1 and SA2 are directly phosphorylated by Plk1.
Alignment of multiple sequences showed that most of the amino acid residues that we found to be phosphorylated in mitotic human Scc1 and SA2 were conserved in orthologs from other vertebrates, but not in more distantly related eukaryotes such as Drosophila, Caenorhabditis elegans, or yeast, where the homology to human cohesin subunits is in overall much lower (unpublished data). When we considered the distribution of the sites, we found that Scc1 phosphorylation sites strikingly clustered around the two separase cleavage sites, whereas SA2 phosphorylation sites were concentrated in the C terminus of the protein (Figure 1C). Clustering of Scc1 phosphorylation sites in the vicinity of separase cleavage sites has also been observed in budding yeast [19]. This indicates that, although individual sites on Scc1 are not conserved from yeast to human, the overall pattern of phosphorylation is conserved. Evolutionary conservation of the overall mitotic phosphorylation pattern, but not of individual sites, has also been observed in the case of the anaphase-promoting complex/cyclosome [26].
Phosphorylation of Scc1 Is Required for Efficient Cleavage by Separase In Vitro
In budding yeast, phosphorylation of Scc1 by the Polo-like kinase Cdc5 enhances the rate of cleavage by separase both in vitro and in vivo [19,20]. We therefore analyzed whether treatment of human Scc1 with Plk1 similarly increases its cleavability by separase (Figure 2). We activated purified human separase-securin complexes by securin destruction in mitotic Xenopus egg extracts and incubated the activated separase with 35S-labeled recombinant Scc1 as a substrate. Under these conditions, separase cleaves Scc1 at the same sites that are cleaved at anaphase onset in vivo, Arg172 and Arg450 ([15]; Figure 2A). When untreated Scc1 was incubated with separase, efficient cleavage could only be detected at the more N-terminal site, resulting in the formation of one N-terminal and one C-terminal cleavage product which could be seen by Phosphorimager analysis (Figure 2A and 2B). The C-terminal cleavage product could also be detected by immunoblotting using antibodies to a myc-epitope tag on the C-terminus of Scc1 (Figure 2A). When the cleavage reaction was carried out in the presence of active recombinant Plk1, cleavage at the first Scc1 site was slightly enhanced, and cleavage at the second site now became apparent (Figure 2A and 2B), consistent with the possibility that phosphorylation of Scc1 by Plk1 increases the cleavability of Scc1. A cleavage product that corresponds to the fragment in between the two cleavage sites could not be detected under these conditions (Figure 2A), indicating that separase cleaves either one or the other site in one Scc1 molecule, but not or only rarely both.
Figure 2 Plk1 Facilitates Cleavage of Human Scc1 by Separase In Vitro
(A) Recombinant, 35S-labeled, wild-type and mutant Scc1 (see Figure 1C) tagged with 9xmyc at the C terminus were incubated with human separase. Recombinant human GST-Plk1 was added to the reaction mixtures where indicated. Samples were withdrawn from the reactions at the indicated time points and analyzed by SDS-PAGE followed by immunoblotting (anti-myc) and Phosphorimager analysis (35S exposure). Arrows indicate full length Scc1-myc (fl), C- and N-terminal fragments resulting from cleavage at Arg172 (Ct #1, Nt #1, respectively), and C- and N-terminal fragments resulting from cleavage at Arg450 (Ct #2, Nt #2, respectively). The lower parts of the membrane or gels were exposed longer than the upper parts. The enhancement of cleavage at Arg172 by Plk1 can be seen particularly well by comparing the intensities of the N-terminal fragments (Nt #1). Note that in the autoradiographs a band can be seen (particularly clearly in the lanes representing the zero time points) that has almost the same electrophoretic mobility as cleavage product Ct #1. This band is distinct from Ct #1 because it migrates a slightly shorter distance and because it is also present in the absence of separase. This band was therefore not included in the quantification in (B).
(B) Quantification of the abundance of Scc1-myc and the Scc1-myc cleavage fragments in the assay shown in the left autoradiograph of (A). For the quantification, autoradiographs of identical exposure were used. The sum of the intensities of full-length and all cleavage fragments was set to 100%. Signal intensities for N- and C-terminal fragments resulting from cleavage at the same site were summed.
(C) Chromatin fractions were prepared from HeLa cells stably expressing either wild-type Scc1-myc or the mutant Scc1-S454A-myc, and were incubated in either interphase or mitotic Xenopus egg extract. Mitosis-specific cleavage of Scc1 was detected by immunoblotting with myc antibodies.
We suspected that the observed enhancement of Scc1's cleavability in the presence of Plk1 might be due to phosphorylation at two sites that are directly adjacent to the cleavage sites, Ser175 and Ser454, which we had found to be phosphorylated in mitosis in vivo (Table 1). We therefore mutated these two residues to alanine, thereby creating mutant Scc1-S175A/S454A (see Figure 1C), and tested the cleavability of this mutant in the absence or presence of Plk1 in vitro. Scc1-S175A/S454A could still be cleaved at the first site, and this reaction was still slightly enhanced in the presence of Plk1, but cleavage at the second site was completely abolished even in the presence of Plk1 (Figure 2A and 2B). This result suggested that cleavage at Arg450 of Scc1 by separase depends on phosphorylation of Ser454. The analysis of a Scc1 mutant in which only Ser454 was changed to alanine (Scc1-S454A) confirmed this notion (unpublished data; see Figure 2C for a related result). Next, we asked whether the enhancement of cleavage at Arg172 upon incubation with Plk1 is due to phosphorylation of any of the other residues we had identified. We therefore created a Scc1 mutant in which nine of the ten identified phosphorylation sites were mutated to alanine (Scc1–9xA; see Figure 1C). Mutation of the tenth site was not possible for technical reasons. When Scc1–9xA was incubated with active separase, we found, as expected, that cleavage at Arg450 was abolished, but cleavage at Arg172 did occur and was still enhanced by the addition of Plk1 (unpublished data). At present, it is therefore unclear how Plk1 enhances cleavage of Scc1 at Arg172. The effect could be due to phosphorylation of one or more of the serine and threonine residues at positions 185–189 (Table 1), which were not included in our mutational analysis; alternatively, it might be due to phosphorylation on one or more residues that our mass spectrometry analysis failed to identify. It also remains a formal possibility that Plk1 enhances cleavage at Arg172 by phosphorylating separase rather than cohesin.
Scc1 Phosphorylation Is Not Essential for the Dissociation of Cohesin from Chromosome Arms and for Progression through Mitosis
To address the physiological significance of Scc1 phosphorylation and the resulting enhanced cleavability of Scc1, we generated cell lines that stably express Scc1-S454A or Scc1–9xA (Figure 3; see also Figure 1C). To be able to distinguish the ectopically expressed Scc1 proteins from endogenous Scc1, we tagged the Scc1 mutants with nine myc epitopes at their C termini. We have shown that such a tag does not detectably compromise the ability of Scc1 to assemble into cohesin complexes [10] that can establish cohesion [15]. Since expression of mutant Scc1 could have deleterious effects on cells, we furthermore used a doxycycline-regulatable promoter to be able to control the level of expression. To avoid potential overexpression artifacts, we screened by immunoblotting for cell lines in which the ectopically expressed Scc1 is present in amounts that are similar to the amounts of endogenous Scc1 when expression is fully induced by doxycycline (Figure 3A). Immunoprecipitation of the ectopically expressed Scc1 with myc antibodies, followed by SDS-PAGE and silver staining, revealed that the mutated forms of Scc1 could associate with Smc1, Smc3, and SA1/2 into cohesin complexes (Figure 3B; note that endogenous untagged Scc1 does not coimmunoprecipitate with Scc1-myc, indicating that only one Scc1 molecule is present per cohesin complex). Sucrose density gradient centrifugation experiments in conjunction with immunoblotting showed that most of the ectopically expressed Scc1 was incorporated into cohesin complexes (Figure S2A). These observations indicate that any phenotypes (but also the absence of phenotypes) that are observed after ectopic expression of Scc1 are not simply caused by overexpression or by the inability of Scc1 to assemble into cohesin complexes.
Figure 3 Characterization of HeLa Cell Lines Stably Expressing Wild-Type or Mutant Forms of Human Scc1 and SA2
(A) Wild-type Scc1 or SA2, or the indicated mutant proteins (see Figure 1C), all tagged with 9xmyc at the C terminus, were stably and inducibly expressed in HeLa tet-on cells. After induction by treatment with 2 μg/ml doxycycline for 1–3 d, cell extracts were prepared from either logarithmically proliferating cells (i, interphase) or from cells arrested in mitosis by nocodazole (m, mitosis), then immunoblotted. In the case of Scc1 cell lines (upper blots), only data from interphase extracts are shown. Exogenous protein was detected by immunoblotting with myc antibodies (lower blots). Since the 9xmyc-tag caused a reduced mobility in SDS-PAGE compared to the endogenous protein, Scc1- and SA2-immunoblots (upper blots) revealed the relative amounts of exogenous and endogenous protein in the different cell lines. The position of molecular weight markers is indicated on the right side.
(B) Extracts were prepared from the different cell lines as indicated. Immunoprecipitation was performed using myc antibodies, followed by SDS-PAGE and silver staining. As a control, the cohesin complex was immunoprecipitated from untransfected HeLa tet-on cells using antibodies to SA2.
(C) Extracts were prepared from SA2-WT-myc or SA2–12xA-myc expressing cells, and fractionated by sucrose density gradient centrifugation (5%–30% sucrose), followed by immunoblotting with antibodies recognizing the proteins indicated on the right (inp. = input/unfractionated sample of the extract).
To analyze whether mutation of Ser454 abolishes cleavage of Scc1 at Arg450 also when Scc1 is part of cohesin complexes that have been loaded onto chromatin in vivo, we incubated chromatin from HeLa cells expressing wild-type Scc1, Scc1-S454A, or Scc1–9xA in Xenopus egg extracts. In this assay, Scc1 is cleaved by separase at Arg172 and Arg450 if the Xenopus egg extract is in a mitotic state [10], presumably because separase and Plx1 are active under these conditions. Whereas wild-type Scc1 was efficiently cleaved at both sites in mitotic egg extract, we did not observe any fragment resulting from cleavage at Arg450 when either Scc1-S454A or Scc1–9xA was analyzed (Figure 2C and unpublished data), further supporting the conclusion that phosphorylation at Ser454 of Scc1 is essential for cleavage at Arg450.
Immunofluorescence analysis demonstrated that the localization of Scc1-S454A and Scc1–9xA was very similar to the one of wild-type Scc1 (Figure S2B and unpublished data). Both mutants were present in nuclei from telophase through interphase until the next mitosis. In prometaphase, the bulk of mutant cohesin complexes had dissociated from chromosome arms, but small amounts remained at centromeres, even if prometaphase was prolonged by treatment of the cells with nocodazole (Figure S2B). Scc1 phosphorylation at the nine mutated sites is therefore essential neither to load cohesin onto chromatin nor to remove cohesin from chromosome arms in early mitosis. Furthermore, we were unable to observe obvious abnormalities at later stages of mitosis and in the overall ability of cells expressing nonphosphorylatable Scc1 to proliferate (unpublished data), indicating that Scc1 phosphorylation at the mutated sites is not essential for the ability of separase to cleave cohesin complexes and to initiate anaphase. The finding that cells expressing Scc1 in which residue 454 cannot be phosphorylated and in which cleavage at Arg450 is therefore compromised (see Figure 2C) do not show anaphase defects is in agreement with our previous observation that Scc1 cleavage at Arg172 is sufficient for the viability of HeLa cells [15].
Phosphorylation of SA2 Is Essential for the Dissociation of Cohesin from Chromosomes during Prophase and Prometaphase
The observation that nonphosphorylatable Scc1 mutants bind chromatin in interphase and dissociate from chromosome arms normally in early mitosis implied that the requirement for Plk1 in cohesin dissociation and the inhibitory effect of cohesin phosphorylation on chromatin binding [16,18] cannot be explained by Scc1 phosphorylation. We therefore asked whether phosphorylation of SA2 might control the association of cohesin with chromosomes. We generated a mutant of SA2 in which serine/threonine residues at 12 sites found to be phosphorylated in mitosis in vivo were mutated to alanine; this mutant was called SA2–12xA (see Figure 1C). We C-terminally tagged this mutant and wild-type SA2 with myc epitopes and expressed both in HeLa cells in a stable and inducible manner, employing the same strategy that we had used for Scc1. Also in this case we isolated cell lines in which the levels of ectopically expressed SA2 are similar to the levels of endogenous SA2 (Figure 3A). Immunoprecipitation experiments with myc antibodies and sucrose density gradient centrifugation showed that both the tagged SA2 wild-type and SA2–12xA proteins were incorporated into cohesin complexes (Figure 3B and 3C).
Since SA2 phosphorylation can be catalyzed by purified Plk1 in vitro and depends on Plx1 in Xenopus egg extracts [16] we asked whether SA2–12xA had lost the ability to be phosphorylated by Plk1. When purified cohesin complexes were incubated with Plk1 and 32P-γ-ATP, approximately 50% less radiolabel was incorporated into SA2–12xA than into wild-type SA2, whereas phosphorylation of Scc1 in the same complexes was not affected (Figure 4A). Because both budding yeast and human Polo-like kinases can phosphorylate many sites in vitro that are not phosphorylated in vivo [26,27], it is possible that the residual phosphorylation of SA2–12xA by Plk1 in vitro occurs on sites that are not phosphorylated in vivo. We therefore analyzed the phosphorylation of wild-type and mutant forms of SA2 under more physiological conditions by incubating 35S-labeled recombinant SA2 in mitotic Xenopus egg extracts. SA2 phosphorylation in these extracts depends on Plx1 [16] and causes an electrophoretic mobility shift (Figure 4B). This shift was partially abolished when three C-terminal phosphorylation sites were mutated (mutant SA2–6/7/11; see Figure 1C), and no shift could be seen with SA2–12xA (Figure 4B). These results indicate that most mitosis-specific phosphorylation sites have been removed from SA2–12xA. The finding that mitosis-specific phosphorylation sites in SA2 are clustered in the C terminus was also confirmed by generating deletion mutants in which SA2 was progressively shortened from the C terminus (see Figure 1C), because SA2's mobility shift was completely abolished when at least 124 C-terminal residues were removed (Figure 4C).
Figure 4 Mutations of the C-Terminal Phosphorylation Sites and C-Terminal Deletions Decrease the Mitotic Phosphorylation of SA2
(A) Cohesin complexes containing wild-type SA2-myc or SA2–12xA-myc were immunopurified by myc antibodies from the respective cell lines. Similar amounts of cohesin were incubated with recombinant GST-Plk1, and the amount of phosphorylation was quantified by 32P incorporation followed by SDS-PAGE, silver staining (top) and Phosphorimager analysis, and quantification using the software program ImageJ.
(B and C) In vitro-translated, 35S-labeled SA2 tagged at the N terminus with 9xmyc was incubated in interphase Xenopus egg extracts, which were induced to enter mitosis at time point 0 min by addition of nondegradable cyclin B Δ90 and okadaic acid. Samples were collected at the indicated time points and analyzed by SDS-PAGE followed by Phosphorimager analysis. The autoradiographs in (B) show phosphorylation site mutants and in (C) they show C-terminal deletion mutants (see Figure 1C). The slower-migrating band represents myc-SA2, whereas the faster-migrating band is presumably generated by translation initiation at an internal start codon.
To be able to address the physiological relevance of SA2 phosphorylation, we first had to determine whether cohesin complexes containing tagged SA2 behave normally in human cells. Immunofluorescence imaging of wild-type SA2-myc showed a localization pattern that is very similar to the pattern found for Scc1 [10,14,15,18]. SA2-myc was nuclear in interphase, and mainly present in a soluble form in the cytoplasm during mitosis (unpublished data). When we extracted mitotic cells to remove the soluble pool, we found a minor fraction bound to chromatin, and in prometaphase and metaphase, SA2-myc was enriched at centromeres as compared to chromosome arms (Figure 5A). Therefore, the tag in SA2 does not seem to interfere with the behavior of cohesin complexes containing SA2-myc.
Figure 5 Phosphorylation of SA2 Is Required for Cohesin Dissociation from Chromosome Arms during Prometaphase
(A) Logarithmically proliferating HeLa cells expressing SA2-WT-myc or SA2–12xA-myc were extracted prior to fixation, and stained with myc antibodies. In the upper set of images, kinetochores were labeled with human CREST serum, and DNA was counterstained with DAPI. In the lower set of images, only SA2-myc staining is shown.
(B) SA2-myc expression was induced by different amounts of doxycycline (Dox.), and cells were arrested in prometaphase by nocodazole (Noc.) treatment for 3 or 10 h. Cells were spun on glass slides, extracted by detergent, fixed, and processed for immunostaining as in (A). Scale bars 10 μm.
When we analyzed the intracellular distribution of the SA2–12xA mutant by immunofluorescence microscopy, we found that this protein was also nuclear throughout interphase and that the nuclear signal could only partially be reduced by removing soluble cohesin complexes by preextraction (unpublished data), indicating that complexes containing SA2–12xA can associate with chromatin like wild-type cohesin. In stark contrast to wild-type SA2, however, SA2–12xA was not strongly enriched at centromeres of prometaphase and metaphase chromosomes, but instead was almost equally abundant on chromosome arms and on centromeres (Figure 5A). A very similar distribution of cohesin on chromosome arms and centromeres has been observed after depletion of Plk1 by RNA interference [18].
These observations indicate that phosphorylation of SA2 is not required for the loading of cohesin onto chromatin, but is essential for its dissociation from chromosome arms during early mitosis. However, it also remained possible that cohesin complexes containing SA2–12xA simply dissociated from chromosomes more slowly than wild-type complexes. To address this possibility, we analyzed cells in which prometaphase was prolonged by treatment with nocodazole. Under these conditions, complexes containing wild-type SA2 dissociated completely from chromosome arms within 3 h but remained at centromeres (Figure 5B), confirming earlier observations made for Scc1 [15,18]. In contrast, SA2–12xA could still be detected clearly on chromosome arms after 3 h and even after 10 h of nocodazole treatment (Figure 5B), excluding the possibility that the dissociation of cohesin complexes containing this mutant is simply slower than the dissociation of wild-type complexes.
Cohesin Complexes Containing Nonphosphorylatable SA2 Are Able to Establish Cohesion
It was also possible that the amino acid exchanges in SA2–12xA had rendered the protein nonfunctional, possibly resulting in the formation of complexes that bound chromatin nonspecifically and therefore did not dissociate from chromosomes in mitosis. Thus, we asked whether cohesin complexes containing SA2–12xA are able to establish sister chromatid cohesion. To answer this question, we again treated cells with nocodazole, which normally results in the loss of cohesion between chromosome arms but not at centromeres, resulting in the formation of X-shaped chromosomes with “open arms” that can be seen by chromosome spreading and Giemsa staining [18]. In this assay, chromosomes from most cells expressing wild-type SA2-myc showed the normal “open arm” phenotype (between 55% and 71%, depending on the cell line analyzed; Figure 6A). In contrast, only 10% of mitotic cells expressing SA2–12xA contained chromosomes whose arms had lost cohesion during the nocodazole arrest, whereas 87% had maintained cohesion between chromosome arms (Figure 6A). This result strongly indicates that the cohesin complexes that contain SA2–12xA and that remain on chromosome arms in prometaphase (see Figure 5B) are able to establish and maintain cohesion between sister chromatids.
Figure 6 The Presence of SA2–12xA on Chromosome Arms Correlates with Cohesion between Sister Chromatid Arms
(A) Cells were cultured in the absence or presence of different amounts of doxycycline as indicated. After arrest in nocodazole for 3 h, cells were fixed, spread on glass slides, and stained with Giemsa (photomicrographs, above). Single chromosomes (indicated by a box) are shown at higher magnification in the lower right corners. The number of cells with chromosome arms that had opened (arms open) or that were connected (arms closed) was scored as indicated (bar graphs, below). Scale bar 10 μm.
(B) Whole-cell extracts were prepared from HeLa cells expressing SA2–12xA-myc after treatment with increasing amounts of doxycycline (0, 0.2, and 2.0 μg/ml). The ratio of exogenous SA2–12xA-myc to endogenous SA2 was visualized by immunoblotting with antibodies to SA2. The position of molecular weight markers is indicated on the right.
Finally, to further confirm this hypothesis, we asked whether the ability of SA2–12xA-expressing cells to maintain arm cohesion during a nocodazole treatment depends on the amount of ectopic protein that is expressed. We therefore treated cells containing SA2–12xA transgenes with different doses of doxycycline and compared the levels of exogenous SA2 with the arm cohesion phenotype. The levels of mutant SA2-myc were well controlled by the amount of doxycycline used (Figure 6B), and there was a clear correlation between the amount of SA2–12xA and the number of cells whose chromosomes maintained arm cohesion during treatment with nocodazole, whereas expression of wild-type SA2-myc had no significant influence on arm cohesion (Figure 6A). Cells of the SA2–12xA cell line maintained arm cohesion slightly more frequently than control cells even if SA2–12xA expression was not induced, but it is possible that small amounts of the ectopic protein are also synthesized in the absence of doxycycline.
These observations rule out the possibility that the different frequencies with which arm cohesion is observed after nocodazole treatment in SA2–12xA and control cells are due to other differences in the cell lines than expression of the different transgenes. We therefore conclude that cohesin complexes containing SA2–12xA are able to maintain and establish cohesion, but that these complexes are unable to dissociate from chromosomes during prophase and prometaphase. Phosphorylation of SA2 at its C terminus therefore appears to be essential for the unloading of cohesin from chromosome arms during early mitosis.
Phosphorylation of SA2 Is Required for Efficient Resolution of Sister Chromatids
Although the major amount of human cohesin dissociates from chromosomes during early mitosis, the physiological importance of this process is still unknown. One reasonable assumption is that cohesin dissociation might be required for binding of condensin complexes and for condensation of chromatin. Like cohesin, condensin complexes contain subunits that are members of the Smc and kleisin protein families [28,29,30], and cohesin dissociation and condensin binding normally coincide in cells [3,13]. However, inhibition of cohesin dissociation by interfering with Plk1/Plx1 or Aurora-B function in human cells or Xenopus egg extracts did not affect the binding of condensin and the overall condensation of chromosomes [16,17,18,31]. In line with these results, we found that condensin binding, compaction, and shortening of chromosomes in a prolonged mitotic arrest was not detectably influenced by expression of SA2–12xA (Figure S3), further strengthening the notion that condensin binding does not require the phosphorylation-dependent dissociation of cohesin complexes from chromosome arms.
Experiments in Xenopus egg extracts showed that in the absence of Plx1 and Aurora-B, chromosome arms on mitotic chromosomes remained very close to each other, i.e., the resolution of sister chromatid arms was impaired [17]. In these experiments, it was difficult to distinguish whether Plx1 and Aurora-B promoted sister chromatid resolution by inhibiting the dissociation of cohesin or by another, independent pathway. We therefore examined how chromosome structure is influenced by expression of nonphosphorylatable SA2. We noticed that sister chromatid arms often stayed in closer proximity in SA2–12xA-expressing cells than in controls (Figure 7A). When we measured the interchromatid distance during prometaphase and metaphase, we found a variation within the same cell line from around 0.4 to 1.1 μm between individual cells (Figure 7B). This variability is presumably caused by different periods of time that cells have spent in mitosis; i.e., cells in which the interchromatid distance was small might have spent shorter time in mitosis than those in which sister chromatids were more resolved. We also found that there was some variability between different cell lines (unpublished data). However, cells expressing wild-type SA2 did not show any prominent difference in the average interchromatid distance in the absence or presence of exogenous SA2, whereas the interchromatid distance was progressively shortened when SA2–12xA-myc was expressed in increasing amounts (Figure 7B).
Figure 7 Phosphorylation of SA2 Is Required for Efficient Resolution of Sister Chromatid Arms during Prometaphase and Metaphase
(A) HeLa cells expressing SA2-WT-myc or SA2–12xA-myc were spread on glass slides and chromosomes were stained with Giemsa. Representative cells from SA2 WT-myc or SA2–12xA-myc cell lines after induction with 2 μg/ml doxycycline are shown. Scale bar 10 μm.
(B) HeLa cells were induced to express SA2-WT-myc or SA2–12xA-myc by different amounts of doxycycline as indicated, and processed as in (A). More than 50 cells in prometaphase or metaphase were selected randomly from each sample. The distance between sister chromatids was determined for five chromosomes in each cell and averaged. Light gray bars indicate average values that have been measured in one or two cells, and darker gray bars indicate average values that have been measured in three or more cells. Diamonds indicate the average distance for all cells in a given sample.
(C) Representative immunofluorescence image of normal anaphase in a cell expressing SA2–12xA-myc. The cell was not extracted prior to fixation, so the soluble pool of SA2–12xA-myc is revealed by myc-staining.
Since resolution of sister chromatid arms happens progressively during prophase and prometaphase, a reduction in the average distance between sister chromatids might also be caused by shortening the time up to metaphase (therefore leaving cells less time to resolve sister chromatids). However, when we compared the percentage of different mitotic stages in SA2–12xA- versus wild-type SA2-expressing cells, we found no indication of a shortening of prometaphase (unpublished data). Phosphorylation of SA2 and dissociation of cohesin from chromosome arms therefore appear to be required for the efficient resolution of sister chromatid arms.
SA2 Phosphorylation Is Not Essential for Anaphase
The prolonged persistence of cohesin on chromosome arms and the closer proximity of sister chromatid arms might cause defects in anaphase, for example because larger amounts of cohesin cannot be cleaved efficiently enough, or because perturbance of chromosome structure might interfere with the separation of sister chromatids. However, we did not observe obvious anaphase defects in cells expressing nonphosphorylatable SA2. Furthermore, in all anaphases that we observed (n > 50), we found that SA2–12xA had completely disappeared from separating chromatids (Figures 7C and S3B). Scc1 cleavage products could be detected in cells expressing SA2–12xA (unpublished data), consistent with the interpretation that the loss of SA2–12xA-containing cohesin complexes from chromatids in anaphase is mediated by separase. These observations indicate that SA2 phosphorylation is required for the dissociation of cohesin from chromosome arms during prophase and prometaphase, but that this dissociation process is not absolutely essential for the initiation of anaphase. These data also further support the notion that separase is able not only to cleave cohesin at centromeres but on chromosome arms as well [18].
Discussion
It has long been known that cohesin's Scc1 and Scc3/SA1/SA2 subunits are specifically phosphorylated in mitosis [2,5,14,16,19,32,33]. In budding yeast it has been shown that phosphorylation of Scc1 by Cdc5 enhances the cleavability of cohesin by separase [19,20]. In vertebrates, however, the functional significance of these modifications has remained unclear. The circumstantial evidence in vertebrate systems that has existed so far points to a role of cohesin phosphorylation in controlling the ability of cohesin to bind chromatin [2,16], not in modulating Scc1 cleavage by separase. It was also unclear whether Plk1, a kinase that is essential for cohesin dissociation from chromosome arms during prophase and prometaphase [16,17,18], regulates cohesin in early mitosis by directly phosphorylating Scc1 or SA2, or by modifying other proteins that might be required for cohesin unloading.
Our analysis of mitotic cohesin regulation in human cells revealed distinct roles for the phosphorylation of Scc1 and SA2. Phosphorylation of human Scc1 enhances the cleavability of this protein by separase, at least at the second of Scc1's two cleavage sites (see Figure 2), and thereby shows that this mode of cohesin regulation is conserved from yeast to humans. Furthermore, our data imply that Scc1 phosphorylation is not required for the dissociation of cohesin from chromosome arms (see Figure S2), again consistent with the situation in yeast where Scc1 is phosphorylated in mitosis [19], yet cohesin complexes do not dissociate from chromosomes until separase is activated [34,35].
In contrast to the data for Scc1, our results show that SA2 phosphorylation is essential for the dissociation of at least some cohesin complexes from chromosome arms (see Figure 5), but this modification does not seem to be necessary for the cleavage of cohesin complexes by separase (Figure 7C and unpublished data). Cells that express nonphosphorylatable versions of SA2 are unable to remove all cohesin complexes from their chromosome arms during prometaphase (see Figure 5A), even if mitosis is prolonged for many hours by treatment with spindle poisons (see Figure 5B); presumably as a consequence, cohesion between sister chromatid arms is not lost in these cells during prolonged prometaphase arrest (see Figure 6). These phenotypes are virtually identical to the cohesin and cohesion phenotypes of cells in which Plk1 has been depleted [18]. It has also been observed that Plk1 can phosphorylate Scc1 and SA2 in vitro, can thereby decrease the ability of cohesin to bind chromatin, and is required for the mitosis-specific phosphorylation of Scc1 and SA1/SA2 in Xenopus egg extracts [16]. Obviously none of these results can exclude the possibility that kinases other than Plk1 contribute to SA2 phosphorylation, nor the possibility that Plk1 may also have to phosphorylate proteins other than SA2 to allow cohesin dissociation, but the simplest interpretation of all the data is that Plk1 is essential for cohesin unloading because it is required for SA2 phosphorylation, which in turn is a prerequisite for cohesin dissociation.
It will be interesting to learn whether this type of regulation is restricted to human SA2, or whether it also applies to paralogs and orthologs of SA2. In addition to SA1 and SA2, a meiosis-specific paralog of the Scc3 family exists in mammals, called SA3 (or STAG3) [36]. Most of the phosphorylation sites that we identified in SA2 are conserved in SA1, whereas SA3 diverges from SA1 and SA2 mostly in its C-terminal sequence (unpublished data). This difference could have important implications for the regulation of meiosis I, where arm cohesion needs to be protected to allow the separation of homologous chromosomes in anaphase I (reviewed in [37]), and where chromosome arms do not separate even if cells are arrested by treatment with spindle poisons [38]. It is possible that the replacement of SA1/2 by SA3 renders meiotic cohesin complexes resistant to Plk1-dependent removal from chromosome arms, and thereby allows the maintenance of arm cohesion until separase is activated. Likewise it will be interesting to analyze whether Scc3 is phosphorylated in budding yeast, in which cohesin dissociation from chromosome arms in early mitosis has not been detected.
How Important Are Scc1 and SA2 Phosphorylation In Vivo?
Somewhat unexpectedly, we found that expression of neither nonphosphorylatable Scc1 nor nonphosphorylatable SA2 blocked progression through mitosis. Although it remains formally possible that our experiments did not identify all mitosis-specific phosphorylation sites and that we therefore did not mutate all critical sites, we consider it more plausible to think that phosphorylation of these proteins is not absolutely essential for progression through mitosis, at least in transformed cultured cells. This notion is supported by the finding that cohesin can be removed from chromosomes (presumably by separase), even in cells in which Plk1 has been depleted and Aurora-B has been inhibited—i.e., under conditions where the early mitotic dissociation of cohesin from chromosome arms is inhibited [18]. The implication is that the early mitotic dissociation of cohesin from chromosomes is not absolutely essential for mitosis, because separase is able to cleave all cohesin complexes that reside on chromosomes at the metaphase-anaphase transition. In this respect, human cells therefore appear to be more similar to budding yeast than previously suspected, in that HeLa cells can also initiate anaphase without first having to remove cohesin from chromosome arms.
Likewise, there are similarities between yeast and HeLa cells in the regulation of Scc1. In both systems, Scc1 phosphorylation enhances its cleavability by separase, but in neither case is this modification essential for viability (this study) [19]. An interesting hint to the possible function of Scc1 phosphorylation comes from the observation that budding yeast cells lacking the securin Pds1 are viable and are able to undergo anaphase, but this ability is dramatically decreased if phosphorylation sites in Scc1 are mutated [19]. Since securin not only inhibits separase but is also required for its activation, yeast cells lacking securin may not have enough separase activity to cleave cohesin if Scc1 is not phosphorylated. Phosphorylation of Scc1 might increase its affinity for separase, and this effect may simply enhance the fidelity of anaphase initiation. Securin is also not essential for viability in human cells, but in its absence the specific activity of separase is decreased [39]. It would be interesting to test whether human cells lacking securin require Scc1 phosphorylation for viability.
Similarly, it is possible that SA2 phosphorylation and the resulting dissociation of cohesin from chromosomes in early mitosis, albeit not being essential, increase the fidelity of chromosome segregation. It is also conceivable that removal of cohesin prior to cleavage is not important for mitosis but for the next interphase. Separase-dependent cohesin removal destroys the Scc1 subunit and thereby renders cohesin nonfunctional. In contrast, phosphorylation-dependent dissociation appears to leave cohesin intact and might thereby enable the rapid reloading of cohesin onto chromatin in telophase, i.e., without the necessity for new Scc1 transcription and translation, which is inhibited during mitosis.
How Does SA2 Phosphorylation Lead to Cohesin Dissociation?
Cohesin is bound to chromatin in an extremely stable manner ([8]; E.R. and J.M.P, unpublished data), and this may be related to the fact that Smc1, Smc3, and Scc1 form a ring-like complex, at least in budding yeast [40,41]. It has been proposed that this protein ring establishes cohesion by encircling the sister chromatid strands [40]. In this model, it is easy to imagine how cleavage of Scc1 releases cohesin from chromatin. However, Scc3 binds to Scc1 and is not required for formation of the ring-like complex, and it is therefore not immediately obvious how phosphorylation of SA2 could lead to dissociation of cohesin from chromosomes. One possibility is that SA2 phosphorylation induces a conformational change in cohesin that opens the ring. Bulk phosphorylation of SA2's C terminus, for example, might considerably change its surface charge, thereby affecting interactions between Scc1 and the Smc1/3 subunits. In its simplest form, this model would predict that SA2 phosphorylation is sufficient for opening of the cohesin ring and thus is sufficient for cohesin dissociation. However, in preliminary experiments, we have been unable to observe cohesin dissociation when we added purified active Plk1 to chromatin (I. Sumara and J.M.P, unpublished data), whereas the simultaneous addition of Plk1 and Xenopus egg extracts to chromatin did enable cohesin dissociation [16]. It is therefore also possible that phosphorylation of SA2 recruits cohesin unloading factors to chromatin (which in the above experiment might have been contributed by the Xenopus extract), which then somehow enable the dissociation of cohesin from chromosomes. In budding yeast and C. elegans, cohesin needs additional factors for its loading onto chromatin [42,43]. Cohesin might similarly need aid for unloading, at least in the absence of Scc1 cleavage. If such additional factors exist and interact with SA2, the cell lines we created might provide a means to isolate the relevant molecules by differential purification of cohesin complexes containing wild-type SA2 and SA2–12xA.
If SA2 phosphorylation results in cohesin unloading by somehow enabling the opening of the cohesin ring without its cleavage, it would be conceivable that this reaction is simply the reverse of the loading process, during which the cohesin ring presumably also has to be opened transiently [44]. However, a prediction of this model would be that SA2 phosphorylation would also be required for the loading of the cohesin complex, whereas we find that complexes containing nonphosphorylatable cohesin can efficiently associate with chromatin and even establish functional cohesion. It is therefore more plausible to hypothesize that SA2 phosphorylation is a modification that is specifically used to remove cohesin from chromosomes in early mitosis by enabling a reaction that is not simply the reverse of the loading reaction.
Which Cohesin Complexes Are Regulated by SA2 Phosphorylation?
SA2 phosphorylation is required for the dissociation of cohesin from chromosome arms, but it does not seem to affect the behavior of cohesin at centromeres. As a consequence, sister chromatid arms are resolved much farther from each other than centromeres during a normal prometaphase, and they can lose cohesion completely if prometaphase is prolonged, whereas cohesion at centromeres is protected. How is this regulation achieved? Recent work in fission yeast has shown that members of the Mei-S332 family of proteins [45] are required for the persistence of cohesin at centromeres in meiosis I [46,47,48,49]. These proteins, called shugoshins or Sgo1, are thought to protect centromeric cohesin in anaphase I from premature cleavage by separase, but they are also found at centromeres in mitotic Drosophila and budding yeast cells [49,50]. In an associated paper by McGuinness et al. [51], we show that an ortholog of Sgo1 is also required for the persistence of cohesin at centromeres and for the maintenance of sister chromatid cohesion during prometaphase in human cells. Remarkably, Sgo1-depleted cells do not show cohesion defects if a nonphosphorylatable form of SA2 is expressed. This observation implies that cohesin normally persists at centromeres because Sgo1 protects cohesin in this chromosomal domain from phosphorylation. To test this hypothesis it will be important to determine whether SA2 phosphorylation does occur at centromeres. Our identification of in vivo phosphorylation sites on SA2 may be an important prerequisite for achieving this goal, because it should enable the generation of phosphospecific antibodies. Likewise, it will be interesting to learn how Sgo1 prevents or antagonizes SA2 phosphorylation, and whether the same mechanism is able to protect centromeric cohesin from separase in meiosis I.
Previous work has highlighted the difference in the regulation of cohesin complexes between chromosome arms and centromeres [10,18], but several observations suggest that there may also be important differences among different populations of cohesin complexes on chromosome arms. When we compared Scc1 cleavage in cells expressing either wild-type or nonphosphorylatable SA2, we noticed that the levels of Scc1 cleavage products in the latter cells were only slightly increased, if at all (unpublished data). If all complexes containing nonphosphorylatable SA2 remained on chromosome arms until prometaphase, we would instead expect to see more Scc1 cleavage in cells containing these complexes than in cells containing wild-type SA2. Furthermore, we noticed that the immunofluorescence intensity of SA2–12xA-myc in interphase cells from which soluble cohesin complexes had been removed by preextraction was clearly higher than the intensity of SA2–12xA-myc on prometaphase and metaphase chromosomes, and in subcellular fractionation and immunoblotting experiments we found that a fraction of SA2–12xA still became soluble in nocodazole-arrested cells (unpublished data). We made similar observations in immunofluorescence experiments in which we analyzed the chromosome association of Scc1-myc in Plk1-depleted cells. Also in these cells, some cohesin still seemed to dissociate from chromosome arms, despite the depletion of Plk1 (T. Hirota and J.M.P, unpublished data). The observation that some cohesin complexes do dissociate from chromosome arms even if Plk1 is depleted or if these complexes contain nonphosphorylatable SA2, whereas others do not, cannot simply be explained by slow dissociation kinetics of cohesin under these conditions, because those complexes that persist on chromosome arms can still be found there after 10 h of prometaphase arrest (see Figure 5B). We therefore favor the hypothesis that there are, in fact, two distinct populations of cohesin on chromosome arms: one whose dissociation depends on Plk1 activity and SA2 phosphorylation, and one whose dissociation does not. Since the population of cohesin complexes whose dissociation depends on Plk1 and SA2 is able to establish cohesion (see Figure 6), it is possible that those complexes that seem to be able to dissociate without Plk1 and SA2 phosphorylation are bound to chromatin in a manner that does not establish cohesion. Such binding modes must exist, because cohesin rebinds to chromatin in telophase [3,13], i.e., long before sister chromatids have been generated by DNA replication. This speculative model makes important predictions, for example that cohesin dissociation from unreplicated DNA should not depend on SA2 phosphorylation; we will attempt to test this prediction in the future.
Materials and Methods
Cell lines and growth conditions
HeLa cells expressing myc-tagged human Scc1 were described previously [15]. HeLa cells expressing mutated Scc1, wild-type SA2, or mutated SA2 were generated by transfecting HeLa tet-on cells (Clontech, Palo Alto, California, United States) with the pTRE2-hygro vector (Clontech) containing the respective cDNA and a 9xmyc tag fused in frame. Hygromycin-resistant clones were selected by growth in medium containing 400 μg/ml hygromycin, and were tested for expression of the myc-tagged protein by immunoblotting after induction with 2 μg/ml doxycycline for 1–3 d. Untransfected HeLa cells were grown in DMEM supplemented with 10% FCS, 0.2 mM L-glutamine, 100 U/ml penicillin, and 100 μg/ml streptomycin. For growth of stably transfected HeLa tet-on cells, the medium was supplemented with 200 μg/ml G418 and 100 μg/ml hygromycin. Hydroxyurea and nocodazole were used at concentrations of 2 mM and 330 nM, respectively.
cDNA mutagenesis
Serine or threonine residues in human Scc1 and SA2 were changed to alanine by mutation of the respective cDNAs using the Quick Change Site-Directed or Multi Site-Directed Mutagenesis kit (Stratagene, La Jolla, California, United States). Where the phosphorylated site could not be assigned to a certain residue within a peptide, we mutated all possible candidate sites to alanine residues. C-terminally truncated versions of SA2 were generated by PCR. For in vitro translation, cDNAs were cloned into pcDNA 3.1 (−)/Myc-His A (Invitrogen) modified to contain a 9xmyc cassette. The expression of cDNAs as 35S-methionine- and 35S-cysteine-labeled proteins was performed using a coupled transcription-translation system in rabbit reticulocyte lysate (Promega, Madison, Wisconsin, United States).
Antibodies
Antibodies specific to phosphorylated threonine were from Cell Signaling Technology (Beverly, Massachusetts, United States; #9381). To detect myc-tagged protein, we used either polyclonal, affinity-purified rabbit anti-myc antibody (Gramsch Laboratories, Schwabhausen, Germany; #CM-100), or monoclonal 9E10 mouse-anti-myc antibody [10]. Polyclonal antibodies to human Scc1, SA1, SA2, Smc1, Smc3, and Smc2 have been described [3,10,30]. Anti-INCENP peptide antibody was raised in rabbit, and the serum was affinity purified. The following peptide was used for immunization: TDQADGPREPPQSARRKRSYC. Human CREST serum was a kind gift of A. Kromminga (Institut für Immunologie, Pathologie und Molekularbiologie, Hamburg, Germany).
Protein purification and fractionation
Endogenous cohesin was purified from HeLa cell extracts by immunoprecipitation using purified SA2 peptide antibodies (antibody 447) crosslinked to Affi-Prep protein A beads (Bio-Rad, Hercules, California, United States) as described [3,16]. For mass spectrometric analysis, cohesin precipitates were first washed with IP buffer (20 mM Tris-HCl [pH 7.5], 100 mM NaCl, 0.2% Nonidet P-40, 20 mM β-glycerophosphate, 10% glycerol, 1 mM NaF, and 0.5 mM DTT) [3] supplemented with 400 mM NaCl, followed by a wash step with IP buffer not containing detergent. Cohesin was eluted from the antibody beads by addition of 1.5 bead volumes of 100 mM glycine (pH 2.2). The samples were titrated to pH 8.5 by addition of NH4HCO3 to a final concentration of 200 mM. Cohesin complexes containing myc-tagged subunits were immunopurified with rabbit anti-myc antibodies (Gramsch Laboratories, #CM-100) using similar conditions. Sucrose density gradient centrifugation was performed as described [15].
Mass spectrometry
A volume of 100 μl of immunopurified cohesin (isolated from about 10 mg of total HeLa protein) was reduced with 1 μg of DTT for 1 h at 37 °C and alkylated with 5 μg of iodoacetamide for 30 min at room temperature in the dark. The proteins were digested in solution with one of the following proteases or combination of proteases: 200 ng of trypsin for 4 h at 37 °C, followed by addition of another 200 ng of trypsin and further incubation for 4 h at 37 °C; 400 ng of Glu-C for 8 h at 25 °C; 400 ng of trypsin overnight at 37 °C followed by 400 ng of Glu-C for 8 h at 25 °C; 400 ng of chymotrypsin or elastase for 4 h at 25 °C (all proteases were sequencing grade; Roche, Basel, Switzerland); or 300 ng of subtilisin (Fluka, from Sigma-Aldrich, St. Louis, Missouri, United States) for 30 min at 37 °C. Generated peptides (100-μl sample) were separated by reversed phase nano-HPLC (LC Packings, Sunnyvale, California, United States) and analyzed using an ion trap mass spectrometer (ThermoFinnigan, from Thermo Electric, Waltham, Massachusetts, United States) as described by Mitulovic et al. [52]. All tandem mass spectra were searched against the human nonredundant protein database by using the SEQUEST program (ThermoFinnigan). Any phosphopeptide matched by computer searching algorithms was verified manually.
In vitro Scc1 cleavage assays
In vitro cleavage assays were performed as described [10] with the exception that for the assays in Figure 2A, in vitro-translated human wild-type or mutant Scc1-myc was used as substrate. To isolate human separase, purified polyclonal antibodies generated against recombinant human separase were used (kindly provided by I. Waizenegger). In some reactions human GST-Plk1 (16) was added in a concentration of approximately 50 ng/μl.
SA2 electrophoretic mobility shift assays
Xenopus interphase egg extracts were supplemented with 1/20 volume of in vitro-translated 35S-labeled SA2, which had C-terminal deletions or mutations of phosphorylation sites. All SA2 constructs used in this assay lacked the 69 N-terminal amino acids, because the start codon was initially misassigned. Extracts were induced to enter mitosis by addition of cyclin B Δ90 and 1 μM okadaic acid as described in Sumara et al. [3]. Samples were collected at the indicated time points and analyzed by SDS-PAGE followed by Phosphorimager analysis (Storm, Amersham Biosciences, Little Chalfont, United Kingdom).
In vitro phosphorylation assay
SA2-myc or SA2–12xA-myc containing cohesin complexes were purified by immunoprecipitation with affinity-purified rabbit anti-myc antibody (Gramsch Laboratories, # CM-100). Conditions of the in vitro phosphorylation assay have been described [26].
Microscopy
For immunofluorescence microscopy, cells were either grown on coverslips or spun onto glass slides using a Cytospin centrifuge (Shandon brand, available from Thermo Electric). Cells were extracted with 0.1% Triton X-100 prior to fixation to remove the soluble pool of cohesin. Fixation, immunostaining, and image acquisition were performed as described [10]. Chromosome spreads followed by Giemsa staining were performed as described [31].
Quantification of interchromatid distance
On pictures of chromosome spreads, a line scan was performed across chromosome arms orthogonal to the long axis of the chromosome using MetaMorph software (Universal Imaging, Downingtown, Pennsylvania, United States). On the line scan, the distance from peak to peak was measured. Five chromosomes were thus analyzed per cell, and the resulting distances were averaged. More than 50 cells were randomly picked per cell line, and thus analyzed.
Supporting Information
Figure S1 Phosphorylation Sites in Human Scc1 and SA2
Sites for Scc1 (A) and SA2 (B) are shown. The sites marked in red and blue were found to be phosphorylated in mitosis by mass spectrometry. Residues marked in red were unambiguously identified, whereas in the regions marked in blue the phosphorylated residue could not be assigned with certainty. The separase recognition sites on human Scc1 (A) are indicated in green. Peptides identified by mass spectrometry are highlighted in yellow. The residues at which SA2 was truncated for the assay in Figure 4C are also indicated (B).
(1.2 MB EPS).
Click here for additional data file.
Figure S2 Phosphorylation of Scc1 Is Not Required for Dissociation of Cohesin from Chromosome Arms during Prometaphase and Metaphase
(A) Extracts were prepared from HeLa cells expressing Scc1-S454A-myc or Scc1–9xA-myc and fractionated by sucrose density gradient centrifugation (5%–30% sucrose), followed by immunoblotting with antibodies recognizing the proteins indicated on the right (inp. = input/unfractionated sample of the extract).
(B) HeLa cells expressing Scc1 WT-myc or Scc1–9xA-myc were either grown logarithmically (0 h Noc) or arrested in prometaphase for 5 h by nocodazole (5 h Noc). Cells were extracted prior to fixation, and stained with myc-antibodies. Kinetochores were labeled with human CREST (calcinosis, Raynaud phenomenon, esophageal dysmotility, sclerodactyly, telangiectasias) serum, and DNA was counterstained with DAPI. Scale bar, 10 μm.
(870 KB JPG).
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Figure S3 Condensation or Condensin Binding Is Not Impaired in SA2–12xA-Expressing Cells
(A) Untransfected HeLa tet-on cells and HeLa cells expressing SA2-WT-myc, or SA2–12xA-myc were arrested with nocodazole for 10 h. Cells were fixed, spread on glass slides, and stained with Giemsa. For each sample, one representative cell is shown. The small bars next to one of the chromosomes in all panels have the same length.
(B) HeLa cells expressing SA2-WT-myc or SA2–12xA-myc were spread on glass slides, extracted prior to fixation, and immunostained as indicated, using an antibody against human Smc2 to reveal condensin. Scale bars in (A) and (B), 10 μm.
(721 KB JPG).
Click here for additional data file.
Accession Numbers
GenBank accession numbers for proteins discussed in this paper are human Scc1 (NP_006256) and human SA2 (NP_006594).
We are grateful to Florine Dupeux and Karin Paiha for excellent technical help, and to Kim Nasmyth for comments on the manuscript. SH is supported by a fellowship from the Human Frontier Science Program, and research in the laboratory of JMP is supported by Boehringer Ingelheim, the Sixth Framework Programme of the European Union via the Integrated Project MitoCheck, the Wiener Wirtschaftsfoerderungsfonds, and the European Molecular Biology Organization.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. ER and JMP conceived the project. ER and KM identified the phosphorylation sites on human cohesin. CMD generated the HeLa cell lines. SH, ER, and BK performed the experiments addressing the functional significance of cohesin phosphorylation. SH and JMP wrote the paper.
¤a Current address: Institute of Molecular and Cellular Biosciences, University of Tokyo, Japan
¤b Current address: Institute of Molecular Biology, University of Zurich, Switzerland
Citation: Hauf S, Roitinger E, Koch B, Dittrich CM, Mechtler K, et al. (2005) Dissociation of cohesin from chromosome arms and loss of arm cohesion during early mitosis depends on phosphorylation of SA2. PLoS Biol 3(3): e69.
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Mitulovic G Smoluch M Chervet JP Steinmacher I Kungl A An improved method for tracking and reducing the void volume in nano HPLC-MS with micro trapping columns Anal Bioanal Chem 2003 376 946 951 12856097
| 15737063 | PMC1054881 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 1; 3(3):e69 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030069 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706410.1371/journal.pbio.0030086Research ArticleCell BiologyMolecular Biology/Structural BiologyHomo (Human)Shugoshin Prevents Dissociation of Cohesin from Centromeres During Mitosis in Vertebrate Cells Cohesion regulation by vertebrate Sgo1McGuinness Barry E
1
Hirota Toru
1
Kudo Nobuaki R
1
Peters Jan-Michael [email protected]
1
Nasmyth Kim [email protected]
1
1Research Institute of Molecular PathologyViennaAustriaHawley R. Scott Academic EditorStowers Institute for Medical ResearchUnited States of America3 2005 1 3 2005 1 3 2005 3 3 e867 9 2004 20 12 2004 Copyright: © 2005 McGuinness et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Dissociation of Cohesin from Chromosome Arms and Loss of Arm Cohesion during Early Mitosis Depends on Phosphorylation of SA2
Chromosome Cohesion: A Cycle of Holding Together and Falling Apart
Separating Sisters: Shugoshin Protects SA2 at Centromeres but Not at Chromosome Arms
Cohesion between sister chromatids is essential for their bi-orientation on mitotic spindles. It is mediated by a multisubunit complex called cohesin. In yeast, proteolytic cleavage of cohesin's α kleisin subunit at the onset of anaphase removes cohesin from both centromeres and chromosome arms and thus triggers sister chromatid separation. In animal cells, most cohesin is removed from chromosome arms during prophase via a separase-independent pathway involving phosphorylation of its Scc3-SA1/2 subunits. Cohesin at centromeres is refractory to this process and persists until metaphase, whereupon its α kleisin subunit is cleaved by separase, which is thought to trigger anaphase. What protects centromeric cohesin from the prophase pathway? Potential candidates are proteins, known as shugoshins, that are homologous to Drosophila MEI-S332 and yeast Sgo1 proteins, which prevent removal of meiotic cohesin complexes from centromeres at the first meiotic division. A vertebrate shugoshin-like protein associates with centromeres during prophase and disappears at the onset of anaphase. Its depletion by RNA interference causes HeLa cells to arrest in mitosis. Most chromosomes bi-orient on a metaphase plate, but precocious loss of centromeric cohesin from chromosomes is accompanied by loss of all sister chromatid cohesion, the departure of individual chromatids from the metaphase plate, and a permanent cell cycle arrest, presumably due to activation of the spindle checkpoint. Remarkably, expression of a version of Scc3-SA2 whose mitotic phosphorylation sites have been mutated to alanine alleviates the precocious loss of sister chromatid cohesion and the mitotic arrest of cells lacking shugoshin. These data suggest that shugoshin prevents phosphorylation of cohesin's Scc3-SA2 subunit at centromeres during mitosis. This ensures that cohesin persists at centromeres until activation of separase causes cleavage of its α kleisin subunit. Centromeric cohesion is one of the hallmarks of mitotic chromosomes. Our results imply that it is not an intrinsically stable property, because it can easily be destroyed by mitotic kinases, which are kept in check by shugoshin.
Shugoshin helps to keep newly replicated chromosomes together by protecting cohesins from phosphorylation, until the moment for the chromosomes to separate has arrived
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Introduction
Cohesion between sister chromatids ensures that traction of sister chromatids towards opposite poles (known as bi-orientation) generates a tug-of-war between microtubules attempting to pull sisters apart and cohesion between them resisting this. The resulting tension is thought to stabilize the attachment of kinetochores to microtubules [1]. Only when all chromosomes have come under tension and have congressed to the metaphase plate do cells destroy the connection holding sister chromatids together, which triggers the simultaneous disjunction of all sister chromatid pairs and traction of sister chromatids to opposite poles of the cell, known as anaphase.
Sister chromatid cohesion depends on a multisubunit complex called cohesin, which is composed of a heterodimer of Smc1 and Smc3 proteins associated with an α kleisin protein called Scc1 [2,3], that is in turn associated with either SA1 or SA2 variants of the Scc3 protein. These proteins create a gigantic ring structure within which DNA molecules might be entrapped [4,5]. Sister chromatid cohesion is destroyed at the metaphase-to-anaphase transition because of cleavage of cohesin's α kleisin (Scc1/Rad21) subunit by a protease called separase [6], whose activity causes cohesin to dissociate from chromatin. For most of the cell cycle, separase activity is inhibited by its phosphorylation by the Cdk1 kinase [7] and by the binding of an inhibitory chaperone called securin [8,9,10]. Separase is eventually activated by proteolysis of the Cdk1 cyclin B subunit and securin, both mediated by a ubiquitin protein ligase called the anaphase-promoting complex or cyclosome (APC/C) [11,12]. Because of the production by unattached kinetochores of an inhibitory form of the Mad2 protein, which binds an essential APC/C cofactor called Cdc20, the APC/C destroys securin and cyclin B only when all chromosomes have successfully bi-oriented [13]. This surveillance mechanism is known as the mitotic spindle checkpoint. Cells lacking any one of cohesin's four subunits fail to bi-orient chromosomes properly and arrest at least transiently in a mitotic state because of inhibition of APC/C-Cdc20 by Mad2 [14,15].
In yeast, cohesin persists at centromeres and along chromosome arms until the onset of anaphase, whereupon it is removed by the APC/C-separase pathway [8]. In animal cells, however, the bulk of cohesin associated with chromatin during G2 dissociates from chromosome arms but not from centromeres during prophase and prometaphase [16]. This so-called “prophase pathway” is thought to be driven not by cleavage of cohesin's α kleisin subunit but instead by hyperphosphorylation of Scc3-SA subunits [17] mediated (directly or indirectly) by the Aurora B [18] and Plk1 mitotic kinases [19]. Crucially, expression of an Scc3-SA2 subunit whose serine and threonine residues phosphorylated during mitosis have been mutated to alanine reduces the dissociation of cohesin from chromosome arms, which, as a consequence, remain more tightly associated [17]. Surprisingly, this mutation does not dramatically interfere with mitosis, presumably because the extra cohesin associated with metaphase chromosomes is efficiently removed by separase upon activation of the APC/C. The function of the prophase pathway is therefore unclear. Centromeric cohesin is not just more slowly removed by the prophase pathway because cohesin remains at centromeres during the prolonged mitotic arrest that is caused by activation of the spindle checkpoint. Under these circumstances, cohesion between chromosome arms is lost entirely, while that between sister centromeres persists [20]. Centromere-specific factors presumably protect cohesin from the prophase pathway.
An analogous process occurs during meiosis, during which cohesin along chromosome arms must be treated differently from cohesin at centromeres. Because of recombination between maternal and paternal chromatids, cohesin along chromosome arms holds homologous centromeres together and enables their bi-orientation. Cleavage of cohesin's α kleisin subunit by separase along chromosome arms destroys this connection and thereby triggers the first meiotic division [21,22]. However, cohesin at centromeres is refractory to separase during meiosis I and therefore persists until metaphase II, and these cohesin complexes are crucial for the bi-orientation of sister chromatids during the second meiotic division. Recent work has shown that a class of proteins associated with meiotic centromeres, called MEI-S332 in Drosophila melanogaster [23] but now known as shugoshins, are essential for cohesin's ability to persist at centromeres after anaphase I. Shugoshin protects cohesin from separase during meiosis I in yeast [24,25,26]. Surprisingly, shugoshins are also found at centromeres during mitosis in yeast [24,27] and D. melanogaster [28]. This observation raises the possibility that shugoshins might have an important, albeit different, function during mitosis.
We show here that a human shugoshin that is possibly orthologous to fly MEI-S332 and yeast Sgo1 proteins [24,25,26] associates with centromeres during prophase and disappears at the onset of anaphase in mitotic tissue culture cells. HeLa cells whose shugoshin has been depleted by RNA interference (RNAi) fail to retain cohesin at centromeres during mitosis, and, as a consequence, their sister chromatids separate asynchronously before anaphase can be initiated, which triggers a prolonged mitotic arrest. Remarkably, expression of nonphosphorylatable Scc3-SA2 alleviates both the precocious loss of sister chromatid cohesion and the mitotic arrest. This suggests that centromeric shugoshin prevents phosphorylation of cohesin's Scc3-SA2 subunit and thereby protects the cohesion between sister centromeres that is essential for mitosis.
Results
The Sgo1/MEI-S332 Protein Accumulates at Centromeres as Cells Enter Mitosis but Disappears during Anaphase
The human and mouse genomes each contain a single gene (hsSgo1 and mmSgo1, respectively) that encodes proteins with a similar structure and weak homology to fungal Sgo1 proteins and MEI-S332 from D. melanogaster. Analysis of cDNAs from various databases as well as RT-PCR products from mRNA obtained from HeLa cells and mouse testis suggest that variable splicing may give rise to several types of protein (Figure S1). To detect these proteins, we raised two antibodies against peptides from the N and C termini that are common to all human splice variants.
To evaluate the specificity of affinity-purified antibodies, we tested whether immunofluorescence signals or bands on Western blots were abolished when Sgo1 mRNA was depleted by transfecting cells with small interfering RNAs (siRNAs) (Figure 1). Because most subsequent experiments addressed Sgo1's role during mitosis, siRNA transfection in HeLa cells was combined with a double thymidine block and release to synchronise cells (Figure 1A). Cells were transfected with siRNAs 8 h after release from the first thymidine block, and 4 h later a second block was imposed by repeat addition of thymidine. After a further 12 h, cells were released from the second block and samples taken at specific times thereafter. Under these circumstances, most cells complete S phase within 7 h of the second release and soon thereafter enter mitosis. Both affinity-purified antibodies detected a 72-kDa protein from a chromatin fraction that was depleted by treatment with an Sgo1-specific siRNA but not by a mock treatment (Figure 1B). This size is consistent with an approximately 60-kDa protein predicted from the longest of the cDNAs analysed.
Figure 1 Centromeric Enrichment of Sgo1 in Mitosis
(A) Schematic overview of cell synchronisation and transfection procedure.
(B) Characterization of Sgo1 antibodies. Affinity-purified Sgo1 antibodies raised against two different regions of Sgo1 recognize the same 72-kDa protein by Western blotting. Synchronised HeLa cells were transfected with water (Mock) or Sgo1 siRNA. Cells were harvested 7 and 11 h after release from thymidine block, and chromatin fractions were resolved by SDS-PAGE and probed with antibodies 94 and 95. A major band at 72 kDa was detected by both antibodies, which disappeared or was greatly reduced upon Sgo1 knockdown. The blot was reprobed with HP-1β antibody as a loading control.
(C) Localisation of Sgo1. Cells were costained for Sgo1 with antibody 94 (shown in green), and with CREST antiserum (shown in red). DNA was counterstained with DAPI. Five different stages of mitosis are shown: (a) prophase, (b) prometaphase, (c) metaphase, (d) anaphase, and (e) telophase. Bar = 10 μm. (f) A single pair of CREST labelled kinetochores are enlarged, showing two adjacent Sgo1 foci as commonly observed for late stage metaphases. Bar = 1 μm.
(D) In situ immunofluorescence of (a) mock-treated or (b) Sgo1 siRNA-transfected HeLa cells, demonstrating the disappearance of the Sgo1 signal following siRNA treatment. The latter shows the typical in situ appearance of an Sgo1-depletion arrested cell (see Figures 3 and 6). Sgo1 was stained with antibody 94 (shown in green), and CREST is shown in red. DNA was counterstained with DAPI.
In situ immunofluorescence of cycling HeLa cells suggested that Sgo1 accumulates at centromeres during mitosis (see Figure 1C). Very similar staining patterns were observed with antibodies against both peptides (Figure S2). The centromeric staining was eliminated in most cells by siRNA depletion (see Figure 1D). Only 16% of all mitotic cells remained positive for Sgo1 staining following siRNA treatment, compared to 99% in mock-treated cells. Sgo1 started to form foci on chromosomes as cells entered prophase (see Figure 1C, part a). During prometaphase, Sgo1 was concentrated, usually as a single focus, on the inner side of the twin (sister kinetochore) structures stained by a CREST (calcinosis, Raynaud's phenomenon, esophageal dysmotility, sclerodactyly, telangiectasias) antiserum (see Figures 1C, part b, and 2A) thought to detect the kinetochore proteins CENP-A/B or C, and Sgo1 overlapped with and covered a slightly larger area than Aurora B during prometaphase (Figure 2B). During metaphase, two Sgo1 foci overlapping with or adjacent to the two CREST signals were observed at many kinetochores (Figure 1C, part f). Centromere-associated Sgo1 was much less abundant during anaphase, but it could nevertheless still be faintly detected adjacent to kinetochores at the leading edge of chromatids (see Figure 1C, part d). No Sgo1 foci could be detected in telophase cells (see Figure 1C, part e). A similar pattern was observed after transfection with a gene expressing a Sgo1-GFP fusion protein (E. Watrin and J.-M. Peters, personal communication). The distribution of Sgo1 during mitosis in HeLa cells resembles that of MEI-S332, its presumptive orthologue in D. melanogaster [28].
Figure 2 Immunofluorescent Staining of Chromosome Spreads from HeLa Cells
Chromosome spreads were stained for Sgo1 with antibody 94 (shown in green), and counterstained with (A) CREST antiserum (bar = 10 μm), and (B) antibody to Aurora B, (both shown in red). DNA was stained with DAPI (shown in blue). Enlarged views show a single Sgo1 focus located between two sister CREST dots (A), and overlapping with Aurora B focus (B). Bar = 1 μm [applies to images b–d in (A) and (B)].
Sgo1 Is Required to Maintain Sister Chromatid Cohesion in Mitotic Cells
To address the function of Sgo1 in HeLa cells, we harvested cells at 2-h intervals after release from a double thymidine block and compared cells whose Sgo1 protein had been depleted by siRNA to mock-treated cells (see Figures 1A, 1B, and 3). Sgo1 depletion had little or no effect on the kinetics of S phase completion or on entry into mitosis (see Figure 3A and 3C). At each time point, chromosomes were spread on glass slides after methanol-acetic acid fixation, and stained with Giemsa. We first measured the fraction of chromosome spreads in a mitotic state, then measured the fraction of seven different categories of mitotic chromosomes amongst mitotic cells (see Figure 3A and 3B). In mock-treated cells, the fraction of mitotic spreads peaked 9 h after release and subsequently declined. The most frequent category at early time points was prophase (see Figure 3B, part a), and the most frequent category at 9 h was metaphase (see Figure 3B, part b). Early-anaphase cells (see Figure 3B, part c) were very rare, whereas telophase cells (see Figure 3B, part d) accumulated later than metaphase cells. Sister chromatids were either tightly associated as during metaphase or fully separated into two equal-sized clusters, as during anaphase or telophase.
Figure 3 Sgo1 Depletion Causes Precocious Sister Separation and Mitotic Arrest
Synchronised HeLa cells were transfected with Sgo1 siRNA or dH2O, harvested at 2-h intervals following release and examined by chromosome spreading and Giemsa staining. 100 cells were scored for mitotic index, and 100 mitotic cells were classified into seven categories based on chromosome configuration as exemplified in (B) for each time point.
(A) The frequency of each category of mitotic cell is given as a percentage of total cell numbers, such that the sum of each column represents the mitotic index.
(B) Representative pictures of seven categories of mitotic cells. Chromosome spreads: (a) prophase; (b) metaphase/metaphase-like; (c) anaphase; and (d) telophase. (e) Early phase of precocious sister disjunction. At this stage, sisters are beginning to separate and some or all presumptive sister pairs are still discernible. Arrowheads indicate chromosomes whose centromeric cohesion seems to be lost. (f) Later phases of sister chromatid separation. Sister pairs at this stage are no longer discernible, remnants of the metaphase plate are still visible, and sisters have not yet hypercondensed. (g) Scattered single chromatids. Sisters are completely separated and distributed randomly in relation to one another, individual chromatids are hypercondensed, giving a “curly” appearance. Note that chromatid separation in normal anaphases (c) are different from precocious sister chromatid separation (e–g), in that disjoined and paired chromatids coexist in the same cell.
(C) A portion of the cells harvested for the analysis in Figure 3A and 3B were ethanol-fixed and their DNA content was analysed by flow cytometry.
Sgo1-depleted cells accumulated in mitosis with similar kinetics, but many failed subsequently to exit from a mitotic state, with the result that nearly 50% of the cells had accumulated in a mitotic state 15 h after release (see Figure 3A). FACS analysis showed that around half of the cells failed to undergo cytokinesis (see Figure 3C). Many Sgo1-depleted cells accumulated at least transiently with chromosomes in a metaphase-like state, but at all time points a large number accumulated with fully condensed or even hypercondensed chromatids that had separated from their sisters (see Figure 3B, part g). At 9 h, when the frequency of metaphase cells peaked in mock-treated cultures, we observed not only a high fraction of metaphase cells but an equally high fraction of spreads in which most sister chromatid pairs had disjoined but chromatids had not yet hypercondensed (see Figure 3B, part f). Although many sisters had separated in these cells, we could also detect remnants of a metaphase plate. We also detected a small fraction of spreads in which this abnormal separation of sister chromatids had only commenced—namely, spreads in which sister chromatids were still aligned but much farther apart than during a normal metaphase (see Figure 3B, part e). These data suggest that Sgo1-depleted cells enter mitosis and align most chromosomes on a metaphase plate, but subsequently fail to undergo anaphase. They then disjoin their sister chromatids asynchronously and in a manner that is not accompanied by directed movement to opposite poles, and, finally, arrest for a prolonged period with hypercondensed, fully separated chromatids. Because the transfected siRNAs could, in principle, interfere with the RNAi machinery that might be necessary for centromeric sister chromatid cohesion [29], we tested whether other siRNAs produce a similar phenotype. Not one of 12 different siRNAs caused the rapid mitotic arrest with separated sister chromatids that is characteristic of cells treated with Sgo1 siRNAs (Figure S3).
The Kinetics of Sister Chromatid Separation in Sgo1-Depleted Cells
To analyse the consequences of Sgo1 depletion with greater temporal resolution, we filmed mock-treated and Sgo1-depleted HeLa cells that stably express histone H2B tagged with enhanced green-fluorescent protein (EGFP), which enabled us to observe the movement of individual chromosomes and chromatids (Figure 4). After mock treatment, all cells that entered mitosis subsequently underwent anaphase. The time from nuclear envelope breakdown (NEBD) to anaphase onset was 33 ± 9 min (average ± standard deviation) (n = 30). In Sgo1-depleted cells, 73% of cells that entered mitosis separated sister chromatids without undergoing anaphase. Although most chromosomes initially congressed to a metaphase plate, many were slow to do so and some never congressed at all (Figure 4A). Although 27% of mitotic cells underwent anaphase, we noticed most of these did so with lagging chromatids (unpublished data). The time from NEBD to the point at which the first sister chromatid was pulled out from the metaphase plate in Sgo1-depleted cells was 39 ± 12 min (n = 60), which is only slightly longer than the time taken by mock-treated cells to commence anaphase or the time taken by the minority of Sgo1-depleted cells that underwent anaphase (34 ± 7 min; n = 23). Cells that disjoined sister chromatids precociously remained in a mitotic state with condensed but fully separated chromatids for a prolonged period (6 h 20 min ± 2 h 3 min) before chromatids decondensed or cells underwent apoptosis. Kinetochore movements in mock-treated and Sgo1-depleted cells were also compared by filming cells expressing a CENP-A protein fused to EGFP (Figure 4C). The results confirmed that most sister kinetochore pairs congressed to a metaphase plate before splitting precociously, which led to collapse of the metaphase plate. It also confirmed the congression defect of some sister kinetochore pairs (Figure 4C, arrows). Our data imply that the highly abnormal separation of sister chromatids seen in Sgo1-depleted cells occurs at around the same time as cells would normally undergo anaphase and not as soon as cells enter mitosis. This suggests that Sgo1 may be required not to build sister chromatid cohesion during S phase, but rather to maintain sister chromatid cohesion during mitosis, a period during which most cohesin is removed from chromosome arms. Our data also show that the abnormal sister chromatid separation of Sgo1-depleted cells is not merely a response to an extended mitotic arrest.
Figure 4 Live Cell Analysis of Sgo1-Depleted Cells
(A) Synchronised HeLa cells expressing EGFP-tagged histone H2B were transfected with Sgo1 siRNA or dH2O as in Figure 1A, and analysed with time-lapse confocal microscopy. Stacks of 12 different z-plane images were obtained every 5 min and projected images for several time points are shown. Note that several chromosomes failed to congress to the metaphase plate, which was followed by progressive sister chromatid separation over time. Typically, a pair of disjoining sister chromatids dissociated simultaneously from the metaphase plate towards opposite poles.
(B) Summary of live cell analysis. Cells prepared as in (A) were examined by time-lapse immunofluorescence microscopy. Mitotic cells were aligned on the time axis according to NEBD as determined from the loss of a defined nuclear boundary. Time from NEBD to anaphase onset/sister chromatid separation (white portion of bars) and from this point to chromosome decondensation or apoptosis (grey portion of bars) was measured.
(C) To follow centromere dynamics, cells expressing EGFP-CENP-A were either mock-treated or Sgo1 siRNA transfected. Images were obtained by time-lapse confocal microscopy. Note that in cells depleted of Sgo1, dots of paired GFP-CENP-A can be found in earlier unaligned chromosomes (arrows). Single dots progressively fell apart in later phases, resulting in collapse of metaphase plate (images in lower row).
Sgo1 Prevents Precocious Dissociation of Cohesin from Centromeres During Mitosis
To test if the abnormal separation of sister chromatids in Sgo1-depleted cells is accompanied by loss of cohesin from chromosomes, we depleted Sgo1 from synchronised HeLa cells that inducibly express a myc-tagged version of the Scc1 α kleisin cohesin subunit (Figure 5) [16]. Cells were harvested 9.5 h after release from the second thymidine block when many had already entered mitosis. After mock treatment, Scc1-myc staining along chromosomes was detectable in two-thirds of mitotic cells, which were identified by being positive for histone H3 phosphorylation (Figure 5A and 5B). Scc1-myc was typically more abundant at centromeres in such cells. This centromere enrichment was more clearly detectable (Figure 5A) if cells had been incubated in the presence of the spindle poison nocodazole for 4 h prior to harvesting, a treatment that induces a prometaphase arrest during which cohesin is completely removed from chromosome arms. Sgo1 depletion greatly reduced the fraction of mitotic cells with chromosomal Scc1-myc staining, in both the presence and the absence of nocodazole (Figure 5A and 5B). Interestingly, we noticed that Scc1-myc was rarely if ever enriched at centromeres in the few mitotic Sgo1-depleted cells whose chromosomes were still associated with Scc1-myc (Figure 5C and 5D). Importantly, Sgo1 depletion did not reduce the amount of Scc1-myc associated with prophase chromosomes (Figure S4). These results are consistent with the notion that the loss of mitotic sister chromatid cohesion caused by Sgo1 depletion is due to dissociation of cohesin from centromeres before cells initiate anaphase.
Figure 5 Sgo1 Is Required for Stable Association of Cohesin at Centromere
(A) Synchronised HeLa cells that inducibly express Scc1-myc were transfected with control or Sgo1 siRNA and processed for immunofluorescence microscopy with or without 4-h treatment with nocodazole. Mitotic cells were spun down on glass slides and analysed for cohesin with antibodies to myc epitope (right photomicrographs). Cells were costained with P-H3 (left photomicrographs, red) to identify cells from prophase to metaphase. DNA was counterstained with DAPI (left photomicrographs, blue). Note that with Sgo1 RNAi, Scc1-myc was undetectable in the majority of P-H3 positive cells (arrows). Cells with myc staining are indicated as staining controls (asterisks). Bar = 10 μm.
(B) Quantification of Scc1-myc staining. Approximately 200 cells with positive staining for P-H3 were assessed for Scc1-myc staining, which was classified as positive, reduced, or negative, as indicated.
(C) Lack of centromere enrichment of cohesin in Sgo1-depleted cells. Mitotic cells were prepared as in (A), and the centromeric enrichment of Scc1-myc was analysed by immunofluorescence microscopy. Merged pictures of Scc1-myc (green) and CREST antigen (red) are shown. Approximately 200 cells were scored for each experiment. Bar = 5 μm.
(D) Centromeric cohesin is not maintained in Sgo1-depleted cells. Mitotic cells were prepared and processed for immunofluorescence as in (C). Representative cells with various levels of Scc1-myc are shown. Note that centromeric staining of Scc1-myc emerges as the bulk of cohesin dissociates from chromosomes in controls (upper panels). However, in Sgo1 RNAi cells, centromeric enrichment is hardly seen at any stage of arm cohesin dissociation (lower photomicrographs). Bar = 10 μm.
Sgo1-Depleted Cells Arrest in a Prometaphase-Like State
To investigate whether the loss of cohesin from centromeres is due to precocious activation of separase, we used in situ immunofluorescence to assess cyclin B1 levels and localization of Mad2 in mock-treated and Sgo1-depleted cells 11 h after their release from the second thymidine block. In mock-treated cells, cyclin B1 was concentrated within the nuclei of prophase cells and on the mitotic spindles of all prometaphase and most metaphase cells, although also throughout their cytoplasm, and was absent from anaphase or telophase cells (Figure 6A). To quantitate this result, we classified mitotic cells into five categories based on DAPI (4′,6′-diamidino-2-phenylindole) staining and scored the fraction of each category that was positive for cyclin B1 (Figure 6B). In mock-treated cells, cyclin B1 was positive in all prometaphase cells, in about 70% of metaphase cells, and in no anaphase cells. This pattern reflects destruction of cyclin B1 shortly before the onset of anaphase. In Sgo1-depleted cells, cyclin B1 was positive in all prometaphase and most metaphase cells. It was also positive in the vast majority of cells that contained metaphase plates with some dispersed chromatids and in cells whose chromatids had largely dispersed from the metaphase plate (Figure 6B). This implies that cyclin B1 destruction never occurs in mitotic Sgo1-depleted cells. This is presumably due to continued activation of the mitotic spindle checkpoint because the chromosomes of mitotic cells lacking Sgo1 always contained foci of Mad2 associated with their centromeres, which is normally seen only in prometaphase in mock-treated cells (Figure 6C and unpublished data). These data suggest that separase is not activated in Sgo1-depleted cells. Their loss of centromeric cohesin is therefore unlikely to be due to Scc1 cleavage.
Figure 6 Sgo1-Depleted Cells Arrest in a Prometaphase-like State
(A) Sgo1 depletion prevents destruction of cyclin B1. Synchronised HeLa cells mock-treated or transfected with Sgo1 siRNA were processed for immunofluorescence microscopy using cyclin B1 antibodies (green) and CREST sera (red) 11 h after release from the second thymidine block. DNA was visualized by DAPI staining (blue). In mock-treated cells, cyclin B1 staining in prophase (a) and in prometaphase (b) disappears as cells segregate their chromosomes upon anaphase entry (c). Sgo1-depleted cells which segregate chromosomes asynchronously and arrest in a mitotic state retain preanaphase levels of cyclin B1 (d–e). Bar = 10 μm.
(B) Mitotic cells shown in (A) were classified into five categories based on DAPI-labelled chromosome configuration, and scored for presence or absence of cyclin B1 staining. Black bars represent the frequency of each category, and red bars represent the percentage of cells that fall into each category and are positive for cyclin B1 staining.
(C) Recruitment of Mad2 in cells depleted of Sgo1. Cells prepared as in (A) were stained with cyclin B1 antibody, and counterstained with Mad2 antibody (red) and DAPI to visualize DNA (blue). (a) Prometaphase mock-transfected cells show Mad2 staining at kinetochores, which is lost as the metaphase plate assembles and disappears before cells enter anaphase (unpublished data). (b) Sgo1-transfected cells which have prematurely segregated sisters remain positive for Mad2 kinetochore staining. Bar = 10 μm.
(D) Aurora B remains at centromeres in Sgo1-depleted cells. Cells prepared as in (A) were stained with Aurora B antibody (green) and counterstained with CREST antiserum (red), and DAPI to visualize DNA (blue). In prometaphase (a) mock-transfected cells, Aurora B is found at centromeres before relocalizing to the central spindle as cells enter anaphase (b). In Sgo1 transfected cells that show precocious sister separation, Aurora B remains localized at centromeres (c).
Another event that normally occurs at the onset of anaphase is the disappearance of Aurora B from inner centromeres and its accumulation at the spindle midzone [30]. Aurora B does not dissociate from centromeres at any stage of the abnormal mitoses of Sgo1-depleted cells, as Aurora B was found adjacent to CREST staining not only in metaphase cells (unpublished data) but also in cells whose chromatids have dispersed from the metaphase plate (Figure 6D). A corollary of this finding is that cohesin is not required to maintain Aurora B at centromeres, contrary to a previous suggestion [15].
Neither Plk1 Depletion nor Aurora B Inhibition Suppresses the Precocious Sister Separation in Sgo1-Depleted Cells
Removal of cohesin from chromosome arms during prophase and prometaphase depends on the activity of mitotic kinases, including Plk1. Might this Plk1-dependent process in normal cells be prevented from attacking centromeric cohesin by Sgo1? To test this idea, we analysed whether synchronised cells depleted for both Sgo1 and Plk1 also lose sister chromatid cohesion when they enter and arrest in mitosis. Mock-treated, Plk1-depleted, Sgo1-depleted, and Sgo1- and Plk1-depleted cells were harvested at 7, 9, and 11 h after release from the thymidine block, and the state of spread chromosomes was analysed by Giemsa staining (as described in Figure 3). The mitotic index of mock-treated cells peaked at 9 h and declined by 11 h, but the mitotic indices of singly or doubly depleted cells continued to rise after 9 h, reaching around 50% by 11 h (Figure 7A). Plk1-depleted cells arrested with hypercondensed, rod-shaped chromosomes whose arms were more tightly associated than those of prometaphase or metaphase mock-treated cells (Figure 7B, part d), which is consistent with previous findings [20]. Sgo1 singly depleted cells arrested with separated chromatids, progressing via the same set of stages described in Figure 3B. Importantly, cells depleted for both Sgo1 and Plk1 also largely arrested with separated chromatids (Figure 7A). The morphology of these chromatids was, however, very different from those seen in Sgo1 singly depleted cells. Of those cells whose sister chromatids had been separated (i.e., those represented by the red bars in Figure 7A), 100% possessed short, curly (presumably coiled) separated chromatids by 11 h in a culture depleted of Sgo1 alone, as shown in Figure 7B, part b. In contrast, 80% of such cells that had been depleted for both Sgo1 and Plk1 possessed separated chromatids that were both longer and straighter, as shown in Figure 7B, part c. Only 12% possessed short, curly chromatids, while 8% possessed chromatids whose arms were still loosely associated with their sisters, as shown in Figure 7B, part a.
Figure 7 Neither Plk Depletion nor Aurora B Inhibition Suppresses the Precocious Sister Separation Seen in Sgo1-Depleted Cells
(A and B) Synchronised HeLa cells were mock-treated or transfected with the indicated combination of Sgo1 and Plk1 siRNA and harvested at the time shown following release from thymidine block. Chromosomes were then spread on glass slides and examined by Giemsa staining. As in Figure 3, the percentage of mitotic cells were calculated out of 200 cells, and mitotic chromosome spreads were then further classified into one of 10 categories (n = 200 mitotic spreads for each time point). (A) The frequency of each category of mitotic cell (see examples in Figures 3B and 7B) is given as a percentage of total cell numbers, such that the sum of each column represents the mitotic index. (B) In addition to the seven categories in Figure 3A and 3B, the following categories (illustrated in Figure 7B) were scored: (a) sister centromeres separated, arms still cohesed; (b) scattered chromatids, same as Figure 3B, part g; (c) sisters are separated and randomly distributed in relation to one another, but are not hypercondensed; and (d) sister chromatids are cohesed along their length, and rod-shaped chromosomes are hypercondensed, as is characteristically seen with Plk1 knockdown [20]. For simplicity, the categories illustrated in Figure 3B, part e and f, and Figure 7B, part a–c, were combined into a single category representing premature sister separation (shown in red). In smaller pictures, bar = 10 μm; in enlarged portions, bar = 2 μm.
(C and D) Synchronised HeLa cells were mock-treated or transfected as indicated. At 6 h after release from the second thymidine block, cells were treated with nocodazole with or without Aurora B inhibitor Hesperadin as indicated, and harvested at hourly intervals up to 3 h thereafter. Mitotic index and chromosome spreads were assessed as in (A). (C) The frequency of each category of mitotic cell (illustrated in Figures 3B and 7D) is given as a percentage of total cell numbers, such that the sum of each column represents the mitotic index. (D) The seven categories scored for Figure 3A and 3B were also scored for this experiment. (a) Scattered chromatids as in Figure 3B, part g; (b) early phase of precocious sister disjunction as in Figure 3B, part e. In addition the following categories (illustrated in Figure 7D) were also scored: (c) Chromosomes have begun to decondense prior to separation, and sister resolution is defective, characteristic of Hesperadin treatment. (d) Centromeres cohesed, and arms opened and hypercondensed, characteristic of nocodazole treatment.
The fact that treatment with Plk1 siRNA dramatically changed the morphology of separated chromatids in Sgo1-depleted cells confirms that both proteins had in fact been effectively depleted. Our data therefore imply (somewhat surprisingly) that Plk1 is not necessary for the precocious separation of sister chromatids induced by Sgo1 depletion. Interestingly, Sgo1 depletion permitted not only sister centromere separation but also that along chromosome arms in cells supposedly lacking Plk1. This raises the possibility that the tight cohesion between sister chromatid arms that persists in Plk1-depleted cells depends on Sgo1.
Aurora B kinase activity has also been implicated in the removal of cohesin from chromosome arms. To address whether this kinase is responsible for the precocious loss of sister centromere cohesion in Sgo1-depleted cells, we examined whether inhibition of Aurora B by the small molecule inhibitor Hesperadin can suppress their sister chromatid separation. Because Aurora B and its yeast equivalent Ipl1 are also required to prevent cell cycle arrest of cells with defective sister chromatid cohesion [31] or cells whose microtubules cannot generate centromeric tension [32,33], it was necessary to maintain Mad2 inhibition of APC/C activity by addition of nocodazole at the same time that Hesperadin was added, 6 h after they had been released from the thymidine block. The former delays by at least 3 h the exit from mitosis of Hesperadin-treated cells [32]. Chromosomes were spread and examined by Giemsa staining after harvesting mock-treated and Sgo1-depleted cells at 6, 7, 8, and 9 h after release (Figure 7C and 7D). Addition of nocodazole caused both mock-treated and Sgo1-depleted cells to accumulate in mitosis between 6 and 9 h after release, which was largely unaffected by Hesperadin addition. Importantly, Hesperadin had only a modest, if any, effect on the precocious separation of sister chromatids in Sgo1-depleted cells (Figure 7C and 7D).
Interestingly, treatment with nocodazole had a clear effect on the arrangement of the separated sisters in Sgo1-depleted cells. By 9 h, up to 73% of mitotically arrested cells resembled the image shown in Figure 7D, part b, where single chromatids lie in the neighbourhood of their presumptive sisters. In the absence of nocodazole, less than 5% of cells fell into this category (Figure 3B, part e). This implies that when sister cohesion is prematurely lost as a result of Sgo1 depletion, spindle pulling forces are not required to separate sisters but they are required to produce the scattered chromatid effect seen in the previous experiments.
We conclude that Hesperadin cannot prevent the precocious separation of sister chromatids induced by Sgo1 depletion, at least when such cells are prevented from exiting mitosis by addition of nocodazole. In a very similar experiment in which nocodazole was omitted, Hesperadin did indeed reduce the number of mitotic cells whose sisters had separated precociously (unpublished data). We believe that this effect is probably a statistical artefact caused by the failure of Sgo1-depleted cells treated with Hesperadin to arrest in mitosis.
The Precocious Sister Separation and Mitotic Arrest of Sgo1-Depleted Cells Is Suppressed by a Nonphosphorylatable Scc3-SA2
In a related paper, Hauf et al. [17] describe the mitosis-specific phosphorylation of 12 serine or threonine residues clustered within the C-terminal domain of cohesin's Scc3-SA2 subunit. Remarkably, a large fraction of Scc3-SA2 protein in which all 12 residues have been mutated to alanine (Scc3-SA2 12xA), which is no longer phosphorylated during mitosis, persists on chromosome arms throughout mitosis and even does so when cells are arrested for prolonged periods in a prometaphase-like state due to nocodazole treatment. The persistence of Scc3-SA2 12xA on chromosomes under these circumstances prevents loss of cohesion between sister chromatid arms in nocodazole-arrested cells, but it does not obviously interfere with mitosis in cycling cells. This implies that phosphorylation of Scc3-SA2 may be largely, if not solely, responsible for the removal of cohesin from chromosome arms during prophase and prometaphase.
We therefore analysed the effects of Sgo1 depletion in cell lines in which either wild-type SA2 or SA2 12xA protein (both tagged with nine myc epitopes) is expressed from a doxycycline-inducible promoter at levels (when induced) that are comparable to endogenous SA2 protein (see Figures 8A and S5). We conducted the experiments on cycling cells that had been treated with (or without) doxycycline for 72 h prior to transfection. Cells were then transfected either with water (mock) or with the Sgo1 siRNA and harvested 18 or 24 h later, and chromosome spreads were examined after Giemsa staining. In mock-transfected cultures, the frequency of mitotic cells remained constant (at around 4–5%) at 18 and 24 h, regardless of the presence or absence of doxycycline or whether wild-type or mutant protein was induced (Figure 8B). In Sgo1-depleted cells, the mitotic indices of cells expressing wild-type Scc3-SA2 myc increased to between 16% and 18% by 24 h; the mitotic index also increased to 14% in cells that expressed only very low levels of Scc3-SA2 12xA-myc in the absence of doxycycline. As expected, most sister chromatids had separated in both sets of these Sgo1-depleted cells. The low frequency of telophase cells suggests that these cells largely failed to undergo anaphase. Remarkably, induction of Scc3-SA2 12xA-myc with doxycycline suppressed the accumulation of mitotic cells caused by Sgo1 depletion. Moreover, only a few of the mitotic cells contained precociously separated sister chromatids, and significant numbers had clearly undergone anaphase and produced telophase cells (Figure 8B). Most metaphase cells possessed chromosomes whose sisters were cohesed at centromeres as well as along their arms. Thus, expression of nonphosphorylatable Scc3-SA2 at physiological levels suppresses both the mitotic arrest and the precocious sister chromatid separation caused by Sgo1 depletion. This indicates that loss of cohesin from centromeres in Sgo1-depleted cells may be due to (hyper-) phosphorylation of Scc3-SA2 at centromeres.
Figure 8 Expression of Nonphosphorylatable Scc3-SA2 Suppresses Sgo1-Depletion Phenotype
(A) Total cell extracts prepared from HeLa cells that, upon doxycycline treatment, inducibly express either a wild-type (SA2-wt) or nonphosphorylatable (SA2–12xA) myc-tagged version of Scc3-SA2 were resolved by SDS-PAGE and probed with anti-SA2 antibody. The lower band represents endogenous Scc3-SA2, the upper band, myc-tagged Scc3-SA2. The first lane contains total cell extract prepared from untagged HeLa cells.
(B) Expression of nonphosphorylatable Scc3-SA2 suppresses sister separation caused by Sgo1 depletion. Cycling HeLa cells carrying inducible myc-tagged versions of Scc3-SA2 were treated with (or without) doxycycline at 2 μg/ml for 72 h prior to and during transfection to induce expression, as indicated. Cells were harvested 18 h and 24 h post transfection, chromosomes were spread on glass slides and Giemsa-stained. As in Figure 7, the percentage of mitotic cells were calculated out of 200 cells, and mitotic chromosome spreads were then further classified into one of five categories (n = 200 mitotic spreads for each time point). The frequency of each category of mitotic cell (see examples in Figures 3B, 7B, and 7D) is given as a percentage of total cell numbers, such that the sum of each column represents the mitotic index.
(C) Nonphosphorylatable Scc3-SA2-mediated suppression is still observed when combined with nocodazole arrest. The indicated treatments were repeated as outlined in (B) in the presence of nocodazole which was added to cultures 4 h posttransfection, i.e., 14 and 20 h prior to harvesting of cells. Samples were processed as in (B), with the addition of a sixth mitotic category.
(D) Live cell analysis of Sgo1 depletion of nonphosphorylatable Scc3-SA2 (SA2–12xA) expressing cells. To follow chromosome behaviour, cells stably expressing EGFP-H2B and inducibly expressing SA2–12xA were used. Expression of SA2–12xA was induced 72 h prior to transfection as in (B). Cells were transfected with Sgo1 siRNA as in Figure 1A and examined by time-lapse fluorescence microscopy. Significant number of cells exit mitosis by expressing nonphosphorylatable Scc3-SA2.
(E) Nonphosphorylatable SA2 (SA2–12xA) is found at centromeres even in the absence of Sgo1. HeLa cells containing either the myc-tagged wild-type (a) or SA2–12xA (b) inducible transgene were either uninduced or induced as in (B) 72 h before transfection. Transfection of Sgo1 siRNA was performed prior to the second thymidine block as in Figure 1A. At 8.5 h after the release from early S phase, mitotic cells were spun down on glass slides and analysed for cohesin localisation by immunofluorescence microscopy using antibodies to the myc epitope (shown in green). Cells were costained with CREST antiserum to label kinetochores (shown in red) DNA was counterstained with DAPI (shown in blue).
(F) Quantification of SA2-myc staining. Samples similar to those described in (E) were stained with myc and P-H3 antibodies (the latter to identify cells from prophase to metaphase). Approximately 200 P-H3-positive cells were assessed for SA2-myc staining, and the percentage of cells that were both P-H3- and SA2-myc-positive was plotted. We believe that the apparent drop in the number of SA2–12xA-myc positive cells observed with depletion of Sgo1 relative to mock transfection is a statistical artefact caused by the mitotic arrest and accumulation of those cells that did not express SA2–12xA-myc (∼30%) but that were depleted of Sgo1.
It is nevertheless possible that nonphosphorylatable Scc3-SA2 suppresses a mitotic defect of Sgo1-depleted cells responsible for their cell cycle arrest, and as a consequence these cells might not have long enough to lose sister chromatid cohesion. To address this possibility, nocodazole was added to doxycycline-induced cells 4 h after transfection (i.e., 14 or 20 h prior to harvesting), which caused both mock-treated and Sgo1-depleted cells to accumulate in mitosis (Figure 8C). In the case of the culture in which Sgo1 had been depleted and wild-type SA2 myc protein induced, a large fraction of mitotic cells (31%) had separated sister chromatids without undergoing anaphase. This fraction was much lower (9%) when SA2 12xA-myc protein had been induced. Most Sgo1-depleted mitotic cells expressing SA2 12xA-myc contained intact arm, as well as centromere, sister chromatid cohesion (Figure 8C). We conclude that SA2 12xA-myc expression suppresses loss of sister chromatid cohesion in Sgo1-depleted cells even when cells have been arrested in mitosis for many hours.
To examine more carefully whether expression of nonphosphorylatable Scc3-SA2 permits cells lacking Sgo1 to undergo anaphase, we created a new cell line that expressed histone H2B tagged with EGFP as well as doxycycline-inducible Scc3-SA2 12xA-myc. Sgo1 was depleted in induced and uninduced cells from the same cell line and the behaviour of chromosomes followed by time-lapse video microscopy (Figure 8D). Three types of mitoses were observed: those in which sister chromatid pairs first congressed to a metaphase plate but then lost cohesion before undergoing anaphase (precocious separation), those in which some chromosomes were slow to congress and failed to undergo anaphase but did not display mass sister separation, and those in which chromosomes congressed to a metaphase plate and then underwent what appeared to be a normal anaphase with unaltered kinetics. Only 23% of uninduced cells depleted for Sgo1 underwent anaphase, and 63% separated their chromatids precociously, while the rest had congression defects. Remarkably, induction of Scc3-SA2 12xA-myc increased the fraction of cells that underwent anaphase from 23% to 60% and reduced the fraction of cells that separated sisters precociously from 63% to 19%. These data suggest that expression of Scc3-SA2 12xA-myc enables cells to undergo anaphase in the absence of Sgo1.
Finally, we addressed whether the loss of mitotic phosphorylation sites on Scc3-SA2 enables cohesin to persist at centromeres as well as along chromosome arms in mitotic Sgo1-depleted cells. To do this, we analysed mitotic chromosomes from Sgo1-depleted cells expressing either Scc3-SA2-myc or Scc3-SA2 12xA-myc. Wild-type SA2 myc-tagged protein was associated with chromosomes in very few cells, while nonphosphorylatable SA2 myc tagged protein was found along the axes of all chromosomes in nearly 40% of cells (Figure 8E and 8F). Crucially, Scc3-SA2 12xA-myc was associated with centromeres as well as chromosome arms. Preventing Scc3-SA2 phosphorylation therefore enables cohesin to persist at centromeres in mitotic cells lacking Sgo1 as well as on chromosome arms in otherwise wild-type mitotic cells. This suggests that the loss of cohesin from centromeres in Sgo1-depleted cells is due to mitosis-specific phosphorylation of SA2.
Discussion
It has long been recognized that cohesion between sister chromatids in the vicinity of centromeres has an especially important role during both meiosis and mitosis. Centromeric cohesion is special during meiosis because it completely resists destruction at the first meiotic division when dissolution of cohesion along chromosome arms triggers the resolution of chiasmata. It is special during mitosis because, unlike cohesion along chromosome arms, centromeric cohesion persists even when cells are arrested in mitosis for prolonged periods by spindle poisons. The work described in this paper suggests, somewhat surprisingly, that both of these special attributes of centromeric cohesion might be conferred by the same protein, namely Sgo1.
Recent work suggests that, in both budding and fission yeast, Sgo1 protects centromeric cohesion at meiosis I by preventing cleavage of cohesin's α kleisin subunit by separase. Surprisingly, both Sgo1 in S. cerevisiae and its probable orthologue in D. melanogaster MEI-S332 are associated with centromeres during mitosis as well as meiosis. We show here that a related protein in human cells concentrates at centromeres during mitosis and disappears from chromosomes during anaphase. Remarkably, depletion of human Sgo1 by RNAi prevents HeLa cells from completing mitosis. Though most chromosomes congress to a metaphase plate in Sgo1-depleted cells, cells never undergo anaphase and instead arrest for a prolonged period in a prometaphase-like state with high cyclin B1 levels. Our data suggest that this is caused by a catastrophic separation of sister chromatids at around the time cells should have normally undergone anaphase, and that this event occurs in the absence of APC/C activation or dissociation of Aurora B from centromeres. This precocious separation is accompanied by loss of cohesin from centromeres as well as chromosome arms and activation of the mitotic spindle checkpoint, as documented by persistent recruitment of Mad2 to kinetochores. Remarkably, expression of a version of cohesin's Scc3-SA2 subunit whose C-terminal domain can no longer be phosphorylated as cells enter mitosis (Scc3-SA2 12xA-myc) not only alleviates the precocious loss of sister chromatid cohesion of Sgo1-depleted cells but also permits a large fraction of them to complete what appears, at least superficially, to be a normal mitosis.
Our findings together with those described by Hauf et al. [17] suggest that phosphorylation of Scc3-SA2 normally triggers dissociation of most cohesin from chromosome arms during prophase and prometaphase, but is prevented from doing so at centromeres by Sgo1, which is more abundant at this location than it is along chromosome arms (Figure 9A). We suggest that in the absence of Sgo1, Scc3-SA2 is removed by the prophase pathway from centromeres as well as from chromosome arms and that this leads to the complete disjunction of sister chromatids before cells can initiate anaphase, which is meanwhile delayed by the mitotic checkpoint (Figure 9A).
Figure 9 Model for Sgo1 Function during Mitosis
(A) During an unperturbed mitosis (Wild Type), arm cohesin (red circles) is removed in a kinase-dependent manner during prophase/prometaphase. Sgo1 protects centromeric cohesin (brown circles) until the metaphase-anaphase transition. Once all chromosomes have successfully bi-oriented on the metaphase plate, Mad2 inhibition of the APC/C is relieved, allowing separase activation. Separase in turn removes cohesin remaining at centromeres through cleavage of the α kleisin Scc1 subunit, allowing the cell to enter anaphase. In the absence of Sgo1 (Sgo1 Depletion), cohesin is removed from the chromosome arms and at the centromere during prophase/prometaphase before chromosomes have properly bi-oriented and been attached to their full complement of spindles. Thus, Mad2 activity continues to maintain the spindle checkpoint, causing cells to arrest for a prolonged period in a prometaphase-like state with separated sister chromatids. Expression of nonphosphorylatable Scc3-SA2 (Sgo1 Depletion + Cohesin containing SA2–12xA) prevents the prophase removal of cohesin both from arms and centromeres, thus allowing cells to proceed through an apparently normal anaphase even in the absence of Sgo1 activity.
(B) The phosphorylation state of cohesin may be the result of a dynamic balance in which Sgo1 somehow functions to antagonize the activity of mitotic kinases, including Plk1 and Aurora B and, potentially, other as-yet unidentified kinases (Kinase X, Y). In this model, when the direction of the reaction is artificially sent towards the hyperphosphorylated state (following Sgo1 knockdown), cohesin efficiently dissociates from chromatin; when the opposite state is favoured (e.g., as a result of Plk1 depletion) cohesin remains tightly associated with chromatin.
The suppression of the mitotic arrest induced by Sgo1 depletion by Scc3-SA2 12xA-myc implies that it is the precocious loss of sister chromatid cohesion and not the lack of some other function of Sgo1 that is largely responsible for the pathological mitotic arrest of Sgo1-depleted cells. Our findings demonstrate for the first time that Sgo1-like proteins promote sister chromatid cohesion by regulating the activity of cohesin. This is inconsistent with the suggestion that Sgo1/MEI-S332 proteins confer a form of sister chromatid cohesion during mitosis that is distinct from that conferred by cohesin [34,35].
Salic et al. [35] have also shown very recently that Sgo1 is required to prevent precocious sister kinetochore splitting. The authors suggested that Sgo1 has an additional role in stabilizing microtubules attached to kinetochores. Their proposal, that a defect in this putative function is the primary cause of the mitotic arrest due to Sgo1 depletion, is inconsistent with our observations. In our view, the simplest, although by no means only, explanation for the mitotic arrest of Sgo1-depleted cells is that loss of sister chromatid cohesion eliminates the tension needed to stabilize the connection between microtubules and kinetochores, which in turn leads to unoccupied kinetochores that recruit Mad2 and generate a form of Mad2 that is capable of inhibiting the APC/C's destruction of securin and cyclin B. Nevertheless, our finding that 14% of Sgo1-depleted cells expressing SA2 12xA-myc had congression defects is consistent with the notion that Sgo1 might also function to regulate kinetochore-microtubule interactions.
How might Sgo1 regulate cohesin? One possibility is that Sgo1 enables cohesin to be refractory to the effects of Scc3-SA2 phosphorylation. If cohesin embraces chromatid fibres within its ring structure, then phosphorylation of Scc3-SA2's C-terminal tail might induce the cohesin ring to open, and this process might be blocked by Sgo1. Alternatively, Sgo1 might prevent cohesin's dissociation by blocking Scc3-SA2's phosphorylation in the first place or by recruiting a phosphatase that reverses its phosphorylation (Figure 9B). Antibodies capable of specifically detecting phosphorylated Scc3-SA2 would make it possible to distinguish the first from the second and third scenarios.
Several lines of evidence suggest that the process against which Sgo1 protects centromeric cohesin does not involve separase-mediated Scc1 cleavage. First, Scc3-SA2 12xA-myc's ability to suppress cohesin's dissociation both from arms in wild-type cells and from centromeres in Sgo1-depleted cells suggests that these two phenomena have a common cause. If the former is separase-independent, then so, presumably, is the latter. Second, the persistence of cyclin B1 throughout the period during which centromeric cohesion is lost in Sgo1-depleted cells implies that the APC/C is not activated and separase must therefore remain associated with its inhibitor chaperone securin. Third, cells expressing Scc3-SA2 12xA-myc proliferate fairly normally, and their timely destruction of sister chromatid cohesion at the onset of anaphase presumably involves the activation of separase by the APC/C. If separase deregulation were responsible for cohesin's precocious disappearance from centromeres in Sgo1-depleted cells, then Scc3-SA2 12xA-myc would have to suppress this defect by delaying Scc1 cleavage, which is difficult to reconcile with the timely onset of anaphase in cells expressing Scc3-SA2 12xA-myc. It is therefore likely that Sgo1 protects centromeric cohesin from different processes during mammalian mitosis and yeast meiosis, from chromosomal dissociation induced by Scc3-SA2 phosphorylation during mitosis, and from Rec8 or Scc1 cleavage during meiosis. Although different, both processes may have a key property in common, namely regulation by Plk1-mediated phosphorylation.
It is noteworthy that many shugoshins, including human Sgo1, disappear from centromeres during anaphase. This event might be triggered by their destruction at the hands of the APC/C along with securin and cyclin B [35]. This event might nevertheless assist in the dissolution of cohesion between sister centromeres at the onset of anaphase by exposing cohesin to the effects of its phosphorylation. Hyperphosphorylation of Scc3-SA2 might promote dissociation of cohesin complexes that had somehow evaded cleavage by separase, while hyperphosphorylation of Scc1 (which could also be triggered by Sgo1's departure) might actually facilitate Scc1's cleavage as occurs in both budding yeast [36] and human cells.
The identity of the mitotic kinases responsible for phosphorylation of Scc3-SA2 remains unclear. Because Plk1 is required for dissociation of cohesin from chromosome arms in HeLa cells containing Sgo1 [20], it was surprising that Plk1's depletion did not suppress the precocious loss of cohesion caused by Sgo1 depletion. This finding indicates that protein kinases other than Plk1 can phosphorylate the C terminus of Scc3-SA2. Because Plk1 is necessary for dissociation of cohesin from chromosome arms in the presence of Sgo1 [20] but not in its absence (Figure 7A), Sgo1 would appear to have a role in inhibiting dissociation of cohesin from chromosome arms as well as from centromeres. Though we could not detect Sgo1 associated with chromosome arms, modest amounts residing at this location might (at least partly) inhibit the prophase pathway. We suggest that Sgo1 antagonizes phosphorylation of Scc3-SA2 by several mitotic protein kinases, including Plk1 (Figure 9).
Our study of human Sgo1 has hitherto been confined to HeLa cells. It will be important in the future to analyse its function in other cell types, especially in untransformed cells, and ultimately in real tissues from mice whose Sgo1 gene could be deleted by homologous recombination. It is curious, for example, that Sgo1's likely orthologue in D. melanogaster, MEI-S332, is not apparently essential for somatic cell divisions, which contrasts with Sgo1's essential function in HeLa cells. It is conceivable that small changes in the kinetics with which cohesin is dissociated from centromeres in Sgo1/MEI-S332-depleted cells is key to whether a cell is able to undergo anaphase before precocious loss of sister chromatid cohesion triggers the mitotic spindle checkpoint.
One of the most surprising implications of our work is that centromeric sister chromatid cohesion, which has long appeared to be a very stable state and to be the cornerstone (not to mention the most recognisable hallmark) of mitotic chromosomes, only exists because shugoshins protect centromeric cohesin from mitotic protein kinases that maraud mitotic chromosomes and threaten to destroy their integrity. This may have important implications for meiotic cells where protection of cohesin by Sgo1-like proteins (in this case, presumably from attack by separase) is essential for the second meiotic division. Chromosome mis-segregation during meiosis is responsible for trisomies such as Down's syndrome and possibly for age-related infertility and a large fraction of spontaneous abortions [37]. Our work raises the possibility that this could be caused by modest misregulation of protein kinases or phosphatases that affect the phosphorylation of centromeric cohesin. Whether Sgo1 protects centromeric cohesin from separase during meiosis I also by preventing cohesin's phosphorylation is an important question for the future.
Materials and Methods
Antibodies
To generate rabbit anti-Sgo1 antibodies, we synthesized two peptides: one (for which the antibody was termed antibody 94) with the sequence YKEPTLASKLRRGDPFTDL (aa 474–492) from the C terminus of a mouse cDNA (identical to corresponding peptide sequence in human protein) and a second (antibody 95) with the sequence QKRSFQDTLEDOKNRMKEKRNKN (aa 7–29) from the N terminus of a mouse cDNA (the corresponding sequence in human protein contains just four substitutions). Each peptide was conjugated with KLH and injected into rabbits. Sera were taken by a standard scheme, and specific antibodies were obtained by affinity purification using antigenic peptide-conjugated columns. For Western blotting, blots were incubated with the primary antibodies overnight at 4 °C at a 1:500 dilution. For immunofluorescence, cells were incubated with the primary antibody overnight at 4 °C at a dilution of 1:100. Other antibodies used in this study were: mouse anti-Aurora B antibody (anti-AIM-1; BD Transduction Laboratories, San Diego, California, United States); CREST serum (gift from A. Kromminga, Hamburg, Germany); mouse anti-cyclin B1 antibody (Santa Cruz Biotechnology, Santa Cruz, California, United States; #GNS1); rabbit anti-Mad2 serum [38]; mouse anti-myc antibody (clone 4A6, Upstate Cell Signaling Solutions, Charlottesville, Virginia, United States); antibody to phosphohistone H3 (Ser10) [P-H3, a mouse monoclonal antibody that detects histone H3 when phosphorylated at serine 10 (Cell Signaling Technology, Beverly, Massachusetts, United States; #6G3)]; rat anti-HP-1β (Serotec Laboratories, Oxford, United Kingdom; #MCA1946); and rabbit anti-SA2 (antibody 466; see [39]).
Cell culture, synchronisation, and transfection
HeLa cells were cultured in DMEM supplemented with 10% FCS, 0.2 mM L-glutamine, 100 units/ml penicillin, and 100 μg/ml streptomycin. For synchronisation, HeLa cells were grown in the presence of 1 mM thymidine (Sigma-Aldrich, St. Louis, Missouri, United States) for 12 h, washed with PBS, and grown in fresh medium for 8 h. Cells were then transfected (see below) in serum-free medium (Opti-MEM, Gibco, San Diego, California, United States) supplemented with 0.3% FCS for 4 h, followed by addition of 1 mM thymidine and FCS to a final concentration of 20%. After a further 12 h, cells were again washed in PBS and transferred to fresh medium. Samples were harvested at various time points up to 15 h after the second release, as described below and in the figure legends. Nocodazole and Hesperadin were each used, as indicated, at a final concentration of 100 ng/ml and 100 nM, respectively. Doxycycline-inducible nonphosphorylatable SA2 HeLa cells were established as described in the accompanying paper [17]. For generation of EGFP-H2B stably expressing line, cells were transfected with plasmid encoding EGFP-H2B [40] using FuGene6 reagent (Roche, Basel, Switzerland), and stable expressants were selected in a complete medium containing 2.0 μg/ml blasticidin-S, and were screened by fluorescence microscopy for expression of EGFP-H2B. HeLa cells stably expressing EGFP-CENP-A were established and characterized as described [41]. For flow cytometric analysis, cells fixed in 70% ethanol were washed with PBS and subsequently stained in PI buffer [10 μg/ml propidium iodide, 10 mM Tris-HCl (pH 7.5), 5 mM MgCl2, and 200 μg/ml RNase A] for 20 min at 37 °C.
RNAi
The oligonucleotide sequence used to target human Sgo1 (oligo Sgo1.1A) was 5′-CAGU
AGAACCUGCU
CAGAA-3′. The oligonucleotide sequence used to target human Plk1 cDNA was 5′-
CGAGCUGCUUAAU
GACGAG-3′ [20]. Synthetic sense and antisense oligonucleotides were purchased from VBC Genomics (Vienna, Austria). For mock transfections, dH2O was used instead of siRNA. Transfection of siRNA duplexes was performed at a final concentration of 150 nM. Experiments performed with a second duplex targeted to a different region of human Sgo1 cDNA (oligo Sgo1.2B) yielded similar results. The oligonucleotide sequence of Sgo1.2B is 5′-GGAUAU
CACCAAUGUCUCC-3′.
Preparation of HeLa cell extracts
To prepare fractionated HeLa cell extract, cells were harvested by washing in ice-cold PBS and scraping from the plate. Cells were pelleted at 1,200 g for 3 min at 4 °C, and washed three times with ice-cold PBS. The cell pellet was resuspended in 200 μl of extraction buffer, which contained 150 mM NaCl, 10 mM NaF, 40 mM β-glycerophosphate, 20 mM HEPES (pH 7.5), 2 mM MgCl2, 10 mM EDTA, 0.5% Triton X-100; 10 ml of this buffer was supplemented with one tablet of Complete Protease Inhibitor (Roche) and 1 mM PMSF. Cells were homogenized with ten strokes in a Dounce homogenizer and incubated on ice for 10 min, followed by another ten strokes. Nuclei and insoluble material were pelleted by centrifugation at 13,000 g for 10 min at 4 °C. The supernatant was completely removed and denatured by addition of an appropriate volume of 4× Laemmli buffer followed by boiling for 10 min at 95 °C. The pellet was resuspended in 4 volumes of 1× Laemmli buffer, boiled at 95 °C for 10 min, and passed two times through a 27-gauge needle to shear DNA.
Immunofluorescence microscopy
For immunostaining, cells were either cultured (and transfected where applicable) directly on glass coverslips, spun onto glass slides using a Cytospin centrifuge (Shandon brand, available from Thermo Electric, Waltham, Massachusetts, United States) for 5 min at 1,500 rpm, or spread on glass slides as described below. Cells were washed with PBS, (in the case of Scc1-myc and SA2-myc staining only, cells were preextracted with 0.1% Triton X-100 in PBS for 2 min followed by PBS wash for 2 min), and fixed in 4% paraformaldehyde in PBS for 15 min. Subsequently, cells were permeabilized with 0.2% Triton X-100 in PBS for 5 min. Cells were blocked in 3% BSA in PBS-T (0.01% Triton X-100) for 1 h prior to incubation with primary antibodies in the same blocking solution. Secondary antibodies were incubated for 1 h at room temperature. The following secondary antibodies conjugated to Alexa Fluor 488 and 568 (Molecular Probes, Eugene, Oregon, United States) were used: goat anti-rabbit, goat anti-mouse, and goat anti-human. DNA was stained by incubating cells in DAPI (1 μg/ml in PBS) for 10 min. Cells were mounted in Vectashield Mounting Medium (Vector Laboratories, Burlingame, California, United States). Images were captured using MetaMorph software (Universal Imaging, Downingtown, Pennsylvania, United States).
Chromosome spreads
To prepare chromosome spreads for Giemsa staining, cells were harvested by trypsinization and pretreated with hypotonic buffer containing 40% medium and 60% tap water for 5 min at room temperature. Cells were fixed with freshly made Carnoy's solution (75% methanol and 25% acetic acid), the fixative was changed three times, and the cells were then stored overnight at −20 °C. For spreading, cells in Carnoy's solution were dropped onto glass slides and dried at room temperature. Slides were stained with 5% Giemsa (Merck, Darmstadt, Germany) at pH 6.8 for 10 min, washed briefly in tap water, air-dried, and mounted with Entellan (Merck).
Chromosome spreads for immunostaining were prepared using the method previously described for spermatocyte spreads [42], as adapted for HeLa cells. Briefly, cells were treated with 330 nM nocodazole for 1 h, and mitotic cells were collected by shaking-off. Cells were incubated in a hypotonic buffer [30 mM Tris (pH 8.2), 50 mM sucrose, and 17 mM sodium citrate] for 7 min and then suspended in 100 mM sucrose (pH 8.2). A small volume of the cell suspension was put on a slide glass that had been dipped in fixative [1% paraformaldehyde, 5 mM sodium borate (pH 9.2), and 0.15% Triton X-100] and dispersed on the slide by continuous tilting. After extensive washing with PBS, lysed nuclei were dried and processed for immunofluorescence microscopy.
Live cell imaging
Cells were grown and synchronised in Lab-Tek chambered cover glasses (Nunc, Roskilde, Denmark) and transfected with siRNA as described in Figure 1. At 6 h after the release from the second thymidine block, medium was changed to CO2-independent medium without phenol red, and the chambers were sealed with silicone grease. Three-dimensional image stacks of live cells were captured over time using either Zeiss 510 confocal microscope (Figure 4A and 4C; Zeiss, Oberkochen, Germany) or Olympus BX51 fluorescence microscope (Figures 4B and 8D; Olympus, Tokyo, Japan).
Supporting Information
Figure S1 Cloning and Expression Analysis of hsSgo1 and mmSgo1
(A) The expression pattern and primary structures of human and mouse Sgo1 (hsSgo1 and mmSgo1, respectively) transcription products were investigated. Total RNA from HeLa cells, mouse testis and mouse thymus were prepared using TRIzol reagent (Invitrogen, Carlsbad, California, United States) according to the manufacturer's instructions. First-strand cDNA was synthesized by SuperScript II reverse transcriptase (Life Technologies, Carlsbad, California, United States) primed by an oligo dT primer, and PCR was performed using the Expand High Fidelity PCR system (Roche). For mmSgo1 cDNA amplification, we designed primers 5′-
CTGAGTGGCCGAGATGAATTTCAC-3′ and 5′-
TGTCCAAGAGACCCTCCTGATCAG-3′ according to GenBank cDNA NM_028232.
We obtained two major bands at 0.9 and 1.8 kb [lane 1 (testis) and lane 2 (thymus)] and cloned them into the pGEM-T vector (Promega, Madison, Wisconsin, United States). Multiple clones were sequenced and PCR error-free sequences mmSgo1A and mmSgo1B were obtained. Note that when the secondary PCRs were done using the PCR reaction above as templates and primers 5′-
AATCTATGACCCACCTGCCTTAGC-3′ and 5′-
GGTTCTGCCCATTGTGCACTGTCT-3′, we saw three minor bands between 0.9 and 1.5 kb (unpublished data), suggesting multiple species of splicing variant.
For hsSgo1 cDNA cloning, we had found two potential splicing variants in the ENSEMBL database (BC001339 and BC017867) that have different 3′ sequences. When primers 5′-
CTGGAGAGCTTCGAAGAGCCTTGA-3′ and 5′-
CCTCTCCTGAAGCAACAGAAAGAG-3′ (designed to amplify products from BC001339) were used, 0.9- and 1.0-kb bands were detected (lane 3), and hsSgo1A–hsSgo1D cDNAs were obtained. When primers 5′-
CTGGAGAGCTTCGAAGAGCCTTGA-3′ and 5′-
CTGAGTGAAACAGACTGTCAACAC-3′ (designed to amplify products from BC017867) were used, six bands between 0.9 and 2.0 kb were detected (lane 4), and cDNA clones obtained from those bands were named hsSgo1E–hsSgo1H and hsSgo1J–hsSgo1L. We could not obtain a clone that encodes the identical open reading frame encoded by BC001339.
(B) To examine the expression of Sgo1 in HeLa cells and various other human and mouse tissues, we performed Northern analysis. Total HeLa RNA was prepared as described in (A), and 20 μg was run on a formaldehyde agarose gel and RNA blots were prepared. A blot was probed with a [32P]-labelled probe (probe H1) generated against the common N terminus region of hsSgo1 [nucleotides (nt) 1–647 of hsSgo1E). Many bands, although faint and ambiguous, were seen (lane 1). We could not assign each signal to a specific cloned cDNA variant; however, these data support the notion that many species of splicing variant are expressed in HeLa cells. When the blot was probed by another probe (probe H2) that is designed to hybridize to the central region specific for hsSgo1E and hsSgo1F cDNAs (nt 665–1454 of hsSgo1E), a signal at ∼2.2 kb was seen (lane 2). Using two probes of hsSgo1, we could confirm that the two relatively long transcripts, hsSgo1E and hsSgo1F, that possess the central region, and the other shorter transcripts that lack the central region, are expressed in HeLa cells.
(C) The tissue distribution of hsSgo1 mRNA was studied using a commercially available Multi Tissue Northern blot (BD Clontech, Palo Alto, California, United States). Multiple, relatively strong, signals were detected in testis, among other organs, on both blots probed by H1 and H2 (upper left blot and lower left blot, respectively). This higher expression of Sgo1 in testis might reflect the fact that testis contains a higher fraction of proliferating cells. Expression analysis of mouse Sgo1 was also performed in parallel. Both a probe against the common N-terminal region of mmSgo1 (probe M1, corresponding to nt 1–610 of mmSgo1A) and an mmSgo1A-specific probe (probe M2, corresponding to nt 704–1339 of mmSgo1A) detected potential mmSgo1 messages in spleen and testis (upper right blot by probe M1, and lower right blot by probe M2, respectively). Mouse Sgo1 messages in spleen were more abundant than in testis, but human Sgo1 message was not detected in human spleen. The biological significance of the different levels of Sgo1 expression observed between human and mouse spleen is unclear. The tested polyA+ RNAs are from spleen (SP), thymus (TH), prostate (PR), testis (TE), ovary (OV), small intestine (SI), colon (CO), peripheral blood leukocyte (PL), heart (HE), brain (BR), lung (LU), liver (LI), skeletal muscle (SM) and kidney (KI). In conclusion, we cloned multiple splicing variants of hsSgo1s and mmSgo1s from HeLa cells and mouse organs. The presence of a variety of messages was also confirmed. However, the functional difference between and biological meaning of such variation remains unknown and requires further study.
(D) Structures of cDNAs for hsSgo1A–hsSgo1H and hsSgo1J–hsSgo1L.
(E) Predicted structures of proteins depicted as cDNAs in (D).
(F) Structures of cDNAs for mmSgo1A and mmSgo1B.
(G) Predicted structures of proteins depicted as cDNAs in (F).
(2.7 MB EPS).
Click here for additional data file.
Figure S2 Sgo1 Antibody Characterisation
Identical Sgo1 localisation patterns were obtained by staining cells with two peptide antibodies raised against different regions of the protein (see Materials and Methods for details).
(A) HeLa cells grown on glass coverslips were paraformaldehyde-fixed and costained with Sgo1 antibody 94 (directed against C-terminal peptide, shown in green) and CREST antiserum (shown in red) to label kinetochores. Five different stages of mitosis are illustrated as indicated.
(B) As (A), except cells were costained with Aurora B antibody (shown in red) to label inner centromere.
(C) As (B), except Sgo1 was stained with antibody 95 (directed against N-terminal peptide, shown in green).
(10 MB EPS).
Click here for additional data file.
Figure S3 Precocious Sister Separation Is Specific to Sgo1 siRNA Treatment
Synchronised HeLa cells were transfected as described in Figures 1 and 3 with siRNA oligonucleotides directed against the following proteins: Aurora B, condensin subunits Cap-D2, Cap-D3, Cap-G, Cap-G2 and Smc2, Cyk4, Ect2, Lamin A, myosin heavy chain Type II (MHC), MKLP1, RhoA, and Sgo1 (two oligonucleotide targets; see Materials and Methods). Cells were harvested 11 h after the second thymidine release and analysed as described for Figure 3A and 3B. The histogram indicates the frequency of each observed category as a percentage of total cells, such that the sum of each column represents the mitotic index. The separated sisters category (represented by the red bars) combines the three categories illustrated in Figure 3B, part e–g.
(1.3 MB EPS).
Click here for additional data file.
Figure S4 No Reduction of Cohesin on Prophase Chromosomes in Sgo1-Depleted Cells
HeLa cells expressing Scc1-myc were mock-treated or Sgo1 siRNA-transfected and harvested as described for Figure 5, cells spun down on glass slides, preextracted prior to fixation, and analysed by immunofluorescence microscopy using myc antibody. Cells were costained with P-H3. Prophase cells, identified by positive P-H3 staining with intact nuclear envelope, were measured for their fluorescence intensities of Scc1-myc and P-H3 using Image-J software. The ratio of fluorescence intensities of myc signals to P-H3 signals was calculated.
(583 KB EPS).
Click here for additional data file.
Figure S5 Induction of SA2-myc Expression upon Doxycycline Treatment
A sample of the cells from the experiment described in Figure 8B were spun onto glass slides and processed for immunofluorescence, staining with myc antibody as described (but cells were not preextracted with 0.1% Triton X-100 prior to fixation). In uninduced cells, a moderate level of leaky expression of the tagged protein is observed in a small number of cells (photomicrographs a, b, e, and f). In induced cells, a high level of the tagged protein is observed in the vast majority of cells (photomicrographs c, d, g, and h). Anti-myc staining of HeLa cells which do not contain any myc-tagged gene expression cassette (photomicrograph i).
(4.7 MB TIF).
Click here for additional data file.
Accession Numbers
The NCBI GeneID (http://www.ncbi.nlm.nih.gov/) and ENSEMBL (http://www.ensembl.org/) accession numbers of the genes discussed in this paper are hsSgo1 (GeneID 151648; ENSEMBL gene ENSG00000129810) and mmSgo1 (Gene ID 72415; ENSEMBL gene ENSMUSG00000023940). The pending GenBank/EMBL/DDBJ accession numbers of the cDNA sequences discussed in Figure S1 are hsSgo1A (AB193056), hsSgo1B (AB193057), hsSgo1C (AB193058), hsSgo1D (AB193059), hsSgo1E (AB193060), hsSgo1F (AB193061), hsSgo1G (AB193062), hsSgo1H (AB193063), hsSgo1J (AB193064), hsSgo1K (AB193065), hsSgo1L (AB193066), mmSgo1A (AB193067), and mmSgo1B (AB193068).
We are grateful to Katja Wassmann, Paris, for anti-Mad2 serum. We also thank Wolfgang Helmhart for technical assistance, Alexander Schleiffer for bioinformatics analysis, Mathias Madalinski for antibody purification, Karin Paiha for BioOptic support, and members of the Nasmyth and Peters lab for helpful discussion and critical reading of manuscript. The Research Institute of Molecular Pathology is funded by Boehringer Ingelheim, and this work was partly supported by grants from the Austrian Industrial Research Promotion Fund and the Austrian Science Fund (KN), European Molecular Biology Organization and Wiener Wirtschaftsförderungsfonds (JMP), a network grant from the European Community (KN and NRK), and the Japanese Society for the Promotion of Sciences (TH).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. BEM, TH, NRK, JMP, and KN conceived and designed the experiments. BEM, TH, and NRK performed the experiments. BEM, TH, and NRK analysed the data. JMP and KN contributed reagents/materials/analysis tools. BEM and KN wrote the paper.
Citation: McGuinness BE, Hirota T, Kudo NR, Peters JM, Nasmyth K (2005) Shugoshin prevents dissociation of cohesin from centromeres during mitosis in vertebrate cells. PLoS Biol 3(3): e86.
Abbreviations
APC/Canaphase-promoting complex or cyclosome
CRESTcalcinosis
DAPI4′,6′-diamidino-2-phenylindole
EGFPenhanced green fluorescent protein
NEBDnuclear envelope breakdown
ntnucleotide(s)
P-H3phosphohistone H3 (Ser10)
RNAiRNA interference
siRNAsmall interfering RNA
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| 15737064 | PMC1054882 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Mar 1; 3(3):e86 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030086 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706510.1371/journal.pbio.0030088Research ArticleEcologyInfectious DiseasesMicrobiologyVirologyZoologyEpidemiology/Public HealthHealth PolicyVirusesAnimalsPredictive Spatial Dynamics and Strategic Planning for Raccoon Rabies Emergence in Ohio Predicting Rabies Emergence in OhioRussell Colin A
1
Smith David L
2
Childs James E
3
Real Leslie A [email protected]
3
1Department of ZoologyUniversity of CambridgeUnited Kingdom2Fogarty International CenterBethesda, Maryland3Department of Biology and Center for Disease Ecology, Emory UniversityAtlanta, GeorgiaUnited States of AmericaLevin Simon Academic EditorPrinceton UniversityUnited States of America3 2005 1 3 2005 1 3 2005 3 3 e8818 8 2004 9 1 2005 Copyright: © 2005 Real et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Forecasting the Path of a Raccoon Rabies Epidemic
Rabies is an important public health concern in North America because of recent epidemics of a rabies virus variant associated with raccoons. The costs associated with surveillance, diagnostic testing, and post-exposure treatment of humans exposed to rabies have fostered coordinated efforts to control rabies spread by distributing an oral rabies vaccine to wild raccoons. Authorities have tried to contain westward expansion of the epidemic front of raccoon-associated rabies via a vaccine corridor established in counties of eastern Ohio, western Pennsylvania, and West Virginia. Although sporadic cases of rabies have been identified in Ohio since oral rabies vaccine distribution in 1998, the first evidence of a significant breach in this vaccine corridor was not detected until 2004 in Lake County, Ohio. Herein, we forecast the spatial spread of rabies in Ohio from this breach using a stochastic spatial model that was first developed for exploratory data analysis in Connecticut and next used to successfully hind-cast wave-front dynamics of rabies spread across New York. The projections, based on expansion from the Lake County breach, are strongly affected by the spread of rabies by rare, but unpredictable long-distance translocation of rabid raccoons; rabies may traverse central Ohio at a rate 2.5-fold greater than previously analyzed wildlife epidemics. Using prior estimates of the impact of local heterogeneities on wave-front propagation and of the time lag between surveillance-based detection of an initial rabies case to full-blown epidemic, specific regions within the state are identified for vaccine delivery and expanded surveillance effort.
A model predicting that the spread of rabies across Ohio will be much more rapid than elsewhere reveals the power of this approach to pro-actively assist targeted surveillance strategies and vaccine delivery
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Introduction
Major recommendations from several Centers for Disease Control and Prevention (CDC) and Institute of Medicine reports on emerging diseases underscore the importance of improving and developing new surveillance strategies to better inform interventions limiting the impact of novel and reemerging disease [1,2,3,4]. By using predictive models of spread we can position ourselves to target surveillance activities and prepare emergency response interventions tailored to the particular feature of an outbreak.
Rabies emergence in the eastern United States and Canada provides an excellent example of how predictive models can help guide surveillance and intervention strategies. Though long endemic in the southeastern U.S., raccoon rabies expanded rapidly along the eastern seaboard during the 1980s and 1990s from an initial focus along the West Virginia–Virginia border; the initial focus was linked to the long-distance translocation (LDT) of rabid animals from Florida [5]. The particular variant of rabies virus associated with raccoons [6,7,8] has spread as a heterogeneous wave away from its original site of introduction and now extends as far north as Ontario [9] and as far west as eastern Ohio, Tennessee, and Alabama [10]. Understanding the context in which models of rabies spread were first developed requires a brief background explanation of the history of epidemic raccoon rabies and the methods used to control spread.
Since late 1997–early 1998, the westward expansion of rabies in raccoons has been curtailed through a massive program of oral rabies vaccine (ORV) delivery focused in eastern Ohio, and later expanded into adjacent border counties in Pennsylvania and West Virginia. Organized by the Ohio Departments of Health, Natural Resources, and Agriculture, in collaboration with the U.S. Department of Agriculture, the CDC, and Canadian agencies, a vaccine cordon sanitaire was constructed to keep raccoon rabies out of Ohio [11,12]; it also served the greater purpose of potentially preventing the raccoon variant's spread throughout the geographic range of raccoons, which, with minor exceptions in the southwest, includes the entire continental U.S. south of Alaska [13]. The campaign was successful, and through 2003, raccoon rabies in Ohio was limited to a few sporadic cases within the vaccine zone [10]. In 2004, this comforting streak of success came to an abrupt end.
On 21 July 2004, approximately 11 km beyond the western extent of the vaccine corridor, a rabid raccoon was detected in Leroy Township, Lake County, Ohio [14] (Figure 1). As of 12 August, at least ten additional rabid raccoons had been detected in Lake County and surrounding counties (Figure 1); given the delay of 3–4 mo between first detection of rabies and development of a full-blown epidemic [15,16,17], and the 40-km linear extent of this outbreak, we assume this is a new rabies focus with the potential to engender continued spread of raccoon rabies into Ohio and areas to the west.
Figure 1 Spatial Location of All Positive Raccoon Rabies Cases in Eastern Ohio as of 11 August 2004
Each sample is identified by number, date of collection, and the laboratory responsible for the positive identification (ODH indicates Ohio Department of Health). To date, more than 300 raccoons have been submitted to the CDC for testing. Ci, Cincinnati; Cl, Cleveland; Co, Columbus; T, Toledo; Y, Youngstown.
Various state agencies have already initiated a remedial wildlife vaccination program to limit further expansion of this epidemic. However, for strategic intervention purposes, determining the expected trajectory and velocity of rabies spread and estimating an effective zone for remedial vaccination would be invaluable [18]. In this paper, we use previously verified mathematical models for predicting rabies spatial dynamics to provide guidance on the likely trajectory and velocity of rabies spread from this emerging focus of infection and to identify specific areas for vaccine delivery and active surveillance.
Results/Discussion
The Raccoon Rabies Epidemic
When the expanding wave front of raccoon rabies reached the borders of Connecticut (CT) and New York (NY), county-level counts of animal rabies data were already being reported monthly to the CDC, as required for this nationally notifiable disease. Previously we analyzed time series data from the county-level data to make predictions about the temporal structure of recurring epidemics and estimated the lag time (approximately 3–4 mo) from detection of raccoon rabies to epizootic development [15,16]. In separate analyses, we arrived at similar estimates for surveillance delays [17]. The county-level data were suitable for assessing a general rate of front movement across the northeastern U.S. but not sufficiently resolved so as to provide information about local environmental heterogeneities that may alter rates of spread.
Concurrent with the collection of county-level data, animal rabies cases occurring within individual townships were also collected by NY and CT state health departments, increasing the spatial resolution of disease reporting data 20-fold above that available from county-level reports. This increase in spatial resolution allowed for the construction of a detailed spatial model of rabies spread.
The Model
The data from NY and CT have been used to parameterize a stochastic spatial model for rabies spread among townships (Figure 2). The model was used for an exploratory data analysis to quantify the spatial dynamics across CT, and we found a 7-fold reduction in local transmission when geographic regions (i.e., townships) were separated by major rivers. In addition, the LDT of rabid animals was incorporated into the model to accommodate these rare, but significant events, often capable of engendering epizootics well in advance of the wave front when larger spatial domains were examined. The spatial model parameterized for CT was used to hind-cast the time to first appearance of raccoon rabies across NY townships using only knowledge of where the disease was introduced into the state and local heterogeneities that may influence the rates of spread [19]. The a priori CT model was able to predict the pattern of spread across NY, as well as provide an estimate of the possible effect of ORV intervention. Having demonstrated the predictive power of the spatial stochastic simulator for CT and NY, we can now use the model and our knowledge of the current outbreak to strategically forecast the spatial spread of rabies in Ohio.
Figure 2 A Stochastic Model Was Used to Simulate the Heterogeneous Spread of Raccoon Rabies on an Irregular Network, Illustrated Here on a Simple Array
An infected township i infects its adjacent neighbor j at rate λij. In addition, township j may become infected because of translocation of rabid raccoons at rate μj. Heterogeneity was incorporated by allowing the local rate of infection from neighboring townships, and the rate of translocation, μj, to be different in different models. Each algorithm for associating a set of rates with rivers defines a stochastic candidate model.
The simulation algorithm involved six steps. (A) For each township, add the rates of infection from all possible routes of infection. (B) Add the townships rates to compute a total rate. (C) Draw a random number to determine the elapsed time. (D) Check to see if any forced townships have become infected in the elapsed interval. (E) If no edges were forced, select a random township to infect. (F) Infect the forced township, update the local rates, and repeat until each township becomes infected (after [17]).
The model transforms Ohio townships into a network; local spread of raccoon rabies among adjacent townships in the Ohio model was predicted where two townships shared at least one common point along their borders. An infected township i was assumed to infect its adjacent neighbor j at rate λij. In addition, long-distance dispersal of rabies was incorporated by assuming a low and constant rate of global infection μj for all uninfected townships regardless of spatial proximity to infected neighbors (Figure 2).
Incorporating environmental heterogeneities into local rates of spread is crucial to predicting the local wave-front movement of terrestrial rabies. Rabies transmission was 7-fold lower when townships were separated by a river in CT, while in NY, the Adirondack mountain range was, and remains, an impenetrable barrier to the incursion of raccoon rabies. Ohio lacks the rough mountainous terrain of NY, but it has several major rivers that could influence westward expansion of raccoon rabies from a focus near the Pennsylvania or West Virginia border; five major Ohio rivers, the Miami, Muskingum, Scioto, Maumee, and Cuyahoga Rivers, were incorporated into our simulations (Figure 3).
Figure 3 Satellite Image of Ohio Topography Illustrating Major River Systems and Location of Vaccine Corridor
Extent and shape of the vaccine corridor is approximated. (Map reproduced with permission from Ohio Department of Natural Resources, Division of Geological Survey.)
The Ohio Forecast
Transposing the model for use in Ohio merits a discussion of the geographic differences between CT, NY, and Ohio. In CT, the Connecticut River bisects the state, running north to south. The river's effect on the epidemic was maximized because the direction of epidemic spread ran orthogonal to the river, i.e., from west to east. Moreover, CT is much smaller than NY or Ohio. When the model was transposed into NY from CT, the townships in the Adirondack Mountains were excluded a priori because none of them had ever reported a case of raccoon rabies. The general triangular shape of NY and the exclusion of the Adirondack townships from the simulation, in conjunction with there being three initial foci within NY, left few townships far from any single initial focus. Thus, the successful hind-cast was based mostly on the relatively predictable local dynamics. In contrast, in Ohio we know of only one focus, in the northeastern quarter of the state, leaving many townships hundreds of kilometers from the epidemic focus. Given the greater distance between the epidemic focus and far reaching townships, LDT of infected individuals in Ohio could have a much more profound impact on the wave-front dynamics than was possible in NY or CT. Given the unpredictable nature of LDT events, we present two projections. In the first projection, we ignored LDT and focused on local rabies wave-front movement: λij = α = 0.66 for townships not separated by a river; λij = β = 0.12 for townships separated by a river; and μ = 0. In the second projection, we used the exact parameters fitted to the CT epidemic: λij = α = 0.66 for townships not separated by a river; λij = β = 0.12 for townships separated by a river; and μ = 0.0002.
The local-spread-only projection for Ohio assumed the rabies wave-front origin was from the townships nearest the center of the Lake County outbreak (case 3 in Figure 1), and the epidemic was simulated without LDT. As the local dynamics of rabies are fairly predictable, this could be considered the best-case scenario. Retaining the parameters for local spread from CT and disallowing any LDT, the rabies epidemic required 70 mo to reach the western corners of the state (Figure 4A). In the absence of LDT, the predicted rate of front propagation is faster than previously reported for other states because of the influence of the permissive zone of central Ohio, in which few environmental impediments exist.
Figure 4 Predicted Trajectory of Rabies in Ohio Based on the Current Outbreak
(A) Predicted time to first appearance (months) of raccoon rabies spreading from Chardon Township and surrounding townships given no vaccine intervention and with LDT excluded from model predictions. The time course of spread is revealed through the contour plot, where each color band indicates a given time interval to arrival at a township. The width of the bands corresponds to velocity of spread, with wider bands associated with more rapid spread. Major cities are Cleveland (Cl), Youngstown (Y), Toledo (T), Columbus (Co), and Cincinnati (Ci). The two black lines labeled A and B correspond to the area where cases have been detected (A) and the expected position of the wave front given a long-tailed distribution of incubation periods.
(B) Predicted time to first appearance (months) of raccoon rabies spreading from Chardon Township and surrounding townships given no vaccine intervention and including estimates of long-distance dispersal modeled in CT and NY .
The projection with LDT was approximately one-third faster than the local-only spread, even with the very low levels of long-distance dispersal modeled. In these simulations rabies spread across central Ohio within 33 mo and covered the state by month 41 (Figure 4B). Passage across the midsection of Ohio was particularly fast; the estimate of 100 km/y far exceeds previous estimates for the rate of spread of raccoon rabies, which typically ranges between 30 and 60 km/y [20,21,22]. By our estimates, if unchecked, rabies will likely spread across Ohio in the same amount of time that it took rabies to transverse CT, even though Ohio is 2.1 times wider than CT. The potential for such rapid spread is quite alarming.
Adding the possibility of global LDT to the model, even at the low rate assumed in the original model for CT, served as a massive promoter of rapid spread. This can be envisaged as the worst-case scenario, with LDT greatly increasing the rate of wave-front propagation across the state. Because of the largely unpredictable nature of LDT, it must be assumed that reality is likely to be somewhere in between the two modeled scenarios, but exactly where is difficult to discern.
The picture that emerges from both scenarios suggests that rabies will spread rapidly through the middle of Ohio, where there are few environmental impediments to disease front expansion. Rivers and mountains in Ohio do not constitute a major barrier to rabies spread. Similarly, rabies will not encounter any impediment like the Adirondack Mountains in NY. With no effective physical barrier across the middle of Ohio, rabies could move more rapidly through this zone then in any previously recorded epizootic.
The design of vaccine barriers to control the Lake County epidemic must allow for the distance the wave front will advance prior to detection of the first laboratory-confirmed case of raccoon variant rabies within a county. This parameter is unknown, although estimates of the delay from the first detected case of raccoon variant rabies to the start of the first epidemic exist [15,16,17,18]: the delay is approximately 3–4 mo. Thus, the detection of the first case of raccoon rabies in a township through passive surveillance indicates roughly where the front was 3–4 mo before.
As no other data exist to help assess the bias in lag between the arrival date of raccoon rabies and the date of detection recorded in the national surveillance database, we set the interval from arrival to detection at 3–4 mo. This parameter defines where to place the inner perimeter line A (Figure 4A). Few estimates of incubation time from natural field situations exist; however, Tinline et al. [18] provided some estimates on the distribution of incubation times for raccoons infected with their homologous variant. We used the 75th percentile in incubation time from Tinline et al. [18] to derive a confidence line for the expected wave-front advance (line B in Figure 4A), adding the incubation time to the calculations for line A; thus the area between perimeter lines A and B defines the vaccination corridor required to target susceptible animals (Figure 4A).
Strategic Planning and Alternative Outbreak Scenarios
Any intervention decisions or strategic plans based on model projections require that models deliver a robust outcome. We explored two additional scenarios for raccoon rabies emergence and spread through Ohio based on potential breach points in the vaccine corridor and consider the utility of the model for selecting sights for enhanced surveillance.
Alternative scenario 1
A breech in the cordon sanitaire could occur within a similar time frame as a LDT, and we considered the effects of independent and concomitant breeching and LDT events in a series of alternative scenarios. Adding such events to the spatial simulations significantly decreases the times to first appearance of raccoon rabies compared to that predicted solely on local spread. Modeling various LDT scenarios is warranted because LDTs occur frequently [23] and have demonstrably played a central role in the epizootiology of raccoon rabies, and are regarded herein and elsewhere as a critical element in our efforts to understand and model the epizootic dynamics of raccoon rabies.
We simulated the pattern of rabies spread associated with a breach in the vaccine corridor near Youngstown (near the current outbreak location) coupled with a forced LDT event into the Athens-Hocking landfill. The Athens-Hocking landfill is the landfill closest to the current front of raccoon rabies outside of Ohio that receives interstate refuse. The LDT event was incorporated into the computer simulation as an initial condition that occurred at month 12 during the simulated spread, in addition to base levels of LDT derived from CT (μ = 0.0002). Spread from the landfill in conjunction with the advancing front originating near Youngstown generated two advancing fronts that converged in the middle of the state (Figure 5A).
Figure 5 Combined Effects of LDT and a Break in the Vaccine Barrier on Pattern of Rabies Spread across Ohio
(A) Predicted time to first appearance (months) of raccoon rabies given a breach of the cordon sanitaire near Youngstown and the LDT of an infected individual into the Athens-Hocking landfill at month 12.
(B) Residual differences between simulations of the Youngstown breach with and without the forced LDT event at the Athens-Hocking landfill. The larger the red squares, the larger the residual difference between the simulations with and without the forced LDT.
Ci, Cincinnati; Cl, Cleveland; Co, Columbus; T, Toledo; Y, Youngstown.
To explore the overall impact of multiple introductions on the time to first appearance we compared simulations with and without the translocation event into the Athens-Hocking landfill. The forced LDT event caused rabies to reach the southern portion of Ohio far earlier than predicted in the absence of the forced LDT. However, the rivers seem to limit the overall additive effect on time of first appearance in western Ohio by acting as natural barriers to spread. There appears to be no additive effect on the rate of disease movement across the middle of the state associated with the union of the advancing wave fronts, as revealed in a plot of the residual differences between simulations (Figure 5B).
Alternative scenario 2
Rabies has a variety of alternative paths of entry into Ohio from infected regions across the cordon sanitaire. In this scenario, we consider the spatial trajectories of rabies spread from a variety of single points of entry and compare these alternative trajectories to determine the most common areas of overlap and, hence, where rabies is most likely to emerge. For example, we consider the trajectory of spread given an introduction around Youngstown and compare this with an introduction around Bridgeport, Ohio (near Wheeling, West Virginia), that would be near the southern end of the vaccine corridor in Ohio.
It is reasonable to assume that the Ohio River serves as a barrier to raccoon movement, but there is the potential for rabid individuals to cross bridges into Ohio. Given an introduction at Bridgeport, Ohio (Figure 6), the model predicts that the initial westward expansion would be halted by the Muskingum River, while northward expansion would be rapid. Following a crossing of the Muskingum, the model predicts the same sort of rapid westward expansion seen in the first scenario.
Figure 6 Combination of Predicted Time to First Appearance Projections from Youngstown Breach and Ohio River Breach Scenarios
Thick black line indicates the minimal distance between the two epidemic foci orthogonal to the temporal contours. Given the trajectories, the path corresponds to the most likely location where the waves will first collide.
Scenario comparison
By jointly considering the predictions from these introduction scenarios, we demonstrate the utility of our model in establishing sentinel points for surveillance. Figure 6 shows the model predictions for spread associated with a river crossing near the southern terminus of the cordon sanitaire overlaid with our projections for spread away from the Youngstown area introduction. From the combination of these two scenarios we see that the first point at which the two epidemic fronts are predicted to meet is not along the Ohio–West Virginia border but further west towards the interior of the state. We can use this juxtaposition of the two trajectories to establish a minimum time path for the epidemic movement (Figure 6) and can suggest sentinel points for surveillance along this line in addition to the surveillance strategies already in place.
The three simulation sets—the current outbreak (with LDT) and alternative scenarios 1 and 2—can be compared to assess the sensitivity of rate estimation on at least these initial conditions. A plot of the maximum residual differences in time to first appearance between all three simulation sets suggests that the predicted velocity and overall trajectory of rabies spread are robust to changes in outbreak origin (Figure 7). Though large residuals exist between the three simulations around the introduction sites, a striking common feature of all simulation outcomes is the rapid spread of raccoon rabies across central Ohio given an average effect of LDT. Maximum differences in the estimated time to raccoon rabies arrival between simulations in central and western Ohio varied by less than 1 mo, irrespective of breach location in the ORV barrier.
Figure 7 Residual Differences between Time to First Appearance from Simulated Epidemics Initiated by Breaches in Different Geographic Locations
The maximum difference between the predicted times to first appearance is indicated in each township by the size and color of the corresponding diamond. (A) represents an epidemic simulated from current outbreak data shown in Figure 1. (B) represents an epidemic simulated from the movement of an infected animal across the cordon sanitaire near Youngstown. (C) represents the introduction of an infected animal from West Virginia at a bridge across the Ohio River.
Conclusions
Experience with parenteral rabies vaccination programs and ORV-based attempts to control wildlife rabies in the U.S. and Europe has demonstrated that all vaccine barriers are, to some extent, permeable and vulnerable to breaches [24,25,26]. Ohio has proven to be no exception.
Considering the vulnerabilities of any vaccine corridor, and the difficulties inherent in anticipating break sites in a vaccine barrier and identifying sites of LDT, the unlimited ability to explore scenarios by mathematical simulation and to compute and compare different design strategies for ORV interventions provide a valuable exercise to plan for real epidemics. From the simulations shown here, a robust pattern of trajectory, an extraordinarily high rate of spread of raccoon rabies through central Ohio, the significant potential impact of LDT, and lack of environmental impediments suggest that a strategy combining early detection and rapid intervention are requisite if control is to succeed. A robust intervention plan is invaluable, even if the exact site or sites to which it is applied prove elusive.
The approach discussed here reveals some of the many gaps in our knowledge of wildlife rabies and in the data available to inform initial conditions for simulations. Without a second measure of the arrival–detection delay, obtained from an independent and extremely sensitive surveillance system (e.g., coupling active sampling of road-killed raccoons, purposeful hunter collections, and kill-trap data), we have no tools to calibrate detection times obtained through the passive national surveillance system.
In western Europe, reinforcement of natural barriers, such as rivers, lakes, and mountains, have been a staple in the successful campaign to control red fox rabies by vaccination [24]. In Massachusetts and NY, pre-existing natural (i.e., the Adirondacks) and artificial (i.e., the Cape Cod canal [27]) impediments to the free movement of raccoons, and, hence, raccoon rabies, were reinforced by vaccine distributions. Unfortunately for the remedial efforts to contain the Lake County focus in Ohio, the benefits derived from enhancement of natural barriers are not an option. With the exception of river systems in southeastern Ohio, such as the Muskingum (for topographic details see Figure 3), there are few natural barriers to augment ORV distribution in the Lake County region or in central Ohio, if required.
As reinforced natural barriers are not an option for rabies control in much of Ohio, the need for rapid remedial intervention by ORV and intensified, active surveillance to estimate the actual epidemic boundaries is immediate. Enhanced surveillance, perhaps by collection and rabies testing of road-killed raccoons, and stockpiling of ORV sufficient to establish a new ORV barrier in central Ohio, are prudent courses of action.
If the Lake County epidemic escapes beyond remedial intervention efforts, the fallback position demands rapid construction of a second cordon sanitaire spanning central Ohio, at a location dictated by the then current position of the epidemic. If the disease is not confined within Ohio, the limits to raccoon variant rabies spread are defined only by the geographic distribution of its host. Nothing short of these activities will contain a rapidly expanding and fast-moving epidemic of raccoon rabies, should ring vaccination and other regional efforts fail to eliminate the current focus around Lake County. The interventions described here would appear as inexpensive alternatives to the uncontrolled westward spread of rabies and the loss of the millions of dollars invested in what would become the vaccine equivalent of the Maginot Line.
We thank Dennis Slate and Anthony Montoney (U.S. Department of Agriculture [USDA] Animal and Plant Health Inspection Service) for their cooperation in this project and for the mapped data for current raccoon cases in Ohio. We also thank CDC for identifying positive raccoon samples. This research was funded through National Institutes of Health grant RO1 AI047498 and USDA grant 0371004129 CA to LAR and by a Gates Cambridge Trust Fellowship to CAR.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CAR, DLS, JEC, and LAR conceived and designed the experiments. CAR performed the experiments. CAR, DLS, JEC, and LAR analyzed the data. CAR, DLS, JEC, and LAR contributed reagents/materials/analysis tools. CAR, DLS, JEC, and LAR wrote the paper.
Citation: Russell CA, Smith DL, Childs JE, Real LA (2005) Predictive spatial dynamics and strategic planning for raccoon rabies emergence in Ohio. PLoS Biol 3(3): e88.
Abbreviations
CDCCenters for Disease Control and Prevention
CTConnecticut
LDTlong-distance translocation
NYNew York
ORVoral rabies vaccine
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| 15737065 | PMC1054883 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Mar 1; 3(3):e88 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030088 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706610.1371/journal.pbio.0030089Research ArticleEvolutionGenetics/Genomics/Gene TherapyMicrobiologyPlant ScienceVirologyVirusesPlantsRecombination Every Day: Abundant Recombination in a Virus during a Single Multi-Cellular Host Infection Recombination in CaMVFroissart Remy
1
¤Roze Denis
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Uzest Marilyne
1
Galibert Lionel
1
Blanc Stephane
1
Michalakis Yannis [email protected]
2
1Biologie et Génétique des Interactions Plante-Parasite, Unité Mixte de Recherche Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)–Institut National de la Recherche Agronomique (INRA)–Ecole National Supérieure Agronomique de Montpellier (ENSAM)TA 41/K, Campus International de Baillarguet, MontpellierFrance2Génétique et Evolution des Maladies Infectieuses, Unité Mixte de Recherche Centre National de la Recherche Scientifique (CNRS)–Institut de Recherche pour le Développement (IRD) 2724MontpellierFranceHull Roger Academic EditorJohn Innes CenterUnited Kingdom3 2005 1 3 2005 1 3 2005 3 3 e8915 9 2004 9 1 2005 Copyright: © 2005 Froissart et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Recombination as a Way of Life: Viruses Do It Every Day
Viral recombination can dramatically impact evolution and epidemiology. In viruses, the recombination rate depends on the frequency of genetic exchange between different viral genomes within an infected host cell and on the frequency at which such co-infections occur. While the recombination rate has been recently evaluated in experimentally co-infected cell cultures for several viruses, direct quantification at the most biologically significant level, that of a host infection, is still lacking. This study fills this gap using the cauliflower mosaic virus as a model. We distributed four neutral markers along the viral genome, and co-inoculated host plants with marker-containing and wild-type viruses. The frequency of recombinant genomes was evaluated 21 d post-inoculation. On average, over 50% of viral genomes recovered after a single host infection were recombinants, clearly indicating that recombination is very frequent in this virus. Estimates of the recombination rate show that all regions of the genome are equally affected by this process. Assuming that ten viral replication cycles occurred during our experiment—based on data on the timing of coat protein detection—the per base and replication cycle recombination rate was on the order of 2 × 10−5 to 4 × 10−5. This first determination of a virus recombination rate during a single multi-cellular host infection indicates that recombination is very frequent in the everyday life of this virus.
An analysis of recombination of the cauliflower mosaic virus during an infection reveals that recombination is extremely frequent and provides the first range of estimates for a plant virus
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Introduction
As increasing numbers of full-length viral sequences become available, recombinant or mosaic viruses are being recognized more frequently [1,2,3]. Recombination events have been demonstrated to be associated with viruses expanding their host range [4,5,6,7] or increasing their virulence [8,9], thus accompanying, or perhaps even being at the origin of, major changes during virus adaptation. It remains unclear, however, whether recombination events represent a highly frequent and significant phenomenon in the everyday life of these viruses.
Viruses can exchange genetic material when at least two different viral genomes co-infect the same host cell. Progeny can then become hybrid through different mechanisms, such as reassortment of segments when the parental genomes are fragmented [10], intra-molecular recombination when polymerases switch templates (in RNA viruses) [11], or homologous or non-homologous recombination (in both RNA and DNA viruses). Quantification of viral recombination in multi-cellular organisms has been attempted under two distinct experimental approaches: in vitro (in cell cultures) [12,13,14,15], and in vivo (in live hosts) [16,17,18]. The in vitro approach, which has so far been applied only to animal viruses, allows the establishment of the “intrinsic” recombination rate in experimentally co-infected cells in cell cultures [14,15,19]. However, it does not necessarily reflect the situation in entire, living hosts, where the frequency of co-infected cells is poorly known and depends on many factors such as the size of the pathogen population, the relative frequency and distribution of the different variants, and host defense mechanisms preventing secondary infection of cells. The in vivo experimental approach is closer to biological conditions and may thus be more informative of what actually happens in “the real world.”
However, as discussed below, numerous experimental constraints have so far precluded an actual quantification of the baseline rate of recombination. First, many experimental designs have used extreme positive selection, where only recombinant genomes were viable (e.g., [13,20,21]). Other studies did not use complementation techniques but detected recombinants by PCR within infected hosts or tissues [18,22,23,24,25], which provides information on their presence but not on their frequency in the viral population. So far, no quantitative PCR or other quantitative method has been applied to evaluate the number of recombinants appearing in an experimentally infected live host. Finally, recent methods based on sequence analysis inferred the population recombination rate, rather than the individual recombination rate [1,26,27]. While results from these methods certainly take in vivo recombination into account, there are other caveats: isolates have often been collected in different hosts—sometimes in different geographical regions—and sometimes the selective neutrality of sequence variation on which these estimates are based is not clearly established. Estimates from such studies by essence address the estimation of the recombination rate at a different evolutionary scale.
Taken together, the currently available information indicates that no viral recombination rate has ever been estimated directly at time and space scales corresponding to a single multi-cellular host infection, although this level is most significant for the biology and evolution of viruses. This study intends to fill this gap by evaluating the recombination frequency of the cauliflower mosaic virus (CaMV) during a single passage in one of its host plants (the turnip Brassica rapa).
CaMV is a pararetrovirus, which is a major grouping containing hepadnaviruses (e.g., hepatitis B virus), badnaviruses (e.g., banana streak virus), and caulimoviruses (e.g., CaMV). Pararetroviruses are characterized by a non-segmented double-stranded DNA genome. After entering the host cell nucleus, the viral DNA accumulates as a minichromosome [28] whose transcription is ensured by the host RNA polymerase II [29]. The CaMV genome consists in approximately 8,000 bp and encodes six viral gene products that have been detected in planta (Figure 1) [30]. Viral proteins P1 to P6 are expressed from two major transcripts, namely a 19S RNA, encoding P6, and a 35S RNA corresponding to the entire genome and serving as mRNA for proteins P1–P5 [31]. Using the pre-genomic 35S RNA as a matrix, the protein P5 (product of gene V) reverse-transcribes the genome into genomic DNA that is concomitantly encapsidated [30].
Figure 1 Genetic Map of CaMV
The CaMV genome is a circular double-stranded DNA of 8,024 bp, represented in the figure by a double line. The thick arrows with different textures represent the organization of open reading frames I to VI, encoding proteins detected in planta. Markers a, b, c, and d were engineered at the positions indicated (see Materials and Methods for precise positions). The inner black arrows represent monocistronic 19S RNA and polycistronic 35S RNA produced by the cellular machinery. The nucleotide position 0 (numbering according to [44]) indicates the origin of replication via reverse transcription, which occurs in the direction indicated by the dotted outermost circle-like arrow. Reverse transcription is accomplished by the viral reverse transcriptase, using the 35S RNA as template [49].
The detection of CaMV recombinants in turnip hosts has been reported numerous times. Some studies have demonstrated the appearance of infectious recombinant viral genomes after inoculation (i) of a host plant with two infectious or non-infectious parental clones [21,32,33,34,35] or (ii) of a transgenic plant containing one CaMV transgene with a CaMV genome missing the corresponding genomic region [36]. While the former revealed inter-genomic viral recombination, the latter demonstrated that CaMV can also recombine with transgenes within the host's genome. Another study based on phylogenetic analyses of various CaMV strains has clearly suggested different origins for different genomic regions and, hence, multiple recombination events during the evolution of this virus [37]. Indirect experimental evidence has indicated that, in some cases, CaMV recombination could occur within the host nucleus, between different viral minichromosomes, presumably through the action of the DNA repair cellular machinery [21,35]. Nevertheless, the mechanism of “template switching” during reverse transcription, predominant in all retroviruses, most certainly also applies to pararetroviruses. For this reason, and on the basis of numerous experimental data, CaMV is generally believed to recombine mostly in the cytoplasm of the host cell, by “legal” template switching between two pre-genomic RNA molecules [21,35,36,38,39], or “illegal” template switching between the 19S and the 35S RNA [36,40]. Under this hypothesis, recombination in CaMV could therefore be considered as operating on a linear template during reverse transcription, with the 5′ and 3′ extremities later ligated to circularize the genomic DNA (position 0 in Figure 1). The above cited studies clearly demonstrate that CaMV is able to recombine. However, since these studies are based on complementation techniques, non-quantitative detection, or phylogenetically based inferences of recombination, they do not inform us on whether recombination is an exceptional event or an “everyday” process shaping the genetic composition of CaMV populations.
In the present work, we aimed at answering this question. To this end, we have constructed a CaMV genome with four genetic markers, demonstrated to be neutral in competition experiments. By co-inoculating host plants with equal amounts of wild-type and marker-containing CaMV particles, we have generated mixed populations in which impressive proportions of recombinants—distributed in several different classes corresponding to exchange of different genomic regions—have been detected and quantified. Altogether, the recombinant genomes averaged over 50% of the population. Further analysis of these data, assuming a number of viral replications during the infection period ranging from five to 20, indicates that the per nucleotide per replication cycle recombination rate of CaMV is of the same order of magnitude, i.e., on the order of a few 10−5, across the entire genome. We thereby provide the first quantification, to our knowledge, of the recombination rate in a virus population during a single passage in a single host.
Results
Recombinant Frequency in CaMV Populations from Co-Infected Plants
From Figure 1, and supposing that all marker-containing genomic regions can recombine, we could predict the detection and quantification of seven classes of recombinant genotypes: +bcd/a+++, a+cd/+b++, ab+d/++c+, abc+/+++d, ++cd/ab++, a++d/+bc+, and a+c+/+b+d. Indeed, all classes were detected, and their frequencies in the ten CaMV populations analyzed are summarized in Table 1.
Table 1 Quantification of Recombinant Genomes in CaMV Populations
a Viral genomes were cloned and analyzed from ten of 24 co-infected plants
b Seven possible classes of recombinants were predicted, their respective frequency in the population is expressed in percentage
c The proportions of recombinants from the seven classes were added to estimate the total percentage of recombinant genomes within each tested plant
Altogether, the proportion of recombinant genomes found in the mixed viral populations was astonishingly high and very similar in the ten co-infected plants analyzed (Table 1, last column), ranging between 44% (plant 5) to 60% (plants 7, 12, and 20), with a mean frequency (± standard error) of 53.8% ± 2.0%. This result indicates that recombination events are very frequent during the invasion of the host plant by CaMV and represents, to our knowledge, the first direct quantification of viral recombination during the infection of a live multi-cellular host.
Probability of Recombination between Various Pairs of Markers
The inferred per generation recombination and interference rates, assuming that CaMV undergoes ten replication cycles during the 21 d between infection and sampling, are given for each of the ten plants in Table 2. Recombination rates between adjacent markers are large, on the order of 0.05 to 0.1. Taking the distance in nucleotides between markers into account yields an average recombination rate per nucleotide and generation on the order of 4 × 10−5. Interestingly, this recombination rate does not vary throughout the genome (Kruskal–Wallis test, p = 0.16).
Table 2 Recombination Parameters for the Viral Populations Sampled from Ten Infected Plants
The various parameters are as follows: r
1, recombination rate between markers a and b; r
2, recombination rate between markers b and c; r
3, recombination rate between markers c and d; i
12, interference between crossovers in segments a–b and b–c; i
23, interference between crossovers in segments b–c and c–d; i
13, interference between crossovers in segments a–b and c–d; i
123, second-order interference accounting for residual interference. The recombination rates are the maximum likelihood estimates (± 95% confidence intervals). The interference parameters were obtained numerically as explained in the Materials and Methods
To relax the assumption of the number of replications during the 21 d, we calculated the recombination parameters assuming five or 20 generations. The effect of the number of generations on the estimates is linear: doubling the number of generations results in a halving of the recombination rate (detailed results not shown). For example, the average recombination rates r
1, r
2, and r
3 assuming 20 generations were equal to 0.05, 0.04, and 0.025, respectively (compare with values in Table 2), yielding per nucleotide per generation recombination rates of 1.9 × 10−5, 2.2 × 10−5 and 1.6 × 10−5.
Inspection of Table 2 also shows that first-order interference coefficients were in general negative, indicating that a crossing over in one genomic segment increases the probability that a crossing over will occur in another genomic segment, while the second-order coefficient parameter had an average value close to zero with a large variance. The mechanism leading to these results will be discussed in the following section.
Discussion
One major breakthrough in the work presented here lies in the space and time scales at which the experiments were performed. Indeed, the processes occurring within the course of a single infection of one multi-cellular host are of obvious biological relevance for any disease. Previous studies on viral recombination suffered from major drawbacks in this respect, basing their conclusions on experiments relying on complementation among non-infectious viruses or between viruses with undetermined relative fitness, on phylogenetically based analyses, or on experiments in cell cultures. For reasons detailed in the Introduction, the first two methods either do not provide information on the frequency of recombination, but only its occurrence, or address the question at a different temporal, and often spatial, scale. Results from cell cultures, on the other hand, impose cell co-infection by different viral variants, potentially overestimating the frequency of recombination events. Our study circumvents these limitations by analyzing viral genotypes sampled from infected plants after the course of a single infection, and therefore the invasion and co-infection of cells in various organs and tissues is very close to natural.
More than half of the genomes (53.8% ± 2.0%; see Table 1) present in a CaMV population after a single passage in its host plant were identified as recombinants, and these data allowed us to infer a per nucleotide per generation recombination rate on the order of 2 × 10−5 to 4 × 10−5. The time length of one generation, i.e., the time required for a given genome to go from one replication to the next, is totally unknown in plant viruses. The only experimental data available on CaMV are based on the kinetics of gene expression in infected protoplasts, where the capsid protein is produced between 48 and 72 h [40]. The reverse transcription and the encapsidation of genomic DNA being two coupled phenomena [30], we judged it reasonable to assume a generation time of 2 d and, thus, an average of ten generations during our experiments. In case this estimate is mistaken, we have verified a linear relationship between r and the number of generations, thereby allowing an immediate adjustment of r if the CaMV generation time is more precisely established. At this point, we must consider that all cloned genomes may not have been through all the successive replication events potentially allowed by the timing of our experiments. It was previously shown that about 95% of CaMV mature virus particles accumulate in compact inclusion bodies [41], where they may be sequestered for a long time, as such inclusions are very frequent in all infected cells, including those in leaves that have been invaded by the virus population for several weeks. The viral population may thus present an age structure that could bias the estimation of the recombination rate. In order to minimize this bias, the clones we analyzed were collected in one young newly formed leaf, where the chances of finding genomes from “unsequestered lines” were assumed to be higher. In any case, our data analysis is conservative, since this age structure can only lead to an underestimation of the recombination rate.
Our results show that interferences between pairs of loci are negative: a recombination event between two loci apparently increases the probability of recombination between another pair of loci. We believe that the most parsimonious explanation of these negative interferences is based on the way the infection builds up within plant hosts. Indeed, one can divide infected host cells into those infected by a single virus genotype and those infected by more than one viral genotype. In the former, analogous to clonal propagation, recombination is undetectable. In the latter, recombination is not only detectable but, as our results indicate, very frequent. Samples consisting of viruses resulting from a mixture of these two types of host cell infections will thus contain viruses with no recombination and viruses with several recombination events, thus yielding an impression of negative interference. These conceptual arguments are supported by mathematical models. It is indeed easy to show (detailed results not shown) that if a proportion F of the population reproduces clonally, analogous to single infections, while the remaining reproduces panmictically, negative interferences could be inferred even if they do not exist. For example, assuming a three-locus model with real recombination rates r
1 and r
2 and interference i
12, the “apparent” recombination and interference parameters, would be r
1 = (1 − F)r1, r
2 = (1 − F)r
2, and i
12 = −(F − i
12)/(1 − F). Interestingly, this example also shows that our estimates of the recombination rate are conservative: that a fraction F of host cells are singly infected while others are multiply infected leads to an underestimation of the recombination rate.
As judged by r
1, r
2, and r
3, calculated between markers a–b, b–c, and c–d, respectively, we found evidence for recombination through the entire CaMV genome. The values for r
1,
r
2, and r
3 are remarkably similar, hence the recombination sites seem to be evenly distributed along the genome. We considered the template-switching model as the major way recombinants are created in CaMV. As already mentioned in the Introduction, hot spots of template switching have been predicted at the position of the 5′ extremities of the 35S and 19S RNAs [21,36,42]. If other recombination mechanisms, such as that associated with second-strand DNA synthesis or with the host cell DNA repair machinery, act significantly, hot spots would be expected at the positions of the sequence interruption Δ1, Δ2, and Δ3 [43]. Due to the design of our experiment and the position of the four markers, we have no information on putative hot spots at positions corresponding to the 5′ end of the 35S RNA and to Δ1 (at nucleotide position 0). Nevertheless, the putative hot spots at the 5′ end of the 19S RNA and at Δ2 and Δ3 (nucleotide positions 4,220 and 1,635, respectively) fall between marker pairs c–d, b–c, and a–b, respectively. Our results indicate that either these hot spots are quantitatively equivalent—though predicted by different recombination mechanisms—or, more likely, that they simply do not exist. Whatever the explanation, what we observe is that the CaMV can exchange any portion of its genome, and thus any gene thereof, with an astonishingly high frequency during the course of a single host infection.
To our knowledge, the viral recombination rate has never previously been quantified experimentally for a plant virus [3]. In contrast, retroviruses and particularly HIV-1 have been extensively investigated in that sense. As we have already discussed for these latter cases, the quantification of the intrinsic recombination rate was carried out in artificially co-infected cell cultures. The estimated intrinsic per nucleotide per generation recombination rate in HIV-1 is on the order of 10−4 [14,15,19], less than one order of magnitude higher than our estimation for CaMV. Because for various reasons detailed above we probably underestimate the within-host CaMV recombination rate, we believe that the intrinsic recombination rate in CaMV is higher and perhaps on the order of that of HIV.
Other pararetroviruses such as plant badnaviruses or vertebrate hepadnaviruses have a similar cycle within their host cells, including steps of nuclear minichromosome, genomic size RNA synthesis, and reverse transcription and encapsidation. Nevertheless, vertebrate hepadnaviruses (e.g., hepatitis B virus) infect hosts that are very different from plants in their biology and physiology, and this could lead to a totally different frequency of cell co-infection during the development of the virus populations. Thus, even though our results can be informative for other pararetroviruses because of the viruses' shared biological characteristics, they should not be extrapolated to vertebrate pararetroviruses without caution.
Materials and Methods
Viral isolates
We used the plasmid pCa37, which is the complete genome of the CaMV isolate Cabb-S, cloned into the pBR322 plasmid at the unique SalI restriction site [44]. To analyze recombination in different regions of the genome, we introduced four genetic markers: a, b, c, and d, at the positions 881, 3,539, 5,365, and 6,943, respectively, thus approximately at four cardinal points of the CaMV circular double-stranded DNA of 8,024 bp (Figure 1). All markers, each corresponding to a single nucleotide change, were introduced by PCR-directed mutagenesis in pCa37, and resulted in the duplication of previously unique restriction sites BsiWI, PstI, MluI, and SacI in a plasmid designated pMark-S. Because, in this study, we targeted the possible exchange of genes between viral genomes, all markers a, b, c, and d were introduced within coding regions corresponding to open reading frames I, IV, V, and VI, respectively. Another important concern was to quantify recombination in the absence of selection, i.e., to create neutral markers. Consequently all markers consist of synonymous mutations (see below).
Production of viral particles and co-inoculation
To generate the parental virus particles, plasmids pCa37 and pMark-S were mechanically inoculated into individual plants as previously described [33]. All plants were turnips (B. rapa cv, “Just Right”) grown under glasshouse conditions at 23 °C with a 16/8 (light/dark) photoperiod. Thirty days post-inoculation, all symptomatic leaves were harvested and viral particles were purified as described earlier [45].
The resulting preparations of parental viruses, designated Cabb-S and Mark-S, were quantified by spectrometry using the formula described by Hull et al. [46]. We fixed the initial frequency of markers to a value of 0.5, and a solution containing 0.1 mg/ml of virus particles of both Cabb-S and Mark-S at a 1:1 ratio was prepared. Plantlets were co-infected by mechanical inoculation of two to three leaves with 20 μl of this virus solution, using abrasive Celite AFA (Fluka, Ronkonkoma, New York, United States). The mixed CaMV population was allowed to grow during 21 d of systemic infection.
Estimation of marker frequency within mixed virus populations
We designed an experimental protocol for quantifying marker frequency within a mixed Cabb-S/Mark-S virus population after a single passage in a host plant. Twenty-four individual plants, inoculated as above with equal amounts of Cabb-S and Mark-S, were harvested 21 d post-inoculation, when symptoms were fully developed. The viral DNA was purified from 200 mg of young newly formed infected leaves according to the protocol described previously [47]. After the precipitation step of this protocol, the viral DNA was resuspended and further purified with the Wizard DNA clean-up kit (Promega, Fitchburg, Wisconsin, United States) in TE 1X (100 mM Tris-HCl and 10 mM EDTA [pH 8]). Aliquots of viral DNA preparations were digested by restriction enzymes corresponding either to marker a, b, c, or d and submitted to a 1% agarose gel electrophoresis, colored by ethydium bromide and exposed to UV. Each individual restriction enzyme cut once in Cabb-S DNA and twice in Mark-S, thus generating DNA fragments of different sizes attributable to one or the other in the mixed population of CaMV genomes. After scanning the agarose gels, we estimated the relative frequency of the two genotypes in each viral DNA preparation and at each marker position, by densitometry using the NIH 1.62 Image program. The statistical analyses of the frequency of the four markers are described below.
Isolation of individual CaMV genomes and identification of recombinants
To identify and quantify the recombinants within the CaMV mixed populations, aliquots from ten of the 24 viral DNA preparations described above were digested by the restriction enzyme SalI, and directly cloned into pUC19 at the corresponding site. In each of the ten viral populations analyzed, 50 full-genome-length clones were digested separately by BsiWI, PstI, MluI, and SacI, to test for the presence of marker a, b, c, and d, respectively. In this experiment, with the marker representing an additional restriction site, we could easily distinguish between the Cabb-S and the Mark-S genotype at all four marker positions, upon agarose gel (1%) electrophoresis of the digested clones. Clones with none or all four markers were parental genotypes, whereas clones harboring 1, 2, or 3 markers were clearly recombinants. Due to the very high number of recombinants detected, markers eventually appearing or disappearing due to spontaneous mutations were neglected.
Statistical analysis
Here we present the different methods we used to quantify recombination in the CaMV genome. Because all these methods assume that the different markers are neutral, we first discuss assumption.
We used two datasets to test the neutrality of markers, both resulting from plants co-infected with a 1:1 ratio of Mark-S and Cabb-S. The first consisted of viral DNA densitometry data derived from 24 plants (described above), where for each plant we have an estimate of the frequency of each marker in the genome population. The second consisted of the restriction of 50 individual full-genome-length viral clones obtained from one co-infected plant (described above), yielding an estimate of the frequency of each marker, and this was repeated on ten different plants. The frequencies of the different markers were 0.508, 0.501, 0.516, and 0.507 for markers a, b, c, and d in the first dataset and 0.521, 0.518, 0.514, and 0.524 in the second dataset. We tested whether these frequencies were significantly different from the expected value under neutrality, 0.5, using either t-tests, for datasets where normality could not be rejected (seven out of eight cases), or Wilcoxon signed-rank non-parametric tests otherwise (marker c in the first dataset). In all cases p-values were larger than 0.05.
There are several cautionary remarks regarding these analyses. First, in all cases we found an excess of markers. Unfortunately, the two datasets cannot be regarded as independent because, even though the methods through which the frequency estimates were obtained were different, the plants used in the second dataset were a subset of the plants of the first. We thus have only four independent estimates in each case, and there is minimal power to detect significant deviations from neutrality with such a small sample size. It should be noted at this stage that deviations from the expected value could also be caused either by slight deviations from the 1:1 ratio in the infecting mixed solution, or by deviations from that ratio in the frequency of the viral particles that actually get into the plants. Second, because of the relatively small sample sizes and low statistical power, the tests presented above could have detected only large deviations.
The results clearly show, however, precisely that the markers do not have large effects, if any, and that therefore recombination estimates would be affected only very slightly by any hypothetical selective effects of the markers. Because of this, along with the fact that the introduced markers provoke silent substitutions in the CaMV genome, we assumed that markers were effectively neutral in the rest of the analysis.
The dataset used to estimate the recombination frequency consisted of the 500 full-genome-length viral clones (50 from each of ten co-infected plants) individually genotyped for each of the four markers. As discussed in detail in the Results, recombination was very frequent and concerned all four markers. Indeed, approximately half of the genotyped clones exhibited a recombinant genotype. It was therefore meaningful to try to obtain quantitative estimates of recombination from our data.
Our aim was to analyze viral recombination in a live host. Consequently, we had to deal with the fact that more than one viral replication cycle occurred during the 21 d that infection lasted in our experiment (we had to wait that long for the disease to develop and to be able to recover sufficient amounts of viral DNA from each infection). Based on the kinetics of gene expression [40], we postulate that each replication cycle lasts between 2 and 3 d, and that therefore seven to ten cycles occurred between infection and the sampling time. In case this assumption is incorrect, we did calculations assuming five, seven, ten, or 20 replication cycles during these 21 d. As shown, the results were not affected qualitatively, and only slightly quantitatively. It is important to note that we assumed that recombination occurred through a template-switching mechanism, and that therefore, from a recombination point of view, the CaMV genome is linear. The reverse transcription starts and finishes at the position 0 in Figure 1, which is the point of circularization of the DNA genome. This implies that changes between contiguous markers a–b, b–c, and c–d can be considered as true recombination whereas those between a and d cannot, as they may simply stem from circularization of DNA, during the synthesis of which the polymerase has switched template once anywhere between a–b, b–c, or c–d.
To estimate the recombination rate between markers, we wrote recurrence equations describing the change in frequency of each genotype over one generation, assuming random mating and no selection (i.e., the standard Wright–Fisher population genetics model). We then expressed the frequency of all possible genotypes n generations later as a function of their initial frequency and of the recombination parameters. Subsequently we calculated the maximum likelihood estimates of the recombination parameters and their asymptotic variances given initial frequencies (we assumed that the two “parental” genotypes, Mark-S and Cabb-S, had equal initial frequencies of 0.5 and that all other genotypes had initial frequencies of zero) and frequencies after n generations (the observed frequencies; as stated above we used different values of n). All algebraic and numerical calculations were carried out with the software Mathematica.
The recombination parameters are the recombination rates between two adjacent loci, e.g., r
1 for the recombination rate between markers a and b, and the interference coefficients, e.g., i
12 for interference between recombination events in the segments between markers a and b and b and c. To define these parameters we followed Christiansen [48], and in particular the recombination distributions for two, three, and four loci (respectively, Tables 2.7, 2.8, and 2.9 of [48]). It is important to realize that given the definitions of these parameters, the estimator of the recombination rate between two loci is not affected by the number of loci considered. In other words, we obtain the same estimation of the recombination rate between markers a and b whether we consider genotypic frequencies at just these two loci, or the frequencies at these two loci plus a third locus, or the complete information to which we have access, the four-marker genotypes. Information on additional loci only affects the estimates of the interference coefficients.
It proved impossible to carry out the calculations for four loci algebraically. Instead, we used a computer program to calculate the expected genotypic frequencies at all four loci after n generations, given the above stated initial frequencies and specified recombination parameters. For each combination of recombination parameters we calculated a Euclidean distance between the vector of the expected genotypic frequencies and the observed genotypic frequencies, and considered that the estimated recombination parameters were those yielding the minimal Euclidean distance. In all cases, the estimated recombination rates between pairs of loci were equal to the second decimal to those estimated algebraically from data for three or two loci.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. RF, SB, and YM conceived and designed the experiments. RF, MU, LG, and SB performed the experiments. RF, DR, SB, and YM analyzed the data. RF, DR, SB, and YM wrote the paper.
¤Current address: Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe Ecologie et Evolution des micro-organismes E 0339, Paris, France
Citation: Froissart R, Roze D, Uzest M, Galibert L, Blanc S, et al. (2005) Recombination every day: Abundant recombination in a virus during a single multi-cellular host infection. PLoS Biol 3(3): e89.
Abbreviation
CaMVcauliflower mosaic virus
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030098SynopsisCell BiologyMolecular Biology/Structural BiologyBiochemistryHomo (Human)Separating Sisters: Shugoshin Protects SA2 at Centromeres but Not at Chromosome Arms Synopsis3 2005 1 3 2005 1 3 2005 3 3 e98Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Dissociation of Cohesin from Chromosome Arms and Loss of Arm Cohesion during Early Mitosis Depends on Phosphorylation of SA2
Chromosome Cohesion: A Cycle of Holding Together and Falling Apart
Shugoshin Prevents Dissociation of Cohesin from Centromeres During Mitosis in Vertebrate Cells
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DNA replication leaves the cell with two identical copies of each chromosome. To ensure their proper segregation during the anaphase stage of mitosis, the members of each pair, called sister chromatids, are held together by a protein complex, aptly named cohesin, that links the two not only at the centromere, but also along the chromatid arms. Anaphase is triggered when cohesin is cleaved, by the equally well-named separase. But cleavage is not the only way to remove cohesin from the chromosome; indeed, in humans and other higher eukaryotes, mitotic kinases such as Plk1 remove the majority of cohesin from chromosome arms—but not from the centromere—during prophase and prometaphase.
These facts raise two questions: what is the precise target of Plk1, and what protects centromeric cohesin from removal by the same pathway? Both questions are addressed in a new article in PLoS Biology. Jan-Michael Peters and colleagues show that phosphorylation of the cohesin subunit SA2, presumably by Plk1, is required for cohesin removal from chromosome arms in early mitosis, while data from Kim Nasmyth and colleagues suggest that a protein called shugoshin protects centromeric SA2 from such phosphorylation.
Premature loss of sister chromatid cohesion
Cohesin is composed of multiple subunits, each of which can be phosphorylated at multiple threonine or serine amino acid residues. These subunits include Scc1 (the target of separase), Smc1, and Smc3, plus Scc3 in yeast, and SA1 or SA2 in humans and other higher eukaryotes. By isolating and analyzing cohesin subunits from cells undergoing mitosis, Peters and colleagues deduced that Scc1, SA1, and SA2 are phosphorylated only during mitosis, suggesting that phosphorylation of one or more of them triggers the breakup of cohesin. Further analysis by mass spectrometry allowed them to identify the exact amino acids that bore the phosphates on each subunit. In Scc1, these were clustered around the known sites of separase cleavage. The researchers showed that phosphorylation at these sites is required for efficient cleavage by the enzyme during anaphase, but is not required to dislodge cohesin specifically from the chromosome arms, as this proceeded essentially normally even after these sites were mutated to prevent their phosphorylation.
The same mutation strategy applied to SA2, on the other hand, revealed that phosphorylation of this subunit is essential for dissociating cohesin from the chromosome arms during prometaphase. Interestingly, the mutations did not prevent the ultimate separation of the chromatids at anaphase. This suggests that separase, once it is activated, can cleave cohesin on the arms as well as at the centromere.
Cohesin at the centromere is removed later in mitosis than cohesin bound to chromatid arms, namely, at the metaphase-to-anaphase transition, suggesting centromeric cohesin is protected by a centromere-specific molecule. Possible candidates would be members of the shugoshin family, which are known to prevent unloading of centromeric cohesin during the first division of meiosis, thus keeping chromatids together as homologous chromosomes are separated.
To investigate human shugoshin's mitotic role, Nasmyth and colleagues depleted shugoshin by RNAi. The result was loss of cohesin not only from the arms but also from the centromere, early separation of chromatids, and failure of anaphase, suggesting that shugoshin protects centromeric cohesin. But how? To find out, the authors examined the effect of shugoshin depletion in cells whose SA2 had been mutated to prevent phosphorylation. Strikingly, these cells underwent mitosis successfully. Together, these results suggest that shugoshin's normal mitotic role is to protect centromeric SA2 from phosphorylation, delaying chromatid separation until the moment when the chromosomes are ready to separate, at which time cohesin is cleaved by separase.
The picture that emerges from these two studies is that sister chromatid cohesion is safeguarded throughout early mitosis by shugoshin, which protects centromeric cohesin from the threat of protein kinases that, in the authors' vivid language, “maraud mitotic chromosomes and threaten to destroy their integrity.” This delicate balance of power between kinases and shugoshin means that any upset in the balance may prevent a cell from dividing properly, which often means not dividing at all. (Also see the Primer “Chromosome Cohesion: A Cycle of Holding Together and Falling Apart” [DOI: 10.1371/journal.pbio.0030094].)
| 0 | PMC1054885 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Mar 1; 3(3):e98 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030098 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030106SynopsisCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaLocation, Location, Location: How Position Affects Gene Expression in the Nucleus Synopsis3 2005 1 3 2005 1 3 2005 3 3 e106Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Cell-by-Cell Dissection of Gene Expression and Chromosomal Interactions Reveals Consequences of Nuclear Reorganization
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Control of gene expression plays a role in determining cell fate, differentiation, and the maintenance of specific cell lineages. In the absence of regulation, aberrant gene expression can lead to developmental defects and disease. As a result, gene expression is highly regulated and that regulation takes many forms. Control mechanisms may be specific to one gene or operate on a gross chromosomal level, ultimately ensuring that genes are expressed at the right time, in the right place.
It is only in the run-up to and during cell division that chromosomes take on the condensed form that enables them to be recognized as discreet structures. During the rest of the cell cycle, interphase chromosomes exist in a relaxed state that at first glance looks like an unraveled ball of wool floating randomly about the nucleus. But a closer look reveals that they are in fact non-randomly organized and compartmentalized, and these groupings have functional ramifications for how genes are expressed or silenced (repressed).
Assessing gene expression and gene location in single cells
What is actually visible is chromatin, a combination of naked DNA and proteins that associate with it. It can exist in two forms: euchromatin and heterochromatin. Actively expressed chromosomal regions (loci) are predominantly located within euchromatin, while loci within heterochromatic regions are silenced. Genetic and cytological evidence indicates that interaction between euchromatic genes and heterochromatin can cause gene silencing. Getting a gene into position for such an interaction may be achieved in two ways. The first is by changing the gene's position on the chromosome to bring it very close to expanses of centromeric heterochromatin, thereby increasing the likelihood for interaction. The second is by changing the position of a section of heterochromatin to place it close to a euchromatic gene. The small regions of heterochromatin involved in this second process seem sufficient to mediate long-range interactions between the affected gene and the larger heterochromatic regions near the centromere, but not so large or powerful as to mediate silencing by themselves. In this issue, Brian Harmon and John Sedat study the functional consequences of long-range chromosomal interactions—consequences that have been inferred in several different organisms but until now have not been analyzed on a cell-by-cell basis or directly verified.
Several Drosophila fruitfly mutants have been identified that exhibit cells in the same organ with varied phenotypes (appearance), though their genotypes (DNA instructions) are the same. This occurs through a phenomenon known as position-effect variegation, in which the expression of variegating genes is determined by their position on the chromosome relative to regions of heterochromatin. Working with fruitflies, the authors labeled three variegating genes and areas of heterochromatin with fluorescent probes and visualized expression of the affected genes in tissues where they are normally expressed. Silenced genes, they discovered, are far closer to heterochromatin than expressed genes, indicating that silenced genes interact with heterochromatin while expressed genes do not.
This study of interactions between a gene and heterochromatin in single cells illustrates unequivocally a direct association between long-range chromosomal interactions and gene silencing. The novel cell-by-cell analysis paves the way for further analysis of this phenomenon and will lead to a greater insight into the understanding and functional significance of nuclear architecture.
| 0 | PMC1054886 | CC BY | 2021-01-05 08:28:12 | no | PLoS Biol. 2005 Mar 1; 3(3):e106 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030106 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1573706710.1371/journal.pbio.0030110Research ArticleBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyVirologyPrimatesHomo (Human)Lineage-Specific Expansions of Retroviral Insertions within the Genomes of African Great Apes but Not Humans and Orangutans Retroviral Insertions within African ApesYohn Chris T
1
Jiang Zhaoshi
2
McGrath Sean D
2
Hayden Karen E
1
Khaitovich Philipp
3
Johnson Matthew E
1
2
Eichler Marla Y
2
McPherson John D
4
Zhao Shaying
5
Pääbo Svante
3
Eichler Evan E [email protected]
2
1Department of Genetics, Case Western Reserve UniversityCleveland, OhioUnited States of America2Department of Genome Sciences, University of Washington School of MedicineSeattle, WashingtonUnited States of America3Max-Planck Institute for Evolutionary AnthropologyLeipzigGermany4Department of Molecular and Human Genetics, Baylor College of MedicineHouston, TexasUnited States of America5The Institute for Genome Research, BethesdaMarylandUnited States of AmericaTristem Mike Academic EditorImperial CollegeUnited Kingdom4 2005 1 3 2005 1 3 2005 3 4 e1104 11 2004 27 1 2005 Copyright: © 2005 Yohn et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
The Chimp Genome Reveals a Retroviral Invasion in Primate Evolution
Retroviral infections of the germline have the potential to episodically alter gene function and genome structure during the course of evolution. Horizontal transmissions between species have been proposed, but little evidence exists for such events in the human/great ape lineage of evolution. Based on analysis of finished BAC chimpanzee genome sequence, we characterize a retroviral element (Pan troglodytes endogenous retrovirus 1 [PTERV1]) that has become integrated in the germline of African great ape and Old World monkey species but is absent from humans and Asian ape genomes. We unambiguously map 287 retroviral integration sites and determine that approximately 95.8% of the insertions occur at non-orthologous regions between closely related species. Phylogenetic analysis of the endogenous retrovirus reveals that the gorilla and chimpanzee elements share a monophyletic origin with a subset of the Old World monkey retroviral elements, but that the average sequence divergence exceeds neutral expectation for a strictly nuclear inherited DNA molecule. Within the chimpanzee, there is a significant integration bias against genes, with only 14 of these insertions mapping within intronic regions. Six out of ten of these genes, for which there are expression data, show significant differences in transcript expression between human and chimpanzee. Our data are consistent with a retroviral infection that bombarded the genomes of chimpanzees and gorillas independently and concurrently, 3–4 million years ago. We speculate on the potential impact of such recent events on the evolution of humans and great apes.
Comparison of human and other primate genomes provides evidence for a retroviral infection that bombarded the genomes of chimpanzees and gorillas 3-4 million years ago
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Introduction
Mammalian genomic sequence is littered with various classes of endogenous retroviruses that have populated genomes during the course of evolution [1,2]. In the case of humans, approximately 8.3% of the genome sequence consists of long terminal repeat (LTR) and endogenous retrovirus elements classified into more than 100 separate repeat families and subfamilies [3,4]. The bulk of human endogenous retrovirus elements are thought to have originated as a result of exogenous retrovirus integration events that occurred early during primate evolution. Based on comparative analyses of orthologous genomic sequence and sequence divergence of flanking LTR elements, the last major genomic infection of the human lineage is estimated to have occurred before the divergence of the Old World and New World monkey lineages (25–35 million years ago) [5,6,7,8]. Since the divergence of chimpanzee and human (5–7 million years ago), only one major family of human endogenous retroviruses (HERVK10) has remained active, and it has generated only three full-length copies with the open reading frame still intact [3]. While new insertions of endogenous retroviral sequences have been described [8,9], most of these are thought to have originated from other previously integrated retroelements [10] or longstanding associations with rare source virus [11]. This apparent wane in activity has led to the view that LTR retroposons have had a history of declining activity in the human lineage and are “teetering on the brink of extinction” [3].
Endogenous retroviruses may arise within genomes by at least two different mechanisms: retrotransposition from a pre-existing endogenous retrovirus (intraspecific transmission) or infection and integration via an exogenous source virus (horizontal transmission). Many cross-species transmissions have been documented and frequently manifest themselves as inconsistencies in the presumed phylogeny of closely related species. During the 1970s and 1980s, Benveniste and colleagues identified, by DNA hybridization and immunological cross-reactivity, several retroviral elements that could be found among more diverse primate/mammalian species but not necessarily among more closely related sister taxa [12,13,14]. Lieber and colleagues, for example, reported the isolation of a particular class of type C retroviruses from a woolly monkey (SSV-SSAV) and gibbon ape (GALV) but not the African great apes [13]. These viruses shared antigenic properties with previously described type C activated endogenous retroviruses of the Asian feral mouse Mus caroli. Cross-species infection from murines to primates was proposed as the likely origin of the retrovirus. A related endogenous retrovirus was subsequently identified in the koala, suggesting a zoonotic transmission from placentals to mammals [15]. Evidence of horizontal transmission for other families of retrovirus has been reported among classes of species as distantly related as avians and mammals [15].
Comparative analyses of closely related genomes have suggested that retroviral cross-species transmissions and genome integrations are a common occurrence during the recent evolutionary history of several species. Murine genomes, in particular, have been bombarded with relatively recent retroviral integrations [16]. In contrast to humans, there is ample evidence that exogenous retrovirus continues to bombard and fix within the genomes of Old World monkey species. Cross-species transmissions and genome integration of retroviruses as recent as 500,000 years ago have been reported between various simian species [17,18]. Differences in the distribution of endogenous retroviruses have even been noted between feral and domesticated mammalian species. The genomes of domestic cats, for example, harbor specific families of endogenous feline leukemia viruses that are not found in the genomes of wild cats [19]. Similarly, the PERV-C (porcine endogenous retrovirus type C) is restricted to domesticated pigs and has not been identified in the genomes of the wild boar from which domestication is thought to have occurred approximately 5,000 years ago [20].
From a functional perspective, the integration of retroviral sequence may have considerable impact. Endogenous retroviruses harbor cryptic mRNA splice sites, polyadenylation signals, and promoter and enhancer sequences. As such, their integration into the genome may significantly alter the expression patterns of nearby genes. Moreover, integrated retroviruses are often preferential sites of methylation and may promote rearrangement of DNA by way of non-allelic homologous recombination between elements. Consequently, these elements have been recognized as potent mutagens [2,21] that may significantly alter the phenotype [22,23]. The mechanism by which such elements originate and differentially spread among closely related species is, therefore, fundamental to our understanding of evolution.
Results
Distribution of Pan troglodytes Endogenous Retrovirus 1 among Primates
During a comparison of human and chimpanzee BAC sequence, we identified several members of a full-length endogenous retrovirus family that were present in chimpanzee but absent in corresponding human genome sequence (Figure 1). Analysis of five full-length insertion sequences revealed that the endogenous retroviral elements (termed Pan troglodytes endogenous retrovirus 1 [PTERV1]) ranged in size from 5 to 8.8 kb in length (Materials and Methods). Translation of the sequence showed strong protein similarity to gammaretroviruses (53%–69%), in particular, murine leukemia virus, feline leukemia virus, porcine endogenous retrovirus type C, and baboon (Papio cynocephalus) endogenous retrovirus (Figure 1). Large deletions (1–2 kb) of the reverse transcriptase in some copies as well as the presence of multiple stop codons in all examined full-length copies indicate that this particular family of endogenous retrovirus is not replication competent.
Figure 1 Identification and Sequence Analysis of PTERV1
(A) A graphical alignment of chimpanzee genomic sequence (AC097267) and an orthologous segment from human Chromosome 16 (Build 34) depicting an example of a PTERV1 (approximately 10 kb) insertion. Aligned sequences are shown in blue (miropeats) [47].
(B) The typical retroviral structure of the insert (gag, pol, env, and LTR) is compared to a baboon (Papio cynocephalus) endogenous retrovirus (PcEV). Regions of nucleotide homology are designated by black blocks and inter-sequence connecting lines. The location of probes (see Table S1) used in genomic library hybridizations, Southern blot analyses, and neighbor-joining tree analyses are shown (red).
We designed two probes to the gag and env portions of PTERV1 (Figure 1; Table S1) and assessed the distribution of PTERV1 among apes and Old World monkeys by Southern analysis. More than 100 copies of the endogenous retrovirus were detected in each African ape and Old World monkey species (Figures 2 and Figure S1). Comparison between DNA digested with methylation-sensitive and -resistant restriction enzymes indicated that most copies were extensively methylated in these species (Figure S2). In contrast, analysis of multiple Asian apes (siamang, gibbon, and orangutan) and a panel of human DNAs showed no hybridization signal. These findings were consistent with early DNA hybrid melting experiments [12] and DNA hybrid electron microscopic studies [14] that indicated that DNA from the African great apes harbored sequences homologous to both colobus monkey and baboon exogenous retroviruses while the genomes of man and Asian apes did not. These data were sometimes used as supporting evidence for an Asian origin of modern humans [12].
Figure 2 Southern Hybridization of PTERV1 among Primates
Species represented include human (HSA), common chimpanzee (PTR), bonobo (PPA), gorilla (GGO), orangutan (PPY), siamang (HSY), white-handed gibbon (HLA), Abyssinian black-and-white colobus monkey (CGU), olive baboon (PHA), rhesus macaque (MMU), and Japanese macaque (MFU). Below each panel, a restriction map (chimpanzee sequence AC097267) is presented in relation to the hybridization probe: PstI (closed circles), PvuII (open circles), and HpaII/MspI (triangles) (see Figures S1 and S2 for additional details).
(A) The absence of PTERV1 among Asian apes and humans is shown in contrast to a generally accepted catarrhine species phylogeny. Primate DNAs have been digested with PstI restriction enzyme, Southern-transferred to nylon membrane, and hybridized with PTERV1 gag probe number 1.
(B) Multiple African great ape species are compared for both the gag probe number 1 and env probe number 3 (Figure 1). Proximity of probe number 1 to the VNTR, which is variable in length between copies (400 bp to 10 kb), reveals hundreds of insertion sites.
(C) Multiple individuals from different subspecies of the olive baboon are compared for both gag probe number 1 and env probe number 3. The pattern of Southern hybridization shows limited intra-specific variation, indicative of either polymorphism in restriction enzyme sites or copy number variation.
In order to further resolve the evolutionary relationship of these endogenous retroviruses, we compared the sites of retroviral integration in the genomes of chimpanzee, gorilla, macaque, and baboon. To this end, we screened large-insert genomic (BAC) libraries for each species using multiple probes from the PTERV1 reference sequence (Table S1). These data allowed us to estimate copy number in each species and to distinguish clones harboring full-length retroviral inserts versus solo LTR elements (Table 1). In addition, we used BAC end sequences from nonhuman primate clones harboring full-length retroviruses to map their locations back to the human genome. We then compared the locations (Figure 3; Table 2) between species to determine whether the sites were non-orthologous. Based on an analysis of 1,467 large-insert clones, we mapped 299 retroviral insertion sites among the four species (Figure 3; Table S2). A total of 275 of the insertion sites mapped unambiguously to non-orthologous locations (Table 2), indicating that the vast majority of elements were lineage-specific (i.e., they emerged after the divergence of gorilla/chimpanzee and macaque/baboon from their common ancestor).
Figure 3 PTERV1 Insertion Sites
Large-insert genomic clones that contained full-length endogenous retrovirus were identified by hybridization from four species: chimpanzee (PTR), gorilla (GGO), baboon (PAN), and rhesus macaque (MMU). End sequencing of large-insert clones (n = 1,467) and alignment against the human genome reference sequence identified 287 insertion sites (see Table S2). A total of 95.8% of these sites were non-orthologous when compared between species. chr, chromosome.
Table 1 PTERV1 BAC Library Hybridizations: Number of BACs (Estimated Copy Number)
a Large-insert BAC libraries were hybridized with radioactive probes corresponding to the gag (probe number 1), env (probe number 3), and LTR (probe number 2) portions of PTERV1. The number of strongly hybridizing positive BACs was used to estimate the copy number based on the depth of coverage of each library. BACs that hybridized with both the gag and env portions were considered to harbor full-length copies of the retrovirus, while BACs that hybridized with the LTR probe but not with gag + env portions were considered to represent solo LTR copies of PTERV1
b The number of hybridization-positive BACs for each probe is shown. The estimated copy number of PTERV1 in different species is indicated in parentheses
Table 2 Cross-Species Retroviral Insertion Mapping
A total of 1,467 BACs that hybridized with PTERV1 gag and env probes were end-sequenced and mapped against the human genome assembly by quality sequence alignment (see Table S2). Insertion sites were refined based on the placement of two or more BACs from a species to the same location. The number of mapped loci and the number of non-overlapping locations with respect to the other species are indicated. A total of 275/287 (275 + 12) = 95.8% of the locations were non-orthologous. Twenty-four locations were ambiguous within the limits of resolution of this study and could, in theory, correspond to 12 orthologous sites (see Table S3)
Within the limits of this BAC-based end-sequencing mapping approach, 24 sites mapped to similar regions of the human reference genome (approximately 160 kb) and could not be definitively resolved as orthologous or non-orthologous (Table S3). We classified these as “ambiguous” overlap loci (Figure 3). If all 24 locations corresponded to insertions that were orthologous for each pair, this would correspond to a maximum of 12 orthologous loci. The number of non-orthologous loci was calculated as 275/287 (275 + 12) or 95.8%. This is almost certainly a lower-bound estimate owing to the limitation of our BAC-based mapping approach to refine the precise locations of the insertions.
We performed two analyses to determine whether these 12 shared map intervals might indeed be orthologous. First, we examined the distribution of shared sites between species (Table S3). We found that the distribution is inconsistent with the generally accepted phylogeny of catarrhine primates [5]. This is particularly relevant for the human/great ape lineage. For example, only one interval is shared by gorilla and chimpanzee; however, two intervals are shared by gorilla and baboon; while three intervals are apparently shared by macaque and chimpanzee. Our Southern analysis shows that human and orangutan completely lack PTERV1 sequence (see Figure 2A). If these sites were truly orthologous and, thus, ancestral in the human/ape ancestor, it would require that at least six of these sites were deleted in the human lineage. Moreover, the same exact six sites would also have had to have been deleted in the orangutan lineage if the generally accepted phylogeny is correct. Such a series of independent deletion events at the same precise locations in the genome is unlikely (Figure S3).
For the three intervals putatively shared between macaque and chimpanzee, we attempted to refine the precise position of the insertions by taking advantage of the available whole-genome shotgun sequences for these two genomes. For each of the three loci, we mapped the precise insertion site in the chimpanzee and then examined the corresponding site in macaque (http://www.ncbi.nlm.nih.gov). In one case, we were unable to refine the map interval owing to the presence of repetitive rich sequences within the interval. In two cases, we were able to refine the map location to single basepair resolution (Figures S4 and S5). Based on this analysis, we determined that the sites were not orthologous between chimpanzee and macaque. It is interesting to note that this level of refined mapping in chimpanzee revealed 4- to 5-bp AT-rich target site duplications in both cases. These findings are consistent with an exogenous retrovirus source since proviral integrations typically target AT-rich DNA ranging from 4 to 6 bp in length [24]. Although the status of the remaining overlapping sites is unknown, these data resolve four additional sites as independent insertion events and suggest that the remainder may similarly be non-orthologous. This apparent independent clustering of retroviral insertions at similar locations may be a consequence of preferential integration bias or the effect of selection pressure against gene regions, limiting the number of effective sites that are tolerated for fixation.
Phylogenetic Analysis of PTERV1
We next examined the phylogenetic relationship of the retroviral elements by comparing portions of the gag and env regions. We chose a total of 103 BAC clones representing distinct loci from the four species and PCR-amplified and sequenced noncontiguous gag and env portions (823 bp) from each clone. Based on sequence analysis, each of the 101 BAC clones contained a single copy of the gag and env portions as determined by analysis of the sequence. These were deemed to be linked to the same endogenous retrovirus insert. Two clones showed “heterozygous” sequence signatures consistent with two or more copies clustered within the BAC clone and were excluded from further analysis. We constructed a phylogenetic tree based on the multiple sequence alignment (Figure 4) of the concatenated gag and env regions. (A similar tree topology with less bootstrap support is obtained if env and gag segments are considered separately [Figure S6A and S6B].) While it is clear that this particular class of endogenous retroviruses shares a common origin, the retroviral phylogeny is inconsistent with the generally accepted primate species tree based on molecular data [5]. The chimpanzee and gorilla PTERV1 elements are most closely related (3%–4% divergence) and belong to a single phylogenetic clade (Table S4). In contrast, macaque and baboon inserts show considerably greater sequence divergence (9%–11%) and a much more stratified phylogeny with little discrimination based on species. This tree topology suggests a polyphyletic origin with at least three groups of Old World virus being distinguished (Figure 4). Interestingly, one of these Old World groups (group 1) shows a possible monophyletic origin with respect to chimpanzee and gorilla.
Figure 4 PTERV1 Phylogenetic Tree
Portions of the gag and env genes (about 823 bp) were resequenced from 101 PTERV1 elements from common chimpanzee (n = 42), gorilla (n = 25), rhesus macaque (n = 14), and olive baboon (n = 20). A neighbor-joining phylogenetic tree shows a monophyletic origin for the gorilla and chimpanzee endogenous retroviruses but a polyphyletic origin among the Old World monkey species. Bootstrap support (n = 10,000 replicates) for individual branches are underlined. Although the retroviral insertions have occurred after speciation, retroviral sequences show greater divergence than expected for a non-coding nuclear DNA element (see Table S4). Table S8 provides a clone key for number designation. Phylogenetic trees showing the gag, env, and LTR segments separately are presented in Figure S6. Sequences 11 and 30 (red) are mapped to one of the 12 ambiguous overlapping loci described in the text (see Table S3). They do not cluster in this phylogenetic tree, which indicates that they are unlikely to be true orthologs.
Since gag and env regions of viruses may experience vastly different selection pressures, we repeated the analysis by examining 295 bp of LTR sequence from a subset of 55 loci (Table S4). Phylogenetic analysis of the LTR segment between species revealed a virtually identical tree topology (Figure S6C) to that of the concatenated gag and env segments. Macaque and baboon interproviral divergence (14%–24%), once again, was significantly greater than that for chimpanzee and gorilla (3%–4%). It should be noted that this degree of divergence is 2-fold greater than the average divergence for a neutral site between gorilla and chimpanzee (approximately 1.6%) [25] and approximately 10-fold greater than estimated neutral divergence rates between macaque and baboon (1.5%, E. E. E., unpublished data).
Identical copies of LTR sequences are created as part of the retrovirus life cycle [2,8]. Consequently, divergence of flanking LTR elements has been used extensively as a metric to estimate the evolutionary age of the source infection. We compared intraproviral LTR sequence divergence from 101 loci in the chimpanzee, gorilla, baboon, and macaque genomes (Figure 5). We designed oligonucleotides within conserved portions of the LTR sequence alignment, PCR-amplified the LTR flanks, sequenced both products from each clone, and compared them for mismatches. Gorilla and chimpanzee LTR elements showed a median divergence of 0.98% and 1.15%, respectively. Using neutral estimates of primate LTR divergence [8], we estimate that a contemporaneous infection occurred in these ancestral gorilla and chimpanzee lineages 3–4 million years ago (see Materials and Methods). LTR divergence among baboon and macaque was significantly less (0.051% and 0.058%, respectively; p < 0.007, one-tailed t test), corresponding to a much more recent origin (approximately 1.5 million years ago). These observations may be reconciled with differences in the phylogenetic tree topology if the Old World monkeys were infected by several diverged viruses while gorilla and chimpanzee were infected by a single closely related exogenous source. In this scenario, most of the genetic differences observed among macaque and baboon endogenous retroviruses are not nuclear in origin but arose as part of normal variation between viruses (see Materials and Methods).
Figure 5 LTR Variation
A total of 101 loci that contained full-length PTERV1 elements were examined for the number of mismatches between left and right LTR flanks (295 bp). Different distributions were obtained for Old World monkeys (baboon, mean = 1.6 ± 1.4; macaque, mean = 1.6 ± 1.5) and great ape species (chimpanzee, mean = 3.4 ± 2.2; gorilla, mean = 2.9 ± 2.3).
We examined the distribution pattern of intraproviral LTR divergence to determine whether the observed pattern was consistent with a single or multiple infections. We modeled the probability of a mutation occurring within a LTR sequence using the Poisson distribution (Table S5). For each of the four species, the distribution of LTR mismatches did not differ significantly from normal statistical fluctuations (Poisson distribution, p > 0.1). While these results are consistent with a single burst of retroviral insertions within each lineage, we cannot exclude the possibility of multiple integrations over a shorter span of 1–2 million years generating a virtually identical pattern. Indeed, based on the presence and absence of hybridizing bands among individuals from the same species (see Figure 2B and 2C), we estimate that 5%–10% of the sites are polymorphic within the baboon and chimpanzee populations. This may be the result of deletion or more recent reinfections of the germline.
PTERV1 Integration Bias and Gene Expression
Integration of retroviral sequence into genomes has long been recognized as a potent mutagen due to the fact that such proviruses frequently alter transcription, disrupt splicing, or become targets of hypermethylation. In this study, for example, the majority of full-length retroviral elements were heavily methylated (see Figure S2), suggesting that most copies had been transcriptionally silenced in transformed and peripheral blood lymphocytes. Studies of proviral integrations followed by inbreeding suggest that 5%–10% of novel insertions result in phenotypic change and/or alter gene expression [22,23]. We examined the distribution of full-length retroviruses within the chimpanzee genome and identified 107 sites of integration (Tables S6 and S7). We defined an integration site as genic if it mapped between the transcription start and stop site of a known annotated human gene. Only 13% (14/107) of retroviral integrations mapped within the introns of genes. Another 75 of the 107 insertion sites were located more than 50 kb upstream or downstream from a transcription start site. This is a significant departure (p < 0.001) from a random model of intron integration (29.2%) and shows a distinctly opposite trend to patterns of somatic retroviral insertion, for which gene-rich regions of the genome are strongly favored (34%–60% of murine leukemia virus and HIV infections) [26,27] (Figure S7). We propose that this 3- to 6-fold bias against gene-rich regions is the direct result of strong purifying selection pressure on the ancestral chimpanzee population.
In order to determine whether the small subset of genes (n = 14) that had been targets of insertions also showed significant changes in expression, we examined gene expression data for humans and chimpanzees in five tissues. This dataset was controlled for DNA sequence single-basepair differences between human and chimpanzee by excluding all probes that did not match perfectly between the two species (P. K and I. Hellman, unpublished data; [28]). Six of the ten genes detected in this dataset showed significant differences in expression levels between human and chimpanzee tissues (Table 3). In five of the six cases, the genes showed reduced levels of gene expression in the chimpanzee when compared to human. A simulation study based on the total number of differentially expressed (approximately 30%) genes revealed that this enrichment is weakly significant (p = 0.0489). The results suggest that retroviral insertion may have had an influence on expression difference between humans and chimps, but because of the small sample size (ten genes), it should be cautioned that the results are far from definitive. Additional studies, including RT-PCR and Northern analysis, with carefully controlled probes and matched tissue sources will be required to fully address this issue.
Table 3 Retroviral Insertions That Map within Genes in the Chimpanzee Genome Assembly
a Insertions were mapped based on the chimpanzee genome assembly (CGSC) and by paired-end sequence analysis (see Materials and Methods)
b Expression differences were based on an Affymetrix (U133A) microarray analysis of five tissues (brain, heart, kidney, liver, and testis) from five chimpanzee and six humans (P. K. and S. P., unpublished data). Tissues with a significant difference in expression are indicated as follows: −1 denotes significant upregulation in chimpanzee, while +1 represents significant upregulation in human. Genes with equivalent or those that were not detected (ND) on the microarray are indicated. Six out of ten genes showed significant differences in gene expression when compared to a control set that showed an approximately 30% expression difference (2,459/7,947 genes) between human and chimpanzee
Discussion
Most human endogenous retroviruses are thought to have emerged as a result of ancient infections more than 25 million years ago [7,8], followed by subsequent retrotransposition events. Several lines of evidence indicate that chimpanzee and gorilla PTERV1 copies arose from an exogenous source. First, there is virtually no overlap (less than 4%) between the location of insertions among chimpanzee, gorilla, macaque, and baboon, making it unlikely that endogenous copies existed in a common ancestor and then became subsequently deleted in the human lineage and orangutan lineage. Second, the PTERV1 phylogenetic tree is inconsistent with the generally accepted species tree for primates, suggesting a horizontal transmission as opposed to a vertical transmission from a common ape ancestor. An alternative explanation may be that the primate phylogeny is grossly incorrect, as has been proposed by a minority of anthropologists [29]. This seems unlikely in light of the extensive molecular evolutionary data that have been collected over the last few years [5,25] that clearly place orangutan as the outgroup species to the human–chimpanzee–gorilla clade and Old World monkeys as an outgroup to the human/ape lineage.
Third, the single nucleotide substitution rate for the viruses is significantly greater than what would be expected for neutral nuclear DNA. The extent of chimpanzee and gorilla substitution, for example, has been estimated at approximately 0.016 ± 0.008 substitutions per site [25], with approximately half of this variation occurring in each lineage. The endogenous retrovirus sequence that we examined showed significantly greater divergence (0.038 ± 0.003) (62 pairwise comparisons). A similar excess of divergence was observed if only intraspecific retroviral divergence was compared (0.028 ± 0.003 and 0.041 ± 0.003 for chimpanzee and gorilla, respectively) (see Table S4). Such an acceleration of neutral substitution would easily be explained if it were composed of a viral and nuclear component (see Materials and Methods). Fourth, if we partition synonymous and non-synonymous substitution sites (see Materials and Methods), we observe a deficiency of amino acid replacement sites (K
a/K
s = 0.63). We observed a similar result (K
a/K
s = 0.44) for one of the ambiguous overlap loci shared between gorilla and chimpanzee (see Table S3). This significant departure from neutrality would be an expected residuum if a portion of PTERV1 sequence variation accrued while being propagated as an infectious virus [11]. If it were solely derived from an ancestral endogenous element, a neutral pattern, as opposed to a relaxed pattern of purifying selection, would be expected. Finally, in the few examples where the insertion sites have been mapped precisely, both the length and the composition of the target site duplications are characteristic of the patterns of retroviral integrations [24].
While these multiple lines of indirect data indicate that PTERV1 likely emerged from an exogenous source, its source reservoir, if it still exists, is unknown. PTERV1 does not share high sequence identity to any known retrovirus. Translation of the protein-encoding portions shows sequence similarity (approximately 50%) to feline leukemia viruses, murine leukemia viruses, and the baboon endogenous retrovirus. Such sequences are known to transfer frequently between the soma of species and occasionally enter the germline [17,18,19,20]. It is interesting that one of the three main branches of Old World monkey PTERV1 may actually share a monophyletic origin with the gorilla and chimpanzee elements. One possible scenario may be that this retrovirus was introduced into the great ape lineages by horizontal transmission, perhaps from contact with an ancient Old World monkey species.
Our data support a model where ancestral chimpanzee and gorilla species were infected independently and contemporaneously by an exogenous source of gammaretrovirus 3–4 million years ago. While similar infections with a related retrovirus appear commonplace among the Old World monkeys, contemporary human and orangutan populations show no molecular vestiges of this infection (see Figure 2). The molecular basis for this historical difference is unclear. While geographic isolation of the African and Asian ape lineages during the Miocene [30,31] might account for part of this difference, the ancestral habitat of early hominids is generally thought to have overlapped, in part, with the African apes [32,33]. Furthermore, both Asian (macaque) and African (baboon) Old World monkeys show evidence of PTERV1 proviral integrations less than 2 million years ago, indicating that the exogenous source virus is either endemic to both continents or that ancestral populations frequented both continents.
Several speculative scenarios may be envisioned to explain the absence of retrovirus in both the orangutan and human lineages. It is possible that the African apes evolved a susceptibility, or humans and Asian apes developed resistance to infection, although in either scenario convergent evolution would have had to have occurred with respect to the viral infections. Studies of the retroviral infection of the Lake Casitas mouse population reveal that such susceptibility/resistance genes may emerge very quickly among closely related strains of mice [34]. Another scenario may be that the lineage that ultimately gave rise to humans did not occupy the same habitat as the ancestral chimpanzee and gorilla lineages. An excursion by early hominids to Eurasia during the time that PTERV1 infected African great apes and then a return to Africa would explain this phylogenetic inconsistency. It is also possible that this effect may have been created by dramatic differences in ancestral population structure. If, for example, the ancestral populations of humans and orangutans were substantially larger than those of the African great apes, the fixation of new insertions (1/2N) would occur much more rapidly within small inbred populations even if similar infection rates existed. A similar model has recently been proposed, albeit in the opposite direction, to explain an increase of “apparent” Alu Ya5 and Yb8 retroposition activity in the human lineage but not in chimpanzees and gorillas [35]. In this regard, it is interesting that documented differences in the patterns of endogenous retrovirus between domesticated and feral species have been attributed to inbreeding [19,20]. There is, however, no evidence to date that the ancestral populations of chimpanzees were smaller than that of humans. Recent studies suggest that ancestral chimpanzee populations, in fact, may have been two to four times larger [36,37] than the effective human population size (greater than 10,000). A dramatic population crash in ancestral gorilla and chimpanzee populations would be required to explain the effect we have observed. Further population genetic studies of contemporary great apes or paleoanthropological work may help to eliminate these and other possible scenarios.
Finally, it is not unreasonable to assume that these ancient infections reduced effective population size if fitness of ancestral populations were compromised by the infection. Recently, such an ancient retroviral infection was predicted to have occurred in chimpanzee based on a completely separate line of reasoning. De Groot and colleagues reported a dramatic reduction of genetic variability of intronic sequence from the major histocompatibility complex (MHC) I human leukocyte antigen loci (A, B, and C) among chimpanzee when compared to human populations [38]. This is a notable exception to other studies that demonstrate 2- to 4-fold greater diversity in chimpanzee populations than in humans [39]. Based on the evolutionary age of some of the new lineages and comparisons between chimpanzee and bonobo, de Groot and colleagues estimated the loss of MHC I diversity to have occurred before subspeciation, 2–3 million years ago. Due to the central role that pathogens play in eliciting immune response against viral infections and the fact that high-frequency MHC I haplotypes target conserved epitopes of the HIV-1 virus, the authors speculated that the unusual pattern of MHC I diversity might be the result of a pandemic retroviral infection that positively selected a small number of lineages to be swept through the population. Our findings are consistent with the timing of this loss of diversity and may represent the genomic vestiges of this retroviral pandemic.
Due to its integration into the germline, this retroviral infection, thus, may have had a double impact. At a genetic level, at least 5% of the retroviral insertions would have resulted in lethality when homozygosed [22]. The 3-fold integration bias against gene insertions may represent a strong signature of this selection. This distribution is in sharp contrast to patterns of somatic retroviral infection [26,27] as well as recent class II human endogenous retroviral elements that map near or within genes [9]. In a background of reduced survival and lowered fecundity, genetic bottlenecks may have been frequent occurrences among ancestral chimpanzee and gorilla populations after speciation [33]. During this time of retroviral crisis, small subsets of retrovirus-induced mutations may have been fixed at an increased frequency. The mutation and fixation of multiple weakly deleterious mutations could, in theory, promote further saltatory and irrevocable changes in phenotypic traits among these progenitor populations. Such episodic mutational events may have simultaneously propelled species differentiation and cemented reproductive barriers between humans and the African great apes. In such a scenario greater sequence divergence over these regions might be expected because of a lack of introgression upon secondary contact among incipient species. It will be interesting to compare patterns of divergence for these sites with those for other genomic regions in humans and African great apes when genome sequence of sufficient quality becomes available.
Materials and Methods
Chimpanzee genome analysis
A computational pipeline was established to identify insertions and deletions between contiguous BAC chimpanzee genome sequence and the human genome assembly (EEE, unpublished data). Using alignment visualization software (PARASIGHT), we detected an 8.45-kb segment present in chimpanzee (AC097267 from 91,434 to 99,886) but absent in human. The sequence was not classified as a repeat (RepeatMasker, version 4.0) but six-frame translation and protein BLAST sequence similarity searches showed significant homology to retroviral sequences including gag, pol, and env proteins (see Figure 1). This family of repeat elements, PTERV1, corresponds to the largest of three retroviral repeat families that were discovered in chimpanzee genome sequence but not in humans (Chimpanzee Genome Sequencing Consortium, unpublished data). Sequence analysis of the chimpanzee genome (November 2003) identified 107 putative full-length copies and 90 solo LTR elements (see Table S7). Since most full-length copies were incompletely assembled, map positions were confirmed by paired end-sequence analysis (see below) in addition to BLAST sequence similarity searches.
Genomic hybridization
Primate DNA was restriction-enzyme-digested, transferred to nylon membrane, and hybridized as described previously [40]. Species included human (Homo sapiens), common chimpanzee (Pan troglodytes), bonobo (Pan paniscus), gorilla (Gorilla gorilla), orangutan (Pongo pygmaeus), siamang (Hylobates syndactylus), white-handed gibbon (Hylobates lar), Abyssinian black-and-white colobus monkey (Colobus guereza), olive baboon (Papio anubis), rhesus macaque (Macaca mulatta), and Japanese macaque (Macaca fuscata) (see Figure 2). In addition, we analyzed multiple individuals (three or four) from each ape lineage and a diversity panel of 16 humans and nine baboons. PCR-amplified products (see Table S1 for PCR oligonucleotide sequence and conditions) corresponding to the gag, env, and LTR portions of PTERV1 were used as radioactive probes as described [41]. Large-insert genomic BAC libraries from chimpanzee (RPCI-43), gorilla (CHORI-255), the olive baboon (RPCI-41), and the rhesus macaque (CHORI-250) were also hybridized. A total of 2,706 BAC clones were obtained that hybridized with PTERV1 gag, env, and LTR probes, while 6,032 clones hybridized solely with the LTR probe (see Table 1). The former were classified as full-length insertions of PTERV1, while the latter was classified as solo LTR elements.
Sequencing.
A total of 1,467 BAC clones containing full-length insertions were end-sequenced and are publicly available from GenBank. In addition, a total of 101 genomic clones (42 chimp, 25 gorilla, 20 baboon, and 14 macaque) corresponding to distinct insertion loci were comparatively sequenced from gag and env portions of the endogenous retrovirus (approximately 823 bp each). Individual clones were also subjected to LTR sequencing, and sequencing variants were analyzed using PolyPhred software [42]. All PCR products (forward and reverse reactions) were directly sequenced, using a modified dye terminator sequencing protocol [41]. Fluorescent traces were analyzed using an Applied Biosystems PRISM 3100 DNA Sequencing System (Perkin-Elmer Applied Biosystems, Norwalk, Connecticut, United States), and the quality of the sequence data was assessed with Phred/Phrap/Consed software [43,44].
Expression analysis
Gene expression differences between human and chimpanzee were assessed as previously described [28] with the following exceptions: five tissues (heart, brain, liver, testis, and kidney) were compared among five chimpanzee and six human samples using Affymetrix (Santa Clara, California, United States) HG U133plus2 arrays. Only those oligonucleotide probe sets that showed a perfect match between human and chimpanzee genomic sequence DNA were considered in this analysis (17,617/54,377). Differentially expressed genes were defined as those that met the following criteria: (i) the gene had to be expressed in all individuals from at least one species (detection p-value of less than 0.065); (ii) the gene had to show a change in expression in the same direction (change p-value [two-tailed] of less than 0.5 or greater than 0.5) in all 30 pairwise comparisons; (iii) different probe sets from the same gene did not conflict. The full dataset that we analyzed consisted of a total of 17,617 probes corresponding to 7,947 genes that showed significant levels of expression in both chimpanzee and human. About 70% of the genes (5,488) showed equivalent levels of expression between both species, while approximately 30% (2,459) showed differential patterns of expression. We identified 14 genes in which retroviral insertions had occurred within the chimpanzee lineage. Ten of these were found in the full expression dataset: four genes showed equivalent expression levels, while six showed differential patterns (largely decreases) in expression. To determine whether this 2-fold increase might be significant, we performed a simulation study. We randomly sampled ten genes from the full dataset (7,947) and calculated the number of genes for which significant differences were observed for each replicate. We repeated this 10,000 times and determined that 9,511 of the replicates showed less than six differentially expressed genes, 374 showed precisely six differentially expressed genes, and 115 showed more than six. Based on this analysis of the control dataset, we determined that enrichment was weakly significant (p = 0.0489).
Evolutionary analyses
End sequences generated from BAC clone inserts (T7 and SP6) were used to position retroviral insertions with respect to the human genome reference (Build 34). We considered only optimal placements after sequence quality rescoring (Phred Q > 25) and masking of common repeats (at least 50 bp of unmasked sequence was required to place an end sequence). We required two independent BAC clones per locus, which allowed mapping intervals to be refined and eliminated potential false positives. A total of 275/287 locations mapped to independent locations. Twelve locations could not be distinguished based on the resolving power of this mapping approach (100–150 kb). Estimates of genetic distance (pairwise deletion) were calculated using Kimura's two-parameter model (where s/v was approximately 2.0) [45]. The elevated substitution rate for PTERV1 is consistent with the total number of substitutions (K
total) being partitioned between a viral component (K
v) and a nuclear component (K
n). For protein-encoding portions of the retrovirus, the average number of synonymous (K
s) and nonsynonymous (K
a) substitutions per site was estimated using the modified Nei–Gojobori method [46]. We calculated evolutionary times of retroviral insertion based on the divergence of LTR flanks (0.13% divergence per million years and r = K/2T) [8]. Phylogenetic trees of multiple aligned sequences (ClustalW) were generated using neighbor-joining distance estimates (MEGA2). Only bootstrap values greater than 80% are indicated in the tree topology (see Figure 4). We modeled the genic distribution of retroviral insertions by random simulation of 107 full-length map positions within the human genome reference sequence. An insertion was classified as intronic if the insertion site mapped within the transcription start and transcription end of a Refseq gene annotation (920 Mb). By this measure, 29.7% (with gaps) and 32.1% (without gaps) of the genome is transcribed. Not once in 10,000 replicates was the measured gene distribution (14/107) observed. Similarly, we estimated the probability that six of the ten genes that showed significant expression differences between human and chimpanzee would have occurred by random chance.
Supporting Information
Figure S1 Southern Analysis of PTERV 1 among Primates
A panel of African great ape DNAs is compared for both the gag probe number 1 by PstI and PvuII restriction enzyme digest. Proximity of probe number 1 to the VNTR sequence and its variable copy number allows the detection of hundreds of insertion sites, showing limited intraspecific variation. Species represented include human (HSA), common chimpanzee (PTR), bonobo (PPA), gorilla (GGO), and orangutan (PPY). Below each panel, a restriction map (chimpanzee reference sequence AC097267) is presented in relation to the hybridization probe: PstI (closed circles), PvuII (open circles), and HpaII/MspI (triangles).
(475 KB PDF).
Click here for additional data file.
Figure S2 Methylation Status of PTERV1 Sequence
Methylation status of the retroviral insertions is compared between peripheral blood DNA and transformed lymphoblast cell lines for baboon (PHA), chimpanzee (PTR), and gorilla (GGO). HpaII does not digest methylated restriction enzyme sites, while MspI is insensitive. The differential patterns suggest most PTERV1 insertions are methylated.
(317 KB PDF).
Click here for additional data file.
Figure S3 Ambiguous Overlap Loci and Primate Phylogeny
The Distribution of Ambiguous Overlap Loci for Human and Great Apes Is Shown (Arcs) with Respect to a Generally Accepted Phylogeny of Catarrhine Primates
If these sites are orthologous, then at least six deletion events would have had to occur independently in orangutan and human at precisely the same positions in both genomes.
(10 KB PDF).
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Figure S4 Resolution of Ambiguous Overlap Locus (RP43–114h16)
(A) Multiple alignment of sequence flanking chimp insertion RP43–114h16 against chimpanzee whole-genome shotgun sequence reads. Precise site of retrovirus integration is indicated by the red arrow, and a 5-bp target site duplication is indicated in the blue box. Note that no chimpanzee sequence reads are contiguous across the region because of the 6-kb retrovirus insertion that extends from the insertion site (data not shown).
(B) Sequence similarity search of sequence flanking chimp insertion against macaque whole-genome shotgun sequence reads. The analysis shows that the chimpanzee insertion site is not shared with macaque. In contrast, human and macaque sequence are contiguous across this site. A macaque retroviral insertion maps to a different location within this 160-kb interval of the genome. This overlap locus is, therefore, resolved as non-orthologous.
(33 KB PDF).
Click here for additional data file.
Figure S5 Resolution of Ambiguous Overlap Locus (RP43–151j13)
(A) Multiple alignment of sequence flanking chimp retroviral insertion RP43–151j13 against chimpanzee whole-genome shotgun sequence reads. Precise site of retrovirus integration is indicated by the red arrow, and a 4-bp target site duplication is indicated by the blue box. Note that no chimpanzee sequence reads are contiguous across the region because of the 6-kb retrovirus insertion that extends from the insertion site (data not shown).
(B) Sequence similarity search of same sequence flanking chimp insertion against macaque whole-genome shotgun sequence reads. The analysis shows that the chimpanzee insertion site is not shared with macaque. Human and macaque sequences are contiguous across the chimpanzee insertion site. A macaque retroviral insertion maps to a different location in this 160-kb region of the genome. This overlap locus is, therefore, resolved as non-orthologous.
(34 KB PDF).
Click here for additional data file.
Figure S6 PTERV1 Phylogenetic Trees
Neighbor-joining phylogenetic trees were constructed for (A) env, (B) gag, and (C) LTR portions of the retrovirus independently, as described in Figure 4. Bootstrap support (n = 10,000 replicates) for individual branches is indicated in italics and is substantially lower owing to the limited number of basepairs compared within each tree (especially in Figure S6B). The general topology of each of the three trees is comparable and shows gorilla and chimpanzee retroviral insertions as a single monophyletic clade.
(47 KB PDF).
Click here for additional data file.
Figure S7 A Random Simulation of 107 Retroviral Insertions within the Human Genome
Genic space was determined as the distance encompassed from transcription start to transcription end of Refseq gene annotations (920 Mb). By this measure, 29.7% (with gaps) and 32.1% (without gaps) of the genome is transcribed. Not once in 10,000 replicates was the measured gene distribution (14/107) observed.
(13 KB PDF).
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Table S1 PCR Primers and Probes
(11 KB PDF).
Click here for additional data file.
Table S2 Mapping of Full-Length Retroviral Insertion Sites on Human Genome (Build 34)
(760 KB PDF).
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Table S3 Retroviral Map Intervals That Potentially Overlap between Species
(50 KB PDF).
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Table S4 Inter- and Intra-Specific Genetic Distance Estimation among PTERV1 Elements
(A) Genetic distance for gag–env portion.
(B) Genetic distance for LTR portion.
(13 KB PDF).
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Table S5 Distribution of LTR Mismatches for Each Species (Observed) Compared with a Poisson Distribution (Expected)
(24 KB PDF).
Click here for additional data file.
Table S6 Mapping of Solo LTR and Full-Length PTERV1 Elements on Human Genome (Build 34)
(66 KB PDF).
Click here for additional data file.
Table S7 PTERV1 Distribution in Chimpanzee Genome
(9 KB PDF).
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Table S8 The Keys (Numbers) and Their Corresponding Clones for Figure 4
(24 KB PDF).
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Accession Numbers
The sequence data described in this paper have been submitted to GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) under accession numbers AY758600–AY760022, AY760471–AY760631, AY760098–AY760470, and AY760640–AY761013.
The GenBank accession numbers for the sequences discussed in this paper are baboon endogenous retrovirus (AF142988), feline leukemia virus (AAC31801), murine leukemia virus (AAA79427), porcine endogenous retrovirus type C (CAC39617), and chimpanzee BAC basepair positions 91,434–99,886 (AC097267).
We thank the large-scale sequencing centers (Baylor College of Medicine, the Broad Institute, MIT, and the Washington University Genome Sequencing Center) for access to all finished sequence, genome assembly, and trace sequence data from the chimpanzee genome prior to publication. We are grateful to Dana Bonaminio, Alexander Alekseyenko, and Anne Morrison for technical assistance; Jerilyn Pecotte and Jeffrey Rogers for providing baboon material used in this study; and Bob Waterston and Maynard Olson for helpful comments in the preparation of this manuscript. Non-human primate materials used in the research were provided by the Southwest National Primate Research Center (P51-RR013986). KEH was supported, in part, by Developmental Biology Training Grant HD-07104–26. This work was supported by grant NIHGM58815 and an Oklahoma Foundation Grant to EEE.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. SP and EEE conceived and designed the experiments. CTY, SDM, KEH, PK, MEJ, MYE, JDM, and SZ performed the experiments. CTY, ZJ, PK, MEJ, and SZ analyzed the data. EEE wrote the paper.
Citation: Yohn CT, Jiang Z, McGrath SD, Hayden KE, Khaitovich P, et al. (2005) Lineage-specific expansions of retroviral insertions within the genomes of African great apes but not humans and orangutans. PLoS Biol 3(4): e110.
Abbreviations
LTRlong terminal repeat
MHCmajor histocompatibility complex
PTERV1
Pan troglodytes endogenous retrovirus 1
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030111SynopsisNeuroscienceRattus (Rat)The Few, the Strong: Rat Cortex Features Small Numbers of Powerful Connections Synopsis3 2005 1 3 2005 1 3 2005 3 3 e111Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits
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How is the brain wired up? Each neuron may connect with hundreds or even thousands of others, and the human brain has a hundred billion neurons. Determining the connection diagram for a whole brain is a truly daunting prospect, and currently well beyond reach. But one way into this thicket is to look for patterns in a small region. In this issue, Dmitri Chklovskii and colleagues show that in the rat visual cortex, some kinds of connection patterns are much more common and much stronger than chance would predict.
To determine the pattern of connections, the researchers placed electrodes into randomly chosen quartets of neurons near each other. They stimulated each in turn, and determined which members responded, and how strongly. Sampling over 800 such quartets, they found 931 actual connections out of a possible 8,050, for an average rate of connectivity of 11.6%. From the group of connected neurons, they then asked about reciprocal connections: what was the likelihood that, if A stimulated B, B stimulated A as well? They found that bidirectionally connected cells were four times as common as expected by chance, a pattern previously observed in other regions of cortex. They asked the same question for groups of three cells, for which there are 16 possible connection patterns. Two patterns stood out as especially significant: (1) A and B talk back and forth with each other, and both listen to C; and (2) A, B, and C all talk with one another. For four cells, although the numbers were too small for statistical analysis, a common over-represented class was chain connections, a kind of a path connecting all four cells that can be drawn without lifting the pencil from the page.
Recording multiple neurons simultaneously
Because the strength with which one neuron stimulates another can be just as important to network function as whether a connection exists at all, the authors examined connection strength as well. They found that connection strengths are distributed broadly, with some connections ten times stronger than the average connection and the strongest 17% of connections contributing half of total synaptic strength. They found that, on average, connections that were part of bidirectional pairs were about 50% stronger than unidirectional ones, and because of this, despite being fewer in number, they disproportionately contributed to the total amount of excitation in the neural network. A similar pattern was found for neuronal triplets—the most highly connected groups of neurons had the strongest connections among them.
Taken together, these results show that neural networks, at least in this portion of the rat brain, are characterized by a vocal minority of unexpectedly strong and reliable connections amidst a large number of weak ones, which suggests the strong ones may play more central roles in local computation or communication. This stands in strong contrast to the usual starting assumption of neural modelers, that connectivity is random. The exact pattern of connectivity seen here for excitatory neurons in one cortical layer (layer 5) may not be universal, and indeed, different patterns have been described in the cerebellum. Nonetheless, the essential feature seen here—“a skeleton of stronger connections in a sea of weaker ones,” as the authors put it—may be an important and common functional feature of brain wiring.
| 0 | PMC1054888 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Mar 1; 3(3):e111 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030111 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030115SynopsisEcologyInfectious DiseasesMicrobiologyVirologyZoologyEpidemiology/Public HealthHealth PolicyVirusesAnimalsForecasting the Path of a Raccoon Rabies Epidemic Synopsis3 2005 1 3 2005 1 3 2005 3 3 e115Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Predictive Spatial Dynamics and Strategic Planning for Raccoon Rabies Emergence in Ohio
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Rabies recently hit the national headlines when a Wisconsin teenager survived after showing full-blown symptoms. Even more remarkable, the girl—who was bitten by a bat—recovered after receiving a novel therapy, since doctors felt her case was too advanced for the standard rabies inoculations to work. Rabies is nearly always fatal if not treated immediately, and continues to pose a serious public health threat. Though most rabies fatalities in the United States stem from bat bites, far more people are treated for raccoon rabies.
A new strain of raccoon rabies started spreading throughout the eastern United States in the mid-1970s, after raccoons caught in Florida were released along the West Virginia–Virginia border to replenish hunting stocks. Some of the imports carried a rabies variant that caused an outbreak in local populations and has been steadily expanding ever since. In 1990, raccoons topped the list of most often reported rabid mammal.
Predicting the spread of raccoon rabies across Ohio
Controlling this re-emerging public health threat depends on predicting the spatial dynamics of the disease—where new outbreaks might occur and how the virus might spread. Toward this end, Leslie Real and colleagues work on probabilistic simulation models that calculate the effects of various factors, such as local transmission rates between townships, ecological barriers to transmission, and long-distance “translocation” rates between townships. (The deliberately released Florida raccoons were one such translocation, but raccoons have also been known to hitch rides on garbage trucks.) As reported elsewhere, these models previously accurately reflected rabies spread in both Connecticut and New York. In a new study reported in PLoS Biology, Real and colleagues apply their model to the likely spread of rabies in Ohio—a potential gateway for spread throughout the Midwest—and find that raccoon rabies could spread throughout the state in just three years.
One strategy for limiting rabies spread is to establish vaccine corridors by distributing vaccine baits—vaccine doses hidden in fishmeal—to wild raccoons. This cordon sanitaire strategy limited rabies in Ohio to sporadic cases from 1997 until 2004, when a rabid animal was detected—11 kilometers beyond the buffer zone—in northeastern Ohio. The authors had previously shown that local transmission was significantly reduced when townships were separated by geographical barriers—the Connecticut River in Connecticut and the Adirondack Mountains in New York. In modeling the likely transmission path in Ohio, the authors incorporated the likely effect of Ohio's five major rivers on transmission from local points along the Pennsylvania or West Virginia border.
Given Ohio's topography (three of its rivers run along the southern and eastern border) and a single point of emergence in the northeast, the authors adjusted their simulations to estimate the potential impact of translocations. Even without the occasional garbage truck ride, because of the lack of ecological barriers in central Ohio, the simulations predict that rabies will spread far faster in Ohio than in New York and Connecticut.
Factoring in those garbage truck rides, the scenario is considerably bleaker: rabies would take just 33 months to spread across central Ohio—compared to 48 months to cross the much smaller state of Connecticut—and cover the state in 41 months. This transmission rate—100 kilometers/year—significantly surpasses previous estimates, which range from 30 to 60 kilometers/year. The potential for such rapid spread, if unchecked, “is quite alarming,” the authors warn. But they also point out that the path of a real epidemic would likely fall somewhere between these two scenarios, given the unpredictable nature of translocations. The authors also simulated potential breech points in the vaccine corridor and found that the Ohio and Muskingum rivers halted viral advance initially. But a raccoon can certainly cross a bridge when the opportunity arises, so any delays would likely be temporary.
Given the unpredictable nature of rabies transmission—challenging efforts to identify potential leaks in vaccine corridors and sites of dispersal—the authors' simulations provide a valuable resource for anticipating alternate outbreak scenarios and preparing multiple game plans to prevent or contain them. They also indicate the best sites for establishing a new vaccine barrier. And given how fast raccoon rabies could spread, Real and colleagues make a strong case that halting its western march depends on a strategy based on early detection and high-powered intervention programs—a sensible approach for any infectious disease.
| 0 | PMC1054889 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 1; 3(3):e115 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030115 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030117SynopsisEvolutionGenetics/Genomics/Gene TherapyMicrobiologyPlant ScienceVirologyVirusesPlantsRecombination as a Way of Life: Viruses Do It Every Day Synopsis3 2005 1 3 2005 1 3 2005 3 3 e117Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Recombination Every Day: Abundant Recombination in a Virus during a Single Multi-Cellular Host Infection
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In theory, a cell's nuclear membrane guards its contents by barring access to potential foes. In reality, pathogens employ a diverse bag of tricks to circumvent this barrier. The murine leukemia virus (a retrovirus), for example, waits until the nuclear membrane degrades during cell division. Other retroviruses, like HIV and so-called pararetroviruses, enlist protein escorts that help them slip through undetected.
Pararetroviruses include both animal viruses, such as hepatitis B, and plant viruses, such as the cauliflower mosaic virus (CaMV). Once inside the nucleus, the double-stranded DNA genome of the CaMV is transcribed into an RNA transcript (called 35S RNA), thanks to the activity of the 35S promoter. (This CaMV promoter is widely used to drive transgenic expression in plants.) Replication proceeds through reverse transcription as a viral enzyme reverse transcribes the 35S RNA into genomic DNA that is then packaged into viral particles.
Turnip infected by cauliflower mosaic virus
During replication, genetic material can pass between different viral genomes when two viral particles infect the same host cell. These exchanges can create novel viruses, much like mutations in bacteria can produce new bacterial strains that show resistance to host defenses and antibiotics. But with little data on viral recombination rates in multicellular organisms, it's unclear how these recombinant viral genomes are influencing host infection. In a new study, Yannis Michalakis and colleagues follow the course of the cauliflower mosaic viral infection in one of its natural hosts, the turnip plant (Brassica rapa), to measure the frequency of viral recombination. Recombination was evident in over half of the recovered viral genomes, suggesting that recombination is routine for this plant virus.
It's thought that CaMV recombination occurs mostly outside the nucleus, in the host's cytoplasm, during reverse transcription. To quantify the frequency of such events, Michalakis and colleagues generated a CaMV genome with four genetic markers and then infected 24 turnip plants with equal amounts of marked and unaltered viruses. Recombination between the two “parent” genomes would produce viral populations with genetic material from both parents. The plants were harvested when full-blown symptoms developed, 21 days after inoculation, and viral DNA was extracted from their leaves to evaluate the occurrence and frequency of recombination.
Assuming that all marker-containing genomes could recombine, the authors predicted that the viruses should produce seven classes of recombinant genotypes, which is what they found. These recombinant genotypes showed up in over 50% of the viral populations—which the authors call an “astonishingly high” proportion. Though little information exists on the length of viral replication cycles in plants, the authors assumed a generation time of two days, which would amount to ten replication cycles over the 21-day experimental period. From this assumption, the authors calculated the recombination rate on the order of 4 × 10−5 per nucleotide base per replication cycle—hardly a rare occurrence. Certain CaMV genomic regions have been predicted as recombination hot spots, but the authors found that the virus “can exchange any portion of its genome… with an astonishingly high frequency during the course of a single host infection.”
By evaluating the recombination behavior of a virus in a living multicellular organism, Michalakis and colleagues created a realistic approximation of recombination events during infection in the field. And since recombination events are linked to both expanded viral infection and increased virulence, understanding the rate of recombination could help shed light on mechanisms underlying the evolution and pathology of a virus—insight that could prove critical for developing methods to inhibit or contain an infection.
| 0 | PMC1054890 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Mar 1; 3(3):e117 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030117 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030126SynopsisBioinformatics/Computational BiologyEvolutionGenetics/Genomics/Gene TherapyVirologyHomo (Human)PrimatesThe Chimp Genome Reveals a Retroviral Invasion in Primate Evolution Synopsis4 2005 1 3 2005 1 3 2005 3 4 e126Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Lineage-Specific Expansions of Retroviral Insertions within the Genomes of African Great Apes but Not Humans and Orangutans
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It's been known for a long time that only 2%–3% of human DNA codes for proteins. Much of the rest of our genomes—often referred to as junk DNA—consists of retroelements: genomic elements that are transcribed into RNA, reverse-transcribed into DNA, and then reinserted into a new spot in the genome. Human endogenous retroviruses make up one class of these retroelements. Retroviruses can insinuate themselves into the host's DNA in either soma (nonreproductive cells) or the germline (sperm or egg).
If the virus invades a nonreproductive cell, infection may spread, but viral DNA will die with the host. A retrovirus is called endogenous when it invades the germline and gets passed on to offspring. Because endogenous retroviruses can alter gene function and genome structure, they can influence the evolution of their host species. Over 8% of our genome is made of these infectious remnants—infections that scientists believe occurred before Old World and New World monkeys diverged (25–35 million years ago).
In a new study, Evan Eichler and colleagues scanned finished chimpanzee genome sequence for endogenous retroviral elements, and found one (called PTERV1) that does not occur in humans. Searching the genomes of a subset of apes and monkeys revealed that the retrovirus had integrated into the germline of African great apes and Old World monkeys—but did not infect humans and Asian apes (orangutan, siamang, and gibbon). This undermines the notion that an ancient infection invaded an ancestral primate lineage, since great apes (including humans) share a common ancestor with Old World monkeys.
Eichler and colleagues found over 100 copies of PTERV1 in each African ape (chimp and gorilla) and Old World monkey (baboon and macaque) species. The authors compared the sites of viral integration in each of these primates and found that few if any of these insertion sites were shared among the primates. It appears therefore that the sequences have not been conserved from a common ancestor, but are specific to each lineage.
PTERV1 contains three structural genes—gag, pol, and env—and regulatory sequences called long terminal repeats (LTRs). To further explore the evolutionary history of the retroviral elements, the authors compared the sequences of gag and pol, as well as the LTR sequences, for each infected primate species. The sequence history, they discovered, did not comport with the established evolutionary history of the primates themselves. Divergence between macaque and baboon was significantly greater than between gorilla and chimp—even though slightly more evolutionary time separates gorilla and chimp than macaque and baboon.
When a retrovirus reproduces, identical copies of LTR sequences are created on either side of the retroviral element; the divergence of LTR sequences within a species can be used to estimate the age of an initial infection. Eichler and colleagues estimate that gorillas and chimps were infected about 3–4 million years ago, and baboon and macaque about 1.5 million years ago. The disconnect between the evolutionary history of the retrovirus and the primates, the authors conclude, could be explained if the Old World monkeys were infected by “several diverged viruses” while gorilla and chimpanzee were infected by a single, though unknown, source.
Phylogenetic tree of retroviral insertions in primates
As for how this retroviral infection bypassed orangutans and humans, the authors offer a number of possible scenarios but dismiss geographic isolation: even though Asian and African apes were mostly isolated during the Miocene era (spanning 24 to 5 million years ago), humans and African apes did overlap. It could be that African apes evolved a susceptibility to infection, for example, or that humans and Asian apes evolved resistance. A better understanding of the evolutionary history and population genetics of great apes will help identify the most likely scenarios. And knowing how these retroviral elements infiltrated some apes while sparing others could provide valuable insights into the process of evolution itself.
| 0 | PMC1054891 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Apr 1; 3(4):e126 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030126 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1575206410.1371/journal.pbio.0030082Research ArticleCell BiologyDevelopmentImmunologyMus (Mouse)Basal Immunoglobulin Signaling Actively Maintains Developmental Stage in Immature B Cells Basal Ig Signaling in Immature B CellsTze Lina E
1
¤Schram Brian R
1
Lam Kong-Peng
2
Hogquist Kristin A
1
Hippen Keli L
1
Liu Jiabin
1
Shinton Susan A
3
Otipoby Kevin L
4
Rodine Peter R
1
Vegoe Amanda L
1
Kraus Manfred
4
Hardy Richard R
3
Schlissel Mark S
5
Rajewsky Klaus
4
Behrens Timothy W [email protected]
1
1Center for Immunology, University of Minnesota Medical SchoolMinneapolis, MinnesotaUnited States of America2Institute of Molecular and Cell BiologySingapore3Fox Chase Cancer Center, PhiladelphiaPennsylvaniaUnited States of America4Center for Blood Research, Harvard Medical SchoolBoston, MassachusettsUnited States of America5Department of Molecular and Cell Biology, University of CaliforniaBerkeley, CaliforniaUnited States of AmericaNemazee David Academic EditorScripps Research InstituteUnited States of America3 2005 8 3 2005 8 3 2005 3 3 e8212 10 2004 30 12 2004 Copyright: © 2005 Tze et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Two-Way Traffic in B Cell Development: Implications for Immune Tolerance
In developing B lymphocytes, a successful V(D)J heavy chain (HC) immunoglobulin (Ig) rearrangement establishes HC allelic exclusion and signals pro-B cells to advance in development to the pre-B stage. A subsequent functional light chain (LC) rearrangement then results in the surface expression of IgM at the immature B cell stage. Here we show that interruption of basal IgM signaling in immature B cells, either by the inducible deletion of surface Ig via Cre-mediated excision or by incubating cells with the tyrosine kinase inhibitor herbimycin A or the phosphatidylinositol 3-kinase inhibitor wortmannin, led to a striking “back-differentiation” of cells to an earlier stage in B cell development, characterized by the expression of pro-B cell genes. Cells undergoing this reversal in development also showed evidence of new LC gene rearrangements, suggesting an important role for basal Ig signaling in the maintenance of LC allelic exclusion. These studies identify a previously unappreciated level of plasticity in the B cell developmental program, and have important implications for our understanding of central tolerance mechanisms.
Gene rearrangement is a hallmark of B cell maturation. By interrupting basal cell signaling through the rearranged IgM receptor, immature B cells "back-differentiate" to an earlier stage in their development
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Introduction
B lymphocytes follow a highly ordered program of development in the bone marrow (BM), beginning with the commitment of lymphoid progenitors to the B lineage and the somatic recombination of heavy chain (HC) immunoglobulin (Ig) alleles [1]. Following an initial diversity (DH) to joining (JH) gene segment rearrangement, usually on both alleles, pro-B cells then rearrange one of many upstream variable (VH) region segments to the D-JH segment, creating the V(D)J joint. These rearrangements require the action of the lymphoid-specific recombination activating genes Rag1 and Rag2, together with a number of ubiquitously expressed DNA repair proteins [2]. Cells with a productive protein-encoding HC rearrangement express HC together with invariant surrogate Ig light chains VpreB and lambda 5 (λ5), and then undergo clonal expansion before efficient initiation of rearrangements at light chain (LC) loci (kappa, κ, or lambda, λ) [3]. A productive LC rearrangement results in the cell surface expression of IgM, which defines the immature B cell stage (IgM+IgD−).
Due to the stochastic nature of V(D)J recombination, B cells express an extremely diverse Ig receptor repertoire (more than 109 specificities). To reduce the potential for autoimmune antibody responses, cells bearing strongly self-reactive Ig receptors are tolerized, either by clonal deletion, functional inactivation through the induction of anergy, or by receptor editing where new LC rearrangements revise the antigen (Ag) specificity of the receptor [4,5]. The maintenance of tolerance also requires that individual B cells express a single Ig HC and LC, since cells bearing multiple receptors could have significant autoimmune potential. In addition, cells bearing receptors in which the two antibody binding sites are not identical would have a reduced ability to bind certain antigens, which could, in turn, compromise downstream antibody effector functions such as complement activation [6].
The process by which cells express a single receptor is called allelic exclusion [3], with a functional Ig rearrangement likely providing a “stop” signal that blocks further rearrangements. In general, the mechanisms that initiate and maintain allelic exclusion are not well understood. HC allelic exclusion requires the expression of a functional membrane-bound HC protein, since mice lacking the Cμ transmembrane domain show a complete block in B cell development at the pro-B stage, and B cells fail to establish HC allelic exclusion [7]. HC allelic exclusion also requires the Ig receptor-associated signaling proteins Igα and Igβ [8,9,10,11]. Less is known about the signaling requirements for LC allelic exclusion, where the situation is complex due to the presence of two κ and two λ alleles and the potential for multiple rearrangements at each locus. LC receptor editing occurs in immature B cells with self-reactive Ig receptors, and continues until a suitable receptor is formed, whereupon further rearrangements are suppressed. Recent studies indicate that receptor editing at LC loci is a common theme in normal B cell development, occurring in approximately 20% or more of B cells during their maturation [12].
Despite the importance of receptor editing in shaping the B cell immune repertoire, our understanding of the mechanisms that drive editing are rudimentary. It is clear that Rag proteins can be re-induced in immature B cells following B cell receptor (BCR) crosslinking by self-antigen, and that this can lead to new rearrangements at LC loci [13,14]. The prevailing view is that positive signaling through crosslinked BCRs drives the editing response. However, in experiments investigating LC receptor editing responses to soluble self-antigen, we found an inverse relationship between levels of surface IgM and LC editing (i.e., low levels of IgM associated with high levels of editing) [15]. These data were consistent with the hypothesis that tonic signals provided by surface BCRs are important for suppressing editing responses in immature B cells. The experiments here were designed to examine this possibility, and the results suggest that basal signaling from the BCR is critical for immature B cells to suppress Rag expression and, surprisingly, to maintain developmental stage.
Results
Model for Inducible Deletion of Surface IgM in Immature B Cells
For the initial series of experiments, we used transgenic mice specifically generated to examine the consequences of inducible deletion of the BCR on B cell function [16]. These animals carry a “floxed” B1-8 HC knock-in allele (B1-8f, flanked by LoxP recombination sites), a 3-83κ LC knock-in allele (3-83κ) [17], and an interferon (IFN)-inducible Cre recombinase transgene (Mx-Cre) [18]. In previous experiments, in vivo treatment of mice carrying these transgenes with type I IFN led to the induction of Cre, followed by the efficient deletion of the B1-8f HC and loss of BCR surface expression. These studies demonstrated that the survival of mature B cells was critically dependent on basal signaling by the BCR [16].
Bone marrow from B1-8f/3-83κ/Mx-Cre and control B1-8f/3-83κ mice was incubated in vitro with interleukin-7 (IL-7), a potent growth factor for early B cell progenitors, for 5 d. At the end of the culture period, flow cytometry revealed an expanded population of IgM+IgD− immature B cells that expressed the B1-8 IgMa allotype and the 3-83κ LC [recognized by the S27 antibody (Figure 1A)]. All of the IgMa allotype-positive cells also stained with an anti-B1-8 idiotype antibody (data not shown). Additional cell surface profiling of these cells is presented in box 4, A small population of B220+ cells was negative for the BCR in both cultures.
Figure 1 Inducible Cre-Mediated Deletion of the B1-8f HC Allele Leads to Loss of Surface Ig from Immature B Cells
(A) Flow cytometry showing surface phenotype of B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre BM after 5-d IL-7 culture.
(B) Flow cytometric analysis of B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre BM culture cells incubated with IFNαβ (1,000 or 5,000 units/ml), in the absence of IL-7, for 1, 2, or 3 d. The cell populations shown are gated on lymphocytes by forward and side scatter, and then for B220. At the end of the 5-d IL-7 culture, more than 90% of the cells were viable and B220+. The numbers shown indicate the percentage of gated cells.
Cells were then washed and incubated with either 1,000 or 5,000 units/ml of IFN αβ for 1, 2, and 3 d, in the absence of IL-7. There was efficient IFN-dependent deletion of surface IgM in B1-8f/3-83κ/Mx-Cre B cells that was apparent by day 1 and was approximately 90% complete at day 3 (Figure 1B). Deletion was more efficient with 5,000 units/ml of IFNαβ than with 1,000 units/ml. Control experiments demonstrated that the cells lacking IgM receptor derived from the IgMhi population following Cre-mediated deletion of HC (described in detail below).
Microarrays to Evaluate Gene Expression in Cells Undergoing Inducible Deletion of IgM
IFN-treated B1-8f/3-83κ IgMhi (Ctrl-Mhi), B1-8f/3-83κ/Mx-Cre IgMhi (Cre-Mhi), and B1-8f/3-83κ/Mx-Cre IgM-deleted (Cre-Mlo) cell populations were sorted from 2-d IFN cultures (Figure 2A), total RNA was isolated, and biotinylated cRNA probes were generated (see Materials and Methods). Probes were hybridized with Affymetrix U74A mouse GeneChip microarrays, which represent approximately 11,000 transcripts, and expression levels were quantified using Affymetrix MicroArray Suite 5.0 software. The data from each array were scaled to correct for minor differences in overall array hybridization intensity, and analyzed to identify genes differentially expressed between Ctrl-Mhi and Cre-Mlo cells.
Figure 2 Microarray Gene Expression Analysis Demonstrates Co-Clustering of Cre-Deleted IgMlo Cells with IgM− Cell Populations
(A) FACS sorting strategy for Ctrl-Mhi, Cre-Mhi, and Cre-Mlo immature B cells incubated with IFNαβ (1,000 units/ml) for 2 d. The numbers shown indicate the percentage of gated cells.
(B) Affymetrix microarrays were used to identify genes differentially expressed between IFN-treated Ctrl-Mhi and Cre-Mlo cells. Analysis identified 327 transcripts that met the following criteria: 2-fold or greater change in mean expression level, a more than 200-unit difference in mean expression values, and Student's t-test p < 0.01. Individual expression values for each gene were divided by the mean of expression levels for three control IgM+ cell populations: IFN-treated Ctrl-Mhi; IgMhi cells sorted from 5-d IL-7 HEL-lg BM cultures (HEL-Mhi); and sorted from normal Balb/c BM (FxE). Other populations included Cre-Mhi, Hardy Fraction D pre-B cells (FxD) sorted from normal Balb/c BM, and lgM− cells sorted from 5-d IL-7 cultures of control B6 BM (B6-M−). Data were transformed into log2 space, and represent fold-differences relative to the IgM+ cell populations (see scale bar). Data from 293 transcripts (duplicates and all but four representative Ig HC and LC transcripts removed) were clustered and visualized using CLUSTER and TREEVIEW [51]. Red represents genes expressed at higher levels, while green represents genes expressed at lower levels, than the mean of IgM+ cells. Each column represents an individual sorted cell population.
Analysis of the array data confirmed that Ig HC gene expression dropped dramatically in the Cre-Mlo cell population (Table 1; two representative probesets are shown), while κ LC gene expression showed a significant approximately 2-fold up-regulation in Cre-Mlo cells. Strikingly, Cre-Mlo cells had elevated levels of mRNA for Rag1 and Rag2, the DNA repair enzyme Ku70, the surrogate light chains VpreB and λ5, as well as the terminal deoxynucleotidyl transferase (TdT), an enzyme thought to play a particularly important role in the generation of junctional diversity of B cell HC alleles.
Table 1 Representative Gene Expression in Cells Undergoing Cre-Mediated Deletion of IgM
a Each value represents the mean expression level from four independent experiments
b Value of 20 is assigned to transcripts with no detectable expression by Affymetrix algorithms
Surface BCR Expression Is Required to Maintain B Cell Developmental Stage
Further examination of the microarray data revealed a large number of genes that were differentially expressed between Ctrl-Mhi and Cre-Mlo cells. A total of 327 transcripts met the following criteria: a 2-fold or greater difference in expression levels, expression value difference more than 200 units, and T-test p < 0.01. In order to better understand the significance of the gene expression changes observed between Ctrl-Mhi and Cre-Mlo cells, we prepared cRNA probes and performed microarray analyses on several additional sorted, control cell populations: (a) IgM− pro-B cells from 5-d IL-7 cultures of BM from nontransgenic C57Bl/6J mice (B6-M−); (b) IgMhi cells from 5-d IL-7 BM cultures from hen egg lysozyme (HEL) Ig transgenic mice [19] (HEL-Mhi); (c) Hardy fraction (Fx) [20] “D” pre-B cells (B220+CD43−IgM−) from normal Balb/c BM (FxD); and (d) Fx “E” immature B cells (B220+IgM+IgD−), also from normal Balb/c BM (FxE).
To visualize the microarray data, expression values for each transcript were first converted to fold-difference ratios by dividing each value by the mean expression level for the IgMhi cell populations (Ctrl-Mhi, HEL-Mhi, and FxE). These ratios were then converted into log2 space, and the data were analyzed by unsupervised hierarchical clustering.
Visualization of the data showed that approximately 60% of the genes were overexpressed (red in Figure 2B) in Cre-Mlo relative to Ctrl-Mhi or Cre-Mhi cells, while approximately 40% were down-regulated (green in Figure 2B). Strikingly, the Cre-Mlo cells clustered with normal pre-B cells (FxD) and with surface IgM-negative cells (B6-M−), rather than with the IgM+ cell populations. This suggested the possibility that Cre-Mlo cells had differentiated back to an earlier stage in B cell development as a consequence of losing IgM receptor expression.
Of the genes that were differentially expressed between Ctrl-Mhi and Cre-Mlo, many are developmentally regulated during normal B cell differentiation (Figure 3 and Table 1). In addition to the gene expression changes noted above, Cre-Mlo cells showed up-regulated mRNA levels for CD43 and the IL-7 receptor (IL-7R), genes selectively expressed in pro-B cells. Cre-Mlo cells also showed elevated levels of mRNA for the cell surface proteins insulin-like growth factor II receptor, CD98, Notch1, transforming growth factor β receptor, and the thrombin receptor; the intracellular signaling molecules serine-threonine kinase 3, protein phosphatase 2Cβ, Son of Sevenless 2, and mitogen-activated protein kinase; and transcription factors such as B lymphocyte-induced maturation protein (BLIMP), c-jun, sex determining region Y-box 4, lymphoid enhancer factor 1, myb, and elk4. Many of the genes expressed at high levels in Cre-Mlo cells were also expressed at elevated levels in FxD and B6-Mneg cell populations, compared with the lower expression levels found in the various IgM+ populations (Figure 3).
Figure 3 Genes Differentially Expressed between Ctrl-Mhi and Cre-Mlo Cell Populations
Shown are representative genes that were generally either (A) up-regulated or (B) down-regulated in Cre-Mlo cells compared with Ctrl-Mhi cells. See Figure 2 legend for details.
A similar situation held for genes that were down-regulated in Cre-Mlo cells compared to Ctrl-Mhi. Transcripts for a large number of surface markers that characterize mature B cells (CD20, CD22, CD23, CD82, CD83, integrin β7, paired-Ig-like receptor-A3 and -B, Mac-2, CXCR5, Fcγ receptor IIB, and ICAM-1/CD54) were expressed at significantly lower levels in Cre-Mlo than in Ctrl-Mhi or Cre-Mhi cells. Other genes down-regulated in Cre-Mlo cells included those encoding class II major histocompatibility complex (MHC) molecules, intracellular signaling molecules (e.g., Vav, Src homology 2 phosphatase-1 (SHP-1), the inositol 1,4,5-trisphosphate receptor, phosphatidylinositol phosphate 5-kinase, MAP4K, mitogen- and stress-activated protein kinase 2, hemopoietic cell kinase, phosphodiesterase 7A, and protein kinase Cγ), and adaptor proteins (e.g., SH3-domain binding protein-2 and -5, and LNK). Finally, the mRNAs for several interesting nuclear proteins were also down-regulated in Cre-Mlo cells, including the transcription factors Oct-2, class II transactivator (CIITA), and early growth response 1, as well as the cell cycle regulators cyclin D2 and cyclin-dependent kinase inhibitor 1a. Nearly all of the down-regulated genes in Cre-Mlo cells were also expressed at low levels in B6-M− pro-B cells and FxD cells. From these data, we conclude that the B cell developmental program has shifted into reverse in immature B cells that have undergone inducible loss of surface IgM expression.
Cre-Mlo Cells Derive from Cre-Mhi Cells
It was important to rule out the possibility that the back-differentiation observed in Cre-Mlo cells might be an artifact of selective expansion and/or survival of IgM− cells that were present at the initiation of the IFN cultures. Cell counts throughout the culture period revealed that the overall number of viable cells was not significantly different between control and BCR-deleting populations (Figure 4A). This was confirmed by similar annexin V and 7-amino-actinomycin D (7-AAD) flow cytometric staining profiles (box 4 (nd data not shown). CFSE [5(6)-carboxyfluorescein diacetate succinimidyl ester] labeling indicated that cells cultured with IFNαβ were not proliferating at significant levels (Figure 4B). We also isolated highly purified populations of IgM+ immature B cells prior to incubation with IFN, and found that these cells similarly underwent the back-differentiation response (Figure S1. Thus, these experiments ruled out a selective expansion of pre-existing IgM− cells in the IFN-treated B1-8f/3-83κ/Mx-Cre cultures.
Figure 4 Gene Expression Phenotype of Cre-Mhi Cell Populations Is Intermediate between Ctrl-Mhi and Cre-Mlo Populations
(A) Viable cell counts were performed in control B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre cell populations at the initiation of culture with medium alone (nil) or with IFNαβ (IFN) 1,000 units/ml, and daily for 3 d. Data are presented as percent of the day 0 cell numbers, and represent 4–7 experiments for each population. Standard errors were less than 10%, and are not shown.
(B) BM cells were cultured for 5 d in IL-7, and were then labeled with CFSE and incubated with 1,000 units/ml IFNαβ or, as a proliferation control, 16 ng/ml IL-7 for an additional 3 d. Flow cytometric analysis of CFSE dye dilution of B220+ cells indicated no significant proliferation of IFN-treated cell populations. Numbers represent percent of gated cells.
(C) A PCR-based assay was used to quantitate the extent of B1-8f deletion in Ctrl-Mhi, Cre-Mhi, and Cre-Mlo populations. Ctrl-Mhi cells contained 100% intact B1-8f alleles, Cre-Mlo cells were 100% deleted, and Cre-Mhi cells had 34 ± 16% average deletion of the B1-8f allele. Expression values for the Ctrl-Mhi transcripts (significantly different between Ctrl-Mhi and Cre-Mlo) were normalized to 1, and relative expression levels of transcripts up- (n = 184) and down-regulated (n = 143) in the three populations were calculated. In both groups of genes, Cre-Mhi cells showed intermediate levels of gene expression between Ctrl-Mhi and Cre-Mlo, indicating that Cre-Mlo cells originated from the Cre-Mhi population.
Additional strong evidence that Cre-Mlo cells were derived from Cre-Mhi cells was the observation that Cre-Mhi cells showed an “intermediate” gene expression phenotype between Ctrl-Mhi and Cre-Mlo cell populations. As shown in Table 1, the level of transcript expression for most of the genes in Cre-Mhi cells was intermediate between Ctrl-Mhi and Cre-Mlo cell populations. Using a PCR-based assay (see Materials and Methods), we determined that about a third of the cells in the Cre-Mhi population (mean 34 ± 16%, range 14–48%) had undergone deletion of the floxed B1-8f allele (Figure 4C). When we compared the 327 transcripts that best discriminated Cre-Mlo from Ctrl-Mhi cells (see Figure 2B), the “intermediate” gene expression phenotype of Cre-Mhi cells was generally observed across the entire spectrum of genes (Figure 4C). The difference in gene expression between Cre-Mhi cells and the other populations was highly statistically significant (Figure 4C).
Reversal in Differentiation Is Confirmed by Analysis of Cell Surface Proteins
The changes in gene expression in Cre-Mlo cells were also reflected at the level of protein expression (Figure 5). After 3–4 d of culture, flow cytometry demonstrated that Cre-Mlo cells up-regulated surface expression of the activation antigens CD69 and CD86, as well as the IL-7R. Proteins down-regulated to varying degrees included B220, integrin β7, CD22, CD21/35, CD24, CD54, PirA/B, and Class II IA/IE. A few of the genes that showed differences at the mRNA level did not show measurable differences in protein expression (e.g., CD23). In nearly every case, the up- or down-regulation of surface protein expression mirrored the changes in mRNA expression. Importantly, the cells within each culture showed a similar cell surface phenotype, confirming that the back-differentiation was occurring across the entire population and not in just a minor subset. Additional control experiments supported the conclusion that back-differentiation of these cells was occurring in response to Cre-mediated loss of BCR expression, and not simply to elevated Cre levels (Figure S2 and S5).
Figure 5 Protein Expression Confirms Reversal of Development in Immature B Cells Losing Surface IgM
B1-8f/3-83κ control and B1-8f/3-83κ/Mx-Cre cell populations were harvested at the end of a 3- or 4-d culture with IFNαβ (3,000 units/ml), stained with mAbs for cell surface proteins, and analyzed by flow cytometry. Shown are the expression levels of B220-gated Ctrl-Mhi (thick line) and Cre-Mlo (thin line) cells at the end of the culture period. Essentially identical results were observed when Cre-Mhi and Cre-Mlo cells were compared (data not shown).
Herbimycin A and Wortmannin also Induce Back-Differentiation of Immature B Cells
To begin to explore the mechanisms by which basal IgM signaling maintains developmental stage, we first examined the influence of blocking tyrosine kinase signaling pathways downstream of the BCR with the inhibitor herbimycin A. For these experiments we used BM from mice carrying both a conventional Ig transgene (HEL-Ig) [19], and a bacterial artificial chromosome transgene containing green fluorescent protein (GFP) under the regulatory control of the Rag2 promoter/enhancer (Rag2-GFP) [14,21]. Previous studies have shown that the expression of GFP from this transgene reflects endogenous Rag2 protein expression in lymphocytes, but with a longer half-life [21].
Bone marrow from HEL-Ig/Rag2-GFP double transgenic (Tg) mice was cultured in IL-7 for 5 d, which generated a population of cells highly enriched for Rag2-GFP−, IgM+IgD− immature B cells (Figure 6A). Cells were then washed and incubated with the tyrosine kinase inhibitor herbimycin A or medium alone for 8 or 24 h. Herbimycin A induced strong Rag2-GFP expression, with about half of the cells GFP+ at 24 h (Figure 6B). The observed GFP response was not due to nonspecific toxicity of herbimycin A treatment, since no GFP expression was observed in the dead or dying annexin-V+ or 7-AAD+ cells in the cultures (data not shown). Incubation of cells with other tyrosine kinase inhibitors (e.g., genistein and piceatannol) also resulted in significant induction of Rag2-GFP (L. E. Tze et al., unpublished data). BCR signaling and B cell development are highly dependent on phosphatidylinositol 3-kinase (PI3K) activity at the plasma membrane [22]. Incubation of HEL-Ig/Rag2-GFP immature B cells with the PI3K inhibitor wortmannin (30 μM) led to a strong induction of Rag expression at 24 h (Figure 6C). Thus, in a single cell assay for induction of Rag protein, interruption of proximal BCR signaling pathways with either herbimycin A or a PI3K inhibitor led to a derepression of Rag2 expression.
Figure 6 Immature B Cells Undergo a Reversal in Development after Blockade of Basal Ig Signaling with Herbimycin A or Wortmannin
(A) Flow cytometric profile of HEL-Ig/Rag2-GFP BM at the end of a 5-d IL-7 culture. Cells are electronically gated by forward and side light scatter, and represent all lymphoid cells in the culture. The majority of cells were Rag-GFP−IgM+IgD− immature B cells.
(B) After a 5-d IL-7 culture, HEL-Ig/Rag2-GFP cells were washed and incubated with medium alone (containing DMSO carrier) or with 300 ng/ml herbimycin A for 8 or 24 h. Cells were then analyzed by flow cytometry, with cells gated based on size and annexin-V exclusion. Numbers represent percent of gated cells.
(C) A representative experiment of Rag2-GFP expression levels in HEL-Ig/Rag2-GFP immature B cells incubated for 24 h with medium alone (black), herbimycin A (300 ng/ml) (green), or wortmannin (30 mM) (pink).
(D and E) HEL-Ig/Rag2-GFP BM was cultured in IL-7 for 5 d, and cells were then incubated with herbimycin A (400 ng/ml) for 1 d. IgM+GFP+ [GFPpos (D)] cells were sorted from herbimycin A treated populations, and IgM+GFP− [GFPneg (D)] cells were sorted from control cultures. In parallel, immature B cells were sorted from the BM of bcl-2 transgenic mice, and cultured with medium alone for 24 h (FxE Ctrl 1 and -2) or 48 h (FxE Ctrl 3 and 4), or with herbimycin A for 24 h (FxE HA-1, -2, -3, and -4) or 48 h (FxE HA-5 and -6). Total RNA was isolated from the cell populations and biotinylated cRNA probes were generated and hybridized to Affymetrix chips (E). All other cell populations were as described in Figure 2. The array data were clustered using the 101 transcripts from the Ctrl-Mhi/Cre-Mlo gene list that were also differentially expressed between normal FxD (pre-B) and FxE (immature B) cells (using the following criteria: 2-fold or greater change in mean expression level, a greater than 200-unit difference in mean expression values, and Student's t-test p < 0.05). HC and LC transcripts were not used for this clustering analysis. The herbimycin A-treated immature B cells, both cultured and from bcl-2 Tg BM, cluster with more primitive cell populations. Data represent log2 fold-differences, with individual expression values for each gene divided by the mean expression value of the Ctrl-Mhi, HEL-Mhi, FxE, GFP−, and FxECtrl populations. Representative genes are annotated.
We then tested whether immature B cells treated with herbimycin A showed similar evidence for global back-differentiation as observed in Cre-Mlo cells. HEL-Ig/Rag2-GFP BM was grown in IL-7 for 5 d, and cells were washed and incubated either in medium alone or with herbimycin A for 24 h. Total RNA was extracted from sorted control Rag2-GFP− (GFPneg in Figure 6D) and herbimycin-treated Rag2-GFP+ (GFPpos in Figure 6D) cells and examined by microarrays. As shown in Figure 6D and 6E, the gene expression patterns of Rag2-GFP+ cells coclustered with those of Cre-Mlo, FxD, and B6-M− cells, consistent with a reversal in development of immature B cells after blockade of signaling pathways with herbimycin A.
Next, we determined whether normal polyclonal immature B cells sorted from BM were capable of moving backward in development in response to loss of basal Ig signaling. This was important because it was possible that the ability of the cultured cells to back-differentiate might, in part, be a consequence of the accelerated development experienced by B cells carrying a pre-rearranged Tg Ig receptor [23]. Because of the increased sensitivity of immature B cells to apoptosis, we sorted immature B cells (B220+IgM+IgD−) to high purity (over 95%) from the BM of mice carrying a B cell-restricted bcl-2 transgene [24]. These immature B cells were then cultured for 1 or 2 d either in medium alone (FxE Ctrl) or with herbimycin A (FxE HA), then total RNA was harvested for microarrays. Strikingly, polyclonal immature B cells treated with herbimycin A showed a reversal in development similar to that observed for the in vitro cultured Ig Tg B cells (Figure 6D and 6E), and clustered with cell populations that lacked an Ig receptor (B6-M−, FxD, Cre-Mlo). The polyclonal immature B cells did not appear to back-differentiate to quite the same degree as the cells carrying a prerearranged BCR. For instance, Rag and Ku70 genes were turned on in the polyclonal FxE cells in response to herbimycin A, similar to Cre-Mlo cells, while TdT, VpreB, and λ5 were not (Figure 6). We conclude that normal immature B cells undergo a reversal in development when treated with herbimycin A.
New LC Rearrangements in Cells Undergoing Back-Differentiation
Finally, we wished to explore the possibility that cells losing basal Ig signaling and showing subsequent induction of Rag might also lose LC allelic exclusion. GFP+ cells were sorted from 24-h herbimycin A cultures of HEL-Ig/Rag2-GFP BM, and GFP− cells were sorted from parallel control cultures. Genomic DNA was isolated and assayed for new endogenous Ig LC rearrangements using quantitative PCR. We observed strong induction of new DNA rearrangements at both κ and λ LC loci following incubation with herbimycin A (Figure 7A). In addition, double-stranded DNA signal end breaks, which are intermediates of active Rag-mediated recombination, were strongly induced in GFP+ herbimycin-treated cells (Figure 7B). Thus, the interruption of basal tyrosine kinase signaling pathways in immature B cells leads to the induction of the recombination machinery (e.g., Rag1 and Rag2) and new endogenous LC rearrangements.
Figure 7 Immature B Cells That Lose Basal Signaling Show Induction of LC Rearrangements
(A) PCR analysis of endogenous Ig light chain rearrangements (V-Jλ3, V-Jλ1, RS, and V-Jκ1) in genomic DNA of FACS-sorted HEL-Ig/Rag2-GFP BM cells incubated with medium alone or with 300 ng/ml herbimycin A for 24 h. IgMa+GFP+ cells were sorted from herbimycin A-treated cultures, and IgMa+GFP− were sorted from control cultures. Data are from three independent experiments. CD14 is a loading control. −, negative control (C57Bl/6J tail DNA); +, positive control (C57Bl/6J spleen DNA).
(B) Genomic DNA from the same cell populations described in (A) was subjected to ligation-mediated PCR to detect double-strand signal end DNA breaks at Jκ1. Controls in right three lanes of blot: H2O, control; −, negative control (S17 stroma); +, positive control (C57Bl/6 BM).
(C) Genomic DNA was extracted from B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre immature B cells 3 d following incubation with IFNαβ. Quantitative PCR analysis was used to determine the fold-induction of LC rearrangements in B1-8f/3-83κ/Mx-Cre immature B cells treated with IFN compared to medium alone. Data represent the mean ± standard deviation of two (V-Jλ1), three (V-Jλ3), or four experiments (V-Jκ1, RS).
Similar rearrangement assays were performed in cells undergoing Cre-mediated deletion of the B1-8f HC. We noted some “leakiness” of basal LC allelic exclusion in that culture system, with low levels of ongoing LC rearrangements even in the absence of deletion of the floxed Ig receptor (unpublished data). Nevertheless, cells losing BCR surface expression following Cre-mediated excision of the B1-8 HC showed a significant induction of LC rearrangements over background after 3 d of culture, ranging from 2- to 5-fold (Figure 7C). These data suggest that basal signaling through the Ig receptor in immature B cells is important for suppressing Rag gene expression and preventing further rearrangements at LC loci.
Discussion
We report here the surprising result that inducible deletion of the BCR from immature B cells using Cre–lox-mediated excision leads to a global movement of cells to an earlier stage in B cell development. Similar findings were observed in immature B cells incubated with the tyrosine kinase inhibitor herbimycin A or the phosphatidylinositol 3-kinase inhibitor wortmannin. Our interpretation of these data is that immature B cells actively maintain their stage in development by constitutive basal Ig signaling through protein tyrosine kinases (PTKs).
Current models for signal transduction in lymphocytes envision a dynamic equilibrium between membrane-proximal protein tyrosine phosphatases (PTPs) and protein tyrosine kinases (PTKs) that serves to maintain cells in a resting state [25]. The BCR-associated molecules Igα and Igβ are phosphorylated on immunoreceptor tyrosine-based activation motifs (ITAMs) by the Src family PTK Lyn [26,27]. Phosphorylated ITAMs, in turn, lead to the recruitment and activation of the PTK Syk, which activates the Tec family PTK Btk. These kinases are counterbalanced by the activity of PTPs such as SHP-1 [28]. In the experiments presented here, incubation of immature B cells with the kinase inhibitors herbimycin A or wortmannin presumably shifts the equilibrium to one dominated by PTPs, resulting in an interruption of basal tyrosine kinase activity.
Loss of tonic BCR signaling in immature B cells, either through deletion of the BCR or incubation with kinase inhibitors, led to rapid changes in gene expression, with the overall picture suggesting a reversal in differentiation to an earlier stage in B cell development. It is intriguing that many transcription factors known to be important in controlling B cell development were modulated by the interruption of basal signaling. For example, the proto-oncogene myb, known to positively transactivate the Rag2 promoter [29], was up-regulated in Cre-Mlo cells, as was BLIMP, a transcriptional repressor that shuts off many B cell genes during terminal differentiation to plasma cells [30]. Oct-2, CIITA, and Egr-1, all known to transactivate B lineage genes, were strongly down-regulated in immature B cells following BCR deletion or treatment with herbimycin. Further dissection of these genetic pathways should provide new insights into the regulatory circuits that serve to actively maintain developmental stage in immature B cells.
A number of control experiments were performed to rule out the possibility that the back-differentiation observed could have been artifactual due either to a selective expansion of IgM− cells present at the initiation of the cultures, or due to nonspecific effects of high-level Cre expression. First, in the Cre deletion experiments, cell counts, annexin V staining, and CFSE labeling demonstrated no evidence for a selective expansion or death of any subpopulation. Second, the Cre-Mhi population, in which approximately a third of the floxed alleles had been deleted, showed an intermediate gene expression phenotype between Ctrl-Mhi and Cre-Mlo, indicating that the changes in gene expression were due to changes within the IgM+ population, since these cells were sorted based on being IgMhi. Third, flow cytometry showed that cell surface protein expression was similar across the entire population of cells for most of the markers, and no small subpopulations of cells were observed that could have contributed to the changes in gene expression profiles. Fourth, at the end of the some of the IL-7 cultures, B cells were isolated to high purity (more than 99% of cells B220+, IgMinterm/hi) before treatment with IFN. After culture with IFN, these cells showed efficient BCR deletion and evidence for back-differentiation (see Figure S3). Finally, to rule out the possibility that nonspecific Cre-mediated DNA breaks at cryptic genomic loxP sites could be inducing DNA damage that was, in turn, responsible for the developmental changes observed, we showed that Cre recombinase had no effect on developmental status when expressed at high levels in control B cells lacking a floxed BCR (Figures S4 and S5). Taken together, these experiments point to a robust back-differentiation of immature B cells upon interruption of basal Ig signaling in this model system.
The Cre-Mhi cells still expressed surface IgM, yet showed early evidence for the back differentiation response (Table 1). One possibility to explain this finding is that Cre-Mhi cells have lowered levels of the BCR in intracellular compartments (e.g., endoplasmic reticulum), and that some portion of the basal signal derives from intracellular BCR complexes. Alternatively, and perhaps more likely, the basal level of signaling provided by the knock-in BCR in this model may be very close to the threshold required for suppressing Rag and the back-differentiation program, and flow cytometry is insufficiently sensitive to detect the small decreases in BCR surface expression levels that accompany the early loss of the floxed HC allele. In support of this, we note that Rag1 levels are nearly 10-fold higher in sorted Ctrl-Mhi cells (mean = 2,330 expression units) by microarray as compared to normal polyclonal immature B cells (FxE; mean = 243), and over 30-fold higher than conventional HEL-Ig Tg immature B cells (mean = 71). Similar differences were noted for Rag2. Suppression of endogenous LC rearrangements was also less efficient in the knock-in model as compared to the HEL system (Figure 7). Thus, these data are consistent with the notion that the B1-8f/3-83κ knock-in receptor provides a relatively weak basal signal, and that cells bearing these receptors are sensitive to very small changes in BCR density.
The idea that threshold signaling through the antigen receptor is required to suppress V(D)J recombination is supported by several observations in T and B cells. In cortical thymocytes, the interaction of the T cell receptor with MHC molecules down-regulates the expression of Rag1 and Rag2 and thereby shuts off further α chain rearrangements [31,32]. Interruption of this interaction with antibodies to the MHC resulted in up-regulation of Rag gene expression [33], indicating that the process is reversible in T cells. In an important series of experiments, Roose and colleagues [34] have recently shown that Jurkat T cells carrying mutations in proximal signaling molecules, including the adaptor molecules LAT and SLP76, have elevated Rag expression and altered gene expression programs. Using a panel of reconstituted Jurkat mutants and a variety of pharmacologic signaling inhibitors, the authors demonstrated that tonic signals through the ERK and Abl kinase pathways are important for suppressing Rag expression in Jurkat T cells and normal mouse thymocytes. B cells bearing a single copy of the anti-class I 3-83 HC and LC knock-in alleles showed poor LC allelic exclusion in mice lacking the cognate class I self-antigen (approximately 10% of B220+ cells idiotype-positive) [35,36]. Conversely, B cells from mice homozygous for both knock-in receptors (i.e., allelic inclusion) showed no evidence for additional LC rearrangements. One possibility is that the “dose” of receptor in mice carrying a single knock-in HC and LC allele did not provide sufficient basal signaling to suppress the recombination machinery, while two copies was sufficient. A similar Ag-receptor mediated suppression of Ig rearrangements was reported in mature IgD+ peripheral B cells treated with LPS and IL-4 [37]. These data support the hypothesis that developing B cells require threshold signals from Ig receptors expressed on the cell surface to block further Ig gene rearrangements.
The exquisite sensitivity of immature B cells to cell surface IgM basal signaling, as demonstrated by the early changes in gene expression in the Cre-Mhi population (Table 1), may be important for normal B cell development at several levels. First, this mechanism may serve as an important quality control mechanism. Cells that fail to rearrange a functional LC will continue to undergo rearrangements until an LC is produced that can be efficiently translated and stably expressed with HC at appropriate levels on the cell surface. This mechanism would also test the ability of HC and LC to pair efficiently and signal, ensuring that the B cell, once mature, can be activated in the periphery upon exposure to antigen. Second, this mechanism might be important for the selection of the naïve B cell VH/VL repertoire [38]. VL genes with strong promoters that drive high-level protein expression in immature B cells might be selectively recruited into the mature pool, while VL genes driven by weaker promoters might fail to provide sufficient basal signals and would be replaced by secondary rearrangements. Similarly, particular individual VH or VL chains, or VHVL pairs, with an enhanced ability to provide basal signaling might be relatively overrepresented in the repertoire.
Finally, we believe that these data have important implications for understanding the regulation of receptor editing in immature B cells. It has been suggested that self-Ag induced receptor editing is driven by the crosslinking of surface Ig on immature B cells, which activates Rag proteins and ultimately results in new LC rearrangements [5]. An alternative hypothesis is that cells triggered to undergo receptor editing may do so primarily in response to the down-regulation of Ig receptors following engagement of Ig with antigen. Our earlier data showing a reciprocal relationship between surface Ig density of immature B cells and receptor editing following Ig crosslinking by soluble antigen [15], and data demonstrating receptor internalization during receptor editing in immature thymocytes [39], are consistent with this idea.
We envision that some developing B cells at the immature stage will initially express high levels of the BCR, and only later during the immature stage encounter self-reactive antigens. These cells would receive a strong initial signal from self-antigen; however, the primary functional consequence of the self-antigen encounter would be the down-regulation of surface Ig via endocytosis. A sufficient loss of the basal signal would result in the re-initiation of Rag and induction of the back-differentiation program, which may be required for an efficient editing response. In support of this, we now have evidence that a back-differentiation program, similar to that observed in the models characterized by a loss of basal signaling, is initiated when immature B cells are incubated with specific antigen and undergo prolonged down-regulation of surface Ig (unpublished data). Together, these findings suggest that basal signals from the BCR control key developmental decisions at the pre-B to immature B cell transition.
Materials and Methods
Mice
Rag2-GFP transgenic mice [21] were kindly provided by Dr. M. Nussenzweig (Rockefeller University, New York, United States), and were bred with HEL-Ig transgenic mice [19] to generate double-Tg animals. The Mx-Cre transgenic [18], B1-8f [16] and 3-83κ [17] mice were intercrossed to generate B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre animals. Each mouse used in these experiments carried a single copy of the knock-in HC and LC Ig alleles. Additonal breedings produced HEL-Ig/Mx-Cre and B1-8f/3-83κ/Rag2-GFP animals. Bcl-2 mice [24] were obtained from Jackson Laboratories (Bar Harbor, Maine, United States). All mice were maintained in specific pathogen-free conditions, and were generally 4–8 wk of age at the time of the experiments.
IL-7 BM culture
Single-cell suspensions of BM cells from HEL-Ig/Rag2-GFP, B1-8f/3-83κ, and B1-8f/3-83κ/Mx-Cre mice were prepared as described [15], and placed into IL-7 BM culture [40,41,42]. Cells were cultured at a concentration of 1.5–2 × 106 cells/ml in complete medium consisting of 1:1 RPMI 1640:EHAA (Mediatech, Washington, DC, United States, and Biofluids, Rockville, Maryland, United States, respectively), 10% heat-inactivated FBS (Life Technologies, Rockville, Maryland, United States), L-glutamine (BioWhittaker, Walkersville, Maryland, United States), penicillin and streptomycin (Mediatech), in the presence of 16 ng/ml recombinant murine IL-7 (R&D Systems, Minneapolis, Minnesota, United States) for 5–7 d. Cells were then washed and re-cultured in complete medium with either herbimycin A (Life Technologies) or wortmannin (Sigma, St. Louis, Missouri, United States) for 8 or 24 h, or with mouse fibroblast IFNαβ (Sigma) or recombinant IFNβ (Biosource, Camrillo, California, United States) for 1–4 d. For some experiments, IgMhi cells were purified at the end of the IL-7 culture using the MACs bead system (Miltenyi Biotech, Bergisch Gladbach, Germany). Recombinant TAT-Cre was used as previously described [43]. For CFSE labeling, cells were incubated in 1× HBSS (BioWhittaker) and 3 μM CFSE (Molecular Probes, Eugene, Oregon, United States) for 5 min at 37 °C, washed, and then cultured in the presence or absence of IFNαβ for 1–4 d.
Flow cytometry and cell sorting
Cells harvested at the end of IL-7, herbimycin A, wortmannin, or IFNαβ cultures were stained in FACS buffer (PBS containing 2.5% FBS and 0.2% sodium azide) with FITC-, PE-, CYC-, APC-, or biotin-conjugated monoclonal antibodies to B220, IgM, IgMa, IgMb, IgDa, CD21/CD35, CD22, CD23, CD24, CD54, CD62 l, CD69, CD86, IL-7Rα, integrin β7, PirA/B, and IA/IE (BD Pharmingen, San Diego, California, United States) or 3-83κ (S27 mAb). Staining with biotinylated antibodies was revealed by SA-PE or SA-APC (BD Pharmingen). In some staining conditions, annexin-V-PE and/or 7-AAD (BD Pharmingen and Calbiochem, San Diego, California, United States) were used to exclude dead cells. For cell sorting, cells treated with IFNαβ or herbimycin A were stained with IgMa-PE and B220-CYC in staining buffer (PBS containing 10% FBS) and sorted by FACSVantage (Becton Dickinson, Mountain View, California, United States). Flow cytometry analyses were performed using CellQuest (Becton Dickinson) and Flowjo (Treestar, San Carlos, California, United States) software.
PCR analysis
Genomic DNA was extracted from unsorted BM cells treated with IFNαβ for 3 d as described [15,44], and from sorted BM cells treated with herbimycin A for 1 d or with IFNαβ for 2 d using TRIzol (Life Technologies). Genomic DNA from the equivalent of 40,000 cells, or serial dilutions thereof, was then subjected to quantitative PCR amplifications using primers specific for CD14, and degenerate Vκ primers and a primer downstream of Jκ1 as described [45,46], with slight modifications for the V-Jκ1 PCR conditions: 45 s at 94 °C, 1 min at 63 °C, and 1.5 min at 72 °C for 27 or 30 cycles [15]. RS recombinations were detected with the following primers: forward primers VDEG1, 5′-
GCGAAGCTTCCCTGATCGCTTCACAGGCAGTGG-3′; VDEG2, 5′-
GCGAAGCTTCCCW
GCTCGCTTCAGTGGCAGTGG-3′; and VDEG3, 5′-
GCGAAGCTTCCCAKM
CAGGTTCAGTGGCAGTGG-3′; and reverse primerRS3′, 5′-
CTCAAATCTGAGCTCAACTGC-3′. The following cycling conditions were used: 45 s at 94 °C, 1 min at 64 °C, and 1 min at 72 °C for 27 or 30 cycles [23]. V-Jλ rearrangements were detected with: forward primer, 5′-
AGGCTGTTGTGACTCAGGAATCTGCA-3′, and reverse primer (JN) 5′-
ACTTACCTAGGACAGTGA-3′ or (J16) 5′-
ACTCACCTAGGACAGT-3′ using the following cycling conditions: 30 s at 94 °C, 1 min at 62 °C, and 1.5 min at 72 °C for 30, 33, or 36 cycles [47,48]. PCR products were resolved on 1% or 1.5% agarose gels and visualized with ethidium bromide staining. Specificity of bands was confirmed by Southern hybridization of nitrocellulose blots with radiolabeled internal oligonucleotides. Double-stranded signal end DNA breaks at Jκ1 were identified as described [49].
The extent of deletion of the B1-8f allele was quantified using a PCR-based assay to amplify across the 3′ loxP site. TRIzol-extracted genomic DNA (5 ng) from sorted cell populations was amplified with: forward primer, 5′-
GAAAGTCCAGGCTGAGCAAAACACCAC-3′; and a reverse primer conjugated with 6-FAM (Integrated DNA Technologies, Coralville, Iowa, United States), 5′-
GGAGACCAATAATCAGAGGGAAGAATAATA-3′. The cycling conditions were 15 cycles of 15 s at 95 °C, 30 s at 55 °C, and 1 min at 72 °C, followed by another 12 cycles of 15 s at 89 °C, 30 s at 55 °C, and 1 min at 72 °C. PCR product for the wild-type allele was 295 bp in length, while the product from the B1-8f allele was 378 bp. The extent of deletion was calculated by the loss of the B1-8f allele. These results were confirmed using the same reverse primer with a forward primer upstream of the 5′ loxP site. The assay was validated using Ctrl-Mhi cells (100% intact B1-8f allele) and Cre-Mlo cells (100% deleted B1-8f allele). PCR products were resolved by electrophoresis on a 3100 Genetic Analyzer with GeneScan-500 ROX size standards (Applied Biosystems, Foster City, California, United States). Quantitation of product intensities was performed using GeneScan Analysis 3.7 (Applied Biosystems).
Microarray gene chip analysis
Total RNA from sorted BM cells treated with IFNαβ or herbimycin A was extracted using TRIzol (Life Technologies) and further purified using an RNAeasy kit (Qiagen, Valencia, California, United States). Total RNA was then subjected to double-stranded cDNA synthesis following the standard Affymetrix protocol (Expression Analysis Technical Manual P/N 700218 rev. 2, Affymetrix, Santa Clara, California, United States). FxD and FxE samples were collected from independent sorts (each sample approximately 2 × 105 cells) and total RNA extracted using TRIzol as described above. Total RNA was then subjected to two rounds of amplification to generate cRNA probes for hybridization, essentially as described [50]. Biotin-labeled cRNA probes (generated using High Yield RNA Transcript Labeling Kit, Enzo Diagnostics, Farmingdale, New York, United States) were chemically fragmented, hybridized to murine U74A and U74Av2 probe arrays (Affymetrix), and scanned at the University of Minnesota Biomedical Genomics Center facility. The U74Av2 probe arrays were subjected to U74A mask corrections, and all target hybridization intensities were scaled to 1,500 arbitrary units using Microarray Suite 5.0 (Affymetrix). Four independent sorts were performed for each of the following samples: Ctrl-Mhi, Cre-Mhi, and Cre-Mlo; two or three sorts were performed for all other samples. Data with probe-set detection p-values less than 0.1 were included in the statistical analysis, and individual probe-set measurements with detection p-values greater than 0.1, and/or signal intensity values 20 or less, were set to a value of 20. We performed all our statistical analyses using MS Excel (Microsoft Office X). To identify genes that were differentially expressed between the Ctrl-Mhi and Cre-Mlo populations, we used the Student's t-test analysis with the assumptions that the sample groups followed a two-tailed distribution and had unequal variance. For visualization of the arrays, each expression value was divided by the mean of the expression values for the relevant IgM+ samples. These ratios were transformed into log2 space, and subjected to centered-average linkage clustering using CLUSTER and visualized by TREEVIEW software [51].
Supporting Information
Figure S1 Cell Surface Profile of B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre B Cells
B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre B cells were collected at the end of 5-d IL-7 cultures, stained with labeled mAbs, and analyzed by flow cytometry. Profiles are compared with wild-type B6 mature IgM+B220hi splenic B cells, and the various compartments of B cells in BM. Second row, IgMhiB220hi recirculating mature B cells; third row, IgM+B220interm immature B cells; and fourth row, IgM−B220lo pro- and pre-B cells).
(1.6 MB JPG).
Click here for additional data file.
Figure S2 Annexin V Staining of B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre B Cells After IFN Culture
BM from control B1-8f/3-83κ and B1-8f/3-83κ/Mx-Cre mice was grown in IL-7 for 5 d. Cells were then washed, and treated with either medium alone or IFNαβ 1,000 units/ml for 1, 2, or 3 d. Cells were harvested and stained for B220, IgMa, and annexin V, and analyzed by flow cytometry. Shown are B220-gated cells. Similar results were obtained using 7-AAD in parallel to detect dead cells. Representative of five experiments.
(2 MB JPG).
Click here for additional data file.
Figure S3 Back-Differentiation of Highly Purified IgMhi Cells
To further address the issue of possible outgrowth or selective survival of IgMlo cells present at the initiation of the IFN cultures, we isolated purified populations of IgMhi (A) B1-8f/3-83κ and (B) B1-8f/3-83κ/Mx-Cre B cells at the end of the 5-d IL-7 cultures by first staining with predetermined optimal concentrations of biotinylated anti-IgM Abs, then isolating cells with streptavidin MACs beads. This reduced the IgMlo population to approximately 0.6% of the B220+ cells prior to the secondary IFN cultures (compare postcolumn with precolumn flow profiles). Purified cell populations were incubated with either medium alone or 5,000 units of IFN for 2 d on irradiated S17 feeder cells, and then analyzed by flow cytometry. Numbers refer to percent of viable gated lymphocytes. IFN induced efficient Cre-mediated deletion of the BCR, and the IgMlo cells in the cultures showed evidence for a reverse in differentiation as determined by elevated IL-7Rα expression and diminished CD22 expression levels. We noticed higher levels of deletion of the BCR in the medium-alone treated cells (in this experiment, 10.1%) compared with earlier experiments, likely due to crosslinking of IgM during the column purification step, and subsequent low level induction of Cre. There was approximately a 30% overall cell loss during the IFN culture and no cell proliferation (data not shown). We conclude that the IgMlo cells present after the IFN culture began as immature IgMhi cells. Representative of three experiments.
(369 KB JPG).
Click here for additional data file.
Figure S4 TAT-Cre Treated HEL-Ig B Cells Do Not Back-Differentiate
To rule out the possibility that the back-differentiation observed was occurring in response to DNA damage due to high level Cre, we used a TAT-Cre fusion protein that was previously shown to induce Cre-mediated deletion of floxed alleles [43].
(A) In initial experiments, we performed titrations with the fusion protein and found dose-dependent deletion of the BCR at 24 h in B1-8f/3-83κ/Rag2-GFP immature B cells. In cells that lost the BCR, there was a significant induction of Rag2-GFP reporter activity, and up-regulation of IL-7Rα (data not shown). These data demonstrate the potency of the TAT-Cre preparation in this system and provide a single-cell analysis for the induction of Rag2-GFP following BCR deletion. Cell counts were similar in TAT-Cre and mock-treated cultures.
(B) The TAT-Cre fusion protein (200 μg/ml) was then introduced into HEL-Ig/Rag2-GFP immature B cells. Intracellular staining with anti-Cre antibodies in permeabilized cells confirmed that the TAT-Cre protein was introduced with good efficiency (note levels at both 2 and 24 h). B1-8f/3-83κ cells were tested in parallel for deletion of IgM (data not shown). Despite high intracellular levels of Cre, HEL-Ig immature B cells showed no evidence for back differentiation as determined by induction of Rag2-GFP, or up-regulation of IL-7R (data not shown). Representative of five independent experiments.
(437 KB JPG).
Click here for additional data file.
Figure S5 HEL-Ig/Mx-Cre Immature B Cells Fail to Back-Differentiate following IFN Treatment
HEL-Ig mice were bred with Mx-Cre animals to generate HEL-Ig/Mx-Cre double transgenic mice. IL-7 BM cultures were established from two mice, and after 5 d IgM+ B cells were purified, washed, and then cultured for 2 d in 5,000 units/ml IFN. In parallel, a BM culture was established from a B1-8f/3-83κ/Mx-Cre mouse. After 2 d, cells were harvested, RNA was isolated, and cRNA probes were generated and hybridized to Affymetrix chips. Data analysis was performed identically to that described for Figure 2.
(A) CD22 levels were measured by flow cytometry for the various cell populations after IFN treatment.
(B) Shown are mean gene expression data for Ctrl-Mhi (n = 4) and Cre-Mlo (n = 4) samples for selected genes (see Figure 2 and Table 1). Fold differences were calculated as mean Cre-Mlo/mean Ctrl-Mhi. Also shown are the average raw data from the IFN-treated HEL-Ig/Mx-Cre samples (n = 2), and the parallel IFN-treated B1-8f/3-83κ/Mx-Cre sample. Fold differences were calculated as IFN-treated B1-8f/3-83κ/Mx-Cre/HEL-Ig/Mx-Cre. Overall, the gene expression patterns of the HEL-Ig/Mx-Cre samples closely mirrored the Ctrl-Mhi samples and showed no evidence for back-differentiation. In the same experiment, analysis of the cell surface profile and gene expression in HEL-Ig/Mx-Cre cells incubated with a PI3K inhibitor showed the expected back-differentiation response (data not shown), indicating that the HEL-Ig/Mx-Cre cells were capable of the response.
(C) Correlation coefficients were measured between the overall gene profiles of the two IFN-treated HEL-Ig/Mx-Cre samples compared with Ctrl-Mhi and Cre-Mlo mean values. Gene profiles of the IFN-treated HEL-Ig/Mx-Cre samples were significantly correlated with the Ctrl-Mhi samples (r = 0.655 and 0.637), and poorly correlated with the Cre-Mhi samples (r = 0.252 and 0.280), in the same range as Ctrl-Mhi vs. Cre-Mlo (r = 0.284).
(673 KB JPG).
Click here for additional data file.
Accession Numbers
The Locuslink, or GeneID (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene, accession numbers of the genes and proteins discussed in this paper are λ5 (16136), Abelson murine leukemia (11350), B220 (19264), BLIMP (12142), Btk (12229), Burkitt lymphoma receptor 1/CXCR5 (12145), CD20 (12482), CD21/35 (12902), CD22 (12483), CD23 (14128), CD24 (12484), CD43 (20737), CD69 (12515) CD82 (12521), CD83 (12522), CD86 (12524), CD98 (20539), CIITA (12265), c-jun (16476), cyclin D2 (12444), cyclin-dependent kinase inhibitor 1a (12575), early growth response 1 (13653), elk4 (13714), Fcγ receptor IIB (14130), hemopoietic cell kinase (15162), ICAM-1/CD54 (15894), Igα (12518) and Igβ (15985), IGF2R (16004), IL-4 (16189), IL-7 (16196), IL-7R (16197), inositol 1,4,5-trisphosphate receptor (16438), integrin β7 (16421), Ku70 (14375), LAT (16797), LNK (16923), lymphoid enhancer factor 1 (16842), Lyn (17096), Mac-2 (16854), mitogen- and stress-activated protein kinase 2 (56613), mitogen-activated protein kinase (29857), MAP4K (26411), myb (17863), Notch1 (18128), Oct-2 (18987), paired-Ig-like receptor-A3 (18726), paired-Ig-like receptor-B (18733), phosphatidylinositol phosphate 5-kinase (18718), phosphodiesterase 7A (18583), PI3K (18708), protein kinase Cγ (18752), protein phosphatase 2Cβ (19043), Rag1 (19373), Rag2 (19374), serine-threonine kinase 3 (56274), sex determining region Y-box 4 (20677), SLP76 (3937), SH3-domain binding protein 2 (24055), SH3-domain binding protein 5 (24056), SHP-1 (15170), Son of Sevenless 2 (20663), Syk (20963), TdT (21673), thrombin receptor (14062), transforming growth factor β receptor (21812), Vav (22324), and VpreB (22362).
The microarray data have been deposited at GEO (http://www.ncbi.nlm.nih.gov/geo/; accession numbers are GSE2227.
We thank M. Nussenzweig for providing Rag2-GFP mice; F. Batliwaga, P. Gregersen, W. Ortmann, E. Baechler, A. Becker, and J. Plumb-Smith for assistance with the microarray experiments; D. Corcoran and D. Fods for assistance in animal husbandry; K. Pape and D. Nemazee for the S27 antibody; J. Peller for assistance in cell sorting; and M. Farrar and M. Jenkins for reviewing the manuscript.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. LET, BRS, KAH, MK, MSS, KR, and TWB conceived and designed the experiments. LET, BRS, KAH, JL, SAS, PRR, ALV, RRH, and MSS performed the experiments. LET, BRS, KPL, KAH, KLH, JL, SAS, MK, RRH, MSS, KR, and TWB analyzed the data. KPL, KLO, and MK contributed reagents/materials/analysis tools. LET, MK, MSS, KR, and TWB wrote the paper.
¤ Current address: Division of Immunology and Genetics, The John Curtin School of Medical Research, Australian National University, Canberra, Australia
Citation: Tze LE, Schram BR, Lam KP, Hogquist KA, Hippen KL, et al. (2005) Basal immunoglobulin signaling actively maintains developmental stage in immature B cells. PLoS Biol 3(3): e82.
Abbreviations
7-AAD7-amino-actinomycin D
Agantigen
BCRB cell receptor
BLIMPB lymphocyte-induced maturation protein
BMbone marrow
CFSE5(6)-carboxyfluorescein diacetate succinimidyl ester
CIITAclass II transactivator
Fxfraction
GFPgreen fluorescent protein
HCheavy chain
HELhen egg lysozyme
IFNinterferon
Igimmunoglobulin
IL-7interleukin-7
IL-7RIL-7 receptor
ITAMimmunoreceptor tyrosine-based activation motif
LClight chain
MHCmajor histocompatibility complex
PI3Kphosphatidylinositol 3-kinase
PTKprotein tyrosine kinase
PTPprotein tyrosine phosphatase
Ragrecombination activating gene
SHP-1Src homology 2 phosphatase-1
TdTterminal deoxynucleotidyl transferase
Tgtransgenic
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| 15752064 | PMC1059451 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 8; 3(3):e82 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030082 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030112SynopsisCell BiologyDevelopmentImmunologyMus (Mouse)Two-Way Traffic in B Cell Development: Implications for Immune Tolerance Synopsis3 2005 8 3 2005 8 3 2005 3 3 e112Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Basal Immunoglobulin Signaling Actively Maintains Developmental Stage in Immature B Cells
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Development generally proceeds in one direction. Undifferentiated, pluripotent cells, which can become several different cell types, first of all become committed to restricted cell lineages. Then, under the control of developmental signals, committed cells gradually take on specialized characteristics, eventually producing mature, functioning cell types. To date, there has been little evidence to suggest that this process is ever reversed during normal development.
Now, however, Timothy Behrens and his colleagues report that the development of B lymphocytes, the antibody-producing cells of the immune system, can be switched into reverse by blocking or removing basal immunoglobulin signaling activity from immature B cells. Their findings have important implications for our understanding of how the immune system is tailored to respond efficiently to foreign antigens while ignoring self antigens and thus avoiding harmful autoimmune reactions.
B lymphocyte development, which occurs in the bone marrow, starts with the commitment of lymphoid progenitors to the B lineage and the somatic rearrangement of the heavy chain (HC) immunoglobulin (Ig) alleles. By stitching together diversity (DH), joining (JH), and variable (VH) region DNA segments, many pro-B cells, each with a single but unique HC allele, are produced. Those cells in which the stitched-together HC allele encodes a functional protein undergo clonal expansion and proceed to the pre-B stage, before repeating the whole rearrangement process for the light chain (LC) Ig alleles. A productive LC rearrangement results in surface expression of IgM, which acts as the B cell receptor (BCR) for antigen for the immature B cell.
During development, any B cells bearing strongly self-reactive Ig receptors are removed—this process is called tolerization—either by clonal deletion, by functional inactivation, or by receptor editing. In this last process, new LC rearrangements revise the antigen specificity of the receptor. Little is known about the mechanisms driving receptor editing, but these new data from Behrens and colleagues suggest that signals provided by surface BCRs might suppress receptor editing in immature B cells.
To test this hypothesis, the researchers used a genetic system to remove the BCR from the cell surface of immature B cells in an inducible manner in vitro, and then compared gene expression patterns in these cells, control immature B cells, and pre-B cells. They discovered that the BCR-deleted cells had a gene expression pattern similar to that of pre-B cells, indicating that the BCR-deleted cells had gone back to an earlier stage of B cell development as a consequence of losing their BCR.
The researchers saw a similar effect on B cell differentiation state when they blocked downstream signaling from the BCR by the use of the tyrosine kinase inhibitor herbimycin A or the phosphatidylinositol 3-kinase inhibitor wortmannin. Finally, the researchers showed that cells undergoing “back-differentiation” also restarted LC rearrangement or receptor editing.
These data, suggest Behrens and co-workers, indicate that immature B cells actively maintain their developmental state by constitutive basal Ig signaling through protein tyrosine kinases. Their findings, they say, throw new light onto how receptor editing might be regulated in immature B cells in order to ensure that tolerance to self antigens develops. The researchers propose that when immature B cells encounter self antigens, down-regulation of surface Ig (BCR) via endocytosis might induce a back-differentiation program and thus induce an efficient editing response to the self antigen. Further experiments are now needed to show that these processes do indeed happen not just in vitro, as revealed here, but also in vivo.
| 0 | PMC1059452 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Mar 8; 3(3):e112 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030112 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1575519510.1371/journal.pmed.0020100Policy ForumEpidemiology/Public HealthHealth PolicyPublic HealthHealth PolicyInternational healthMedicine in Developing CountriesThe Global Threat of Counterfeit Drugs: Why Industry and Governments Must Communicate the Dangers Policy ForumCockburn Robert *Newton Paul N Agyarko E. Kyeremateng Akunyili Dora White Nicholas J Robert Cockburn is a writer and formerly a journalist with The Times, London, United Kingdom. Paul N. Newton is at the Centre for Clinical Vaccinology and Tropical Medicine, Churchill Hospital, University of Oxford, United Kingdom. E. Kyeremateng Agyarko is Chief Executive of the Food and Drug Board, Accra, Ghana. Dora Akunyili is Director-General of the National Agency for Food and Drug Administration and Control, Lagos, Nigeria. Nicholas J. White is at the Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand, and the Centre for Clinical Vaccinology and Tropical Medicine, Churchill Hospital, University of Oxford, United Kingdom.
Competing Interests: NJW is on the editorial board of PLoS Medicine. RC, PNN, EKA, and DA declare that they have no competing interests.
*To whom correspondence should be addressed. E-mail: [email protected] 2005 14 3 2005 2 4 e100Copyright: © 2005 Cockburn et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The production of substandard and fake drugs is a vast and underreported problem, particularly affecting poorer countries. Cockburn and colleagues argue that the pharmaceutical industry and governments must both take action
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Introduction
The production of substandard and fake drugs is a vast and underreported problem, particularly affecting poorer countries. It is an important cause of unnecessary morbidity, mortality, and loss of public confidence in medicines and health structures. The prevalence of counterfeit drugs appears to be rising (see “The Scale of the Problem”) and has not been opposed by close cooperation between drug companies, governments, or international organizations concerned with trade, health, customs and excise, and counterfeiting.
In this article we suggest that many pharmaceutical companies and governments are reluctant to publicize the problem to health staff and the public, apparently motivated by the belief that the publicity will harm the sales of brand-name products in a fiercely competitive business. Publicly, at least, several industry sources say the justification for secrecy is to avoid any alarm that could prevent patients taking their genuine medicines. We argue that this secrecy, and the subsequent lack of public health warnings, is harming patients and that it is also not in the long-term interests of the legitimate pharmaceutical industry. We urge a change to mandatory reporting to governmental authorities, which should also have a legal duty to investigate, issue appropriate public warnings, and share information across borders. This is not a role for the pharmaceutical industry, which has a serious conflict of interest.
While some drug companies have given public warnings to protect patients, others have been criticized for withholding information and, in a recent development in the United States, taken to court for failing to act. The industry is addressing the problem. In 2003, US pharmaceutical companies made an agreement with the US Food and Drug Administration (FDA) that they would report suspected counterfeit drugs to the FDA within five days of discovery (see “Companies That Have Warned”), although this remains a voluntary arrangement. In many poorer countries, where the problem is at its worst, there are no similar governmental and industry initiatives.
The Scale of the Problem
It has been estimated that up to 15% of all sold drugs are fake, and in parts of Africa and Asia this figure exceeds 50% ([1,2,3,4,5,6,7]; R. Jones, FDA spokesperson, E-mail statement, 18 November 2004). The FDA estimates that fake drugs comprise approximately 10% of the global medicine market (R. Jones, FDA spokesperson, E-mail statement, 18 November 2004). This estimate suggests annual criminal sales in excess of US$35,000,000,000 [1,2]. The number of investigations of possible counterfeit drugs by the FDA has jumped from about five per year in the 1990s to more than 20 per year since 2000 (Figure 1).
Figure 1 The Number of Investigations of Possible Counterfeit Drugs by the FDA Has Been Rising
(Figure: Margaret Shear, Public Library of Science, adapted from [39])
Most of the literature on fake drugs derives from local investigative journalism [6,8,9,10,11,12,13,14], with little scientific public health enquiry relative to the enormous scale of this criminal enterprise. The effects on patients of counterfeit medicines are difficult to detect and quantify, and are mostly hidden in public health statistics. The estimate of 192,000 patients killed by fake drugs in China in 2001 gives an indication of the scale of human suffering (see Sidebar).
Secrecy and Counterfeit Medicines
Most data on the epidemiology of counterfeit drugs are kept secret by the pharmaceutical industry and by governmental agencies. Drug companies employ investigators to track down and facilitate the shutting down of counterfeit industries, but this occurs very much in private.
There are no reliable accessible databases whereby health workers or the public can access current details of which products are being faked in a locality. It is obviously correct that information on anti-counterfeiting strategies and the sources of undercover intelligence should not be released, but we believe that the information on what drug is being counterfeited, and where, should be public knowledge [1].
Government Reluctance
Governments are also often reluctant to publicize problems with the quality of the drug supply in their country. This is reflected in the lack of action in much of the world regarding the problem of counterfeits, relative to their large impact on public health. The World Health Organization (WHO) has a reporting system and some of the information is publicly available [15]. The public information, crucially, does not include the country or region where the fakes were identified. However, the WHO has received no reports of counterfeit drugs from member countries after 2002, and it received only 84 reports between 1999 and 2002 [16,17].
In some countries, government officials have been accused of involvement in the false certification of counterfeit drugs, while in others, governmental agencies have been criticized for suppressing information [9,18]. The WHO in the Western Pacific region, an area severely affected by counterfeit drugs, is planning a rapid alert system for expediting the sharing of warnings and information between governments in the region.
Pharmaceutical Industry Survey
We wrote to the Pharmaceutical Security Institute (PSI) (see Box 1), which collates information on fake drugs obtained by the industry, asking whether they currently forwarded reports of counterfeit drugs to the relevant governments and the WHO. This question was not answered, but the PSI (in a letter dated 29 July 2003) informed us that, “Since its inception, it was recognized that a great deal of this information it [the PSI] contains would remain confidential and would not be disseminated. There is proprietary information that cannot be disclosed, either to peer member companies or to the general audience. Consequently, at this time the dissemination of information…is restricted and limited.” The letter added that the PSI encourages its members to report counterfeiting incidents to the appropriate authorities, and that it fully supports the voluntary reporting to the FDA. We also wrote to 21 major companies, of the more than 70 pharmaceutical companies with offices in the United Kingdom, asking for information on the companies' policies on what action should be taken and who should be told when one of their products was found to be counterfeited. We have received replies from six companies; one (Merck Sharp and Dohme) declined to give any information, while three (GlaxoSmithKline [GSK], Bristol-Myers Squibb, and Novartis) stated that they would inform the local drug regulatory authority if they were notified that one of their products was being counterfeited.
Box 1. The Pharmaceutical Security Institute
The PSI is a not-for-profit corporation formed by the major drug companies to collate their fake drug information to cooperate in fighting the racket. Based in Vienna, Virginia, United States, the PSI holds the only known comprehensive and updated source of fake drug information. The PSI Web site (www.psi-inc.org) states, “On a daily basis, many individuals unknowingly risk death or serious injury to their health by taking counterfeit pharmaceuticals.” But its databank, which health workers see as holding key information to prevent patients from taking life-threatening fakes, is not accessible to the WHO, health authorities, or the public. Such is the secrecy of the PSI's information, that access is restricted even between its member companies, which include the 15 largest drug manufacturers.
Figure 2 Genuine and Fake Guilin Pharma Artesunate Blister Pack Holograms Found in Mainland Southeast Asia
(A) is the genuine hologram attached to the blister packs of the genuine Guilin Pharma artesunate. The red arrow points to a legend stating “GUILIN PHARMA”, which is visible with the naked eye as a thin strip below the waves, but can only be read with a microscope (letters are about 0.1 mm high).
(B) is a fake artesunate blister pack hologram: the upper red ring shows that the hologram has crescents, rather than a continuous blank line, between mountain and waves, and the lower ring shows that there is no “GUILIN PHARMA” legend.
(C) is also a fake artesunate blister pack hologram: the red ring shows that the “GUILIN PHARMA” legend is present but the letters are of larger font than those on the genuine hologram and can be read with the naked eye (letters are about 0.3 mm high).
A warning sheet giving more details and photographs is available in [47].
(Photos: Paul Newton, Wellcome Trust SE Asian Tropical Medicine Research Units)
Paucity of Warnings about Fake Drugs
That many pharmaceutical companies, professional organizations, and governments, both in developed and developing countries are not releasing warnings is manifested by the paucity of warnings relative to the scale of the problem. The industry's history of secrecy over data about fake drugs, and claims of a commercial motivation, go back over 20 years. In 1982, a spokesperson for the Association of the British Pharmaceutical Industry said, “It is difficult to declare a [fake drug] problem without damaging legitimate business” [13]. This impression of secrecy is supported by historical statements, such as the following: “The Society [Royal Pharmaceutical Society of Great Britain] is not issuing press releases [about counterfeit drugs] because it believes that as much as possible should be done behind the scenes…and that no great publicity should be sought because it could damage public confidence in medicines” [19]. But the Royal Pharmaceutical Society of Great Britain has recently revised its position. David Pruce, Director of Practice and Quality Improvement for the organization, told us (E-mail letter, 14 February 2005), “If there is a risk that a patient has been dispensed a counterfeit medicine, then it is vital that they are informed. There have been two recent cases in Great Britain where counterfeit medicines appeared in the legitimate pharmacy supply chain. The public announcement of the problem of the counterfeit medicines was therefore entirely proper and necessary.” He added, “It is important that news stories of this type are handled responsibly so that the public's confidence in their medicines is not undermined. This could deter patients from taking genuine medicines.”
Recent Examples of Counterfeit Drugs
Approximately one-third to one-half of packets of artesunate tablets, the pivotal, life-saving anti-malarial drug, recently bought in Southeast Asia were fakes, containing no active ingredient at all. A nongovernmental organization in a Southeast Asian country bought 100,000 inexpensive “artesunate” tablets only to find that they were counterfeit [7,39]. See Figure 2 for examples of fake artesunate being sold in mainland Southeast Asia.
A total of 192,000 Chinese patients are reported to have died in 2001 from fake drugs, and in the same year Chinese authorities “closed 1,300 factories while investigating 480,000 cases of counterfeit drugs worth 57 million USD” [12]. In 2004, Chinese authorities arrested 22 manufacturers of grossly substandard infant milk powder and closed three factories after the death of over 50 infants [40].
In North America, counterfeit atorvastatin [41], erythropoietin [41], growth hormone [33], filgrastim [33,41], gemcitabine [36,37], and paclitaxel [36,37] have been reported recently.
Nigeria recently threatened to ban the import of all drugs from India, a major supplier, because of the high prevalence of counterfeits amongst the imports [42].
In Haiti, Nigeria, Bangladesh, India, and Argentina, more than 500 patients, predominantly children, are known to have died from the use of the toxin diethylene glycol in the manufacture of fake paracetamol syrup [43,44,45].
During the 1995 meningitis epidemic in Niger, the authorities received a donation of 88,000 Pasteur Merieux and SmithKline Beecham vaccines from neighbouring Nigeria. The drugs were found to be counterfeit, with no traces of active product. Some 60,000 people were inoculated with the fake vaccines [24].
The recent discovery of counterfeit antiretrovirals (stavudine-lamivudine-nevirapine and lamivudine-zidovudine) in central Africa [46] raises the prospect of a disastrous setback in the treatment of AIDS in sub-Saharan Africa, unless vigorous action is taken now.
This assessment, that the dangers of causing alarm amongst the general public could outweigh the benefits of disclosure, remains widespread in public statements. A spokesperson for the Association of British Pharmaceutical Industries, Marjorie Syddall, wrote (E-mail letter, 20 October 2003), “A company should be completely satisfied that a medicine is counterfeit before informing the authorities, but more importantly still, before it makes this information known to the public—so that no unnecessary alarm is caused.”
Commercial Motivation—“Cut-Throat Competition”
Chris Jenkins, a founding member of the PSI, now Associate Director of Pinkerton Consulting and Investigations, told us (E-mail statement, 9 December 2004), “It is necessary to keep fake drug information confidential for commercial reasons…to avoid media leaks and to prevent the possibility of rival drug companies taking unfair commercial advantage of a victim company.” He explained, “At the outset, we [the PSI] were against having data online that anyone could interrogate…If a patient came to harm as a result of a counterfeit product, the company's good reputation is in danger of disappearing, together with a loss of confidence in the products… The one thing we were trying very hard to do was to keep it [data] out of the hands of the commercial people in any of the companies…The importance of meeting sales' targets is such that you can even find cut-throat competition between different operating divisions of the same company, let alone between two companies competing in the same market with similar drugs.”
The WHO 1999 guidelines for the development of measures to combat counterfeit drugs states that “the reluctance of the pharmaceutical industry, wholesalers and retailers to report drug counterfeiting to the national drug regulatory authorities could impede the national authorities from successfully taking measures against counterfeiting”, and suggests “the compulsory reporting to the relevant authorities of any incidents in which counterfeits are detected or involved” [20]. A recent review of the law and counterfeit drugs calls for the “eradication of the clandestine status of records and counterfeit drug information” [21]. At the International Conference of Drug Regulatory Authorities in Madrid in February 2004, it was stated by the WHO that “the drugs industry had a great deal of data but was ‘very reluctant to make them available’” [17].
Information Strictly Confidential
In the US it was reported that it had been “very difficult to obtain citable factual information about the extent of the problem of counterfeit drugs. Drug companies keep the information they have strictly confidential” [22]. In 1989, the British Department of Health and Glaxo (now a part of GlaxoSmithKline) were criticized for not publicizing information about the discovery in Britain of fake Glaxo Ventolin asthma inhalers. London's The Times obtained the fake Ventolin's licence and batch numbers for a story, prompting the release of the information. Warning letters, drafted by Glaxo and the Department of Health, were sent to all 14,000 pharmacists in Britain five weeks after the fake's discovery [8]. In 1998, the company Schering do Brasil was accused of keeping secret the discovery of oral contraceptive pills made of wheat flour for 30 days while they carried out their own investigation [23]. According to the Far Eastern Economic Review, the company was fined US$2.5 million by the Brazilian government [6]. Schering do Brasil informed us (E-mail letter, 17 February 2005) that “Federal Justice cancelled the fine in 2002 after the company appealed”. In Niger, in 1995, one of the fake meningitis vaccines originating from Nigeria was labelled as made by SmithKline Beecham, but Le Monde reported that the company did not act against the counterfeiters, afraid that it might damage trade [24].
Fake Paediatric Anti-Malarial Drugs
The need to release fake drug information is acute in Africa, where a resurgence of malaria is killing an estimated one million people a year, the vast majority of them children under five [25]. One example highlights the problems encountered. One of us (K. Agyarko) found counterfeits of the GSK paediatric anti-malarial syrup halofantrine (Halfan) in August 2002 in Ghana. That month he prepared a public health warning. Agyarko and his deputy told the BBC [26] that he also alerted GSK's Ghana agent, who visited him with staff from GSK's London headquarters and took away samples of the fake Halfan. Agyarko publicly stated (on 23 September 2002, at the First Global Forum on Pharmaceutical Anticounterfeiting in Geneva, Switzerland) [26] that he was asked by GSK to withhold his public warning because it would “damage” their product. After his meeting with GSK, no warning was issued. In a written statement (E-mail letter, 24 October 2003), GSK denied receiving Agyarko's fake Halfan alert and said the company was “not provided with any samples of fakes by the authorities in Ghana”.
After a year of enquiries, resulting in a BBC Radio programme (BBC Radio 4, “File on 4”, 5 October 2004) [26], GSK reversed its position and said that its local agent had “bumped into” Agyarko and had received his alert and samples of fake Halfan syrup. In a new statement (E-mail letter, 5 October 2004) GSK said: “At no point was any pressure put on the Ghanaian authorities not to issue a public warning on fake Halfan.” GSK's vice president of communications, Louise A. Dunn, told us (E-mail letter, 6 October 2004), “There was some confusion over the interactions with Mr Agyarko. The key point here is that there was no wrong doing…”
However, the Ghana incident needs to be viewed in the context of the wider illegal trade in fake Halfan syrup identified in West Africa, and GSK's reluctance to give us details about this trade. We asked GSK whether it had issued any public warnings about fake Halfan syrup, but the question was not answered. The only reference to counterfeit halofantrine syrup that we have been able to find in the public domain was published in a specialist technical journal that described the mass spectroscopy analysis of fake halofantrine syrups by the GSK Medicines Research Centre [27] and demonstrated that the fake syrups contained two potentially harmful sulphonamide drugs, but no halofantrine. We wrote to GSK (letter, 20 June 2003) asking when and where discoveries of fake Halfan were made, and whom GSK had informed about them. GSK told us only that “counterfeit Halfan is present in Nigeria and Sierra Leone” (letter, 21 July 2003). It gave no details of preparation type or discovery dates.
Fake GSK Halfan syrup was discovered in Nigeria in June 2002 by the Nigerian National Agency for Food and Drug Administration and Control. NAFDAC alerted GSK and issued a public health warning in June 2002 in the regular NAFDAC fake drug bulletin [28], giving the fake Halfan syrup's identifying details. The NAFDAC's Dora Akunyili told BBC Radio (5 October 2004): “It is more dangerous not to alert the public. We will still issue a warning even if we find it in only one shop. If you find any fake drug product in only one shop you can be sure it is in many villages…We don't defend companies. We are defending the people” [26].
Figure 3 Poster Advertising the Second Global Forum on Pharmaceutical Counterfeiting
(Figure: Ian Lancaster, Reconnaissance International)
The Pharmaceutical Board of Sierra Leone, which handles fake drug cases, was not informed by GSK of any discoveries of fake GSK Halfan syrup, according to its director Michael J. Lansana (E-mail letter, 21 January 2004), although it did receive a report of counterfeit adult Halfan caplets from GSK. Later, GSK told us (E-mail letter, 6 October 2004) the fake Halfan syrup it had tested was found in Sierra Leone in late 2001, and that it had informed Sierra Leone's Minister of Health and Sanitation of the find.
Only a single report of counterfeit halofantrine, which does not specify details of preparation type or location, is given in the WHO Counterfeit Drug Reports for 1999–October 2000 [15].
Cross-Border Threats and Cooperation
The fake Halfan syrup cases highlight the importance of communication and cross-border cooperation, and the need for industry and governments to inform neighbouring countries when a fake is found. The global distribution and the scale of the racket in fake adult Halfan capsules was clear in December 2000, when Belgian customs seized 57,600 packs of fake GSK Halfan capsules (and 4,400 packs of fake GSK Ampiclox [ampicillin] and 11,000 packs of fake GSK Amoxil [amoxicillin]) en route from China to Nigeria. The counterfeiters in China were found to be preparing to export 43 tons of 17 brands of drugs from seven international pharmaceutical companies [29].
Companies That Have Warned
Sometimes pharmaceutical companies have publicized information to alert health workers and patients and governments to the dangers of counterfeited or tampered products. For example, Johnson and Johnson, Serono, Hoechst, Wellcome Foundation (now part of GSK), GSK, and Genentech have publicized information on their drugs that have been counterfeited or tampered with. In 1982, cyanide-laced paracetamol killed seven people in the US. The pharmaceutical company whose product had been tampered with, Johnson and Johnson, issued alerts and cooperated with the investigation, and although the financial cost to the company was large, its long-term reputation was probably enhanced. Other companies, at least initially, did not take advantage of the disaster for their own financial gain [30]. In 2002, Johnson and Johnson issued 200,000 letters to health-care professionals in the US warning them of fake Procrit (erythropoetin) within one week of being notified of a severe counterfeit problem [31]. In 1982, Hoechst voluntarily took out magazine adverts in Lebanon to warn pharmacists and customers of a fake of its drug Daonil (glibenclamide) for the treatment of diabetes mellitus [13]. In 2001, Serono was told by the FDA to issue a public warning to hospitals, clinics, and patients in seven US states after the discovery of a counterfeit of its drug Serostim, a human growth hormone used in the treatment of AIDS and other conditions [32]. In 1984, in Thailand, the Wellcome Foundation (now part of GSK) publicized the discovery of fakes of its antibiotic Septrin (co-trimoxazole) that lacked any active ingredients, and the company's efforts to stop its production. Wellcome also had reports that the fakes were being imported into the UK, which it made public along with the warning that it sent to the British Embassy in Bangkok [14]. In 2001, GSK made public the discovery of fakes of its AIDS treatment Combivir (zidovudine + lamivudine) [32], and Genentech publicized information on fakes of Neupogen (filgrastim) [33].
The Pharmaceutical Research and Manufacturers of America announced in April 2003 that, from 1 May 2003, its 60 members would voluntarily report to the FDA “within five working days of determining that there is a reasonable basis to believe their product has been counterfeited” [34]. This is an important local development but it should be mandated by law and become a global standard. Indeed, we have not found one country where drug companies have a legal duty to report discoveries of counterfeits of their products to public health or trade authorities.
The Sharing of Information on Counterfeit Medicines
We suggest that the pharmaceutical industry, which is such a benefit to our health, is harming both patients and itself by not vigorously warning the public of fake products when they arise. Apart from the moral imperative, there is the prospect of growing legal pressure on drug companies to take responsibility for fakes of their products. In Britain, there are proposals to introduce a charge of “corporate killing” for companies who have contributed to the deaths of customers [35] that could also apply to drug companies if they do not take reasonable steps to warn the public of a fake product.
Drug Companies Sued in the US
Already, the US has seen the first court case brought against two drug companies for allegedly failing to act to protect customers over a fake drug discovery. In 2002, a Kansas City pharmacist was jailed for diluting the anticancer drugs Gemzar (gemcitabine) and Taxol (paclitaxel). The victims and dead patients' families sued the drug companies, Eli Lilly and Myers Squibb, for not taking steps to stop him. The companies argued that they had no duty to protect the plaintiffs from the pharmacist's criminal acts, but a newspaper reported that Eli Lilly and Myers Squibb settled out of court, apparently for US$72 million, avoiding a legal precedent that would hold drug companies liable for not disseminating such information [36,37].
Chris Jenkins suggests that the PSI could face a legal challenge to open its fake drug databases (E-mail, 9 December 2004): “Only the PSI had an overview of the known racket…In theory, every fake drug case reported by the companies should be on there.” He is concerned that private investigators could be liable for fake drug data they obtain for client companies.
Governments Must Enforce a Legal Responsibility
We believe that the industry, along with pharmacists, health workers, and governments, needs to extend the “behind the scenes” fight against fakes to a public collaborative approach with a legal responsibility to report suspected counterfeits to drug regulatory authorities, in a similar way to the reporting of “notifiable” infectious diseases. The drug regulatory authorities, accountable to the consumers of drugs, should have a statutory duty to investigate and disseminate the information, with the interests of patients as the prime concern. Drug regulatory authorities in economically poor countries will need additional financial support.
We recognize that false information could seriously damage a company and that information should be verified and used prudently. We also recognize that careful public information measures will be needed to prevent patients from stopping the use of genuine products, but suggest that this is possible as pharmaceutical companies can, and have, alerted the public in collaboration with government agencies (see above). However, the decision to warn the public should not be made by the pharmaceutical industry alone, which has a serious conflict of interest. We believe that the long-term interests of both the industry and patients are best served by more openness and social responsibility to public health. Company staff and shareholders should not be in a position to adjudicate conflicts between commercial gain and public health—such adjudication should be in the hands of government departments accountable to the public.
Aviation Industry Model
The UK Civil Aviation Authority provides a model: suspected unapproved aircraft parts must, by law, be reported to it [38]. When a report of a counterfeit drug is confirmed, the drug regulatory authorities should be responsible for assessing the public health importance of the information and deciding when and how to alert the country's police, trade, customs authorities, and public, and also the drug regulatory authorities of other countries that may be affected, with the assistance of Interpol as required. If a drug regulatory authority is confident, for example, that the fake drug has been intercepted before it has reached the pharmacies, a public alert may not be necessary. The “confusion” reported in the GSK Halfan syrup case also illustrates the great importance for both companies and government departments to keep a secure paper trail of information so that it is clear what has happened and when.
The pharmaceutical company is also a victim of the counterfeiter and should be supported by governmental authorities if it reports promptly. Individuals who report information on counterfeit drugs should remain anonymous and be protected from the criminal counterfeiting underworld, which may exact retribution. International agreements between companies to avoid taking advantage of competitors' misfortunes, when precipitated by rumors or confirmed reports of fake drugs, may facilitate enhanced cooperation within the pharmaceutical industry.
International Convention against Counterfeit Drugs
The Madrid meeting in 2004 considered a proposed international framework convention on counterfeit drugs, presented by the WHO, to promote international cooperation and the exchange of information [17]. If enacted this could be a very important contribution to improving drug quality. The effective control of the global epidemic of counterfeit and substandard drugs will not be easy, and will need a multifaceted approach: the provision of effective, available, and inexpensive drugs; the enforcement of drug regulation; more openness by governments as to the scale of the problem; more effective police action against the counterfeiters and those who may be corrupt allies within government and industry; enhanced cooperation between the industry, police, customs, and drug regulators; and enhanced education of patients, drug sellers, and health workers [4,5,20]. We urge the industry and governments to act, through the sharing of crucial public health information, to facilitate the protection of patients and improve the quality of an apparently deteriorating drug supply.
Counterfeit Drug Conference in Paris
On 15–17 March 2005, the Second Global Forum on Pharmaceutical Anticounterfeiting will convene in Paris, where representatives of the major pharmaceutical companies, governments, medical and scientific professionals, law enforcement agencies, nongovermental organizations, and private investigators will meet to discuss the growing problem that threatens patients and the pharmaceutical industry (Figure 3
).
A collection of counterfeit pharmaceutical drugs seized by the NAFDAC in Nigeria
(Photograph: NAFDAC/International Chamber of Commerce Counterfeiting Intelligence Bureau)
We are very grateful to Dr. Lembit Rago, WHO Essential Drugs and Medicines Department; Dr. Allan Schapira, WHO Roll Back Malaria Department; John Anderson, Chairman of the Global Anti-Counterfeiting Group; Mark Morris, Kansas City Star; Erik Madsen, Interpol; Danièle Letoré, Genevensis; and Marie Ose, Agence France Presse. We are very grateful to Professor Marcus Reidenberg, Cornell University, and Michael Lansana, Head of the Pharmaceutical Board of Sierra Leone, for their support for the arguments put forward in this article. PN and NJW are supported by the Wellcome Trust of the UK.
Author contributions. All authors researched the information for this review, RC and PNN wrote the first draft, and all authors contributed to writing the final version of the manuscript.
Citation: Cockburn R, Newton PN, Agyarko EK, Akunyili D, White NJ (2005) The global threat of counterfeit drugs: Why industry and governments must communicate the dangers. PLoS Med 2(4): e100.
Abbreviations
FDAUS Food and Drug Administration
GSKGlaxoSmithKline
NAFDACNigerian National Agency for Food and Drug Administration and Control
PSIPharmaceutical Security Institute
WHOWorld Health Organization
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| 15755195 | PMC1062889 | CC BY | 2021-01-05 11:13:38 | no | PLoS Med. 2005 Apr 14; 2(4):e100 | utf-8 | PLoS Med | 2,005 | 10.1371/journal.pmed.0020100 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9221553584310.1186/bcr922Research ArticleThe Na+–H+ exchanger-1 induces cytoskeletal changes involving reciprocal RhoA and Rac1 signaling, resulting in motility and invasion in MDA-MB-435 cells Paradiso Angelo [email protected] Rosa Angela [email protected] Antonia [email protected] Anna [email protected] Lorenzo [email protected] Massimo [email protected] Valeria [email protected] Stephan J [email protected] Laboratory of Clinical & Experimental Oncology, Oncology Institute of Bari, Bari, Italy2 Department of General and Environmental Physiology, University of Bari, Bari, Italy3 International Agency for Research on Cancer, World Health Organization, Unit of Infection and Cancer, Lyon, France2004 13 9 2004 6 6 R616 R628 28 4 2004 22 6 2004 9 7 2004 21 7 2004 Copyright © 2004 Paradiso et al; BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
An increasing body of evidence shows that the tumour microenvironment is essential in driving neoplastic progression. The low serum component of this microenvironment stimulates motility/invasion in human breast cancer cells via activation of the Na+–H+ exchanger (NHE) isoform 1, but the signal transduction systems that underlie this process are still poorly understood. We undertook the present study to elucidate the role and pattern of regulation by the Rho GTPases of this serum deprivation-dependent activation of both NHE1 and subsequent invasive characteristics, such as pseudopodia and invadiopodia protrusion, directed cell motility and penetration of normal tissues.
Methods
The present study was performed in a well characterized human mammary epithelial cell line representing late stage metastatic progression, MDA-MB-435. The activity of RhoA and Rac1 was modified using their dominant negative and constitutively active mutants and the activity of NHE1, cell motility/invasion, F-actin content and cell shape were measured.
Results
We show for the first time that serum deprivation induces NHE1-dependent morphological and cytoskeletal changes in metastatic cells via a reciprocal interaction of RhoA and Rac1, resulting in increased chemotaxis and invasion. Deprivation changed cell shape by reducing the amount of F-actin and inducing the formation of leading edge pseudopodia. Serum deprivation inhibited RhoA activity and stimulated Rac1 activity. Rac1 and RhoA were antagonistic regulators of both basal and stimulated tumour cell NHE1 activity. The regulation of NHE1 activity by RhoA and Rac1 in both conditions was mediated by an alteration in intracellular proton affinity of the exchanger. Interestingly, the role of each of these G-proteins was reversed during serum deprivation; basal NHE1 activity was regulated positively by RhoA and negatively by Rac1, whereas RhoA negatively and Rac1 positively directed the stimulation of NHE1 during serum deprivation. Importantly, the same pattern of RhoA and Rac1 regulation found for NHE1 activity was observed in both basal and serum deprivation dependent increases in motility, invasion and actin cytoskeletal organization.
Conclusion
Our findings suggest that the reported antagonistic roles of RhoA and Rac1 in cell motility/invasion and cytoskeletal organization may be due, in part, to their concerted action on NHE1 activity as a convergence point.
cytoskeletonmetastasisNHE1invasionRho GTPases
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Introduction
Tumour invasion and metastasis associated with neoplastic progression are the major causes of cancer deaths. The invasive process occurs through a complex series of interactions with the host tissue, resulting in infiltration and penetration of normal tissue by cancer cells [1]. Recent advances have highlighted the importance of the acid component of the tumour microenvironment in driving invasive capacity and subsequent malignant progression [2-4]. The activity of the Na+–H+ exchanger (NHE) isoform 1 is known to play a role in acidifying the tumour microenvironment [5], and the nutrient-deprived conditions that are common to the tumour microenvironment activate tumour cell NHE1, which in turn stimulates increased motility and invasive capability [6]. This role played by Na+–H+ exchanger activity in driving tumour cell motility has been corroborated in transformed renal cells [7,8] and in ascites hepatoma cells [9], emphasizing the importance of understanding the mechanisms that control Na+–H+ exchanger activity in tumour cells.
The Rho family of small GTPases has been shown to play a major role in regulating the rearrangement of the actin cytoskeleton in response to cell stimulation [10] and to be involved in the regulation of a variety of other cellular processes, such as organization of the microfilamental network, cell–cell contact, motility and apoptosis [11]. Early studies in fibroblasts suggested that Cdc42, Rac1 and RhoA are organized in hierarchical cascades in which activated Cdc42 activates Rac1, in turn activating RhoA [10]. It is now known that these G-proteins can be organized in many other ways. For example, Rac1 or Cdc42 have been shown to oppose or modify RhoA action in the regulation of a number of processes, including actin cytoskeleton remodelling [12], axon guidance [13], dendrite branching [14] and cell migration [15-17]. Thus, it is clear that the interactions and associations of the various members of the Rho family are cell/tissue-specific or function-specific, or both. Despite the evidence that Rho GTPases are overexpressed in tumours [18] and play an important role in mitogenesis, proliferation and invasiveness [19], our understanding of how they interact among themselves in tumour cells to regulate these processes is still incomplete and represents an important issue in oncology [20].
Molecular control of actin organization is probably at the core of cell motility, and it is of importance that we gain an understanding of the mechanisms that underlie the regulation of actin dynamics so that we may appreciate how motility is regulated. Recent studies conducted in smooth muscle cells have led to a model (for review see [21,22]) that associates RhoA with contraction via activation of Rho kinase (ROCK) and ROCK-dependent phosphorylation of myosin phosphatase, thereby inactivating it and resulting in an increase in the phosphorylation state of myosin light chain and enhancement of myosin binding to actin filaments. Rac1 via activation of the p21-activated kinase (PAK) antagonizes this process by blocking the activity of myosin light chain kinase. As recently discussed [23], this mechanism pertains to smooth muscle cell contraction and cannot fully explain the effects of Rho family proteins on the actin cytoskeleton in other cell types, such as epithelial cells. Two other mechanisms have been described in which Rac1 downregulates RhoA activity via a redox-dependent mechanism [17] and by stimulating RhoA degradation via Smurf1 [24]. Recently, however, a further possible cross-regulatory mechanism has emerged. Pioneering studies in fibroblasts have shown that NHE1, a regulator of intracellular pH (pHi), can play a direct role in controlling actin dynamics and subsequent motility through a protein–protein interaction with the cytoskeletal adaptor protein ezrin, and that, in those cells, RhoA-dependent modulation of cytoskeletal dynamics and motility occurred via direct regulation of NHE1 activity [25,26]. There is increasing evidence that activity of the NHE1 is essential for motility in various cell types [6-9,26]. Accordingly, the questions are whether RhoA and Rac1 reciprocally regulate motility in tumour cells of epithelial origin, and if so then do they act via a coordinated regulation of NHE1 activity?
We previously reported that serum deprivation, a common component of the tumour microenvironmental, stimulates NHE1 in human epithelial breast cancer cells and drives increased cellular motility and invasive ability via the activated NHE1 [6]. In light of this essential role played by NHE1 in regulating motility in these cells, the present study was undertaken to characterize the role and pattern of regulation by Rho GTPases of this serum deprivation-dependent activation of both NHE1 and subsequent basic invasive characteristics, such as pseudopodia and invadiopodia protrusion, directed cell motility and penetration of normal tissues [27,28]. The study was conducted in a well characterized human mammary epithelial cell line that represents a late phase in metastatic progression, namely MDA-MB-435 [6]. We observed that serum deprivation inhibits RhoA activity and stimulates Rac1 activity and, using dominant negative and constitutively active mutants, that Rac1 and RhoA are antagonistic regulators of tumour cell NHE1 activity. As was observed in the regulation of this phenomenon by phosphoinositide-3 kinase (PI3K) [6], a reversal of RhoA and Rac1 regulatory action on NHE1 activity found in serum replete conditions was found during serum deprivation-dependent upregulation of NHE1. Although serum deprivation reversed the regulatory actions of Rac1 and RhoA on NHE1 activity, the basic pattern of antagonism of action between these two G-proteins was maintained. Furthermore, the same pattern of RhoA and Rac1 regulation found for NHE1 activity was observed in both basal and serum deprivation-dependent increases in cell invasion and motility. Together with the recent reports demonstrating the direct role of NHE1 in controlling actin dynamics in fibroblasts [25,26], our findings suggest that the reported antagonistic roles of RhoA and Rac1 in cell motility and cytoskeletal organization may also be due, in part, to their concerted action on NHE1 activity as a convergence point.
Methods
Cells and construction of expression vectors containing RhoA mutants
MDA-MB-435 cells were cultured as previously described [6]. Minus serum growth medium was complete Dulbeccos modified Eagle's medium (DMEM; GibcoBRL, Milano, Italy) without serum, in which the osmolarity, if necessary, had been adjusted to be identical to the plus serum medium by the addition of the necessary amount of mannitol. To deprive cells of serum, monolayers that had grown to confluency in complete DMEM with 10% foetal calf serum were washed two times in the minus serum DMEM and then the same volume of minus serum medium was added and the monolayers replaced in the incubator. After the indicated times the experiments were conducted.
pCEFL plasmids containing the dominant negative N19RhoA, N17Rac1 and N17Cdc42 mutants were kindly provided by Dr PP Di Fiore (European Institute of Oncology, Milan, Italy) and pEXV plasmids containing the constitutively active V14RhoA and V12Rac1 mutants by Dr MH Symons (Picower Institute for Molecular Research, Manhasset, NY, USA). These cDNAs were subcloned into the pBabe puro expression vector containing a haemagglutinin (HA) tag. Ten micrograms of plasmid cDNA or empty vector was incubated with 100 μl LipoTaxi reagent (Stratagene, La Jolla, CA, USA) in 1 ml of simple DMEM growth medium for 30 min at room temperature. Serum was added to 3% and 200 μl of this mixture was pipetted onto confluent monolayers on glass coverslips and placed in an incubator at 5% CO2 and 37°C for 6 hours. This mixture was then replaced with fresh complete medium (10% serum) for 24 hours in normal growth conditions. Cells were then treated and NHE1 activity, motility, or invasion were measured. The level of transfection was determined using Western blot of whole cell extracts using anti-HA antibody (Santa Cruz, Santa Cruz, CA, USA). The percentage of transfection with different concentrations of cDNA (0, 2.5, 5 and 10 μg plasmid cDNA) was evaluated by immunofluorescence microscopy of cell monolayers on coverslips using the same anti-HA antibody. As shown in Fig. 1 the transfection efficiency for both the dominant negative (dn) N17Rac1 cDNA and N19RhoA cDNA was commensurate with cDNA concentration and reached approximately 90% when cells were transfected with 10 μg plasmid cDNA. The transfection efficiencies observed with the other cDNAs utilized in the study were very similar to those for N19RhoA and N17Rac1 (data not shown).
Intracellular pH and H+ efflux rate determinations
Intracellular, cytoplasmic pH (pHi) was measured spectrofluorimetrically at 37°C with the fluorescent pH sensitive probe 2',7'-bis(carboxyethyl)-5, 6-carboxy-fluorescein (BCECF), and trapped intracellularly in cell monolayers grown on glass coverslips as previously described [6]. NHE1 activity was measured by monitoring pHi recovery after an intracellular acid load produced with the NH4Cl prepulse technique. The initial rate of Na+-dependent alkalinization was determined by linear regression analysis of the first 15 points taken at 4 s intervals after the readdition of sodium. The use of CO2/HCO3 free solutions minimizes the likelihood that Na+-dependent HCO3 transport was responsible for the observed changes in pHi. The pHi dependence of intracellular buffer capacity (βi; i.e. the buffering power of all non-HCO3, non-CO2 buffers) was computed using the NH4 pulse method, and the actual activity of the exchanger in terms of proton flux rate (mmol/l H+/min) was determined by multiplying the rates of pHi change by the cells' intrinsic buffering capacity (βi) at the pHi at which the measurement was taken. Preliminary experiments demonstrated that 2 μmol/l of the specific NHE1 inhibitor 5-(N, N-dimethyl)-amiloride (DMA; Sigma, Milano, Italy) almost completely blocked NHE1 activity, whereas transfection with empty pBabe vector had no effect on NHE1 activity (data not shown).
Motility and invasion
A quantitative measure of the degree of in vitro motility of the cells was obtained in BW25 Boyden Chambers (Neuro Probe Inc., Cabin John, MD, USA) using 8 μm polycarbonate membranes (Poretics, Livermore, CA, USA) coated with 5 μg collagen I as previously described [6]. After 4 hours of incubation, cells were fixed and stained (DiffQuick; Baxter, Oakland, CA, USA), those cells that had not transversed the filter were removed, and randomly chosen fields were photographed at 100× magnification using a Nikon Eclipse E800 microscope equipped with an MRC-1024 imaging system (Bio-Rad Laboratories, Milano, Italy). The number of cells traversing the filter in four random fields for each filter were counted from these images.
A quantitative measure of the degree of in vitro invasion was measured as the ability to infiltrate into live, confluent MCF-10A monolayers, essentially as previously described [6,29]. MDA-MB-435 cells were metabolically loaded with 3H-thymidine for the 24 hours prior to the experiment, cells were trypsinized, and a standard curve of incorporated 3H-dT/cell measured for each treatment. Tumour cells (80,000) were added in suspension to the complete medium of the confluent MCF-10A monolayers. Culture dishes were returned to the incubator for 8 hours and then unattached cells were removed by vigorous washing and mechanical agitation two times with phosphate-buffered saline (PBS). The number of MDA-MB-435 cells that had invaded the MCF-10A monolayer was calculated by measuring the incorporated 3H-thymidine for each MCF-10A monolayer in a Packard TopCount NXT® microplate scintillation counter (Packard Instruments, Inc., Palo Alto, CA, USA), and the number of cells present computed using the standard curve of disintegrations per min/cell for each treatment. Experiments standardizing this assay against the Boyden Chamber matrigel assay demonstrated high correlation between the two techniques.
Expression and activity of RhoA and Rac1
RhoA and Rac1 activity were assessed using the RhoA-binding domain of Rhotekin or Rac1-binding domain of PAK-1, respectively, in kits supplied from Upstate Biotechnology (Lake Placid, NY, USA). In brief, 3 × 106 cells were plated onto 10 cm cell culture dishes and after 24 hours they were treated as indicated. After the indicated time, cells were extracted using RIPA buffer (50 mmol/l Tris, pH 7.2, 500 mmol/l NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, 1% SDS, 10 mmol/l MgCl2, 0.5 μg/ml leupeptin, 0.7 μg/ml pepstatin, 4 μg/ml aprotinin, and 2 mmol/l PMSF). After centrifugation at 14,000 g for 3 min, the extracts were incubated for 45 min at 4°C with glutathione beads coupled with glutathione S-transferase (GST)–RBD (Rho-binding domain of Rhotekin) fusion protein or GST-PAK-1 (Upstate Biotechnology), and then washed three times with Tris buffer (pH 7.2), containing 1% Triton X-100, 150 mmol/l NaCl and 10 mmol/l MgCl2. The RhoA or Rac1 content in these samples or in 50 μg protein of cell homogenate was determined by immunoblotting samples using rabbit anti-RhoA antibody (Santa Cruz) or anti-Rac1 antibody (Upstate Biotechnology).
Fluorescence microscopy
For analysis of actin cytoskeleton organization, cells were plated on coverslips until they were approximately 60% confluent, at which time they were transfected with empty vector or dn-RhoA or dn-Rac1 and, after 24 hours of incubation, they were either subjected or not subjected to serum starvation in the presence or absence of 2 μmol/l DMA. After 24 hours, cells were fixed with 4% paraformaldehyde in PBS for 15 min, and after residual formaldehyde had been quenched with 50 mmol/l NH4Cl in PBS for 10 min the cells were permeabilized with 0.2% Triton X-100 in PBS for 10 min. Filamentous actin (F-actin) was stained with fluorescein isothiocyanate-conjugated phalloidin (Sigma) in PBS (0.5 U/ml) for 1 hour, whereas HA-positive cells were determined with anti-HA antibody (Santa Cruz) at a 1:100 dilution. Microscopy was performed using an MRC-1024 imaging system (Bio-Rad Laboratories) equipped with a Nikon Eclipse E800 microscope and a Nikon Plan Apo 40-by-1.0 or 60-by-1.4 oil immersion objective lens.
Analysis of F-actin content
Actin polymerization was detected using fluorescent phalloidin and analyzed by confocal microscopy and fluorescence activated cell sorting analysis. Analytic flow cytometric measurements were performed basically as previously described [30], using a Hewlett-Packard 9153C flow cytometer with argon laser excitation at 488 nm and detection through a 515–540 nm bandpass filter. Ten thousand cells in each sample were analyzed. For determination of F-actin amount the gate was defined as corresponding to scatter parameters on a dot plot of viable control cells (nondeprived). Phalloidin–fluorescein isothiocyanate fluorescence intensities from the gated population were presented as a histogram and the geometrical mean value was used as a measure of F-actin content.
Kinetic and statistical analysis
The kinetic coefficients for proton dependency of exchange activity were estimated using nonlinear curve-fitting regression utilizing the iterative Marquardt procedure on the KALIDIOGRAPH® (Abelbeck Software, Reading, PA, USA) program. The proton data were fitted to the Hill equation – V = (Vmax [H]n)/(Km + [H]n) – in which V is the experimental transport velocity, Vmax is the calculated maximum transport velocity, [H] is the cytosolic proton concentration, Km is the apparent affinity for protons, and n is the apparent Hill coefficient.
Results
NHE1 lies upstream of serum deprivation-dependent actin cytoskeletal reorganization
We previously showed that serum deprivation selectively stimulates NHE1 activity in breast cancer cells and their subsequent NHE1-dependent motility and invasion [6]. Recently, mounting evidence has shown that NHE1-dependent regulation of motility in normal cells occurs by direct NHE1-dependent reorganization of the actin cytoskeleton [25,26]. To evaluate whether NHE1 is upstream of actin reorganization during serum deprivation in cancer cells, we followed the serum deprivation-dependent remodelling of the F-actin cytoskeleton (Fig. 2a) and quantity of F-actin (Fig. 2b,2c) in the presence or absence of the specific NHE1 inhibitor DMA. As can be seen in Fig. 2a, control cells were primarily fusiform in shape with thin, uniform, parallel actin filaments (stress fibres) departing from single points and extending throughout the length of the cell. Serum deprivation provoked a complex and dramatic reorganization of the actin cytoskeleton, resulting in a reduction in the number of stress fibres with an uneven thickening of the remaining stress fibres. Consistent with the model proposed by the group of Barber [25,26], inhibition of NHE1 during serum deprivation with 2 μmol/l DMA completely abrogated the observed serum deprivation-dependent cytoskeletal reorganization, and depolymerization suggesting that stimulation of NHE1 is indeed up-stream of actin cytoskeletal reorganization. As can be seen in Fig. 2c, treatment with the actin cytoskeleton disruptor cytochalasin B (5 μmol/l for 15 min) reduced the amount of F-actin by approximately 75%. The lack of significant inhibitory effect by cytochalasin B treatment on NHE1 activity (absence versus presence of cytochalsin B: 16.2 ± 2.7 versus 20.6 ± 4.7 mmol/l intracellular H+ concentration/min [27% increase], n = 7; P = 0.58) further supports this hypothesis [31].
Serum deprivation activates Rac1 and inactivates RhoA
The usual model for RhoA-dependent alterations in motility is linked to RhoA action upon the actin cytoskeleton via the phosphorylation/inactivation of myosin phosphatase by the RhoA effector p160ROCK [21,22]. However, mounting evidence has suggested the existence of an alterative system in which RhoA/ROCK directly alters NHE1 activity [32] followed by the NHE1-dependent reorganization of the actin cytoskeleton [25,26]. It was recently reported that the tight junction protein NZO-3 increases kidney cell motility via a decrease in stress fibre number due to an inhibition of RhoA activity [33], and dihydromotuporamine C decreases cancer cell motility and invasion via an increase in stress fibre number that was due to a stimulation of Rho activity [34].
As a starting point for our study of the regulatory pattern of RhoA and Rac1 in the serum deprivation regulation of NHE activity and the migration/invasion of MDA-MB-435 cells, we measured the effect of serum deprivation on the activation status of RhoA and Rac1. To assess RhoA and Rac1 activation states, we used the GST fusion proteins of RBD to capture GTP-bound RhoA, and PAK-1 Rac-binding domain to capture GTP-bound Rac-1 from cell extracts. As shown in Fig. 3, serum deprivation resulted in a significant decrease in the amount of RhoA retained by RBD (panel a) and in a significant increase in Rac1 retained by PAK-1 Rac-binding domain (panel b). In contrast, serum deprivation had no effect on total cellular expression of either RhoA or Rac-1, demonstrating that it is indeed changes in their activity and not expression that drives their regulatory control of NHE activity. A similar pattern of inverse correlation between levels of active RhoA and active Rac1 was previously described in epithelial cells and fibroblasts [35,36].
RhoA and Rac1 reciprocally regulate the activity of NHE1 in basal and serum deprivation stimulated conditions
To address the question of whether Rho proteins are involved in the regulation of tumour cell NHE1 activity, we compared the effect of transient transfection of HA-tagged dn (Fig. 4a) or constitutively active (ca; Fig. 4b) mutants of Rho family proteins on both the basal activity of the NHE1 and its activation by serum deprivation. The relative level of transfection was determined using Western blot of whole cell extracts using anti-HA antibody (the inserts in the respective bar graphs in Fig. 4). The transfection efficiency for the various cDNAs was commensurate with cDNA concentration and reached approximately 90% when the cells were transfected with 10 μg plasmid cDNA (see the Methods section, above).
In cells not subjected to serum deprivation, the basal level of NHE1 activity was regulated reciprocally by these mutant constructs; specifically, NHE1 was slightly but significantly stimulated by inactivation of Rac1 induced by transfection with dn-N17Rac1 (stripped bar), and slightly but significantly inhibited by inactivation of RhoA induced by transfection with dn-N19RhoA (hatched bar). In serum deprived conditions NHE1 activity was still regulated reciprocally but the role of these G-proteins was reversed; inactivation of RhoA with N19RhoA potentiated the serum deprivation-dependent stimulation of the NHE1 (hatched bar), whereas inactivation of Rac1 with N17Rac1 blocked this stimulation (stripped bar). Inactivation of Cdc42 with dn-N17Cdc42 inhibited NHE1 activity to a similar degree in both nondeprived and deprived conditions (stippled bars).
These data suggest that RhoA and Rac1 play antagonistic roles in the regulation of both basal and serum deprivation-induced NHE1 activity, and that there is a reversal of their regulatory action with serum deprivation. To confirm this hypothesis, cells were transfected with constitutively active mutants of RhoA and Rac1. Activating RhoA with V14RhoA blocked serum deprivation-mediated stimulation of the NHE1 whereas the constitutively active V12Rac1 potentiated NHE1 stimulation by serum deprivation (Fig. 4b). Treating the cells with either a RhoA-specific pharmacological inhibitor (C3 exotoxin) or activator (CNF-1) produced a pattern identical to that observed with the RhoA mutated constructs (data not shown).
NHE1 activity is finely regulated by intracellular proton concentration via an allosteric proton regulatory site, and we previously showed that serum deprivation upregulated tumour cell NHE activity via an increased H+ affinity of this site, resulting in an alkaline shift of the pK value for intracellular H+ [6]. To determine whether RhoA and Rac1 modulate the serum deprivation-dependent upregulation of the NHE1 via this same mechanism, we analyzed the effect of the dominant negative mutants of RhoA or Rac1 on the dependence of NHE1 activity on pHi in serum deprived cells, as previously described [6]. Fig. 5 shows the relationship of NHE1 activity (Δmmol/l intracellular H+ concentration/min) to pHi in 24 hr serum complete (open circles) and serum deprived cells minus (closed circles) or plus transfection with dn-N17Rac1 (squares) or dn-N19RhoA (triangles) in a typical experiment. Serum deprivation shifted the pK value for intracellular H+ to alkaline values (alkaline shift) and increased the slope of the relationship. The observed alkaline shift in serum deprived conditions in the tumour cells is consistent with an increased capacity for net acid extrusion. In the cells transfected with dn-N17Rac1 (squares) serum deprivation no longer had any significant effect on the alkaline shift produced by serum deprivation, whereas transfection with dn-N19RhoA potentiated this alkaline shift. Kinetic analysis on five independent experiments for each treatment demonstrated that the Vmax and H+ affinity increased in MDA-MB-435 cells upon serum deprivation (Table 1) without a change in the Hill coefficient (napp remained approximately 2). Also, transfection with dn-N17Rac1 blocked the serum deprivation-dependent kinetic shifts whereas transfection with dn-N19RhoA potentiated them. These data suggest that one of the mechanisms by which RhoA and Rac1 regulate the serum deprivation-dependent increases in NHE1 activity is by modifying the affinity of the internal proton regulatory site of the NHE1 for protons.
Effect of dominant negative RhoA and Rac1 on serum deprivation-induced alterations in cell shape and actin organization
In order to determine the alterations in F-actin cytoskeletal organization associated with the above changes in cell shape, we next incubated the cells with tetramethylrhodamine 5-isothiocyanate (TRITC)-labelled phalloidin in order to stain F-actin in the different treatments. As can be seen in Fig. 6a, control cells were primarily fusiform in shape with thin, uniform, parallel actin filaments (stress fibres) departing from single points and extending throughout the length of the cell, with the presence of lamellipodia (arrow), but there were few dominant, elongated pseudopodia and few actin containing, fine cell extensions (termed invadopodia [37,38]). Serum deprivation provoked a complex and dramatic reorganization of the actin cytoskeleton that resulted in a reduction in number of stress fibres and lamellipodia, with an uneven thickening of the remaining stress fibres and an increase in both dominant leading-edge pseudopodia (arrowhead) observed at the ends of the cells and in invadopodia (asterisk). Interestingly, transfection with mutant constructs that increase either basal or deprivation-stimulated NHE1 activity and motility (e.g. dn-Rac1, ca-RhoA, dn-RhoA plus deprivation or ca-Rac1 plus deprivation) greatly augmented all of the serum deprivation-induced alterations in cell shape and actin organization. There was an increase in invadopodia in all conditions such as to produce a halo-like effect around the cell. Furthermore, in all conditions that increased NHE1 and motility, except for dn-Rac1, there was increased length and complexity of the pseudopodia that reached its maximum development in the condition of dn-RhoA plus deprivation (see also the larger magnification of the end of an elongated dominant leading-edge pseudopodia; Fig. 6b). A densiometric analysis (AutoDeblur 9.1; AutoQuant Imaging, Inc., New York, NY, USA; data not shown) confirmed that F-actin pixel density decreased with serum deprivation and in those treatments that increased invasion and motility (dn-Rac1, ca-RhoA, dn-RhoA plus deprivation or ca-Rac1 plus deprivation).
RhoA and Rac1 reciprocally regulate directed motility and invasive capacity of MDA-MB-435 cells via the activity of NHE1
We previously demonstrated that NHE1 activity plays a fundamental role in motility and invasion in MDA-MB-435 cells [6], and so we analyzed the ability of dominant negative and constitutively active mutants of RhoA or Rac1 to modify the serum deprivation-induced motility and invasive ability of MDA-MB-435 cells. As differential apoptosis could affect the outcome of these measurements, the effects of transfection of these mutants of RhoA and Rac1 on apoptosis were analyzed and we observed that neither 1 day of serum deprivation nor transfection of any plasmid had any significant effect on apoptosis in either basal or serum deprived states (data not shown).
As shown in Fig. 7, 24 hours of serum deprivation (D) significantly increased basal, non-serum-deprived (ND) MDA-MB-435 cell motility, measured as their ability to cross a collagen I layer in a Boyden Chamber (Fig. 7a,7b) and their capacity to invade a confluent MCF-10A cell monolayer (Fig. 7c,7d). This serum deprivation-dependent stimulation of motility and invasion was strongly inhibited in the cells transfected with either dn-N17Rac1 or ca-V14RhoA and was potentiated in the cells transfected with dn-N19RhoA or ca-V12Rac1. Furthermore, in non-serum-deprived conditions (ND), the basal level of cellular motility was regulated opposite to that observed during deprivation by these mutant constructs. That is, motility was slightly but significantly stimulated by transfection with either dn-N17Rac1 or ca-V14RhoA and slightly but significantly inhibited by transfection with dn-N19RhoA or ca-V12Rac1 (values expressed as cells traversed/filter [mean ± standard error of the mean]: control 2148 ± 80, dn-Rac1 3456 ± 263, ca-RhoA 3055 ± 104, dn-RhoA 1727 ± 94, ca-Rac1 1287 ± 55; n = 5 for each treatment).
Altogether, these data strongly suggest that regulation of both motility and invasion closely followed regulation of NHE1 activity. Furthermore, incubation with 2 μmol/l of the specific NHE1 inhibitor DMA almost completely abrogated both basal motility and invasion and their stimulation by serum deprivation, and the potentiation by either dn-N19RhoA or ca-V12Rac1, supporting our previous observation [6] and the findings of others [7-9] that the NHE1 plays a fundamental role in tumour cell motility and invasion. Serum deprived cells that had traversed the collagen coated filter also exhibited a more motile shape than did the serum replete cells (Fig. 7a), and this increase was potentiated in the cells transfected with dn-N19RhoA and attenuated in cells transfected with dn-N17Rac1.
Discussion
Rho family GTPases are greatly overexpressed in breast tumours [18] and RhoA is necessary for Ras-mediated transformation [19] and metastatic spread [39]. This suggests that these G-proteins play a critical role in neoplastic progression, but the mechanistic basis of their action in tumour cells is still not completely clear [20]. Here we demonstrate that the Rho family of GTPases are involved in the regulation of basal NHE1 activity and especially in the serum deprivation-induced stimulation of its activity. The regulation of both basal NHE1 activity and its upregulation by serum deprivation are linked to a reciprocity of RhoA and Rac1 action (Fig. 4a,4b). We propose that in these breast tumour cells this reciprocal antagonism between RhoA and Rac1 precisely coordinates the specificity and/or magnitude of the pathophysiological response of NHE1 to serum deprivation, with concomitant increases in motility and invasion and hence malignant progression [6].
The reciprocal antagonism between the actions of Rac1 and RhoA on breast cancer cell NHE1 activity, motility and invasive capacity is identical to that reported for movement/migration [21,22] and actin cytoskeleton remodelling [12,40] in other cell systems. Recent evidence supports a prominent role for NHE1 in coordinating motility [6-9] by selectively regulating cytoskeletal events such as focal adhesion assembly and turnover, cortical cytoskeleton dynamics and tyrosine phosphorylation of focal adhesion kinase, with consequent impaired recruitment and assembly of integrins, paxillin and vinculin at focal contacts [41]. Importantly, it was recently demonstrated that NHE1 can regulate these processes through direct interaction with the ERM family cytoskeletal linker protein ezrin [25,26]. In the present study we observed that elevated NHE1 activity is upstream from serum deprivation-dependent actin remodelling (Fig. 2) and the consequent increase in motility and invasion (Fig. 6); furthermore, we found that RhoA and Rac1 act on these processes through a modification of NHE1 activity. This association of increased motility with a decrease in stress fibre number driven by inhibition of RhoA activity is similar to that recently reported for the tight junction protein NZO-3 in controlling renal cell motility [33], and inhibition of motility and invasion by dihydromotuporamine C [34] or by degradation of RhoA by Smurf1 in controlling Mv1Lu cell motility [24], suggesting a common mechanism. In the latter work RhoA degradation was observed primarily in the cell protrusions, and we are currently determining whether inhibition of RhoA by serum deprivation occurs preferentially in the leading edge pseudopodia of the MDA-MB-435 cells.
Altogether these studies strongly suggest that in our cell system the reported antagonistic roles of RhoA and Rac1 in cytoskeletal organization, motility and invasion may also be due to their concerted, convergent action on NHE1 activity. Thus, RhoA and Rac1 integrate cytoskeletal reorganization, which is necessary to direct motility via protein–protein interactions of the NHE1 with other focal adhesion components, and thus they facilitate or inhibit downstream signalling and/or cytoskeletal cascades.
What remains unclear is whether the RhoA-dependent and Rac1-dependent regulation actin organization via NHE1 forms an integral part of the myosin light chain kinase/myosin phosphatase cycle [21,22], or whether it is part of an independent, parallel system that may cooperate to produce the characteristic integration of cell movement by RhoA and Rac1 antagonism. As discussed previously [23], this mechanism concerns smooth muscle cell contraction and cannot fully explain the effects of Rho family proteins on the actin cytoskeleton in other cell types, such as epithelial derived tumour cells. The studies performed to date focused on specific cell types and/or experimental systems, and so it is difficult to determine whether these two mechanisms coexist and to interpret possible interactions between the RhoA/Rac1 modulation of motility via the regulation of NHE1 activity [25,26] or via the myosin phosphatase/myosin light chain kinase cycle [21,22]. Furthermore, it will be necessary to determine whether the redox-dependent downregulation of RhoA by Rac1 with subsequent regulation of cytoskeletion and motility described in HeLa cells [17] has NHE1 as a downstream target. In this way we can begin to acquire an organized view of how the actin cytoskeleton and associated process of motility are regulated by the Rho family of GTPases, and to gain further insights into the mechanisms that underlie the variability of action of these GTPases on actin structures.
Conclusion
The present study defines the roles of RhoA and Rac1 in the regulation of Na+–H+ exchange, and consequently of motility/invasion in tumour cells and in the stimulation of these processes that is unique to tumour cells when confronted with serum deprivation – a common environmental condition in tumours. The regulation of both basal NHE1 activity and its upregulation by serum deprivation are linked to a reciprocity of the actions of RhoA and Rac1. Interestingly, the role of each of these G-proteins is reversed during serum deprivation; basal NHE1 activity is regulated positively by RhoA and negatively by Rac1, whereas RhoA negatively and Rac1 positively directs the stimulation of NHE1 during serum deprivation (Figs 4 and 5).
The findings presented here extend those of our earlier studies documenting the effects of serum deprivation and the role of PI3K on pH regulation, in which we postulated that the observed inversion of PI3K regulatory action contributes, in part, to the upregulation of the NHE1 by serum deprivation in tumour cells [6]. That deprivation-induced switch from positive to negative PI3K regulation may reflect a shift in the coupling of active Rho GTPases to different signalling pathways. We postulate that it is this inversion of RhoA/Rac1 function, together with PI3K regulatory action, that contributes, in part, to the upregulation of NHE1 by serum deprivation in MDA-MB-435 cells, and hence to increased invasion and malignant progression.
To date, the mechanism that drives this shift in RhoA/Rac1 function is not clear but growing evidence indicates that a delicate balance between positive and negative actions of a signal cascade can determine the specificity and/or magnitude of a physiological or pathophysiological response. A particularly important area in biology is the study of mechanisms that underlie dynamic organization of signalling networks in response to specific cellular signals. In the currently developing paradigm, it is becoming ever clearer that specificity of response in signal transduction is attained through the compartmentalization of signalling proteins and effectors close to their activators, regulatory elements and targets to ensure tight regulation and specificity of action of signalling cascades [39]. The assembly of these multimolecular complexes or modules at the cellular site of action is accomplished by scaffolding proteins that have the capacity, through simultaneous interaction with multiple signalling proteins, to integrate diverse signalling pathways [40]. A possibility is that serum deprivation alters the balance of expression or activity of a scaffolding protein, resulting in the creation of new spatiotemporal combinations and/or altered integration of signal components. Finally, the reversal in the roles of Rac1 and RhoA in the regulation of NHE1 activity and motility observed here might explain some seemingly contradictory reports in which activating RhoA can lead to either increased or decreased motility and invasiveness [18]. These discrepancies possibly reflect different serum treatments of the tumour cells after RhoA activation and point out the peril of an across-the-board inhibition of RhoA as a possible therapy. Importantly, these results also unify recent advances concerning the mechanisms underlying tumour invasive potential: the RhoA-ROCK activation of NHE1 [41,42], the RhoA-ROCK mediation of invasion [43,44] and role of the NHE1 in driving tumour cell motility [6-9] and invasion [6]. Further studies are now needed to elucidate the signal transduction components that lie upstream and downstream of RhoA and Rac1 action, and to clarify the interactions between the RhoA-and Rac1-dependent signal transduction pathways that are involved in their reciprocal antagonism. It will be of key interest to investigate the interrelationships between these activities and to define the effectors and downstream mediators that are involved in RhoA-and Rac1-transduced mitogenesis, transformation and invasiveness.
Competing Interests
None declared.
Abbreviations
ca = constitutively active; dn = dominant negative; DMA = 5-(N, N-dimethyl)-amiloride; DMEM = Dulbecco's modified Eagle's medium; GST = glutathione S-transferase; HA = haemagglutinin; NHE = Na+/H+ exchanger; PAK = p21-activated kinase; PBS = phosphate-buffered saline; pHi = intracellular pH; PI3K = phosphoinositide-3 kinase; RBD = Rho-binding domain of Rhotekin; ROCK = Rho kinase.
Acknowledgement
We thank Dr PP Di Fiore of the European Institute of Oncology, Milan, Italy for the dominant negative N19RhoA, N17Rac1 and N17Cdc42 plasmids, and Dr MH Symons of the Picower Institute for Molecular Research, Manhasset, NY, USA for the constitutively active V14RhoA and V12Rac1 plasmids. This work was supported by PSO CNR-MIUR Grant CU03.00304, FIRB Grant RBAU01B3A3-001 and the Italian Association for Cancer Research (l'AIRC). We thank CEGBA (Centro di Eccellenza di Genomica in Campo Biomedico ed Agrario). A Bagorda is the recipient of a fellowship from l'AIRC.
Figures and Tables
Figure 1 Efficiency of transfection with Rho family constructs. MDA-MB-435 cell monolayers were transfected with variable concentrations of the pBabe vector containing either the dominant negative (a) N17Rac1 or (b) 17NCdc42 construct. Forty-eight hours later the monolayers were fixed with 4% paraformaldehyde, and the expression of the haemagglutinin (HA) tag visualized by immunofluorescent microscopy with an anti-HA antibody. The percentage of cells expressing HA-tagged N17Rac1 increased in a concentration-dependent manner and reached approximately 90% when the cells were transfected with 10 μg plasmid cDNA.
Figure 2 Role of Na+–H+ exchanger (NHE)1 in serum deprivation-dependent rearrangement of the actin cytoskeleton. (a) To examine the role of NHE1 in serum deprivation-induced reorganization of the actin cytoskeleton underlying motility and invasive ability, MDA-MB-435 cell monolayers were left in fresh whole growth medium (nondeprived [ND]) or were serum deprived (D) in the presence or absence of 2 μmol/l of the specific NHE1 inhibitor 5-(N, N-dimethyl)-amiloride (DMA) for 1 day. The fluorescent photomicrographs show cell groups at a magnification of 40× (bar = 10 μm). (b) typical one-parameter fluorescence distributions of MDA-MB-435 cells labelled with phalloidin–fluorescein isothiocyanate in the above experimental conditions. The lighter coloured curve is from control, ND cells. Cyt B is the fluorescence distribution after treatment with the actin cytoskeleton disruptor cytochalasin B (5 μmol/l for 15 min). (c) Relative intracellular F-actin content measured by flow cytometry in MDA-MB-435 cells in the above experimental conditions. Mean ± standard error. n = 9; **P < 0.001 versus ND cells.
Figure 3 Serum deprivation activates Rac1 and inactivates RhoA without changes in total cellular expression. MDA-MB-435 cells were nondeprived (t0) or deprived of serum for 6 or 24 hours and cell extracts were assayed for (a) Rhotekin or (b) p21-activated kinase (PAK-1)-binding activity, respectively, as described in the Methods section. Representative immunoblots from these experiments are shown. For these experiments, the RhoA bound to the RBD or Rac1 bound to PAK-1 (upper panels) were normalized to the total RhoA or Rac1 content of cell extracts (lower panels). GST, glutathione-S-transferase; RBD, Rho-binding domain of Rhotekin (upper panel) or Rac-binding domain (lower panel); WB, Western blot.
Figure 4 Effect of Rho family mutants on the basal and serum deprivation stimulation of Na+–H+ exchanger (NHE)1 activity. To determine the role of Rho family G-proteins in the regulation of basal and serum deprivation stimulated Na+–H+ exchanger (NHE)1 activity, monolayers were transfected with empty pBabe vector or vector containing mutated Rho family genes. After 24 hours, medium was replaced with either serum replete or deplete medium for an additional 24 hours. Intracellular pH recoveries were measured and the initial rate of the NHE1 activity calculated as Na+-dependent H+ efflux, as described in the Methods section. All measurements were conducted in nominally HCO3-free, HEPES-buffered solutions in order to measure only the NHE. (a) Effect of dominant negative (dn) Rho family mutants on the serum deprivation-dependent upregulation of NHE1 activity. (b) Effect of constitutively active (ca) RhoA and Rac1 mutants on the serum deprivation-dependent upregulation of NHE1 activity. Data for control cells are shown in the gray solid bars, whereas the stippled bars represent data for Cdc42, stripped bars represent data for Rac1 and hatched bars represent data for RhoA. Data are expressed as means ± standard error for between 9 and 20 observations in each condition. *P < 0.01, **P < 0.001 versus control values; +P < 0.01, ++P < 0.001 versus deprived values. Insets show the level of transfection of haemagglutinin (HA)-tagged cDNA in a typical experiment by Western blot of whole cell extracts using anti-HA antibody.
Figure 5 Effect of dominant negative (dn) RhoA and Rac1 mutants on the intracellular (pHi) dependence of net acid extrusion rate. To examine the mechanism of RhoA and Rac1 modification of the serum deprivation-dependent increase in Na+–H+ exchanger (NHE)1 activity, the intracellular pH (pHi) dependence of NHE1 activity (Δmmol/l H+ i/minute) in MDA-MB-435 cell monolayers was measured. Monolayers were left in fresh whole growth medium (open circles) or serum deprived (closed circles) plus dn-N19RhoA (triangles) or dn-N17Rac1 (squares) for 24 hours. Results from a typical experiment are shown. The plots were computed as described in the Methods section. H+ i, intracellular proton concentration.
Figure 6 Role of RhoA and Rac1 in serum deprivation-dependent rearrangement of the actin cytoskeleton and cell shape. To examine the role of RhoA and Rac1 in serum deprivation-induced reorganization of the actin cytoskeleton underlying motility and invasive ability, MDA-MB-435 cell monolayers were (a) left in fresh whole growth medium (nondeprived) or were serum deprived (deprived) for 1 day after transfection with empty pBabe vector (Cont) or with dominant negative (dn)-N19RhoA, constitutively active (ca)-V14RhoA, dn-Rac1 or V12Rac1 inserted in the same vector (bar = 10 μm). Arrows indicate the presence of lamellipodia whereas pseudopodia are indicated by arrowheads and invadopodia along the cell surface or at the end of an dominant leading edge pseudopodia are indicated by an asterist. (b) The complex actin organization of a long, dominant leading edge pseudopodia extension in a cell transfected with dn-RhoA and deprived of serum at a higher magnification (bar = 5 μm).
Figure 7 Effect of dominant negative (dn) and constitutively active (ca) RhoA and Rac1 mutants on the Na+–H+ exchanger (NHE)1-dependent stimulationof cell motility, invasive capacity and cell shape by serum deprivation. To examine the effect of RhoA and Rac1 on serum deprivation-induced motility and invasive ability of the cells, MDA-MB-435 cell monolayers were left in fresh whole growth medium (nondeprived [ND]) or were serum deprived (D) after having been transfected with dn or ca mutants of RhoA (stippled bars) or Rac1 (stripped bars), or empty vector (empty bars) for 1 day. (a) For motility, cells were then trypsinized, centrifuged and resuspended in serum-free medium with or without 2 μmol/l 5-(N, N-dimethyl)-amiloride (DMA). Cells (20,000) were added to the upper chamber of a Boyden Chamber, in which the lower chamber containing medium plus 1% serum was separated by a collagen I coated filter with 8 μm pores. After 4 hours of incubation, filters were removed, processed as described in the Methods section, and the cells that had traversed the filter were photomicrographed (20×). The bar is 10 μm. Circles are the 8 μm pores of of the filter. (b) As described in the Methods section, the number of cells that tranversed the collagen I coated filter were counted in photomicrographs taken at 100×. Values are expressed as mean ± standard error. n = 4; **P < 0.001 versus nondeprived cells. For invasion, cells were metabolically loaded with 3H-thymidine for the 24 hours prior to the experiment, MDA-MB-435 cells were trypinsized, centrifuged and resuspended in serum-free medium, and 30,000 cells were added to the confluent MCF-10A monolayer in each well of a 96 well culture plate. After 8 hours incubation, noninvaded cells were removed and (c) the monolayer was photographed at 10× or (d) the number of invaded cells determined as described in the Methods section by counting in a Packard TopCount NXT® microplate scintillation counter. Values are expressed as mean ± standard error. n = 16; **P < 0.001 versus nondeprived cells.
Table 1 Effect of serum deprivation on the kinetic parameters of the allosteric proton regulatory site in human breast cell monolayers
Kinetic parameter ND D D + dn-RhoA D + dn-Rac1
Vmax 19.6 ± 0.75 26.2 ± 0.86* 31.3 ± 0.88† 21.6 ± 0.9
K' for H+ 0.150 ± 0.02 0.113 ± 0.05† 0.105 ± 0.07† 0.137 ± 0.023
napp 2.19 2.16 2.03 2.23
Values are expressed as mean ± standard error for Vmax (mmol/l H+/minute) and H+ apparent K' (μmol/l) measured at 135 mmol/l external sodium. The Hill coefficient (napp) was derived at 135 mmol/l external sodium. Confluent cell monolayers were either kept in serum complete medium (nondeprived [ND]) or were placed in serum free growth medium (D) minus or plus dominant negative (dn)-RhoA or dn-Rac1 for 24 hours prior to measurement of ΔH+. n = 5; *P < 0.01, †P < 0.001, versus ND.
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| 15535843 | PMC1064074 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2004 Sep 13; 6(6):R616-R628 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr922 | oa_comm |
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Breast Cancer Res
Breast Cancer Res
Breast Cancer Research : BCR
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BioMed Central
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Research Article
The progesterone receptor Val660→Leu polymorphism and breast cancer risk
De Vivo Immaculata [email protected]
Hankinson Susan E 12
Colditz Graham A 124
Hunter David J 1234
1 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
2 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
3 Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
4 Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
2004
22 9 2004
6 6 R636R639
16 4 2004
26 5 2004
13 7 2004
5 8 2004
Copyright © 2004 De Vivo et al.; licensee BioMed Central Ltd.
2004
De Vivo et al.; licensee BioMed Central Ltd.
This is an Open Access article: distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licences/by/2.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Background
Recent evidence suggests a role for progesterone in breast cancer development and tumorigenesis. Progesterone exerts its effect on target cells by interacting with its receptor; thus, genetic variations, which might cause alterations in the biological function in the progesterone receptor (PGR), can potentially contribute to an individual's susceptibility to breast cancer. It has been reported that the PROGINS allele, which is in complete linkage disequilibrium with a missense substitution in exon 4 (G/T, valine→leucine, at codon 660), is associated with a decreased risk for breast cancer.
Methods
Using a nested case-control study design within the Nurses' Health Study cohort, we genotyped 1252 cases and 1660 matched controls with the use of the Taqman assay.
Results
We did not observe any association of breast cancer risk with carrying the G/T (Val660→Leu) polymorphism (odds ratio 1.10, 95% confidence interval 0.93–1.30). In addition, we did not observe an interaction between this allele and menopausal status and family history of breast cancer as reported previously.
Conclusion
Overall, our study does not support an association between the Val660→Leu PROGINS polymorphism and breast cancer risk.
breast cancer
linkage disequilibrium
polymorphism
progesterone receptor
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Introduction
Until recently, the role of progesterone on mammary gland tumorigenesis was not well understood. Data from epidemiological studies revealed a higher risk for breast cancer in postmenopausal women who used a combination of estrogens and progestins, in comparison with those women who used estrogens alone [1,2]. As demonstrated in the progesterone receptor knockout mouse, the physiological effects of progesterone are completely dependent on the presence of its receptor gene, PGR, which exists as a single-copy gene. The PGR gene uses separate promoters and translational start sites to produce two protein isoforms, hPR-A and hPR-B [3-5], that are identical except for an additional 165 amino acids present only in the amino terminus of hPR-B [6,7]. Although hPR-B shares many important structural domains with hPR-A, the two isoforms are functionally distinct transcription factors [8] that mediate their own response genes and physiological effects with little overlap [9,10]. The progesterone receptor knockout mouse, in which the functional activity of both hPR-A and hPR-B were simultaneously ablated, revealed that progesterone is required for the formation of ductal and alveolar structures during pregnancy [11,12]. Selective ablation of PR-B in a mouse model, resulting in the exclusive production of PR-A, revealed that PR-B is necessary for breast formation [13]. Given the evidence described above for the role of progesterone in breast cancer causation, we proposed that variations in the PGR gene might predispose women to breast cancer. Several studies have investigated the Val660 →Leu G/T polymorphism and the PROGINS Alu insertion, which are in complete linkage disequilibrium [14], in association with breast cancer [15-17]. In this study we focused on the Val660→Leu G/T polymorphism that has been reported to be associated with a decreased risk of breast cancer [17].
Materials and methods
Detailed information about this nested case-control study and exposure data has been reported previously [18]. The protocol was approved by the Committee on Human Subjects, Brigham and Women's Hospital. Genotyping assays were performed by the 5' nuclease assay (TaqMan®) by the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA). TaqMan primers, probes, and conditions for genotyping assays are available from the authors on request. Genotyping was performed by laboratory personnel blinded to case-control status, and blinded quality control samples were inserted to validate genotyping procedures. Concordance for the blinded samples was 100%.
Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by using conditional and unconditional logistic regression. In addition to the matching variables, we adjusted for breast cancer risk factors: body mass index (BMI) (kg/m2) at age 18 years, weight gain since age 18 years, age at menarche, parity/age at first birth, duration of postmenopausal hormone use, first-degree family history of breast cancer, and history of benign breast disease. We also adjusted for age at menopause in analyses limited to postmenopausal women. Indicator variables for all genotypes were created by using the wild-type genotype as the reference category in the regression models. Because of the low prevalence of homozygous variants, we combined heterozygotes and homozygotes in the logistic regression analysis. Interactions between genotypes and breast cancer risk factors were evaluated by including appropriate interaction terms in unconditional logistic regression models. The nominal likelihood ratio test was used to assess the statistical significance of these interactions. We used SAS version 8.0 (SAS Institute, Cary, NC) for all analyses. We tested Hardy–Weinberg agreement by using a χ2 test.
Results and discussion
Our study included a total of 1323 incident breast cancer cases, diagnosed after blood draw to 1 June 2000, and 1854 matched controls. Of these, 1134 cases and 1640 controls were postmenopausal at blood draw, and 112 cases and 121 controls were premenopausal; menopausal status was uncertain in 77 cases and 93 controls. The mean age of cases at blood draw was 57.3 years; for controls it was 57.9 years. Cases and controls had similar mean BMI at blood draw (25.5 versus 25.5 kg/m2) and weight gain since age 18 years (11.6 versus 11.3 kg). In comparison with controls, cases had similar ages at menarche (12.5 versus 12.6 years), first birth (23.0 versus 23.0 years) and age at menopause (48.2 versus 47.9 years). The proportion of women with a first-degree family history of breast cancer was significantly higher among the cases (20.0% versus 15.0%). Cases were also more likely to have a history of benign breast disease (64.0% versus 51.0%) and a longer duration of postmenopausal hormone use (50.3% versus 49.7% current users for five or more years). The study population was predominantly Caucasian (89% of cases, 86% of controls).
The prevalence of the variant carriers was similar to that in a previous report for Caucasian women [14]: 31% for the cases and 29% for the controls. The genotype distribution of the Val660→Leu polymorphism among the cases and controls was in Hardy–Weinberg equilibrium. We did not observe a statistically significant association of breast cancer among carriers of the Val660→Leu G/T polymorphism. Too few homozygote variants were available in which to analyze the heterozygotes and homozygotes separately. Compared with the G/G wild-type genotype, the adjusted OR for women with G/T and T/T was 1.10 (95% CI 0.93–1.30) (Table 1). Because the previously reported inverse association was confined to premenopausal women, we stratified by menopausal status and observed no association among premenopausal women (adjusted OR 1.21 [95% CI 0.64–2.28]) although we had a relatively small number of women for this analysis (Table 1). The Val660→Leu polymorphism has been suggested to modify the association between family history of breast cancer and breast cancer [17]. We observed no statistically significant interactions between the Val660→Leu polymorphism and first-degree family history of breast cancer. In addition, we selected BMI, history of benign breast disease, and hormone replacement therapy use among postmenopausal women as potential effect modifiers based on biological plausibility. We observed no significant interactions between the Val660→Leu polymorphism and any of these risk factors.
Table 1 Association between the progesterone receptor exon 4 (Val660→Leu) G/T polymorphism and breast cancer risk
Genotypea Cases, n (%) Controls, n (%) Crude OR (95% CI) Adjusted OR (95 % CI)
G/G (Val/Val) 869 (69) 1186 (71) 1.0b 1.0c
G/T+T/T(Val/Leu+Leu/Leu) 383 (31) 474 (29) 1.08 (0.92–1.27) 1.10 (0.93–1.30)
Premenopausal women
G/G(Val/Val) 68 (65) 79 (72) 1.0d 1.0e
G/T+T/T(Val/Leu+Leu/Leu) 36 (35) 31 (28) 1.35 (0.75–2.41) 1.21 (0.64–2.28)
Postmenopausal women
G/G(Val/Val) 745 (69) 1044 (71) 1.0f 1.0g
G/T+T/T(Val/Leu+Leu/Leu) 330 (31) 427 (29) 1.09 (0.92–1.29) 1.08 (0.91–1.28)
aNumbers may vary because of missing genotypes.
bConditional logistic regression adjusted for the following matching variables: age, menopausal status, postmenopausal hormone use at blood draw, date at blood draw, time at blood draw, and fasting status.
cConditional logistic regression adjusted for matching variables and age at menarche, age at menopause, age at first birth/parity, Body Mass Index (BMI) at age 18 years, weight gain since age 18 years, benign breast disease, first-degree family history of breast cancer, and duration of postmenopausal hormone use.
dUnconditional logistic regression adjusted for matching variables listed in footnote b.
eUnconditional logistic regression adjusted for matching variables in footnote b and other covariates listed in footnote c.
fUnconditional logistic regression adjusted for the following matching variables: age, menopausal status, postmenopausal use at blood draw, date at blood draw, time at blood draw, and fasting status.
gUnconditional logistic regression adjusted for matching variables in footnote f and age at menarche, age at first birth/parity, BMI at age 18 years, weight gain since age 18 years, benign breast disease, first-degree family history of breast cancer, and duration of postmenopausal hormone use.
Conclusions
Our data do not support an inverse association between the Val660→Leu polymorphism and breast cancer risk as reported previously [17]. These results are consistent with recent studies of mostly Caucasian women in which no association was observed between this polymorphism and breast cancer risk in either premenopausal or postmenopausal women [15,16,19,20]. Most notable was the study by Spurdle and colleagues, in which a substantial number of premenopausal cases (n = 769) were evaluated [19]. We had limited power to study this association in premenopausal women, but this is the largest study of postmenopausal women reported so far. The large sample size, prospective design and extensive relevant life-style information are among the strengths of this study. In conclusion, our results suggest that there is no association between the Val660→Leu polymorphism and breast cancer risk despite the substantial power of the study (more than 80% power to detect an OR of 0.75 or less for the carrier genotype).
Competing interests
None declared.
Abbreviations
BMI = body mass index; CI = confidence interval; LD = linkage disequilibrium; OR = odds ratio PGR = progesterone receptor.
Acknowledgments
We thank the participants of the Nurses' Health Study for their exceptional dedication and commitment to the study, Dr Hardeep Ranu for genotyping and Pati Soule for laboratory support. This research was supported by NIH grants CA82838 (ID) and CA49449 (SEH), and American Cancer Society grant RSG-00-061-04-CCE (ID).
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| 15535845 | PMC1064075 | CC BY | 2021-04-27 23:14:50 | no | Breast Cancer Res. 2004 Sep 22; 6(6):R636-R639 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr928 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9291553584710.1186/bcr929Research ArticleGenetic polymorphism in the manganese superoxide dismutase gene, antioxidant intake, and breast cancer risk: results from the Shanghai Breast Cancer Study Cai Qiuyin [email protected] Xiao-Ou 1Wen Wanqing 1Cheng Jia-Rong 2Dai Qi 1Gao Yu-Tang 2Zheng Wei [email protected] Department of Medicine and Vanderbilt–Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA2 Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China2004 22 9 2004 6 6 R647 R655 23 5 2004 20 7 2004 3 8 2004 9 8 2004 Copyright © 2004 Cai et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
It has been suggested that oxidative stress and mitochondrial DNA damage play important roles in breast cancer carcinogenesis. Manganese superoxide dismutase (MnSOD) is a major enzyme that is responsible for the detoxification of reactive oxygen species in the mitochondria. A T → C substitution in the MnSOD gene results in a Val → Ala change at the -9 position of the mitochondrial targeting sequence (Val-9Ala), which alters the protein secondary structure and thus affects transport of MnSOD into the mitochondria.
Methods
We evaluated this genetic polymorphism in association with breast cancer risk using data from the Shanghai Breast Cancer Study, a population-based case–control study conducted in urban Shanghai from 1996 to 1998. The MnSOD Val-9Ala polymorphism was examined in 1125 breast cancer cases and 1197 age-frequency-matched control individual.
Results
Breast cancer risk was slightly elevated in women with Ala/Ala genotype (odds ratio [OR] 1.3, 95% confidence interval [CI] 0.7–2.3), particularly among premenopausal women (OR 1.8, 95% CI 0.9–3.7), as compared with those with Val/Val genotype. The increased risk with the Ala/Ala genotype was stronger among premenopausal women with a higher body mass index (OR 2.5, 95% CI 0.9–7.0) and more years of menstruation (OR 2.6, 95% CI 0.8–8.0). The risk among premenopausal women was further increased twofold to threefold among those with a low intake of fruits, vegetables, vitamin supplements, selenium, or antioxidant vitamins, including carotenes and vitamins A, C, and E. However, the frequency of the Ala allele was low (14%) in the study population, and most of the ORs provided above were not statistically significant.
Conclusion
The present study provides some evidence that genetic polymorphism in the MnSOD gene may be associated with increased risk of breast cancer among Chinese women with high levels of oxidative stress or low intake of antioxidants. Studies with a larger sample size are needed to confirm the findings.
antioxidantbreast cancercase-control studyMnSODpolymorphism
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Introduction
More than 90% of the body's oxygen is consumed by the electron transport chain in mitochondria [1], and about 1–5% of it is released as superoxide (O2•-) and hydrogen peroxide (H2O2) [2]. Reactive oxygen species (ROS) may also be generated from estrogen metabolism through catechol estrogen redox cycling [3,4]. Because of a high level of internally generated ROS, lack of histone protection, and a low level of DNA repair, mitochondrial DNA is particularly vulnerable to oxidative damage [5]. It has been suggested that mitochondrial DNA damage may play an important role in breast carcinogenesis [5,6]. Manganese superoxide dismutase (MnSOD) and glutathione peroxidase (GPX) 1 are two major enzymes that are responsible for ROS detoxification in mitochondria [7,8]. The MnSOD gene, which is composed of five exons and four introns, is localized to chromosome 6q25 [9,10]. A T → C substitution, resulting in a Val → Ala change at the -9 position (Val-9Ala), which alters the secondary structure of the protein [11], has been noted to affect transport of MnSOD into the mitochondria [12,13]. Another polymorphism (Ile58Thr) in exon 3 affects the stability of MnSOD and reduces protein amount and enzyme activity [14,15]. Cells that over-expressed the Ile58 allele had higher MnSOD activity than did cells that over-expressed the Thr58 allele [16]. It is biologically plausible that the Val-9Ala and Ile58Thr polymorphisms play an important role in ROS detoxification, thus affecting the risk for developing cancer, particularly among individuals with a higher level of oxidative stress or who are deprived of other antioxidative protection, such as through a low level of antioxidant intake.
Four studies examined the association of the Val-9Ala polymorphism with breast cancer risk, with mixed results [17-20]. Two hospital-based case–control studies found a moderate elevation in risk among women carrying the Ala/Ala genotype [17,18]. A population-based case–control study found no overall association with this polymorphism [19]. Recently, a case–control study conducted using data from the Ontario Familial Breast Cancer Registry found no association with this polymorphism [20]. All four studies were conducted among Caucasian women and had relatively small sample sizes, and most of them did not include a comprehensive assessment of environmental exposures or lifestyle data. Using data from the Shanghai Breast Cancer Study, a large-scale population-based case–control study conducted in urban Shanghai from 1996 to 1998, we evaluated the association of the MnSOD gene polymorphism with breast cancer risk, in conjunction with conditions related to oxidative stress and dietary intake of antioxidants.
Methods
Study participants
The cases and control individuals evaluated in this study were participants of the Shanghai Breast Cancer Study, a population-based case–control study. Detailed study methods were published elsewhere [21]. Briefly, the study included 1459 women aged between 25 and 64 years, who were diagnosed with breast cancer between August 1996 and March 1998, and 1556 age-frequency-matched control individuals. The study protocol was approved by committees of all relevant institutions for the study of humans in research. All study participants were permanent residents of urban Shanghai who had no prior history of cancer and were alive at the time of interview. Through a rapid case ascertainment system, supplemented by the population-based Shanghai Cancer Registry, a total of 1602 eligible patients with breast cancer were identified during the study period, and interviews were completed in-person by 1459 (91%) of them. The major reasons for nonparticipation were refusal (109 [6.8%]), death before the interview (17 [1.1%]), and inability to locate the person (17 [1.1%]). Cancer diagnoses for all patients were confirmed by two senior study pathologists by review of tumor slides.
Control individuals were selected using the Shanghai Resident Registry, a population registry containing demographic information for all residents of urban Shanghai, and were frequency matched for age (5-year intervals) to the expected age distribution of the cases in a 1:1 ratio. The inclusion criteria for control individuals were identical to those for the cases but with the exception of a diagnosis of breast cancer. Of the 1724 eligible women, 1556 (90.3%) completed interviews in-person. The remaining women were not included in the study either because of refusal (166 [9.6%]) or death before the interview (2 [0.1%]).
A structured questionnaire was used to elicit detailed information on demographic factors, menstrual and reproductive histories, hormone use, dietary habits, prior disease history, physical activity, tobacco and alcohol use, weight, and family history of cancer. For all participants, current weight, circumference of the waist and hips, and height while sitting and standing were measured. Blood samples were obtained from 1193 (82%) cases and 1310 (84%) control individuals who completed in-person interviews. These samples were processed on the same day, typically within 6 hours of sample collection, and stored at -70°C until bioassays were performed.
Usual dietary habits over the past 5 years were assessed by in-person interview, using a validated quantitative food frequency questionnaire [22,23]. The food frequency questionnaire included 76 food items or groups, 30 fresh vegetables and nine fruits, covering over 85% of foods commonly consumed in Shanghai. During the in-person interview, each study participant was first asked how frequently she consumed a specific food or group of foods (daily, weekly, monthly, yearly, or never), followed by a question on how many liangs (= 50 g) of food were eaten per unit time (day, week, month, or year) during the previous 5-year period, ignoring any recent changes in usual dietary intake within the 5-year period. Total dietary intakes of vitamin A (mg), carotene (mg), vitamin C (mg), vitamin E (mg), and selenium (μg) were calculated based on data from the Chinese Food Composition Table [24].
Genotyping method
Genomic DNA was extracted from buffy coat fractions. The MnSOD genotypes were determined with PCR–restriction fragment length polymorphism methods, as reported previously [17,25] but with some modification. Briefly, the primers for the Val-9Ala polymorphism were 5'-ACCAGCAGGCAGCTGGCGCCGG-3' (forward) and 5'-GCGTTGATGTGAGGTTCCAG-3' (reverse). The primers for the Ile58Thr polymorphism were 5'-AGCTGGTCCCATTATCTAATAG-3' (forward) and 5'-TCAGTGCAGGCTGAAGAGAT-3' (reverse). The PCR was performed in a PTC-200 Peltier Thermal Cycler (MJ Research Inc., Waltham, MA, USA). Each 20 μl of PCR mixture contained 5 ng DNA, 1× PCR buffer with 1.5 mmol/l MgCl2, 0.16 mmol/l each of dNTP, 0.5 μmol/l of each primer, and 1 unit of HotStarTaq™ DNA polymerase (Qiagen Inc., Valencia, CA, USA). The reaction mixture was initially denatured at 95°C for 15 min, followed by 35 cycles of 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s. The PCR was completed by a final extension cycle at 72°C for 7 min. The PCR products were digested by the NgoMIV and EcoRV restriction endonucleases for the Val-9Ala and Ile58Thr polymorphisms, respectively. The DNA fragments were then separated using 3% Nusieve/Agarose gel and visualized by ethidium bromide staining.
For the Val-9Ala polymorphism, the PCR product (107 bp) with C allele (Ala) was digested to two fragments (89 bp and 18 bp), whereas the PCR product with T allele (Val) cannot be cut by NgoMIV. For the Ile58Thr polymorphism the PCR product (139 bp) with T allele (Ile) was digested to two fragments (117 bp and 22 bp), whereas the PCR product with C allele (Thr) cannot be cut by EcoRV. No Ile58Thr polymorphism was found in 400 individuals in our study, and we did not perform genotyping for this polymorphism in all participants.
The laboratory staff was blind to the identity of the participants. Quality control (QC) samples were included in the genotyping assays. Each 96-well plate contained one water, two CEPH 1347-02 DNA, two blinded QC DNA, and two unblinded QC DNA samples. The blinded and unblinded QC samples were taken from the second tube of study samples included in the study. The Val-9Ala genotypes determined for the blinded QC samples were in complete agreement with the genotypes determined for the study samples. Genotyping data were obtained from 1125 cases and 1197 control individuals. The major reasons for incomplete genotyping were insufficient DNA and unsuccessful PCR amplification.
Statistic analysis
To evaluate case–control differences for categorical data, including genotype distribution and continuous variables, χ2 test and t-test were used, respectively. Conditional logistic regression models adjusted for age were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to measure the strength of the association between the MnSOD gene Val-9Ala polymorphism and breast cancer risk [26,27]. Analyses stratified by menopausal status and age were conducted to check homogeneity of the association. Additional analyses stratified by body mass index (BMI), years of menstruation, and intake of fruits, vegetables, vitamin supplements, selenium, or antioxidant vitamins were conducted to evaluate the potential modifying effects of these variables on the association between the MnSOD genotypes and breast cancer risk. A composite dietary antioxidant index was derived to incorporate information on intake of four antioxidant nutrients (i.e. selenium and vitamins A, C, and E) [28]. Intake of each antioxidant nutrient was first standardized by subtracting the mean and dividing by the standard deviation, and then the antioxidant index was created by summing the standardized intake of these four antioxidant nutrients with equal weight [28]. All statistical tests were two-sided.
Results
The distribution of selected demographic characteristics and major risk factors for breast cancer are shown in Table 1. Breast cancer cases and controls were similar in age and level of education. Elevated risks were observed for all known major breast cancer risk factors [29], including a prior history of breast fibroadenoma, physical inactivity, higher waist-to-hip ratio, higher BMI, early onset of menarche, late onset of menopause, and late age at first live birth. No apparent differences were found between individuals with genotyping data and those included in the whole study in any of the major known risk factors, demographic characteristics, and dietary antioxidant intake (data not shown), indicating that the chance of selection bias in this study is likely to be small.
The frequencies of the Ala allele were 14.3% and 14.0% in cases and control individuals, respectively. Genotype frequencies were 73.9% (Val/Val), 23.6 (Val/Ala), and 2.5% (Ala/Ala) for cases, and the respective frequencies were 73.9%, 24.2%, and 1.9% for control individuals (Table 2). The MnSOD Val-9Ala polymorphism was in Hardy–Weinberg equilibrium for both cases and control individuals. No Ile58Thr polymorphism was found in our study population. Thus, we report here the results for the Val-9Ala polymorphism.
Compared with women with the Val/Val genotype, breast cancer risk was slightly but not statistically significantly elevated for women with the Ala/Ala genotype (age-adjusted OR 1.3, 95% CI 0.7–2.3; Table 2). The Val/Ala genotype was unrelated to risk and was combined with the Val/Val genotype in some subsequent analyses. Additional adjustment for physical activity, BMI, waist-to-hip ratio, age at menarche, number of pregnancies, age at first birth, and family history of breast cancer had no appreciable effect on age-adjusted ORs, regardless of whether the analyses were done among all participants or stratified by menopausal status or age. Thus, only the age-adjusted ORs are presented. The elevated risk associated with the Ala/Ala genotype was restricted to premenopausal women (OR 1.8, 95% CI 0.9–3.7; for postmenopausal women OR 0.8, 95% CI 0.3–2.0) and women younger than 45 years (OR 1.8, 95% CI 0.7–4.3; for women older than 45 years OR 1.1, 95% CI 0.5–2.2).
Further analyses were conducted to evaluate the association of the MnSOD Val-9Ala polymorphism and breast cancer risk by duration (years) of menstruation and BMI, factors that are related to the duration and level of estrogen exposure (Table 3). The ORs for the Ala/Ala genotype were higher among premenopausal women with a higher BMI (OR 2.5, 95% CI 0.9–7.0) or a longer duration of menstruation (OR 2.6, 95% CI 0.8–8.0). Again, the ORs were not statistically significant. This increased risk was not observed in postmenopausal women (Table 3).
We further evaluated the association of the MnSOD Val-9Ala polymorphism with breast cancer risk by dietary antioxidant intake (Table 4). Intriguingly, the positive association with the Ala/Ala genotype among premenopausal women was consistently found to be stronger among those who had a low intake of fruits, vegetables, selenium, or antioxidant vitamins, including carotenes and vitamins A, C, and E, than among those who had a higher intake of these dietary factors. This pattern of association suggests a modifying effect, although neither the ORs nor the interaction tests were statistically significant, perhaps as a result of the small numbers of participants in the subgroups. The modifying effect of these antioxidant intakes on the association between the MnSOD Val-9Ala polymorphism and breast cancer risk was less apparent in the analyses among postmenopausal women. However, the postmenopausal case–control numbers were small. To illustrate the joint effects of dietary antioxidant intake, we further evaluated the association of the MnSOD Val-9Ala polymorphism with breast cancer risk by dietary antioxidant index. ORs for the Ala/Ala genotype were 2.4 (95% CI 0.6–9.5) and 2.2 (95% CI 0.5–9.4) among premenopausal and postmenopausal women, respectively, who had a lower dietary antioxidant index.
Discussion
Ambrosone and colleagues previously reported that Val/Val genotype was significantly associated with an increased risk of breast cancer among premenopausal Caucasian women, particularly those who had a low intake of fruits and vegetables and of dietary ascorbic acid and α-tocopherol [17]. In this large-scale, population-based, case–control study, we found that breast cancer risk was slightly but not significantly elevated in Chinese women with the Ala/Ala genotype as compared with women with the Val/Val genotype, particularly among premenopausal women who had a low intake of fruits, vegetables, selenium, or antioxidant vitamins. A significant association of this polymorphism with breast cancer risk was not observed in present study, which might partly be due to low Ala allele frequency in Chinese women.
We observed in this study that women carrying the Ala/Ala genotype who had a higher BMI or a longer duration of menstruation were at higher risk of breast cancer, particularly among premenopausal women. Some ROS may be generated from estrogen metabolism through catechol redox cycling [3,4]. Mitrunen and coworkers [18] conducted a case–control study among 483 cases and 482 controls in a Finnish Caucasian population, and reported that the Ala allele was associated with breast cancer risk, with an OR of 1.5 in the Ala/Ala or Val/Ala groups compared with the Val/Val group. Postmenopausal women who had used estrogen replacement therapy and carried either the Ala/Ala or Val/Ala genotype had a 2.5-fold higher risk for breast cancer. Women who had used oral contraceptives and carried the Ala/Ala or Val/Ala genotype had a 3.0-fold higher risk for breast cancer [18]. More recently, Egan and coworkers [19] conducted a population-based case–control study among 476 cases and 502 controls in an American population. Overall, relative risks were not significantly elevated in women with one (OR 1.27, 95 CI 0.91–1.77) or two (OR 1.18, 95% CI 0.81–1.73) Ala alleles, as compared with the Val/Val genotype. Risk, however, was increased among premenopausal women carrying the Val/Ala genotype (OR 1.88), but not among women carrying the Ala/Ala genotype (OR 0.94) [19]. Women carrying the Ala/Ala or Val/Ala genotype who had used oral contraceptives or had higher BMI also had an increased risk for breast cancer [19]. Because of the low frequency of the Ala/Ala genotype and low percentage of estrogen use among Chinese women, we are unable to perform the analyses stratified by these exogenous estrogen exposures.
Several studies have evaluated the association of the MnSOD Val-9Ala polymorphism with other cancers, although the results were inconsistent among cancer sites. Recently, Woodson and coworkers [30] reported that the Ala/Ala genotype was associated with a 1.7-fold (95% CI 0.96–3.08) increased risk for prostate cancer as compared with the Val/Val genotype. No association of this polymorphism with colorectal adenomas was found in a sigmoidoscopy-based case–control study [31]. Recently, Lin and coworkers [32] reported no association of the MnSOD Val-9Ala polymorphism with lung cancer risk in a case–control study conducted in Taiwan. Interestingly, Wang and coworkers [33] reported that the Val allele was associated with lung cancer risk with ORs of 1.34 (95% CI 1.05–1.70) and 1.67 (95% CI 1.27–2.20) in the Val/Ala and Val/Val group, respectively. These differences in the associations of the MnSOD genotype with breast and lung cancers suggest that the role played by MnSOD in carcinogenesis may vary for different tumors. The mechanism underlying this tumor type difference remains to be investigated.
The SODs are the first and most important line of antioxidant enzyme defense against ROS and particularly O2•- radical. It was predicted that MnSOD Val-9Ala polymorphism might alter transfer of the MnSOD enzyme into mitochondria [11,12]. Recently, Sutton and coworkers [13] reported that the Ala-MnSOD precursor generated 30–40% more of the active, matricial, and processed MnSOD homotetramer in mitochondrial matrix than did Val-MnSOD. Some human tumor cells lost MnSOD activity, and this loss has been shown to be responsible for at least part of the malignant phenotype [34,35]. MnSOD knockout mice exhibited increased oxidative DNA damage [36]. Over-expression of MnSOD in MCF-7 cell suppressed the malignant phenotype, as evidenced by decreased cell proliferation, clonogenic fraction in soft agar culture, and tumor growth in nude mice [37]. Recently, Soini and coworkers [38] reported that MnSOD expression is less frequent in the tumor cells of invasive breast carcinomas than in in situ carcinomas or non-neoplastic breast epithelial cells.
Three distinct isoforms of SOD have been identified in mammals. These three isoforms are encoded by three distinct genes located on different chromosomes [10]. All three SOD genes are polymorphic. It would be interesting to analyze jointly all of the three genes to evaluate any gene–gene interaction in relation to breast cancer risk. O2•-, if not scavenged by SOD, may react with nitric oxide radical (NO•) to form the strong oxidant peroxynitrite (ONOO-) [39]. MnSOD dismutates O2•- to H2O2, which is further detoxified by GPX1 in mitochondria. If not be quenched, H2O2 will be converted to the more toxic hydroxyl radical (•OH). Thus, study of the joint effect of the MnSOD, nitric oxide synthase, and GPX1 gene polymorphisms might provide further information on the role played by oxidative stress in cancer risk.
Frequencies of the Val and Ala alleles of the MnSOD Val-9Ala polymorphism in our control population were 86.0% and 14.0%, respectively. The minor allele (Ala) frequency (14.0%) is comparable with that in Japanese (14.1%) [40] and Chinese (11.5%) [41] populations, but substantially lower than that in Caucasian populations [17-20]. The low frequency of the Ala allele in our study population contributes to a wider range of 95% CIs in OR estimations and limits the statistical power for stratified analyses. Small numbers of individuals, such as in the subgroups stratified by menopausal status in the present study, may result in unstable OR estimates. Studies with a larger sample size are needed to confirm the findings.
The current study has many strengths. First, the high participation rate and the population-based study design substantially reduce selection bias. Second, Chinese women living in Shanghai are relatively homogenous in their ethnic background; over 98% are from a single ethnic group (Han Chinese). Therefore, the potential confounding effect of ethnicity is not a major concern in this study. Third, the extensive information collected on lifestyle factors allowed comprehensive evaluation of their interaction with genetic polymorphisms. The risk estimates derived from age-adjusted and multivariable adjusted analyses were similar, indicating that a confounding effect is unlikely to be a concern in this study.
Conclusion
In this population-based case–control study conducted in Chinese women, we found that the MnSOD Ala/Ala genotype was associated with a slightly but nonsignificantly elevated risk of breast cancer. The positive association was more evident among premenopausal women, particularly among those who consumed a low level of antioxidant vitamins or with high levels of oxidative stress. However, the study is limited by the low frequency of the Ala allele in the Chinese population, and most of the ORs were not statistically significant. Studies with a larger sample size are needed to confirm the findings.
Author contributions
QC, X-OS, and WZ participated in interpretation of results and writing the manuscript. QC participated in laboratory assays. WW conducted the statistical analyses. J-RC, QD, and Y-TG participated in field operation.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
BMI = body mass index; bp = base pairs; CI = confidence interval; GPX = glutathione peroxidase; MnSOD = manganese superoxide dismutase; OR = odds ratio; PCR = polymerase chain reaction; QC = quality control; ROS = reactive oxygen species.
Acknowledgements
We thank Dr Fan Jin for her valuable contribution in coordinating the field operation, Nabela Hamm and Qing Wang for their excellent technical laboratory assistance, and Bethanie Hull for technical assistance in manuscript preparation. This study would not have been possible without the support of all of the study participants and research staff of the Shanghai Breast Cancer Study. This research was supported by USPHS grants RO1CA64277 and RO1CA90899 from the National Cancer Institute and grant DAMA17-02-1-0603 from the US Department of Defense.
Figures and Tables
Table 1 Comparisons of cases and controls by selected demographic characteristics and major risk factors in the Shanghai Breast Cancer Study
Characteristic Cases (n = 1125) Controls (n = 1197) P-valuea
Demographic factors
Age (years [mean ± SD]) 47.63 ± 8.00 47.20 ± 8.78 0.217
Education, lower than middle school (%) 12.27 14.79 0.124
Major risk factors
Breast cancer in first-degree relatives (%) 3.38 2.26 0.101
Ever had breast fibroadenoma (%) 9.78 5.26 < 0.001
Age at menarche (years [mean ± SD]) 14.41 ± 1.64 14.71 ± 1.73 0.004
Age at first live birthb (years [mean ± SD]) 26.82 ± 4.07 26.17 ± 3.83 < 0.001
Age at menopausec (years [mean ± SD]) 48.17 ± 4.65 47.46 ± 4.95 0.036
Physically active in past 10 years (%) 19.40 26.00 < 0.001
Body mass index (mean ± SD) 23.54 ± 3.39 23.25 ± 3.45 0.047
Waist-to-hip ratio (mean ± SD) 0.81 ± 0.06 0.80 ± 0.06 0.004
aFrom χ2 test (categorical variables) or T test (continuous variables). bAmong parous women.
cAmong postmenopausal women. SD, standard deviation.
Table 2 Association between the manganese superoxide dismutase polymorphism and breast cancer risk in the Shanghai Breast Cancer Study
Genotype Cases (n [%]) Controls (n [%]) ORa 95% CI
All subjects
Val/Val 831 (73.9) 884 (73.9) 1.0 Reference
Val/Ala 266 (23.6) 290 (24.2) 1.0 0.8–1.2
Ala/Ala 28 (2.5) 23 (1.9) 1.3 0.7–2.3
Val/Val and Val/Ala 1097 (97.5) 1174 (98.1) 1.0 Reference
Ala/Ala 28 (2.5) 23 (1.9) 1.3 0.7–2.3
Stratified analyses by menopausal status
Premenopausal women
Val/Val and Val/Ala 729 (97.2) 751 (98.4) 1.0 Reference
Ala/Ala 21 (2.8) 12 (1.6) 1.8 0.9–3.7
Postmenopausal women
Val/Val and Val/Ala 363 (98.1) 419 (97.4) 1.0 Reference
Ala/Ala 7 (1.9) 11 (2.6) 0.8 0.3–2.0
P for interaction = 0.147
Stratified analyses by age
Age < 45 years
Val/Val and Val/Ala 442 (97.1) 488 (98.4) 1.0 Reference
Ala/Ala 13 (2.9) 8 (1.6) 1.8 0.7–4.3
Age ≥ 45 years
Val/Val and Val/Ala 655 (97.8) 686 (97.9) 1.0 Reference
Ala/Ala 15 (2.2) 15 (2.1) 1.1 0.5–2.2
P for interaction = 0.380
aAdjusted for age. CI, confidence interval; OR, odds ratio.
Table 3 Associations of breast cancer with the manganese superoxide dismutase polymorphism, stratified by lifestyle factors, in the Shanghai Breast Cancer Study
Stratified variable (by median) MnSOD genotypes
Val/Val Val/Ala Ala/Ala
na ORb (95% CI) na ORb (95% CI) na ORb (95% CI)
Premenopausal women
BMI
≤ Median 250/275 1.0 (ref.) 73/99 0.9 (0.6–1.2) 8/7 1.3 (0.5–3.7)
> Median 300/288 1.0 (ref.) 106/89 1.1 (0.8–1.6) 13/5 2.5 (0.9–7.0)
Years of menstruationc
≤ Median 203/286 1.0 (ref.) 77/91 1.2 (0.8–1.7) 8/8 1.4 (0.5–3.7)
> Median 347/277 1.0 (ref.) 102/97 0.8 (0.6–1.2) 13/4 2.6 (0.8–8.0)
Postmenopausal women
BMI
≤ Median 126/156 1.0 (ref.) 39/52 1.0 (0.6–1.6) 4/7 0.7 (0.2–2.5)
> Median 152/161 1.0 (ref.) 46/50 1.0 (0.6–1.6) 3/4 0.8 (0.2–3.7)
Years of menstruationc
≤ Median 128/172 1.0 (ref.) 44/54 1.1 (0.7–1.8) 2/6 0.4 (0.1–2.2)
> Median 150/145 1.0 (ref.) 41/48 0.8 (0.5–1.3) 5/5 1.0 (0.3–3.5)
aNumber of cases/controls. bAdjusted for age. cYears of menstruation = menopausal age or age at interview for premenopausal women – menarche age. CI, confidence interval; MnSOD, manganese superoxide dismutase; OR, odds ratio; ref., reference.
Table 4 Associations of breast cancer with the manganese superoxide dismutase polymorphism polymorphism, stratified by food and nutrient intake, in the Shanghai Breast Cancer Study
Stratified variable Premenopausal women Postmenopausal women
Val/Val Val/Ala Ala/Ala Val/Val Val/Ala Ala/Ala
na ORb (95% CI) na ORb
(95% CI) na ORb
(95% CI) na ORb (95% CI) na ORb
(95% CI) na ORb
(95% CI)
Total vegetables
≤ Median 273/290 1.0 (ref.) 89/88 1.1 (0.8–1.6) 9/4 2.4 (0.7–8.0) 145/164 1.0 (ref.) 49/44 1.3 (0.8–2.0) 4/6 0.7 (0.2–2.7)
> Median 277/273 1.0 (ref.) 90/100 0.9 (0.6–1.2) 12/8 1.5 (0.6–3.7) 133/153 1.0 (ref.) 36/58 0.7 (0.5–1.2) 3/5 0.8 (0.2–3.3)
Total fruits
≤ Median 260/281 1.0 (ref.) 95/98 1.1 (0.8–1.5) 7/3 2.6 (0.7–10.0) 152/166 1.0 (ref.) 38/44 1.0 (0.6–1.6) 4/5 1.0 (0.3–3.8)
> Median 290/282 1.0 (ref.) 84/90 0.9 (0.6–1.2) 14/9 1.5 (0.7–3.6) 126/151 1.0 (ref.) 47/58 1.0 (0.6–1.5) 3/6 0.6 (0.1–2.3)
Vitamin A
≤ Median 238/286 1.0 (ref.) 86/92 1.1 (0.8–1.6) 10/4 2.9 (0.9–9.2) 111/165 1.0 (ref.) 29/46 1.0 (0.6–1.6) 5/4 1.9 (0.5–7.3)
> Median 312/277 1.0 (ref.) 93/96 0.9 (0.6–1.2) 11/8 1.3 (0.5–3.3) 167/152 1.0 (ref.) 55/56 0.9 (0.6–1.4) 2/7 0.3 (0.1–1.3)
Carotene
≤ Median 281/287 1.0 (ref.) 88/90 1.0 (0.7–1.5) 10/4 2.6 (0.8–8.4) 131/157 1.0 (ref.) 41/52 0.9 (0.6–1.5) 3/6 0.6 (0.1–2.4)
> Median 269/276 1.0 (ref.) 91/98 0.9 (0.7–1.3) 11/8 1.4 (0.5–3.5) 147/160 1.0 (ref.) 44/50 1.0 (0.6–1.6) 4/5 0.9 (0.2–3.4)
Vitamin E
≤ Median 288/282 1.0 (ref.) 96/95 1.0 (0.7–1.4) 10/5 1.9 (0.7–5.7) 156/155 1.0 (ref.) 48/53 0.9 (0.6–1.5) 4/7 0.6 (0.2–2.1)
> Median 262/281 1.0 (ref.) 83/93 1.0 (0.7–1.4) 11/7 1.8 (0.7–4.9) 122/162 1.0 (ref.) 37/49 1.1 (0.6–1.7) 3/4 1.0 (0.2–4.8)
Vitamin C
≤ Median 275/282 1.0 (ref.) 92/96 1.0 (0.7–1.4) 12/4 3.1 (1.0–9.8) 137/161 1.0 (ref.) 43/48 1.1 (0.7–1.7) 5/6 1.0 (0.3–3.3)
> Median 275/281 1.0 (ref.) 87/92 1.0 (0.7–1.3) 9/8 1.1 (0.4–3.0) 141/156 1.0 (ref.) 42/54 0.9 (0.6–1.4) 2/5 0.5 (0.1–2.5)
Selenium
≤ Median 256/278 1.0 (ref.) 78/101 0.9 (0.6–1.2) 6/3 2.1 (0.5–8.6) 115/164 1.0 (ref.) 37/48 1.1 (0.7–1.8) 5/3 2.3 (0.5–9.9)
> Median 294/285 1.0 (ref.) 101/87 1.1 (0.8–1.6) 15/9 1.7 (0.7–3.9) 163/153 1.0 (ref.) 48/54 0.9 (0.5–1.3) 2/8 0.3 (0.1–1.2)
Vitamin supplement
No 488/480 1.0 (ref.) 162/174 0.9 (0.7–1.2) 20/10 1.9 (0.9–4.2) 227/255 1.0 (ref.) 70/80 1.0 (0.7–1.5) 5/10 0.6 (0.2–1.7)
Yes 62/83 1.0 (ref.) 17/14 1.6 (0.7–3.6) 1/2 0.7 (0.1–7.8) 51/62 1.0 (ref.) 15/22 0.9 (0.4–2.0) 2/1 2.1 (0.2–24.3)
Dietary antioxidant indexc
≤ Median 262/280 1.0 (ref.) 89/98 1.0 (0.7–1.4) 7/3 2.4 (0.6–9.5) 126/161 1.0 (ref.) 43/51 1.1 (0.7–1.8) 5/3 2.2 (0.5–9.4)
> Median 288/283 1.0 (ref.) 90/90 1.0 (0.7–1.4) 14/9 1.6 (0.7–3.8) 152/156 1.0 (ref.) 42/51 0.9 (0.5–1.4) 2/8 0.3 (0.1–1.2)
aNumber of cases/controls. bAdjusted for age. cDietary antioxidant index was derived as described in the Methods section. CI, confidence interval; OR, odds ratio; ref., reference.
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| 15535847 | PMC1064076 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Sep 22; 6(6):R647-R655 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr929 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9301553584910.1186/bcr930Research ArticleProteotypic classification of spontaneous and transgenic mammary neoplasms Mikaelian Igor [email protected] Natalie [email protected] Gary A [email protected] Karen [email protected] Barbara B [email protected] Janan T [email protected] John P [email protected] The Jackson Laboratory, Bar Harbor, Maine, USA2004 6 10 2004 6 6 R668 R679 26 5 2004 9 7 2004 16 7 2004 9 8 2004 Copyright © 2004 Mikaelian et al. licensee BioMed Central LtdThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Mammary tumors in mice are categorized by using morphologic and architectural criteria. Immunolabeling for terminal differentiation markers was compared among a variety of mouse mammary neoplasms because expression of terminal differentiation markers, and especially of keratins, provides important information on the origin of neoplastic cells and their degree of differentiation.
Methods
Expression patterns for terminal differentiation markers were used to characterize tumor types and to study tumor progression in transgenic mouse models of mammary neoplasia (mice overexpressing Neu (Erbb2), Hras, Myc, Notch4, SV40-TAg, Tgfa, and Wnt1), in spontaneous mammary carcinomas, and in mammary neoplasms associated with infection by the mouse mammary tumor virus (MMTV).
Results
On the basis of the expression of terminal differentiation markers, three types of neoplasm were identified: first, simple carcinomas composed exclusively of cells with a luminal phenotype are characteristic of neoplasms arising in mice transgenic for Neu, Hras, Myc, Notch4, and SV40-TAg; second, 'complex carcinomas' displaying luminal and myoepithelial differentiation are characteristic of type P tumors arising in mice transgenic for Wnt1, neoplasms arising in mice infected by the MMTV, and spontaneous adenosquamous carcinomas; and third, 'carcinomas with epithelial to mesenchymal transition (EMT)' are a characteristic feature of tumor progression in Hras-, Myc-, and SV40-TAg-induced mammary neoplasms and PL/J and SJL/J mouse strains, and display de novo expression of myoepithelial and mesenchymal cell markers. In sharp contrast, EMT was not detected in papillary adenocarcinomas arising in BALB/cJ mice, spontaneous adenoacanthomas, neoplasms associated with MMTV-infection, or in neoplasms arising in mice transgenic for Neu and Wnt1.
Conclusions
Immunohistochemical profiles of complex neoplasms are consistent with a stem cell origin, whereas simple carcinomas might originate from a cell committed to the luminal lineage. In addition, these results suggest that the initiating oncogenic events determine the morphologic features associated with cancer progression because EMT is observed only in certain types of neoplasm.
cancerepithelial to mesenchymal transitionkeratinmammary glandneoplasia
==== Body
Introduction
Architectural and cytological patterns have been the basis for mammary tumor categorization for more than a century [1]. Over the past 20 years, immunohistochemistry has added a molecular dimension to the categorization of mammary neoplasms: altered expression of oncogenes, oncosuppressor genes, hormone receptors, and cytoskeletal proteins have been identified as useful indicators of disease outcome. Gene microarray analysis has confirmed the importance of many of these histological prognosticators [2-4], which – in conjunction with clinical features such as lymph node status, tumor size, and tumor grade [5] – are undeniably useful. However, these prognosticators do not seem to be useful to identify the pathways that lead to tumorigenesis and cancer progression.
Transgenic mouse models of mammary cancer have added considerably to our knowledge of human breast tumorigenesis. More specifically, in addition to dissecting the molecular mechanisms underlying mammary development and the initial steps of mammary neoplastic transformation, mouse models have shown that activation of a particular pathway is reflected in specific histologic patterns [6,7].
In view of the evidence that terminal differentiation markers are important pathway markers [7] and prognostic predictors [4,8-10], we compared the expression of terminal differentiation markers and some morphologic features of spontaneous and transgene-induced mammary tumors in mice. This study corroborates molecular [2,3,11-14] and immunohistochemical [4,7,15] studies in human cancer patients and mouse models of human cancer recommending that, in addition to the broadly accepted and well-documented use of architectural features [6,7,16-19], diagnostic criteria should also take into account the cell types into which neoplastic cells differentiate.
We have identified neoplastic cells with luminal, myoepithelial, and mesenchymal phenotypes. Myoepithelial differentiation is 'constitutive' to specific tumor types, for example adenosquamous carcinomas, adenomyoepitheliomas, and adenocarcinomas associated with the active mouse mammary tumor virus (MMTV) and the wingless-related MMTV integration site 1 (Wnt1) transgene. In contrast, in neoplasms dependent on the myelocytomatosis oncogene (Myc), Harvey rat sarcoma viral oncogene 1 (Hras), and SV40 T antigen (SV40-TAg) transgenes, myoepithelial differentiation is an important event in the specific type of tumor progression characterized by epithelial to mesenchymal transition (EMT). The occurrence in some transgenic models of distinct proteotypic expression patterns, defined as groups of neoplasms expressing a specific set of terminal differentiation markers, suggests an as yet unreported degree of variability in transgenic models, perhaps reflective of the diversity of second-hit mutations in these tumors. Finally, specific types of spontaneous mammary tumors are histologically and immunohistochemically indistinguishable from some of the transgene-induced mammary tumor models. This provides the background information to test the hypothesis that tumor phenotype is a predictor of tumor genotype.
Methods
Mice
The pathology database [20] of The Jackson Laboratory was searched from 1987 to 2001 for spontaneous mammary tumors in production and research colonies. Slides were reviewed and categorized on the basis of standardized criteria [17-19,21-23].
In addition, newborns of the following strains were obtained from The Jackson Laboratory Induced Mutant Resource (Bar Harbor, ME): C57BL/6J-Tg(WapTAg)1Knw, FVB/N-Tg(WapNotch4)10Rnc/J, B6SJL-Tg(Wnt1)1Hev/J (hereafter abbreviated B6SJL-Wnt1), FVB/NJ-Tg(Wnt1)1Hev/J (hereafter abbreviated FVB-Wnt1), FVB/N-Tg(MMTVneu)202Mul/J, FVB/N-Tg(WapMyc)212Bri/J, FVB/N-Tg(WapHRAS)69Lln YSJL/J, and FVB/NJ-Tg(MMTVTGFA)254Rjc/J and B6D2-Tg(MMTVTGFA)254Rjc/J. Mice were weaned at 3 weeks of age and genotyped by polymerase chain reaction. Detailed information on the genotyping protocols and breeding conditions are available online at . These mice were aged until they developed mammary masses, which were fixed by immersion in Fekete's acid–alcohol–formalin. Slides were sectioned at 5–6 μm and stained with hematoxylin and eosin (H&E).
Immunohistochemistry
Consecutive sections (5–6 μm thick) on Superfrost Plus slides (Fisher Scientific, Fair Lawn, NJ) of paraffin-embedded neoplasms were immunolabeled for α-smooth muscle actin (SMA; Sigma, St Louis, MO), keratin 1 (KRT2-1, hereafter abbreviated K1; BabCo, Richmond, CA), keratin 5 (KRT2-5, hereafter abbreviated K5; BabCo), keratin 6 (KRT2-6, hereafter abbreviated K6; BabCo), keratin 10 (K1-10, hereafter abbreviated K10; Babco), keratin 13 (KRT13, hereafter abbreviated K13; Sigma), keratin 14 (KRT1-14, hereafter abbreviated K14; BabCo), keratin 17 (KRT1-17, hereafter abbreviated K17; PA Coulombe, The Johns Hopkins University, Baltimore, MD) [24], keratins 8/18 (KRT8 and KRT18, hereafter abbreviated K8/18; Progen, Heidelberg, Germany), vimentin (Biomeda, Foster City, CA), filaggrin (BabCo), involucrin (Babco), loricrin (Babco), and trichohyalin (T-T Sun, The New York University School of Medicine, New York, NY) as described previously [25]. All these antibodies were mouse-specific except the antibodies against K13, trichohyalin, and vimentin, which were raised against human proteins, the antibody against K8/18, which was raised against bovine keratins, and the antibody against SMA, which was raised against avian proteins. The anti-SMA antibody recognizes aortic α2 smooth muscle actin (ACTA2) and enteric γ2 smooth muscle actin [26].
In brief, the method was as follows. Deparaffinized slides were gradually hydrated. Endogenous peroxidase activity was quenched by incubation with 3% hydrogen peroxide in methanol for 20 min at room temperature (20–25°C). Heat-mediated antigen retrieval in citrate buffer at pH 6.0 was used for the antibodies targeted at K8/18 and vimentin. Slides were washed and were incubated for 30 min with blocking serum (10% normal fetal calf serum diluted in phosphate-buffered saline). Excess blocking serum was blotted and the slides were incubated overnight at 4°C with primary antibodies diluted in phosphate-buffered saline. Secondary biotinylated anti-mouse or anti-rabbit antibody was applied for 30 min at room temperature, followed by incubation with the avidin-biotin complex (45 min). The reaction was developed with the substrate diaminobenzidine (Sigma) and the slides were counterstained with Mayer's hematoxylin. All spontaneous tumors were labeled for MMTV gp27 Gag, gp36 Env, and gp52 Env [27]. A mouse was identified as positive for MMTV if immunolabeling for at least one of the markers was detected in the tumor or nearby tissues.
Images were captured with a DP70 digital camera (Olympus, Melville, NY) on an Olympus BX41 microscope and color enhanced and balanced for contrast with Photoshop 6.0 (Adobe, San Jose, CA). Additional photomicrographs of H&E-stained and immunolabeled slides are archived in the Mouse Tumor Biology Database, where they can be viewed on-line at [28].
Slide interpretation and scoring of immunolabeling intensity
Cell types were defined on the basis of a previous study that evaluated the expression of the same terminal differentiation markers in the developing mammary gland [29].
Expression of K5, K14, K17, and/or SMA was associated with a direct contact with a basement membrane and the morphology of a myoepithelial cell defined myoepithelial cell differentiation. Absence of labeling for any of the markers, or labeling for K8/18 along with a polygonal morphology, defined luminal cells.
Image analysis (Photoshop 6.0; Adobe, San Jose, CA) was used to evaluate the ratio of spindloid neoplastic cells on H&E-stained sections on all tumors with EMT. EMT was considered significant when more than 1% of the neoplasm was composed of neoplastic cells having a spindloid to fusiform shape and blending into the stroma. These cells had an oval to elongated nucleus with less clumped chromatin than epithelial neoplastic cells. Their cytoplasm was more acidophilic than epithelial neoplastic cells.
Squamous differentiation was defined by the presence of one or more of the following features: the formation of a large core of cornified material, the formation of keratin pearls, the cornification of individual cells, or the presence of trichohyalin granules or keratohyalin granules. Squamous differentiation was confirmed by immunohistochemical expression of at least one of the following markers in the areas of squamous differentiation: K1, K6, K10, filaggrin, trichohyalin, involucrin, or loricrin, none of which is normally expressed in cycling mammary glands [29].
The intensity of immunolabeling was scored on a scale of 0 to 3 (immunolabeling levels: 0 = none; 1 = weak; 2 = moderate; 3 = intense). The grade for each cell type was the product of average labeling intensity and the relative percentage of cells (0%, 0; up to 1%, 1; 2–5%, 2; 6–25%, 3; 26–50%, 4; more than 50%, 5) labeled at the predominant intensity. The outcome for each neoplasm was the sum of the grades for each cell type. If immunolabeling was detected in less than 10 cells in a tumor, the tumor was considered to be negative for this marker.
Morphologic criteria
All tumors were evaluated (presence/absence) on H&E-stained sections for myoepithelial differentiation, EMT, squamous differentiation, vascular invasion, proteinaceous or lipid droplets in neoplastic cells, and ductal differentiation (defined as tubular structures lined by a basal layer of cells with myoepithelial differentiation and a luminal layer of cuboidal cells). Type P tumors are neoplasms composed of a branching network of blind ducts lined by an epithelium at least two cells thick and terminated by structures resembling the terminal end buds of the pubertal mammary gland [7]. Macrocysts are large cysts lined by an epithelium one or two cells thick that occasionally forms small glands. Lactation-responsive plaques are discoid masses composed of contoured and anastomosed mammary acini at the periphery and ducts at the center.
Results
Histologic tumor types (Table 1)
Tumors were categorized histologically using the recommendations of the Mouse Models of Human Cancers Consortium categorization scheme [17-19] and the specific nomenclature applying to certain models [22] or tumor types [21,23] when applicable.
Spontaneous tumors were papillary adenocarcinomas with papillae lined by a single layer of cells (n = 6; Fig. 1), papillary adenocarcinomas with papillae lined by two layers of cells (n = 2; Fig. 2), glandular adenocarcinomas with EMT (n = 4; Fig. 3), microacinar adenocarcinomas in C3H/HeJ mice (n = 8; Fig. 4), type P tumors in C3H/HeJ mice (n = 3), adenomyoepitheliomas in BALBc/J mice (n = 5; Fig. 5), and adenosquamous carcinomas (n = 7; Fig. 6).
All Hras-induced (n = 4; Fig. 7) and Neu-induced (n = 10) tumors as well as some Myc-induced (n = 4; Fig. 8c,8e,8f) and SV40-TAg-induced tumors (data not shown) had a solid pattern. Some SV40-TAg-induced (n = 5; Fig. 9a,9b) and Myc-induced tumors (n = 9; Fig. 8a,8b,8d) as well as all Notch4-induced tumors (n = 6) had a glandular pattern. Some Myc-induced (n = 13; Fig. 8), SV40-TAg-induced (n = 3; Fig. 9), and Hras-induced (n = 3) carcinomas displayed areas of EMT. Some SV40-TAg-induced tumors (n = 4) had a papillary pattern with papillae lined by epithelial cells that piled up in a disorderly fashion (data not shown). Preneoplastic lesions of Tgfa-transgenic mice consisted of macrocysts (n = 8; Fig. 10a) and lactation-responsive plaques (n = 3; Fig. 10b). All Wnt1-induced tumors (n = 13; Fig. 11) exhibited a type P tumor pattern.
All spontaneous type P tumors (n = 9), all microacinar carcinomas in C3H/HeJ mice (n = 8), and one of two papillary carcinomas in one C3H/HeJ mouse showed immunolabeling for MMTV (data not shown). Proteinaceous and/or lipid secretion was identified in a small number of tumors in most transgenic models and spontaneous tumor types (Fig. 2a,2b), although it was observed most consistently in macrocysts and in adenosquamous carcinomas (Fig. 6a,6c,6d,6e).
Terminal differentiation proteins expression patterns
The neoplasms were categorized into three groups: those with a pure luminal phenotype (simple carcinomas), those that constitutively expressed myoepithelial and luminal markers, and those that were characterized by EMT. Hampe and Misdorp [30] defined the term complex as 'any type of neoplasm or proliferation composed of cells resembling both secretory epithelial and myoepithelial cells'. We therefore used the term 'constitutively complex carcinomas' to indicate the tumors in which luminal and myoepithelial cells were arranged as they would be in a normal mammary gland, suggesting that these two types of cells originated from the same progenitor cell. The tumors that expressed myoepithelial markers in the areas of EMT only are designated 'acquired complex carcinomas' to indicate that myoepithelial differentiation was absent in the areas representing the original phenotype of the neoplasms, before the occurrence of EMT.
Tumor categorization was then redesigned to take into account not only the architecture of the neoplastic process [17-19] but also myoepithelial differentiation and EMT, which were not always apparent on H&E-stained sections. Keratins 5, 14, and 17, which are generally considered to be markers of myoepithelial cells [4], were occasionally identified in suprabasal cells in a variety of neoplasms (Figs 6f,6h,6i,8c,8e,8f,11a). α-Smooth muscle actin, another marker of myoepithelial cells, was expressed by suprabasal cells only in MMTV-associated and Wnt1-induced type P tumors (Fig. 12c), a feature previously noted by Li and colleagues [13].
Neoplasms with a pure luminal phenotype (simple carcinomas) were identified in all transgenic models of mammary carcinogenesis (Fig. 7, Table 1) with the notable exception of Wnt1-induced carcinomas that had a constitutively complex phenotype. Most (six of nine) glandular carcinomas arising in mice transgenic for Myc showed a few areas of myoepithelial differentiation that were interpreted as early EMT rather than the neoplasms arising from a progenitor cell common to the luminal and myoepithelial phenotypes; hence these neoplasms with minimal myoepithelial differentiation were categorized as 'simple' carcinomas. The only spontaneous neoplasms composed exclusively of cells with a luminal phenotype were four of six papillary carcinomas that, on H&E sections, were characterized by papillae lined by an epithelium one cell thick.
Constitutively complex carcinomas consisted of spontaneous papillary carcinomas lined by an epithelium two cells thick (Fig. 2), microacinar adenocarcinomas arising in MMTV-infected C3H/HeJ mice (Fig. 4), type P tumors of MMTV-infected C3H/HeJ mice (Fig. 12) and Wnt1-transgenic mice (Fig. 11), adenomyoepitheliomas (Fig. 5), adenosquamous carcinomas (Fig. 6), and lactation-responsive plaques and most (six of eight) macrocysts of Tgfa-transgenic mice (Fig. 10). Neoplastic cells with a myoepithelial phenotype were abundant and formed an almost continuous layer surrounding cells with a luminal phenotype in microacinar carcinomas (Fig. 4) and in spontaneous papillary carcinomas lined by an epithelium two cells thick (Fig. 2a). In type P tumors, myoepithelial differentiation was most prominent in the areas of ductal metaplasia and was minimal or absent in the frond-like areas of the neoplasms (Fig. 12a). In adenosquamous carcinomas, myoepithelial differentiation was most prominent in the areas of squamous differentiation (Fig. 6f,6i), and was often absent in the glandular areas.
In adenomyoepitheliomas (n = 5), large proportions of suprabasal cells were labeled for K5, K6, K8/18, K14, and K17, with K1 being expressed in only two tumors, and with duct formation and cornification being identified in four and three neoplasms, respectively. The morphology of neoplastic cells in adenomyoepitheliomas varied, ranging from fusiform to cuboidal. The proteotypic pattern of these cells is not characteristic of any specific cell type, and these cells were categorized as 'cells with myoepithelial differentiation' when in contact with a basement membrane and as 'luminal cells' when not in contact with a basement membrane. Neoplastic cells in adenomyoepitheliomas were difficult to differentiate from stromal myofibroblasts and, as reported earlier [31], they were negative for SMA.
In lactation-responsive plaques of Tgfa-transgenic mice, myoepithelial differentiation was most prominent in the areas of ductal differentiation located at the center of these lesions (Fig. 10b). The peripheral portions of lactation-responsive plaques, which morphologically resemble mammary acini, generally lacked myoepithelial differentiation. Macrocysts were predominantly composed of cells with a luminal phenotype (Table 1) that occasionally rested directly on the basement membrane. Segmental portions of six of eight macrocysts had myoepithelial cells (Fig. 10a).
Immunohistochemistry indicated that the descriptive term 'papillary carcinoma' encompasses a variety of neoplasms. First, most (four of six) spontaneous papillary carcinomas of BALB/cJ mice (Fig. 1) and all (four of four) papillary carcinomas with a pseudostratified epithelium in SV40-TAg-transgenic mice were exclusively composed of cells with a luminal phenotype (simple carcinomas). Cells with myoepithelial differentiation, identified in two of six spontaneous papillary carcinomas of BALB/cJ mice, were scarce and might represent entrapped remnants of the normal mammary gland, because they were located at the periphery of the neoplasm. In spontaneous papillary adenocarcinomas characterized by two layers of cells lining neoplastic papillae, the basal layer was continuous and had a myoepithelial phenotype (Fig. 2a), whereas cells of the luminal layer expressed K8/18 (Fig. 2b).
Type P tumors, adenomyoepitheliomas, and adenosquamous carcinomas were characterized by the presence of subpopulations of cells with a high mitotic index that did not express terminal differentiation markers, or expressed them only weakly. In type P tumors, these cells were grouped at the ends of the fronds, the morphology and immunohistochemistry of which mimicked terminal end buds of the pubertal mammary gland [29].
Adenomyoepitheliomas (Fig. 5a,5b) and adenosquamous carcinomas (Fig. 6f,6g,6h,6i) were populated by individual cells and clusters of cells with a large open-faced nucleus and a moderate amount of pale cytoplasm with the appearance of ground glass. These cells were haphazardly distributed in the solid areas of adenomyoepitheliomas and in the viable epithelium enclosing cornified debris in adenosquamous carcinomas. Similar populations of neoplastic cells with low expression of terminal differentiation markers were not detected in the other neoplasms.
Carcinomas in B6SJL-Wnt1-and FBV-Wnt1-transgenic mice were essentially similar and had the classical appearance of a type P tumor. However, they differed slightly with regard to their proteotypic patterns: squamous differentiation and expression of K5 and K8/18 was higher in B6SJL-Wnt1-than in FBV-Wnt1-transgenic mice (Table 1; Fig. 11a,11b).
Tumor progression
EMT, an important morphologic marker of tumor progression [32,33], was detected in 23 tumors. It was most commonly observed in Myc-induced carcinomas although it was also identified in Hras-and in SV40-TAg-induced carcinomas, and in carcinomas spontaneously arising in SJL/J and PL/J mice. Various proportions of neoplastic cells with a mesenchymal phenotype expressed vimentin (Figs 3, 8b), K5 (Fig. 8c), K14 (Fig. 8e), and K17 (Figs 8f, 9b). These cells consistently expressed SMA but could not be differentiated from myofibroblasts on the basis of the expression of this antigen alone (Fig. 8a). EMT was also associated with increased expression of K8/18 (Figs 8d, 9a). In most cases (20 of 23), a small number of cells with a myoepithelial phenotype were present in the glandular or solid areas in the immediate vicinity of the areas of EMT (Fig. 8c,8f).
Morphologic progression was also observed in microacinar adenocarcinomas associated with MMTV infection in C3H/HeJ mice, where it was characterized by focal to multifocal acquisition of a solid pattern. Acquisition of the solid pattern was associated with a loss of immunolabeling for MMTV proteins (data not shown) by neoplastic cells, but the expression of terminal differentiation markers remained unaltered.
Neu-induced carcinomas did not display a morphologic alteration suggestive of tumor progression, although the tumors evaluated included both metastatic and non-metastatic neoplasms (data not shown). The markers tested in this study did not segregate the three cell types classically described in Neu-and Hras-induced tumors [7]. EMT was not observed in Notch4-induced carcinomas, although the glandular pattern of these neoplasms is reminiscent of Myc-and SV40-TAg-induced carcinomas.
Pathway pathology
Three histologic features of mammary tumors have recently been related to the activation of specific pathways: myoepithelial differentiation has been associated with activation of the Wnt pathway [7], squamous metaplasia has been attributed to β-catenin stabilization [34-37], and alveolar differentiation and milk secretion are dependent on signal transducer and activator of transcription 5a [38]. As expected, all Wnt1-induced carcinomas and spontaneous type P tumors displayed myoepithelial differentiation. In addition, myoepithelial differentiation, as assessed by routine H&E histology or immunohistochemistry, was identified in spontaneous papillary carcinomas with a bi-stratified epithelium, MMTV-associated microacinar carcinomas, adenomyoepitheliomas, and adenosquamous carcinomas.
Squamous metaplasia associated with expression of the full array of terminal differentiation markers of the suprabasal layers of the epidermis was found only in adenosquamous carcinomas (Fig. 6a,6b,6c,6d,6e,6f,6g,6h,6i). However, squamous metaplasia was also identified in some MMTV-associated (7 of 9) and Wnt1-induced (12 of 13) type P tumors, Myc-induced carcinomas (7 of 19), adenomyoepitheliomas (3 of 5), and spontaneous carcinomas exhibiting EMT (1 of 4). In spite of areas histologically consistent with squamous differentiation, MMTV-associated and transgene-induced type P tumors express only a limited set of suprabasal markers of the epidermis (Table 1). For example, K1 and K10, two markers of the stratum spinosum, were seldom expressed (4 of 30 and 6 of 30 carcinomas, respectively) in neoplasms other than adenosquamous carcinomas that exhibited cornification, whereas involucrin, a marker of the stratum corneum, was expressed in 19 of 30 such neoplasms.
Discussion
In the present study, interpretation of immunohistochemistry results determined the following: first, that expression of terminal differentiation markers categorizes spontaneous and transgenic models of mammary neoplasia into 'simple' carcinomas, 'complex' carcinomas, and carcinomas with EMT; second, that most neoplasms are composed of multiple cell populations; and third, that some transgenic and spontaneous neoplasms are histologically and immunohistochemically indistinguishable, and they provide an opportunity to test the hypothesis that tumor phenotype predicts tumor genotype [6,7].
Proteotypic patterns: luminal, luminal/myoepithelial, and luminal with EMT
Like human breast tumors [14], most mouse mammary tumors are exclusively composed of neoplastic cells with a luminal phenotype, suggesting that they developed from a progenitor cell committed to the luminal lineage. Progression to EMT through a myoepithelial stage was observed in Myc-, Hras-, and SV40-TAg-induced carcinomas, a subset of spontaneous mammary carcinomas in PL/J and SJL/J mice, and has also been reported in mice transgenic for Tgfa [39] and matrix metalloproteinase 3 [40]. Three major pathways have been implicated in EMT: the pathway controlled by the activation of Hras and Src, the transforming growth factor β pathway, and the Wnt pathway [32]. Evaluation of the expression of target genes of each of these pathways is needed to determine the molecular mechanisms associated with EMT in mouse models of mammary carcinogenesis.
Type P tumors and microacinar carcinomas constitutively express myoepithelial markers, whereas expression of these markers is an indicator of progression in Myc-, Hras-, and SV40-TAg-induced carcinomas, as it is in some types of human breast cancer [4,15,41]. As a consequence, misinterpretations are likely to occur if tumors are evaluated for EMT with whole-tumor methodologies such as gene or protein array technologies.
The genetic background of transgenic mouse models of mammary tumors might account for differences in the penetrance, latency, and phenotype of mammary proliferating lesions [42-44]. The histological phenotype of tumors arising in Wnt1-transgenic mice was similar, regardless of the background. However, the background strain influenced the proteotypic pattern of these tumors. This observation emphasizes the importance of the background strain in genetically engineered mice, suggests the presence of tumor modifier genes, and indicates possible similar differences in humans.
Cell populations
The presence of cells negative or weakly positive for terminal differentiation markers establishes the presence of compartments of less differentiated cells in specific types of mammary carcinomas. This observation supports the hypothesis that undifferentiated cells, so-called 'mammary stem' cells, are the source of mammary tumors [45,46]. The histologic phenotype and the proteotypic pattern of type P tumors constitutes the most striking evidence for this hypothesis: the fronds in these carcinomas caricatured terminal end buds of the pubertal mammary gland, one of the compartments in which mammary stem cells are found [47,48]. Gene array data also support a stem cell origin for neoplasms arising in mice transgenic for Wnt1 [13]. In adenomyoepitheliomas and adenosquamous carcinomas, the location of cells that did not express terminal differentiation markers is suggestive of a ductal origin for these neoplasms. Interestingly, a second compartment of mammary stem cells is located in ductal suprabasal cells [49].
In addition to viable cells, a large proportion of neoplasms contained areas of cornification and necrosis. This heterogeneous group of non-viable cells has recently been named 'non-tumorigenic cancer cells' [50]. These cells need to be differentiated from neoplastic cells with self-renewal potential, including those cells with invasive or metastatic potential. Because the populations of 'non-tumorigenic cancer cells' might account for a significant proportion of some neoplasms, they might interfere with studies aiming at evaluating RNA or protein expression or genetic damage at the whole-tumor level.
Pathway pathology
Cardiff and colleagues [6] initially proposed that tumor architecture and terminal differentiation markers can predict tumor genotype. The concept has subsequently been refined [7] and is supported by molecular data [12,13,51]. The present study confirmed that simple carcinomas with a solid pattern are typical of tumors driven by the Neu and Ras pathways, whereas type P tumors are a feature of neoplasms arising in mice transgenic for Wnt1. That type P tumors are also found in MMTV-infected mice was expected because Wnt1 is activated in 75% of mammary carcinomas arising in C3H/HeJ mice [52], and this observation supports the validity of the pathway pathology hypothesis.
Although promising, the pathology pathway concept has some limitations that are illustrated by our data. For example, Myc-induced carcinomas express large amounts of whey acidic protein and casein β [53], which should correlate with histologic evidence of proteinaceous secretion. However, only 5 of 20 Myc-induced carcinomas examined in this study had evidence of proteinaceous or lipid secretion. In addition, the pathology pathway concept predicts that Myc-, Hras-, and Neu-induced carcinomas should share similar pathways, distinct from those of Wnt1-induced carcinomas. However, Myc-and Wnt1-induced neoplasms are independent of cyclin D1, whereas Hras-and Neu-induced carcinomas are dependent on cyclin D1 [12,54], indicating that tumor phenotype and tumor genotype are not consistently matched.
Conclusions
The observations that mammary neoplasms of mice transgenic for H-Ras, Myc, Neu, Notch4 and SV40-Tag, and that papillary carcinomas of BALB/cJ mice are exclusively composed of cells with a luminal phenotype, are consistent with the hypothesis that these neoplasms arise from a cell committed to the luminal lineage. The fact that adenomyoepitheliomas, adenoacanthomas, microacinar carcinomas, and type P tumors have a complex phenotype is consistent with a mammary stem cell origin. In addition, it is apparent that cancer progression in mammary carcinomas of mice transgenic for H-Ras, Myc, and SV40-Tag is often associated with EMT. However, EMT does not occur in mammary tumors of mice transgenic for Tgfa, Neu, Notch4, and Wnt1, which indicates that the morphologic features associated with cancer progression are determined by the initiating oncogenic events.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
B6SJL-Wnt1 = B6SJL-Tg(Wnt1)1Hev/J; EMT = epithelial to mesenchymal transition; FVB-Wnt1 = FVB/NJ-Tg(Wnt1)1Hev/J; H&E = hematoxylin and eosin; Hras = Harvey rat sarcoma viral oncogene 1; K = keratin; MMTV = mouse mammary tumor virus; Myc = myelocytomatosis oncogene; SMA = α-smooth muscle actin; Wnt1 = wingless-related MMTV integration site 1.
Acknowledgements
We thank CM Conley, HJ Miller, J Miller, EF Taylor, and SL Williamson for technical assistance. We are grateful to RT Bronson, TV Golovkina, and BA Richards-Smith for critical review of the manuscript. This study was supported in part by National Institutes of Health grants CA34196, CA88327, CA89713, and RR173.
Figures and Tables
Figure 1 Papillary adenocarcinoma lined by an epithelium one cell thick in a BALB/cJ mouse: neoplastic cells lack myoepithelial differentiation. Myofibroblasts are present in the stroma of the neoplasm. Scale bar, 80 μm.
Figure 2 Papillary adenocarcinoma lined by an epithelium two cells thick: there is myoepithelial differentiation of the basal layer (a), whereas suprabasal cells have a luminal phenotype (b). Scale bar, 80 μm.
Figure 3 Epithelial to mesenchymal transition (EMT) in a spontaneous neoplasm: neoplastic cells with a spindloid phenotype are labeled strongly for vimentin in a glandular carcinoma with EMT in an SJL/J mouse. Scale bar, 80 μm.
Figure 4 Microacinar carcinoma in a C3H/HeJ mouse infected by the mouse mammary tumor virus. Microacinar carcinomas are characterized by a microacinar pattern with prominent myoepithelial differentiation. Scale bar, 80 μm.
Figure 5 Adenomyoepithelioma in a BALB/cJ mouse: individual cells (a, arrowhead), clusters of cells (a, arrow), and occasionally large areas (b) are not labeled by antibodies against terminal differentiation markers of myoepithelial (a) and luminal cells (b) of the mammary gland. Scale bar, 80 μm.
Figure 6 Adenosquamous carcinoma in a BALB/cJ mouse: squamous differentiation is commonly associated with the presence of keratohyalin (a, arrowhead) or trichohyalin (b, arrowhead) granules. Squamous differentiation is 'complete', with expression of markers of all layers of the epidermis (c-e). Individual cells and small clusters of cells fail to express, or express only weakly, any terminal differentiation marker (f-i, arrowheads), whereas myoepithelial differentiation is observed in the areas of squamous differentiation (f, i, arrows). Scale bars, 50 μm (a, b) and 80 μm (c-i).
Figure 7 Solid carcinoma from a mouse transgenic for Hras: expression of α-smooth muscle actin in carcinomas is restricted to stromal myofibroblasts and vascular smooth muscle fibers. A similar observation was made for carcinomas arising in mice transgenic for Neu (Erbb2) and Notch 4 and in most mice transgenic for SV40-TAg (not shown). Scale bar, 320 μm.
Figure 8 Epithelial to mesenchymal transition (EMT) in a mouse transgenic for Myc: EMT is associated with expression of terminal differentiation markers of mesenchymal (a,b), myoepithelial (c,e,f) and luminal (d) cells, with de novo acquisition of a myoepithelial phenotype (c,e,f, arrows) by neoplastic cells. Scale bars, 80 μm (a-d) and 160 μm (e, f). 'De novo' acquisition of the myoepithelial phenotype is defined as neoplastic cells displaying a myoepithelial phenotype in a portion of the neoplasm limited to the vicinity of the areas of EMT, whereas myoepithelial differentiation was lacking in the rest of the neoplasm (not shown).
Figure 9 Epithelial to mesenchymal transition (EMT) in a mouse transgenic for SV40-TAg: EMT is associated with increased expression of luminal (a) and myoepithelial (b) terminal differentiation markers. Scale bars, 80 μm (a) and 160 μm (b).
Figure 10 Neoplasms from Tgfa-transgenic mice: myoepithelial differentiation is present in macrocysts (a, arrows<) and in lactation-responsive plaques (b, arrows). Scale bars, 80 μm (a) and 160 μm (b).
Figure 11 Influence of the background strain on protein expression in type P tumors: the expression of K5 (a,b) and K8/18 (not shown) is higher, and squamous differentiation (not shown) is more prominent in B6SJL-Wnt1 than in FVB/NJ-Wnt1 mice. Scale bar, 80 μm.
Figure 12 Type P tumors arise in mice infected by the mouse mammary tumor virus. Myoepithelial differentiation is prominent in ductal areas (a, arrowheads), whereas suprabasal cells have a luminal phenotype and label for K8/18 (b). Clusters and individual suprabasal cells are labeled by antibodies against α-smooth muscle actin (c) and in cells with myoepithelial differentiation (arrowheads). Scale bars, 320 μm (a), 160 μm (b) and 80 μm (c).
Table 1 Proteotypic Classification of Spontaneous and Transgenic Mammary Neoplasms Based on Terminal Differentiation Markersa.
Type Strain/ Transgeneb Main patternc Myoepithelial differentiationd EMT (%)e K1 K5 K6 K8/18 K10 K14 K17 FLG IVL LOR THH SMA VIM
Simple FVB-Myc Glandular 6/9 4/9 (<1%) + + + ++ +
FVB-Notch4 Glandular 0/6 0 + + +
C57BL6/J-Tag Glandular 1/5 0 + + +
BALB/cJ (mostly) Papillary, 1 cell thick 2/6 0 + + ++ ++ ++
C57BL6/J-Tag Papillary 0/4 0 + + +
FVB-Myc Solid 1/4 3/4 (<1%) + + +
FVB-Neu Solid 0/10 0
Complex FVB and B6D2-Tgfa Macrocyst 6/8 0 + + +++ + + +
FVB and B6D2-Tgfa Lactation plaque 3/3 0 ++ + +++ ++ + +
BALB/cJ Adenomyoepithelioma 5/5 0 + +++ ++ ++ +++ ++
BALB/cJ (mostly) Adenoacanthoma 7/7 0 ++ ++ ++ ++ ++ +++ +++ ++ ++ + + +
C3H/HeJ Microacinar 8/8 0 ++ + ++ ++ +
BALB/cJ, C3H/HeJ Papillary, 2 cells thick 2/2 0 ++ ++ + ++ ++ +++
C3H/HeJ Type P tumor 9/9 0 ++ + ++ ++ ++ + +++
FVB-Wnt1 Type P tumor 6/6 0 ++ ++ +++ ++ ++ + + ++
B6SJL-Wnt1 Type P tumor 7/7 0 + + + ++ + ++
EMT C57BL6/J-Tag Glandular with EMT 2/3 3/3 (6%) +++ +++ ++ ++ +
FVB-Myc Glandular with EMT 4/6 6/6 (20%) + + ++ ++ ++ + +
PL/J and SJL/J Glandular with EMT 4/4 4/4 (38%) ++ + +++ ++ ++ + ++ ++
FVB-Hras Solid with EMT 3/4 3/4 (<1%) + ++ + + +
adata are coded, with "+++" representing highest grades; the intensity of immunolabeling was scored 0–3 (immunolabeling levels: 0 = none; 1 = weak; 2 = moderate; 3 = intense); the grade for each cell type was the product of average labeling intensity and the relative percentage of cells (0: 0; ≤ 1%: 1; 2–5%: 2; 6–25%: 3; 26–50%: 4; >50%: 5) labeled at the predominant intensity; outcome for each neoplasm was the sum of the grades for each cell type and the data presented are the average for each group; white boxes indicate an average grade <1; "+" indicates an average grade ≥ 1 and <5; "++" indicates an average grade ≥ 5 and <10; "+++" indicates an average grade ≥ 10; EMT, epithelial to mesenchymal transition; FLG, filaggrin; IVL, involucrin; K1, keratin 1; K5, keratin 5; K6, keratin 6; K8/18, keratins 8 and 18; K10, keratin 10; K17, keratin 17; LOR, loricrin; THH, trichohyalin; SMA, α-smooth muscle actin; VIM, vimentin.
bAbsence of information for the transgene indicates that the tumor arose in a non-transgenic mouse.
cBased on the recommendations of the Annapolis meeting [17–19].
dTumors with myoepithelial differentiation/tumors examined; carcinomas with minimal myoepithelial differentiation arising in mice transgenic for Myc were categorized as "simple carcinomas" because myoepithelial differentiation was interpreted as evidence of early EMT.
eTumors with EMT/ tumors examined; the data in parenthesis indicate the average percentage of the tumors comprised of neoplastic cells with a mesenchymal phenotype.
==== Refs
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| 15535849 | PMC1064077 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Oct 6; 6(6):R668-R679 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr930 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9311553584810.1186/bcr931Research ArticleBirthweight, parental age, birth order and breast cancer risk in African-American and white women: a population-based case–control study Hodgson M Elizabeth [email protected] Beth [email protected] Robert C [email protected] Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA2 School of Public Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia3 Department of Epidemiology, School of Public Health, and Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA2004 22 9 2004 6 6 R656 R667 28 4 2004 1 6 2004 8 7 2004 9 8 2004 Copyright © 2004 Hodgson et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Much recent work has focused on hypotheses that very early life exposures influence adult cancer risk. For breast cancer it has been hypothesized that high in utero estrogen exposure may increase risk.
Methods
We used data from the Carolina Breast Cancer Study, a population-based case–control study of incident breast cancer in North Carolina, to examine associations for three possible surrogates of high prenatal estrogen exposure: weight at birth, maternal age, and birth order. We also examined paternal age. Birthweight analyses were conducted for white and African-American women born in North Carolina on or after 1949 (196 cases, 167 controls). Maternal age was analyzed for US born participants younger than 49 years of age (280 cases, 236 controls).
Results
There was a weak inverse association between birthweight in the highest tertile and breast cancer overall (odds ratio [OR] 0.7, 95% confidence interval [CI] 0.4–1.2), although associations differed by race (OR 0.5, 95% CI 0.2–1.0, and OR 1.0, 95% CI 0.5–2.1 for African-American and white women, respectively). For maternal age there was an approximately threefold increase in risk in women whose mothers were older than 22 years of age, relative to 19–22 years of age, when the women were born. After adjustment for maternal age, older paternal age increased risk in the oldest and youngest age categories (relative to 23–27 years of age at the woman's birth: OR 1.6, 95% CI 0.8–3.1 for age 15–22 years; OR 1.2, 95% CI 0.7–2.2 for age 28–34 years; and OR 1.5, 95% CI 0.7–3.2 for age 35–56 years). There was no association with older paternal age for white women alone. After adjustment for maternal age (265 cases, 224 controls), a birth order of fifth or higher relative to first had an inverse association with breast cancer for women younger than 49 years old (OR 0.6, 95% CI 0.3–1.3).
Conclusion
Although the CIs are wide, these results lend support to the possibility that the prenatal period is important for subsequent breast cancer risk, but they do not support the estrogen hypothesis as a unifying theory for the influence of this period.
African-Americanbirthweightbreast cancermaternal ageprenatal
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Introduction
Recent epidemiologic studies have investigated the possibility that very early life exposures increase adult cancer risk. Trichopoulos [1] postulated that a highly estrogenic intrauterine environment would create a 'fertile soil' for carcinogenesis in breast tissue and lead to higher risk for breast cancer later in life. Because retrospective prenatal hormone measurements cannot be obtained for large numbers of people, he and others proposed that birth and maternal characteristics be investigated as surrogates for a highly estrogenic intrauterine environment. These birth characteristics include high birthweight, maternal age 20–24 years at birth, and low birth rank. Much work during the past decade has been done on birthweight in particular. There is an apparent modest positive association between high birthweight and breast cancer that is stronger in younger women, which is consistent with the estrogen hypothesis. Data on other surrogates of intrauterine estrogen levels have been less consistent [2].
Despite an overall higher incidence of breast cancer in white women, incidence and mortality rates are higher in young African-American women than in young white women [3,4]. This crossover in incidence rates, occurring at about age 40 years for women diagnosed between 1950 and 1969, was documented in the Third National Cancer Survey [4]; SEER (Surveillance, Epidemiology and End Results) data from 1997 document a shift in the crossover to approximately 45 years of age [3]. Consequently, it is important to investigate relationships between putative causes of breast cancer and breast cancer incidence in younger African-American women. To our knowledge no studies published to date have specifically addressed the relationships between prenatal or birth characteristics and breast cancer in African-American women.
The goal of this study was to characterize the relationships of birthweight, maternal age, paternal age, and birth order with breast cancer in African-American and white women in a population-based study. We analyzed data from a subset of women participating in the Carolina Breast Cancer Study (CBCS) [5], a population-based case–control study that over-sampled younger women and African-American women.
Methods
Study design and supplemental data collection
The CBCS (phase I) is a population-based, case–control study of incident invasive breast cancer conducted between May 1993 and December 1996 in 24 counties of central and eastern North Carolina [5]. Participants gave informed consent using forms approved by the Institutional Review Board of the University of North Carolina School of Medicine, which were in compliance with the Helsinki Declaration. Cases (n = 861) were women aged 18–74 years, who were mentally competent and resident in the study area at the time of selection with a first diagnosis of histologically confirmed primary invasive breast cancer. They were identified in cooperation with the North Carolina Central Cancer Registry [6] using a randomized recruitment protocol [7] to over-sample African-American women and women under 50 years of age. Potential controls were identified by North Carolina Division of Motor Vehicles (women aged 20–64 years) and/or Health Care Financing Administration (women aged 65–74 years) lists and had no previous or current history of breast cancer. Controls (n = 790) were frequency matched by race and 5-year age group to cases. Trained nurse interviewers collected information and obtained height and weight measurements during interviews conducted at the participant's home. To obtain birthweight and parental ages we requested birth records for all study participants born in the USA on or after 1 January 1948.
Analytic datasets
Maternal age was analyzed in the subset of women for whom birth records with maternal age were available (Table 1) and was categorized as 15–18 years, 19–22 years, 23–27 years, or 28–44 years, based on homogeneity of risk apparent in smoothed lowess curves [8]. Paternal age was available for 92.7% of women with maternal age data, and was categorized as 15–22 years, 23–27 years, 28–34 years, or 35–56 years, by the same method. Maternal and paternal age distributions did not permit identical categorizations.
Birthweight analyses were restricted to women born in North Carolina (NC-born; Table 1). Of case and control birth records, 96% and 97%, respectively, were located and nearly all (97.0% and 94.9%, respectively) recorded birthweight. A restricted birthweight dataset was constructed that excluded women who had any of the following indicators of a possibly poorly measured birthweight: noninstitutional birth, birth attendant other than a physician, and a birthweight recorded only in pounds. Overall, birthweight was recorded only in pounds more often for African-American than for white women (28% versus 7.4%). African-American women were more likely than white women to have been born at home, were less likely to have been delivered by a physician at home, and were less likely to have had a birthweight recorded in pounds and ounces under any birth circumstances. Hence, a disproportionate number of African-American women were excluded from the restricted dataset. Birthweights were converted from pounds and ounces to grams for analysis. Race-specific tertiles were derived from white or African-American controls.
Birth order was analyzed twice, first in the full CBCS dataset and then in the subset of women for whom there was information on maternal age (Table 1). Birth order was self-reported and was a categorized as first, second to fourth, and fifth or higher.
Statistical analysis
Unconditional logistic regression was used for all analyses. Odds ratios (ORs) and 95% confidence intervals (CIs) were the primary measure of association. PROC GENMOD in SAS (SAS Institute Inc., Cary, NC, USA) was used with an offset term to account for the age and race specific sampling probabilities used to identify eligible participants [7]. All estimates presented are adjusted for, at minimum, age and sampling fractions. Except for birthweight, parental age, and body mass index (BMI), all variables were based on self-report. Age at diagnosis (cases) or selection (controls) was categorized using the same 5-year age groups as the sampling protocol (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, and 65–69 years). The proportion of non-African-American participants who classified themselves as non-white was under 1%, and so, for the purposes of this study, non-African-Americans were classified as white.
Age at menarche (≤12 years or >12 years), age at first full-term pregnancy (nulliparous, <26 years, ≥26 years), lactation (nulliparous, ever breastfed, or never breastfed), parity (none, one, two, or three or more), BMI (≤25 kg/m2 or >25 kg/m2, calculated from nurse interviewers' measurements of height and weight), first degree family history of breast cancer (positive if a mother, father, or full sibling had breast cancer), and menopausal status were considered potential confounders. Women younger than 50 years were considered postmenopausal if they had undergone natural menopause, bilateral oophorectomy, or irradiation to the ovaries. Multivariable logistic models were used to adjust for potential confounders [9]. A potential confounder was included in the model based on a >15% change in the β coefficient for any level of the birth characteristic relative to the referent in either white or African-American women. Lowess curves were generated using Stata version 7.0 (Stata Corporation, College Station, TX, USA). All other analyses were done using SAS version 8.01.
Results
Racial distributions for analytic datasets are presented in Table 1. Overall, the proportion of African-American women was higher for the younger NC-born women (i.e. those eligible for the birthweight analysis) than for the full CBCS or for younger CBCS participants (born on or after 1 January 1948). Those eligible for the maternal and paternal age analyses were under 48 years of age at selection/diagnosis. Consequently, the proportion of postmenopausal women was much lower in this group than in the full dataset (11% versus 55%), as was mean age at menopause (controls 39.2 ± 6.1 years versus 44.4 ± 7.3 years). There were somewhat higher proportions of women with first births at age greater than 26 years, no family history of breast cancer, household income above the study median, higher educational level, and nonrural childhoods. Only minor differences emerged between the women eligible for the parental age analyses and those for whom parental age was obtained. This subgroup of women was slightly more likely to have had rural childhoods and lower education. The birthweight analysis was restricted to younger NC-born women. These women reported, on average, only slightly lower educational level, more rural childhoods, lower household income, lower age at first birth, and higher BMI than did younger women overall.
No important differences in breast cancer risk factors emerged between those eligible for the birthweight study and those for whom birthweight was obtained. ORs for age at menarche, age at first pregnancy, and lactation were virtually identical in the full CBCS and all analytic datasets. Differences in ORs between the full CBCS dataset and the birthweight dataset were, as expected, due to age restriction in the latter. In the birthweight dataset, ORs for family history were slightly higher (OR 1.6, 95% CI 1.0–2.7 versus OR 1.4, 95% CI 1.0–1.9), whereas ORs for the following were slightly lower: BMI greater than 25 kg/m2 (OR 0.6, 95% CI 0.4–0.8 versus OR 0.8, 95% CI 0.6–1.0), postmenopausal status (OR 0.7, 95% CI 0.4–1.5 versus OR 0.9, 95% CI 0.7–1.2), and parity of three or greater (OR 0.5, 95% CI 0.2–1.1 versus OR 0.8, 95% CI 0.6–1.1).
Risk factor distributions
The distributions of birthweight, parental age, and birth order are presented in Table 2. Birth records were unavailable from some states, increasing the proportion of NC-born women in the maternal age dataset. The birthweight dataset was restricted to NC-born participants because of unavailability of birthweight on most out-of-state birth records. Ages at diagnosis/selection were similar in analytic datasets and relevant subgroups of CBCS cases and controls. Maternal age, paternal age, and birth order were also similarly distributed in datasets including only younger women. As expected, the frequency of birth orders higher than fourth was lower among the younger women.
Birthweight
Tables 3 and 4 present ORs and 95% CIs for the association between birthweight categories and breast cancer in the full and restricted datasets, combined and by race, respectively. Overall, there was a weak inverse association between higher birthweight and breast cancer in the full dataset but not in the restricted dataset. Higher birthweight was inversely associated with breast cancer among African-American women in the full and restricted datasets, although CIs were wide. There was no association for higher birthweight in white women for the full dataset and a modest but statistically nonsignificant positive association for the restricted dataset. There was no association between lower birthweight and breast cancer for white or African-American women in the full birthweight dataset. As has historically been the case in North Carolina and elsewhere [10], mean and median birthweight and lower and upper limits of birthweight distributions among controls were higher for whites than for African-Americans. Neither prenatal characteristics nor adult BMI were strongly correlated with birthweight (data not shown).
Maternal and paternal age
ORs for maternal and paternal age and breast cancer are presented in Table 5. After adjustment, older maternal age (>22 years of age) increased ORs approximately threefold, whereas older paternal age (>27 years of age) was more weakly associated with breast cancer. Maternal and paternal ages, as categorized, were moderately correlated (Spearman correlation coefficient 0.73, 95% CI 0.69–0.78). Parental ages were somewhat correlated with birth order (Spearman correlation coefficients 0.47, 95% CI 0.40–0.54 and 0.43, 95% CI 0.35–0.50 for maternal and paternal ages, respectively). After full adjustment, the OR was elevated and of borderline statistical significance for maternal age 15–18 years among African-American women but not among white women (Table 6). ORs for maternal age over 22 years were increased twofold to fivefold, with 95% CIs usually excluding the null, for both white and African-American women. The odds of breast cancer for all categories of maternal age were slightly stronger for first-born participants, although this was not statistically significant (data not shown).
After adjustment for maternal age, birth order, adult BMI, and household income, there was no association between paternal age and breast cancer among white women. For African-American women, ORs were elevated for both younger (15–22 years of age) and older (35–56 years of age) paternal ages, although CIs were wide. There was no substantial difference in results when the maternal/paternal age datasets were restricted to women born on or after 1 January 1949. Among controls, parental ages were distributed similarly by race, with African-American participants having slightly higher mean maternal and paternal ages than whites.
Birth order
Results for analyses of birth order are shown in Tables 7 and 8. In the full CBCS dataset, birth order (categorized as first, second to fourth, or fifth or higher) was not associated with breast cancer, overall or by race. In the full CBCS, mean birth order was higher for African-Americans than for whites; this pattern was stronger in younger CBCS participants. No potential confounders met the criteria for model inclusion. Finer categorization of birth order did not change the results. For younger women, adjustment for maternal age, adult BMI, and household income revealed a weak, statistically nonsignificant, inverse relationship between higher birth order and odds of breast cancer, which did not differ appreciably by race.
Discussion
We examined the relationships between breast cancer and birthweight, parental age, and birth order among women younger than 49 years of age residing in North Carolina. Overall, there was a weak inverse association with higher birthweight, which was stronger in the full dataset than in the restricted dataset. For white women in the study there was no overall association between birthweight and breast cancer. Among white women born in medical facilities, birthweight in the highest tertile was positively associated with breast cancer, but CIs were wide and included the null. Higher birthweight was inversely associated with breast cancer for African-American women regardless of delivery setting, but again CIs were wide for all estimates. Most previous studies have reported weak to modest positive associations between higher birthweight and breast cancer [11-22], with some showing a positive dose response [11,16-19]. The only previous report of an overall weak inverse association between higher birthweight and breast cancer was from an Asian population [23], although similar results were reported among older, white women [16,19]. Two studies have shown no association [24,25]. With the exception of the Asian case–control study [23], previous studies of birthweight have been done in exclusively or predominantly white populations: seven large record-based, nested case–control studies in Scandinavian cohorts [11,12,14,16,17,22,24]; five population-based, case–control studies in the USA [13,19,20,25,26]; one US cohort study [18]; and one cohort study [21] and a cross-sectional study [15] conducted in the UK. All previous studies of birthweight and breast cancer included younger women; 10 presented results for premenopausal or younger women separately [13,14,16-21,23,26]. Associations were generally positive in younger women, with ORs ranging from 1.25 (95% CI 1.0–1.6) [17] to 3.5 (95% CI 1.3–9.4) [16] for birthweights of 4000 g or greater, as compared with the ORs of 1.4–1.5 for birthweight greater than 3458 g among younger white women with reliable birthweights observed in the present study. No previous studies have estimated ORs for African-American women.
In the USA maternal report and self-report have been the most common sources of birthweight information, rather than birth records. Andersson and coworkers [27] conducted an analysis of agreement between self-reported birthweight and birth records. They found that, despite good overall agreement (Spearman correlation coefficient 0.76), 31% of self-reported birthweights differed from birth record data by 500 g or more, and that this level of misclassification led to both underestimation and overestimation of the magnitude and significance of various effect estimates. Moreover, nonresponse can be sizable (ranging from 12% [19] to 24% [28]) and may reflect bias toward healthier [29], more educated, and/or more communicative mothers. In the USA, birthweight was only routinely recorded on birth records of younger women, the group in which the association between birthweight and breast cancer appears to be strongest [2]. The two US studies that used birth records (conducted in Hawaii [26] and New York state [13]) employed a design similar to that of the present study – a population-based, case–control study using cases born in the state where they were recruited. Both observed minimal, statistically nonsignificant, increased risks for breast cancer among women in the highest tertile of birthweight relative to those in the central tertile. The present study is intermediate in sample size between these two studies, which included 74 and 484 cases, respectively.
In addition to using birth records to decrease misclassification, we performed a separate birthweight analysis for the subset of women who were delivered by physicians in hospitals or doctors' clinics and had their birthweight recorded in pounds and ounces. During the 1950s, home birth and delivery by lay midwives was common practice in North Carolina, particularly among African-Americans and in rural areas, and this could have affected data collection [10]. Additionally, participants delivered in a medical setting comprise a subgroup of women more closely comparable to previous study participants than do women born at home. Results for white women from this subset were in agreement with previous literature, with a small positive association between birthweight and breast cancer, whereas birthweight remained inversely associated for African-American women in the restricted dataset with comparable delivery circumstances. Although analyses in this restricted group potentially reduce birthweight misclassification, results may have limited generalizability to less medically advantaged populations.
Although the search strategy employed in this study limited nonlocatable birth records to 3.6% of the study population, and records missing birthweight to 4.0% of locatable records (i.e. data available for 92% and 93% of eligible cases and controls, respectively), the missing records were predominantly those of African-American women who self-reported birth in the more rural counties of North Carolina.
The inverse associations between higher birthweight and breast cancer seen in this study could also partly be explained by selection bias in the full CBCS dataset if either the case group under-represented the proportion of high birthweight women in the underlying case population or the control group under-represented the proportion of normal weight births in the underlying population. Because birthweights in North Carolina have been increasing over time, more strongly in whites than in African Americans [30], younger white women would be expected on average to have the highest birthweights, and this group is slightly over-represented rather than under-represented in the case population. Some under-representation of African-American women in the control population (36.5% response rate for younger African-Americans) could have contributed to an upward bias in the control birthweights.
In the context of a relatively disadvantaged population such as this one, a higher birthweight may be a surrogate for a different constellation of prenatal and postnatal influences than in a relatively advantaged population. Rather than viewing birthweight solely as an indicator of a highly estrogenic prenatal environment, or even specific physiologic processes, birthweight can also be viewed more globally as an indicator of the prepregnancy health of the mother [31,32]. In this context, it could be considered predictive of the general overall health of the daughter as well and perhaps of a decreased susceptibility to some etiologic agents. Socioeconomic status, based on study participants' self-reported current household income, was not found to be a confounder in this study, but it is probably a poor surrogate for complex environmental influences such as early diet, physical activity, or childhood residence. If there is either a general or breast cancer specific survival advantage to having a higher birthweight, then one would expect to see an inverse association between birthweight and breast cancer among older women. This was found in one study of birthweight and breast cancer [19] but not in another [18], although an apparent protective effect of higher birthweight has been found for other chronic diseases [19,33]. Inasmuch as birthweight is a good surrogate for higher intrauterine estrogen levels, these data do not support the hypothesis that in utero estrogen exposure increases risk for breast cancer.
One limitation of our birthweight analysis is that, lacking an accurate measure of gestational age, it is not possible to interpret fully the association between lower birthweight and breast cancer. Although Andersson and coworkers [11] reported increases in risk associated with birthweight after adjustment for gestational age, particularly after additional adjustment for age at menarche, evidence is inconsistent for gestational age as a strong confounder [2,14]. In the birthweight analyses, power (the probability of correctly rejecting the null hypothesis) to detect an OR of 1.5 at the 95% confidence level was low for whites and African-Americans (0.33 and 0.61, respectively). Similarly, power to detect an OR of 0.5 was low for whites and African-Americans (0.25 and 0.50, respectively). Therefore chance cannot be ruled out as an explanation for the results. Although stratification allowed us to characterize the relationships between these early life factors and breast cancer in African-American women, power to detect an overall effect was decreased. Because this is the only study to date that presents data on African-American women, further research should be undertaken.
Older maternal age exhibited a moderate positive association with breast cancer in this study. Study participants whose mothers were aged 23 years or older at the participant's birth had approximately twofold to fourfold higher odds of breast cancer than did women whose mothers were between 19 and 22 years of age. Although African-American women whose mothers were aged under 19 years also had elevated odds of breast cancer, the pattern did not differ appreciably between white and African-American women. Although the magnitude of OR for women whose mothers were aged 23–27 years was greater than in previous studies, the findings were consistent with the majority of previous reports: weak positive associations with older maternal age [13,24-26,34-42], with stronger associations (approximate doubling in the oldest categories) found for younger women [13,26,43]. Several studies, however, reported no association with older maternal age [12,17,19,43,44]. Innes and coworkers [13] reported a similar J-shaped relationship between maternal age and breast cancer for women diagnosed before age 33 years; the lowest risk was for those aged 20–24 years, with an approximate doubling of odds for women whose mothers were older than 35 years old at their birth. Collectively, these data do not support highest risk being associated with maternal age 20–24 years, as predicted by the estrogen theory [1]. Recent evidence, however, indicates that the association between maternal age and levels of pregnancy estrogens may be weaker than was previously thought [45,46]. Alternatively, older oocytes may have sustained more genetic damage over time and/or DNA repair may be deficient in older mothers [35,47].
The pattern of association between having an older father (older paternal age) and breast cancer was somewhat different for white and African-American women. After adjustment for maternal age and birth order, paternal age was not associated with breast cancer for white women. This is consistent with previous reports for white women showing little to no effect of paternal age [34,38,41,42]. For African-American women in the present study, positive associations with breast cancer were seen for those with the youngest (age 15–22 years) and oldest (age 35–56 years) fathers at their birth, even after adjustment for maternal age and birth order. This is broadly consistent with the only previous study of paternal age to include African-Americans (10.1 % of participants, 52 matched case–control pairs) [13]. In that study Innes and coworkers found an elevated risk associated with older paternal age, after adjustment for maternal age and birth order (OR 1.3, 95% CI 0.9–1.7 for age 30–34 years; OR 1.2, 95% CI 0.8–1.7 for age 35–39 years; and OR 1.3, 95% CI 0.8–2.0 for age ≥40 years), and there was some suggestion of effect modification by race. Although speculative, the differing risk patterns for paternal age in white and African-American women may reflect differing exposures for white and African-American men at that time and place, which could have affected mutation rates.
Birth records with parental age could only be obtained for 76% of CBCS participants born in or after 1948, and were missing almost exclusively by participants' state of birth. Breast cancer mortality is generally higher in the midwest and northeast regions of the USA than in the southeast [48], and so if participants with higher birthweights from those areas were systematically excluded then bias toward the null would be expected. However, there was no regional pattern to the missing birth records; therefore, this was unlikely to have introduced substantial bias. Paternal information was collected only when the mother was married. Although the proportion of unmarried parents is small, this could have introduced bias.
In women younger than 50 years of age, higher birth order exhibited a weak inverse association with breast cancer only after adjustment for maternal age. No association was seen in the full CBCS, which included women aged up to 74 years, although data were not available to adjust for maternal age. This is consistent with the majority of previous studies, which have shown either a weak inverse association with breast cancer [19,26,36,49] or no association [22,24,37]. A weak positive relationship (OR 1.05, 95% CI 1.01–1.10 per 1 unit increase in birth order) was found by Hemminki and Mutanen [50]. Few studies were able to adjust for maternal age. Because pregnancy estrogens appear to be highest in first pregnancies and decline in successive pregnancies [51,52], these results lend some support to the theory that prenatal estrogen exposure may influence breast cancer later in life. Birth order was self-reported and may have been misclassified. Although the number of previous maternal pregnancies could be a better measure of prenatal estrogen exposure than live birth order, we could not assess this because of poor data quality on the birth records.
Younger African-American women are at higher risk for breast cancer than younger white women [3,4]. Birthweight, patterns of parental age at birth, and birth order continue to vary by race [53]. Our study has several important strengths. Use of birth records as the source of birthweight information improved accuracy of the exposure measurement, eliminated recall bias caused by self-report, and reduced possible selection bias from maternal report. Similarly, birth records improved data quality for parental age. Using a population-based case–control study made it possible to evaluate a wider range of adult-life risk factors as potential confounders and/or effect modifiers than is generally possible in a registry-based study.
Conclusion
Taken as a whole, the results for birthweight, parental age, and birth order from the present study do not support the estrogen exposure hypothesis as a unifying theory for prenatal influence on adult breast cancer. This emphasizes the importance of further investigating the influence of prenatal factors on breast cancer risk, particularly in multiple populations. Additional hypotheses must be pursued, including the association between birthweight and other hormonal exposures such as insulin-like growth factor I, and between maternal age and endogenous and exogenous mutagenic exposures. Methodologic difficulties involved in investigating prenatal exposures in nonwhite and/or disadvantaged populations are not trivial; nonetheless, this type of investigation must be done to fully understand life course processes that can culminate in breast cancer among women of any background.
Author contributions
MEH, BN and RCM participated in the interpretation of results and writing of the manuscript. MEH performed data collection, data entry, and statistical analyses.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
BMI = body mass index; CBCS = Carolina Breast Cancer Study; CI = confidence interval; OR = odds ratio.
Acknowledgements
The CBCS was supported in part by the Program of Research Excellence (SPORE) in Breast Cancer (NIH/NCI P50-CA58223) and conducted through UNC-Chapel Hill and the Lineberger Comprehensive Cancer Center. M Elizabeth Hodgson was supported by a National Cancer Institute training grant for cancer epidemiology at the University of North Carolina-Chapel Hill School of Public Health. The authors thank Sandra L Deming, Thao Vo, Vani Vannappagari, Olga L Sarmiento (University of North Carolina at Chapel Hill, Department of Epidemiology), and Patricia Plummer (CBCS nurse interviewer) for insightful discussions and Dianne Vann (North Carolina Central Cancer Registry, Department of Environment Health and Natural Resources) for invaluable assistance collecting birth records.
Figures and Tables
Table 1 Cases and controls in Carolina Breast Cancer Study (CBCS) analytic datasets by race
African-American White
Analytic dataset Main exposure Cases (%) Controls (%) Cases (%) Controls (%)
CBCS, entire Birth order 335 (100.0) 332 (100.0) 526 (100.0) 458 (100.0)
CBCS, born 1948 or later 131 (39.1) 135 (40.7) 235 (44.7) 181 (39.5)
Maternal age dataset Birth order, Maternal age 107 (31.9) 116 (34.9) 173 (32.9) 121 (26.4)
Paternal age dataset Paternal age 95 (28.4) 100 (30.1) 171 (32.5) 118 (25.8)
CBCS, NC born 1949 or later 99 (29.6) 96 (28.9) 112 (21.3) 85 (18.6)
Birthweight, full dataset Birthweight 86 (25.7) 89 (26.8) 110 (20.9) 78 (17.0)
Birthweight, restricted dataset Birthweight 49 (14.6) 37 (11.1) 98 (18.6) 63 (13.8)
Table 2 Prenatal Factors in Carolina Breast Cancer Study (CBCS) Analytic Datasets
CBCS Analytic subgroups
CBCS, full dataset CBCS, born 1948+ Maternal age dataset Birthweight dataset
Cases Controls Cases Controls Cases Controls Cases Controls
n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%)
Number 861 790 366 316 280 237 196 167
Age at selection/diagnosis (years)
Mean ± SD 50.26 ± 11.93 51.83 ± 11.65 39.59 ± 5.29 40.65 ± 4.72 39.19 ± 5.43 40.68 ± 4.63 38.39 ± 5.29 39.95 ± 4.32
Median 48 49 41 42 40 42 39 41
Range 23–74 21–74 23–48 21–47 23–48 26–47 23–47 26–47
Birthplace
NC born 583 (67.7) 550 (69.6) 235 (64.2) 207 (65.5) 220 (78.6) 198 (83.5) 196 (100.0) 167 (100.0)
US born (outside NC) 261 (30.3) 218 (27.6) 121 (33.1) 98 (31.0) 60 (21.4) 39 (16.5) 0 (0.0) 0 (0.0)
Non-US born 17 (2.0) 22 (2.8) 10 (2.7) 11 (3.5) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
Prenatal factors
Birthweight
Lower tertile NA NA NA NA NA NA 73 (37.2) 57 (34.1)
Central tertile 70 (35.7) 54 (32.3)
Upper tertile 53 (27.0) 56 (33.5)
Mean ± SD 3210.7 ± 526.1 3262.5 ± 550.3
Maternal age (years)
15–18 NA NA NA NA 31 (11.1) 31 (13.1) 21 (10.7) 22 (13.3)
19–22 51 (18.2) 80 (33.8) 34 (17.3) 53 (31.9)
23–27 87 (31.1) 49 (20.7) 60 (30.6) 38 (22.9)
28–44 111 (39.6) 77 (32.5) 81 (41.3) 53 (31.9)
Mean ± SD 26.46 ± 6.48 25.18 ± 6.66 26.63 ± 6.46) 25.34 ± 6.82
Paternal age (years)
15–22 NA NA NA NA 39 (14.7) 35 (16.1) 26 (14.1) 21 (14.0)
23–27 73 (27.5) 69 (31.8) 47 (25.4) 50 (33.3)
28–34 87 (32.8) 67 (30.9) 66 (35.7) 49 (32.7)
35–56 66 (24.9) 46 (21.2) 46 (24.9) 30 (20.0)
Mean ± SD 29.97 ± 7.10 29.54 ± 7.46 30.24 ± 7.06) 29.71 ± 7.48
Birth order (self-report)
First 297 (34.8) 268 (34.1) 117 (32.3) 108 (34.3) 88 (31.5) 80 (33.9) 56 (28.6) 53 (31.9)
Second-fourth 406 (47.5) 377 (48.0) 194 (53.6) 158 (50.2) 149 (53.4) 115 (48.7) 107 (54.6) 81 (48.8)
Fifth or higher 151 (17.7) 140 (17.8) 51 (14.1) 49 (15.6) 42 (15.1) 41 (17.4) 33 (16.8) 32 (19.3)
Mean ± SD 2.86 ± 2.30 2.87 ± 2.32 2.76 ± 2.12 2.74 ± 2.15 2.83 ± 2.22 2.84 ± 2.25 2.95 ± 2.22 3.01 ± 2.35
NA, not available
Table 3 Birthweight distributions and odds ratios for breast cancer in African-American and white women combined
Minimally adjusted ORa Fully adjusted ORb
Cases Controls ORa 95% CI Cases Controls ORb 95% CI
Full birthweight dataset n = 196 n = 167 n = 191 n = 161
Lower tertilec 73 57 0.9 0.6–1.6 72 55 1.0 0.6–1.7
Central tertile 70 54 Ref. 69 51 Ref.
Upper tertile 53 56 0.7 0.4–1.2 50 55 0.7 0.4–1.2
Mean ± SD (g) 3262 ± 550
Median (g) 3232
Range (g) 1021–4621
Restricted datasetd n = 147 n = 100 n = 143 n = 97
Lower tertilec 59 43 0.9 0.5–1.7 58 41 1.0 0.5–0.9
Central tertile 48 32 Ref. 47 31 Ref.
Upper tertile 40 25 1.0 0.5–2.1 38 25 0.9 0.4–1.9
Mean ± SD (g) 3210 ± 482
Median (g) 3232
Range (g) 2041–4621
aAdjusted for age, race and sampling fractions. bAdjusted for age, race, sampling fractions, history of previous biopsy, maternal age, and adult body mass index >25 kg/m2. cTertiles are race specific with cutpoints derived from controls. White women: <3062 g, 3062–3458 g, >3458 g; African American women: <3146 g, 3146–3486 g, >3486 g. dBirthweight measured in pounds and ounces and participant delivered in a medical facility by a physician. CI, confidence interval; OR, odds ratio.
Table 4 Birthweight distributions and odds ratios for breast cancer among African-American and white women by race
Minimally adjusted ORa Fully adjusted ORb
African-American White African-American White
Cases Controls ORa 95% CI Cases Controls ORa 95% CI Cases Controls ORb 95% CI Cases Controls ORb 95% CI
Full birthweight dataset n = 86 n = 89 n = 110 n = 78 n = 83 n = 85 n = 108 n = 76
Lower tertilec 38 30 1.0 0.5–2.1 35 27 0.9 0.4–1.8 38 29 1.1 0.5–2.2 34 26 0.9 0.4–2.0
Central tertile 33 29 Ref. 37 25 Ref. 32 27 Ref. 37 24 Ref.
Upper tertile 15 30 0.5 0.2–1.0 38 26 1.0 0.5–2.1 13 29 0.4 0.2–1.0 37 26 0.9 0.4–2.0
Mean ± SD (g) 3251 ± 584 3276 ± 513
Median (g) 3211 3239
Range (g) 1021–4536 1843–4734
Restricted datasetd n = 49 n = 37 n = 98 n = 63 n = 47 n = 36 n = 96 n = 61
Lower tertilec 26 19 0.7 0.3–2.0 33 24 1.0 0.4–2.1 26 18 0.8 0.3–2.4 32 23 1.1 0.5–2.4
Central tertile 17 9 Ref. 31 23 Ref. 16 9 Ref. 31 22 Ref.
Upper tertile 6 9 0.4 0.1–1.4 34 16 1.5 0.7–3.4 5 9 0.3 0.1–1.2 33 16 1.4 0.6–3.2
Mean ± SD (g) 3184 ± 463) 3225 ± 497)
Median (g) 3147 3232
Range (g) 2296–4337 2041–4621
aAdjusted for age and sampling fractions. bAdjusted for age, sampling fractions, history of previous biopsy, maternal age, and adult body mass index >25 kg/m2. cTertiles are race specific with cutpoints derived from controls. White women: <3062 g, 3062–3458 g, >3458 g; African American women: <3146 g, 3146–3486 g, >3486 g. dBirthweight measured in pounds and ounces and participant delivered in a medical facility by a physician. CI, confidence interval; OR, odds ratio.
Table 5 Parental age distributions and odds ratios for breast cancer among African-American and white women combined
Minimally adjusted ORa Fully adjusted ORb
Case Control OR 95% CI Case Control OR 95% CI
Maternal age (years)c n = 280 n = 236 n = 263 n = 235
15–18 31 31 1.8 0.9–3.4 30 29 1.8 0.9–3.5
19–22 (ref.) 51 80 Ref. 48 77 Ref.
23–27 87 49 3.0 1.8–5.0 85 48 3.5 2.0–5.9
28–44 111 77 2.5 1.6–4.0 100 81 3.0 1.8–5.2
Mean ± SD 26.5 ± 6.5 25.2 ± 6.7
Median 26 23
Range 17–43 15–43
Paternal age (years)d n = 266 n = 218 n = 251 n = 206
15–22 39 35 1.0 0.6–1.8 38 33 1.6 0.8–3.1
23–27 (refs) 73 70 Ref 69 65 Ref
28–34 87 67 1.3 0.8–2.1 83 62 1.2 0.7–2.2
35–56 67 46 1.6 0.9–2.6 61 46 1.5 0.7–3.2
Mean ± SD 30 ± 7.1) 29.5 ± 7.5)
Median 29 28
Range 17–53 15–56
aAdjusted for age, race, and sampling fractions. bAdjusted for age, race, sampling fractions, and other covariates as specified. cAdditional covariates for fully adjusted OR: household income ≥$30,000 and birth order.dAdditional covariates for fully adjusted OR: adult body mass index >25 kg/m2, household income ≥$30,000, birth order, and maternal age.
Table 6 Parental age distributions and odds ratios for breast cancer among African-American and white women by race
Minimally adjusted ORa Fully adjusted ORb
African-American White African-American White
Case Control OR 95% CI Case Control OR 95% CI Case Control OR 95% CI Case Control OR 95% CI
Maternal age (years)c n = 107 n = 116 n = 173 n = 120 n = 99 n = 118 n = 164 n = 117
15–18 19 17 2.3 0.9–5.5 12 14 1.1 0.5–2.9 18 15 2.7 1.0–7.0 12 14 1.0 0.4–2.7
19–22 (ref.) 18 33 Ref. 33 47 Ref. 17 33 Ref. 31 44 Ref.
23–27 28 28 1.9 0.9–4.3 59 21 4.2 2.1–8.3 27 28 2.4 1.0–5.4 58 20 4.8 2.3–9.8
28–44 42 38 2.2 1.1–4.6 69 39 2.7 1.5–4.9 37 42 3.3 1.4–7.4 63 39 2.8 1.4–5.4
Mean ± SD 26.4 ± 7.3 25.5 ± 7.0 26.5 ± 5.9 24.9 ± 6.3
Median 26 23 26 22
Range 15–43 16–38 16–43 15–44
Paternal age (years)d n = 95 n = 100 n = 171 n = 118 n = 89 n = 94 n = 162 n = 112
15–22 16 15 1.6 0.6–3.9 23 20 0.7 0.3–1.5 16 14 2.7 1.0–7.3 22 19 1.1 0.5–2.5
23–27 (ref.) 22 30 Ref. 51 40 Ref. 20 27 Ref. 49 38 Ref.
28–34 23 28 1.3 0.6–2.8 64 39 1.3 0.8–2.4 22 26 1.3 0.5–3.4 61 36 1.2 0.6–2.4
35–56 34 27 1.9 0.9–4.0 33 19 1.4 0.7–2.8 31 27 2.1 0.8–5.8 30 19 1.1 0.4–2.9
Mean ± SD 30.9 ± 6 30.4 ± 8.0 29.5 ± 6.8 28.8 ± 6.9
Median 30 29 29 27
Range 18–52 19–54 17–53 15–56
aAdjusted for age and sampling fractions. bAdjusted for age, sampling fractions, and other covariates as specified. cAdditional covariates for fully adjusted OR: household income ≥$30,000 and birth order. dAdditional covariates for fully adjusted OR: adult body mass index >25 kg/m2, household income ≥$30,000, birth order, and maternal age.
Table 7 Birth order distributions and odds ratios for breast cancer among white and African-American women combined
Minimally adjusted ORa Fully adjusted ORb
Case Control OR 95% CI Case Control OR 95% CI
Full CBCSc n = 854 n = 785 n = 854 n = 785
1st born (ref.) 297 268 Ref. 297 268 Ref.
2nd–4th born 406 377 0.9 0.7–1.1 406 377 0.9 0.7–1.1
5th–14th born 151 140 1.0 0.8–1.3 151 140 1.0 0.8–1.3
Mean ± SD 2.9 ± 2.3 2.9 ± 2.3
Median 2.0 2.0
Range 1–14 1–14
Born ≥1948d,e n = 362 n = 315 n = 164 n = 224
1st born (ref.) 117 108 Ref. 84 76 Ref.
2nd–4th born 194 158 1.1 0.8–1.6 144 109 0.9 0.6–1.4
≥5th born 51 49 1.0 0.6–1.7 37 39 0.6 0.3–1.3
Mean ± SD 2.8 ± 2.1 2.7 ± 1.2 2.8 ± 2.2 2.9 ± 2.3
Median 2.0 2.0 2.0 2.0
Range 1–14 1–14 1–14 1–14
1Adjusted for age, race, and sampling fractions. 2Adjusted for age, race, sampling fractions, and other covariates as specified. 3Additional covariates for fully adjusted OR: none. 4Additional covariates for fully adjusted OR: adult body mass index >25 kg/m2, household income ≥$30,000 and maternal age.5Restricted to participants born on or after 1 January 1948. CBCS, Carolina Breast Cancer Study; CI, confidence interval; OR, odds ratio.
Table 8 Birth order distributions and odds ratios for breast cancer among white and African-American women by race
Minimally adjusted ORa Fully adjusted ORb
African-American White African-American White
Case Control OR 95% CI Case Control OR 95% CI Case Control OR 95% CI Case Control OR 95% CI
Full CBCSc n = 331 n = 329 n = 523 n = 456 n = 331 n = 329 n = 523 n = 456
1st born (ref.) 106 103 Ref. 191 165 Ref. 106 103 Ref. 191 165 Ref.
2nd–4th born 142 151 0.8 0.6–1.2 264 226 1.0 0.7–1.3 142 151 0.8 0.6–1.2 264 226 1.0 0.7–1.3
≥5th born 83 75 1.0 0.7–1.5 68 65 1.0 0.7–1.5 83 75 1.0 0.7–1.5 68 65 1.0 0.7–1.5
Mean ± SD 3.6 ± 2.7 3.3 ± 2.7 2.6 ± 1.9 2.6 ± 2.0
Median 2.0 3.3 2.0 2.0
Range 1–14 1–14 1–12 1–11
Born ≥1948d,e n = 129 n = 134 n = 233 n = 181 n = 101 n = 109 n = 164 n = 115
1st born (ref.) 37 41 Ref. 80 67 Ref. 29 31 Ref. 55 45 Ref.
2nd–4th born 56 61 1.0 0.5–1.7 138 97 1.2 0.8–1.9 46 50 0.7 0.4–1.5 98 59 1.0 0.6–1.8
≥5th born 36 32 1.2 0.6–2.3 15 17 0.7 0.3–1.6 26 28 0.7 0.3–1.6 11 11 0.5 0.2–1.5
Mean ± SD 3.6 ± 2.7) 3.3 ± 2.6) 2.3 ± 1.5) 2.3 ± 1.6)
Median 3.0 2.5 2.0 2.0
Range 1–14 1–14 1–10 1–11
1Adjusted for age and sampling fractions. 2Adjusted for age, sampling fractions, and other covariates as specified. 3Additional covariates for fully adjusted OR: none. 4Additional covariates for fully adjusted OR: adult body mass index >25 kg/m2, household income ≥$30,000 and maternal age. 5Restricted to participants born on or after 1 January 1948. CBCS, Carolina Breast Cancer Study; CI, confidence interval; OR, odds ratio.
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| 15535848 | PMC1064078 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Sep 22; 6(6):R656-R667 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr931 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9331553584410.1186/bcr933Research ArticleFrequency of CHEK2 mutations in a population based, case–control study of breast cancer in young women Friedrichsen Danielle M [email protected] Kathleen E [email protected] David R [email protected] Janet R [email protected] Elaine A [email protected] Divisions of Clinical Research and Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA2 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA3 School of Public, Health and Community Medicine, Department of Epidemiology, University of Washington, Seattle, Washington, USA2004 22 9 2004 6 6 R629 R635 10 5 2004 6 7 2004 5 8 2004 11 8 2004 Copyright © Friedrichsen et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
The cell-cycle checkpoint kinase (CHEK)2 protein truncating mutation 1100delC has been associated with increased risk for breast or prostate cancer. Multiple studies have found an elevated frequency of the 1100delC variant in specific stratifications of breast cancer patients with a family history of the disease, including BRCA1/BRCA2 negative families and families with a history of bilateral disease or male breast cancer. However, the 1100delC mutation has only been investigated in a few population-based studies and none from North America.
Methods
We report here on the frequency of three CHEK2 variants that alter protein function – 1100delC, R145W, and I175T – in 506 cases and 459 controls from a population based, case–control study of breast cancer conducted in young women from western Washington.
Results
There was a suggestive enrichment in the 1100delC variant in the cases (1.2%) as compared with the controls (0.4%), but this was based on small numbers of carriers and the differences were not statistically significant. The 1100delC variant was more frequent in cases with a first-degree family history of breast cancer (4.3%; P = 0.02) and slightly enriched in cases with a family history of ovarian cancer (4.4%; P = 0.09).
Conclusion
The CHEK2 variants are rare in the western Washington population and, based on accumulated evidence across studies, are unlikely to be major breast cancer susceptibility genes. Thus, screening for the 1100delC variant may have limited usefulness in breast cancer prevention programs in the USA.
breast cancercase–control studyCHEK2population based
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Introduction
Cell-cycle checkpoint kinase (CHEK)2 has been shown to play a role in cell cycle regulation, apoptosis, and DNA repair, at least in part through phosphorylation of p53 and BRCA1 in response to DNA damage [1,2]. Several studies have reported associations of germline mutations in CHEK2, especially the 1100delC mutation, with increased susceptibility to breast and prostate cancer [3-8]. Although CHEK2 germline variants other than 1100delC have been associated with prostate cancer risk, these have not yet been shown to be enriched in breast cancer cases [3,4,9,10].
The association between the CHEK2 1100delC variant and risk for breast cancer was initially reported by the CHEK2 Breast Cancer Consortium [5]. They found that the frequency of the variant was greater among breast cancer patients with a positive family history of breast cancer who do not carry germline mutations in the BRCA1 or BRCA2 genes, and in families with male breast cancer, as compared with healthy control individuals from the UK, The Netherlands, and North America [5]. Additionally, they noted that the frequency of the 1100delC variant did not differ significantly between breast cancer patients and matched control individuals from a population-based series of young women from the UK (age < 45 years) and of older women from The Netherlands (age ≥ 55 years) [5]. However, neither population-based series included frequency data after stratifying for family history characteristics.
Several additional studies have addressed the association of the 1100delC variant and breast cancer risk in unique populations. In a Finnish study conducted by Vahteristo and coworkers [6], the frequency of the 1100delC mutation was observed to be slightly but not significantly higher in an unselected cohort of breast cancer patients than in control individuals (identified from the Finnish Red Cross Blood Service). Significant enrichment of the variant was found among index cases with a first-degree or second-degree relative with breast or ovarian cancer, and in women with bilateral breast cancer as compared with patients with unilateral disease. Finally, analysis of the variant in a set of patients with positive family history who were not BRCA1 or BRCA2 germline mutation carriers demonstrated a significantly elevated frequency of the 1100delC variant as compared with controls. These findings from Vahteristo and coworkers [6] and recent work from Oldenburg and colleagues [7] are similar to data from the CHEK2 Breast Cancer Consortium, and suggest a significant role played by the 1100delC variant in breast cancer among women with a positive family history of breast cancer whose disease is not attributable to germline mutations in BRCA1 or BRCA2.
Finally, a study from New York by Offit and coworkers [8] reported a lower frequency of 1100delC carriers in both breast cancer cases and controls as compared with previous studies that largely included Northern European individuals. The 1100delC mutation was identified in 1.0% of cases, which was not statistically different (P = 0.10) from that observed among controls (0.3%), who were volunteers from the New York Cancer Project. Compared with the general population frequency in New York, the 1100delC variant appears to be even rarer among breast cancer patients from Spain [11] and India [12], where studies to date have reported no individuals with the 1100delC variant.
To understand better the association of CHEK2 variants and breast cancer risk in the general population in the USA, we analyzed the frequency of three CHEK2 variants – 1100delC, R145W, and I175T, each of which reportedly alters CHEK2 protein function – in a population based, case–control study of 506 breast cancer cases diagnosed before age 45 years from western Washington state, and a set of 459 frequency matched control individuals.
Methods
Study population
A characterization of the study population has previously been reported and is summarized only briefly [13,14]. Cases were identified through the Cancer Surveillance System of Western Washington, a population-based cancer registry and a participant in the National Cancer Institute's Surveillance, Epidemiology, and End Results Program (SEER). Control individuals were identified through random digit dialing and were frequency matched to the cases on 5-year age group and reference year [15]. The study identified all incident first primary breast cancer cases diagnosed before age 45 years, from May 1 1990 to December 31 1992, in women of all races and ethnic backgrounds, who were residents of King, Pierce and Snohomish counties at the time of diagnosis. Information on potential risk factors for breast cancer, including family history, was obtained through a structured in-person interview. The reference date for the interview, a date beyond which exposure information was not collected, was the month and year of diagnosis for cases and a randomly assigned date for controls. Interviews were completed for 642 cases (84.0%) and 608 controls (73.8% overall response rate). Blood was collected from 540 interviewed cases and 476 interviewed controls.
Tested cases tended to be older than untested cases from the study (P = 0.001) whereas no such age-related differences were seen in controls. Untested cases were more likely to have advanced stage disease (51.0% of tested and 41.2% of untested cases had local stage disease, 30.2% and 40.4% had regional disease, and 1.4% and 5.9% had distant disease; P = 0.001) and were more likely to be deceased at the last follow up in June 2002 (16.6% of tested and 48.8% of untested cases were deceased; P < 0.001). For 40% of participants, blood collection was not attempted until after the initial interview, probably accounting in part for these differences. We observed no difference in cases or controls between those tested and untested with regard to family history.
Molecular methods
Batches of DNA for genotyping were constructed to contain both case and control samples, and genotyping personnel were blinded as to the case–control status of samples. Previously described specific primers for CHEK2 exon 10 were used for PCR amplification [16]: 5'-TTA ATT TAA GCA AAA TTA AAT GTC-3' and 5'-GGC ATG GTG GTG TGC ATC-3'. Genomic DNA (25 ng) was amplified using the AccuPrime TAQ DNA polymerase system (Invitrogen, Carlsbad, CA, USA). Touchdown PCR conditions for the 1100delC amplicon were as follows: denaturation at 94°C for 1 min then 94°C for 30 s, 60°C for 30 s, and 68°C for 30 s for seven cycles with the annealing temperature decreasing by 1°C for each cycle, followed by an additional 28 cycles of 94°C for 30 s, 54°C for 30 s, and 68°C for 30 s. The resulting 556 bp amplicon was analyzed by unidirectional DNA sequencing with the reverse primer. The R145W and I175T variants were sequenced from a 409 bp amplicon generated using the following primers: 5'-TTG CCT TCT TAG GCT ATT TTC C-3' and 5'-AAA GGT TCC ATT GCC ACT GT-3'. As above, 25 ng genomic DNA with AccuPrime TAQ DNA polymerase was amplified by touchdown PCR, in which the starting annealing temperature was 64°C and the final annealing temperature was 58°C. For sequencing, the Applied Biosystems Big Dye Terminator Ready Reaction Mix (Foster City, CA, USA) was used in accordance with the manufacturer's recommended protocol.
Genotyping was conducted in 506 cases and 459 controls. Valid results for all participants were obtained for the R145W and I175T variants, whereas results for one case and one control were not obtained for the 1100delC variant.
Analysis
To assess the relationship between CHEK2 variants and breast cancer risk, logistic regression was used to obtain odds ratios as estimates of the relative risk and 95% confidence intervals [17]. All analyses were completed using Stata statistical software (StataCorp LP, College Station, TX, USA).
Because reference age and year were matching variables for the frequency matching employed in the original study, all risk estimates presented are age (continuous)-and reference year (exact)-adjusted.
A subset of the samples analyzed in the study had been screened previously for germline mutations in the breast cancer susceptibility genes BRCA1 and BRCA2 [18,19]. Cases were selected for BRCA1/BRCA2 screening on the basis of an age of diagnosis under 35 years and/or a first-degree family history of breast cancer (n = 134). In addition, 235 controls were tested for mutations in the BRCA1 gene, and 37 of these controls were additionally tested for BRCA2. Overall, 110 cases and 33 controls were available for consideration of CHEK2 variant frequencies in BRCA1/BRCA2 mutation negative subjects.
Results
Study population characteristics
Tested cases and controls were generally similar with regard to age, menopausal status, and racial distribution (Table 1). Approximately 90% of all participants were Caucasian. Cases more often reported a family history of breast cancer than did controls, particularly a first-degree family history, which was reported by 19.0% of cases and 8.1% of controls.
Variants and risk for breast cancer
Overall, no statistically significant differences were observed in frequency between cases and controls for any of the three variants tested (Table 2) and all three variants studied were uncommon. For the 1100delC mutation, a 2.9-fold increased risk was observed among cases compared with controls, because six (1.2%) out of 505 cases and two (0.4%) out of 458 controls carried the variant. However, the confidence interval did not exclude 1 and thus chance cannot be excluded as an explanation (95% confidence interval 0.6–14.6).
We examined the frequencies of the CHEK2 variants according to age, race, and family history features of the probands (Table 3). All 1100delC deletion carriers were Caucasian. Among the cases, 0.7% (2/280) of those with no family history of breast cancer, none (0/120) with only a second-degree family history, and 4.3% (4/94) of those with a first-degree family history were found to carry the 1100delC variant (P = 0.02). Among controls, 0.3% (1/294) of those with no family history, 0.9% (1/115) of those with only a second-degree family history, and none (0/36) of the controls with a first-degree family history carried the 1100delC variant.
One case, or 2.4% of those with a family history of bilateral breast cancer, and one control, or 3.2% of those with a similar family history, were carriers. Cases with a positive first-degree or second-degree family history of ovarian cancer carried an 1100delC variant more frequently (4.4% [2/45]) than did cases with no such family history (0.9% [4/461]; P = 0.09). No controls (0/27) with such family history were carriers. Furthermore, 9.1% (2/22) of the cases with a positive family history of both breast and ovarian cancer were found to carry the 1100delC variant (P = 0.07). In the overall dataset, only three cases and two controls reported a family history of male breast cancer, and none carried the 1100delC variant.
The R145W variant was rare in this data set, with only one case and no controls carrying the variant (Table 2). The carrier case was diagnosed before age 30 years, was Caucasian, and reported one first-degree relative with breast cancer who was diagnosed after age 45 years and no family history of ovarian cancer, bilateral breast cancer, or male breast cancer (data not shown).
The I175T change was observed in two (0.4%) of 506 cases and four (0.9%) of 459 controls (odds ratio 0.5, 95% confidence interval 0.1–2.6; Table 2). One control carrier was non-Caucasian. One case (0.4%) and three controls (1.0%) with no family history of breast cancer carried the I175T change. The other case carrying this variant was in the group with a first-degree family history of breast cancer (1/94 [1.1%]) and the other control carrying this variant was among controls with a second-degree family history (1/115 [0.9%]). None of the I175T carriers had a family history of ovarian cancer, bilateral breast cancer, or male breast cancer.
Non-BRCA1/BRCA2 carriers and CHEK2 mutations
Because some studies suggest that the CHEK2 1100delC variant acts as a breast cancer modifier in non-BRCA1/BRCA2 families only [5-7], we considered the subset of women known not to be BRCA1 or BRCA2 germline mutation carriers. Within this subset of 110 cases and 33 controls, four (3.6%) cases and no (0%) controls carried the 1100delC variant (P = 0.27), one case and no controls carried the R145W change, and one case and no controls carried the I175T change. No CHEK2 variants were observed in any known BRCA1 or BRCA2 mutation carrier.
Discussion
We analyzed three CHEK2 variants that are known to disrupt protein function (1100delC, R145W, and I175T) in a population based, case–control study of breast cancer among young North American women. The 1100delC variant is a protein truncating mutation that abrogates CHEK2 kinase activity [20]. R145W has been shown to have disrupted kinase activity [20,21] and I175T is deficient in binding and phosphorylation of Cdc25A and in binding to BRCA1 and p53 [20-22]. Although an enrichment in the 1100delC variant and a reduction in I175T carriers in the cases were noted, no statistically significant association between any of the CHEK2 variants and breast cancer risk was observed. The absolute number of participants carrying CHEK2 variants was relatively small, and thus there was limited power to examine frequencies according to family history features. Nonetheless, among cases there was some suggestion that the 1100delC variant may be slightly more frequent in those with a positive first-degree family history of breast cancer (P = 0.02) and in those with any family history of ovarian cancer (P = 0.09). However, in agreement with two other breast cancer studies [9,10], we observed no suggestive correlation between the R145W and I175T CHEK2 variants and breast cancer risk. No CHEK2 variants were seen among women found previously to carry a BRCA1/BRCA2 mutation.
Our overall frequency results for 1100delC of 1.2% for cases and 0.4% for controls are similar to the frequencies reported previously from the UK, Philadelphia, and New York [5,6,8]. In a population based series of individuals from the UK and The Netherlands, the frequency of the 1100delC variant was higher among cases, but did not differ significantly from a set of matched controls (1.3% and 2.5% for cases and 0.3% and 1.2% for controls, respectively) [5]. Likewise, the frequency of 1100delC in a Finnish series of breast cancer patients was similar to that reported among control individuals from the Finnish Red Cross Blood Transfusion Service (2.0% and 1.4%, respectively; P = 0.18) [6]. Finally, in North America the 1100delC variant was identified in 1.6% of index cases from breast cancer families in Philadelphia and in 0.6% of control individuals (from the same neighborhood or spouses marrying into a breast cancer family from the same area) [5]. In New York examination of the 1100delC variant in 192 women with a family history of breast cancer, 92 women with a personal history of breast cancer, and 16 male breast cancer patients [8] revealed a mutation frequency of 1.0%, which did not differ significantly from the frequency of 0.3% found in volunteers for the New York Cancer Project (P = 0.10).
Several previously published studies [5-7,23] reported an elevated frequency of the CHEK2 1100delC variant in specific stratifications of breast cancer patients. Specifically, individuals with positive family history (especially those who are BRCA1/BRCA2 mutation negative), patients with bilateral disease, and patients with a family history of male breast cancer had a higher occurrence of 1100delC variants as compared with control individuals. We found no CHEK2 variants in women with a family history of male breast cancer, but there were only five individuals with such a history in our entire sample. This finding is similar to those of other recent studies that did not find an association between 1100delC and risk for male breast cancer [24-26]. Although the frequency of 1100delC carriers was higher in cases (2.4%) and controls (3.2%) with a family history of bilateral disease as compared with cases (0.7%) and controls (0.3%) with no family history and cases (1.8%) and controls (0) with only a family history of unilateral disease, this was based on sparse data and family history of bilaterality contributed no insights beyond family history overall.
After stratifying by family history, we did observe an elevated frequency of 1100delC carriers among cases with a first-degree family history (4.4%; P = 0.02). Although our numbers are small, this frequency is similar to the frequencies reported by others. Vahteristo and coworkers [6] reported that, among 1035 breast cancer patients, 3.1% of those with at least one affected first-degree or second-degree relative were 1100delC carriers. Additionally, in index cases with a family history of breast cancer, Meijers-Heijboer and coworkers [5] observed that 3.0% (31/1036) were 1100delC carriers.
Thus far, the most convincing evidence for an association between the 1100delC variant and breast cancer risk is in families who do not carry BRCA1/BRCA2 germline mutations [5-7]. However, the 1100delC frequency in BRCA1/BRCA2 mutation positive families did not differ significantly from the frequency observed among controls [5,6]. In this study we observed that four of 110 cases (3.6%) and none of 33 controls who were known to be BRCA1 or BRCA2 negative carried the CHEK2 1100delC variant. The number of women in the present study with a first-degree family history of breast cancer who tested negative for BRCA1/BRCA2 mutations (71 cases, 27 controls) does not offer adequate power to detect differences in the frequency of CHEK2 variants within this stratification.
The significance of the CHEK2 1100delC mutation in individuals with a family history of ovarian cancer is not as well understood. Vahteristo and coworkers [6] found no association between the 1100delC variant and ovarian cancer family history among women with familial breast cancer (0/40). However, Meijers-Heijboer and colleagues [5] reported that 4.0% of index cases or 4.3% of all cases with at least one family member with ovarian cancer carried the 1100delC variant (P = 0.016). This is compatible with the frequency we observed (2/45 [4.4%]) among breast cancer cases with a family history of ovarian cancer. Although the numbers are small, our data suggests that further investigation into the association between the CHEK2 1100delC mutation and ovarian cancer risk is warranted.
The results of our study should be assessed with regard to its limits. Specifically, there are differences between tested and untested women. Tested cases were more likely to be alive, older, and have a less advanced stage of cancer than untested cases. Thus, the generalizability of these results, although from a population-based study, must be viewed within that context. As noted earlier, the literature is diverse in terms of its estimates of CHEK2 mutation frequency. Although the overall sample size of our study was generous (965 women), the frequency of the CHEK2 variants turned out to be quite low. As a result, the study had somewhat reduced power, particularly for assessing mutation frequency according to various family history characteristics.
Conclusion
The population based, case–control study of young women (age at diagnosis < 45 years) presented here does not identify any of the 1100delC, R145W, and I175T variants as major factors in breast cancer susceptibility in western Washington. After stratification by family history characteristics, an association with first-degree family history of breast cancer and possibly family history of ovarian cancer was observed. However, no particular relationship was found with family history of bilateriality or family history of male breast cancer. These results suggest that incorporation of any CHEK2 variants into a breast cancer screening program among Caucasian women in the US would be premature. Additional studies, particularly of women with a family history of breast cancer who do not carry mutations in the BRCA1 or BRCA2 genes, are warranted.
Competing interests
None declared.
Abbreviations
bp = base pairs; CHEK = cell-cycle checkpoint kinase; PCR = polymerase chain reaction.
Acknowledgements
The authors thank the study participants and area physicians for their generous contributions to this study, Kay Byron for programming assistance, and Cecilia O'Brien for project coordination efforts. This research was supported in part by grants and contracts R01-CA-63697, R01-CA-63705, N01-CP9567, R01-CA-59736, and N01-CN-67009 from the National Cancer Institute, FHCRC Interdisciplinary Training Grant CA80416 and K05CA90754-03 to EAO.
Figures and Tables
Table 1 Characteristics of cases and controls
Characteristic Cases (n = 506) Controls (n = 459)
n % n %
Age at reference (years)
< 35 62 12.3 80 17.4
35+ 444 87.7 379 82.6
Race
White 450 88.9 412 89.8
Nonwhite 56 11.1 47 10.2
Family history
None 280 56.7 294 66.1
First degree 94 19.0 36 8.1
Second degree 120 24.3 115 25.8
Unknown 12 14
Menopausal status
Premenopausal 445 88.3 392 85.8
Postmenopausal 59 11.7 65 14.2
Unknown 2 2
Table 2 Association of CHEK2 variants with breast cancer risk
CHEK2 statusa Cases (n = 506) Controls (n = 459) ORb 95% CI
n % n %
1100del C
Noncarrier 499 98.8 456 99.6 1.0 Reference
Carrier 6 1.2 2 0.4 2.9 (0.6–14.6)
R145W
Noncarrier 505 99.8 459 100.0 1.0 Reference
Carrier 1 0.2 0 - - -
I175T
Noncarrier 504 99.6 455 99.1 1.0 Reference
Carrier 2 0.4 4 0.9 0.5 (0.1–2.6)
aAll carriers are heterozygous. bAdjusted for age at reference and reference year. CI, confidence interval; OR, odds ratio.
Table 3 Frequency of CHEK2 variants in cases and controls according to age and family history features
Characteristic All women 1100delC+ I175T+
Cases Controls Cases Controls Cases Controls
n n n % 95% CI n % 95% CI n % 95% CI n % 95% CI
Age (years)
< 30 10 16 0 0 0 0
30–34 52 64 0 0 0 1 1.6 0.04–8.4
35–39 156 140 1 0.6 0.02–3.5 1 0.7 0.02–3.9 1 0.6 0.02–3.5 0
40–44 288 239 5 1.7 0.6–4.0 1 0.4 0.01–2.3 1 0.3 0.009–1.9 3 1.3 0.3–3.6
Family History of breast cancera
None 280 294 2 0.7 0.09–2.6 1 0.3 0.009–1.9 1 0.4 0.009–2.0 3 1.0 0.2–3.0
First-degree 94 36 4 4.3 1.2–10.5 0 1 1.1 0.03–5.8 0
Second-degree only 120 115 0 1 0.9 0.02–4.7 0 1 0.9 0.02–4.7
Number of relatives with breast cancera
None 280 294 2 0.7 0.09–2.6 1 0.3 0.009–1.9 1 0.4 0.009–2.0 3 1.0 0.2–3.0
1 137 114 3 2.2 0.5–6.3 1 0.9 0.02–4.8 1 0.7 0.02–4.0 0
2 51 29 1 2.0 0.05–10.4 0 0 1 3.4 0.09–17.8
3+ 26 8 0 0 0 0
Family history of ovarian cancer
None 461 432 4 0.9 0.2–2.2 2 0.5 0.06–1.7 2 0.4 0.05–1.6 4 0.9 0.3–2.4
1st or 2nd degree 45 27 2 4.4 0.5–15.1 0 0 0
Family history of breast and/or ovarian cancera
No breast/no ovarian 258 278 2 0.8 0.09–2.8 1 0.4 0.009–2.0 1 0.4 0.01–2.1 3 1.1 0.2–3.1
No breast/yes ovarian 22 16 0 0 0 0
Yes breast/no ovarian 192 141 2 1.0 0.1–3.7 1 0.7 0.02–3.9 1 0.5 0.01–2.9 1 0.7 0.02–3.9
Yes breast/yes ovarian 22 10 2 9.1 1.1–29.2 0 0 0
aTwelve cases and 14 controls had missing information on their family history and are excluded. There were no CHEK2 variants in these women.
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| 15535844 | PMC1064080 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Sep 22; 6(6):R629-R635 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr933 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9341553585010.1186/bcr934Research ArticleRatios of involved nodes in early breast cancer Vinh-Hung Vincent [email protected] Claire [email protected] Donald I [email protected] Gábor [email protected] de Steene Jan [email protected] Patricia [email protected] Georges [email protected] Mia [email protected] Guy [email protected] Melanie [email protected] Oncology Center, Academic Hospital, AZ-VUB, Vrije Universiteit Brussel, Jette, Belgium2 Student Management Healthcare Data, BISI-VUB, Computer Science and Medical Informatics, Vrije Universiteit Brussel, Jette, Belgium3 The University of New Mexico, Cancer Research and Treatment Center, Albuquerque, New Mexico, USA4 Decision Analyst, Burlington, Vermont, USA5 Surgical Pathology, Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary6 Radiation Oncology, Saskatchewan Cancer Agency, Regina, Canada7 Gynecologic Oncology and Senology, Geneva University Hospitals, Geneva, Switzerland2004 6 10 2004 6 6 R680 R688 29 3 2004 7 5 2004 9 8 2004 26 8 2004 Copyright © 2004 Vinh-Hung et al.; licensee BioMed Central LtdThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
The number of lymph nodes found to be involved in an axillary dissection is among the most powerful prognostic factors in breast cancer, but it is confounded by the number of lymph nodes that have been examined. We investigate an idea that has surfaced recently in the literature (since 1999), namely that the proportion of node-positive lymph nodes (or a function thereof) is a much better predictor of survival than the number of excised and node-positive lymph nodes, alone or together.
Methods
The data were abstracted from 83,686 cases registered in the Surveillance, Epidemiology, and End Results (SEER) program of women diagnosed with nonmetastatic T1–T2 primary breast carcinoma between 1988 and 1997, in whom axillary node dissection was performed. The end-point was death from breast cancer. Cox models based on different expressions of nodal involvement were compared using the Nagelkerke R2 index (R2N). Ratios were modeled as percentage and as log odds of involved nodes. Log odds were estimated in a way that avoids singularities (zero values) by using the empirical logistic transform.
Results
In node-negative cases both the number of nodes excised and the log odds were significant, with hazard ratios of 0.991 (95% confidence interval 0.986–0.997) and 1.150 (1.058–1.249), respectively, but without improving R2N. In node-positive cases the hazard ratios were 1.003–1.088 for the number of involved nodes, 0.966–1.005 for the number of excised nodes, 1.015–1.017 for the percentage, and 1.344–1.381 for the log odds. R2N improved from 0.067 (no nodal covariate) to 0.102 (models based on counts only) and to 0.108 (models based on ratios).
Discussion
Ratios are simple optimal predictors, in that they provide at least the same prognostic value as the more traditional staging based on counting of involved nodes, without replacing them with a needlessly complicated alternative. They can be viewed as a per patient standardization in which the number of involved nodes is standardized to the number of nodes excised. In an extension to the study, ratios were validated in a comparison with categorized staging measures using blinded data from the San Jose–Monterey cancer registry. A ratio based prognostic index was also derived. It improved the Nottingham Prognostic Index without compromising on simplicity.
axillary lymph node ratiobreast neoplasmfunctional formloco-regionallog oddsnodal ratioNottingham Prognostic Indexpredictive utilityprognostic factorsproportional hazardsproportion basedratio-based prognostic indexSEER programstagingsurvival
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Introduction
Breast cancer is the most common neoplasm in women. Nodal status as determined by pathologic examination of lymph nodes has repeatedly been shown to be the single most important predictor of survival in breast cancer [1]. The absolute number of pathologically involved nodes has also been shown to be an important prognostic factor in breast cancer survival [2-6]. The extent of lymph node involvement is incorporated into prognostic indices such as the Nottingham Prognostic Index [7-9] (see Additional files 1,2,3,4,5,6). Old lymph node stage measures categorized cases according to whether they had none, one to three, or four or more involved nodes, and recently according to more detailed subdivisions [10,11] (see Additional files 2,3,4,5 and 7).
However, several authors have noted the inherent confounding by the number of excised nodes [12,13]. To address the variability of nodal examination, an intuitive approach is to use the proportion or the percentage of involved nodes, as was suggested by Rostgaard and coworkers [14]. The proportion can immediately be derived from pathology reports that clearly state the total number of lymph nodes examined and the total number of involved nodes [1,15]. The proportion has received increasing attention in the literature, providing a reference base on which its clinical relevance may be discussed [13,14,16-27].
In this report the modeling utility of the proportion of involved nodes is compared with the absolute numbers of involved nodes and of examined nodes. There is a one-to-one correspondence between proportion and ratio between involved and uninvolved nodes. A previous study hinted at an apparent linear relationship with survival between involved and uninvolved nodes (Fig. 1) [28], and therefore this report also examines the utility of expressing ratios as odds instead of proportions.
The absolute numbers considered in the study were the number of nodes examined (excised; nx), the number of involved nodes (np), and the number of uninvolved nodes (nn).
Methods
The SEER (Surveillance, Epidemiology, and End Results) program of the USA [29] provides extensive cancer incidence data from 11 population-based registries. The data used in the present study were extracted from nine of those registries: San Francisco-Oakland, Connecticut, Metropolitan Detroit, Hawaii, Iowa, New Mexico, Seattle (Puget Sound), Utah, and Metropolitan Atlanta.
Selected patients were women without a previous history of cancer who presented with a noninflammatory invasive breast carcinoma, which was histologically confirmed and diagnosed between 1988 and 1997, with specified tumor size no larger than 50 mm (T1 and T2), strictly confined to breasts without distant metastasis, and in which curative surgery and axillary lymph node dissection were performed with removal of at least one node. Cases with involvement of skin, hypodermis or pectoral muscles, or with deep fixation were excluded. Patients who had undergone subcutaneous mastectomy, radical mastectomy, or preoperative or intraoperative radiotherapy were excluded. Data on systemic treatment were not available and therefore could not be taken into account. Certain records were rejected because of data quality concerns: uncertain sequence of treatment, nonhospital based data records, month of diagnosis unknown, or race unknown. Examination of outliers (scarce and extreme values) resulted in further exclusion of cases with more than 50 nodes examined, '0 months' of follow up, and age at diagnosis under 25 years or older than 95 years.
The follow-up cut-off date was 31 December 1999. The survival end event was defined as death from breast cancer.
The proportions of involved nodes were expressed as percentages ([np/nx] × 100%). The log odds of nodal involvement were computed using the empirical logistic transform: L = Loge([np + 0.5]/ [nn + 0.5]) [30]. The transform, also called the sample logit, avoids singularities caused by null observations, and is the least biased estimator of the true log odds [31]. (Note that, with hindsight, Fig. 1 shows a logarithmic relationship.) Unadjusted mortality (the number of patients who died divided by the number of patients at risk) as a function of the ratios was used for descriptive purposes.
The utilities of the percentage and log odds were evaluated in different multivariate Cox proportional hazards models [32]. The numbers np and nx, the percentage (np/nx) × 100%, and the L transform were entered as quantitative continuous variables in different combinations. The corresponding hazard ratios were each time computed within a Cox model that included tumor size, age at diagnosis, and year of diagnosis modeled as quantitative continuous variables; and the registry area, race, marital status, tumor topography, histologic type and grade, estrogen and progesterone receptor status, type of primary surgery, and administration of postoperative radiotherapy modeled as qualitative variables. The qualitative variables were converted or expanded as needed into dummy variables to allow binary coding ('married' versus 'not married', 'high grade' versus 'not high grade', for example, and so on). A first order interaction between type of surgery and postoperative radiotherapy was included for consistency with a previous analysis [33]. The models were computed in all cases irrespective of nodal status, and then as a function of positive or negative nodal status. The functional forms were assessed using the generalized additive model procedure [34].
The Nagelkerke R2 index (R2N) was used to score the different Cox models [35]. R2 represents the proportion of variation explained by covariates in regression models [35-37]. R2N divides R2 by its maximum attainable value to scale it to within the range 0–1. R2N is close to 1 for a perfectly predictive model, and close to 0 for a model that does not discriminate between short and long survival times.
Statistical analyses were performed using Splus (Insightful Corporation, Seattle, WA, USA) statistical software.
Results
In the 2002 SEER release [29], 188,410 women were diagnosed with breast tumors from 1988 to 1997, of whom 132,457 had a hospital based histopathologic diagnosis of unilateral invasive carcinoma. A total of 83,686 cases matched the selection criteria; 58,070 were node-negative and 25,616 node-positive. The median follow-up time was 73 months (range 1–143 months) for patients still alive at the follow-up cut-off date (31 December 1999). Characteristics of the patients were presented elsewhere [33]. Except for some additional cases due to updated registration minus the exclusion of outliers resulting in 90 fewer cases, there were no noticeable differences in the distribution of the characteristics.
The median number of nodes examined was 15 (range 1–50, mean ± standard deviation 15.4 ± 6.5). Among the node-positive patients, the median number of involved nodes was 2 (1–46, 4.1 ± 4.8).
Table 1 shows the distribution of the percentages of involved nodes. Figure 2 is a plot of the corresponding breast cancer mortality, which appears to increase linearly with the np/nx percentage.
Table 2 shows the distribution of the log odds for nodal involvement. Figure 3 plots the corresponding breast cancer mortality. There is an initial, almost flat segment for values of L ≤ -3, which is followed by a steeply sloping upward segment. The initial flat segment corresponds mostly to node-negative cases. The sloping upward segment corresponds to node-positive cases, with more positive L values indicating more involved nodes and/or fewer uninvolved nodes. There is an overlap between node-negative and node-positive cases for L values between -3.5 and -1.
In multivariate analyses, np and nx exhibited marked nonlinearity and widely diverging confidence intervals (Fig. 4a,4b). The linearity improved for the percentage (np/nx) × 100% and the L transform, which also showed more homogeneously distributed confidence intervals (Fig. 4c,4d).
The upper section of Table 3 shows a comparison between proportional hazards models that included different combinations of np, nx, np/nx, and L for all patients, irrespective of nodal status. Based on R2N, the best predictive covariate was L (model 6), with a small improvement contributed by nx (model 10). In the model with nx alone (model 3), nx was statistically significant but its contribution to global model fit appeared negligible because the R2N did not change from the baseline 0.069 (model 1). The contribution of np alone was substantial, with a change of R2N from baseline 0.069 to 0.093 (model 2). However, adding np and/or nx onto L or onto np/nx provided no improvement, except in the already mentioned model 10.
The middle section of Table 3 shows multivariate analysis performed for node-positive cases only. Models based on separately expressed numbers provided the lowest R2N (models 2–4). The largest R2N values were all observed in models incorporating L or np/nx (models 5–11). The simplest model appeared to be based on np/nx alone (model 5; R2N = 0.108). A small improvement was contributed by np (model 7; R2N = 0.109).
The lower section of Table 3 shows the analysis performed for node-negative cases only. Because np, by definition, equals 0, there are only four models. They show that nx (model 3) and L (model 6) are statistically significant, but these variables either alone or in combination did not improve the index R2N.
The multivariate computations were also performed by considering death from any cause as the end-point. There were no notable discrepancies.
Discussion
Although many data were evaluated in the present study, there are weaknesses. The data are heterogeneous. Histopathologic characteristics such as grade could not be verified. Neoadjuvant systemic treatment might have modified the yield of nodes [38]. Important information such as how patients were selected for any particular treatment is missing. Undocumented comorbidity might have affected the extent of nodal dissection. An imbalance in the delivery of chemotherapy or hormone therapy could have affected the distribution of deaths. For all of these reasons, the present results should be considered explorative and must be validated independently.
Since about 1999 a growing number of studies have investigated nodal ratios. In the studies that compared the numbers of involved nodes with ratios in multivariate models, the majority found that ratios were better than numbers as prognostic indicators [13,16,18,19,24,26]. Ratios (expressed as percentages or log odds) have a better prognostic impact than do isolated numbers and, unlike numbers, they are not associated with inconsistent findings. Part of the explanation might be that a ratio can be interpreted as a form of standardization in which the number of involved nodes found in a patient is standardized to the number of nodes examined in that same patient [20]. It is noteworthy that the hazard ratios for np/nx were almost unaffected by the model (column np/nx [%] in Table 3), whereas the hazard ratios for np and/or nx exhibited more variability (columns np and nx in Table 3).
As a prognostic factor, np/nx appears the most convenient. Figure 2 shows that, for node-positive cases presenting with 0–10% involved nodes, the crude breast cancer mortality risk for an average follow up of 6 years is about 5%, and with 90–100% involved nodes the mortality is about 45%. For any intermediate value for the percentage of node involvement, the mortality risk is easily interpolated.
The estimated log odds L provided results very similar to those with np/nx. Overall, L improves on np/nx when all cases are considered together (column L in Table 3). The log odds appears useful for integrating node-negative and node-positive cases while avoiding more complex modeling, which we performed previously [39]. However, there is a range of L values in which node-negative and node-positive cases overlap (Table 2, Figure 3). In an analysis of all cases pooled, the overlap might blur the prognostic difference between node-negative status based on a very small number of excised nodes, and node-positive status based on a large number of excised nodes but with few involved nodes. The literature on the log odds of node involvement is scarce, and the utility of the L transform needs independent confirmation.
The present findings indicate that the favorable survival attributed to higher numbers of nodes removed, as suggested by Krag and Single [40], might be due to different model specifications. The number of patients is huge and statistical significance can easily be demonstrated but without necessarily implying any major clinical impact. Undoubtedly, the uncertainty about node negativity increases when nx (the number of excised nodes) is small. However, the predictive utility attributable to nx is exceedingly small (Table 3, lower section). This dissociation between statistical significance and predictive utility appears counterintuitive. Nevertheless, it is in keeping with findings from Fisher and coworkers [41], who noted that prognosis was unaffected by the number of excised nodes when nodal status was reported to be negative. This is also supported by a recent report based on 3800 patients [42] in which the number of excised nodes was predictive of the risk for recurrence in node-positive but not in node-negative patients.
Sentinel node biopsy has gained wide acceptance since 1997 and it is used to determine the need for axillary dissection [43]. Because our selection of patients was from 1988 until 1997, it is unlikely that sentinel nodes could have represented any substantial part of the present study. The prognostic impact of one involved node in patients who had one node removed in this study cannot be extrapolated to the patient found with one involved node in a sentinel node procedure. However, in the prediction of nonsentinel node involvement when one or more sentinel nodes are found to be involved, Cserni and coworkers [44] reported that the number of sentinel nodes and the percentage of positive sentinel nodes were jointly significant predictors. A closely related finding that also highlights the predictive role of ratios was reported in a recent Australian study [45], in which the prediction model was determined by patient age, by the number of sentinel nodes, and by the proportion of involved sentinel nodes.
Conclusion
We found the percentage of involved nodes to be the most directly useful indicator of nodal involvement, but this is limited to node-positive cases. The log odds of nodal involvement performed equally well in node-positive and node-negative patients. The log odds might provide a unified approach to the modeling of nodal involvement. The present results and the growing literature argue that ratios should be considered in the staging of axillary dissection.
Competing interests
The author(s) declare that they have no completing interests.
Abbreviations
L = empirical logistic transform (estimated log odds); nn = number of axillary lymph nodes free from tumor involvement; np = number of pathologically involved axillary lymph nodes; nx = number of axillary lymph nodes examined (excised); R2N = Nagelkerke R2 index.
Supplementary Material
Additional File 1
Text on deriving a ratio based prognostic index.
Click here for file
Additional File 2
Table evaluating nodal staging measures using breast cancer data from the San Jose–Monterey registry: all cases irrespective of nodal status.
Click here for file
Additional File 3
Table evaluating nodal staging measures using breast cancer data from the San Jose–Monterey registry: node-positive patients.
Click here for file
Additional File 4
Table providing a simulation of small datasets of 300 breast cancer patients, irrespective of nodal status.
Click here for file
Additional File 5
Table providing a simulation of small datasets of 300 node-positive breast cancer patients
Click here for file
Additional File 6
Figure showing Kaplan–Meier survival estimates for T1–T2 breast cancer abstracted from the San Jose–Monterey registry.
Click here for file
Additional File 7
Text on ratios and the TNM categorization.
Click here for file
Acknowledgements
We are grateful to the population that made the SEER data available. We are grateful to the reviewers who placed signposts to new directions, gave precious information, raised the research stakes, and finally sent us to explore unexpected realms. Use of the R2 owes to queries and answers from Lawrence Hunsicker and Terry Therneau posted on the mail-list [email protected]. Part of this work was originally discussed in the [email protected], an experimental mail-list for data studies in oncology. Gábor Cserni was supported by a János Bolyai Research Fellowship from the Hungarian Academy of Sciences. This work was initially presented at the 26th Annual San Antonio Breast Cancer Symposium [46].
Figures and Tables
Figure 1 Joint effect of the numbers of involved nodes (npos) and uninvolved nodes (nneg) on survival in T1–T2 breast cancer. Part of the contour plot was partially filled at the corners by padding. The pattern of isoprobability contours radiating from the origin suggests that similar ratios of involved/uninvolved nodes were associated with similar Kaplan–Meier survival estimates (for example. 8 npos/10 nneg has approximately the same 75% [contour line 0.75] 5-year survival chance as 4 npos/5 nneg). Reproduced with permission from Vin-Hung and coworkers [28]. Colors were omitted in the original publication.
Figure 2 Unadjusted breast-cancer mortality as a function of the percentage of involved nodes in T1–T2 breast cancer based on the SEER (Surveillance, Epidemiology, and End Results) program data. Dot size computed as a step function of the number of patients at risk: smallest dots 1–20 patients and the largest dots >200 patients. The straight line highlights the trend but should not be interpreted as the basis for extrapolation.
Figure 3 Unadjusted breast cancer mortality as a function of the estimated log odds of nodal involvement in T1–T2 breast cancer. Red dots are node-negative patients, and blue are node-positive patients. The smallest dots represent 1–20 patients and the largest dots represent >200 patients. The straight lines highlight the different slopes but should not be interpreted as the basis for extrapolation (they would extrapolate to <0% or >100% mortalities).
Figure 4 Adjusted breast cancer mortality in T1–T2 node-positive breast cancer as a function of (a) number of nodes examined, (b) number of involved nodes, (c) percentage of involved nodes, and (d) log odds of involved nodes. Dotted lines indicate the 95% confidence interval. Plots are based on multivariate models that included all non-nodal covariates listed in the Methods section and a single nodal covariate. Analysis of cases pooled irrespective of nodal status revealed a more marked nonlinearity in plot c between 0% and 20% involved nodes. Otherwise, different combinations of nodal covariates gave similar plots.
Table 1 Distribution of the percentages of involved nodes and corresponding unadjusted mortality
Percentage of involved nodes (np/nx [%]) Number of patients at risk All-cause mortality (%) Breast cancer mortality (%) Non-breast-cancer mortality (%)
0 58070 15.9 5.2 10.7
1–10 8695 21.6 11.7 9.9
11–20 6350 26.3 15.7 10.6
21–30 3003 30.8 19.9 10.9
32–40 2055 33.1 22.7 10.4
41–50 1487 41.0 29.6 11.4
51–60 889 37.8 28.2 9.6
61–70 719 47.0 33.7 13.4
71–80 717 50.3 40.3 10.0
81–90 658 52.6 40.4 12.2
91–100 1043 58.6 45.2 13.4
np, number of pathologically involved axillary lymph nodes; nx, number of axillary lymph nodes examined (excised).
Table 2 Distribution of the estimated log odds of nodal involvement, and corresponding unadjusted mortality
L All patients Node-negative patients Node-positive patients
Number of patients at risk Breast cancer mortality Non-breast-cancer mortality Number of patients at risk Breast cancer mortality Non-breast-cancer mortality Number of patients at risk Breast cancer mortality Non-breast-cancer mortality
L ≤ -4 3053 5.4 9.2 3053 5.4 9.2 0
-4 < L ≤ -3 44695 5.3 10.2 44503 5.2 10.2 192 13.5 7.8
-3 < L ≤ -2 17619 8.0 11.5 10187 5.4 12.8 7432 11.7 9.7
-2 < L ≤ -1 9173 15.4 10.9 327 4.0 19.3 8846 15.8 10.6
-1 < L ≤ 0 5120 24.0 11.0 0 5120 24.0 11.0
0 < L ≤ 1 1943 31.9 11.3 0 1943 31.9 11.3
1 < L ≤ 2 1113 40.3 11.1 0 1113 40.3 11.1
2 < L ≤ 3 536 44.4 13.8 0 536 44.4 13.8
3 < L ≤ 4 376 47.6 13.0 0 376 47.6 13.0
4 < L 58 56.9 10.3 0 58 56.9 10.3
L = empirical logistic transform (estimated log odds).
Table 3 Comparison of models
Patients studied R2N Hazard ratios (95% confidence interval)
np nx np/nx (%) L
All cases
1: no nodal variables 0.069
2: np 0.093 1.093 (1.090–1.097)
3: nx 0.069 0.998 (0.995–1.001)
4: np, nx 0.096 1.112 (1.108–1.116) 0.970 (0.966–0.974)
5: np/nx (%) 0.104 1.023 (1.022–1.024)
6: L 0.108 1.459 (1.442–1.477)
7: np, np/nx (%) 0.104 1.010 (1.004–1.017) 1.021 (1.020–1.023)
8: nx, np/nx (%) 0.104 1.002 (0.999–1.005) 1.023 (1.022–1.024)
9: np, L 0.108 0.998 (0.992–1.005) 1.466 (1.435–1.497)
10: nx, L 0.109 1.009 (1.006–1.013) 1.462 (1.445–1.479)
11: np, nx, np/nx (%) 0.104 1.013 (1.004–1.022) 0.998 (0.994–1.002) 1.021 (1.019–1.022)
12: np, nx, L 0.109 0.967 (0.957–0.976) 1.021 (1.017–1.026) . 1.599 (1.554–1.646)
Node-positive patients
1: no nodal variables 0.067
2: np 0.095 1.066 (1.062–1.071)
3: nx 0.067 0.996 (0.992–1.000)
4: np, nx 0.102 1.088 (1.083–1.093) 0.966 (0.961–0.971)
5: np/nx (%) 0.108 1.017 (1.016–1.018)
6: L 0.108 1.379 (1.355–1.403)
7: np, np/nx (%) 0.109 1.013 (1.006–1.020) 1.015 (1.014–1.017)
8: nx, np/nx (%) 0.108 1.005 (1.001–1.009) 1.017 (1.016–1.018)
9: np, L 0.108 1.008 (1.001–1.016) 1.344 (1.306–1.384)
10: nx, L 0.108 1.005 (1.001–1.009) 1.381 (1.357–1.405)
11: np, nx, np/nx (%) 0.109 1.016 (1.005–1.028) 0.998 (0.991–1.004) 1.015 (1.013–1.017)
12: np, nx, L 0.108 1.003 (0.990–1.017) 1.003 (0.996–1.010) 1.366 (1.304–1.431)
Node-negative patients
1: no nodal variables 0.045 NA
3: nx 0.045 NA 0.991 (0.986–0.997)
6: L 0.045 NA 1.150 (1.058–1.249)
10: nx, L 0.045 NA 1.001 (0.984–1.019) 1.169 (0.902–1.514)
Shown are all nodal status combined, and node-positive patients and node-negative patients separately. A hazard ratio >1 indicates increased risk for death from breast cancer. All models are multivariate, adjusting for the effect of covariates listed in the Methods section: tumor size, age at diagnosis, year of diagnosis, registry area, race, marital status, tumor topography, histologic type and grade, estrogen and progesterone receptor status, type of primary surgery, and administration of postoperative radiotherapy. NA, not applicable; np, number of involved nodes; nx, number of nodes examined; np/nx (%), percentage of nodes involved; L, log-odds of node involvement; R2N, Nagelkerke R2 index of global model fit (0 = lack of fit, 1 = perfect fit).
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| 15535850 | PMC1064081 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Oct 6; 6(6):R680-R688 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr934 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9351553584610.1186/bcr935Research ArticleOver-expression of lysophosphatidic acid receptor-2 in human invasive ductal carcinoma Kitayama Joji [email protected] Dai 1Sako Akihiro 1Ishikawa Makoto 1Hama Kotaro 2Aoki Junken 2Arai Hiroyuki 2Nagawa Hirokazu 11 Department of Surgical Oncology, The University of Tokyo, Tokyo, Japan2 Department of Pharmaceutical Science, The University of Tokyo, Tokyo, Japan2004 22 9 2004 6 6 R640 R646 15 5 2004 21 7 2004 10 8 2004 26 8 2004 Copyright © 2004 Kitayama et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Lysophosphatidic acid (LPA) is a bioactive phospholipid with diverse effects on various cells. It interacts with at least three G-protein-coupled transmembrane receptors, namely LPA1, LPA2 and LPA3, whose expression in various tumours has not been fully characterized. In the present study we characterized the expression profile of LPA receptors in human breast cancer tissue and assessed the possible roles of each receptor.
Methods
The relative expression levels of each receptor's mRNA against β-actin mRNA was examined in surgically resected invasive ductal carcinomas and normal gland tissue using real-time RT-PCR. LPA2 expression was also examined immunohistochemically using a rat anti-LPA2 monoclonal antibody.
Results
In 25 cases normal and cancer tissue contained LPA1 mRNA at similar levels, whereas the expression level of LPA2 mRNA was significantly increased in cancer tissue as compared with its normal counterpart (3479.0 ± 426.6 versus 1287.3 ± 466.8; P < 0.05). LPA3 was weakly expressed in both cancer and normal gland tissue. In 48 (57%) out of 84 cases, enhanced expression of LPA2 protein was confirmed in carcinoma cells as compared with normal mammary epithelium by immunohistochemistry. Over-expression of LPA2 was detected in 17 (45%) out of 38 premenopausal women, as compared with 31 (67%) out of 46 postmenopausal women, and the difference was statistically significant (P < 0.05).
Conclusion
These findings suggest that upregulation of LPA2 may play a role in carcinogenesis, particularly in postmenopausal breast cancer.
carcinogenesisimmunohistochemistrylysophosphatidic acidRT-PCR
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Introduction
Lysophosphatidic acid (LPA; 1- or 2-acyl-sn-glycero-3-phosphate), the simplest glycerophospholipid, mediates a broad range of cellular responses including smooth muscle cell contraction, platelet aggregation, neurite retraction/cell rounding, regulation of cell proliferation, protection from apoptosis, modulation of chemotaxis and transcellular migration [1-3]. Previous reports have also demonstrated that LPA stimulates proliferation, migration and production of matrix metalloproteinases and angiogenic factors in ovarian cancer cell lines [4-9]. Moreover, a high level of LPA has been detected in ascitic fluid from ovarian cancer patients [4,10,11]. These findings suggest that LPA may be a mediator of tumour development and progression [12].
At least three receptors – LPA1/Edg-2, LPA2/Edg-4 and LPA3/Edg-7 – have been identified as specific receptors for LPA [13,14]. These LPA receptors differ with respect to their distribution in various tissues. LPA1 is broadly expressed in various normal tissues, whereas expression levels of LPA2 and LPA3 are more restricted, which may account for the various biological effects of LPA [13,14]. In recent studies LPA1 and LPA2 were found to have redundant functions in mediating multiple endogenous LPA responses, including phospholipase C activation, calcium mobilization, cell proliferation and stress fibre formation in mouse embryonic fibroblast cells [15,16]. However, in the same study the different phenotypes of knockout mice for each receptor suggested distinct roles for LPA1 and LPA2 in vivo.
Previous studies also found that malignant transformation resulted in aberrant expression of LPA2 or LPA3 in ovarian or thyroid cancers, suggesting that LPA may play a role in tumour biology and that shifts in LPA receptor expression are related to carcinogenesis [17-20]. However, patterns of expression of these LPA receptors in other tumours have not been adequately examined. In the present study we characterized the expression patterns of LPA receptors in human breast cancer tissue (another common form of cancer in females) and analyzed correlations with other clinical and pathological findings to determine the possible role played by each LPA receptor in the development of breast cancer.
Methods
Patients and materials
In the first part of the study, mRNA expression for each LPA receptor was evaluated in 25 cases of invasive ductal carcinoma, which were surgically resected in the Department of Surgery, University of Tokyo, from 1998 to 2003, and in six samples of adjacent normal gland tissue. In the next part of the study, protein expression of LPA2 was evaluated by immunohistochemical staining in the 25 cases and an additional 59 cases of invasive ductal carcinoma, which were resected from 1992 to 1997, also in the Department of Surgery, University of Tokyo.
All of the resected primary tumours and regional lymph nodes were histologically examined by haematoxylin–eosin staining, in accordance with the International Union Against Cancer TNM classification. Several discrete histological parameters and lymph node metastasis were also examined. Oestrogen receptor levels were evaluated using enzyme immunoassay of frozen tumour specimens with cutoff levels for positivity of 5 fmol/mg protein.
Isolation of total RNA and reverse transcription
The tumour tissue resected from the primary lesion and paired nontumour tissue (taken 10 cm away from the neoplasm) were immediately frozen in liquid nitrogen and kept at -80°C until extraction of RNA. Total RNA was extracted from each sample using the acid guanidine isothiocyanate/phenol/chloroform extraction method. One microgram of total RNA was reverse transcribed using a SuperScript First-Strand Synthesis System (Invitrogen Co., Carlsbad, CA, USA). The reverse transcription reaction was carried out in a total volume of 20 μl, in accordance with the manufacturer's instructions. The cDNA was stored at -20°C until use.
Preparation of cDNA calibrators
We used pcDNA3 vector (Invitrogen) containing LPA1, LPA2, or LPA3 as the standard cDNA for each LPA receptor [14]. cDNA for human β-actin was prepared using human colon cDNA as the template DNA and the following oligonucleotides: CCATCGAATTCACCACCATGGATGATGATATCGCCGCGCTC and AAGGTGCGGCCGCCTAGAAGCATTTGCGGTGGACGAT. The resulting DNA fragments were digested by EcoRI/NotI and ligated into pBlueScript (Strategene, La Jolla, CA, USA). The DNA sequence of cDNA prepared by RT-PCR was confirmed by DNA sequencing and used to calibrate for β-actin.
Real-time fluorescence quantitative PCR
The primers used in the analysis of LPA1, LPA2, LPA3 and β-actin gene expression are given in Table 1. All the primers were designed using Primer Express software (Applied Biosystems, Foster, CA, USA). Real-time PCR reactions were conducted in an ABI PRISM 7000 (Perkin-Elmer/Applied Biosystems, Foster, CA, USA) using SYBR Green I (Perkin-Elmer) with the following profile: one step at 50°C for 2 min, one step at 95°C for 10 min, and 40 cycles at 95°C for 30 s and 60°C for 1 min. Thermocycling was done in a final volume of 20 μl containing 1 μl of cDNA sample. The ABI PRISM software constructed the calibration curve by plotting the crossing point against the logarithm of the number of copies for each calibrator. The number of copies in unknown samples was calculated by comparing their crossing points with the calibration curve. To correct for differences in both RNA quality and quantity between samples, data were normalized using the ratio of the target cDNA concentration to that of β-actin. Both RT and PCR reactions were performed in triplicate, and the mean values were calculated against against β-actin.
Monoclonal antibodies against human LPA2
A peptide consisting of the carboxyl-terminal 17 amino acids of human LPA2 (335–351) was conjugated with keyhole limpet haemocyanin. The conjugate was injected into the hind foot pads of WKY/Izm rats using Freund's complete adjuvant. The enlarged medial iliac lymph nodes from the rats were used for cell fusion with mouse myeloma cell line PAI. The antibody-secreting hybridoma cells were selected by screening with enzyme-linked immunosorbent assay, immunofluorescence and Western blotting. Several clones were established. In this study, monoclonal antibody from clone 10D5 (rat IgG1) was used for immunohistochemical staining.
Specificity of the monoclonal antibody against human LPA2 used in the present study (clone 10D5) was confirmed as follows. HeLa cells were transfected with LPA1-pcDNA3, LPA2-pcDNA3, LPA3-pcDNA3, or empty pcDNA3 vector using lipofectamine 2000 (Invitrogen). Twenty-four hours after transfection, the cells were rinsed with phosphate-buffered saline (PBS) and recovered using a cell scraper. Protein (10 μg) was separated on SDS-PAGE (12.5% polyacrylamide) and transferred onto nitrocellulose transfer membrane (Schleicher & Schuell, Einbeck, Germany). Using the enhanced chemiluminescence (ECL) method, the expression of each receptor was confirmed using anti-LPA1, anti-LPA2, or anti-LPA3 antibodies (Fig. 1).
Immunohistochemical study of LPA2
Expression of LPA2 was examined by immunohistochemical staining using the anti-LPA2 antibody. Sections (3 μm thick) were deparaffinized in xylene, hydrated through a gradually diluted ethanol series, and heated in a microwave oven for two 7-min cycles (500 W). After being rinsed in PBS, endogenous peroxidase activity was inhibited by incubation with 0.3% hydrogen peroxide in 100% methanol for 30 min. After washing three times in PBS, nonspecific reaction was blocked by incubation with PBS containing 5% skimmed milk for 30 min at room temperature, and then the sections were incubated with normal rabbit serum for 30 min. The sections were incubated overnight at 4°C in humid chambers with the primary antibody to LPA2 at a dilution of 1/100. As negative control, anti-LPA1 monoclonal antibody (rat IgG1), as well as control rat IgG (Nichirei, Tokyo, Japan), was used. After washing three times with PBS, the sections were incubated with biotinylated rabbit antirat immunoglobulin for 30 min. After washing again with PBS, the slides were treated with peroxidase-conjugated streptavidin for 30 min, and developed by immersion in 0.01% hydrogen peroxide and 0.05% diaminobenzidine tetrahydrochloride for 3 min, followed by light counterstaining with Mayer's haematoxylin. Immunoreactivity was assessed by two evaluators who had no knowledge of the background features. When more than half of the carcinoma cells in these fields exhibited significantly stronger staining intensity than the corresponding epithelium from normal mammary glands, these tumours were defined as having high expression of LPA2. In other tumours, most of the carcinoma cells exhibited only weak, if any, immunostaining, and, if present, the staining intensity was similar to that of normal epithelium; these tumours were thus defined as having low expression of LPA2.
Statistical analysis
All statistical calculations were conducted using StatView-J 5.0 statistical software (SAS Institute, Cary, NC, USA). mRNA levels were compared using unpaired Student's t-test, and relationships between the over-expression of LPA2 and clinicopathological features were examined by Fisher's exact test. P < 0.05 was considered statistically significant.
Results
Expression of LPA receptors at the mRNA level
The relative expression level of each LPA receptor's mRNA was quantitatively evaluated by real-time RT-PCR against that of β-actin (Fig. 2). A significant level of LPA1 mRNA was detected in both normal and carcinoma tissues, and expression levels did not differ significantly between tissues (1438.5 ± 425.5 versus 1675.3 ± 299.8). However, the expression level of LPA2 in cancer tissue was significantly greater than that in normal tissue (3479.0 ± 426.6 versus 1287.3 ± 466.8; P < 0.05). In comparison, LPA3 was weakly expressed in both normal and cancer tissue, although one cancer contained a relatively high level of LPA3 transcript. When the ratio of LPA2 to LPA1 was calculated, cancer tissue exhibited a threefold higher ratio than did normal gland tissue (3.69 ± 0.96 versus 1.12 ± 0.43; P = 0.0016).
Expression of LPA2 receptor in the immunohistochemical study
The expression of LPA2 in the same tumours was also evaluated at the protein level by immunohistochemical staining (Fig. 3). In 15 out of 25 (60%) carcinomas, enhanced staining for LPA2 was clearly detected in comparison with normal tissue. In these carcinoma cells, LPA2 was detectable in the cytoplasm and in the cell membrane, but not in the nucleus. Some of the normal epithelium in the same specimens, as well as interstitial cells, also stained slightly, but the intensity was significantly weaker than that in carcinoma cells in the 15 tumours. Thus, these cases were categorized as tumours with high LPA2 expression. In the other 10 tumours, carcinoma cells exhibited apparently the same staining intensity as normal epithelium and were categorized as having low expression of LPA2.
When the immunoreactivity was compared with the relative ratio of LPA2 mRNA against β-actin, 11 out of the 12 tumours (92%) with ratios greater than 3000 had high expression of LPA2, whereas 10 out of 13 tumours with a ratio below 3000 had low expression (Table 1). Thus, the results of the immunohistochemical study exhibited significant correlation with the results of quantitative mRNA study in breast cancer tissue (P < 0.001).
Relationship between LPA2 expression and clinical and pathological factors
Because LPA2 expression showed a strong correlation between mRNA and protein levels in 25 cases, we performed immunostaining of LPA2 in an additional 59 cases. The relations between LPA2 expression and clinical or pathological findings in these 84 cases are presented in Table 2. High expression of LPA2 was frequently detected in relatively old postmenopausal patients. High expression of LPA2 was detected in 17 out of 38 (45%) premenopausal patients and in 31 out of 46 (67%) postmenopausal women; this difference was statistically significant (P < 0.05). LPA2 expression did not correlate with oestrogen receptor expression. The frequency of nodal or distant metastasis tended to be higher in tumours with low expression of LPA2, although the difference was not statistically significant.
Discussion
In the present study we found that breast cancer frequently exhibited enhanced expression of LPA2 mRNA as compared with normal breast gland tissue, although the expression level of LPA1 and that of LPA3 were not significantly different from those in normal tissue. Over-expression of LPA2 was confirmed at the protein level in some of these cases by immunohistochemical staining. Previous studies showed that LPA1 is widely expressed in various tissues, whereas LPA2 and LPA3 are known to be highly expressed in malignant cells, suggesting the potential role of these receptors in the pathophysiology of cancer [5,18,19,21,22]. Our findings regarding LPA2 are basically consistent with those results and suggest that upregulation of the LPA2 gene is a common feature of carcinogenesis in various organs.
Protein expression of LPA2 in various organs has not been investigated thus far because of the lack of an appropriate antibody against LPA2. Our antibody clearly detected significantly enhanced expression of LPA2 in more than half of the ductal carcinoma cells, and the staining pattern exhibited a strong correlation with the mRNA data obtained with quantitative RT-PCR. Normal epithelium of mammary glands also showed slight immunoreactivity, but the staining intensity was markedly weaker than that of carcinoma cells in these cases. Positive staining was mostly detected in the cytosol and cellular membrane of carcinoma cells, although the distribution of immunoreactivity differed among the cells in each case, indicating that the cellular distribution of LPA2 is highly heterogeneous, even within the same tumour.
The exact role played over-expression of LPA2 in breast carcinogenesis is not yet clear. However, in ovarian cancer cells the proliferative responses to LPA of ovarian cancer cells with high levels of LPA1 receptor were uniformly weaker than those of cells with low LPA1 receptor levels [22]. Moreover, LPA1 has been reported to transduce the inhibitory signal to LPA2-mediated proliferative responses and concurrently elicits apoptosis and anoikis [23]. On the other hand, LPA has been shown to have an antiapoptotic effect on various cells, although the LPA receptor that mediates this response has not been determined [24,25]. Deng and coworkers [26] reported that LPA prevents apoptosis of rat intestinal epithelial cells (IEC-6) induced by radiation and chemotherapeutic agents. In that study they used receptor specific inhibitors and concluded that the antiapoptotic effect was mediated through both LPA1 and LPA2. Thus, LPA1 can mediate both antiapoptotic and apoptotic effects, and this is apparently dependent on the cell type and circumstances. Taken together, these findings indicate that the stimulation of LPA1 receptor has suppressive effects, at least in part, on cell proliferation as compared with the LPA2 receptor. Thus, the enhanced expression of LPA2 with relatively reduced expression of LPA1 appears to promote mammary epithelial cell survival in LPA-rich circumstances and to favour development of breast cancer.
Immunohistochemical finding showed that LPA2 is upregulated more frequently in postmenopausal than in premenopausal women, suggesting that over-expression of LPA2 is more strongly related to the carcinogenesis of postmenopausal breast cancer. It is well known that a high-fat diet has a positive association with development of breast cancer, especially postmenopausal breast cancer, although some conflicting findings have also been reported [27-32]. A high-fat diet is often associated with the insulin resistance syndrome, and hyperinsulinaemia and high level of insulin-like growth factor (IGF) have also been reported to be markers of increased breast cancer risk [33-36]. Recently, receptors for IGF-1 were shown to be functionally expressed in mammary epithelium [37-39]. Moreover, the IGF-mediated intracellular signal closely interacts with the LPA-mediated signal, although the specific mechanism is not yet known [40-43]. Therefore, our data raise the possibility that a shift in LPA receptor expression may alter the effects of the LPA on IGF-mediated signal in mammary epithelial cells, which may be closely associated with adiposity-related carcinogenesis of breast cancer. The crosstalk between the LPA1- or LPA2-mediated signal and the IGF- or insulin-mediated signal remains an interesting subject for future study.
In summary, we found preferential expression of LPA2 in ductal carcinoma as compared with normal epithelium in human mammary gland tissue. Over-expression of LPA2 was more prominent in postmenopausal women. A shift in LPA receptor expression in mammary epethelium may be a key event, especially in adiposity-related breast carcinogenesis. LPA and its receptors could represent new chemopreventive targets in a novel therapeutic strategy in human breast cancer.
Conclusion
Human ductal carcinoma cells frequently exhibited enhanced expression of LPA2 as compared with normal epithelium in human mammary gland tissue, especially in postmenopausal women. This suggests that upregulation of LPA2 may be one of the key events in carcinogenesis, especially in adiposity-related breast cancer.
Competing interests
The authors declare that they have no competing interests.
Abbreviations
IGF = insulin-like growth factor; LPA = lysophosphatidic acid; PBS = phosphate-buffered saline; PCR = polymerase chain reaction; RT = reverse transcription.
Acknowledgements
This work was supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports and Culture of Japan, and a Grant from the Ministry of Health and Welfare of Japan.
Figures and Tables
Figure 1 Specificity of anti-human lysophosphatidic acid receptor 2 (LPA2) antibody. The cross-reactivity of anti-human LPA2 (clone 10D5) was examined by Western blotting. Cell lysate of HeLa cells transfected with human lysophosphatidic acid receptor 1 (LPA1; lane1), LPA2 (lane 2), LPA3 (lane 3), or empty vector (lane4) were loaded onto SDS-PAGE. After the proteins were transferred onto nitrocellulose membrane, the LPA2 protein was detected using antihuman LPA2 antibody (clone 10D5) in an enhanced chemiluminescence (ECL) system. 10D5 did not exhibit cross-reactivity to LPA1 or LPA3.
Figure 2 Relative expression level of lysophosphatidic acid receptors' mRNA against β-actin in cancer tissue and normal mammary gland tissue, evaluated using real-time RT-PCR: (a) Lysophosphatidic acid receptor 1 (LPA1), (b) LPA2 and (c) LPA3. Y axis indicates the cDNA copies for lysophosphatidic acid receptors divided by that of β-actin in each case. *P < 0.05.
Figure 3 Staining patterns of lysophosphatidic acid receptor 2 (LPA2) in normal mammary epithelium (upper left) and carcinoma tissues (upper right) of a representative case with high expression, and in another tumour tissue sample with low expression of LPA2 (lower left). Some of the normal epithelial cells in the same specimens, as well as interstitial cells, are slightly stained, but the intensity is significantly weaker than that in carcinoma cells with high expression of LPA2. LPA2 can be detected in the cytoplasm as well as in the cell membrane, but not in the nucleus (lower right).
Table 1 Lysophosphatidic acid receptor 2 (LPA2) expression evaluated by RT-PCR and immunohistochemistry
Immunohistochemistry findings Relative mRNA expression P
<3000 >3000
Low expression 10 1
High expression 3 11 <0.001
Table 2 The relationship between lysophosphatidic acid receptor 2 (LPA2) expression and clinical/pathological factor
LPA2 expression Low (36) High (48) P
Age (years)
<50 20 (56%) 16 (33%) 0.042
≥50 16 (44%) 32 (67%)
Tumour size
T1 (≤2.0 cm) 16 (44%) 17 (35%) NS
T2 (>2.0 cm to ≤5.0 cm) 18 (50%) 25 (52%)
T3 (>5.0 cm) 2 (6%) 6 (13%)
Nuclear grade
I 5 (14%) 5 (10%) NS
II 24 (67%) 31 (65%)
III 7 (19%) 12 (25%)
Menopausal status
Premenopausal 21 (58%) 17 (35%) 0.037
Postmenopausal 15 (42%) 31 (65%)
Oestrogen receptor status
Negative 10 (55%) 27 (56%) NS
Positive 8 (45%) 21 (44%)
Nodal metastasis
Negative 12 (33%) 21 (44%) NS
Positive 24 (67%) 27 (56%)
Distant metastasis
Negative 27 (75%) 41 (85%) NS
Positive 9 (25%) 7 (15%)
Numbers in parentheses indicate percentages in the same expression pattern. NS, not significant.
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| 15535846 | PMC1064082 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Sep 22; 6(6):R640-R646 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr935 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9371553585110.1186/bcr937Research ArticleMenopausal status dependence of the timing of breast cancer recurrence after surgical removal of the primary tumour Demicheli Romano [email protected] Gianni [email protected] William JM [email protected] Michael W [email protected] Pinuccia [email protected] Istituto Nazionale Tumori, Milano, Italy2 Dorn VA Medical Center, Columbia, South Carolina, USA3 Department of Surgery, Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA2004 11 10 2004 6 6 R689 R696 2 6 2004 15 7 2004 12 8 2004 31 8 2004 Copyright © 2004 Demicheli et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Information on the metastasis process in breast cancer patients undergoing primary tumour removal may be extracted from an analysis of the timing of clinical recurrence.
Methods
The hazard rate for local-regional and/or distant recurrence as the first event during the first 4 years after surgery was studied in 1173 patients undergoing mastectomy alone as primary treatment for operable breast cancer. Subset analyses were performed according to tumour size, axillary nodal status and menopausal status.
Results
A sharp two-peaked hazard function was observed for node-positive pre-menopausal patients, whereas results from node-positive post-menopausal women always displayed a single broad peak. The first narrow peak among pre-menopausal women showed a very steep rise to a maximum about 8–10 months after mastectomy. The second peak was considerably broader, reaching its maximum at 28–30 months. Post-menopausal patients displayed a wide, nearly symmetrical peak with maximum risk at about 18–20 months. Peaks displayed increasing height with increasing axillary lymph node involvement. No multi-peaked pattern was evident for either pre-menopausal or post-menopausal node-negative patients; however, this finding should be considered cautiously because of the limited number of events. Tumour size influenced recurrence risk but not its timing. Findings resulting from the different subsets of patients were remarkably coherent and each observed peak maintained the same position on the time axis in all analysed subsets.
Conclusions
The risk of early recurrence for node positive patients is dependent on menopausal status. The amount of axillary nodal involvement and the tumour size modulate the risk value at any given time. For pre-menopausal node-positive patients, the abrupt increase of the first narrow peak of the recurrence risk suggests a triggering event that synchronises early risk. We suggest that this event is the surgical removal of the primary tumour. The later, broader, more symmetrical risk peaks indicate that some features of the corresponding metastatic development may present stochastic traits. A metastasis development model incorporating tumour dormancy in specific micro-metastatic phases, stochastic transitions between them and sudden acceleration of the metastatic process by surgery can explain these risk dynamics.
breast cancermenopausal statusmetastasis development modelrecurrence timingtumour dormancySee related Commentary:
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Introduction
The 1970s and 1980s witnessed a revolution in the conventional approach to the treatment of primary breast cancer. Early in the 1970s, the favourable results of postoperative systemic adjuvant therapy in women with positive axillary lymph nodes [1,2] started an avalanche of clinical trials that explored the role of several systemic treatments in different subsets of patients. The beneficial results were confirmed by a few overviews of randomised trials [3,4], and reports from individual studies proved that the benefit continued at 20 years of follow-up [5]. However, the significant, albeit moderate, improvement of disease-free survival and overall survival achieved by earlier adjuvant therapy trials has improved only slightly during subsequent years, despite a spate of new active drugs and the use of higher drug doses [6]. The benefits of adjuvant therapy have therefore apparently reached a plateau, and it is unlikely that further improvements will be obtained without a more complete and accurate understanding of the biology of the tumour–host system at the time of treatment.
Surgical resection of primary tumour removal may either 'cure' a significant fraction of patients, or it may even change the 'natural' recurrence and death timing for some others, by accelerating the metastatic development [7,8]. Some specific biological mechanisms supporting this effect have been elucidated: in animals given surgery, a growth-stimulating factor was found in serum [9] and a switch of micro-metastatic foci to the angiogenic phenotype, due to withdrawal of an angiogenesis inhibitor from the primary tumour, was demonstrated [10]. Despite these provocative data, the residual tumour growth dynamics underlying the beneficial results of all adjuvant systemic treatments is virtually unknown in humans.
Careful inspection of the timing of tumour recurrence after resection can be of considerable interest. The recurrence risk pattern in a given follow-up span, a useful estimate of which is the hazard function [11], provided information on the biological behaviour of metastases. The hazard functions for local-regional recurrences and distant metastases for breast cancer patients undergoing mastectomy alone [12] proved to be double-peaked, with an early peak at about 18 months after surgery, a second peak at about 60 months, and a plateau-like tail extending out to 15 years, the maximum period analysed. These findings were confirmed by a similar investigation on node-positive patients receiving adjuvant CMF (cyclophosphamide, methotrexate, fluorouracil) treatment [13], and a double-peaked hazard function was also demonstrated for the timing of death after primary tumour resection [14-16]. A reasonable hypothesis to explain these findings is that the early peak of the hazard function for recurrence is generated by the sudden acceleration of the metastatic process due to surgery. This hypothesis was the cornerstone for a biological model for the development of breast cancer metastasis, incorporating tumour dormancy in specific micro-metastatic phases and stochastic transitions between them [17]. A computer simulation of the model generated double-peaked relapse histograms reasonably similar to clinical data [18].
The model and the results of its computer simulation suggest that the first peak may result from the superimposition of metastatic recurrences with different dynamics. Here we report a detailed analysis of the first peak of the hazard rate curve, that is, of the recurrence timing during the first 4 years, for patients undergoing mastectomy without any adjuvant chemotherapy or radiotherapy. Our findings suggest that metastatic growth dynamics after primary tumour removal is markedly different depending on menopausal status and, perhaps, axillary nodal involvement. These results support the concept that surgery may have differential perturbing effects on tumour growth kinetics depending on both tumour and host traits. These results strengthen the leading lines of the proposed model for metastasis development and may be relevant to adjuvant treatment strategies.
Methods
All patients with mastectomy alone as primary treatment who entered into three different clinical trials for operable breast cancer were retrospectively evaluated for this analysis. The trials were performed at the Milan Cancer Institute between 1964 and 1980 to compare mastectomy with other surgical or combined therapeutic approaches. Before surgery, all patients underwent standard staging: complete physical examination, X-ray study of chest, skull, spine and pelvis, bilateral mammography, electrocardiogram, complete haemogram and routine biochemical tests. The primary tumour was treated by radical or modified radical mastectomy. No patient received postoperative radiotherapy or chemotherapy. After surgery, follow-up was performed as follows: physical examination, biochemical tests and chest X-ray every 6–8 months during the first 3 years and once a year thereafter; skeletal survey and mammography once a year. In the presence of controversial clinical findings, examinations were performed more often than originally planned. Appropriate radiological, radioisotopic and surgical investigations were performed whenever recurrence was suspected or clinically evident. Particular attention was paid to assessing the recurrence time by carefully reviewing clinical, radiological and laboratory documentation. More detailed characteristics of the studied series are reported in reference 12.
Treatment failure (recurrence) was defined as the first clinically documented evidence of new disease manifestation(s) in either local-regional area(s) (namely chest wall, axilla and/or ipsilateral supra-clavicular region) or distant site(s) or any combination of these sites (classified as distant). Contra-lateral breast lesions may be either second primaries (confounding factors in the present investigation) or true metastases. Their annual incidence, together with the absence of a link with clinical prognostic factors of the primary breast cancer, suggest that most of them can be considered as second primary breast cancers [12,19]. Contra-lateral tumours were therefore considered second primaries.
Recurrence-free survival was considered as the time elapsed from the date of surgery to the first documented evidence of treatment failure; clinical evaluation without recurrence, death without recurrence, and second primary cancer (including contra-lateral breast cancer) were considered censored events.
Our application involved dividing the time axis into discrete intervals; the timing of the recurrence risk was studied by estimating the recurrence hazard rate, namely the conditional probability of manifesting recurrence in a time interval, given that the patient is clinically free of any recurrence at the beginning of the interval. The discrete hazards of recurrence and their standard deviations were calculated by means of the life-table method over 4 years. Because the event-specific rates display some instability arising from random variation, a kernel-like smoothing procedure [20] was adopted to aid the reader in visualising the underlying pattern, and the smoothed curves were represented graphically. Time intervals of 1, 2, 3, 6 and 12 months were used in a preliminary smoothing analysis that showed that a 3-month interval is a good compromise between smoothing data and displaying structure. We therefore used the hazard rate per 3-month interval.
Menopausal status, which was collected and recorded at the time of primary tumour diagnosis, was defined as 'post-menopausal' if 1 year had elapsed since the last menstrual period. Cumulative survival curves were compared by two-sided log-rank test.
Results
The main clinical characteristics of the 1173 analysed patients, all undergoing regular follow-up examinations in accordance with protocol requirements, are reported in Table 1. A total of 368 patients suffered from treatment failure within 4 years of surgery: the tumour recurrence was local-regional in 95 cases and distant in 273. A total of 749 patients were surviving and were at risk of recurrence at the end of the fourth year. Patients were lost to follow-up at a rate of 1.6% during the analysed years.
As reported and discussed elsewhere [12], the hazard rate for recurrence of all patients during 10 years of follow-up (Fig. 1a) displays a double-peaked pattern. In the present study we focused on the first peak (Fig. 1b), which shows an asymmetrical pattern with an early steep rise, reaching the maximum recurrence risk at the end of the first year, followed by a slight decrease lasting about 1.5 years and then by a more distinct decline until the end of the fourth year. This shape suggests that the recurrence risk curve may result from the superimposition of differently timed peaks.
As a first step, the hazard rate pattern by type of recurrence (local-regional versus distant) was studied and, because their timing proved to be remarkably similar, in the further analysis both recurrences were combined. Patients were then allocated to different subsets according to tumour size (T1 versus T2–T3) or to axillary nodal status (positive versus negative) or to menopausal status (pre-menopausal versus post-menopausal). The resulting hazard rate curves indicated that the recurrence risk pattern is definitely correlated to menopausal status, with a double-peaked curve for pre-menopausal patients (Fig. 2a) and a single-peaked curve for post-menopausal women (Fig. 2b). Tumour size did not show a noticeable influence on the recurrence risk pattern, and this finding was confirmed by the analysis of further subsets of patients (T1 and T2–T3 N-pre-menopausal, T1 and T2–T3 N+ pre-menopausal, T1 and T2–T3 N- post-menopausal, T1 and T2–T3 N+ post-menopausal), in whom tumour size (as well as type of recurrence) failed to change the hazard rate pattern (data not shown).
Pre-menopausal patients were allocated to subsets of node-negative, node-positive, one to three nodes positive and more than three nodes positive, and the hazard rates were estimated (Fig. 3). The resulting curves show that for node-positive patients, the recurrence risk has a quite narrow early peak, with a very steep rise to a maximum at about 8–10 months after mastectomy, and a second wider increase peaking at about 28–30 months. The recurrence pattern and the peak position do not change for patients with different axillary involvement, whereas the peak height is positively correlated with the extent of nodal invasion.
A similar analysis was performed in post-menopausal patients. All subsets of patients displayed a wide, nearly symmetrical recurrence risk peak, with a maximum at 18–24 months and a height similar to the corresponding estimates of pre-menopausal patients (Fig. 4). Even for these patients the extent of axillary node invasion was positively correlated to the peak height.
The recurrence risk for node-negative patients displays an initial regular increase reaching a maximum at about 18–24 months after surgery, with a mild decrease afterwards. No multi-peaked pattern was evident or even suggested for either pre-menopausal (Fig. 3a) or post-menopausal (Fig. 4a) women. However, it should be noted that of the 598 node-negative patients at risk, only 74 recurrences were observed during the 4 years under investigation, 43 of which occurred within 2 years. The reported results, mainly those from the third and subsequent years, should therefore be very cautiously considered.
Discussion
First of all, it should be emphasised that findings resulting from the different subsets of patients are notably coherent. A remarkably stable two-peaked hazard function, with increasing peak height associated with the increasing axillary lymph node involvement, describes the risk of recurrence for pre-menopausal patients, whereas, quite differently, post-menopausal women always display a single peak. Moreover, each observed peak maintains the same position on the time axis in all analysed subsets of pre-menopausal and post-menopausal women. The occurrence of these peaks by chance, that is, resulting from random fluctuations of the recurrence timing, should therefore be considered very unlikely. Obviously, our findings must be confirmed by similar analyses of other comparable databases.
We add here some important details to the previously reported investigation on the recurrence risk pattern of this same series [12]. Indeed, we found that even though pre-menopausal and post-menopausal patients display similar long time results (10-year recurrence-free survival: 56% versus 54%; not significant) and similar overall recurrence pattern (early surge during the first 4 years, a second minor increase at about 5 years and a tapering phase afterwards), the early recurrence dynamics may be very different between pre-menopausal and post-menopausal women.
A very early significant difference in the recurrence risk between pre-menopausal and post-menopausal patients had already been noted [21]. In the present analysis we found that these differences cover all the first 4 years and persist in all patient subsets. Summarising, the recurrence risk for pre-menopausal patients displays an abrupt increase at the third 3-month period; it quickly reaches its prominent maximum level (about four times the level of the previous 6 months) and is followed by a second fairly symmetrical and considerably broader peak appearing at about 18 months of follow-up, with its maximum at 28–30 months after surgery. In comparison, the recurrence risk for post-menopausal patients is nearly symmetrical and, after a smooth increase, reaches its maximum level at about 18 months after primary tumour removal. These findings are clear in node-positive patients, and the higher the nodal involvement, the higher the amplitude of peaks. The limited number of events prevents us from drawing any conclusion for node-negative patients. Tumour size affects the recurrence risk value at a given time but does not affect the pattern of the hazard rate distribution (that is, the occurrence of peaks).
It is difficult not to be impressed by the particular shape of the first recurrence peak of pre-menopausal node-positive patients and by the difference between it and the others of both menopausal subsets. It is also difficult, in the presence of a such an abrupt and prominent increase of the recurrence risk, to imagine something different from a triggering event resulting in recurrence synchronisation.
As a first step, some possible non-biology-based explanations of this finding should be considered. The peak could be related to inadequate staging evaluation, because at the time of the study no bone scan, liver ultrasound or computed tomography scan staging was done. Patients with undiscovered metastatic disease at diagnosis would therefore have displayed recurrence at the initial post-treatment screening, thus contributing to the first peak. However, it should be considered that, for newly diagnosed otherwise asymptomatic operable patients, (1) very few cases prove to be metastatic at standard staging today (less than 5%) [22] and (2) bone scan ultrasound and computed tomography scan surveys have a very low detection rate (less than 1%) [23,24] and are not routinely recommended in these patients [25]. Even chest X-ray was considered unnecessary at staging of asymptomatic operable breast cancer, because of its very low diagnostic yield (0.1%) [26]. Therefore, even if a few missed metastatic patients at primary tumour diagnosis might not have been excluded, they would have had little influence on the observed first peak of recurrence risk involving, for pre-menopausal node-positive patients, 22% of eligible patients and 37% of recurrences within 10 years. Moreover, because up to 75% of breast cancer recurrences are detected by the patient and reported to the physician within a mean time of 1 month [27,28], we can reliably consider the observed recurrence timing as widely independent of the planned follow-up timing. Last but not least, post-menopausal patients, who underwent the same clinical management as pre-menopausal ones, did not display an early peak. A significant role of screening and diagnostics can therefore reasonably be ruled out and a biology-based explanation should be investigated.
The first sharp peak, in our opinion, has a biological explanation and may result from some triggering event changing the unperturbed history of the disease. We suggest that primary tumour surgical removal is probably this event, selectively operating on some micro-metastases within similar biological conditions. This triggering effect could be modulated by the surgery extent [29]. However, in the present investigation data about the different surgical approaches (namely radical versus modified radical mastectomy) were not considered suitable for such an analysis, which would be more usefully performed for trials comparing mastectomy with breast-conserving surgery. The likely triggering effect of mastectomy is also supported by the peak position, implying a rapid activation and growth within 8–10 months. This is in quite good agreement with estimates of tumour growth after dormancy release (30 ± 8 weeks or less) obtained by very different methodology [30].
Assuming tumour dormancy release and the estimate of 8–10 months or less for tumour development from micro-metastasis to clinical metastasis, the hazard function should be considered as a description of the dormancy escape kinetics. A symmetrical, bell-shaped peak in the curve of hazard rate against time provides information about both the mean value (the peak position) and the variance (the peak width) of the timing of the constituent recurrences. The temporally stable position of the recurrence peak therefore suggests a common cause for recurrence development, a narrow peak indicates that the underlying event is most probably deterministic in nature, and a considerably wider peak suggests that some features of the transition from dormancy to growth may also present stochastic traits.
We recently proposed a model [17,18,31] in which breast cancer metastasis development may include successive steps: first, single cells (or nests containing a few cells), where most of malignant cells are non-dividing; second, non-angiogenic micro-metastases (and angiogenic ones in the presence of anti-angiogenic factors) that cannot grow more than the size of avascular foci; and third, vascularised metastases that will reach the clinical level. This orderly process is apparently perturbed by surgical removal of the primary tumour, which can stimulate cells to proliferate and/or remove angiogenic inhibition, thus resulting, for some patients, in sudden acceleration of the metastatic process.
The proposed model both reasonably explains and takes support from the findings of this study. The early sharp peak of the recurrence risk for pre-menopausal patients can be ascribed to a considerable switching of micro-metastatic foci to the angiogenic phenotype via the withdrawal of angiogenesis inhibitor(s) [10], and the sharpness of this peak supports the deterministic character of the triggering event. For post-menopausal patients, for whom the first sharp peak is absent, this effect may be much more modest. Other broader peaks may result from the induced proliferation of single cells through one or more growth-stimulating factors [9], resulting in successive avascular micrometastases, followed by the 'spontaneous' switching to the angiogenic phenotype, a process with stochastic characteristics, in keeping with peak shapes.
As regards differences between pre-menopausal and post-menopausal patients, it should be taken into account that peculiar conditions relevant to breast cancer development, in particular conditions acting on angiogenetic traits, may be dependent on the host hormone milieu. Angiogenesis in invasive breast carcinoma, as evaluated by micro-vessel counts, is associated inversely with patient age, and higher vascular intensity is observed in younger patients [32,33]. Vascular endothelial growth factor, the first member of a family of glycoproteins with considerable stimulating activity for angiogenesis and lymphangiogenesis, waxes and wanes in normal breast tissue within each menstrual cycle [34]. Angiogenin, another potent angiogenic factor, displays a significant difference between serum levels drawn in the proliferative and secretory phases of the menstrual cycle [35]. Plasma levels of endostatin, a negative regulator of angiogenesis, are significantly higher in post-menopausal patients [36]. It is therefore reasonable to assume that some conditions related to menopausal status may select neoplastic cell populations with peculiar traits relative to angiogenesis. A further factor may have a role: it has been suggested that timing of surgery in the menstrual cycle might modulate the tumour–host relationship and ultimately the disease outcome [37]. This occurrence is restricted, obviously, to pre-menopausal patients.
The findings of this investigation would be much more complete if other prognostic and predictive factors could be analysed. However, this database, like most databases of patients undergoing surgery alone without any adjuvant treatment, goes back to years when factors other than TNM (tumor, nodes, metastases) staging and menopausal status were poorly known or completely unknown.
Conclusions
Early metastatic growth dynamics shows a structured pattern that is correlated to menopausal status. Indeed, during the first 4 years, node-positive pre-menopausal patients display a split of the hazard function surge into two peaks, while post-menopausal patients always display a single peak. The timing and shape of peaks suggest the occurrence of some triggering event, correlated with primary tumour surgical removal, resulting in recurrence synchronisation, which seems to be especially important for pre-menopausal node-positive patients. In addition, findings support the hypothesis that some features of metastatic development may present stochastic traits. A metastasis development model incorporating tumour dormancy, stochastic transitions between specific micro-metastatic phases and sudden acceleration of the metastatic process due to surgery may be considered appropriate and to fit these findings well.
The different post-resection relapse dynamics between pre-menopausal and post-menopausal patients may have important implications for adjuvant systemic treatments. For adjuvant chemotherapy the proposed picture makes it easier to understand retrospectively why adjuvant chemotherapy was found to be most effective in pre-menopausal patients, who during the first months after primary tumour removal would display an enhancement of actively proliferating, and hence more chemo-sensitive, micro-metastases [38]. Most importantly, the prominent role of angiogenesis in the surgery-driven dormancy escape for pre-menopausal patients suggests that early (preoperative) antiangiogenic treatment might favourably influence prognosis.
Competing interests
The authors declare that they have no competing interests.
Figures and Tables
Figure 1 Hazard rate for breast cancer recurrence (local-regional plus distant) after mastectomy alone as primary treatment in 1173 patients. Calculated values (squares) with standard deviations (oblongs) and interpolation curve covering 10 years of follow-up (a) and magnification of the first 4 years (b).
Figure 2 Hazard rate for breast cancer recurrence (local-regional plus distant) after mastectomy alone as primary treatment in 1173 patients: (a) pre-menopausal patients; (b) post-menopausal patients. Calculated values (squares) with standard deviations (oblongs) and interpolation curve covering the first 4 years of follow-up.
Figure 3 Hazard rate for breast cancer recurrence (local-regional plus distant) after mastectomy alone as primary treatment in pre-menopausal patients: (a) node-negative versus node-positive; (b) node-positive (one to three) lymph nodes; (c) node-positive (more than three) lymph nodes. Calculated values (squares and diamonds) with standard deviations (oblongs) and interpolation curve covering the first 4 years of follow-up (N-, 34 events; N+(1–3), 61 events; N+(>3), 60 events).
Figure 4 Hazard rate for breast cancer recurrence (local-regional plus distant) after mastectomy alone as primary treatment in post-menopausal patients: (a) node-negative versus node-positive; (b) node-positive (one to three) lymph nodes; (c), node-positive (more than three) lymph nodes. Calculated values (squares and diamonds) with standard deviations (oblongs) and interpolation curve covering the first 4 years of follow-up (N-, 40 events; N+(1-3), 84 events; N+(>3), 88 events).
Table 1 Main patient characteristics
Parameter Value
Total number 1173
Median age, years (range) 52 (23–82)
Pre-menopausal 517
Tumour size
T1 222
T2 265
T3 30
Axillary nodes
N- 266
N+(1–3) 158
N+(>3) 93
Post-menopausal 656
Tumour size
T1 237
T2 363
T3 56
Axillary nodes
N- 332
N+(1–3) 184
N+(>3) 140
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| 15535851 | PMC1064084 | CC BY | 2021-01-04 16:04:31 | no | Breast Cancer Res. 2004 Oct 11; 6(6):R689-R696 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr937 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9391564216610.1186/bcr939Research ArticleThe p53-dependent apoptotic pathway of breast cancer cells (BC-M1) induced by the bis-type bioreductive compound aziridinylnaphthoquinone Yang Yu-Ping 1Kuo Hsien-Shou 1Tsai Hsin-Da 1Peng Yi-Chen 1Lin Yuh-Ling [email protected] Department of Biochemistry, College of Medicine, Taipei Medical University, Taiwan, ROC2 Department of Medicine, College of Medicine, Fu-Jen Catholic University, Taipei Hsien, Taiwan, ROC2005 4 11 2004 7 1 R19 R27 4 5 2004 27 5 2004 4 8 2004 6 9 2004 Copyright © 2004 Yang et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Several aziridinylbenzoquinone drugs have undergone clinical trials as potential antitumor drugs. These bioreductive compounds are designed to kill cells preferentially within the hypoxia tumor microenvironment. The bioreductive compound of bis-type naphthoquinone synthesized in our laboratory, 2-aziridin-1-yl-3-[(2-{2-[(3-aziridin-1-yl-1,4-dioxo-1,4-dihydronaphthalen-2-yl)thio]ethoxy}ethyl)thio]naphthoquinone (AZ-1), had the most potent death effect on the breast cancer cells BC-M1 in our previous screening. In the present study, we determined that the mechanism of the death effect of BC-M1 cells induced by AZ-1 was mediated by the apoptosis pathway.
Methods
We evaluated the cytotoxicity of AZ-1 and the anti-breast cancer drugs tamoxifen and paclitaxel to BC-M1 cells and MCF-7 cells by the MTT assay and measured the apoptosis phenomena by Hoechst 33258 staining for apoptotic bodies. We also quantified the sub-G1 peak area and the ratio of the CH2/CH3 peak area of the cell membrane in BC-M1 cells by flow cytometry and 1H-NMR spectra, respectively.
The apoptosis-related protein expressions, including p53, p21, the RNA-relating protein T-cell restricted intracellular antigen-related protein, cyclin-dependent kinase 2 (cell cycle regulating kinase) and pro-caspase 3, were detected by western blot, and the caspase-3 enzyme activity was also quantified by an assay kit.
Results
AZ-1 induced two of the breast cancer cell lines, with IC50 = 0.51 μM in BC-M1 cells and with IC50= 0.57 μM in MCF-7 cells, and showed less cytotoxicity to normal fibroblast cells (skin fibroblasts) with IC50= 5.6 μM. There was a 10-fold difference between two breast cancer cell lines and normal fibroblasts. Of the two anti-breast cancer drugs, tamoxifen showed IC50= 0.12 μM to BC-M1 cells and paclitaxel had much less sensitivity than AZ-1. The expression of p53 protein increased from 0.5 to 1.0 μM AZ-1 and decreased at 2.0 μM AZ-1. The p21 protein increased from 0.5 μM AZ-1, with the highest at 2 μM AZ-1. Regarding the AZ-1 compound-induced BC-M1 cells mediating the apoptosis pathway, the apoptotic body formation, the sub-G1 peak area, the ratio of CH2/CH3 of phospholipids in the cell membrane and the enzyme activity of caspase-3 were all in direct proportion with the dose-dependent increase of the concentration of AZ-1. The death effect-related proteins, including T-cell restricted intracellular antigen-related protein, cyclin-dependent kinase 2, and pro-caspase-3, all dose-dependently decreased with AZ-1 concentration.
Conclusions
The AZ-1-induced cell death of BC-M1 cells mediating the apoptosis pathway might be associated with p53 protein expression, and AZ-1 could have the chance to be a candidate drug for anti-breast cancer following more experimental evidence, such as animal models.
apoptosisbioreductive compoundbis-type aziridinylnaphthoquinonebreast cancer cells (BC-M1 and MCF-7)
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Introduction
The bioreductive drugs, aziridinylbenzoquinones, are a class of compounds designed to exploit one of the features of solid tumor biology caused by an inadequate blood supply to the solid tumor; namely, tumor hypoxia. Such regions generally are resistant to radiation and other oxygen-requiring treatment [1-4]. The ideal bioreductive drug should be administered as an inactive prodrug that is only activated under low-oxygen conditions by one-electron or two-electron reductase [5]. The aziridine-substituted benzoquinones such as mitomycin C, RH1, and E09 are three principal aziridinylquinone-class hypoxia-specific cytotoxins that are being developed for clinical use [6-8]. These agents are composed of an aziridinyl moiety on a quinone structure, and they convert on reductive metabolism to a bifunctional alkylating species that can cross-link DNA in the major grove that interacts predominantly on guanine-N7 [9]. These agents probably produce their major cytotoxic activities through the formation of DNA cross-links.
Tumor tissue with lower oxidative reduction (redox) potential relative to most normal tissue could increase reductive activation of these quinone derivatives in the tumor [2]. The selectivity of bioreductive drugs is therefore governed not only by the difference in oxygen tension between the tumor and normal tissue, but also by levels of enzymes catalyzing bioreductive activation such as DT-diaphorase [4,10,11]. This fact led to the publication in 1990 of the concept of 'enzyme-directed bioreductive development' by Workman and Walton [12].
In many cases, the biological activity of quinone is attributed to the ability of accepted electrons to form the corresponding radical anion or dianion species. A quinone moiety substituted with aziridine has been shown to be a potent alkylating agent as a result of bioreduction by the one-electron reducing enzymes (e.g. NADPH cytochrome P450 reductase, cytochrome b5 reductase) or by two-electron reducing enzymes ([NADP]H oxidoreductase, NQO1) to form the corresponding aziridinyl hydroquinone [13-15]. The hydroquinone moiety in the corresponding aziridinyl hydroquinone effectively changes the pK value of the aziridine ring such that it is protonated and becomes activated toward nucleophilic attack under physiological pH.
In previous work in our laboratory, we biosynthesized the bis-type of bioreductive aziridinylnaphthoquinone series compounds and evaluated their anti-cancer activity [16,17]. In the case of di-aziridinyl-substituted quinone, this highly cytotoxic bifunctional alkylating agent can cross-link DNA in cells that results in induction of a complex cellular mechanism leading to cell death by apoptosis or necrosis [18]. Based on our previous results, the compound 2-aziridin-1-yl-3-[(2-{2-[(3-aziridin-1-yl-1,4-dioxo-1,4-dihydronaphthalen-2-yl)thio]ethoxy}ethyl)thio]naphthoquinone (AZ-1) had the most potent death effect on breast cancer cells (BC-M1) and less cytotoxicity to normal fibroblast cells (skin fibroblasts). In this study, we were interested in determining the cytotoxicity of AZ-1 to our localized cell line (BC-M1 cells) and to determine the mechanism by which this occurs. We compared the death effect of two cell lines (BC-M1 and the estrogen-receptor-positive cell line MCF-7) induced by AZ-1, and also compared the cytotoxicity of AZ-1 with two clinical anti-breast cancer therapies, tamoxifen (aromatase inhibitors) and paclitaxel, in the cell viability of BC-M1 cells.
Materials and methods
Materials
RPMI 1640 medium, DMEM medium, fetal bovine serum, 2 mM L-glutamine, MEM non-essential amino acid, trypsin-EDTA solution, PBS, Hank's balanced salt solution, pencillin-streptomycine and fungizone were purchased from Gibco Laboratories (Grand Island, NY, USA). NaHCO3-, MTT, trypan blue, EDTA, propidium iodide, Hoechst 33258, paclitaxel and tamoxifen were purchased from Sigma Chemical Co. (St Louis, MO, USA). The primary antibodies of T-cell restricted intracellular antigen-related protein (TIAR), pro-caspase and p53, cyclin-dependent kinase (cdk) 2 and cyclin B were purchased from BD Transduction Laboratories (BD Bioscience, Palo Alto, CA, USA). Peroxidase-conjugated affinity-purified goat anti-mouse IgG was purchased from Jackson Immuno Research Laboratories (West Grove, PA, USA). The chamber slide was purchased from NUNC (Roskilde, Denmark). All other chemicals were purchased from Merck (Darmstadt, Germany). AZ-1 was obtained from total synthesis in our laboratory, was dissolved in dimethsulfoxide before experimental use and was aliquoted to be stored at -20°C, with stability for several years.
Methods
Human cell lines cultured
The BC-M1 (human breast adenocarcinoma) cell line was cultured in RPMI 1640 medium with 10% fetal bovine serum, 2 mM L-glutamine, and 25 mM Hepes. Skin fibroblasts and MCF-7 cells were cultured in DMEM medium with 10% fetal bovine serum, 2 mM L-glutamine, and MEM nonessential amino acid. The cell culture media for the two cell lines all contained pencillin-streptomycine and fungizone. All cells were incubated in a humidified atmosphere of 5% CO2 at 37°C. Cell cultures were subcultured once or twice weekly using trypsin-EDTA to detach the cell from their culture flask. The numbers of cells were counted after trypsinization by a Neubauer hemocytometer (VWR Scientific Corp., Philadelphia, PA, USA).
Cytotoxicity determined by MTT for cell viability
The MTT assay was performed according to the method of Skehan and colleagues [19]. One day before drug application, cells were seeded in 96-well flat-bottomed microtiter plates (3000–5000 cells/well). Cells were incubated for 24 hours with various drugs, and applied as serial dilutions (100 μl/well) at various concentrations. Twenty microliters of MTT (5 mg/ml) were added to each well and incubated for 4 hours at 37°C. The formazan product was dissolved by adding 100 μl dimethylsulfoxide to each well, and the plates were read at 550 nm. All measurements were performed in triplicate and each experiment was repeated at least three times. The IC50 value was calculated from the 50% formazan formation compared with a control without addition of drugs.
Apoptoic body stained by Hoechst 33258
The cells were cultured in RPMI 1640 complete medium for BC-M1 cells and in DMEM complete medium for MCF-7 cells on a chamber slide (1 × 104 cells/ml). Various concentrations of AZ-1 compound were added and incubated at 37°C. After 24 hours of incubation, the cultured medium was removed and the cells were fixed by acetic acid/methanol (1:3) solution for 10 min. In the following step, the fixed solution was removed and cells were air-dried for another 10 min. The cell was stained by Hoechst 33258 stain solution (0.5 μg/ml in Hank's balanced salt solution) at room temperature for 30 min. After staining, the solution was removed, the cell was washed three times with distilled water and then one drop of mounting solution (0.1 M citric acid:0.2 M disodium phosphate:glycerol, 1:1:2) was added before being covered by a cover slide. Apoptotic cells showed blue, peripherally clumped or fragmented chromatin.
Apoptosis analysis by flow cytometry
The apoptotic nuclei of BC-M1 cells induced by AZ-1 were also identified by the flow cytometry analysis method as described by Dive and colleagues with minor modification [20]. The BC-M1 cells were treated with various concentrations of AZ-1 for 24 hours. Cells were harvested and DNA was stained with propidium iodide. The DNA content was measured by flow cytometry (Becton Dickinson FACScan, San Jose, CA, USA).
Western blot analysis
This analysis method was that according to Bacus and colleagues with slight modifications [21]. Briefly, cells were collected from a 100 mm cultured dish after challenge by various concentrations of AZ-1 compound at 24 hours. Cell pellets were spun-down by centrifugation (1000 × g × 20 min). Pellets were resuspended in cold buffer (10 mM HEPES [pH 7.9], 1.5 mM MgCl2, 10 mM KCl, 0.5 mM dithiothreitol, 0.5 mM phenylmethylsulfonyl fluoride, 1 mM benzamidine, 30 mg/ml leupeptin, 5 mg/ml aprotinin, and 5 mg/ml pepstatin A; all from Sigma) and were incubated on ice for 5 min and lysed by sonication. Cell lysate (25 μg) was separated by 12% SDS-PAGE and transferred onto polyvinylidene difluoride membranes (Amersham, Little Chalfont, UK). Blots were incubated with blocking buffer (11 mM Tris-vase [pH 7.4], 154 mM NaCl, and 5% skim milk), washed by washing buffer (11 mM Tris-vase [pH 7.4], 154 mM NaCl, and 0.1% Tween-20) and incubated with specific antibodies to probe specific proteins. The primary antibodies were from mouse anti-human monoclonal antibodies (Imgenex Co., San Diego, CA, USA). The secondary antibody (Jackson ImmunoResearch Laboratories) was conjugated with horseradish peroxidase at an appropriate dilution by blocking buffer. The dilution factors for the primary and secondary antibodies were various (dependent on different proteins) and 1:5000, respectively. The primary antibodies used were a mouse monoclonal antibody to GAPDH and β-actin with dilution factors 1:2000 (Biogenesis, Poole, UK) and 1:10,000 (Calbiochem, San Diego, CA, USA), respectively. Immunodetection was carried out using enhanced chemiluminescence (NEN, Boston, MA, USA) detection system. Quantification of various protein expressions were achieved by measuring the intensity of chemiluminescence of the second antibody using a densitometer (BioRad Gel Doc 2000 software, analyzed using Gel Doc; BioRad Laboratories, Hercules, CA, USA) The values in the relative expression-quantifying table of various proteins represent the relative amounts of protein expression with respect to GAPDH or β-actin expression divided by its control.
Caspase-3 activity assay
The BC-M1 cells were treated with various concentrations of AZ-1 for 24 hours. The cells were harvested and washed with PBS buffer twice. The caspase-3 activity of BC-M1 cells challenged by AZ-1 was measured by the CPP32/caspase-3 protease colorimetric assay kit, with the assay method according to the procedure described by the manufacturer (Chemicon, Temecula, CA, USA). Briefly, the cells were first lysed by cold lysis buffer (supplied by the assay kit) for 10 min. The cell lysate was then centrifuged to remove the cell debris, and the protein concentration determined by Barford reagent (BioRad Laboratories). The supernatant was ready to detect the caspase-3 activity.
Apoptosis analysis by 1H-NMR spectra
1H-NMR spectroscopy was performed in vitro using methods previously described by Francis and colleagues with minor modification [22]. Briefly, 5 × 107 cells were harvested and washed twice with PBS medium made with D2O (90% purity), suspended in a final volume of 500 μl, and were placed immediately on ice until data acquisition. Samples were analyzed on a 400 MHz high-resolution Brucker CSI Omega spectrometer (Brucker, Karlsruhe, Germany) at 18°C, with pulse acquisition, a 90° flip angle, a repetition time of 10 s, 64 or 128 excitations (depending on the desired signal-to-noise ratio), 8 k points, and a 5 kHz bandwidth. A coaxial tube filled with trimethylsialoproponic acid, 0.1% solution in D2O, was used as reference (0.0 ppm) for each experiment. The relative areas underneath the CH2 and CH3 resonance curves (at 1.3 ppm and 0.9 ppm, respectively) were calculated by integration of the proton spectrum using the trough between the CH2 and CH3 resonances as a baseline reference.
Results
The cytoxicity of AZ-1 to BC-M1 cells and MCF-7 cells
The cytotoxicities of the IC50 value in AZ-1 to BC-M1 cells and MCF-7 cells were 0.51 μM and 0.57 μM in a dose-dependent manner, respectively. The response of these two cell lines (MCF-7 and BC-M1) to AZ-1 was very similar to cell viability in a dose-dependent manner. In the normal fibroblast cell (skin fibroblast) there was still a 90% survival rate at 2 μM (Fig. 1). According to the time-dependent treatment of AZ-1 using an IC50 concentration of 0.51 μM to BC-M1 cells, the cell viability was less than 10% after 36 hours of treatment in a time-dependent manner (Fig. 2). The two clinical therapies paclitaxel and taxmoxifen were compared regarding cytoxicity in BC-M1 cells with AZ-1, and the estrogen receptor antagonist taxmoxifen and AZ-1 were more potent than paclitaxel. The taxmoxifen had a low IC50 value of 0.05 μM, which is lower than AZ-1, with a plateau concentration from 0.05 μM to 2 μM (Fig. 3).
Apoptosis assay by flow cytometry and Hoechst staining
Analysis of the DNA content of cells was used to determine whether cell apoptosis was induced by AZ-1. The data of the sub-G1 area indicated that BC-M1 cells had a significant population of cell apoptosis in the sub-G1 area on treatment with AZ-1 for 24 hours compared with dimethylsulfoxide alone (Fig. 4). The sub-G1 area started at 1.0 μM AZ-1 and exhibited a large increase in apoptotic cells identified as subcellular populations with decreasing DNA. The BC-M1 cells treated with 2.0 μM AZ-1 exhibited the largest apoptotic area (29.2%). This is similar to the apoptotic bodies observed using the Hoechst stain (Fig. 5). The Hoechst staining method was used to identify the apoptotic nuclei in BC-M1 cells and MCF-7 cells. Apoptotic cells that contained the apoptotic bodies showed blue peripherally clumped or fragmented chromatin, as indicated by arrows in Figs 5 and 6. From the visual observation of the Hoechst staining results, the BC-M1 cells treated with 2.0 μM had the highest number of apoptotic bodies (Fig. 5). The formation of apoptotic bodies was observed obviously at concentrations as low as 0.5 μM AZ-1 in MCF-7 cells (Fig. 6).
Tumor cell apoptosis associated with protein expression and caspase-3 activity
We next set out to determine whether the induction of BC-M1 cell apoptosis by AZ-1 was associated with expression of apoptosis-related proteins. We found that AZ-1 induced changes in BC-M1 cell expression of the checkpoint protein p53 and the cell arrest protein p21 in a dose-related manner (Fig. 7). The p53 protein showed increasing expression from 11% to 43% at 0.5 μM and 1 μM AZ-1, and then decreased to 31% at the 2 μM concentration of AZ-1, and p21 protein increased from 6% to 22% from the 0.5 μM to 2 μM concentrations of AZ-1 added to BC-M1 cells for 24 hours compared with control, respectively. The other proteins including TIAR, pro-caspase protein and cell cdk2 also showed a dose-dependent decreasing manner (Fig. 8). From the western blot results and the values in the relative protein expression-quantifying table (Figs 7 and 8) it was revealed that the expression of proteins was affected by various concentrations of AZ-1 in BC-M1 cells after 24 hours of treatment, and these relative protein expressions were compared with the control. The cdk2, pro-caspase protein and TIAR were reduced to about 62%, 85% and 65%, and to 40%, 67% and 57% when BC-M1 cells treated with 1 μM AZ-1 and 2 μM AZ-1 for 24 hours compared with control, respectively. Based on the results of western blot analysis in the expression of pro-caspase protein, we determined the enzyme activity of caspase-3 in BC-M1 cells after challenge by various concentrations of AZ-1 from 0.5 μM to 4 μM for 24 hours. The enzyme activity of caspase-3 dose-dependently increased with concentrations of AZ-1. The activity was more than twofold over the control in BC-M1 cells after 3 μM AZ-1 treatment for 24 hours (Fig. 9).
Assay of the apoptosis signal by 1H-NMR
According to some previous reports, the ratio of the CH2 and CH3 peak area on the cell membrane was directly in proportion with the signal of apoptosis. From our results we also observed the same phenomena that the ratio of the CH2 and CH3 peak area was increasing according to the concentration of AZ-1 (Fig. 10). It was about 1.7-fold higher than the control at 2 μM AZ-1 treatment in BC-M1 cells.
Discussion
Breast cancer is the most common malignancy in women, and it is highly curable if diagnosed at early stage. It is now well established that adjuvant systemic therapy improves survival in patients with early-stage breast cancer [23,24]. Treatment options for early-stage breast cancer include chemotherapy (e.g. anthrocyclines, taxanes) and hormone therapy (e.g. tamoxifen, aromatase inhibitor). Estrogen mediates its functions through two specific intracellular receptors, estrogen receptor alpha and estrogen receptor beta, which act as hormone-dependent transcriptional regulators [25,26]. The estrogen receptor pathway plays a critical role in the pathophysiology of human breast cancer. Overexpression of estrogen receptor alpha is a well-established prognostic and predictive factor in breast cancer patients. The prognostic significance of estrogen receptor beta is not well defined [27-30].
Our previous study on the different series of bis-aziridinylnaphthoquinone compounds identified that they exhibit a more potent response toward the solid tumors than the circulation tumors [17]. This result was supported by other reports that there are differences in the reductive metabolism between the solid tumors and the circulation tumors [2]. Considering the importance of all the cellular reductases (e.g. NADPH cytochrome P450 reductase, cytochrome b5 reductase, [NADP]H oxidoreductase, NQO1) in response to the whole cellular reductive metabolism, these reductases are probably involved in bioactivation of AZ-1. The bioreductive drugs AZQ, mitomycin C and E09, however, have been developed to exploit the oxygen deficiency in the hypoxic fraction of solid tumors on the premise that hypoxic cells should show a greater propensity for reductive metabolism than well-oxygenated cells [2,31-33].
The major events involved in tissue homeostasis are proliferation, differentiation, and apoptosis. The processes of cell reproduction normally take place through an ordered process, generally known as the cell cycle. The tumor suppressor gene p53 is a multifunctional protein mainly responsible for maintaining genomic integrity, and it is the most frequently mutated gene in human tumors [34]. In response to DNA damage, aberrant growth signals, or chemotherapeutic drugs, p53 is stabilized and induces apoptosis and/or cell cycle arrest. While the mechanisms of p53-dependent apoptosis are not understood well, p53-dependent cycle arrest is primarily mediated by the cdk inhibitor p21 [34-36]. p21 regulates the cellular repair response to damaged DNA [37].
In the cytotoxicity results, AZ-1 induced the death effect of BC-M1 cells and MCF-7 cells in a dose-dependent and time-dependent manner in BC-M1 cells (Figs 1 and 2). From the results shown in Fig. 3, the hormone antagonist taxmoxifen and AZ-1 were more potent than paclitaxel to our local cell line BC-M1. This indicates that the BC-M1 cell is more sensitive to the hormone antagonist drug and our bioreductive compound AZ-1 than to a nonhormone antagonist such as paclitaxel. We assumed that the BC-M1 cell is an estrogen receptor-positive cell line. According to the results of Figs 4 and 5, the apoptosis phenomena were observed in BC-M1 cells induced by AZ-1 for 24 hours. We saw the apoptotic bodies increasing in direct proportion with the concentration of AZ-1 based on the sub-G1 area measurement and Hoechst staining (Figs 4 and 5). The apoptotic bodies were increasing slightly in 0.5 μM AZ-1, and were most abundant at a concentration of 2.0 μM. The apoptotic bodies were also observed in MCF-7 cells after treatment by AZ-1 for 24 hours (Fig. 6). For the mechanism of the apoptotic pathway, we found that p53 might be involved in the death effect of BC-M1 cells induced by the bioreductive compound AZ-1 in this study. Figure 7 shows that the expression of the p53 protein of BC-M1 cells was upregulated from 0.5 μM, was highest at 1.0 μM and downregulated at 2 μM AZ-1 for a 24-hour challenge. The response of the expression sequence in p21 protein was upregulation from 1.0 μM to 2.0 μM AZ-1 treatment to BC-M1 cells, which was later than for p53 protein. The p21 protein is the inhibitor of cdk2, the results of Fig. 8 showing that it was decreasing to 40% of the cdk2 at 2.0 μM AZ-1 and was without any disturbance to cdk2 expression in 0.5 μM AZ-1.
TIAR is a membrane of RNA recognition motif-type RNA-binding protein and also regulates the general translational arrest that accompanies environmental stress [38]. In the roles of the RNA-binding protein, TIAR involved the connections between the eukaryotic initiation factor kinase system, mRNA stability, and cellular chaperone levels [39,40]. Mutant mice lacking TIAR exhibit partial embryonic lethality and defective germ-cell maturation, implicating this protein in certain aspects of vertebrate development [41]. In DT40 cells, TIAR is also required for cell viability that involved the splicing of the exons [42]. In our results, the expression of TIAR protein was decreased with the concentrations of AZ-1 increasing in BC-M1 cells. We therefore proposed that TIAR was involved in the death effect of BC-M1 cells induced by AZ-1, which might play a role in cell viability, without direct proportion to apoptosis correlation.
According to the report of Schmidt and colleagues, p53 expression was correlated with the resistance against paclitaxel [43]. From this report, none of the tumors with p53 expression (11 patients) responded to paclitaxel. In contrast, 10 of the 22 patients without p53 expression showed an objective response. This phenomena was also supported by the report of Takara and colleagues [44], whereby the doses of paclitaxel used in BC-M1 cells are much higher than the usual IC50 values for most breast cancer cell lines reported in the literature (Fig. 3). This was also supported by the data from our results (Fig. 7) that the p53 protein really existed in BC-M1 cells, and with function. The report from Zhang and colleagues failed to detect the p53 protein in normal breast epithelial cells, and p53 positivity was 24% and 30% in intraductal and invasive cancer tissues, respectively [45]. The p53 protein could therefore be detected in tumor cells such as BC-M1 cells in untreated conditions, as in our results.
Caspases are a large family of cysteine proteases, most of them playing central roles in the execution of apoptosis [46]. The pro-caspase protein was in its inactive form when the cell was in the rest state and the protein expression was decreasing to convert to the active form of caspase-3 in the process of apoptosis [47]. In our result, the expression of pro-caspase protein was in inverse proportion to the concentrations of AZ-1 (Fig. 8, lane 2), and compared the enzyme activity assay of caspase-3 that was in direct proportion to AZ-1 concentration treatment to BC-M1 cells for 24 hours, as observed in Fig. 9. The apoptosis pathway of BC-M1 cells induced by AZ-1 compound was mediated by caspase-3. In this study, we also approached the apoptosis phenomena by the NMR spectra method. Some reports provided evidence that NMR spectroscopy allows the early detection (after 6 hours) of apoptosis-induced cellular changes in cells by observing signals from small and mobile molecular species, particularly the ratio of intensities of the CH2 and CH3 resonance [22,48]. Figure 10 shows that we observed that the ratio of CH2 and CH3 in BC-M1 cells induced by AZ-1 for 24 hours was also in direct proportion with the concentration of AZ-1. This is also one of the pieces of evidence to prove the apoptosis phenomena in BC-M1 cells induced by AZ-1.
Conclusion
The apoptosis pathway in BC-M1 cells induced by various concentrations of AZ-1 was initially triggered by the activation of p53 protein and then upregulation of the p21 protein to inhibit the cyclin kinase cdk2 expression to arrest BC-M1 cell temporality and, finally, into the apoptosis process. In BC-M1 cells, the arrest and apoptosis processes, the activation of caspase-3 activity, apoptotic body formation, the ratio of CH2 and CH3 increasing and the expression of the RNA-binding protein TIAR decreasing were all involved in the death effect of BC-M1 cells induced by this bioreductive compound. From this study, AZ-1 could be used as a cocktail in combination with other anti-breast cancer drugs to cure the hypoxia tumor tissue of breast cancer patients to prevent cancer cell recurrence.
Abbreviations
AZ-1 = 2-aziridin-1-yl-3-[(2-{2-[(3-aziridin-1-yl-1,4-dioxo-1,4-dihydronaphthalen-2-yl)thio]ethoxy}ethyl)thio]naphthoquinone; cdk = cyclin-dependent kinase; DMEM = Dulbecco's modified Eagle's medium; IC50 = inhibition concentration of 50% cell growth; NMR = nuclear magnetic resonance; PBS = phosphate-buffered saline; TIAR = T-cell restricted intracellular antigen-related protein.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgment
The authors thank the National Science Council of Taiwan for generous financial support to this research (NSC 93-2320-B-030-013).
Figures and Tables
Figure 1 Bis-aziridinylnapthoquinone (AZ-1) inhibited the proliferation of three cell lines, skin fibroblasts (SF), BC-M1 and MCF-7. Cells were seeded for 18 hours before the addition of AZ-1 with various concentrations. The death effects of these three cell lines induced by various concentrations of AZ-1 were compared by the MTT assay. The MTT assay was used to determine the cell viability after an additional 24 hours of culture. Data are from quadruplet wells and are representative of three separate experiments.
Figure 2 Bis-aziridinylnapthoquinone (AZ-1) inhibited the proliferation of cell line BC-M1. Shown is the cell viability of BC-M1 cells at various times using the IC50 dose (0.51 μM) of AZ-1 challenge. The MTT assay was used to determine the cell viability. Data are from quadruplet wells and are representative of three separate experiments.
Figure 3 Comparison of the death effect of BC-M1 cells induced by various concentrations of bis-aziridinylnapthoquinone (AZ-1), paclitaxel and tamoxifen by the MTT assay. The MTT assay was used to determine the cell viability after an additional 24 hours of culture. Data are from quadruplet wells and are representative of three separate experiments.
Figure 4 Apoptosis induced by bis-aziridinylnapthoquinone (AZ-1). BC-M1 cells were treated in 24-hour culture with (a) dimethylsulfoxide control, (b) 0.2 μM AZ-1, (c) 1.0 μM AZ-1, or (d) 2.0 μM AZ-1 before propidium iodide staining and analysis of the DNA content. Apoptosis is apparent from the large population of cells with increased DNA content in the sub-G1 area (M1 area). FL2-H, fluorescence two-peak high.
Figure 5 Apoptosis induced by bis-aziridinylnapthoquinone (AZ-1) in BC-M1 cells. The BC-M1 cells were treated in 24-hour culture with (a) dimethylsulfoxide control, (b) 0.2 μM AZ-1, (c) 1.0 μM AZ-1, or (d) 2.0 μM AZ-1 before Hoechst staining and analysis of the DNA nuclei. Apoptotic cells showed as blue, peripherally clumped or fragmented chromatin as indicated by the arrows.
Figure 6 Apoptosis induced by bis-aziridinylnapthoquinone (AZ-1) in MCF-7 cells. The MCF-7 cells were treated in 24-hour culture with (a) dimethylsulfoxide control, (b) 0.2 μM AZ-1, (c) 1.0 μM AZ-1, or (d) 2.0 μM AZ-1 before Hoechst staining and analysis of the DNA nuclei. Apoptotic cells showed blue, peripherally clumped or fragmented chromatin as indicated by the arrows.
Figure 7 Bis-aziridinylnapthoquinone (AZ-1) alters the expression of apoptotic proteins p53 and p21. The protein expression of p53 and p21 were assessed by immunoblot. The BC-M1 cells were treated with various concentration of AZ-1 (lanes 1–4: 0.2 μM, 0.5 μM, 1.0 μM, and 2.0 μM) for 24 hours and cell lysate was prepared for analysis. Lane 5, untreated control (C). β-actin was an internal control on BC-M1 cells.
Figure 8 Bis-aziridinylnapthoquinone (AZ-1) alters the expression of apoptotic proteins. The protein expression of T-cell restricted intracellular antigen-related protein (TIAR), pro-caspase 3 and cyclin-dependent kinase (cdk2) were assessed by immunoblot. The BC-M1 cells were treated with various concentration of AZ-1 (lanes 2–4: 0.2 μM, 1.0 μM, and 2.0 μM) for 24 hours and cell lysate was prepared for analysis. Lane 1, untreated control (C). GADPH was an interna l control on BC-M1 cells.
Figure 9 The enzyme activity of caspase 3 in BC-M1 cells was induced by bis-aziridinylnapthoquinone (AZ-1). Cells were seeded for 24 hours before the addition of AZ-1 in various concentrations. The cell lysate was prepared for analysis by a CPP32/caspase 3 colorimetric assay kit according to the manufacturer's instructions. The enzyme activity is shown as the fold increase relative to the control.
Figure 10 Plot of the ratio of CH2/CH3 peak area according to NMR spectra as the dose of bis-aziridinylnapthoquinone (AZ-1)-treated BC-M1 cells. Cells were seeded for 24 hours before the addition of AZ-1 in various concentrations. The ratio of CH2/CH3 signal intensity was plotted against the various concentrations of AZ-1.
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| 15642166 | PMC1064093 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 4; 7(1):R19-R27 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr939 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9401564215710.1186/bcr940Research ArticlePresence of papillomavirus sequences in condylomatous lesions of the mamillae and in invasive carcinoma of the breast de Villiers Ethel-Michele [email protected] Robert E [email protected] Hausen Harald [email protected] Charles E [email protected] Division for the Characterization of Tumorviruses, Deutsches Krebsforschungszentrum, Heidelberg, Germany2 Lower Columbia Pathologists, Longview, Washington, USA2005 22 10 2004 7 1 R1 R11 10 5 2004 9 7 2004 7 8 2004 7 9 2004 Copyright © 2004 de Villiers et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Viruses including Epstein–Barr virus (EBV), a human equivalent of murine mammary tumour virus (MMTV) and human papillomavirus (HPV) have been implicated in the aetiology of human breast cancer. We report the presence of HPV DNA sequences in areolar tissue and tumour tissue samples from female patients with breast carcinoma. The presence of virus in the areolar–nipple complex suggests to us a potential pathogenic mechanism.
Methods
Polymerase chain reaction (PCR) was undertaken to amplify HPV types in areolar and tumour tissue from breast cancer cases. In situ hybridisation supported the PCR findings and localised the virus in nipple, areolar and tumour tissue.
Results
Papillomavirus DNA was present in 25 of 29 samples of breast carcinoma and in 20 of 29 samples from the corresponding mamilla. The most prevalent type in both carcinomas and nipples was HPV 11, followed by HPV 6. Other types detected were HPV 16, 23, 27 and 57 (nipples and carcinomas), HPV 20, 21, 32, 37, 38, 66 and GA3-1 (nipples only) and HPV 3, 15, 24, 87 and DL473 (carcinomas only). Multiple types were demonstrated in seven carcinomas and ten nipple samples.
Conclusions
The data demonstrate the occurrence of HPV in nipple and areolar tissues in patients with breast carcinoma. The authors postulate a retrograde ductular pattern of viral spread that may have pathogenic significance.
areolar tissuebreast carcinomapapillomavirus
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Introduction
Breast cancer is the most frequently diagnosed cancer among women in the USA, and the incidence of breast carcinoma has increased by more than 40% over 25 years [1]. The aetiology of breast cancer remains unknown. Many risk factors have been associated with the pathogenesis of this disease, including family history, hormones, cigarette smoking and alcohol consumption [2-7]. Hormones, cigarette smoking and family history have also been demonstrated to enhance infections with papillomaviruses, mainly the high-risk human papillomavirus (HPV) types involved in the aetiology of cervical carcinoma [8].
Viral infection as an aetiological factor has been addressed in other studies. The data obtained from studies investigating the presence of viral sequences in breast cancer biopsies and cell lines have been controversial. A role for herpes viruses, and specifically Epstein–Barr virus (EBV), in the aetiology of this cancer has been questioned [9,10]. A human equivalent of the murine mammary tumour virus (MMTV) has been described in breast tumours, as well as in breast cancer cell lines [11]. These results have been questioned and negated by others (R Schmidt and H zur Hausen, unpublished results) [12]. Immortalisation of normal breast epithelial cells by the HPV types HPV 16 and HPV 18 [13] has been used to study the functions of the viral early genes E6 and E7 in different cellular pathways [14-18]. However, the presence of HPV in malignant tumours of the breast has been controversial. Several studies reported positive results [19-25], whereas others reported negative results [26-28]. In the present study we report the presence of HPV DNA in biopsy samples from mammary carcinomas, as well as these viral sequences in the tissue of the corresponding mamilla.
Materials and methods
Samples and DNA extraction
Samples from breast tumour tissue for the HPV analysis were supplied by two of the authors (RES and CEB). The 29 cases used for the retrospective study and demonstration of papillomavirus sequences were selected from routine mastectomy specimens undertaken in a course of treatment for carcinoma of the breast. Case selection was based on the availability of adequate nipple and tumour tissue from the same patient and the presence of histological features suggestive of HPV infection in nipple and areolar sections. The patients, all female, ranged in age from 30 to 88 years, with an average age of 61.3 years. Twelve tumours arose in the right breast and 17 in the left. Five tumours were subareolar and 24 were located at a distance from the nipple and areola. Tumours ranged in size from 6 mm to 15 cm. One patient received cytoxan chemotherapy before surgery. In all other cases primary treatment was surgical as represented by the specimen. One patient had undergone contralateral mastectomy ten years earlier for a localised breast cancer, which was judged clinically and pathologically to represent a separate primary tumour. Among the 29 patients there were three with remote histories of malignancy without evidence of recurrence (one cutaneous malignant melanoma, one renal cell carcinoma and one soft-tissue malignancy). One patient had undergone hysterectomy for high-grade cervical dysplasia. There were no patients with documented cervical carcinoma. No patients were immune-impaired or immunosuppressed.
Formalin-fixed paraffin-embedded samples from both the nipple (n = 29) and the carcinoma of the breast (n = 29) from the same patient were sectioned, and the total DNA was extracted from each sample. Great care was taken during sectioning to avoid any contamination between samples. Two different individuals sectioned the blocks in several small random groups at different time intervals over a period of one week. The microtome was cleaned thoroughly and exposed to ultraviolet between samples; in addition, new blades were used for each sample. Deparaffinisation was performed by rotation overnight in 1 ml of xylene, followed by centrifugation and subsequent removal of the supernatant. This step was repeated twice (each for one hour) with fresh xylene. The xylene was in turn removed by rotation in 1 ml of 100% ethanol for one hour followed by centrifugation and subsequent replacement of the supernatant with 90% ethanol for 45 min, and repeated by steps of 80% ethanol for 45 min and 70% ethanol for 45 min. Samples were freeze-dried, and total cellular DNA was extracted as described previously [29]. The DNA was digested with Proteinase K and subjected to extraction with phenol followed by extraction with chloroform/isoamyl alcohol. The extracted DNA was precipitated with ethanol and the pellet was resuspended in 10 mM Tris/HCl, pH 8.0.
PCR analysis and cloning
DNA (50–100 μg) of each sample was amplified by polymerase chain reaction (PCR). The quality of the DNA obtained from the fixed samples was controlled by amplification with primers to detect the β-actin gene [30]. The primers RS42 and KM29 were initially used to amplify the 536-base-pair fragment of the β-actin gene. If this size of fragment could not be amplified, the primers PC03 and PC04 were used to amplify the 110-base-pair (bp) fragment of the β-actin gene. Papillomavirus sequences were amplified by three different methods, each targeting highly conserved regions within the L1 open reading frame of papillomaviruses. These included the GP5+/GP6+ primers [31], the CP primers using modified conditions as previously described [32] and the FAP primers [33]. All three primer combinations are routinely used on individual samples tested in our laboratory. We initially modified the PCR conditions for each described method to include the amplification of the largest number of individual HPV types possible. Cloned DNA of each known HPV type was used to optimise these conditions. Amplification with the GP primers was performed in 2 mM MgCl2 using 40 cycles with an annealing temperature of 40°C. The expected size of the amplicon was 140–150 bp. The expected size with the FAP primers is 480 bp after 45 cycles in 3.5 mM MgCl2 and an annealing temperature of 50°C. The nested amplification approach with the CP primers results in an amplicon size of 370–400 bp. The initial amplification was performed in 2 mM MgCl2 and 40 cycles at 50°C annealing temperature, after which an aliquot was re-amplified with nested primers but with the same PCR conditions. All amplicons were eluted after gel electrophoresis, purified and cloned. At least ten fragments of each amplicon were sequenced. Sequencing was performed with either the Sequenase 2.0 DNA Sequencing Kit (USB, Cleveland, OH) or an ABI Model Sequencer with Big Dye Terminator chemistry (Perkin Elmer Applied Biosystems Division, Dreieich, Germany). All precautions were taken to avoid contamination before or during extraction of the DNA from the embedded formalin-fixed samples. After initial results had been obtained, a second sectioning was performed on the same embedded samples and DNA was again extracted to ensure that no contamination had taken place during the first round of experiments. This second round of analyses was performed in different rooms and by different individuals.
Sequence analysis
Sequences were compared with all available data banks with the aid of the Husar software package (Deutsches Krebsforschungszentrum).
In situ hybridisation
In situ hybridisation was performed as described by Kawase and colleagues [34]. Papillomavirus probes were digoxigenin-labelled by PCR in accordance with the manufacturer's instructions (PCR DIG Probe Synthesis Kit, catalogue no. 1636090; Roche, Mannheim, Germany). The FAP primers were used to label HPV 6 and HPV 11 DNA and the CP primers to label HPV 16 DNA. Denaturation was performed in a Biozym cycler for 5 min at 95°C followed by hybridisation overnight at 37°C under stringent conditions as described previously [35]. This was followed by washing with solutions in accordance with instructions in the Genpoint Kit (catalogue no. K0620; Dako, Hamburg, Germany). Anti-digoxigenin-AP antibodies (anti-digoxigenin-AP, Fab fragments, catalogue no. 1093274; Roche), blocking reagent (catalogue no. 1096176; Roche) and Nitro Blue Tetrazolium/5-bromo-4-chloroindol-3-yl phosphate (catalogue no. 1681451; Roche) were applied for visualisation of the signals. Sections from each sample were used for in situ hybridisation. Reading of the samples was blinded and performed by one of us (HzH).
Results
Nipple specimens were reviewed and selected by two of us (CEB, RES) for histological features consistent with human papillomavirus infections. Features identified included epithelial hyperplasia, hypergranulosis, parakeratosis, and distorted nuclear shape associated with nuclear clearing and pyknosis suggestive of koilocytosis. In addition, squamous metaplasia of lactiferous ducts and shedding of metaplastic cellular elements into lactiferous ducts and sinuses were noted (Fig. 1). In addition to the 29 invasive carcinomas, ductal carcinoma in situ was identified in lactiferous ducts in four samples and small benign papillomas were present in three samples. Most invasive carcinoma samples (18 of 29) displayed a duct cell carcinoma pattern (Table 1). Other types included medullary carcinoma (five cases), tubular carcinoma (three cases), lobular carcinoma (two cases) and mucinous carcinoma (one case). DNA extracted from the samples taken from the nipple and the corresponding carcinoma of each breast (29 pairs) was analysed by PCR for the presence of papillomavirus sequences. The quality of the DNA was controlled by amplification of the β-actin gene. The 536-base-pair fragment of this gene was amplified in 47 of 58 samples. The 110-base-pair fragment was amplified in the remaining 11 samples (Table 2). Intact papillomavirus particles are resistant to degeneration. Extraction of DNA from fixed tissue can harbour intact viral genomes even under conditions in which the cellular DNA has been degraded. The three different methods used for amplification of the papillomavirus DNA were chosen to ensure amplification of all known papillomavirus types, as well as putative new types. All three primer combinations (GP, CP and FAP) are routinely used on individual samples tested in our laboratory. We initially modified the PCR conditions for each described method to include the amplification of the largest number of individual HPV types possible. Cloned genomic DNA of each known HPV type was used to optimise these conditions. The GP primers have been designed to amplify the majority of papillomavirus types associated with mucosal lesions. The FAP primers, in contrast, were initially designed to amplify the majority of papillomavirus types associated with cutaneous lesions. The amplicon size described is 480 bp, but in our hands smaller fragments identified as papillomavirus sequences (after cloning and sequencing) were also generated, depending on the HPV type. The CP primers were originally designed to amplify all the epidermodysplasia verruciformis-associated HPV types, but under modified conditions a large spectrum of mucosal types are amplified as well. Combining the three methods with the modified PCR conditions allows us to amplify all known HPV types with more or less equal efficiency. The amplicons generated, using any of the described methods, often constitute cellular sequences depending on the viral load of the sample.
Normal breast and areolar tissue was not available for use as control samples. The described methods of testing have been used routinely in our laboratory over several years in the analyses of tissues from a large variety of organs. We have analysed large numbers of samples from benign and malignant tumours of the oesophagus, head and neck and skin, as well as normal skin tissue for the presence of papillomavirus DNA with identical methods. HPV positivity varied depending on the type of tumour and normal tissue [29,36-38] (E-MdV, unpublished data). The present study describes the first series of tissue of the breast analysed in our laboratory for the presence of papillomavirus DNA. Sequence analyses showed that 25 (86%) of the breast carcinoma samples and 20 (69%) of the samples from the mamilla harboured papillomavirus sequences. The results for each sample using each of the primer combinations are presented in Table 2. Only one pair of samples were both negative for HPV DNA. HPV DNA was present in 17 (59%) pairs of samples, namely nipple as well as carcinoma, whereas papillomavirus DNA was detected only in the carcinomatous tissue in eight cases (28%) and only in the nipple in three cases (10%) (Table 3). HPV 11 DNA was present in both carcinoma and nipple samples in seven cases, HPV 6 in one case and HPV 57 in two cases. The most prevalent HPV types were HPV 11 (19 carcinomas and 8 nipples) followed by HPV 6 (7 carcinomas and 6 nipples) (Table 4). Other HPV types detected were HPV 16 (2 carcinomas and 3 nipples), HPV 57 (2 carcinomas and 3 nipples), HPV 27 (2 carcinomas and 4 nipples), HPV 66 (2 nipples), candHPV 87 (4 carcinomas), HPV 37 (4 nipples) and the putative new HPV type DL250 (HPV 9-related) in two carcinomas. HPV types detected in single samples of the nipple were HPV 20, 21, 23, 32, 38 and GA3-1 (HPV 8-related) and HPV 3, 15, 23, 24 and DL473 (HPV 15-related) in carcinoma samples. Multiple HPV types were demonstrated in eight carcinomas (24%) and ten nipple (34.5%) samples.
In situ hybridisation was performed on all samples that had previously been positive by PCR amplification for HPV 6, HPV 11 or HPV 16. Positive signals were clearly distinguishable from the surrounding negative tissue (Fig. 2). All carcinoma samples that had been negative for one of these three HPV types by PCR were also subjected to in situ hybridisation with each of the respective labelled HPV probes. No positive hybridisation signal was observed in any of these samples. In situ hybridisation supported the presence of HPV 6, 11 and 16 in nipple and areolar samples and in all tumour tissue in which these type-specific HPV sequences were detected by PCR analysis.
Discussion
The ubiquitous distribution of papillomavirus infections of the skin and genital and oral mucosa has been documented [39-41]. Infection with specific papillomavirus types has been shown to be a necessary but not sufficient cause in the pathogenesis of malignant genital tumours. The malignant tumours generally develop after long latency periods during which additional cellular modifications occur within the infected cell [8]. The E6 and E7 genes of the most prevalent high-risk HPV types, HPV 16 and HPV 18, modulate cellular pathways, thereby regulating proliferation and cell survival [8,42]. In contrast, the E6 and E7 proteins of the low-risk types, HPV 6 and HPV 11, do not influence these cellular pathways in the same manner, although they have occasionally been demonstrated in premalignant and malignant tumours. Additional cofactors are probably needed to modulate cellular proteins so as to immortalise and transform the infected cells [8]. The mechanism through which other HPV types, mainly associated with cutaneous lesions, might be involved in the pathogenesis of tumours has received little attention. However, preliminary data indicate that several molecular pathways are probably followed, depending on the HPV type involved [43].
The detection of papillomavirus infections in tissues largely depends on the method used. Described methods vary greatly in their sensitivity and specificity. The HPV types HPV 6, 11, 16, 18, 31, 33, 35, 39, 52 and 58 are frequently associated with genital lesions and are therefore most often targeted for HPV detection. Tests to demonstrate any of the 96 characterised HPV types in tissues require extensive analyses, including the sequencing of cloned amplimers. Most published studies therefore used methods restricted to the detection of specific, single, or combinations or groups of HPV types. The use of type-specific primers may increase the number of positive samples but is biased with regard to the HPV types involved, because other HPV types present cannot be detected.
The present study investigated breast samples by using DNA PCR amplification of two conserved regions within the L1 open reading frame of the papillomavirus genome. Two of the primer combinations used (CP and GP primers) amplify an overlapping region within the L1 open reading frame. By using all three primer combinations to amplify the DNA of a single sample we are able to detect all known HPV types and also putative new HPV types. In addition, the cloning of the amplified product, in combination with sequencing of the cloned inserts, allows us to distinguish individual HPV types present in one sample, including multiple infections in one sample. Studies applying the techniques described here have demonstrated a larger spectrum of papillomavirus types associated with different types of benign and malignant tumours and argue that the historical grouping into 'mucosal' and 'cutaneous' HPV types can no longer be upheld [44]. We are not aware of any study outside our group that has analysed tissues for the presence of infection by any HPV types as extensively as the study described here.
Other investigators have analysed nipple and areolar specimens for the presence of HPV DNA. HPV 6/11 was detected in one of 20 papillomas of the nipple, whereas ten nipple duct adenomas were negative [45] and seven low-risk and high-risk HPV types were not present in 20 cases of Paget's disease of the nipple [46]. Single cases of papillomas of the nipple resembling condylomata acuminata harboured HPV 6 DNA [47] and HPV 41 DNA [48]. Reports on the detection of HPV 16, HPV 18 and/or HPV 33 DNA in breast carcinoma samples range between 11% and 68% positivity [19-25]. The present study detected HPV 16 DNA in 7% of the carcinoma and in 10% of the nipple samples. The only other high-risk HPV type, HPV 66, was present in two samples from the nipple. The reasons for the differences in published reports are unclear, but may be attributed to numbers of samples tested, methodological differences or the demographics of the samples tested. In contrast, several other studies failed to demonstrate HPV sequences in tumours of the breast: all of 15 papillomas and 28 breast carcinoma samples were negative for HPV 6, 16 and 18 DNA [26], no HPV 16 and HPV 18 DNA could be detected in 26 fine needle aspirates and four breast carcinoma biopsies [28], and no HPV DNA was found in 80 samples of breast carcinomas [27]. HPV 16 and HPV 18 oncogenes immortalise normal human mammary cells under in vitro conditions [14-18]. It is unclear whether these data truly represent the in vivo situation.
The present study detected papillomavirus sequences in 86% (25/29) of the breast carcinoma samples and 69% (20 of 29) of the nipple samples. HPV 6 and HPV 11 infections were most prevalent in our collection of samples. Either of the two HPV types was present in 20 of 29 (69%) of the breast carcinoma biopsies and in 12 of 29 (41%) of the samples of the mamilla. We detected the simultaneous presence of more than one HPV type in about one-third of the samples. HPV types associated with mucosal lesions, and also several associated with skin lesions, were detected. HPV 27 and HPV 57 are closely related HPV types. HPV 27 is very frequently found in cutaneous warts, whereas HPV 57 has been demonstrated in mucosal and cutaneous lesions [49]. Several other HPV types found in the breast samples have historically been associated with the rare hereditary disease epidermodysplasia verruciformis but have more recently been shown in non-melanoma skin cancers and their precursors in both immunosuppressed and immunocompetent patients [44].
The confirmation of PCR-derived evidence of HPV sequences in nipple, areolar and tumour tissue by in situ hybridisation in our hands lends substantive support to the conjecture that viral sequences are present and not an artefact in the tissues we have examined. The absence of HPV DNA in several samples, as well as the detection of different HPV types in the carcinoma versus the nipple of the same patient, might be attributed to the viral load at the time of sampling in combination with the sensitivity of the tests performed. The tissue sections also harboured unaffected tissue, leading to the dilution of virus-containing cells. In addition, the amplification of viral DNA by PCR with degenerate or consensus primers will detect replicating viral sequences and is not sensitive enough to detect all individual HPV types with equal sensitivity in a mixed population of cells. The in situ hybridisation technique is also less sensitive and will not detect a single viral genome copy per cell.
HPV 6 and HPV 11 are regarded as low-risk HPV types because they have only rarely been demonstrated in premalignant or malignant tissue, and the early genes E6 and E7 do not immortalise primary keratinocytes in vitro [50]. HPV 6 DNA has been found in carcinomas [51,52] and giant condylomas (Buschke–Löwenstein tumours) [53,54]. The molecular mechanism by which these low-risk HPV types induce or participate in the transformation of cells has not been resolved. Duplications of the long control region [54-57], minimal sequence dissimilarities [58,59] and integration [60] of HPV 6 and/or HPV 11 genomes have been shown in malignant tissue.
The mere presence of the low-risk HPV 6 and 11 in most of the mamillae and in the concomitant breast carcinoma samples does not prove its role in the aetiology of the disease, although it merits further investigation. Unfortunately, because of the previous fixation of our samples, RNA from these tumours was not available for the detection of viral transcripts as additional support of our results. Relatively few studies have been performed in an attempt to unravel the intracellular mechanisms through which HPV 6 or 11 may immortalise cells, and information is still fractional in comparison with data on the high-risk HPV types. Introduction of the HPV 6 E6 into normal mammary cells leads to immortalisation and reduced levels of the p53 protein [61]. Binding of the HPV 6 E6 protein modulates the function of the cellular proteins Bak [62], Gps2 [63] and Zyxin [64]. Additional in vitro studies investigating the influence of hormones, smoke adducts and genetic factors on the interaction of the HPV 6 and 11 proteins with cellular proteins should provide valuable information on a possible role for HPV 6 and 11 in the pathogenesis of breast carcinoma. The data presented in this study also indicate a strong need for epidemiological studies correlating HPV, cigarette smoking, hormone use and family history to substantiate in vitro findings.
This study suggests that human papillomavirus may infect the epithelium of the nipple and areola. Human papillomavirus infection may be identified with recognisable histological features comparable to human papillomavirus infection at other sites. Furthermore, this study is consistent with a pathogenic mechanism involving transfer in a retrograde fashion via the nipple, areola, lactiferous ducts and sinuses of the human papillomavirus. If confirmed, the pathogenic mechanism proposed may have significant implications for the prevention and treatment of breast carcinoma and the identification of individuals at risk for carcinoma development. Examination of cellular samples of nipple and areola inclusive of cytological examination and methods for identifying the presence of human papillomavirus might aid early diagnosis and perhaps therapy.
Conclusions
The data demonstrate the occurrence of HPV in nipple and areolar tissues in patients with breast carcinoma. The presence of papillomavirus DNA in most mamillae and concomitant breast carcinoma samples merits further investigation.
Competing interests
The author(s) declare that they have no competing interests.
Abbreviations
EBV = Epstein–Barr virus; HPV = human papillomavirus; MMTV = murine mammary tumour virus; PCR = polymerase chain reaction.
Acknowledgements
We thank Corinne Whitley, Elsbeth Schneider, Romana Cop, Karin Gunst, Helene Rahn and Imke Grewe at the Deutsches Krebsforschungszentrum and Kari Bradshaw, Joyce Kangas and Connie Gibbons at Lower Columbia Pathologists for excellent technical support. This study was supported in part by The Healthcare Foundation, Cowlitz County, Washington. The study was reviewed by the IRB committee at St John Medical Center, Longview, Washington (acting chairperson Dane Moseson MD) and accepted as conforming to the institution's guidelines with regard to retroactive studies employing human tissue samples.
Figures and Tables
Figure 1 Histological features of the carcinomatous breast (magnification × 100). Detailed histology of (a) is seen in (b–d). AS, anucleate squames; asterisks, interstitial lymphocytic infiltrates; G, galactophore; K, keratin; LS, lactiferous sinus; LD, lactiferous duct; SG, sebaceous gland (tangenial cut); SGD, sweat gland duct; SH, simple epithelial hyperplasia; PH, papillary epithelial hyperplasia.
Figure 2 Papillomavirus sequences detected by in situ hybridisation. (a) Case 10012, in the nipple epithelium (magnification × 40). (b) Case 10041, in the nipple epithelium (magnification × 40). (c) Case 10012, in tumour tissue (magnification × 100). (d) Case 10014, in tumour tissue (magnification × 100).
Table 1 Samples analysed for HPV sequences
Carcinoma type Location Number tested HPV DNA positive
Invasive ductal Carcinoma 18 16
Nipple 11
Medullary Carcinoma 5 4
Nipple 3
Tubular Carcinoma 3 3
Nipple 3
Lobular Carcinoma 2 1
Nipple 2
Mucinous Carcinoma 1 1
Nipple 1
Total carcinoma Carcinoma 29 25 (86%)
Nipple 20 (69%)
HPV, human papillomavirus.
Table 2 Presence of HPV DNA: results obtained after amplification with respective primers
Carcinoma type Patient number Location β-actin HPV
KM29/RS42 (536 bp) PC03/PC04 (110 bp) GP CP FAP
Invasive duct cell 1 Carcinoma + HPV11 - HPV11
Nipple + HPV16 HPV27 HPV11, HPV57
2 Carcinoma + HPV11 - HPV11
Nipple - + HPV11 - HPV6, HPV11
3 Carcinoma + - - HPV11
Carcinoma + HPV16 HPV27 -
4 Carcinoma + - - HPV11
Nipple - + - - -
5 Carcinoma + - - HPV 11
Nipple + - - HPV3, HPV11, HPV20, HPV21
6 Carcinoma + HPV11, HPV16 HPV6, HPV27, HPV57 HPV3, HPV6, HPV57
Nipple + - - HPV6, HPV11, HPV57
7 Carcinoma + - - -
Nipple + - - -
8 Carcinoma + - DL250 (HPV9-related) -
Nipple + - - -
9 Carcinoma + - DL473 (HPV15-related) -
Nipple + - - -
10 Carcinoma + - HPV6 -
Nipple + - - -
11 Carcinoma + - HPV23 -
Nipple + - GA3-1 (HPV8-related) -
12 Carcinoma + HPV11 - -
Nipple + - HPV37 -
13 Carcinoma - + HPV11 - HPV11
Nipple - + - - HPV6
14 Carcinoma - + - HPV 24 -
Nipple + - - -
15 Carcinoma + HPV11 HPV6, DL250 HPV11, HPV87
Nipple + HPV66 - -
16 Carcinoma + - - HPV11
Nipple + - - -
17 Carcinoma + - - -
Nipple - + HPV6, HPV66 HPV37 -
18 Carcinoma + HPV11 - HPV11
Nipple + HPV6 HPV37 -
Medullary 19 Carcinoma + HPV11 - HPV11
Nipple - + - - HPV11
20 Carcinoma + - - -
Nipple + HPV11 - -
21 Carcinoma + HPV11 HPV6 HPV6, HPV87
Nipple + - HPV23 -
22 Carcinoma + HPV11 HPV6 HPV6, HPV87
Nipple + - - -
23 Carcinoma + - HPV 6 HPV11
Nipple + - - -
Tubular 24 Carcinoma + - - HPV57
Nipple - + HPV6, HPV57 HPV6, HPV57 HPV6, HPV57
25 Carcinoma + - - HPV11
Nipple - + HPV16, HPV27 - HPV11
26 Carcinoma + HPV11 HPV15 HPV87
Nipple - + - HPV27, HPV37 -
Lobular 27 Carcinoma + HPV11, HPV16 HPV27 -
Nipple + - - HPV11
28 Carcinoma + - - -
Nipple + - - -
Mucinous 29 Carcinoma - + - HPV6 HPV11
Nipple + - HPV38 -
bp, base pairs; HPV, human papillomavirus.
Table 3 Papillomavirus sequences demonstrated
Tumor pairs Number HPV typesa
Carcinoma+ nipple+ 17 Same type in both samples: 11 (7×)b, 6 (1×)b, 57 (2×)
Different types: 3, 15, 16, 20, 21, 23, 27, 37, 38, 66, 87, DL250, DL473, GA3-1
Carcinoma- nipple+ 3 6, 11, 32, 37, 66
Carcinoma+ nipple- 8 6, 11, 24, 87, DL250, DL473
Carcinoma- nipple- 1
aInfection with multiple human papillomavirus (HPV) types was demonstrated in eight carcinoma samples (27.6%) and ten nipple samples (34.5%).
bOne pair harboured HPV 6, HPV 11 and HPV 57 DNA.
Table 4 Types of papillomavirus detected
Carcinoma type Location HPV types
6 11 16 27 37 57 66 87 DL250 Others
Intraductal Carcinoma 3 11 1 1 - 1 - 1 2 3, 23, 24, DL473
Nipple 5 4 2 2 3 2 2 - - 20, 21, GA3-1
Lobular Carcinoma - 1 1 1 - - - - - -
Nipple - 1 - - - - - - - 32
Tubular Carcinoma - 2 - - - 1 - 1 - 15
Nipple 1 1 1 2 1 1 - - - -
Medullary Carcinoma 3 4 - - - - - 2 - -
Nipple - 2 - - - - - - - 23
Mucinous Carcinoma 1 1 - - - - - - - -
Nipple - - - - - - - - - 38
Total carcinoma (%) Carcinoma 7 (24.1) 19 (65.5) 2 (6.9) 2 - 2 - 4 (13.8) 2
Nipple 6 (21) 8 (27.6) 3 (10.3) 4 4 3 2 - -
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| 15642157 | PMC1064094 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Oct 22; 7(1):R1-R11 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr940 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9411564216110.1186/bcr941Research ArticlePolychlorinated biphenyls, cytochrome P450 1A1 (CYP1A1) polymorphisms, and breast cancer risk among African American women and white women in North Carolina: a population-based case-control study Li Yu [email protected] Robert C [email protected] Douglas A [email protected] Lisa [email protected] Chiu-Kit J 1Newman Beth [email protected] Kathleen [email protected] Department of Epidemiology, School of Public Heath, University of North Carolina, Chapel Hill, North Carolina, USA2 Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA3 National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA4 School of Public Health, Queensland University of Technology, Kelvin Grove, Queensland, Australia2005 22 10 2004 7 1 R12 R18 18 6 2004 22 7 2004 3 9 2004 9 9 2004 Copyright © 2004 Li et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Epidemiologic studies have not shown a strong relationship between blood levels of polychlorinated biphenyls (PCBs) and breast cancer risk. However, two recent studies showed a stronger association among postmenopausal white women with the inducible M2 polymorphism in the cytochrome P450 1A1 (CYP1A1) gene.
Methods
In a population-based case-control study, we evaluated breast cancer risk in relation to PCBs and the CYP1A1 polymorphisms M1 (also known as CYP1A1*2A), M2 (CYP1A1*2C), M3 (CYP1A1*3), and M4 (CYP1A1*4). The study population consisted of 612 patients (242 African American, 370 white) and 599 controls (242 African American, 357 white).
Results
There was no evidence of strong joint effects between CYP1A1 M1-containing genotypes and total PCBs in African American or white women. Statistically significant multiplicative interactions were observed between CYP1A1 M2-containing genotypes and elevated plasma total PCBs among white women (P value for likelihood ratio test = 0.02). Multiplicative interactions were also observed between CYP1A1 M3-containing genotypes and elevated total PCBs among African American women (P value for likelihood ratio test = 0.10).
Conclusions
Our results confirm previous reports that CYP1A1 M2-containing genotypes modify the association between PCB exposure and risk of breast cancer. We present additional evidence suggesting that CYP1A1 M3-containing genotypes modify the effects of PCB exposure among African American women. Additional studies are warranted, and meta-analyses combining results across studies will be needed to generate more precise estimates of the joint effects of PCBs and CYP1A1 genotypes.
breast cancerCYP1A1polychlorinated biphenyls
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Introduction
The role of organochlorine compounds such as polychlorinated biphenyls (PCBs) as etiologic agents for breast cancer has been investigated in several epidemiologic studies, but findings are inconsistent [1]. Initial studies examining breast tissue or serum showed significantly higher concentrations of PCBs in patients with breast cancer compared with controls [2-6], but more recent studies observed no association [7-10] or an inverse association [11] with breast cancer.
Several mechanisms have been proposed to explain how PCB exposure might increase the risk of breast cancer [12-15]. Owing to their lipophilic nature, organochlorine compounds undergo lifelong sequestration in animal and human adipose tissues. Adipose tissue within the human breast contains measurable levels of PCBs [16]. Metabolism of PCBs within breast tissue can generate reactive intermediates that cause oxidative damage and react with DNA. The proposed mechanism involves cytochrome P450 1A1 (CYP1A1), a gene that is inducible by PCBs [17,18]. CYP1A1 encodes aryl hydrocarbon hydrolase (AHH), an enzyme involved in the production of reactive epoxide intermediates from polycyclic aromatic hydrocarbons, steroid hormones, and other aromatic compounds [19]. On the basis of this model, individuals with higher CYP1A1 activity would be at increased risk of breast cancer when exposed to high levels of PCBs.
Several polymorphisms have been identified in CYP1A1, some of which lead to more highly inducible AHH activity [19-22]. CYP1A1 polymorphisms include M1 (T→C substitution at nucleotide 3801 in the 3'-noncoding region), M2 (A→G substitution at nucleotide 2455 leading to an amino acid change of isoleucine to valine at codon 462), M3 (T→C substitution at nucleotide 3205 in the 3'-noncoding region), and M4 (C→A substitution at nucleotide 2453 leading to an amino acid change of threonine to asparagine at codon 461). Studies of CYP1A1 in cultured human lymphocytes showed significantly elevated levels of inducible enzyme activity among persons with M2 genotypes [20,22]. Crofts and colleagues [21] reported that M2 alleles were associated with increased CYP1A1 inducibility at the mRNA level, and a threefold elevation in AHH enzyme activity. The M1 allele also encodes an inducible form of CYP1A1 [19,22,23].
Two recent epidemiologic studies showed an increased risk of breast cancer among postmenopausal white women with at least one M2 (valine) variant allele of CYP1A1 and a high serum level of PCBs [24,25]. These findings suggest that interactions between CYP1A1 polymorphisms and PCBs could be involved in the development of breast cancer, and genotyping for CYP1A1 could help to resolve inconsistencies in past epidemiologic studies. We used data from the Carolina Breast Cancer Study (CBCS), a population-based case-control study of African American and white women in North Carolina, to evaluate the effects of PCBs and CYP1A1 genotypes in relation to breast cancer risk.
Materials and methods
Study population
CBCS study participants were women aged 20–74 years residing in 24 contiguous counties in central and eastern North Carolina [26]. Patients were women with a first diagnosis of invasive breast cancer identified through a rapid ascertainment system implemented in cooperation with the North Carolina Central Cancer registry. Controls were selected from records of the North Carolina Division of Motor Vehicles and US Health Care Financing Administration, and were frequency-matched to patients on the basis of race and age. Information was collected through in-person interviews and included reproductive history, diet and lifestyle factors, a detailed family history of cancer, and occupational history. About 98% of participants who were interviewed agreed to give a 30 ml blood sample at the time of interview. Informed consent to obtain DNA and measurement of plasma concentration of PCBs was sought with the use of a form approved by the Institutional Review Board of the UNC School of Medicine in compliance with the Helsinki Declaration.
The CBCS was conducted in two phases. Phase I of the study was conducted between May 1993 and December 1996 [26]. Phase II of the study was conducted between 1996 and 2001. The total numbers of participants enrolled in Phase I of the CBCS were 861 patients (335 African American, 526 white) and 790 controls (332 African American and 526 white). The response rates were 76% for patients and 55% for controls. Genotyping for CYP1A1 was performed on the first 688 breast cancer patients and 702 controls enrolled in Phase I of the CBCS. Laboratory assays for organochlorines were conducted on blood samples from 748 patients and 659 controls in Phase I of the CBCS [26]. The present analyses were based on Phase I participants for whom both CYP1A1 genotyping and organochlorine measurements were available: 612 patients (242 African American, 370 white) and 599 controls (242 African American, 357 white).
Laboratory methods
Methods for identification of plasma PCBs on CBCS plasma samples using gas chromatography/electron capture detection have been described previously [26]. Methods for genotyping of CYP1A1 on CBCS samples using restriction fragment length polymorphism–polymerase chain reaction have been described previously [27]. In a newly proposed nomenclature for CYP1A1 alleles, the M1 allele corresponds to CYP1A1*2A, M2 to CYP1A1*2C, M3 to CYP1A1*3, and M4 to CYP1A1*4 [28]. Data were missing on the following CYP1A1 genotypes due to unreadable gels or failure to amplify: M1 (10 patients and 7 controls), M2 (none missing), M3 (10 patients and 7 controls), and M4 (none missing). To correct for differences in plasma concentrations of chlorinated hydrocarbons attributable to blood lipids, the quantity of different lipid components in the plasma of each subject was measured with an automated enzymic assay, as described previously [26].
Statistical methods
Imputed total PCB values were used in the data analyses. Imputation was performed by setting zero values and values below the detection limit (0.0125 ng/ml) to the detection limit divided by the square root of 2 (26). Hornung and Reed [29] recommended this method of imputation as an accurate approach for computing means and standard deviations for variables that include nondetectable values. In the present study, imputation did not affect the estimation of odds ratios (ORs) for breast cancer and total PCBs, because individuals in the lowest category (that is, below the median) remained in the lowest category after imputation. Lipid-adjusted total PCBs levels were calculated with Equation 2 of Phillips and colleagues [30]. Univariate analyses were performed for total PCBs, lipid-adjusted total PCBs, low to moderate chlorinated PCBs, highly chlorinated PCBs, and individual PCB congeners 99, 105, 118, and 153. Among controls in the CBCS, levels of individual PCB congeners and congener groups were highly intercorrelated [31]. ORs for the joint effects of CYP1A1 genotypes and PCB exposure did not differ substantially according to specific PCB congeners or congener groups, and ORs were similar to results obtained with total PCBs. We did not observe differences in ORs for breast cancer and PCB exposure, or the joint effects of CYP1A1 genotypes and PCBs, when we analyzed PCB congeners 99, 105, 118, or 153 alone, as suggested by Demers and colleagues [32]. Therefore, only results for lipid-adjusted total PCBs are presented.
Adjusted ORs for breast cancer and 95% confidence intervals (CIs) were calculated from unconditional logistic regression models. Total PCBs were categorized as greater than or equal to versus below the median, on the basis of the distribution in African American or white controls separately. PROC GENMOD of software package SAS (version 8.1; SAS Institute, Cary, NC) was used to incorporate offsets derived from the sampling probabilities used to identify eligible participants. Covariates included age, race (whites/African Americans), parity (nulliparous, 1 or ≥ 2), use of hormone replacement therapy (never or ever), oral contraceptive use (never or ever), breast feeding (never or ever), smoking (never, current, or former smoker), alcohol consumption (yes/no), family history of breast cancer (yes/no), benign breast biopsy (yes/no), income (less than $30,000/year or at least $30,000/year), and education (lower than high school, or high school and above). Continuous covariates, which included height, waist/hip ratio, and body mass index, were categorized on the basis of the median values of each variable in all controls.
ORs did not differ after adjusting for additional covariates, so results are presented for African American and white women adjusting for sampling fractions and age. Menopausal status was defined as described previously [27] and used to stratify analyses of CYP1A1 genotypes and PCB levels. Stratified analyses were also conducted on the basis of smoking history (ever or never). In addition, we estimated the increase in odds of breast cancer per 0.10 ng/ml increase in total PCB levels by coding lipid-adjusted total PCBs as a continuous variable, and stratifying on CYP1A1 genotypes. Linearity in the logit was tested using the Box–Tidwell transformation test as implemented in SAS (version 8.1). The assumption of linearity was not violated in premenopausal or postmenopausal African American or white women.
To assess the interaction on the additive scale between PCB levels and CYP1A1 genotypes, indicator variables were created for each category of joint exposure of PCBs and CYP1A1 genotype. Women with the homozygous common (non-M1, non-M2, non-M3, or non-M4) genotypes and the lowest level of exposure to PCBs were used as a common reference group. Interaction contrast ratios (ICRs) and 95% CIs were calculated for joint effects of the M1 genotype and PCBs [33]. ICR values greater than zero indicate greater than additive effects (synergy), and 95% CIs for the ICR that exclude zero can be used as a test for statistical significance at an alpha level of 0.05. To test for interaction on a multiplicative scale, likelihood ratio tests (LRTs) were conducted comparing logistic regression models with main effect terms for lipid-adjusted total PCBs and CYP1A1 genotypes and a product interaction term compared with models with main effects only. P values for LRTs less than 0.20 were considered statistically significant [34].
To test for independence of environmental exposure and genotype, means, medians, and distributions of total PCBs were determined in relation to CYP1A1 genotypes among African American and white controls. Medians for lipid-adjusted total PCBs were compared by using the Wilcoxon rank sum test. There were no statistically significant differences in medians for total PCBs comparing participants with CYP1A1 M1-containing versus non-M1-containing genotypes, M2 versus non-M2, M3 versus non-M3, or M4 versus non-M4 genotypes.
Results
Genotype frequencies for CYP1A1, and ORs for breast cancer, have previously been reported for the CBCS [27]. In brief, genotype frequencies for CYP1A1 M1- and M3-containing genotypes were higher among African Americans than among whites, whereas M2- and M4-containing genotypes were more prevalent among whites. ORs for CYP1A1 genotypes and breast cancer were close to the null in African Americans and whites [27]. The association between plasma levels of total PCBs and breast cancer in the CBCS was reported previously [26]. A weak positive association for high levels of PCBs and breast cancer was found among African American women. ORs were close to the null for PCBs and breast cancer in white women.
ORs for breast cancer estimating the joint effects of total PCBs and CYP1A1 M1 genotypes on an additive scale are presented in Table 1. ORs were slightly elevated for PCB exposure greater than or equal to the median among premenopausal African American women regardless of CYP1A1 M1 genotype. There was no evidence for strong joint effects of PCBs and CYP1A1 M1-containing genotypes. ICRs were close to zero in African American and white women, and LRTs were not statistically significant. Joint effects for PCBs and CYP1A1 M2-containing genotypes among white women are presented in Table 2. Greater than additive joint effects were observed among all participants, with an ICR greater than zero and an associated P value of 0.03. Although imprecise, joint effects seemed to be stronger among premenopausal than among postmenopausal women. P values for LRTs were 0.02 among all participants, 0.007 among premenopausal women, and 0.66 among postmenopausal women.
ORs estimating the joint effects of PCBs and CYP1A1 M3 genotype among African American women are presented in Table 3. ORs were slightly elevated for women with elevated PCB levels and CYP1A1 M3-containing genotypes. An ICR greater than zero was observed in postmenopausal women. P values for LRTs were 0.10 in all participants, 0.71 among premenopausal women, and 0.11 in postmenopausal women. Joint effects of PCBs and CYP1A1 M4 genotypes among white women are presented in Table 4. An ICR greater than zero (P = 0.03) was observed among postmenopausal women. The P value for the LRT in postmenopausal women was 0.07. ICRs greater than zero for the joint effects of exposures with ORs less than 1.0 can be interpreted as antagonism [33]. Results were similar among smokers and non-smokers (data not shown).
ORs for breast cancer per 0.10 ng/ml increase in lipid-adjusted total PCBs showed only slight differences according to CYP1A1 M2 genotypes in white women. ORs for premenopausal women were 1.3 (95% CI 0.7–2.3) for those with CYP1A1 M2-containing genotypes and 1.0 (95% CI 0.9–1.1) for non-M2 genotypes. The corresponding ORs for postmenopausal women were 1.2 (95% CI 0.8–1.8) and 1.1 (1.0–1.2).
Discussion
We estimated the joint effects of plasma levels of total PCBs and CYP1A1 genotypes in association with breast cancer, using previously collected data from a population-based case-control study of African American and white women in North Carolina. ORs were slightly elevated for premenopausal white women with CYP1A1 M2-containing genotypes and lipid-adjusted total PCBs greater than the median, and for postmenopausal African American women with M3-containing genotypes and total PCBs greater than the median. ORs and ICRs were imprecise because of small sample size, and many of our results could be due to chance. However, biologic evidence and previous epidemiologic studies support the possibility of causal interaction between CYP1A1 genotypes (in particular, M2-containing genotypes) and PCB exposure in the etiology of breast cancer.
PCBs are metabolized by cytochrome P450 enzymes, activate CYP1A1, and produce free-radical-induced oxidative DNA damage in breast tissue [35]. With regard to CYP1A1 M1-containing genotypes, a previous study by Laden and colleagues [25] did not report a positive association for M1-containing genotypes and high levels of PCBs in postmenopausal women: the OR for women in the highest one-third of lipid-adjusted total PCBs (at least 0.67 μg per g lipid) and M1-containing genotypes was 1.1 (95% CI 0.5–2.5) compared with women in the lowest one-third of PCBs with non-M1-containing genotypes. In our data set, the corresponding age-adjusted OR for lipid-adjusted total PCBs of 0.67 ng/ml or more and CYP1A1 M1-containing genotypes among postmenopausal white women was 1.8 (95% CI 0.5–6.9).
With regard to CYP1A1 M2-containing genotypes, Moysich and colleagues [24] reported an OR for breast cancer of 2.9 (95% CI 1.2–7.5) in postmenopausal women with total PCBs between 3.72 and 19.04 ng/g and CYP1A1 M2-containing genotypes compared with women with low PCBs and non-M2-containing genotypes. In our data set, the corresponding age-adjusted OR for total PCBs of 3.73 ng/ml or more (not lipid-adjusted) and CYP1A1 M2-containing genotypes was 6.3 (95% CI 0.7–55.0). Laden and colleagues [25] reported an OR of 2.8 (95% CI 1.0–7.8) for women with the highest one-third of lipid-adjusted total PCBs (0.67 μg per g lipid) and CYP1A1 M2-containing genotypes, compared with women in the lowest one-third of PCBs and non-M2-containing genotypes. The corresponding OR in our data set for lipid-adjusted total PCBs ≥ 0.67 ng/ml and CYP1A1 M2-containing genotypes was 4.7 (95% CI 0.5–43.1). It would be helpful to combine individual-level results across these three studies to obtain more precise estimates of the joint effects of CYP1A1 genotypes and high levels of PCB exposure.
A strength of our study is the fact that we included both African American and white women, and we examined the effects of the four known CYP1A1 alleles: M1, M2, M3, and M4. Previous studies [24,25] included only white women and did not estimate ORs for CYP1A1 M3- or M4-containing genotypes. In previous analyses of this data set [26], African American women showed higher plasma levels of PCBs and a stronger relationship between total PCBs and breast cancer than that in white women. Our results suggest that further study of CYP1A1 M3-containing genotypes in African American women is warranted, particularly in combination with PCB exposure. Another strength of our study is that fact that we estimated joint effects for specific PCB congener groups and CYP1A1 genotypes. PCB congener groups may differ in biologic activity in ways that are relevant to breast cancer etiology [35-39]. However, because of the strong intercorrelation of specific congeners with total PCBs, we were unable to distinguish strong congener-specific effects.
A weakness of our study was the fact that, because of the small sample size, we were unable to estimate ORs with precision in all of the subgroups of interest. Moysich and colleagues [24] reported that breast cancer risk was significantly increased among women with elevated PCB body burden and CYP1A1 M2 genotypes who had ever smoked cigarettes. Laden and colleagues [25] observed similar results. We did not observe differences in ORs for the joint effects of total PCBs and CYP1A1 genotypes according to smoking status. However, we lacked power to address the effects of smoking dose or duration. ORs were imprecise when we subdivided study participants on the basis of menopausal status, and any differences could be due to chance. Owing to the case-control study design, patients were enrolled after diagnosis of breast cancer. As described previously [26], we did not observe evidence for disease-related changes in plasma organochlorine levels when we adjusted for weight loss or gain and stage at diagnosis. Limited information on diet was collected in this study. We did not observe correlations between total PCB levels and the consumption of fruits, vegetables, or fish (data not shown), but the confounding effects of other dietary exposures cannot be ruled out.
Conclusions
Data from the CBCS provides evidence that subgroups of women with CYP1A1 M2 and M3 polymorphisms and high levels of PCB exposure might have a modestly elevated risk of breast cancer. Our results confirm findings from previous studies with respect to the highly inducible CYP1A1 M2 genotype, and suggest that M3-containing genotypes might also modify risk of breast cancer associated with PCB exposure in African American women. Additional studies with a large sample size are warranted to confirm or refute these findings. In addition, meta-analyses combining individual-level data from epidemiologic studies of CYP1A1 and breast cancer will be needed to generate more precise estimates of the joint effects of PCBs and CYP1A1 genotypes.
Abbreviations
AHH = aryl hydrocarbon hydrolase; CBCS = Carolina Breast Cancer Study; CI = confidence interval; CYP1A1 = cytochrome P450 1A1; ICR = interaction contrast ratio; LRT = likelihood ratio test; OR = odds ratio; PCBs = polychlorinated biphenyls.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
YL and LC conducted the laboratory analyses; YL, RM, and C-KT conducted the statistical analyses; YL, RM, DB, LC, C-KT, BN and KC participated in writing the manuscript.
Acknowledgements
The authors wish to thank the nurse–interviewers for the CBCS for their important contributions, and the Molecular Epidemiology Core Laboratory of the Lineberger Comprehensive Cancer Research Center at UNC for technical support. The authors also thank Dr Kirsten Moysich and three anonymous reviewers for helpful comments on the manuscript. This research was funded in part by the Specialized Program of Research Excellence (SPORE) in Breast Cancer (NIH/NCI P50-CA58223), Lineberger Comprehensive Cancer Center Core Grant (P30-CA16086), Pesticides and Breast Cancer in North Carolina (NIH/NIEHS R01-ES07128), Center for Environmental Health and Susceptibility (NIEHS P30-ES10126), and Superfund Basic Research Program (NIEHS P42-ES05948).
Figures and Tables
Table 1 Odds ratios for breast cancer and total PCBs in relation to CYP1A1 M1 genotypes in African American and white women
Total PCBsa CYP1A1 M1 genotype All participants Premenopausal Postmenopausal
Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b
African Americans
<0.430 Non-M1 66/75 Referent 46/51 Referent 20/24 Referent
≥0.430 Non-M1 75/67 1.5 (0.9–2.5) 21/16 1.9 (0.9–4.2) 54/51 1.0 (0.5–2.3)
<0.430 Any M1 42/46 1.0 (0.6–1.7) 35/33 1.2 (0.6–2.2) 7/13 0.6 (0.2–1.8)
≥0.430 Any M1 59/54 1.4 (0.8–2.5) 17/14 1.7 (0.8–4.1) 42/40 1.0 (0.5–2.3)
ICR (95% CI) 0.0 (-0.9,0.9) -0.3 (-2.2,1.6) 0.4 (-0.5,1.3)
Whites
<0.349 Non-M1 174/133 Referent 118/83 Referent 56/50 Referent
≥0.349 Non-M1 122 148 0.7 (0.5–1.0) 38/43 0.6 (0.4–1.1) 84/105 0.8 (0.5–1.3)
<0.349 Any M1 45/44 0.8 (0.5–1.2) 29/31 0.7 (0.4–1.2) 16/13 1.1 (0.5–2.5)
≥0.349 Any M1 29/32 0.8 (0.4–1.4) 11/10 0.7 (0.3–1.9) 18/22 0.8 (0.4–1.7)
ICR (95% CI) 0.4 (-0.2,0.9) 0.5 (-0.3,1.2) 0.0 (-1.1,1.0)
aLipid-adjusted total imputed PCBs (ng/ml).
bOdds ratios adjusted for age and sampling fractions.
Any M1, M1/M1 or M1/non-M1; CI, confidence interval; ICR, interaction contrast ratio; non-M1, non-M1/non-M1.
Table 2 Odds ratios for breast cancer and total PCBs in relation to CYP1A1 M2 genotypes in white women
Total PCBsa CYP1A1 M2 genotype All participants Premenopausal Postmenopausal
Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b
<0.349 Non-M2 210/158 Referent 141/98 Referent 69/60 Referent
≥0.349 Non-M2 138/166 0.7 (0.5–1.0) 45/51 0.6 (0.4–1.0) 93/115 0.8 (0.5–1.2)
<0.349 Any M2 11/20 0.4 (0.2–0.8) 8/16 0.3 (0.1–0.8) 3/4 0.7 (0.1–3.1)
≥0.349 Any M2 15/14 0.9 (0.4–1.9) 6/2 2.1 (0.4–10.6) 9/12 0.7 (0.3–1.9)
ICR (95% CI) 0.8 (0.1, 1.6) 2.1 (-1.3,5.5) 0.3 (-0.9,1.5)
aLipid-adjusted total imputed PCBs (ng/ml).
bOdds ratios adjusted for age and sampling fractions.
Any M1, M1/M1 or M1/non-M1; CI, confidence interval; ICR, interaction contrast ratio; non-M1, non-M1/non-M1.
Table 3 Odds ratios for breast cancer and total PCBs in relation to CYP1A1 M3 genotypes in African American women
Total PCBsa CYP1A1 M3 genotype All participants Premenopausal Postmenopausal
Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b
<0.430 Non-M3 95/95 Referent 73/70 Referent 22/25 Referent
≥0.430 Non-M3 105/100 1.3 (0.8–2.0) 31/24 1.6 (0.8–3.2) 74/76 1.0 (0.5–2.0)
<0.430 Any M3 13/26 0.6 (0.3–1.2) 8/14 0.7 (0.3–1.7) 5/12 0.5 (0.2–1.6)
≥0.430 Any M3 29/21 1.6 (0.8–3.2) 7/6 1.5 (0.5–4.7) 22/15 1.5 (0.6–3.6)
ICR (95% CI) 0.8 (-0.3,1.9) 0.2 (-1.7,2.1) 1.0 (-0.2,2.1)
aLipid-adjusted total imputed PCBs (ng/ml).
bOdds ratios adjusted for age and sampling fractions.
Any M1, M1/M1 or M1/non-M1; CI, confidence interval; ICR, interaction contrast ratio; non-M1, non-M1/non-M1.
Table 4 Odds ratios for breast cancer and total PCBs in relation to CYP1A1 M4 genotypes in white women
Total PCBsa CYP1A1 M4 genotype All participants Premenopausal Postmenopausal
Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b Patients/controls OR (95% CI)b
<0.349 Non-M4 202/159 Referent 132/103 Referent 70/56 Referent
≥0.349 Non-M4 139/163 0.7 (0.5–1.0) 46/48 0.8 (0.5–1.2) 93/115 0.7 (0.4–1.1)
<0.349 Any M4 19/19 0.8 (0.4–1.6) 17/11 1.2 (0.5–2.7) 2/8 0.2 (0.0–0.9)
≥0.349 Any M4 14/17 0.7 (0.3–1.6) 5/5 0.8 (0.2–2.9) 9/12 0.7 (0.3–1.7)
ICR (95% CI) 0.2 (-0.6,1.0) -0.1 (-1.6,1.3) 0.8 (0.1,1.5)
aLipid-adjusted total imputed PCBs (ng/ml).
bOdds ratios adjusted for age and sampling fractions.
Any M1, M1/M1 or M1/non-M1; CI, confidence interval; ICR, interaction contrast ratio; non-M1, non-M1/non-M1.
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| 15642161 | PMC1064095 | CC BY | 2021-01-04 16:54:48 | no | Breast Cancer Res. 2005 Oct 22; 7(1):R12-R18 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr941 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9481564216810.1186/bcr948Research ArticleImaging in situ breast carcinoma (with or without an invasive component) with technetium-99m pentavalent dimercaptosuccinic acid and technetium-99m 2-methoxy isobutyl isonitrile scintimammography Papantoniou Vassilios [email protected] Spyridon [email protected] Ekaterini [email protected] Varvara 1Souvatzoglou Michael [email protected] Maria [email protected] Lydia [email protected] Dimitrios [email protected] Androniki [email protected] Maria [email protected] Artemis 6Pirmettis Ioannis [email protected] John [email protected] Cherry [email protected] Department of Nuclear Medicine, 'Alexandra' University Hospital, Athens, Greece2 Department of Pathology, 'Alexandra' University Hospital, Athens, Greece3 Department of Pathology, University of Athens School of Medicine, Athens, Greece4 Department of Obstetrics and Gynecology, 'Alexandra' University Hospital, Athens, Greece5 Department of Internal Medicine, 'Metropolitan' Hospital, Athens, Greece6 Department of Radiology, 'Alexandra' University Hospital, Athens, Greece7 Institute of Radioisotopes – Radiodiagnostic Products, National Center for Scientific Research 'Demokritos', Athens, Greece2005 8 11 2004 7 1 R33 R45 25 3 2004 17 6 2004 19 8 2004 24 9 2004 Copyright © 2004 Papantoniou et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
The aim of the study was to retrospectively define specific features of the technetium-99m pentavalent dimercaptosuccinic acid (99mTc-(V)DMSA) and technetium-99m 2-methoxy isobutyl isonitrile (99mTc-Sestamibi [99mTc-MIBI]) distribution in ductal breast carcinoma in situ and lobular breast carcinoma in situ (DCIS/LCIS), in relation to mammographic, histological and immunohistochemical parameters.
Materials and methods
One hundred and two patients with suspicious palpation or mammographic findings were submitted preoperatively to scintimammography (a total of 72 patients with 99mTc-(V)DMSA and a total of 75 patients with 99mTc-Sestamibi, 45 patients receiving both radiotracers). Images were acquired at 10 min and 60 min, and were evaluated for a pattern of diffuse radiotracer accumulation. The tumor-to-background ratios were correlated (T-pair test) with mammographic, histological and immunohistochemical characteristics.
Results
Histology confirmed malignancy in 46/102 patients: 20/46 patients had DCIS/LCIS, with or without coexistent invasive lesions, and 26/46 patients had isolated invasive carcinomas. Diffuse 99mTc-(V)DMSA accumulation was noticed in 18/19 cases and 99mTc-Sestamibi in 6/13 DCIS/LCIS cases. Epithelial hyperplasia demonstrated a similar accumulation pattern. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value for each tracer were calculated. Solely for 99mTc-(V)DMSA, the tumor-to-background ratio was significantly higher at 60 min than at 10 min and the diffuse uptake was significantly associated with suspicious microcalcifications, with the cell proliferation index ≥ 40% and with c-erbB-2 ≥ 10%.
Conclusion
99mTc-(V)DMSA showed high sensitivity and 99mTc-Sestamibi showed high specificity in detecting in situ breast carcinoma (99mTc-(V)DMSA especially in cases with increased cell proliferation), and these radiotracers could provide clinicians with preoperative information not always obtainable by mammography.
ductal carcinoma in situextensive intraductal carcinomalobular breast carcinoma in situscintimammography99mTc-(V)DMSA99mTc-Sestamibi
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Introduction
Ductal carcinoma in situ (DCIS) of the breast, alone or in the presence of an invasive tumor (invasive ductal carcinoma [IDC]), presents a problem in its detection and treatment. DCIS represents a number of biologically different processes that exhibit variable frequencies of occult invasion and variable risks for local recurrence, after attempts at excision biopsy or lumpectomy. The term 'extensive intraductal component' or 'extensive intraductal carcinoma' (EIC) is defined as DCIS within and around an invasive tumor, comprising at least 25% of the neoplasm. Of all IDC cases, 15–30% are considered to have EIC [1]. Tumors that are predominantly DCIS and appear with focal invasion are also classified as EIC.
DCIS not within the confines of the invasive tumor, but frequently in the adjacent breast tissue, makes breast-conserving surgical treatment less safe with regard to the recurrence rate. The great majority of recurrences are observed at or near the site of the primary tumor. On pathological and radiological evaluation of mastectomy specimens, some studies [1] have shown that EIC tumors commonly had DCIS not only in their immediate vicinity, but also as far as 4–6 cm from the invasive tumor. The risk of local recurrence after a breast-conserving procedure can be evaluated according to the histological type, the extent of the lesion and the state of the surgical margins. It has been reported that the local recurrence rate after a limited resection of the tumor, followed by radiotherapy, is significantly greater for EIC-positive patients than for EIC-negative patients [2,3]. If the real extent of EIC was known preoperatively, it might be possible to safely limit the breast resection, since an initial wide excision would constitute overtreatment for the majority of patients not having a substantial amount of DCIS outside the invasive tumor. The fact that younger patients are more likely than older ones to have EIC, and that the association between EIC and breast cancer recurrence is stronger in premenopausal women than in postmenopausal women, renders the detection of such lesions imperative [1].
Mammographic evidence of microcalcifications with a certain form and distribution is presently the only method of detecting the presence of DCIS. The failure of mammography to recognize DCIS or EIC in radiologically dense breasts in young populations raises a significant problem.
Scintimammography with technetium-99m 2-methoxy isobutyl isonitrile (99mTc-Sestamibi [99mTc-MIBI]) has been used to detect primary breast cancer [4-8]. It concentrates in cancer cells by an energy-requiring transport mechanism and a transmembrane electronegative potential, in addition to nonspecific mechanisms, and it is stored within the mitochondria. Only a few reports [9,10] have discussed the efficacy of 99mTc-Sestamibi in detecting DCIS.
Technetium-99m pentavalent dimercaptosuccinic acid (99mTc-(V)DMSA) is a tumor-seeking agent already known for its efficacy in detecting medullary thyroid carcinoma. Its mechanism of accumulation has been thought to be related to the structural similarity between the (V)DMSA core and the phosphate anion (PO4 3-), which is avidly taken up by some cancer cells [11,12]. Denoyer and colleagues recently reported that the radiotracer uptake is specifically mediated by NaPi cotransporter type III in cancer cells, similar to phosphate ions, which enter cells via NaPi cotransporters [13], while other reports suggest that its uptake in tumors is related to glucose metabolism-mediated acidosis [14]. 99mTc-(V)DMSA has been tried out in identifying invasive and preinvasive breast cancer [15-17].
The present study retrospectively assessed the capability of 99mTc-(V)DMSA and 99mTc-Sestamibi to image DCIS and EIC. The study also investigated whether this was related to the presence of suspicious microcalcifications on mammography, stromal reaction, lymphocytic infiltration and immunohistochemical parameters such as the cell proliferation index (Ki-67), c-erbB-2, and p53 expression.
Materials and methods
Patients and scintimammography
A total of 102 women (mean age ± standard deviation [SD], 62.5 ± 12.49 years) referred with suspicious breast lesions on physical examination and/or an abnormal mammogram underwent 99mTc-Sestamibi and/or 99mTc-(V)DMSA scintimammography prior to any surgical intervention. All patients were intended to have studies performed with both agents. The first agent to be used was selected on a random basis. Not all patients managed to have the second study performed preoperatively, for various reasons. Forty-five patients underwent both 99mTc-(V)DMSA and 99mTc-Sestamibi breast scintigraphy at separate sessions, in a head-to-head, double-phase study with a 48-hour time interval. Twenty-seven patients underwent only 99mTc-(V)DMSA and 30 patients underwent only 99mTc-Sestamibi scintimammography. A total of 72/102 patients therefore underwent 99mTc-(V)DMSA and a total of 75/102 patients underwent 99mTc-Sestamibi scintigraphy. Inclusion and exclusion criteria for entry into the study are summarized in Table 1. The study was in accordance with the ethical principles of the Declaration of Helsinki.
Early and late planar images (at approximately 10–20 min and 60–70 min, respectively) were acquired, in the lateral prone and anterior supine positions, after intravenous administration of 925–1100 MBq radiotracer. Acquisitions were obtained using a special positioning pad (PBI-2 Scintimammography Pad Set®; Pinestar Technology Inc., Greenville, PA, USA).
Scintimammography was performed using a single-head γ camera (Sophycamera DS7®; Sopha Medical Vision International, Buc Cedex, France), equipped with a high-resolution parallel hole collimator connected to a dedicated computer (Sophy NxT®; Sopha Medical Vision International). The matrix was 256 × 256 pixels and the photopeak was centered at 140 keV, with a symmetrical 10% window. The acquisition time for images with both radiotracers varied between 7 min and 10 min per view (the time required to acquire 2,000,000–2,500,000 counts per image). Tomographic imaging (single-photon emission computerized tomography) was not performed since it has not been found to be superior to planar imaging, regarding the sensitivity and the specificity, for invasive breast tumors [18] and since no consensus has been reached regarding its utility. It was considered that no additional information would be obtained (except for axillary lymph node involvement detection), and tomographic imaging is time consuming and relatively inconvenient for the patient.
99mTc-(V)DMSA was prepared using a domestically available kit (DMS(V)/Demoscan®; National Center of Physical Sciences, Institute of Radioisotopes and Radiodiagnostics 'DEMOKRITOS', Athens, Greece). The kit for the preparation of 99mTc-Sestamibi (Cardiolite®) was obtained from Bristol-Myers Squibb GmbH (Regensburg, Germany). Both pharmaceuticals were labeled with technetium-99m within the Nuclear Medicine Department.
Diagnosis was made by histopathology of the specimens obtained surgically. The scintigraphic results and the mammograms were compared with histologic and immunohistochemical findings.
Image analysis
Scintigrams were retrospectively evaluated, regarding the site, shape, pattern, extent, homogeneity and dispersion of radiotracer distribution. The background count rate of normal breast tissue depends on various factors (breast size, injected radioactivity dose, etc.) and is readily distinguishable from tracer accumulation in pathological tissue. Any increased focal radiotracer accumulation was assessed as suggestive of invasive breast cancer, whereas any other pattern of more widespread diffuse uptake was assessed as representing noninvasive lesions [10,15,16]. We set and evaluated as the criterion for the scintigraphic detection of noninvasive breast carcinoma the presence of any pattern of increased radiotracer accumulation other than focal activity (i.e. diffuse heterogeneous and diffuse homogeneous), independently of the coexistence of any focal increased activity. Accordingly, two experienced nuclear medicine physicians blinded to any clinical and pathologic data characterized the scintimammograms as positive or negative. Disagreement between them was resolved by consensus or by obtaining a third opinion.
Radiotracer accumulation in breast tumors was evaluated in early and delayed images (at 10–20 min and 60–70 min post injection, respectively). This was performed visually and semiquantitatively. The tumor-to-background (T/B) ratio was calculated by drawing regions of interest of standardized size and shape over the site of the greatest radioactivity within the areas of diffusely increased tracer uptake – assumed to represent noninvasive in situ lesions – and over the surrounding normal breast tissue. The same ratio was calculated for the areas of focal increased tracer uptake (presumably representing invasive tumor). In the late (60 min) 99mTc-(V)DMSA images of some cases with extensive DCIS and associated IDC, the margins of the focal uptake area – corresponding to invasive disease – were not very clearly demarcated from the diffuse uptake. Nevertheless, the invasive component was usually clearly visible on the early (10 min) image. Thus, by comparing early and late images, that area was avoided when creating the region of interest for diffuse uptake.
Mammography
All women underwent conventional X-ray mammography using cranio-caudal and medio-lateral projections. Images were interpreted by two experienced radiologists and were characterized as positive or negative with regard to the presence or the absence of suspicious branching and clustered coarse granular-type microcalcifications, suggestive of in situ carcinoma [19]. Disagreement was resolved by consensus or by obtaining a third opinion.
Histopathology and immunostaining
The assessment of the extent of the in situ carcinoma was based on the number of histologic slices containing the lesion. Whole specimens were examined in serial sections and the final major diameter of the lesion was estimated by multiplying the number of slices containing the lesion by 0.3 cm, which is the medium size of a section. The EIC was defined as DCIS within and around an invasive tumor, comprising at least 25% of the neoplasm.
In order to investigate whether any of these factors is associated with the diffuse pattern of tracer distribution, an immunohistochemical method (avidin biotin peroxidase-horseradish peroxidase complex) was performed on paraffin-embedded breast tissue sections for in situ carcinomas, for the demonstration of Ki-67, c-erbB-2 and p53 expression. A semiquantitative estimation of these levels was performed, based on the staining intensity and the percentage of positive cells. Regarding Ki-67, a threshold of 40–45% of cells with positive staining is generally used for separating 'moderate' staining from 'intense' staining. Overexpression for c-erbB-2 is defined, in cases of invasive carcinoma, by dense membrane staining in ≥ 10% of the epithelial cells. Similarly, p53 overexpression is defined by staining in ≥ 10% of the epithelial cell nuclei. Although there is no consensus for defining c-erbB-2 and p53 overexpression in noninvasive breast carcinoma, the latter generally has the same pattern as invasive breast carcinoma and thus the same thresholds could be used [20].
Data analysis
The T-pair test was applied to the T/B ratios of diffuse lesions between early and late acquisitions. The T/B ratios in the late (60 min) images for both tracers were also correlated with the presence of or the absence of suspicious mammographic microcalcifications, with the stromal reaction, with lymphocytic infiltration, with Ki-67 values < 40% and ≥ 40%, with c-erbB-2 values < 10% and ≥ 10%, and with p53 values < 10% and ≥ 10%, in order to assess any significant difference in diffuse tracer uptake between these populations. Finally, linear regression univariate analysis was performed to reveal any possible correlation between the T/B ratio at 60 min and the tumor size. For all tests, P < 0.05 was considered statistically significant.
In vitro assay of 99mTc-(V)DMSA uptake and micro-autoradiographic study
An in vivo autoradiographic study performed on tumor specimens excised immediately after scintimammography could provide precise data about 99mTc-(V)DMSA localization on DCIS areas. Such a study was attempted, but the radioactivity of the tumor specimen was not sufficient. This was not surprising, since, according to the literature [21], in vivo autoradiographic studies in mice require approximately 37 MBq (1 mCi or 1221 MBq/kg body weight), a dose that is approximately 66 times higher than the injected dose per kilogram of body weight in humans. Given the impossibility of implementing the study as described, the tumor specimen was thus used for an in vitro autoradiographic study. The frozen tumor tissue of one patient with DCIS was sliced into serial sections in the cryostat microtome chamber (Microm HM 505 N®; Microm Laborgerate GmbH, Walldorf, Germany), was mounted onto gelatin-coated slides, was dried at 37°C for 1 hour and was then incubated in a solution containing 99mTc-(V)DMSA. The sections were then exposed to Kodac X-OMATT XAR® film (Eastman Kodak Co., Rochester, NY, USA) in an autoradiographic cassette for 24 hours.
Results
Breast cancer was histologically confirmed in 46/102 women. Of these women, 26/46 had invasive carcinomas, mainly ductal (IDC) and lobular (invasive lobular carcinoma [ILC]). This was found to be the prominent histological lesion in this group of IDC patients, and these patients were therefore classified as EIC-negative. The other 20/46 cases were diagnosed as having carcinoma in situ. Of these, 18/20 presented DCIS: in four cases (patients 9, 12, 17, and 18; see Table 2) DCIS was the sole histological finding, while the majority (14 patients) had invasive carcinoma (IDC) associated with extensive DCIS clearly outside the confines of the invasive tumor, and thus these patients were characterized as EIC-positive. Both the remaining 2/20 cases had extensive lobular carcinoma in situ (LCIS), associated with an invasive component (ILC).
EIC was found more frequently in younger women (mean age ± SD, 57.1 ± 13.7 years for EIC-positive patients versus 65.8 ± 8.0 years for EIC-negative patients; P < 0.05). The size of the invasive tumors (IDC/ILC) associated with extensive DCIS/LCIS ranged from 0.7 to 6.0 cm (mean ± SD, 2.75 ± 1.50 cm), and the size of in situ carcinomas ranged from 0.8 to 9.0 cm (mean ± SD, 4.92 ± 1.93 cm). The basic data of these 20 DCIS/LCIS cases are presented on an individual basis in Table 2.
Benign breast lesions were found in the remaining 56/102 women. Among them, epithelial hyperplasia (usual type hyperplasia and/or atypical type hyperplasia) was present in 14/56 patients, with or without fibrosis, adenosis and ductal dilatation.
All scintimammograms were characterized as positive or negative for in situ carcinoma, with reference to the diagnostic criteria set for the detection of DCIS/LCIS (i.e. presence of the pattern of diffusely increased tracer uptake, regardless of the presence of any focal increased activity). Any study displaying only focal uptake was therefore a (true or false) negative for the in situ criterion (Fig. 1). Comparative statistics (sensitivity, specificity, accuracy, positive predictive value and negative predictive value) for each radiotracer, in the total patient groups studied with each one and in the subgroup studied with both (head-to-head study), are presented in Table 3. The interobserver agreement rate between the two physicians assessing the breast scans was 92%.
Of the 20/46 breast cancer patients with DCIS/LCIS, 19/20 underwent 99mTc-(V)DMSA and 13/20 underwent 99mTc-Sestamibi scintimammography (12/20 were part of the head-to-head subgroup of 45 patients that underwent both). Locally diffuse, heterogeneous (patchy), poorly circumscribed increased 99mTc-(V)DMSA accumulation, sometimes covering and surrounding focal increased accumulation (if present), was noticed in 16/17 of DCIS cases (Figs 2,3,4) and in 2/2 LCIS cases (Fig. 5), a total of 18/19 (95%) patients. The one lesion that was not detected had a size of 0.8 cm (patient 9). A similar pattern of distribution was found with 99mTc-Sestamibi, but only in 6/13 (46%) patients. Among those 12/20 DCIS/LCIS patients that underwent both examinations, this pattern of tracer distribution was noticed in 11/12 patients (92%) with 99mTc-(V)DMSA and in 5/12 patients (42%) with 99mTc-Sestamibi (Tables 3 and 4).
In women with histologically confirmed usual type hyperplasia and/or atypical type hyperplasia, a similar pattern of intense diffuse, not patchy, but more homogeneous, 99mTc-(V)DMSA distribution was observed in 10/14 patients (71%). Diffuse accumulation of 99mTc-Sestamibi in sites of epithelial hyperplasia was observed in 6/14 patients (43%), yet it was less intense in comparison with 99mTc-(V)DMSA (Fig. 6). In the absence of epithelial hyperplasia, the benign histological lesions of fibrosis, adenosis and ductal dilatation alone did not accumulate either of the two radiotracers. These results are also presented in Table 4.
In the series studied, the T/B ratio for 99mTc-(V)DMSA uptake in the sites of diffuse accumulation pattern demonstrated a tendency to increase over time (Figs 2,3,4,5,6). Semiquantitative analysis (T/B ratio) performed separately in DCIS and in IDC revealed a statistically significant increase in late images only of the diffuse 99mTc-(V)DMSA accumulation in DCIS (mean ± SD, 1.27 ± 0.22 at 10 min versus 1.76 ± 0.25 at 60 min; P < 0.01). On the contrary, T/B ratios for the focal 99mTc-(V)DMSA uptake in invasive tumors were not significantly increased in the late acquisitions (mean ± SD, 1.76 ± 0.28 at 10 min versus 1.94 ± 0.28 at 60 min; P > 0.05). The pattern of diffuse 99mTc-(V)DMSA distribution observed in epithelial hyperplasia demonstrated a similar tendency to increase over time. The T/B ratios for 99mTc-Sestamibi did not demonstrate significant variability with time, either in DCIS or in IDC (mean ± SD for DCIS, 1.34 ± 0.29 at 10 min versus 1.33 ± 0.33 at 60 min; P > 0.05; and mean ± SD for IDC, 2.06 ± 0.97 at 10 min versus 1.98 ± 0.84 at 60 min; P > 0.05). Similarly, the diffuse 99mTc-Sestamibi distribution observed in epithelial hyperplasia did not appear to increase significantly over time. Patients with LCIS and associated ILC gave parallel results, but due to the limited number of cases (n = 2) they could not be statistically evaluated.
Linear regression univariate analysis revealed no statistically significant correlation between the 99mTc-(V)DMSA T/B ratio at 60 min and tumor size (r = 0.28, P > 0.1).
Mammography depicted suspicious microcalcifications in 10/20 (50%) patients with DCIS/LCIS (Table 2). The diffuse uptake of 99mTc-(V)DMSA in DCIS/LCIS at 60 min was significantly higher in patients with suspicious microcalcifications on mammography as compared with patients without (mean ± SD T/B ratio, 1.81 ± 0.05 versus 1.4 ± 0.07, respectively; P = 0.003). 99mTc-Sestamibi diffuse uptake at 60 min was not significantly different between the patient groups with and without microcalcifications (mean ± SD T/B ratio, 1.49 ± 0.19 versus 1.31 ± 0.09, respectively; P = 0.49).
An intense stromal reaction was found in 1/20 patient, a moderate stromal reaction in 4/20 patients, and a mild stromal reaction in 3/20 patients with DCIS/LCIS; a total of 8/20 patients (40%). Microcalcifications combined with stromal reaction appeared in 6/20 cases. Mild or moderate lymphocytic infiltration was noted in 4/20 patients (20%). Pure comedo was the most prominent type (10/18) among DCIS (Table 2). No statistically significant difference was found in 99mTc-(V)DMSA or 99mTc-Sestamibi uptake between patients with stromal reaction and/or lymphocytic infiltration and those patients without.
With reference to the studied immunohistochemical parameters in DCIS/LCIS patients, the Ki-67 value ranged from 10% to 55% (mean ± SD, 33.4 ± 13.4%), p53 ranged from 0% to 27% (mean ± SD, 6.4 ± 8.8%) and c-erbB-2 ranged from 0% to 80% (mean ± SD, 21.7 ± 26.4%) (Table 2).
The uptake of 99mTc-(V)DMSA was significantly higher in patients with Ki-67 overexpression (≥ 40%) as compared with patients with Ki-67 levels < 40% (mean ± SD T/B ratio at 60 min, 1.86 ± 0.027 versus 1.52 ± 0.047, respectively; P = 0.025]. No significant difference was found in 99mTc-Sestamibi scans in relation to Ki-67 levels ≥ 40% and < 40% (mean ± SD T/B ratio at 60 min, 1.51 ± 0.27 versus 1.24 ± 0.08, respectively; P = 0.46).
Overexpression of c-erbB-2 (≥ 10%) was associated with higher 99mTc-(V)DMSA uptake in DCIS/LCIS (mean ± SD T/B ratio at 60 min, 1.89 ± 0.01 versus 1.52 ± 0.09 for values ≥ 10% and < 10%, respectively; P = 0.004). 99mTc-Sestamibi uptake was not significantly different between women with c-erbB-2 values ≥ 10% and < 10% (mean ± SD T/B ratio at 60 min, 1.43 ± 0.06 versus 1.3 ± 0.16, respectively; P = 0.64).
Diffuse uptake of both 99mTc-(V)DMSA and 99mTc-Sestamibi was not significantly different with regard to p53 values < 10% and ≥ 10% (mean ± SD T/B ratio at 60 min, 1.71 ± 0.068 versus 1.72 ± 0.06, respectively, for 99mTc-(V)DMSA; P = 0.93; and 1.60 ± 0.11 versus 1.29 ± 0.17, respectively, for 99mTc-Sestamibi; P = 0.78).
The c-erbB-2 levels were significantly higher in DCIS cases with coexistent IDC (EIC-positive) than in EIC-negative patients (P < 0.05), whereas p53 expression did not demonstrate such a statistically significant difference.
In vitro autoradiography of the breast tumor of one patient with DCIS revealed a very good correlation between the regional distribution of 99mTc-(V)DMSA radioactivity and the distribution of DCIS cell clumps, as observed after hematoxylin & eosin staining of the same sections (Fig. 7).
Discussion
In situ breast carcinoma tumors are very difficult to identify, and the only available method to date for their detection is the evaluation of the shape and distribution of microcalcifications on mammography, with all the limitations inherent in this method (dense breasts, scars, implants, etc.).
Scintigraphic imaging of EIC, DCIS or epithelial hyperplasia with 99mTc-(V)DMSA has already been reported [15,16]. Few reports have been published concerning the ability of 99mTc-Sestamibi not actually to detect DCIS, but rather to improve invasive breast cancer detection in the presence of DCIS [9,10], and these studies seem to be contradictory. Howarth and colleagues [10] reported no change in sensitivity in the presence or absence of carcinoma in situ, and furthermore that there is no relationship between 99mTc-Sestamibi uptake and DCIS, except in the presence of an invasive tumor. Several other authors, however, have suggested that diffuse 99mTc-Sestamibi uptake in benign breast disorders was associated with proliferative changes [15,22,23], demonstrating an increased risk of developing into breast cancer, while other workers described hormonal influence to be the cause of diffuse breast uptake on scintimammography [24]. In the present study, hormonal influence or the menstrual cycle could not be responsible for this kind of tracer distribution, since all but two women were postmenopausal and they were not on any hormone replacement therapy.
In the group of patients with breast malignancy, the detected invasive tumors (IDC/ILC) presented mostly with focal increased uptake of both radiotracers. On the contrary, the pattern of diffuse heterogeneous (patchy) uptake was mainly observed in cases with DCIS, independently of the presence of an invasive tumor. Among the 26 invasive tumors not associated with extensive amounts of in situ carcinoma, only a couple of cases gave false positive results (for DCIS) with 99mTc-(V)DMSA (Table 4): one consisted of multiple microscopic foci of IDC, and the other case presented IDC with intense lymphocytic infiltration.
LCIS was found in two cases, and due to this limited number specific scintigraphic results could not be statistically evaluated. They could have been considered as false positive findings for DCIS, yet their scintigraphic appearance was the same; they both revealed a similar diffuse inhomogeneous pattern of 99mTc-(V)DMSA uptake, apart from the focal uptake corresponding to the coexistent ILC. They could therefore not be separated from DCIS, except after histological examination of the biopsied tissue, and thus they are studied together in this series. If this diffuse uptake was attributable to the lobular invasive component, known for its peculiar pattern of growth and local invasion, then it should have been clearly visible from the early (10 min) images, simultaneously with the focal uptake. Yet the extensive diffuse 99mTc-(V)DMSA uptake was prominent in the late (60 min) image, whereas the focal tracer uptake was also visible in the early image (Fig. 4). 99mTc-Sestamibi, known for its efficacy in imaging invasive carcinoma, in this case managed to image only the focal uptake in both early and late images.
The diffuse pattern of radiotracer uptake (not patchy, but more homogeneous) was also noticed in some cases of epithelial hyperplasia, more prominently with 99mTc-(V)DMSA. Epithelial hyperplasia was therefore the major source of false positive results. The discrimination between atypical epithelial hyperplasia and in situ carcinoma is certainly not easy, even on frozen section. Yet any false positive scan attributable to hyperplasia should not be considered a major disadvantage, since it may lead to a search but not to an incorrect final decision concerning excision. This is because scintigraphic data (in addition to suspicious microcalcifications, if present) can serve as a guide and provide the surgeon with useful information preoperatively, in order to decide the extent of the search and to facilitate a guided biopsy, rather than directly determine whether an excision has to be performed.
Increased cell proliferation activity (Ki-67 ≥ 40%) was found to be significantly correlated with the diffuse 99mTc-(V)DMSA uptake in breast carcinoma in situ. Papantoniou and colleagues [25] recently reported this factor to be independently correlated with focal 99mTc-(V)DMSA accumulation in invasive breast cancer, while their preliminary reports [26] revealed that cases of usual type hyperplasia imaged with 99mTc-(V)DMSA tend to be related to elevated Ki-67 values and are therefore at risk of developing breast malignancy, according to the literature [27-31]. Cutrone and colleagues claimed that increased proliferative activity is found not only in invasive cancers, but also in precancerous lesions [27].
Shaaban and colleagues [28], along with several other reports [29,30], suggested that benign breast lesions such as epithelial hyperplasia, when associated with increased levels of Ki-67 and estrogen receptors type A, define a subset of hyperplastic lesions with high risk of subsequent breast cancer development. Such patients could be selected for prophylactic antiestrogen therapy to diminish the proliferative activity [28,31]. The observed tendency of the 99mTc-(V)DMSA diffuse uptake pattern to increase over time (being more notable on the delayed 60-min images), as well as its relation to elevated proliferative cellular activity (Ki-67), could be considered indicative of the existence of a distinct primary pathway for the mechanism of its accumulation in tumor tissue. This is considered to reflect the tracer's uptake in structures that participate in mitotic activity of cancerous and precancerous cell populations [25]. The possibility for this finding to be due to locally increased vascularity, angiogenesis or vessel permeability is therefore highly unlikely; if that was the case, then this diffuse pattern would have been equally clearly visible in the early 10-min images.
On the contrary, diffuse 99mTc-Sestamibi distribution was not found to be significantly correlated with Ki-67 expression. Palmedo and colleagues [32] reported that the 99mTc-Sestamibi concentration was one-half that of 99mTc-(V)DMSA in an experimental model of rats bearing poorly differentiated breast tumours. Cutrone and colleagues found a moderate correlation between 99mTc-Sestamibi uptake and the degree of cellular proliferation [27], while Cwikla and colleagues did not [33]. Angiogenesis and an oxidative metabolism seem to be favorable factors for 99mTc-Sestamibi tumor uptake, rather than proliferative activity [25,29]. This could provide an explanation for the lower sensitivity of this radiotracer, as compared with 99mTc-(V)DMSA, in imaging such diffuse lesions.
Overexpression of c-erbB-2 (≥ 10%) is found in almost 60% of cases of high-grade comedo-type DCIS, in 10–40% of IDC and in only a few cases of ILC [34,35]. An increased c-erbB-2 level was a factor significantly correlated with diffuse 99mTc-(V)DMSA uptake (but not with 99mTc-Sestamibi uptake). This appears reasonable, since oncoprotein overexpression is usually found in aggressive in situ carcinomas with increased levels of cell proliferative activity.
The measurement of Ki-67 and c-erbB-2 on histological specimens is not related to the size of the tumor. In the present series, the T/B ratio for 99mTc-(V)DMSA in the late image was not found to be significantly correlated with tumor size. It is therefore considered that the correlation found for the 99mTc-(V)DMSA T/B ratio to Ki-67 and/or c-erbB-2 expression is independent of tumor size.
p53 overexpression is known to reflect tumor aggressiveness and a decreased disease-free interval following therapy. An intriguing finding requiring further investigation, however, is that p53 overexpression, unlike that of Ki-67 and c-erbB-2, was not significantly correlated with this pattern of uptake of either tracer in the present series.
DCIS of the comedo type is usually accompanied by a desmoplastic stromal reaction with pronounced neovascularization. Elastosis is also more common in DCIS [36]. A variable inflammatory infiltrate is present in the periductal stroma. This consists of lymphocytes and histiocytes, in amounts ranging from sparse to abundant [37]. The suggestion that stromal reaction and/or lymphocytic infiltration could be considered mediators for the diffuse radiotracer uptake was not confirmed in the studied population.
Suspicious mammographic microcalcifications were observed in only one-half of the DCIS/LCIS cases, whereas 99mTc-(V)DMSA revealed a diffuse uptake pattern in the vast majority (18/19 patients). Nevertheless, diffuse uptake for 99mTc-(V)DMSA (but not for 99mTc-Sestamibi) was found to be significantly increased in women with suspicious microcalcifications as compared with those without. A possible explanation for this could be that clustered microcalcifications are usually found in more aggressive types of DCIS (such as comedo type), which tend to be related to increased Ki-67 levels. The majority of DCIS found in this series were of comedo type. Although it has been reported for the comedo type that suspicious microcalcifications approximately correspond to the histologically confirmed size, their extent in the present study was substantially smaller in comparison with the spread of diffuse 99mTc-(V)DMSA uptake, which in turn was found to correlate very well with the histologically confirmed size of DCIS. These findings imply that calcium deposits may not represent a 99mTc-(V)DMSA uptake mechanism, as was previously presumed [16].
Examining scintimammograms in combination with mammography could help limit false positive results. In the presence of suspicious microcalcifications, a diffuse 99mTc-(V)DMSA uptake could be considered more suggestive of in situ carcinoma. In the absence of microcalcifications, however, a diffuse 99mTc-(V)DMSA uptake could imply the possibility of a false positive study, probably due to epithelial hyperplasia.
In vitro 99mTc-(V)DMSA autoradiography of one tumor specimen demonstrated a very good correlation between radioactivity uptake and the distribution of foci of DCIS cells. This is considered a strong indication for the tracer localization in these cell clumps. Autoradiography nevertheless needs to be performed in a larger series of tumor specimens to verify the findings.
Conclusion
99mTc-(V)DMSA displayed an excellent sensitivity and negative predictive value in detecting DCIS/LCIS, especially in cases associated with increased cell proliferation (Ki-67) and c-erbB-2 overexpression, independent of the presence of suspicious microcalcifications. Its relatively lower specificity and positive predictive value, caused by false positive scans mainly attributable to epithelial hyperplasia, should not be considered a major disadvantage, since to date there is no diagnostic technique other than suspicious mammographic microcalcifications to define high-risk breast areas that should be biopsied. 99mTc-Sestamibi appeared to be less sensitive, yet it demonstrated a very good specificity and could also be useful in preinvasive lesion imaging.
In our opinion, any diffuse tracer accumulation, either as an isolated finding or extending beyond the margins of a focal, well-circumscribed accumulation, could be considered to probably correspond with highly proliferating tissue and to possibly represent DCIS/LCIS or epithelial hyperplasia. Since the difference between in situ carcinoma and atypical type hyperplasia cannot always be discriminated, even histologically, we note the potential usefulness of scintimammography in imaging these lesions and making them accessible to biopsy. There is, however, a need for a larger series of patients to verify these preliminary observations, as well as prospective studies to estimate its reliability in affecting treatment decisions.
Abbreviations
DCIS = ductal carcinoma in situ; EIC = extensive intraductal carcinoma; IDC = invasive ductal carcinoma; ILC = invasive lobular carcinoma; Ki-67 = cell proliferation index; LCIS = lobular carcinoma in situ; SD = standard deviation; T/B, tumor-to-background; 99mTc-(V)DMSA = technetium-99m pentavalent dimercaptosuccinic acid; 99mTc-Sestamibi (99mTc-MIBI) = technetium-99m 2-methoxy isobutyl isonitrile.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
VP is the guarantor of integrity of the entire study and was responsible for study concepts, study design, literature research, data acquisition, data analysis/interpretation, statistical analysis, manuscript preparation, manuscript definition of intellectual content, manuscript editing, and manuscript revision/review. ST participated in data acquisition, data analysis/interpretation, statistical analysis, manuscript preparation, manuscript editing, and manuscript revision/review. EM, VV, MSou, LN, and AT participated in data acquisition, data analysis/interpretation, and manuscript preparation. MSot and IP participated in experimental studies (autoradiography in vitro), data acquisition, data analysis/interpretation, and manuscript preparation. DL, AL, and MM participated in clinical studies, data acquisition, data analysis/interpretation, and manuscript preparation. JK participated in data acquisition, data analysis/interpretation, statistical analysis, and manuscript preparation. CZ participated in data analysis/interpretation, manuscript preparation, manuscript definition of intellectual content, manuscript editing, and manuscript revision/review. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank Evaggelia Kounadi for helping with the statistical analysis of the data and M Vassilios Makrypoulias for technical support.
Figures and Tables
Figure 1 A 3-cm infiltrating ductal carcinoma of the right breast. Scintimammography, right lateral projection: 99mTc-(V)DMSA at 10 min and 60 min (upper row, i-ii); 99mTc-Sestamibi (99mTc-MIBI) at 13 min and 55 min (bottom row, iii-iv). Focal radiotracer accumulation (arrowhead) in the upper breast hemisphere, corresponding to the invasive tumor, visible in early and late images with both radiotracers. There is no diffuse uptake pattern in the breast; the studies are considered negative for in situ carcinoma.
Figure 2 A 5-cm infiltrating ductal carcinoma, grade 2, with 9-cm, comedo-type, ductal carcinoma in situ of the right breast (patient 4). (a) Mammography, medio-lateral projection. Nodule with spiky margins (transparent arrow), close to the chest wall in the upper breast hemisphere. (b) Scintimammography, right lateral projection: 99mTc-Sestamibi (99mTc-MIBI) at 5 min and 60 min (upper row, i-ii); 99mTc-(V)DMSA at 10 min and 60 min (bottom row, iii-iv). Spindle-shaped focal accumulation (arrowhead) in the area corresponding to the radiological abnormality. The 60-min 99mTc-(V)DMSA image additionally reveals an extensive area of diffuse heterogeneous patchy tracer uptake (arrow) extending below and anterior to the margins of the focal accumulation. Lymph node involvement in the axilla is visible (curved arrow).
Figure 3 A 6-cm infiltrating ductal carcinoma, grade 2, with coexistent 5-cm ductal carcinoma in situ, comedo type, of the left breast (patient 1). (a) Mammography, medio-lateral projection. Multinodular opacity with abnormal radiating spicules and clustered microcalcifications (transparent arrow) behind the nipple. (b) Scintimammography, left lateral projection: 99mTc-Sestamibi (99mTc-MIBI) at 10 min and 65 min (upper row, i-ii); 99mTc-(V)DMSA at 15 min and 60 min (bottom row, iii-iv). Increased bifocal 99mTc-Sestamibi uptake (arrowheads) behind the nipple, clearly defining the invasive component of the tumor. Focal 99mTc-(V)DMSA accumulation in the same area (arrowhead), with additional diffuse uptake (arrow) extending inferiorly, more prominent at 60 min and corresponding to the in situ tumor component. No diffuse pattern is imaged with 99mTc-Sestamibi. (c) Scintimammography, left lateral projection: 99mTc-(V)DMSA at 60 min (same as (b) iv), with regions of interest (ROIs) drawn. ROI selection for diffuse uptake with each tracer is based on the comparison between early and late images (see text).
Figure 4 A 4.5-cm ductal carcinoma in situ of the left breast, comedo, solid and cribriform type (patient 18). (a) Mammography, medio-lateral projection. Microcalcifications (transparent arrow) behind the nipple. (b) Scintimammography, left lateral projection: 99mTc-(V)DMSA at 10 min and 60 min (i-ii). Diffuse semi-lunar accumulation (arrow) extending behind the nipple, more prominent in the late image. 99mTc-Sestamibi scan was not performed in this patient.
Figure 5 A 4-cm infiltrating lobular carcinoma, grade 3, associated with 6-cm lobular carcinoma in situ (LCIS) of the left breast in a 65-year-old woman (patient 20). (a) Mammography, cranio-caudal projection. Nodular opacity (asterisk) in the inner hemisphere of the breast. (b) Scintimammography, left lateral projection: 99mTc-Sestamibi (99mTc-MIBI) at 10 min and 60 min (upper row, i-ii); 99mTc-(V)DMSA at 10 min and 60 min (bottom row, iii-iv). Focal increased uptake (arrowhead) between upper and lower breast hemisphere, imaged by both radiotracers, corresponding to an invasive tumor. A diffuse patchy 'V'-shaped tracer accumulation (arrows), surrounding the focal activity and extending anterior to it, is revealed with 99mTc-(V)DMSA at 60 min only (iv). It corresponds to LCIS.
Figure 6 Atypical epithelial hyperplasia of the right breast in a 41-year-old woman. Scintimammography, right lateral projection: 99mTc-(V)DMSA at 15 min and 75 min (upper row, i-ii); 99mTc-Sestamibi (99mTc-MIBI) at 10 min and 70 min (bottom row, iii-iv). Increased diffuse homogeneous 99mTc-(V)DMSA uptake (arrow) at early and late acquisitions, more prominent in the late image. Very faint (hardly visible) 99mTc-Sestamibi activity (arrow) in the same area.
Figure 7 Ductal carcinoma in situ (DCIS), comedo, solid and cribriform type (patient 18). (a) Tumor section (hematoxylin & eosin, × 25). Regions of DCIS (arrowheads), within normal breast tissue (asterisk). (b) In vitro 99mTc-(V)DMSA autoradiogram of the same section (× 25). Distribution of the radioactivity in the same tumor section. The sites of intense tracer uptake (curved arrow) appear darker. (c) Overlay of stained tumor section and autoradiogram. The histologically detected lesion is well correlated with the tissue sites of intense 99mTc-(V)DMSA uptake.
Table 1 Inclusion and exclusion criteria of the study
Inclusion criteria Exclusion criteria
Female sex Pregnancy
Age older than 21 years Recurrent disease
Not pregnant Previous mastectomy
Suspicious lesion of the breast (on palpation or mammography) Fine needle aspiration within 1 week prior to scintimammography
Recommendation for excisional biopsy after mammography Core biopsy during the previous 4 weeks
Informed consent of the patient Previous chemotherapy
Medically unstable patient (severe arrhythmia, heart failure or recent surgery)
Table 2 Data of the 20 patients diagnosed with ductal carcinoma in situ/lobular carcinoma in situ (DCIS/LCIS)
Patient Age (years) Histology Grade of invasive carcinoma (IDC/ILC) Size of IDC/ILC (cm) Type of DCIS/LCIS Size of DCIS/LCIS (cm) Mammography with suspicious microcalcifications Scintimammography Stromal reaction Lymphocytic infiltration p53 (%) c-erb B-2 (%) Ki-67 (%)
99mTc-(V) DMSA 99mTc-Sestamibi
Focal uptake Diffuse uptake Focal uptake Diffuse uptake
1 50 IDC + DCIS II 6.0 Comedo 5.0 (+) (+) (+) (+) (+) - - 8 8 50
2 58 IDC + DCIS III 2.5 Comedo 5.0 (-) (+) (+) (+) (-) - - 0 0 25
3 49 IDC + DCIS I 1.0 Comedo 6.0 (+) (-) (+) (-) (-) ++ - 0 20 55
4 65 IDC + DCIS II 5.0 Comedo 9.0 (-) (+) (+) (+) (+) - - 10 50 35
5 54 IDC + DCIS II 5.0 Comedo 7.0 (+) (+) (+) (+) (+) - - 0 50 45
6 48 IDC + DCIS II 2.5 Solid 5.0 (-) (+) (+) (-) (-) ++ + 0 0 15
7 77 IDC + DCIS III 3.0 Comedo + cribriform 4.0 (+) (+) (+) (+) (-) + - 0 0 35
8 40 IDC + DCIS II 1.5/1.8/2.0 (multifocal) Comedo 6.0 (+) (+) (+) (+) (-) + - 10 10 10
9 50 DCIS Comedo 0.8 (-) (-) (-) (-) (-) - - 8 15 32
10 53 IDC + DCIS II 1.5 Comedo 4.0 (-) (+) (+) (+) (+) - - 25 0 35
11 86 IDC + DCIS III 2.0 Comedo 3.0 (-) (+) (+) Not performed - - 0 0 42
12 66 DCIS - 3.0 (-) (-) (+) Not performed - - 0 0 35
13 71 IDC + DCIS II 0.7 Cribriform 8.0 (+) (+) (+) Not performed +++ + 0 80 55
14 60 IDC + DCIS II 2.9 Comedo + micropapillary 5.0 (-) (+) (+) (+) (+) - - 27 75 15
15 50 IDC + DCIS II 2.0 Micropapillary 2.0 (-) Not performed (+) (+) - - 0 0 31
16 38 IDC + DCIS III 4.3 Comedo 4.0 (+) (+) (+) Not performed + ++ 0 50 45
17 61 DCIS Comedo + solid 5.0 (+) (-) (+) Not performed - - 20 10 15
18 71 DCIS Comedo + solid + cribriform 4.5 (+) (-) (+) Not performed - - 13 40 35
19 67 ILC + LCIS II 2.5 6.0 (+) (+) (+) Not performed ++ - 0 0 20
20 65 ILC + LCIS III 4.0 6.0 (-) (+) (+) (+) (-) ++ ++ 6 25 38
Patients 1–10, patient 14 and patient 20 belong to the 45 patients of the head-to-head subgroup that underwent both scintigraphic examinations. IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; 99mTc-(V)DMSA, technetium-99m pentavalent dimercaptosuccinic acid; 99mTc-Sestamibi (99mTc-MIBI), technetium-99m 2-methoxy isobutyl isonitrile; (+), positive scan or mammogram; (-), negative scan or mammogram; -, absent stromal reaction or lymphocytic infiltration; +, mild stromal reaction or lymphocytic infiltration; ++, moderate stromal reaction or lymphocytic infiltration; +++, intense stromal reaction or lymphocytic infiltration.
Table 3 Statistics for each radiotracer concerning in situ carcinoma detection, in the total group of patients studied and in the subgroup that underwent both examinations (double phase study)
99mTc-(V)DMSA radiotracer 99mTc-Sestamibi radiotracer
Head-to-head patients (45/102) Total patients (72/102) Head-to-head patients (45/102) Total patients (75/102)
Sensitivity 92% 95% 42% 46%
Specificity 91% 77% 94% 90%
Accuracy 91% 82% 80% 83%
Positive predictive value 79% 60% 71% 50%
Negative predictive value 97% 98% 82% 89%
99mTc-(V)DMSA, technetium-99m pentavalent dimercaptosuccinic acid; 99mTc-Sestamibi (99mTc-MIBI), technetium-99m 2-methoxy isobutyl isonitrile.
Table 4 Scintigraphic identification of breast carcinoma in situ with 99mTc-(V)DMSA and 99mTc-Sestamibi: classification of the studied population (n = 102) according to the histological diagnosis and the scintimammographic criterion set for ductal carcinoma in situ/lobular carcinoma in situ (DCIS/LCIS)
Malignant lesions (n = 46) Benign lesions (n = 56)
Histology Carcinoma in situ, alone, or with invasive component (DCIS/LCIS ± IDC/ILC)
(n = 20) Invasive carcinoma (IDC/ILC)
(n = 26) Lesions with usual type, or atypical hyperplasia (UTH/ATH)
(n = 14) Lesions without hyperplasia (fibrosis, adenosis and ductal dilatation)
(n = 42)
Radiotracer 99mTc-(V)DMSA
(n = 19) 99mTc-Sestamibi
(n = 13) 99mTc-(V)DMSA
(n = 25) 99mTc-Sestamibi
(n = 20) 99mTc-(V)DMSA
(n = 14) 99mTc-Sestamibi
(n = 14) 99mTc-(V)DMSA
(n = 14) 99mTc-Sestamibi
(n = 28)
Results
(-)
(n = 28) (+)
(n = 18) (-)
(n = 1) (+)
(n = 6) (-)
(n = 7) (+)
(n = 2) (-)
(n = 23) (+)
(n = 0) (-)
(n = 20) (+)
(n = 10) (-)
(n = 4) (+)
(n = 6) (-)
(n = 8) (+)
(n = 0) (-)
(n = 14) (+)
(n = 0)
Classification TP FN TP FN FP TN FP TN FP TN FP TN FP TN FP TN
99mTc-(V)DMSA, technetium-99m pentavalent dimercaptosuccinic acid; 99mTc-Sestamibi (99mTc-MIBI), technetium-99m 2-methoxy isobutyl isonitrile; EIC, extensive intraductal component; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; UTH, usual type hyperplasia; ATH, atypical type hyperplasia; (+), positive scan; (-), negative scan; TP, true positive; FN, false negative; FP, false positive; TN, true negative.
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| 15642168 | PMC1064097 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Nov 8; 7(1):R33-R45 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr948 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9491564216910.1186/bcr949Research ArticleBreast fibroblasts modulate epithelial cell proliferation in three-dimensional in vitro co-culture Sadlonova Andrea [email protected] Zdenek [email protected] Martin R [email protected] Damon B [email protected] Sandra R [email protected] Grier P [email protected] Jaideep V [email protected] Danny R [email protected] Andra R [email protected] Department of Pathology, The University of Alabama at Birmingham, Alabama, USA2 Department of Pediatrics, The University of Alabama at Birmingham, Alabama, USA3 Department of Pharmacology and Toxicology, The University of Alabama at Birmingham, Alabama, USA4 Department of Biostatistics, The University of Alabama at Birmingham, Alabama, USA5 Drug Discovery Division, Southern Research Institute, Birmingham, Alabama, USA2005 8 11 2004 7 1 R46 R59 15 12 2003 26 1 2004 2 9 2004 24 9 2004 Copyright © 2004 Sadlonova et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Background
Stromal fibroblasts associated with in situ and invasive breast carcinoma differ phenotypically from fibroblasts associated with normal breast epithelium, and these alterations in carcinoma-associated fibroblasts (CAF) may promote breast carcinogenesis and cancer progression. A better understanding of the changes that occur in fibroblasts during carcinogenesis and their influence on epithelial cell growth and behavior could lead to novel strategies for the prevention and treatment of breast cancer. To this end, the effect of CAF and normal breast-associated fibroblasts (NAF) on the growth of epithelial cells representative of pre-neoplastic breast disease was assessed.
Methods
NAF and CAF were grown with the nontumorigenic MCF10A epithelial cells and their more transformed, tumorigenic derivative, MCF10AT cells, in direct three-dimensional co-cultures on basement membrane material. The proliferation and apoptosis of MCF10A cells and MCF10AT cells were assessed by 5-bromo-2'-deoxyuridine labeling and TUNEL assay, respectively. Additionally, NAF and CAF were compared for expression of insulin-like growth factor II as a potential mediator of their effects on epithelial cell growth, by ELISA and by quantitative, real-time PCR.
Results
In relatively low numbers, both NAF and CAF suppressed proliferation of MCF10A cells. However, only NAF and not CAF significantly inhibited proliferation of the more transformed MCF10AT cells. The degree of growth inhibition varied among NAF or CAF from different individuals. In greater numbers, NAF and CAF have less inhibitory effect on epithelial cell growth. The rate of epithelial cell apoptosis was not affected by NAF or CAF. Mean insulin-like growth factor II levels were not significantly different in NAF versus CAF and did not correlate with the fibroblast effect on epithelial cell proliferation.
Conclusion
Both NAF and CAF have the ability to inhibit the growth of pre-cancerous breast epithelial cells. NAF have greater inhibitory capacity than CAF, suggesting that the ability of fibroblasts to inhibit epithelial cell proliferation is lost during breast carcinogenesis. Furthermore, as the degree of transformation of the epithelial cells increased they became resistant to the growth-inhibitory effects of CAF. Insulin-like growth factor II could not be implicated as a contributor to this differential effect of NAF and CAF on epithelial cell growth.
breast cancerbreast epitheliumfibroblastinsulin-like growth factorstroma
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Introduction
The structure and homeostasis of normal breast parenchyma is maintained by dynamic interactions between breast epithelial cells and their associated stroma. These stromal elements include the vasculature, adipocytes, resident immune cells, and fibroblasts with their numerous cellular products, including various growth factors and extracellular matrix (ECM) components. In breast cancers, the stroma differs from stroma found in normal breast. The stromal alterations that accompany most invasive breast carcinomas are morphologically characterized by an enhanced accumulation of fibroblasts and a modified, collagenized extracellular matrix. Breast carcinoma-associated fibroblasts (CAF) have been reported to express increased amounts of specific ECM molecules, various molecules that modulate the ECM, and several peptide growth factors, including insulin-like growth factor (IGF) II, in comparison with fibroblasts in histologically normal breast (i.e. normal breast-associated fibroblasts [NAF]) [1,2].
Fibroblasts surrounding ductal carcinoma in situ (DCIS), prior to the development of invasive carcinoma, also differ from those in histologically normal breast tissue. Some of the molecular alterations found in CAF also have been documented, by immunohistochemistry and by in situ hybridization, in the fibroblasts surrounding the DCIS [3-6]. This suggests an accumulation of alterations in stromal fibroblasts (i.e. a progression from NAF to CAF) surrounding the breast epithelium as it progresses from normal to hyperplasia to DCIS and invasive cancer.
The role that these stromal changes play in the development and progression of breast cancer and their effect on fibroblast–epithelial cell interactions is a current topic of much interest. It is theorized that CAF act to enhance breast cancer progression [2], and much of the experimental evidence to date supports this contention [7-14]. However, assessment of the effect of CAF has concentrated on established breast cancers. The focus in the present article is on the effect of fibroblasts on the growth of epithelial cells derived from benign breast disease, specifically proliferative breast disease (i.e. MCF10A cells and MCF10AT cells), and the potential role of fibroblast–epithelial cell interactions to promote the development of breast cancer. If CAF promote epithelial cell growth to a greater degree than NAF, could preventing the alterations that occur in fibroblasts surrounding epithelial lesions during carcinogenesis inhibit the progression of the epithelial lesion? To address this possibility, the key signaling and regulatory pathways mediating the effects of fibroblast–epithelial cell interactions, as well as the way in which these interactions are altered during carcinogenesis, must be identified.
Studies to date indicate that the IGF system may play a role in the stromal–epithelial interactions that affect breast cancer progression. IGF I and IGF II both function in cellular growth, in differentiation, and in survival in all tissues. Signaling by IGF I and IGF II through their principal receptor, the insulin-like growth factor receptor, can promote cell cycle progression and can inhibit apoptosis [15]. In breast cancer, the normal regulation and functioning of the IGF system is altered, and the insulin-like growth factor receptor is expressed in 39–93% of breast cancers [16]. By in situ mRNA analysis and immunohistochemistry, IGF II expression is reported to be increased in the stromal cells within some breast cancers in comparison with the stromal cells adjacent to normal breast epithelium [17]. In the present study, the effect of NAF and CAF on the growth of pre-cancerous breast epithelial cells was compared. To explore a potential role for IGF II in the growth modulation of breast epithelial cells by NAF and CAF, the level of expression of IGF II in CAF versus that in NAF was assessed.
NAF and CAF were grown in direct contact co-cultures with MCF10A breast epithelial cells and MCF10AT breast epithelial cells, both of which are considered representative of pre-invasive breast disease. MCF10A cells were derived from benign proliferative breast disease. These cells carry a deletion of the chromosomal locus containing p16 and p14ARF, and amplification of MYC [18,19]. MCF10A cells were transfected with mutated T24 H-ras to yield the MCF10AT cells. When suspended in the basement membrane material Matrigel®, the MCF10AT cells persist as xenografts in nude mice. The cells initially form structures that resemble normal breast epithelium, and then gradually undergo transition to structures resembling proliferative breast disease and DCIS. Approximately 25% of the MCF10AT xenografts develop invasive carcinoma. The MCF10AT model thus reflects temporally and morphologically high-risk human proliferative breast disease [20,21].
Our in vitro model consists of a three-dimensional (3D) direct co-culture system in Matrigel® similar to that utilized by Debnath and colleagues and by Shekhar and colleagues [19,22]. A 3D system was selected over standard monolayer cultures because it more closely simulates in vivo growth [19]. When grown in Matrigel®, human luminal breast epithelial cells, primary or immortalized, form spherical polarized structures that resemble normal lobular acini. This spatial organization of cells determines how cells perceive and respond to signals from the stromal microenvironment [23].
Importantly, it has been demonstrated that intracellular signaling directing the proliferation of breast epithelial cells differs in cells grown in two dimensions versus those grown in three dimensions [23-25]. Incorporation of NAF and CAF in this 3D culture system allowed assessment of soluble and insoluble secreted factors and of direct contact factors in the fibroblast–epithelial interactions influencing epithelial cell growth.
Methods
Maintenance of epithelial cell lines
MCF10A cells (American Type Culture Collection, Manassas, VA, USA) and MCF10AT cells (Karmanos Cancer Institute, Detroit, MI, USA) were cultivated in DMEM/Ham's F-12 (Cambrex, Walkersville, MD, USA) supplemented with 0.1 μg/ml cholera toxin (Calbiochem, San Diego, CA, USA), 10 μg/ml insulin (Sigma, St Louis, MO, USA), 0.5 μg/ml hydrocortisone (Sigma), 0.02 μg/ml epidermal growth factor (Upstate Biotechnology, Lake Placid, NY, USA) and 5% horse serum (Invitrogen, Carlsbad, CA, USA). Subconfluent cultures (80–90% confluence) were utilized in experiments.
Isolation, characterization and maintenance of fibroblast cultures
Fibroblasts were derived from mammary reduction specimens (NAF) and from primary breast cancers (CAF). The tissues were remnants of diagnostic surgical specimens and were obtained from The University of Alabama at Birmingham Tissue Procurement Facility after Institutional Review Board approval. H&E-stained, frozen histologic sections were prepared from each tissue sample to confirm benignity or malignancy. The tissue samples from the breast reduction specimens consisted predominantly of adipose tissue, but interspersed fibrous areas were selected for fibroblast isolation. The tissue was minced and digested for 18–24 hours at 37°C in DMEM (Vitacell, Manassas, VA, USA) with 10% fetal bovine serum (Invitrogen) supplemented with 100 U/ml streptomycin, 100 μg/ml penicillin, 2.5 μg/ml Fungizone (GibcoBRL, Life Technologies, Grand Island, NY, USA), 150 U/ml hyaluronidase (Sigma) and 200 U/ml collagenase type III (GibcoBRL). The digested tissue was centrifuged at 100 relative centrifugal force and plated in T25 tissue culture flasks with DMEM and 10% fetal bovine serum. Differential trypsinization was applied during subculturing to select for the growth of fibroblasts [26].
Early passages (below passage 9) of all fibroblasts were subjected to immunocytochemical evaluation with anti-vimentin (mouse IgG1, clone V9; Neomarkers, Fremont, CA, USA), anti-epithelial membrane antigen (mouse IgG2a, clone ZCE113; Zymed, San Francisco, CA, USA), and anti-cytokeratin (CK) 5/CK 8 (mouse IgG1, clone C-50; Neomarkers) as confirmation of their stromal origin (i.e. strong vimentin expression, and absence of epithelial membrane antigen and CK 5/CK 8). Epithelial membrane antigen and CK 8 are expressed primarily in luminal breast epithelial cells, whereas CK 5 is found in myoepithelial cells [27]. For immunocytochemical evaluation, fibroblasts were grown on glass coverslips, fixed in 70% ethanol and were permeabilized with acetone. Fibroblasts were incubated in 3% hydrogen peroxide followed by 3% goat serum at room temperature. Anti-vimentin (0.5 μg/ml), anti-epithelial membrane antigen (1 μg/ml), or anti-CK 5/CK 8 (0.3 μg/ml) were applied for 1 hour at room temperature. Secondary detection was accomplished with a streptavidin/horseradish peroxidase secondary detection system (Signet Laboratories, Dedham, MA, USA) and diaminobenzidine (BioGenex, San Ramon, CA, USA). Harris hematoxylin was used as a counterstain.
The fibroblasts were routinely maintained in DMEM and 10% fetal bovine serum. Subconfluent cultures (70–90% confluence) of lower passages (below passage 9) were utilized for the experiments described. Only early-passage fibroblast cultures are used in experiments to more closely simulate their in vivo phenotype. It has been shown that early-passage (below passage 9) colonic primary fibroblasts maintain many of their in vivo characteristics, including expression of alpha-smooth muscle actin, collagen IV and laminin 1 [28]. Multiple NAF and CAF cultures were utilized because of potential variation among fibroblasts from different individuals and different breast cancers.
Preparation of the 3D cultures
The effect of NAF and CAF on MCF10A breast epithelial cells and MCF10AT breast epithelial cells was studied using a 3D in vitro model. In co-cultures, epithelial cells and fibroblasts were mixed in Human Endothelial–SFM Basal Growth Media (Invitrogen) supplemented with 10 ng/ml epidermal growth factor and 20 ng/ml basic fibroblast growth factor (Invitrogen). This was followed by dispersal on 100 μl basement membrane material (Growth Factor Reduced Matrigel®; BD Biosciences, Bedford, MA, USA) in each well of eight-well chamber slides (Lab-Tek® Chamber Slide™ System; Nalge Nunc International, Naperville, IL, USA).
The ratio of epithelial cells to fibroblasts (E:F) ranged from 2:1 to 1:3 by varying the number of fibroblasts while keeping the number of epithelial cells constant (100,000 cells/well). Controls consisted of 3D monocultures of MCF10A cells and MCF10AT cells (100,000 cells/well). All cultures were incubated in a 37°C, 5% CO2 humidified incubator for 14 days with supplementation of fresh Human Endothelial-SFM Basal Growth Media supplemented with 10 ng/ml epidermal growth factor and 20 ng/ml basic fibroblast growth factor at 4-day intervals.
Morphologic development was observed by phase contrast microscopy. 5-bromo-2'-deoxyuridine (BrdU) (0.2 mg/ml; Calbiochem) was applied to all cultures for 24 hours. The cultures were removed from the chamber slides, were embedded in HistoGel specimen processing gel (Richard-Allan, Kalamazoo, MI, USA), were fixed in 10% neutral-buffered formalin, and were embedded in paraffin. Histologic sections were prepared for H&E staining and immunocytochemistry.
BrdU detection by immunocytochemistry
Distinction of epithelial cells from fibroblasts in the 3D cultures was accomplished by examination of the cell morphology and location within the culture. To ensure that only epithelial cells were counted, however, immunostaining with anti-BrdU (mouse IgG1, clone Bu20a; DAKO, Carpinteria, CA, USA) was followed by immunostaining with anti-CK 5/CK 8, expressed only in MCF10A cells and MCF10AT cells in 3D cultures. The staining entailed pretreatment with low-temperature antigen retrieval (i.e. incubation in 0.01 M citric acid monohydrate, pH 6.0, for 2 hours in an 80°C water bath), followed by sequential incubation in 1 N HCl, 3% hydrogen peroxide, 1% goat serum, and anti-BrdU (3 μg/ml). Secondary detection was as previously described. This was followed by incubation with anti-CK 5/CK 8 (4 μg/ml) and secondary detection using a streptavidin/alkaline phosphatase reagent (Signet Laboratories) and New Fuchsin (BioGenex) as the chromogen. The slides were lightly counterstained with Harris hematoxylin.
The resulting dual coloration of anti-BrdU (brown nucleus) and anti-CK 5/CK 8 (pink cytoplasm) enabled identification of proliferating epithelial cells. A BrdU-labeling index in the epithelial cells was determined by calculating the percentage of epithelial cells with nuclear staining for anti-BrdU in complete cross-sections of the 3D cultures. A minimum of 500 epithelial cells was counted. Negative controls consisted of histologic sections of each 3D culture processed without the addition of primary antibodies.
BrdU detection by flow cytometry
Cells were gently removed from 3D cultures after treatment with Dispase (BD Biosciences Discovery Labware, Bedford, MA, USA), followed by 5–10 mM EDTA. Recovered cells were washed with cold PBS. To distinguish co-cultured epithelial cells from fibroblasts, allophycocyanin-conjugated anti-EpCAM (mouse IgG1, clone EBA-1; BD Biosciences Immunocytometry Systems, San Jose, CA, USA) was used to label MCF10A cells and MCF10AT cells. The cells were permeabilized and fixed (BD Cytofix/Cytoperm and Perm/Wash kit; BD Biosciences Pharmingen, San Diego, CA, USA). Prior to staining with FITC-conjugated anti-BrdU (mouse IgG1, clone B44; BD Biosciences Immunocytometry Systems), cells were treated with DNase I (Roche Diagnostics GmbH, Penzberg, Germany). Samples were analyzed on a BD FACS Calibur™ flow cytometer (BD Biosciences). The percentage of BrdU-labeled epithelial cells (positive for anti-EpCAM and anti-BrdU) was calculated from the total acquired events.
Assessment of apoptosis by TUNEL assay
Apoptosis was quantified in epithelial cells in 3D cultures by the TUNEL Assay (ApopTag Peroxidase In Situ Apoptosis Kit; Integren Co., Purchase, NY, USA) as per the manufacturer's instructions. In the TUNEL assay, terminal deoxynucleotide transferase was used to label fragmented DNA with digoxigenin-linked nucleotides. These nucleotides were then detected using an anti-digoxigenin antibody. Negative controls consisted of histologic sections of each 3D culture processed without the addition of terminal deoxynucleotide transferase. Sequential sections of each 3D culture were stained with anti-CK 5/CK 8, as previously described, and were used to aid in identification of epithelial cells in co-cultures. The percentage of epithelial cells with nuclear staining was determined in complete cross-sections of the 3D cultures. A minimum of 500 epithelial cells was counted.
Preparation of cell lysates
Cells were gently removed from Matrigel® in 3D co-cultures of MCF10AT cells (100,000 cells/well) with fibroblasts (50,000 cells/well) and monocultures of MCF10AT cells (100,000 cells/well) or fibroblasts (50,000 cells/well) using dispase, as previously described. The released cells were washed with cold PBS to remove residual Matrigel®. Cell lysates were prepared from 3D cultures and monolayer cultures with NEB lysis buffer (Cell Signaling Technology, Beverly, MA, USA) containing 20 mM Tris–HCl (pH 7.5), 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 1% Triton, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4, 1 μg/ml leupeptin, 10 mM sodium fluoride, a Complete Mini Protease Inhibitor Cocktail tablet (Roche, Indianapolis, IN, USA) and 1 mM phenylmethylsulfonylfluoride, and were cleared by centrifugation. All lysates were concentrated with Millipore Microcon® Centrifugal Filter Devices YM-3 (Millipore Corporation, Bedford, MA, USA). The protein concentration of lysates was measured with the BCA Protein Assay Kit (Pierce, Rockford, IL, USA).
Enzyme-linked immunosorbent assay
The protein levels of IGF II in cell lysates were measured by ELISA for human IGF II (DSL-10-9100 Active™ IGF-II ELISA; Diagnostic System Laboratories, Webster, TX, USA) as per the manufacturer's instructions. The kit includes a modified version of the standard acid–ethanol extraction prior to ELISA. The ELISA for IGF II shows no detectable cross-reactivity with IGF I, insulin, IGF binding protein 1, and IGF binding protein 3. The minimum detection limit of the assay is 0.25 ng/ml. The amount of IGF II present in lysates from cell lines and 3D cultures was normalized to the total protein concentration.
Quantitative real-time PCR
RNA isolation (Trizol reagent; GibcoBRL) was followed by RNA clean-up with RNeasy columns (Qiagen, Valencia, CA, USA). Spectrophotometric ratios of A260 to A280 were greater than 1.8. The forward, reverse and probe oligonucleotides were synthesized and purified by HPLC (Applied Biosystems, Foster City, CA, USA) after complete evaluation of the IGF II and ribosomal S9 gene sequences (GenBank database) (IGF II forward, 5'-GTCGATGCTGGTGCTTCTCA-3' and reverse, 5'-GGGCGGTAAGCAGCAATG-3' ; probe, 5'-6FAMCTTCTTGGCCTTCGCCTCGTGCTTAMRA-3' ; ribosomal S9 forward, 5'-ATCCGCCAGCGCCATA-3' and reverse, 5'-TCAATGTGCTTCTGGGAATCC-3' ; probe, 5'-6FAMAGCAGGTGGTGAACATCCCGTCCTTTAMRA-3') using the Primer Express software (Applied Biosystems).
Fluorescent signal data were collected by the ABI Prism 7700 Sequence Detection System (Applied Biosystems). The log-linear phase of amplification was monitored to obtain the threshold cycle (defined as the fractional cycle number at which the amount of the amplified target reaches a fixed threshold) values for each RNA sample. Ribosomal S9 was used as the internal reference and was selected because it exhibits minimal variability in tissues of different origins [29]. The comparative threshold cycle method was employed to determine IGF II expression levels in each sample relative to a calibrator, in this case MCF10AT cells [29,30]. Each sample was run in triplicate.
Statistical analysis
A relationship between BrdU labeling or apoptotic indexes and co-culture with NAF or with CAF was assessed by multiple linear regression analysis to allow combining replicate experiments performed on different days. Results of ELISA and quantitative real-time PCR were compared by t test. Outliers were eliminated prior to analysis using a basic outlier test, where a high outlier was defined as a number greater than quartile 3 + 1.5 (interquartile range) and a low outlier was less than quartile 1 – 1.5 (interquartile range).
Results
Fibroblasts alter the 3D morphology of MCF10A cells and MCF10AT cells
The immunocytochemical characterization of fibroblasts used in the described experiments confirmed their stromal nature. Immunostaining for vimentin was strongly positive and staining for epithelial membrane antigen and CK 5/CK 8 was negative.
In 3D monocultures, both MCF10A cells and MCF10AT cells initially form a lattice-like network of duct-like structures. After several days, the lattice-like network is replaced by a predominance of rounded epithelial cell groups (spheroids) (Fig. 1a,1b,1c,1d). MCF10A cells form small, rounded spheroids (Figs 1b and 2a). MCF10AT cells aggregate into larger solid groups or sheets with extensive squamous metaplasia (Figs 1d and 2c). The formation of larger three-dimensional structures and the abnormal differentiation (i.e. squamous metaplasia) of MCF10AT cells supports their greater degree of transformation.
In 3D co-cultures with NAF or with CAF, the epithelial cells and fibroblasts form large rounded structures (Fig. 1e). In histologic sections of co-cultures with NAF or with CAF, MCF10A cells and MCF10AT cells form spheroids or sheets within Matrigel®, as seen in monocultures, and these epithelial groups surround an aggregate of fibroblasts (Fig. 2b,2d). These 3D co-cultures resemble a terminal duct-lobular unit in the normal breast or in proliferative breast disease/DCIS (Fig. 3a,3b,3c), in that epithelial cells are arranged in groups (similar to an acinus or a terminal duct) surrounded by a laminin-rich basement membrane with fibroblasts located outside the basement membrane and separating epithelial groups. The 3D in vitro model differs from normal breast or in situ breast disease in vivo, however, in that the number of fibroblasts centrally located and between epithelial cell groups is greater than that typically seen in vivo. Additionally, the fibroblasts in the in vitro model are surrounded by an ECM rich in laminin and collagen IV [31], whereas in vivo collagen I would typically predominate [32].
In 3D co-cultures, both NAF and CAF markedly suppressed squamous metaplasia of MCF10AT cells as observed on H&E-stained sections of monocultures, thereby normalizing the morphology to a glandular phenotype (Fig. 2c,2d). No obvious differences between NAF and CAF in morphology, growth pattern or adhesion to substrate, in either a monolayer or the 3D culture, were observed.
NAF and CAF affect the rate of proliferation of MCF10A cells and MCF10AT cells in 3D co-culture
In replicate co-cultures of MCF10A cells with three different NAF and CAF grown in an E:F of 2:1, both types of fibroblasts significantly reduced proliferation of MCF10A cells. The mean BrdU-labeling index of MCF10A cells, when measured by immunocytochemistry, was decreased by 47% in co-culture with NAF (n = 19, P = 0.009) and by 39% in co-culture with CAF (n = 19, P = 0.024) relative to the MCF10A monoculture (Table 1 and Fig. 4). The BrdU-labeling index of MCF10AT cells was reduced by 49% in the presence of NAF (n = 20, P = 0.013), relative to the MCF10AT monoculture, whereas co-culture with CAF failed to significantly lower the MCF10AT BrdU-labeling index (n = 22, P = 0.935) (Table 2 and Fig. 4). The effect of NAF versus CAF on the rate of proliferation of MCF10AT cells was significantly different (P < 0.001). The effect was further confirmed by repeating the co-cultures to measure the BrdU-labeling index by flow cytometry, rather than by immunocytochemistry (Fig. 5).
There was variability among NAF cultures and among CAF cultures in their ability to suppress proliferation of MCF10A cells and MCF10AT cells (Tables 1 and 2) in this 3D culture system, potentially reflecting heterogeneity among the individuals from which the fibroblasts were derived. Because of this variability, detection of a significant difference in the function of NAF and CAF required many replicates and multiple fibroblast cultures derived from different individuals.
In a prior report, CAF was found to promote, rather than inhibit, the growth of MCF10A cells in a similar 3D co-culture system [22]. One of several possible explanations for this discrepancy between the prior result and the present result is a difference in E:F. Shekhar and colleagues used an E:F of 1:1 rather than the E:F of 2:1 we initially used [22]. The number of fibroblasts has been shown to have an effect on the response of epithelial cells [7,9,14]. We therefore repeated the 3D co-cultures of MCF10A cells using NAF-2 and CAF-1 with increasing numbers of fibroblasts (i.e. a decreasing E:F) (Fig. 6). BrdU labeling was assessed by immunocytochemistry of histologic sections of 3D cultures.
As previously, NAF-2 at an E:F of 2:1 suppressed proliferation of MCF10A cells. However, with increasing numbers of NAF-2, this suppression effect was gradually weakened (P = 0.043). Although we found no significant difference in the suppressive effect of NAF-2 in an E:F of 2:1 versus an E:F of 1:1 or of 1:2, there was a significantly greater rate of proliferation of MCF10A cells with NAF-2 in an E:F of 1:3 compared with in an E:F of 2:1 (P = 0.028). More importantly, CAF-1 at an E:F of 1:1 did not significantly suppress proliferation, whereas our original ratio of 2:1 did (P = 0.025). CAF-1 at an E:F of 1:2 also conferred a higher rate of proliferation of MCF10A cells than the E:F of 2:1, but this did not reach statistical significance (P = 0.054). At an E:F of 1:3, however, CAF-1 caused a decrease in proliferation of MCF10A cells and enhanced cell death, as assessed by microscopic morphology. At an E:F of 1:3, the total number of viable MCF10A cells was reduced in co-culture with both NAF-2 and CAF-1; however, this reduction was more marked with CAF-1.
Neither NAF nor CAF had a significant effect on the rate of apoptosis of MCF10A cells or MCF10AT cells when grown at an E:F of 2:1 after 2 weeks of co-culture, as assessed by TUNEL assay (Fig. 7).
Quantities of IGF II are no different in NAF versus CAF
As an initial attempt to identify differences between NAF and CAF that explain our observed results, expression of IGF II in NAF and in CAF was assessed. A higher level of expression of IGF II in CAF than in NAF may provide an explanation for the higher rate of proliferation of MCF10AT cells allowed by CAF in comparison with NAF.
ELISA performed on cell lysates of NAF and CAF cultures demonstrated variability in expression of IGF II among cultures, but no significant difference was observed in the mean IGF II quantity between NAF and CAF in monolayer cultures (Table 3) or in 3D monocultures (Table 4). Although in monolayer cultures more CAF than NAF had IGF II levels > 10 ng/μg protein, the mean levels of IGF II were similar (NAF versus CAF, P > 0.05) (Table 3). This comparison included five different NAF cultures and six different CAF cultures. ELISA was also performed on NAF-conditioned media and CAF-conditioned media but, despite the concentration of samples, the levels were too low for reliable quantification by ELISA (data not shown).
Additionally, because IGF II mRNA was previously reported to be expressed at a higher level in CAF than in NAF, IGF II mRNA was assessed by quantitative real-time PCR in monolayer cultures of NAF and CAF. The relative expression levels for each fibroblast culture are provided in Fig. 8. Although more CAF cultures than NAF cultures expressed IGF II mRNA at relatively high levels, the mean relative expression level of IGF II mRNA for NAF (4.2, n = 5) and for CAF (7.7, n = 5) did not differ significantly (P = 0.390).
Co-culture of MCF10A cells or MCF10AT cells may enhance expression of IGF II in fibroblasts, as has been reported in fibroblasts co-cultured with MCF-7 cells [33]. Furthermore, expression of IGF II may be enhanced to a greater degree in CAF versus NAF, thus possibly explaining the greater rate of proliferation of MCF10AT cells in co-culture with CAF than with NAF. 3D culture of NAF and CAF also could alter expression of IGF II in comparison of the monolayer culture. To investigate these possibilities, IGF II levels were assessed by ELISA in 3D monocultures of the same three NAF cultures and three CAF cultures used in previous co-cultures, in 3D monocultures of MCF10AT cells, and in 3D co-cultures of MCF10AT cells with NAF and CAF in an E:F of 2:1, identical to those co-cultures assessed for BrdU labeling previously (Table 4). In the 3D cultures, fibroblasts expressed IGF II at a significantly higher level than the epithelial cells (P < 0.01), and there was no difference in IGF II between NAF and CAF (P > 0.05). In co-cultures, the overall expression of IGF II was lower than in fibroblast monocultures. This latter result is expected because of the addition of a relatively large number of MCF10AT cells (E:F of 2:1) that express IGF II at a lower level than fibroblasts. Furthermore, there was no difference in mean IGF II levels in co-cultures with NAF versus CAF. The IGF II levels in 3D cultures were generally higher than in monolayer cultures, and this was particularly true when comparing 3D monocultures of fibroblasts versus two-dimensional monocultures of fibroblasts. The potential reasons for this include an effect of 3D growth, or the presence of Growth Factor Reduced Matrigel® on the expression of IGF II, and/or deviations between the ELISA runs.
When the protein levels of IGF II in 3D monocultures of fibroblasts or in 3D co-cultures were correlated with the rate of proliferation of MCF10AT cells in co-culture with the matching NAF or CAF, no significant correlation was observed (r = 0.030 or r = 0.258, respectively; P > 0.05, Pearson Product Moment Correlation).
Discussion
Our results indicate that both NAF and CAF have the ability to inhibit epithelial cell proliferation and to induce glandular differentiation to a more normal phenotype. However, CAF have less inhibitory capacity than NAF. At relatively low concentrations of fibroblasts (E:F of 2:1) NAF can suppress proliferation of both MCF10A cells and MCF10AT cells, whereas CAF can suppress proliferation of MCF10A cells but not the more transformed MCF10AT cells. In vivo NAF may thus have an inhibitory and regulatory effect on the proliferation of normal epithelial cells. This suppressive ability may be lost or reduced as epithelial lesions gradually progress from hyperplasia to DCIS and invasive cancer, and correspondingly NAF become CAF.
Differences in gene expression between CAF and NAF have been documented by examination of human breast cancers by immunohistochemistry and in situ hybridization, and also by analysis of cultures of fibroblasts isolated from breast cancers. These documented characteristics of breast-derived CAF are thoroughly reviewed by Kunz-Schughart and Knuechel [1,2], and include an increased expression of several growth factors [3,34], of ECM molecules [35-37], and of proteases and protease inhibitors involved in modulating the ECM [5,6]. Many of these differences in the expression profile of CAF in comparison with NAF have the potential to enhance the development, growth and progression of breast carcinoma [1]. Furthermore, a subset of the phenotypic alterations documented in CAF have been identified in fibroblasts surrounding DCIS by examining these lesions using immunohistochemistry or in situ hybridization [3-6]. However, many of the changes observed in CAF have not yet been examined in DCIS, and it is therefore quite possible that fibroblasts surrounding DCIS share more features with CAF than are currently documented.
Attempts to actually demonstrate a promotional effect of CAF on the growth of epithelial cell lines derived from breast cancer are limited in number and have met with somewhat conflicting results. A variety of different methods were used in these studies, complicating comparison among them. In most in vitro analyses, however, direct and indirect co-culture with CAF increased the growth of MCF-7 breast cancer cells compared with MCF-7 cells alone [7,12,14,38]. Co-culture of NAF with MCF-7 cells caused both growth promotion [7,10,11,13,39,40] and inhibition [8] of MCF-7 cells. In contrast, co-culture of NAF and CAF with the breast cancer cell line MDA-MB-231 had no significant effect on epithelial cell growth [10,11]. In vivo, NAF and CAF increased growth of the MCF-7 xenografts or NAF had no effect [7,38]. Only a few reports have addressed the effects of fibroblast–epithelial interactions on the growth of nontransformed or nontumorigenic breast epithelial cells, rather than on the growth of breast carcinoma cells. NAF and CAF have been reported to stimulate the proliferation of normal human breast epithelial cells [7,39] or to have no effect on the rate of proliferation of normal breast epithelial cells immortalized by SV40 large-T antigen [8]. The overriding observations from these previous studies of fibroblast–epithelial interactions are that co-cultured fibroblasts affect the growth of epithelial cells, but this growth is dependent on the source of the fibroblasts, the characteristics of the epithelial cells, and the culture conditions utilized.
In a co-culture system similar to that presented here, NAF (two different cultures) inhibited the growth, measured by direct counting of total viable cells in co-cultures, of MCF10A cells and MCF10AT-EIII8 cells – whereas CAF induced the growth of both epithelial cell lines [22]. While our findings for NAF are similar to the previous results, we did not find a promotional effect of CAF on epithelial cell growth. This discrepancy may be a result of the interindividual variation found in fibroblast cultures and/or differences in epithelial cells (MCF10AT cells versus MCF10AT-EIII8 cells, an estrogen-induced derivative of the MCF10AT cells). In addition, the total number of viable cells present (fibroblasts and epithelial cells) in co-cultures were counted, whereas in the current study only proliferation of epithelial cells was measured. In the previous study, an E:F ratio of 1:1 was utilized compared with the current E:F of 2:1. In support of the latter, we found that CAF at lower E:F values of 1:1 and 1:2 no longer inhibited the growth of MCF10A cells. Although not identical, this result is more in keeping with that of Shekhar and colleagues [22].
Varying E:F ratios have been utilized in a few prior studies of the effect of fibroblasts on the growth of breast carcinoma cells. Ratios have varied from a great predominance of epithelial cells [9] to a predominance of fibroblasts [7,14]. In these previous studies, an increasing proportion of breast fibroblasts, either NAF or CAF, correlates with an increase in growth of co-cultured cancerous breast epithelial cells to a plateau where no further enhancement of growth is seen. This is in general concordance with our results, where the inhibitory effect of both NAF and CAF on proliferation of MCF10A cells was less with increasing numbers of fibroblasts, particularly for NAF. This suggests the presence of fibroblast-derived factors that both inhibit and promote the proliferation of epithelial cells; at higher concentrations of fibroblasts, the promotional effect predominates.
To be biologically relevant, the most meaningful ratio of epithelial cells to fibroblasts depends on the lesion or tissue being modeled. In the present study, the intent was to model proliferative breast disease and DCIS, the putative precursors of invasive carcinoma, which are believed to have their origins in terminal ducts within terminal duct-lobular units [41]. Microscopic examination of such intraductal lesions in the human breast reveals a range of E:F, depending on the extent of fibrosis of the lesion. In the normal terminal duct-lobular unit and in situ lesions depicted in Fig. 3, the E:F varies from 3:1 to 2:1. These ratios were determined by counting epithelial cells (both luminal and myoepithelial cells) and stromal cells identified as fibroblasts and located within or in close proximity to the terminal duct-lobular unit involved. It is probable that those fibroblasts in proximity to the epithelial structures have the greatest influence on epithelial cell behavior. Our choice to use an initial E:F of 2:1 is therefore appropriate, whereas lower ratios with many more fibroblasts are less common within terminal duct-lobular units in vivo.
Prior attempts to identify fibroblast-derived factors that are mediating the effect of NAF and CAF on breast epithelial cell growth have identified IGF I and/or IGF II as partially contributing to the mitogenic effect of fibroblasts [11]. Previous studies have reported that IGF II was expressed at a moderate to high level in 43–57% of breast cancers by in situ hybridization and by immunohistochemistry [17,42,43], with localization primarily to stromal fibroblasts or vessel walls [17,42], making IGF II a potential candidate to mediate fibroblast–epithelial interactions. Additionally, in cultures of CAF and NAF, IGF II mRNA was detected at higher levels more frequently in CAF than in NAF in some studies [33,44], but not in other studies [45,46].
In the current study, IGF II levels in fibroblasts were assessed in both monolayer cultures and 3D cultures by ELISA and quantitative real-time PCR. While more CAF cultures than NAF cultures had relatively higher levels of IGF II mRNA or protein, no significant differences in mean quantities of IGF II mRNA or protein between these CAF and NAF were observed. We also found no difference in IGF II expression in the 3D co-cultures of MCF10AT cells prepared with NAF versus CAF, suggesting that MCF10AT cells do not alter IGF II expression to a different degree in co-cultured NAF and CAF. Furthermore, we found no correlation between proliferation of MCF10AT cells in co-culture and the level of IGF II protein in 3D fibroblast monocultures or 3D co-cultures. Differences in IGF II expression in NAF and CAF are therefore unlikely to explain the difference in the effect of CAF versus that of NAF on proliferation of the epithelial cells described in this study. Our results do not eliminate a role for the IGF system in these fibroblast–epithelial cell interactions as other family members, such as IGF II receptor and a multitude of IGF binding proteins, may be mediating these interactions in other ways.
Prior studies by other workers [33,44-46] and the current work underscore the variability in expression of IGF II and in the growth inhibitory effect of NAF and CAF, and emphasize the importance of including several NAF and CAF cultures from different individuals in studies of fibroblast–epithelial cell interactions. The interindividual heterogeneity observed among NAF and CAF complicates assessment of mechanisms underlying fibroblast–epithelial interactions. Broad generalizations based on the results of experiments using only one or two fibroblast cultures should be avoided.
In conclusion, both NAF and CAF have the ability to suppress breast epithelial cell proliferation; however, the capacity of CAF to inhibit proliferation is less than that of NAF. This suggests that there are differences in either secreted factors or intercellular interactions between NAF and CAF that render CAF less effective in inhibiting proliferation, particularly of more transformed epithelial cells. Furthermore, differences between the phenotypes of the H-ras overexpressing MCF10AT cells and the parental MCF10A cells cause MCF10AT cells to be more resistant to the suppressive effect of fibroblasts. Future work to identify the key fibroblast–epithelial interactions mediating these effects may reveal mechanisms to allow restoration of the inhibitory of effect of fibroblasts during the carcinogenic process.
Abbreviations
3D = three-dimensional; BrdU = 5-bromo-2'-deoxyuridine; CAF = carcinoma-associated fibroblasts; CK = cytokeratin; DCIS = ductal carcinoma in situ; DMEM = Dulbecco's modified Eagle's medium; ECM = extracellular matrix; E:F = ratio of epithelial cells to fibroblasts; ELISA = enzyme-linked immunosorbent assay; FITC = fluorescein isothiocyanate; H&E = hematoxylin and eosin; HPLC = high-performance liquid chromatography; IGF = insulin-like growth factor; NAF = normal breast-associated fibroblasts; PBS = phosphate-buffered saline; PCR = polymerase chain reaction.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AS carried out experimental design and conduct, and manuscript preparation. ZN provided technical assistance with flow cytometry. MRJ provided technical assistance with quantitative real-time PCR, and carried out manuscript preparation. DBB provided technical assistance with the conduction of experiments. SRG provided assistance with the conduction of experiments. GPP provided assistance with the statistical analysis. JVT provided technical assistance and carried out manuscript preparation. DRW carried out experimental design and manuscript preparation. ARF was the principal investigator responsible for overall experimental design and conduct, and manuscript preparation.
Acknowledgements
This work was supported by National Institutes of Health grant R03 CA097472-01 and by the National Foundation for Cancer Research. The authors would like to thank William E Grizzle for advice in experimental design, Cynthia Moore for assistance with immunohistochemical staining, and the High Resolution Imaging Facility at The University of Alabama at Birmingham for technical assistance with phase-contrast imaging.
Figures and Tables
Figure 1 MCF10A cells and MCF10AT cells in monoculture and in co-culture with fibroblasts. (a), (b) MCF10A cells and (c), (d) MCF10AT cells in monoculture initially form a lattice/scaffold arrangement (a, c). After several days of culture, spheroidal structures become more prominent (b, d). (e) MCF10AT cells in co-culture with fibroblasts form three-dimensional rounded structures. Similar structures are formed by MCF10A cells in co-culture with fibroblasts (phase contrast, 100 × magnification; scale bar, 200 μm).
Figure 2 H&E-stained histologic sections of MCF10A cell and MCF10AT cell monocultures and co-cultures with fibroblasts. (a) MCF10A cells form small spheroids. (b) MCF10A cells in co-culture with fibroblasts are located adjacent to the fibroblast aggregate (F) and maintain smaller spheroids. (c) In monoculture, MCF10AT cells form larger rounded three-dimensional structures. (d) In co-culture, MCF10AT cells form solid sheets and rounded groups of cells located adjacent to the fibroblasts (F). The occurrence of squamous metaplasia (SM) is more evident in MCF10AT monocultures, while it is suppressed in co-cultures. Overall, MCF10A cells have less squamous metaplasia than MCF10AT cultures (400 × magnification; scale bar, 50 μm).
Figure 3 Distribution and relative quantities of fibroblasts and epithelial cells in a normal terminal duct-lobular unit, hyperplasia and ductal carcinoma in situ (DCIS). (a) A terminal duct-lobular unit with epithelial cells (arrowheads) arranged in acini and intralobular terminal ducts separated by stroma-containing fibroblasts (arrows) in a ratio of epithelial cells to fibroblasts (E:F) of 2.7:1. (b) Ductal hyperplasia with a proliferation of epithelial cells (arrowheads) filling and expanding terminal ducts separated by reactive stroma including fibroblasts (arrows) in an E:F of 3.3:1. (c) High-grade DCIS, with epithelial cells (arrowheads) demonstrating markedly atypical nuclei, involving terminal ducts separated by reactive stroma including fibroblasts (arrows) in an E:F of 2:1 (200 × magnification; scale bar, 50 μm).
Figure 4 Proliferation of MCF10A cells and MCF10AT cells grown in monoculture and co-culture with fibroblasts. The rate of proliferation of MCF10A cells and MCF10AT cells, as measured by the 5-bromo-2'-deoxyuridine (BrdU) labeling index (assessed by immunocytochemistry), was significantly reduced in co-cultures of MCF10A cells with both normal breast-associated fibroblasts (NAF) (P = 0.009) and carcinoma-associated fibroblasts (CAF) (P = 0.024) compared with the MCF10A monoculture (control). The rate of proliferation of MCF10AT cells was significantly suppressed by NAF (P = 0.013) but not by CAF (P = 0. 935) in comparison with the MCF10AT monoculture (control).
Figure 5 5-Bromo-2'-deoxyuridine (BrdU) labeling, assessed by flow cytometry, of MCF10AT monocultures and co-cultures with normal breast-associated fibroblasts (NAF) and carcinoma-associated fibroblasts (CAF). These data are representative of replicate experiments indicating that NAF suppress proliferation of MCF10AT cells to a greater extent than do CAF. Again some variability in extent of suppression is present among individual NAF cultures and individual CAF cultures.
Figure 6 Relative 5-bromo-2'-deoxyuridine (BrdU) indices of MCF10A cells in co-culture with varying quantities of normal breast-associated fibroblast NAF-2 and carcinoma-associated fibroblast CAF-1. With increasing numbers of NAF-2, the mean rate of proliferation of co-cultured MCF10A cells increased, with a significant difference in BrdU-labeling index observed between a ratio of epithelial cells to fibroblasts (E:F) of 2:1 versus an E:F of 1:3 (P < 0.05). With increasing numbers of CAF-1, the mean rate of proliferation was highest at an E:F of 1:1. The rate of proliferation at an E:F of 1:1 was significantly higher than that at an E:F of 2:1 (P < 0.05). At an E:F of 1:3, CAF-1 caused a decreased proliferation of and enhanced cell death of MCF10A cells.
Figure 7 TUNEL assay for MCF10A cells and MCF10AT cells in co-culture with normal breast-associated fibroblasts (NAF) and carcinoma-associated fibroblasts (CAF). Co-culture with NAF and CAF had no significant effect on the rate of apoptosis of MCF10A cells and MCF10AT cells in comparison with epithelial cell monoculture controls.
Figure 8 Comparative expression of insulin-like growth factor (IGF) II mRNA. IGF II expression levels in MCF10A cells, MCF10AT cells, normal breast-associated fibroblasts (NAF) and carcinoma-associated fibroblasts (CAF) were determined by quantitative real-time PCR. All expression levels are relative to the calibrator, MCF10AT cells. The error bars represent the standard deviation of triplicate assays for each sample. Comparison of the mean expression level between NAF (mean = 4.2) and CAF (mean = 7.7) did not reach statistical significance (P = 0.39, t test).
Table 1 5-Bromo-2'-deoxyuridine (BrdU) labeling* of MCF10A cells grown in monoculture (control group) and in co-cultures with normal breast-associated fibroblasts (NAF) and carcinoma-associated fibroblasts (CAF)
Culture BrdU-labeling indices of MCF10A cells (mean ± standard error of the mean) BrdU-labeling indices of MCF10A cells (group mean ± standard error of the mean) Comparison of BrdU-labeling indices of MCF10A cells between groups (linear regression)
MCF10A (n = 6) 30.3 ± 3.0 30.3 ± 3.0 MCF10A vs MCF10A + NAF (P = 0.009) MCF10A + NAF vs MCF10A + CAF (P = 0.501)
NAF-1 + MCF10A (n = 7) 21.9 ± 4.2 16.1 ± 2.6 (n = 19)
NAF-2 + MCF10A (n = 6) 10.7 ± 3.6
NAF-3 + MCF10A (n = 6) 14.7 ± 5.0
CAF-1 + MCF10A (n = 8) 15.2 ± 2.0 18.5 ± 2.5 (n = 19) MCF10A vs MCF10A + CAF (P = 0.024)
CAF-2 + MCF10A (n = 6) 15.5 ± 4.2
CAF-3 + MCF10A (n = 5) 27.6 ± 6.7
*Assessed by immunocytochemistry
Table 2 5-Bromo-2'-deoxyuridine (BrdU) labeling* of MCF10AT cells grown in monoculture and in co-cultures with normal breast-associated fibroblasts (NAF) and carcinoma-associated fibroblasts (CAF)
Culture BrdU-labeling indices of MCF10AT cells (mean ± standard error of the mean) BrdU-labeling indices of MCF10AT cells (group mean ± standard error of the mean) Comparison of BrdU-labeling indices of MCF10AT cells between groups (linear regression)
MCF10AT (n = 6) 27.7 ± 7.2 27.7 ± 7.2 MCF10AT vs MCF10AT + NAF (P = 0.013) MCF10AT + NAF vs MCF10AT + CAF (P < 0.001)
NAF-1+ MCF10AT (n = 7) 17.1 ± 2.8 14.1 ± 1.4 (n = 20)
NAF-2 + MCF10AT (n = 6) 13.8 ± 1.9
NAF-3 + MCF10AT (n = 7) 11.4 ± 1.9
CAF-1 + MCF10AT (n = 8) 25.9 ± 4.9 25.5 ± 2.8 (n = 22) MCF10AT vs MCF10AT + CAF (P = 0.935)
CAF-2 + MCF10AT (n = 8) 26.6 ± 3.2
CAF-3 + MCF10AT (n = 6) 23.5 ± 7.4
*Assessed by immunocytochemistry
Table 3 Insulin-like growth factor (IGF) II ELISA for normal breast-associated fibroblasts (NAF), carcinoma-associated fibroblasts (CAF), MCF10A cells and MCF10AT cells in monolayer cultures
Cell type IGF II level (normalized to total protein)
Level (ng/μg) Mean (ng/μg)
NAF
NAF-1 11.63
NAF-2 6.87
NAF-3 10.31
NAF-4 7.21
NAF-5 2.92 7.79
CAF
CAF-1 11.80
CAF-2 10.58
CAF-3 10.59
CAF-4 11.27
CAF-5 10.21
CAF-6 14.07 11.42
Breast epithelial cells
MCF10A 5.56
MCF10AT 4.17 4.87
Table 4 Insulin-like growth factor (IGF) II ELISA for normal breast-associated fibroblasts (NAF), carcinoma-associated fibroblasts (CAF) and MCF10AT cell monocultures and co-cultures in a three-dimensional in vitro model
Cell type IGF II level (normalized to total protein)
Monoculture Co-culture with MCF10AT cells
Level (ng/μg) Mean (ng/μg) Level (ng/μg) Mean (ng/μg)
NAF
NAF-1 49.25 9.68
NAF-2 45.00 10.22
NAF-3 68.14 54.13 10.27 10.06
CAF
CAF-1 70.64 10.01
CAF-2 52.37 10.88
CAF-3 52.96 58.65 9.96 10.28
Breast epithelial cells
MCF10AT 9.98
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| 15642169 | PMC1064098 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 8; 7(1):R46-R59 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr949 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9501564216710.1186/bcr950Research ArticleSystemic chemotherapy induces microsatellite instability in the peripheral blood mononuclear cells of breast cancer patients Fonseca Fernando LA [email protected] Ana Aleksandra VL [email protected] Israel [email protected] Vitor 4Costa Luciano J [email protected] Aparecida A [email protected] Giglio Auro [email protected] ABC Foundation School of Medicine, Hematology and Oncology, Santo Andre, São Paulo, Brazil2 Faculdade de Medicina da Universidade de São Paulo, Hematology Post Graduate Section, São Paulo, Brazil3 Hemocentro de São Paulo, Tumor Biology, São Paulo, Brazil4 Instituto Adolfo Lutz, Anatomic Pathology, São Paulo, Brazil5 Faculdade de Medicina da Universidade de São Paulo, Pathology Post Graduate Section, São Paulo, Brazil6 ABC Foundation School of Medicine, Biochemistry, São Paulo, Brazil2005 4 11 2004 7 1 R28 R32 13 5 2004 15 7 2004 15 9 2004 24 9 2004 Copyright © 2004 Fonseca et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Systemic chemotherapy is an important part of treatment for breast cancer. We conducted the present study to evaluate whether systemic chemotherapy could produce microsatellite instability (MSI) in the peripheral blood mononuclear cell fraction of breast cancer patients.
Methods
We studied 119 sequential blood samples from 30 previously untreated breast cancer patients before, during and after chemotherapy. For comparison, we also evaluated 20 women who had no relevant medical history (control group).
Results
In 27 out of 30 patients we observed MSI in at least one sample, and six patients had loss of heterozygosity. We found a significant correlation between the number of MSI events per sample and chemotherapy with alkylating agents (P < 0.0001). We also observed an inverse correlation between the percentage of cells positive for hMSH2 and the number of MSI events per sample (P = 0.00019) and use of alkylating agents (P = 0.019).
Conclusion
We conclude that systemic chemotherapy may induce MSI and loss of heterozygosity in peripheral blood mononuclear cells from breast cancer patients receiving alkylating agents, possibly mediated by a chemotherapy-induced decrease in the expression of hMSH2. These effects may be related to the generation of secondary leukaemia in some patients, and may also intensify the genetic instability of tumours and increase resistance to treatment.
cyclophosphamidemicrosatellite repeatsneoplasmsproliferating cell nuclear antigensecond primary
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Introduction
Systemic chemotherapy is an important part of treatment for breast cancer in both the adjuvant and palliative settings. Despite the consistent improvement in overall survival afforded by systemic adjuvant chemotherapy, about 1% of patients develop secondary leukaemia and/or myelodysplasia, probably as a consequence of the genotoxic effects of this type of treatment [1]. Deficiencies in the DNA mismatch repair system leading to microsatellite instability (MSI) may produce a syndrome of familial predisposition to colon, endometrial and upper gastrointestinal cancers, known as hereditary nonpolyposis colon cancer [2]. The DNA mismatch repair system depends on the coordinated interplay of several proteins encoded by various genes, including hMLH1, hMSH2, hPMS1 and hPMS2 [2]. Several groups have reported a significant association between MSI and treatment-related secondary acute myeloid leukaemia (AML) and myelodysplasia [3-5]. In some studies MSI in treatment-related secondary AML/myelodysplasia cases was accompanied by hMLH1 hypermethylation [3] and MSH2 polymorphisms [6].
In order to evaluate whether systemic chemotherapy could produce MSI in normal peripheral blood mononuclear cells (PBMCs), we analyzed PBMCs from breast cancer patients collected before, during and after systemic chemotherapy.
Methods
Collection and preparation of samples
This protocol was approved by our institutional review board. We obtained blood samples from 33 patients with histologically confirmed breast cancer after informed consent had been obtained. We had only the initial sample in three patients, and so we could not include them in the study. We therefore studied 119 sequential blood samples from 30 previously untreated breast cancer patients, collected at 3-month intervals before, during and after receiving systemic treatment (13 adjuvant, 12 neoadjuvant and 5 palliative). Three patients initially received hormones (two adjuvant and one palliative).
Chemotherapy combinations containing cyclophosphamide were classified as alkylating regimens (FAC [5-fluorouracil, doxorubicin and cyclophosphamide], AC [doxorubicin and cyclophosphamide], CMF [cyclophosphamide, methotrexate, and 5-fluorouracil]). We also studied PBMCs from 20 normal control women, who had no relevant previous medical history, by immunocytochemistry. Afterward, we collected peripheral blood from these 20 normal control women on two occasions with a 3-month interval to evaluate MSI using the TP53Alu and PCR15.1 markers (described below).
Each sample consisted of 20 ml venous blood, from which we separated the PBMCs by Ficoll gradient (Ficoll Hypaque; Organon Teknica®, Durham, NC, USA), yielding a final concentration of 1.0 × 106 cells/ml; we sent part of the sample for cytospin for immunocytochemical studies and part for DNA extraction. DNA was extracted from mononuclear fraction by the use of Trizol (Invitrogen, Carlsbad, CA, USA), in accordance with the manufacturer's instructions. For immunocytochemistry analysis, assay slides were prepared (described in detail elsewhere [7]).
Microsatellite analysis
The sequences of all primers used for six microsatellite loci (BAT-26, BAT-40, MFD-28, MFD-41, TP53, PCR15.1, TP53ALU) and the PCR protocols used to study each of these markers were described previously [8]. Briefly, PCR products were denatured and subjected to electrophoresis in Gene Gel Clean 15/24 (Amershan Pharmacia Biotech AB, Uppsala, Sweden) for 90 min at 600 V and 8°C, and then silver stained using a Hoefer Automed Gel Stainer (Amershan Pharmacia Biotech AB). MSI was defined by the appearance of novel bands and loss of heterozygosity (LOH) whenever disappearance of previously visualized bands occurred, as described previously [8]. Three independent observers analyzed the results of each gel for LOH and MSI.
Immunocytochemistry
For immunostaining for proliferating nuclear antigen (PCNA), hMLH1, hPMS1, hPMS2, hMSH2 and TP-53 proteins, we used an avidin–biotin–peroxidase complex and 3,3'-diaminobenzidine as chromogen. All antibodies were supplied by Santa Cruz Biotechnology (Santa Cruz, CA, USA), and for each clone we employed the following dilutions: PC-10 (anti-PCNA), 1:1000; C-20 (anti-hMLH1), 1:25; K-20 (anti-hPMS1), 1:25; C-20 (anti-hPMS2), 1:200; N-20 (anti-hMSH2), 1:200; and Pab1801 (anti-P53), 1:400. Endogenous peroxidase activity was blocked by incubation with H2O2 and washed in tap water. The cytospin slides were then treated with 7% skimmed milk for 60 min to block nonspecific protein binding. Each antibody was applied separately, and cytospins were incubated for 18 hours at 3°C. After 3 washes in phosphate-buffered saline, antigen-bound primary antibody was detected using a standard streptavidin–biotin complex (Strep AB Complex; Dako, Carpinteria, CA, USA). After brief washing, slides were incubated with diaminobenzidine and H2O2 for 10 min and then counterstained with haematoxylin, dehydrated in graded alcohols and cleared in xylene. Two independent observers scored 300 cells/slide as positive or negative according to the presence of nuclear staining for each of the aforementioned antibodies. Then, the results from these two observers were averaged to obtain a percentage of positive cells per sample.
Statistical methods
We analyzed correlations between categorical variables using the χ2 or Fisher's exact test. We used analysis of variance to study correlations between continuous and categorical variables, and simple regression to analyze correlations between continuous variables.
Results
We studied 30 patients with median age 52 years (range 25–80 years). Twelve patients had stage II, 13 had stage III and five had stage IV breast cancer. We evaluated samples from 20 normal control women by immunocytochemistry for PCNA, hMLH1, hPMS2, hMSH2 and TP-53 (Table 1); the raw data for each of the included patients appear in Additional file 1.
We observed MSI in 40 out of the 119 samples collected, and MSI was observed in 27 out of 30 patients in at least one sample. We observed LOH at the PCR15.1 locus in six patients. Whereas MSI was usually transient, disappearing in the next collected sample, LOH was persistent in all subsequent samples in five of the six observed cases (Fig. 1).
In order to ensure that our findings in the patients studied were not due to chance, we studied 20 normal blood donors (women who had no relevant medical history; see Additional file 2). In those women blood samples were collected twice, with a 3-month interval between samples. We studied both samples from each of these normal women for MSI using TP53ALU and PCR15.1 markers. In only one normal woman was any MSI identified (one additional band was detected in the PCR15-1 marker after 3 months). None of them had LOH (data not shown). Therefore, in only one blood sample out of 40 drawn from these 20 normal women was there any evidence of MSI, as compared with 28 out of 119 samples from the breast cancer patients studied using the same markers (P = 0.018).
We observed a significant correlation between the number of MSI events per sample and the use of chemotherapy (P = 0.005), especially chemotherapy containing alkylating agents (P < 0.0001; Fig. 2). We also observed an inverse correlation between the number of MSI events per sample and the percentage of cells positive for MSH2 by immunocytochemistry (P = 0.00019) and especially for the MFD41 marker (P = 0.000638). The percentage of cells positive for MSH2 by immunocytochemistry was inversely correlated with the use of chemotherapy regimens containing alkylating agents (P = 0.019). We found no significant correlations between the number of MSI events or the presence of LOH and the frequency of cells positive for TP53, hMLH1, hPMS1 and hPMS2.
In the present study, the percentage of cells positive for PCNA, as determined by immunocytochemistry, was directly correlated with use of radiation therapy (P = 0.046) and with the percentage of MSH2-positive cells (P = 0.000096), and inversely correlated with the presence of LOH (P = 0.043), but it was not correlated with the number of MSI events per sample.
Discussion
Systemic chemotherapy administered to breast cancer patients induces secondary leukaemia and myelodysplasia in about 1% [1]. In our study, in 90% of patients MSI was noted in at least one of the samples. This finding does not appear due to chance because only one of the sequential blood samples drawn from 20 normal donor women exhibited MSI. Furthermore, we observed a significant and direct correlation between the number of MSI events per sample and the use of chemotherapy, especially with regimens containing alkylating agents. We believe that our data reflect the genotoxic effects of chemotherapy on normal cells because in only one normal control woman out of 20 was there evidence of MSI (after 3 months) and with only one of the two surveyed MSI markers. Furthermore, no LOH was observed within this cohort of normal women. Therefore, because MSI and LOH occurred at significantly greater frequency among patients with breast cancer, it is unlikely that our findings could be due to normal variations in MSI occurrence or to technical artifacts.
We also noted a significant, inverse correlation between the number of MSI events per sample and the expression of hMSH2 by immunocytochemistry. Because we noted fluctuation in hMSH2 levels throughout the treatment of several patients, it is possible that hMSH2 downregulation occurs at the post-transcriptional level and could be related to the use of chemotherapy based on alkylating agents. Interestingly, Watanabe and coworkers [9] described induction of MSI in ovarian tumours from patients studied before and after they received cisplatin-based chemotherapy, but in their study the cisplatin-induced MSI correlated with a significant decrease in hMLH1 expression.
We believe that the transient decrease in hMSH2 expression that we noted might have predisposed to an increased number of MSI events. In fact, Zhu and coworkers [10] also described abnormal hMSH-2 expression in about one-third of cases of AML, mainly in those with treatment-related secondary AML and in patients who were elderly.
PCNA is a 36 kDa nuclear protein that functions as a cofactor for δ-DNA polymerase, which is regulated in a cell cycle dependent manner. PCNA expression also increases when cells are actively engaged in DNA repair [11]. The correlation of PCNA with LOH but not with the number of MSI events found in the present study may indicate that PCNA may have greater expression in situations of more intense DNA damage that tend to be long-lasting. Interestingly, our group previously showed that PCNA over-expression was associated with resistance to chemotherapy AML and chronic lymphocytic leukaemia [12,13].
Conclusion
We conclude that systemic chemotherapy may induce MSI and LOH in PBMCs from breast cancer patients receiving chemotherapy regimens, especially chemotherapy regimens containing alkylating agents. Furthermore, it is possible that these alterations are associated with a chemotherapy-induced decrease in the expression of hMSH2. These effects may be related to the generation of secondary leukaemia in some patients, and may also intensify the genetic instability of tumours and increase resistance to treatment.
Abbreviations
AML = acute myeloid leukaemia; LOH = loss of heterozygosity; MSI = microsatellite instability; PBMC = peripheral blood mononuclear cell; PCNA = proliferating nuclear antigen; PCR = polymerase chain reaction.
Competing interests
The author(s) declare that they have no competing interests.
Supplementary Material
Additional File 1
An Excel file showing raw data for each patient studied.
Click here for file
Additional File 2
A figure showing the results of testing in 20 normal women. The 20 normal women were tested for MSI using the PCR15.1 and TP53Alu markers. Each normal woman's sample corresponds to one letter (e.g. 'A' represents the first sample of the first control woman and 'A1' her second sample, collected 3 months latter). We noted only one case (S and S1) in which the second sample showed an extra band with the marker PCR15.1 (indicated by an arrow).
Click here for file
Acknowledgement
This study was supported by grant FAPESP N° 00/04681-2. This work was previously presented as a poster at the 2004 ASCO meeting in New Orleans.
Figures and Tables
Figure 1 Loss of heterozygosity (LOH) and microsatellite instability (MSI) occurrence. (a) The clinical course of one of the studied patients who received three cycles of neoadjuvant paclitaxel (P) followed by four cycles of neoadjuvant doxorubicin with cyclophosphamide (AC) before surgery (Sx) and radiation therapy (Rxt). Each star below the straight line indicates a blood sample collection (time intervals between collections are not to scale). (b) Single-strand conformation polymorphism (SSCP) gels for two MSI markers PCR15.1 and ALU from the patient represented in panel a, showing the occurrence of LOH and MSI (black arrows). Each lane corresponds to one of the blood samples collected from this patient. Note that MSI disappears whereas LOH persists.
Figure 2 Correlations between microsatellite instability (MSI) events, treatment, and hMSH2-positive cells. Significant correlations between (a) number of MSI events and type of treatment received by patients during which samples were collected (2, alkylating chemotherapy; 3, nonalkylating chemotherapy; 4, radiation therapy; 5, hormone therapy; and 6, off treatment), (b) percentage of hMSH2-positive cells and number of MSI events (MSI and loss of heterozygosity [LOH]), and (c) percentage of hMSH2-positive cells and type of treatment received by patients during which samples were collected (1, before treatment; 2, alkylating chemotherapy; 3, nonalkylating chemotherapy; 4, radiation therapy; 5, hormone therapy; 6, off treatment). Values are expressed as means and standard errors (for P values see text).
Table 1 Peripheral blood mononuclear cells positive for each of the studied markers by immunocytochemistry
Protein Percentage
PCNA 15.93 ± 4.38
hMLH1 0.25 ± 1.11
hPMS1 0.6 ± 0.99
hPMS2 0 ± 0
hMSH2 72.7 ± 20.33
TP53 0 ± 0
Values are expressed as mean ± standard deviation (%).
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| 15642167 | PMC1064099 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Nov 4; 7(1):R28-R32 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr950 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9511564217110.1186/bcr951Research ArticleVariants in estrogen-biosynthesis genes CYP17 and CYP19 and breast cancer risk: a family-based genetic association study Ahsan Habibul [email protected] Alice S [email protected] Yu [email protected] Ruby T [email protected] Steven P 4Wang Qiao 2Gurvich Irina 2Santella Regina M 25Regina [email protected] Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA2 Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, USA3 Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA4 Department of Psychiatry, Columbia University College of Physicians and Surgeons and the New York State Psychiatric Institute, New York, New York USA5 Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA2005 11 11 2004 7 1 R71 R81 10 6 2004 2 8 2004 2 9 2004 29 9 2004 Copyright © 2004 Ahsan et al. licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Case-control studies have reported inconsistent results concerning breast cancer risk and polymorphisms in genes that control endogenous estrogen biosynthesis. We report findings from the first family-based association study examining associations between female breast cancer risk and polymorphisms in two key estrogen-biosynthesis genes CYP17 (T→C promoter polymorphism) and CYP19 (TTTA repeat polymorphism).
Methods
We conducted the study among 278 nuclear families containing one or more daughters with breast cancer, with a total of 1123 family members (702 with available constitutional DNA and questionnaire data and 421 without them). These nuclear families were selected from breast cancer families participating in the Metropolitan New York Registry, one of the six centers of the National Cancer Institute's Breast Cancer Family Registry. We used likelihood-based statistical methods to examine allelic associations.
Results
We found the CYP19 allele with 11 TTTA repeats to be associated with breast cancer risk in these families. We also found that maternal (but not paternal) carrier status of CYP19 alleles with 11 repeats tended to be associated with breast cancer risk in daughters (independently of the daughters' own genotype), suggesting a possible in utero effect of CYP19. We found no association of a woman's breast cancer risk either with her own or with her mother's CYP17 genotype.
Conclusion
This family-based study indicates that a woman's personal and maternal carrier status of CYP19 11 TTTA repeat allele might be related to increased breast cancer risk. However, because this is the first study to report an association between CYP19 11 TTTA repeat allele and breast cancer, and because multiple comparisons have been made, the associations should be interpreted with caution and need confirmation in future family-based studies.
breast cancercyp17cyp19estrogen biosynthesis genesfamily-based design
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Introduction
Cumulative exposure to circulating estrogen is considered to be of primary importance in breast cancer etiology. Estrogen biosynthesis, cellular binding and metabolism involve many steps, and the genes controlling these steps may contribute to inherent variability in breast cancer susceptibility. Endogenous estrogen is produced predominantly in the ovarian theca cells in premenopausal women and in the breast stromal adipose cells in postmenopausal women. The present study focuses on CYP17 and CYP19, two key genes that control the biosynthesis of estradiol and estrones from their lipid precursors and are expressed in these cells. CYP17 controls two successive early steps of endogenous estrogen biosynthesis by converting pregnenolone and progesterone to precursors of androgen and estrogen. CYP19, also known as aromatase, controls the terminal step of estrogen biosynthesis by converting 19-carbon steroids (testosterone and androstenedione) to 18-carbon estrogens (estradiol and estrone).
A T→C single-nucleotide polymorphism in the 5' promoter region of the CYP17 gene and a TTTA repeat polymorphism in the exon 4–intron 5 boundary region of the CYP19 gene have been investigated in breast cancer by several studies, with inconsistent results [1,2]. For both polymorphisms the variant alleles are considered to be related to an increased biosynthesis of endogenous estrogen. The CYP17 T→C polymorphism is thought to create an Sp1-type (CCACC) promoter site (although one study did not confirm this [3]) and is associated with an increased serum estrogen level [4,5]. After Feigelson and colleagues first published their study [6] showing a higher risk of breast cancer in relation to the CYP17 C allele among non-Caucasian women, many other authors attempted to replicate this in other populations. Although some studies confirmed this initial finding, others did not. All studies reporting an increased risk, including the original study, found the increased risk in one or more certain subgroups of women studied, for example women with advanced disease [6], women aged less than 40 years [7], women aged less than 40 years with family history [8], women aged more than 55 years [9], and women also carrying other genetic polymorphisms [10]. Two studies found that women carrying CYP17 C allele are less likely to use hormone replacement therapy [5,11] and three studies found that the protective effect of later age at menarche is stronger among women who do not carry the C allele [5,6,12]. A recent meta-analysis concluded that the CYP17 T→C polymorphism is not a significant independent risk factor for breast cancer [2].
The CYP19 gene contains a variable number (range 7–13) of TTTA repeats in the exon 4–intron 5 boundary region, creating polymorphisms that have been examined in five studies [13-17]. Kristensen and colleagues [13] and subsequently others found a roughly twofold to fourfold elevated risk in relation to certain numbers of CYP19 TTTA repeat polymorphisms. Although one small study found a higher risk in relation to the TTTA seven repeats allele, (TTTA)7 [14], most studies reporting an association found elevated risks in relation to one of the higher number of TTTA repeat alleles: 10 repeats, (TTTA)12 [13]; 12 repeats, (TTTA)10 [15,16]; or 10 or more repeats, (TTTA)≥10 [17]. A meta-analysis published in 1999 based on some of the earlier studies found that women carrying the CYP19 (TTTA)10 allele were at higher risk of breast cancer [1].
All published studies of association between the CYP17 and CYP19 polymorphisms and breast cancer discussed above used a classical case-control design. A recent meta-analysis of CYP17 T→C polymorphism indicates substantial differences in genotype frequencies in case-control studies conducted in different populations [2], with proportions of carriers ranging from 0.46 in the UK [18] to 0.79 in Japan [19] and proportions of homozygotes ranging from 11% in Finland [12] to 36% in Taiwan [10]. Similarly, the allele frequency of the CYP19 (TTTA)10 allele ranges from 0.5% [15] to 1.8% [14]. Given that case-control studies can be susceptible to population stratification bias, it is important to examine these potentially important biologically plausible hypotheses in family-based studies that are free from such bias. In this study we examine the association between the CYP17 promoter T→C and CYP19 TTTA repeat polymorphisms and female breast cancer by using a family-based design among nuclear families participating in the Metropolitan New York Registry (MNYR), one of the six international centers of the National Cancer Institute's Breast Cancer Family Registry project. Although other polymorphisms in the CYP17 and CYP19 genes have been reported, we focused on these two polymorphisms because they have been studied most extensively both in relation to their potential associations with breast cancer and also in relation to their influence on circulating estrogens.
All published studies focused on the relationship between a woman's own constitutional genotype and her breast cancer risk. A body of recent literature has provided limited data suggesting that a woman's breast cancer risk might be related not only to her own endogenous estrogens during adolescence and adulthood, but also to her prenatal exposure; that is, her exposure in utero to her maternal circulating estrogens [20-25]. In addition to the main association between a woman's own genotype and her breast cancer status, the family-based design of the present study allows us to address this hypothesis indirectly, by examining the association between maternal carrier status of CYP17 or CYP19 gene variants (that is, exposure in utero to an altered level of maternal estrogens) and breast cancer status in daughters.
Methods
Selection of study participants
Since 1995 the MNYR has been recruiting families with breast and/or ovarian cancers in clinical and community settings within the metropolitan New York area. Families meeting one or more of the following criteria are invited to participate: a female less than 45 years of age at diagnosis of breast cancer; a female with both breast and ovarian cancer; three or more relatives with breast or ovarian cancer diagnosed at age 45 years or more, or any male with breast cancer. After identification of a proband he/she is invited to participate in the registry and his/her family's eligibility is assessed. If the family is eligible and the proband agrees to participate, after appropriate informed consent, he/she is interviewed either in person or by phone with an epidemiology questionnaire and a family-history questionnaire. The proband is also asked to provide permission to contact family members. Blood or buccal samples are also collected and participants are provided with a self-administered dietary questionnaire to be returned by mail. Once family members consent to participate, data and blood or buccal samples from the family members are also collected in a similar manner. For members affected with cancer, tumor tissue samples are collected and reviewed pathologically. Genomic DNA from white blood cells or buccal samples has been collected for participants who donated biological samples. So far, the MNYR has enrolled 1158 families and more than 3900 total participants.
For this study we restricted attention to nuclear families with at least one affected daughter and at least one parent and/or sibling for whom DNA samples were available. Of the 1158 families enrolled in the MNYR so far, 278 families met these eligibility criteria. Subjects can participate in the MNYR with or without completion of the full epidemiology questionnaire and/or blood samples. There were 1123 family members in the 278 eligible nuclear families, of whom 702 completed the full epidemiology questionnaire and provided blood samples. However, accurate data on relevant variables for the statistical method used in this study (see below) for the remaining 421 members were available from the family-history questionnaire completed by the 702 members. There was 99% concordance in data on age and affected status between women who completed the full epidemiology questionnaire and women who did not.
Laboratory analysis
We evaluated association between the T→C single-nucleotide polymorphism in the promoter region of the CYP17 gene and the tetranucleotide (TTTA) repeat polymorphism in the exon 4–intron 5 boundary of the CYP19 gene. A total of 23 subjects could not be genotyped for CYP17, and 26 subjects could not be genotyped for CYP19. Genotype data were available on a total of 679 members (from 277 nuclear families) for CYP17 and 676 members (from 278 nuclear families) for CYP19.
The CYP17 promoter polymorphism was determined with template-directed primer extension and detection by fluorescence polarization in a 96-microwell-based format [26,27]. In brief, DNA isolated from blood cells by salting out was used for genotyping subjects. First, the target DNA was amplified by polymerase chain reaction (PCR; using forward primer 5'-TTTAAAAGGCCTCCTTGTGC-3' and reverse primer 5'-TTGGGCCAAAACAAATAAGC-3') to generate products in the range 100–200 base pairs. After amplification by PCR, the primers were digested with shrimp alkaline phosphatase and Escherichia coli exonuclease I. Then single-nucleotide extension was performed in the presence of the appropriate allele-specific ddNTPs differentially fluorescence-labeled with either R110 or tetramethylrhodamine purchased from NEN Life Sciences (Boston, MA). For the single-nucleotide extension reaction both forward and reverse probes were tested to select the optimum (the forward probe 5'-GCCACAGCTCTTCTACTCCAC-3') on the basis of clear signal differences. The incorporation resulted in diminished rotation of the fluor compared with the ddNTP. Finally, the fluorescence polarization was read on a fluorescence polarization microplate reader (Tecan Polarion, Research Triangle Park, NC). The reader generates the genotype data on the basis of the distinct separations (with appropriate cut-offs) of the fluorescent intensity values for different alleles in comparison with internal controls.
The CYP19 TTTA repeats were determined by PCR amplification (using the forward primer 5'-GTCTATGAATGTGCCTTTTT-3' and the reverse primer 5'-GTTTGACTCCGTGTGTTTGA-3') followed by analysis on an ABI 377 system with GenScan software on the basis of the separations on gel according to the differences in the number of TTTA repeats.
All laboratory assays were performed with laboratory personnel blinded to the subject's disease status or family relationships. In addition to assay-specific quality-control samples, 10% of samples were reassayed after relabeling to keep laboratory staff blinded to its identity.
Statistical analysis
We used the Family Genetic Analysis Program (FGAP [28], freely available at ) to test the null hypothesis of no association between genotype and breast cancer risk in nuclear families. The FGAP computes two test statistics: the nonfounder statistic (NFS), a generalization of the transmission disequilibrium test (TDT) [29,30], which evaluates transmission disequilibrium from parents to offspring, and the founder statistic (FS), which compares the distribution of parental genotypes with that expected under the null hypothesis of no association. The FGAP statistics fully exploit data from families with variable numbers of affected/unaffected members with variable (known/unknown) patterns of parental genotypes. They are similar to, but can be more powerful than, those available in the software FBAT [31]. (See [32] for a comparison of the methods.)
On the basis of the previous evidence [6,13,15,17], we hypothesized that breast cancer risk is elevated among carriers of the CYP17 C allele and the CYP19 variant alleles with 10 or more TTTA repeats, namely the (TTTA)10, (TTTA)11, (TTTA)12, and (TTTA)13 alleles. The data analysis was focused on two specific components of the study hypotheses: first, whether a woman's carrier status of the hypothesized alleles is associated with her breast cancer status, and second, whether a mother's carrier status of the hypothesized alleles is associated with her daughter's breast cancer risk. For testing the first component of a hypothesis, we applied the FS and NFS to assess whether specific genotypes of each of the studied genes are related to breast cancer. Because FS and NFS follow a normal Gaussian distribution under the null hypothesis, the assessment of statistical significance of the association can be done on the basis of the deviation of these statistics from the standard critical values under normal distribution.
For simplicity, we describe these analyses for the CYP17 gene as applied to nuclear families consisting of two parents and at least one daughter. Parents may be untyped and the mother's breast cancer status may be unknown. The test statistics, which are likelihood-based score statistics, are obtained by summing the score contributions from each family. These family-specific scores are obtained in three steps.
In the first step we imputed a probability distribution for the genotypes of each pair of parents, conditional on the observed genotypes of all family members. To do this, we obtained maximum-likelihood estimates of the genotypes TT, TC and CC for each of a pair of parents, given the observed genotypes in the family. These estimates do not require the assumption of Hardy–Weinberg frequencies for parental genotypes. If, for example, both parents' genotypes were known, then the probabilities are degenerate at the observed genotypes. Similarly, if both parents' genotypes were unknown but two offspring had observed CYP17 genotypes TT and CC, then the parental distributions are degenerate at TC because both parents must be heterozygotic.
In the second step we used the inferred parental genotype distribution and the offspring's observed genotypes to test whether heterozygous parents were equally likely to transmit T and C alleles to affected daughters. This evaluation is based on the NFS. Under the null hypothesis of equal transmission of T and C alleles from parents to affected daughters, the NFS has an asymptotic standard Gaussian distribution. The NFS generalizes the TDT to families with untyped parents and to families with both affected and unaffected daughters. It can be considerably more powerful than the sibling TDT test [33] when applied to families without unaffected daughters.
In the final step we used the inferred parental genotypes (and the mothers' breast cancer phenotypes) in the FS to compare the parental genotype distribution with the expected distribution under the null hypothesis of no association. This statistic treats the affected and unaffected mothers like cases and controls in a case-control study. However, each parent's contribution is weighted in proportion to his/her number of affected and unaffected daughters, so that parents of many affected daughters receive higher weights than do those of few affected daughters.
To test the second component of our hypothesis, namely the association between maternal carrier status and daughter's breast cancer status, we evaluated whether the genotypes of mothers with more affected daughters differ from those of mothers with less affected daughters. Such deviation might be expected if some aspect of a daughter's environment in utero, governed by the mother's genotype, influences the daughter's risk of subsequent breast cancer development. The FS was adapted to evaluate this question by comparing the observed or imputed genotypes of mothers of affected daughters with the genotypes expected in the parental population. It is a weighted sum of differences between each mother's observed (or inferred) C allele count and the average C count in the population. In symbols, each family's contribution to this sum is proportional to the quantity (nA - nU)(Cobs - Cexp), where nA and nU are,, respectively, the numbers of affected and unaffected daughters in the family, and Cobs and Cexp are the observed and expected C-allele counts for the mother. Under the null hypothesis of no association between maternal genotype and daughters' breast cancer risks, Cobs has a mean value Cexp, so Cobs - Cexp = 0 in expectation for all families. Thus the FS has expectation zero and the correct type I error rate regardless of the actual numbers of affected and unaffected daughters in each family. Under the alternative hypothesis that maternal C-allele count is associated with daughters' breast cancer risks, one expects that Cobs - Cexp > 0, and thus families with many affected daughters and few unaffected daughters (that is, nA - nU >> 0) contribute larger values to the FS than those with few affected daughters or those with many unaffected daughters. A statistically significant value of the FS when restricted to the mothers (with an insignificant value when restricted to the fathers) would provide evidence for this association.
When the null hypothesis is rejected, it is useful to estimate a measure of association between genotype and risk, such as the odds ratio, and to evaluate the effects of potential confounding by hormonal factors. To do so, we also performed conditional logistic regression analyses [34,35] on all the available sibships containing at least one affected sibling and at least one unaffected sibling who had provided blood samples and relevant epidemiology data for statistical adjustment (165 sibships for CYP17 and 169 sibships for CYP19).
Results
Of the 277 nuclear families eligible for CYP17 analyses, 229 were Caucasian, 4 were African American, 41 were Hispanic, and 3 were Asian American. Of the 278 nuclear families eligible for CYP19 analyses, 229 were Caucasian, 4 were African American, 42 were Hispanic, and 3 were Asian American. Table 1 shows the distribution of the study subjects according to CYP17 and CYP19 genotypes, by family position and breast cancer status. The numbers in each cell represent the number of specific type of family members in our study population carrying a particular genotype. The number of TTTA repeats in intron 4 of the CYP19 gene ranged between 7 and 13 in our study population, with the (TTTA)7 and (TTTA)11 alleles being the most frequent (allele frequencies 53.9% and 28.8%, respectively). These frequencies are consistent with those found in Caucasian populations in other studies in the USA [15]. The frequency of the CYP17 variant C allele was 42.8% in this study population, which is similar to that found in other studies conducted in Caucasians [4].
The distribution of the nuclear families according to mother's and father's carrier status and mother's and daughter's affected status is presented in Table 2. A majority (about 55%) of the nuclear families contained one affected and one unaffected daughter. The majority of the nuclear families had one or more parents who did not have the genotyping information available.
Table 3 presents the FS and NFS for testing the associations between the a priori hypothesized CYP17 and CYP19 variant alleles and breast cancer. Each test statistic has an approximately standard Gaussian distribution under the null hypothesis of no association between genotype and breast cancer risk. A positive value of a NFS reflects excess transmission of the variant allele to affected daughters, and a negative value represents fewer such transmissions than expected under the null. Thus a test statistic that is negative but large in absolute value would suggest that the variant allele is associated with reduced risk. We computed the FS and NFS under recessive, dominant, and additive models. For the dominant models, the number of affected daughters carrying one or more copies of the variant alleles was compared with that expected from the parental genotypes in accordance with Mendelian expectation. Similarly, for the recessive models, the number of affected daughters homozygous for the variant allele was compared with that expected under Mendelian expectation. For the additive models, the total variant allele count in affected daughters was compared with that expected from the parental genotypes in accordance with Mendelian expectation. On the basis of the literature, we hypothesized a priori that CYP19 alleles with 10 or more TTTA repeats would be associated with breast cancer. In addition, we examined the association between the CYP19 genotype and breast cancer by defining the variant allele(s) by treating each of the 10 or more repeat alleles, (TTTA)10, (TTTA)11, (TTTA)12 and (TTTA)13, separately as the variant allele under each of the three models (realizing that this might have increased the chance of our finding of a statistically significant association; see the Discussion section).
As seen in Table 3, the NFS for association between the (TTTA)11 allele and breast cancer under the dominant model is 1.83, which is higher than the critical value (1.65) for a one-tailed test statistic, suggesting that affected daughters were more likely to receive the (TTTA)11 allele from their parents (irrespective of their ethnic distribution) than unaffected daughters. Like the NFS, the FS was also statistically significant under the dominant model, supporting an association between the CYP19 (TTTA)11 allele and breast cancer among the parents in these families. The results for CYP19 TTTA≥10 alleles did not show a consistent association, because only the FS was statistically significant under the dominant model. None of the other specific CYP19 alleles showed a consistent association with breast cancer on the basis of the NFS and FS (results not shown). Although the FS found an association between the CYP19 (TTTA)13 allele and breast cancer, this was not supported by the more robust NFS (results not shown).
Neither the FS nor the NFS suggested any significant association between the CYP17 variant C allele and breast cancer, under any of the models of FGAP analyses (see Table 3).
Table 4 presents the results of conditional logistic regression analysis comparing the CYP17 and CYP19 genotypes between affected and unaffected sisters. These results, adjusted for age (in years), hormone replacement use (ever/never), oral contraceptive use (ever/never), age at menarche (in years) and term pregnancies (yes/no), are similar to the FGAP results although because of the smaller number of available sibships the associations did not achieve statistical significance. As seen in Table 4, carriers of the CYP19 (TTTA)11 allele had an increased risk of breast cancer (odds ratio 1.8; 95% confidence interval 0.9–3.5).
Table 5 presents results relating maternal and paternal carrier statuses for the variants of estrogen-biosynthesis genes CYP17 and CYP19 to breast cancer risk in daughters. Mothers of affected daughters were more likely to carry the CYP19 (TTTA)11 allele than expected in the parental population. There were no such associations between the paternal carrier status of (TTTA)11 and any of the other CYP19 alleles and breast cancer in daughters. For this hypothesis, the findings for analysis involving CYP19 (TTTA)≥10 corroborated that for (TTTA)11 alleles. Although maternal carrier status of the CYP17 C allele tended to be positively associated with daughter's breast cancer, this association was not specific to the mothers but was also present among the fathers.
Discussion
Despite a sound biological basis for the role of estrogen-biosynthesis genes in breast cancer, the findings of studies investigating the relationship between these genes and breast cancer have not been consistent. Employing a case-control design, many of these prior studies, especially those examining the CYP17 gene–breast cancer relationships, produced conflicting results. Although in comparison with CYP17 a smaller number of studies investigated the association of breast cancer with CYP19, findings for CYP19 have been more consistent, with most studies showing a positive association between CYP19 alleles with a higher number (10, 12, or 10 or more) of TTTA repeats and breast cancer [13,15-17].
Using a family-based design we investigated the relationships between the CYP17 and CYP19 gene variants and breast cancer in families participating in the MNYR. Like many of the previous case-control studies, the present study did not find any association between the CYP17 C (variant) allele and breast cancer. However, our findings support an association between certain alleles of the CYP19 intron 4 TTTA repeat polymorphism and breast cancer. On the basis of the previous studies we defined each of the CYP19 alleles with 10, 11, 12, or 13 TTTA repeats as the 'variant' allele and examined each association with breast cancer. Unlike some of the previous case-control studies we did not find the CYP19 (TTTA)10 or (TTTA)12 alleles to be associated with breast cancer. However, we found the CYP19 (TTTA)11 allele to be significantly associated with breast cancer in these nuclear families, under a dominant model. Although we also observed a significantly positive association between the CYP19 (TTTA)13 allele and breast cancer among the parents in these families, we did not observe excess transmission from parents to affected daughters, suggesting that the association might be due to chance or bias. The evidence of an increased risk in relation to the CYP19 (TTTA)11 allele was also observed in the conditional logistic regression analysis adjusting for potential confounding variables among the subset of families containing discordant sibships. However, because of the reduced power of these analyses among only a subset of families [36], results of these discordant sibship analyses did not achieve statistical significance.
In addition to evaluating associations between a woman's breast cancer risk and her own constitutional genotype, we also evaluated whether maternal genotypes are associated with the breast cancer risk in the daughters (independent of the daughter's own genotype). We found that the maternal (but not the paternal) genotypes of the CYP19 (TTTA)11 allele conferred a non-significantly elevated breast cancer risk to daughters. This effect was also observed when all (TTTA)≥10 alleles were treated as the variant allele. This association is consistent with evidence from the previous literature on the association between exposure to hormonal factors in utero and breast cancer risk in adulthood [20]. Although the association might be due to chance, if confirmed in subsequent studies it will have important implications in advancing our understanding of the breast cancer etiology.
Some limitations of the present study merit consideration. The major limitation concerns statistical power. The analysis, which is based on 287 nuclear families, might not have had enough power to detect small increases in risk associated with certain of the CYP17 genotypes. For example, we lacked power to evaluate interactions between genotypes for CYP17 and CYP19 and both endogenous and exogenous hormonal characteristics, such as age at menarche, timing and number of pregnancies and the use of exogenous hormones. In addition, although there is evidence for variations in the allele frequencies of the studied polymorphisms across ethnic groups, we lacked statistical power to conduct ethnicity-specific analyses. The evaluation of such analyses will be the subject of a separate future analysis, based on additional numbers of Breast Cancer Family Registry families.
Although the hypotheses examined in this study are not novel, the study design (which is free from population stratification bias) and the analytical approach have not been applied to these hypotheses in previous studies. Several limitations of this study require caution when interpreting the findings. First, the selection of nuclear families participating in this study from the MNYR was not population-based. Although this might limit the generalizability of the findings it should not affect the validity of the observed associations. Second, although it is possible for variations in the number of nucleotide repeats in hormone-related genes to be associated with cancer risk, such an association is less plausible biologically for the TTTA repeat numbers in the CYP19 gene. This is because the TTTA polymorphism is in the intronic region of the gene and so it is less likely that the variant alleles of the gene are directly associated with the functional status of endogenous estrogens in the body. Nevertheless, it is possible that one or more of the CYP19 TTTA alleles, including the (TTTA)11 allele, are in linkage disequilibrium with other functionally relevant alleles, as suggested by other studies [16]. Third, the present study compared multiple CYP19 TTTA alleles with breast cancer under different models. Although it is possible that multiple comparisons might have led to the observed associations, the consistency of the associations involving the CYP19 (TTTA)11 allele across both parents and transmission to offspring as well as the similarity between the associations with both constitutional and maternal genotypes suggest that these findings might have a biological basis. Further, the fact that the association was observed under specific susceptibility models and was consistent with conditional logistic regression analysis might be suggestive of the specificity of the finding.
Conclusion
This family-based study found that the CYP19 (TTTA)11 allele is associated with breast cancer risk among families participating in a breast cancer family registry. The study also suggests that maternal carrier status of the CYP19 (TTTA)11 allele might be associated with breast cancer in daughters in these families. These associations might have important implications for understanding the etiology and risk prediction of breast cancer. However, because this is the first study to report an association with the CYP19 (TTTA)11 allele, and because multiple comparisons have been made, the associations reported in this study should be interpreted with caution and need to be confirmed in future family-based studies.
Abbreviations
FGAP = Family Genetic Analysis Program; FS = founder statistic; MNYR = Metropolitan New York Registry; NFS = nonfounder statistic; PCR = polymerase chain reaction; TDT = transmission disequilibrium test.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
This research was supported by US Department of Defense grant no. DAMD17-00-1-0213 and National Cancer Institute under RFA no. CA-95-003, Breast Cancer Family Registry, through cooperative agreement with Columbia University.
Figures and Tables
Table 1 Genotype distribution of the study population by gender, family relationship and affected status
Mothers Daughters
Genotype Affected Unaffected Affected Unaffected Fathers Sons Total
CYP17
CC 2 10 47 41 6 11 117
CT 8 32 147 108 30 22 347
TT 2 19 107 61 8 18 215
Unknown 64 140 0 0 233 0 437
Total 76 201 301 210 277 51 1116
CYP19 (no. of TTTA repeats)
7/7 1 15 88 55 7 21 187
7/8 0 10 34 29 4 5 82
7/9 0 0 1 0 1 0 2
7/10 0 1 5 6 2 0 14
7/11 9 19 106 71 14 19 238
7/12 0 2 9 8 0 0 19
7/13 0 0 0 0 1 0 1
8/8 0 0 4 1 0 0 5
8/10 0 1 3 2 0 0 6
8/11 1 2 24 16 1 3 47
8/12 0 0 1 2 0 0 3
8/13 0 0 0 1 0 0 1
9/11 0 0 0 1 0 0 1
10/11 0 0 2 3 0 2 7
11/11 0 5 19 16 1 1 42
11/12 0 1 4 2 0 0 7
11/13 1 0 2 1 1 0 5
11/not 11a 1 3 0 0 5 0 9
Unknown 63 143 0 0 241 0 447
Total 76 202 302 214 278 51 1123
aIndicates those whose genotype cannot be inferred for both alleles; the other allele could be 7, 8, or 12. Two of these nine observations, one an unaffected mother and the other the father in the same nuclear family, will be excluded when the allele with 10 or more repeats is selected as bad allele, because either them could be 11/12.
Table 2 Distribution of participating nuclear families according to mother's breast cancer status, mother's and father's carrier status of the CYP17 and CYP19 variant alleles, and number of affected and unaffected daughters
Number of nuclear families according to mother's breast cancer status and genotype Number of nuclear families according to father's genotype
Number of daughters affected/unaffected Affected Unaffected Total Total
Carrier Non-carrier Unknown Carrier Non-carrier Unknown Carrier Non-carrier Unknown
CYP17
1/0 3 0 19 17 12 29 80 12 2 66 80
1/1 5 2 38 16 6 85 152 19 4 129 152
1/2 0 0 2 4 0 14 20 4 2 14 20
1/3 0 0 1 2 0 0 3 0 0 3 3
2/0 2 0 3 1 1 9 16 0 0 16 16
2/1 0 0 0 0 0 2 2 0 0 2 2
2+/2+ 0 0 1 2 0 1 4 1 0 3 4
Total 10 2 64 42 19 140 277 36 8 233 277
CYP19 > = 10 repeats
1/0 4 0 17 14 13 29 77 7 4 66 77
1/1 5 1 40 12 9 88 155 10 6 139 155
1/2 1 0 1 5 1 14 22 5 2 15 22
1/3 1 0 0 1 0 0 2 1 0 1 2
2/0 1 0 4 1 1 9 16 1 0 15 16
2/1 0 0 0 0 0 2 2 0 0 2 2
2+/2+ 0 0 1 0 1 2 4 0 0 4 4
Total 12 1 63 33 25 144 278 24 12 242 278
CYP19 = 11 repeats
1/0 4 0 17 11 16 29 77 6 5 66 77
1/1 5 1 40 11 10 88 155 9 7 139 155
1/2 1 0 1 6 1 13 22 5 3 14 22
1/3 1 0 0 1 0 0 2 1 0 1 2
2/0 1 0 4 1 1 9 16 1 0 15 16
2/1 0 0 0 0 0 2 2 0 0 2 2
2+/2+ 0 0 1 0 1 2 4 0 0 4 4
Total 12 1 63 30 29 143 278 22 15 241 278
Table 3 Association between the CYP19 and CYP17 variant alleles and breast cancer
Nonfounder statistic Founder statistic
Variant allele(s) Estimated allele frequency (%) Recessive model Dominant model Additive model Recessive model Dominant model Additive model
CYP17
C 42.46 - 1.01 - 1.52 - 1.85 0.40 1.08 1.01
P 0.16 0.06 0.03 0.34 0.14 0.16
CYP19
(TTTA)≥10 33.71 - 1.24 1.26 0.32 - 0.32 1.66 1.13
P 0.11 0.10 0.38 0.37 0.05 0.13
(TTTA)11 28.78 - 1.09 1.83 0.97 - 1.50 1.96 0.89
P 0.14 0.03 0.17 0.07 0.03 0.19
P values are based on one-tailed test statistics. Values that are statistically significant at one-tailed test are displayed in bold type.
Table 4 Conditional logistic regression analysis of discordant sibships for the association between CYP17 and CYP19 genotypes and breast cancer
Gene (sibling sets/cases/controls) Affected (n) Unaffected (n) Adjusted odds ratios for breast cancer (95% CI)
CYP17 (165/171/188)
Dominant model TT 59 56 1.00
TC/CC 112 132 0.86 (0.47–1.59)
Recessive model TC/TT 146 154 1.00
CC 25 34 0.61 (0.27–1.41)
General model TT 59 56 1.00
TC 87 98 0.86 (0.47–1.59)
CC 25 34 0.55 (0.21–1.42)
Additive model (trend per allele) 0.77 (0.49–1.21)
CYP19 (no. of TTTA repeats) (169/175/193)
Dominant model (TTTA)<10(TTTA)<10 67 78 1.00
(TTTA)≥10(TTTA)<10/(TTTA)≥10(TTTA)≥10 108 115 1.24 (0.63–2.46)
Recessive model (TTTA)<10(TTTA)<10/(TTTA)≥10(TTTA)<10 159 173 1.00
(TTTA)≥10(TTTA)≥10 16 20 0.82 (0.30–2.24)
General model (TTTA)<10(TTTA)<10 67 78 1.00
(TTTA)≥10(TTTA)<10 92 95 1.26 (0.64–2.51)
(TTTA)≥10(TTTA)≥10 16 20 0.98 (0.30–3.18)
Additive model (trend per allele) 1.11 (0.65–1.89)
Dominant model (TTTA)other(TTTA)other 77 95 1.00
(TTTA)11(TTTA)other/(TTTA)11(TTTA)11 98 98 1.77 (0.90–3.47)
Recessive model (TTTA)other(TTTA)other/(TTTA)11(TTTA)other 165 179 1.00
(TTTA)11(TTTA)11 10 14 0.66 (0.19–2.33)
General model (TTTA)other(TTTA)other 77 95 1.00
(TTTA)11(TTTA)other 88 84 1.84 (0.93–3.63)
(TTTA)11(TTTA)11 10 14 1.04 (0.27–4.08)
Additive model (trend per allele) 1.38 (0.79–2.40)
Odds ratios were adjusted for age (in years), hormone replacement use (ever/never), oral contraceptive use (ever/never), age at menarche (in years), full term pregnancies (yes/no). Each sibling set had at least one breast cancer case and one sister control. All the subjects included in the analysis had information for all the covariate variables. CI, confidence interval.
Table 5 Association between parental carrier status of the variant allele(s) and breast cancer risk in daughters
Test statistic
Mothers' carrier status and disease risk in daughters Fathers' carrier status and disease risk in daughters
Variant allele(s) Additive Dominant Additive Dominant
CYP17
C 1.47 1.09 1.40 1.07
P 0.07 0.14 0.08 0.14
CYP19
(TTTA)≥10 1.73 1.65 0.95 0.45
P 0.04 0.05 0.17 0.33
(TTTA)11 1.52 1.96 - 0.11 0.18
P 0.06 0.03 0.46 0.43
The test statistic was calculated under the additive model. P values are based on one-tailed test statistics.
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| 15642171 | PMC1064100 | CC BY | 2021-01-04 16:54:48 | no | Breast Cancer Res. 2005 Nov 11; 7(1):R71-R81 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr951 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9531564217310.1186/bcr953Research ArticleHistopathological features of breast tumours in BRCA1, BRCA2 and mutation-negative breast cancer families Eerola Hannaleena [email protected]ä Päivi [email protected] Anitta [email protected]äki Kristiina [email protected] Carl [email protected] Heli [email protected] Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland2 Department of Pathology, Helsinki University Central Hospital, Helsinki, Finland3 Department of Obstetrics and Gynaecology, Helsinki University Central Hospital, Helsinki, Finland4 Department of Clinical Genetics, Helsinki University Central Hospital, Helsinki, Finland2005 19 11 2004 7 1 R93 R100 15 6 2004 4 8 2004 31 8 2004 30 9 2004 Copyright © 2004 Eerola et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Histopathological features of BRCA1 and BRCA2 tumours have previously been characterised and compared with unselected breast tumours; however, familial non-BRCA1/2 tumours are less well known. The aim of this study was to characterise familial non-BRCA1/2 tumours and to evaluate routine immunohistochemical and pathological markers that could help us to further distinguish families carrying BRCA1/2 mutations from other breast cancer families.
Methods
Breast cancer tissue specimens (n = 262) from 25 BRCA1, 20 BRCA2 and 74 non-BRCA1/2 families were studied on a tumour tissue microarray. Immunohistochemical staining of oestrogen receptor (ER), progesterone receptor (PgR) and p53 as well as the histology and grade of these three groups were compared with each other and with the respective information on 862 unselected control patients from the archives of the Pathology Department of Helsinki University Central Hospital. Immunohistochemical staining of erbB2 was also performed among familial cases.
Results
BRCA1-associated cancers were diagnosed younger and were more ER-negative and PgR-negative, p53-positive and of higher grade than the other tumours. However, in multivariate analysis the independent factors compared with non-BRCA1/2 tumours were age, grade and PgR negativity. BRCA2 cases did not have such distinctive features compared with non-BRCA1/2 tumours or with unselected control tumours. Familial cases without BRCA1/2 mutations had tumours of lower grade than the other groups.
Conclusions
BRCA1 families differed from mutation-negative families by age, grade and PgR status, whereas ER status was not an independent marker.
breast canceroestrogen receptorhereditaryp53progesterone receptor
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Introduction
Women predisposed to hereditary or familial breast cancer form a heterogeneous group. It would be useful if we could identify carriers of the high-risk BRCA1 and BRCA2 genes and target the expensive and time-consuming genetic testing to individuals who most probably carry those mutations. Besides family history, histopathological markers could also be useful in distinguishing patients and families likely to carry a BRCA1/2 germline mutation from mutation-negative families and breast cancer patients in general.
Several studies have compared the characteristics of breast cancers in BRCA1 carriers and in sporadic controls. Distinct features between BRCA1-associated tumours have been found, such as high tumour grade, oestrogen receptor (ER) negativity, and overexpression of p53 [1-3]. In addition, negativity for progesterone receptor (PgR) [3,4], a higher proportion of medullary and atypical medullary carcinomas [5,6], and tumours with a low expression of c-erbB-2 [1,4,6] have been detected. Besides the higher proportion of medullary histology, a higher frequency of ductal carcinoma has also been reported [7,8]. Recently, cDNA expression analyses have suggested a basal epithelial phenotype for BRCA1 tumours [9] and expression of cytokeratins 5/6 has been associated with BRCA1 tumours [10].
Among BRCA2-associated tumours, a slight increase has been observed in the incidence of lobular or tubulolobular carcinomas [11,12]. However, results are inconsistent, and in most cases no significant difference has been found between BRCA2-associated tumours and sporadic cancers [1,4,6,13]. Complementary DNA (cDNA) expression analysis has suggested distinct expression profiles for BRCA2 tumours as well [14].
It has been clear for some time that in many families hereditary susceptibility is not due to the BRCA1 or BRCA2 genes [15,16]. Only a few studies have evaluated the features of this large group of families. It would be crucial for genetic counselling and for our understanding of tumour development to learn more about these patients. Lakhani and colleagues [17] studied the pathology of 82 familial breast cancers not attributable to BRCA1 or BRCA2 mutations, and they found these non-BRCA1/2 cancers to be of lower grade, to show less pleomorphism and to have a lower mitotic count than sporadic cancers or BRCA mutation-positive cancers. No other features differed significantly in their study. However, they did not examine immunohistochemical characteristics. There has been only one study so far that has characterised the immunohistochemical features of familial non-BRCA1/2 cancers: Palacios and colleagues [18] studied immunohistochemical staining and histopathology and compared 37 non-BRCA1/2 cancers with 20 BRCA1-associated and 18 BRCA2-associated cancers, and also with unselected control cancers. They similarly found those to be of lower grade than in all the other three groups. In comparison with sporadic cancers they were also more frequently p53-negative and erbB2-negative, and expressed reduced E-cadherin and β-catenin. However, the number of patients in this study was small and it was restricted to only a univariate analysis of the studied parameters.
Most of the previous studies on BRCA1-associated and BRCA2-associated cancers have studied highly selected patient groups, but in the present study families were collected with a simple criterion of at least three first-degree or second-degree relatives with breast or ovarian cancer with no restriction on age. We studied an extensive material of 152 non-BRCA1/2 tumours, and also 110 tumours from BRCA1/2 families for histopathological features as well as for the immunohistochemical expression of ER, PgR, p53 and erbB2. We describe here the histopathological profile of the tumours originating from non-BRCA1/2 breast cancer families and also present a multivariate analysis to find the independent markers that can further help in distinguishing especially BRCA1 mutation-positive families from other familial cases.
Patients and methods
Familial breast cancer patients were identified and collected by a systematic screening for family history at the Department of Oncology, Helsinki University Central Hospital, as described previously [19]. We defined breast cancer families by the selection criterion of at least three first-degree or second-degree relatives with breast or ovarian cancer (including the proband). We confirmed the genealogy of the families through population registries, and cancer diagnoses through the Finnish Cancer Registry. In this study we included 25 BRCA1 families, 20 BRCA2 families and 74 families not associated with either of these genes (non-BRCA1/2 families) (Table 1). All families had previously been tested for BRCA1 and BRCA2 mutations by mutation analysis of the whole coding sequences and exon/intron boundaries of the genes as described [16,20] or tested for all previously reported 18 Finnish BRCA1 and BRCA2 mutations [16,20-22].
We collected all the paraffin blocks of all the primary breast cancers that were available (n = 262) from these families. However, cases tested to be non-carriers in the mutation-positive families were excluded. In total, 51 cancers from the 25 BRCA1 families, 59 cancers from the 20 BRCA2 families and 152 cancers from the 74 non-BRCA1/2 families were included in this study. We studied the haematoxylin and eosin sections of the original blocks to achieve histological diagnosis and grading. Grading was performed according to Scarff-Bloom-Richardson modified by Elston and Ellis [23]. The most representative area of the tumour was punched to produce a hereditary breast cancer tissue microarray including two cores (diameter 0.6 mm) from all of the original blocks. The array block of non-BRCA1/2 cases was described previously by Vahteristo and colleagues [24]. All of the microarray slides were stained with routine methods by antibodies against ER, PgR, p53 and erbB2 in the same pathology laboratory as our controls. In brief, 5 μm sections were cut from paraffin-embedded blocks, deparaffinated in xylene, and dehydrated in a series of graded alcohols. The sections were pretreated in a microwave oven and incubated overnight with antibody. Antibodies for ER (dilution 1:50) and c-erbB-2 (NCL-CB11; dilution 1:400) were purchased from Novocastra (Newcastle upon Tyne, UK), and those for PgR (dilution 1:250) and p53 (dilution 1:100) were from Dako (Copenhagen, Denmark). The evaluation of the staining results was similar to that used in routine diagnostics and samples were considered positive when 10%, 10% and 20% of the cells were stained for ER, PgR and p53, respectively. Samples having a moderate or intense staining of the entire membrane in more than 10% of the tumour cells (2+ and 3+) were considered to be c-erbB-2-positive. Other staining patterns were considered to be negative (0 and 1+).
As a control group we drew from the archives of the Pathology Department at Helsinki University 862 unselected breast cancer tumours from the years 1997–2001 that were scored by the same pathologist (PH) as the tumours of the hereditary breast cancer patients.
Statistical analysis was conducted with SPSS version 8.0 for Windows. We tested the differences in continuous variables by the Mann–Whitney test and in dichotomous variables by the χ2 test or Fisher's exact test. In multivariate analysis, we used logistic regression analysis (stepwise backwards logistic regression, 99%). All P values are two-sided.
Permissions for this study were obtained from the ethics committees of the Department of Oncology and the Department of Obstetrics and Gynaecology, Helsinki University Central Hospital, and of the Ministry of Social Affairs and Health in Finland. Blood and tumour samples were used in this study with written informed consent from probands and family members.
Results
BRCA1-associated cancers were diagnosed at a younger age than unselected breast cancers (median ages 44 and 56 years, respectively; P ≤ 0.0005) and they were more often ER-negative and PgR-negative, p53-positive and of higher grade (Table 2). The frequency of medullary histology was also higher. In logistic regression analysis, taking into account all of these factors, the independent factors were age (P ≤ 0.0005), ER status (P = 0.0597), PgR status (P = 0.0170) and medullary histology (P = 0.0636).
In comparison with familial non-BRCA1/2 cancers (median age 55 years), a univariate analysis of BRCA1 tumours showed similar differences from unselected cancers (Table 2). However, in multivariate analysis, taking into account the same factors, the independent factors were age (P = 0.0012), grade (P = 0.0014) and PgR negativity (P = 0.0196) (Table 3). We did not find any significant differences between groups of tumours from carriers with different mutations or when comparing tumours from the carriers of the mutations at the 5' end or the 3' end of the gene.
BRCA2-associated cancers were diagnosed at younger age (median age 47 years) than unselected breast cancers (median age 56 years, P ≤ 0.0005). They were also more often ER-negative and PgR-negative (Table 2). In multivariate analysis, the independent factors were age (P ≤ 0.0005), PgR status (P = 0.0365) and p53 status (P = 0.0318).
When compared with familial non-BRCA1/2 cancers, the BRCA2-associated cancers were diagnosed at a younger age (median age 47 years; among non-BRCA1/2 patients the median age was 55 years; P ≤ 0.0005). No other variable differed significantly from non-BRCA1/2 cancers (Table 2). We did not find any significant differences between tumours originating from different mutation carriers or when comparing tumours from the carriers of mutations from the OCCR (Ovarian Cancer Cluster region) (n = 7) and the end of the gene. In a logistic regression analysis comparing BRCA2-associated cancers with non-BRCA1/2 cancers and taking into account age, grade and all tested histological and immunohistochemical factors, the only independently significant factor was age (P = 0.0001) (Table 2).
Among familial non-BRCA1/2 patients, the median age of onset was marginally younger (55.0 years) than among the unselected controls (56.0 years, P = 0.060). The frequency of grade I and II tumours was much higher among the non-BRCA1/2 group than in the unselected control group (odds ratio 1.8, P = 0.009) (Table 2) or in the BRCA1 and BRCA2 groups. In a multiple regression analysis comparing non-BRCA1/2 tumours with unselected controls, grade (odds ratio 0.54, P ≤ 0.00005) and ER status (odds ratio 2.4 for negative ER status, P = 0.0006) were the independent significant factors.
The erbB2 results were very similar in the three groups of familial cases, with 18.6%, 15.1% and 17.4% of the BRCA1, BRCA2 and non-BRCA1/2 tumours, respectively, expressing the erbB2 antigen (Table 2).
Discussion
Although genetic testing for BRCA1 and BRCA2 mutations is available, it is expensive, time-consuming and stressful to patients. Several models have therefore been developed for evaluating the probability of carrying a BRCA1 or BRCA2 mutation [25-30]. In our previous study of Finnish breast cancer families [15,29], multivariate analysis suggested simple family history criteria for breast cancer onset under the age of 40 years and the presence of ovarian cancer to be most strongly associated with BRCA1/2 mutation status [29]. However, in addition to family history, it is important to find other markers that could help to identify mutation carriers. In many countries, markers that are already routinely used, such as ER, PgR, p53 and the grade of the tumour, could serve as an excellent tool to aid distinguishing families because this information is easily available. In this study, we found that BRCA1-associated cancers have a different histological profile and can be distinguished from other familial cancers. Specifically, multivariate analysis revealed age, grade and PgR negativity as the independent factors distinguishing BRCA1 tumours from familial non-BRCA1/2 tumours. BRCA2-associated cancers did not differ greatly, but those were also significantly younger and there was a trend towards higher grade than among familial non-BRCA1/2 cancers.
ER negativity has previously been highlighted to be linked to BRCA1 tumours. It was obvious in this study too. Our results are consistent with, for example, the study of Lakhani and colleagues [4], in which BRCA1 cancers were clearly more ER-negative (90%) than unselected control cancers (35%). However, in our study the overall percentages of ER-negative cancers were much lower for both groups (67% among BRCA1 and 19% among unselected controls). This might be due to a different age distribution because the prevalence of ER-negative cases is higher among young breast cancer patients [31,32] and our familial cases were not selected on the basis of young age of diagnosis. Furthermore, there is a very high coverage mammography screening (in the general age group 50–60 years) in Finland, aiding in finding asymptomatic cancers, which are more often ER-positive as well as being of lower grade and PgR-positive [33].
In this study, we specifically sought to compare BRCA1 cancers with familial non-BRCA1/2 cancers as well, which is the relevant question in a clinical and genetic counselling setting. Surprisingly, in this analysis the ER status was not an independent marker in multiple regression. More important markers were age, PgR status and grade. ER status was dependent on age and grade. Palacios and colleagues [18] also compared BRCA1 cases with non-BRCA1/2 cases (cases from families with at least three affected with breast cancer, one of them diagnosed under 50 years of age). In a univariate analysis, the results showing ER and PgR negativity, p53 overexpression and high grade were very similar to ours. However, multivariate analysis was not used for evaluating independent markers. Recently, cDNA expression analyses have suggested a basal epithelial phenotype for BRCA1 tumours [9]. Epithelial phenotype is associated with breast cancers that express neither ER nor erbB2, a feature that also occurs in BRCA1-mutation carriers [10]. A large majority (20 of 22 tumours) of BRCA1-associated tumours were also ER-negative and erbB2-negative in our study, in accordance with the high frequency of ER-negative tumours among BRCA1 carriers. The frequency of erbB2 expression was similar in all three groups of familial tumours.
BRCA2-associated tumours did not differ significantly from familial non-BRCA1/2 tumours, although they were diagnosed at an earlier age. Our results on BRCA2 cancers were quite similar to those of Palacios and colleagues [18] and Lakhani and colleagues [17], although in the former study the BRCA2 cancers were more ER-positive and PgR-positive (on the basis of smaller sample set of 14 cases).
BRCA1 and BRCA2 mutations have been detected in quite a low proportion of breast cancer families. In Finland, among families with three affected first-degree or second-degree relatives without age restrictions, the proportions were 10% and 11% for BRCA1 and BRCA2, respectively [16,20]. Thus an important and large group of families is not due to mutations in the BRCA1 and BRCA2 genes. In this study, the familial non-BRCA1/2 cancers were diagnosed at a marginally younger age than those among unselected cases, and were more often of lower grade than the control cancers or BRCA1 and BRCA2 cancers. These factors might be influenced by recall bias because patients having affected relatives undergo diagnostic procedures earlier than women with no family history of breast cancer. However, no such influence is seen for BRCA1 or BRCA2 cases, which often have a much stronger family background of cancer. Furthermore, a lower grade among familial non-BRCA1/2 cases has been detected elsewhere as well [17,18].
In comparison with BRCA1 or BRCA2 cancers, familial non-BRCA1/2 cancers represented a much greater number of ER-positive and PgR-positive cases; however, in comparison with the unselected control group they were more receptor-negative. The frequency of ER-positive cancers among our control cancers is quite high, which might be due to a later year of diagnosis than for non-BRCA1/2 cancers and in general because of age distribution [34], mammography screening [33] or ethnic differences [35] in different populations.
Conclusions
In this report we have characterised familial non-BRCA1/2 tumours and evaluated routine immunohistochemical and pathological markers that could help us to further distinguish families carrying BRCA1/2 mutations from other breast cancer families. It is noteworthy here that, although ER negativity has been considered a hallmark of BRCA1 tumours, logistic regression analysis indicated that this was not an independent marker but was dependent on the age of diagnosis and tumour grade. When considering the possibility of mutation testing in the context of genetic counselling, for instance, it would be important to consider the tumour characteristics specifically in comparison with those from other breast cancer families. It also seems crucial to consider the histopathological features with regard to the age of the patients. In this study, the independent markers that distinguished BRCA1 carrier tumours from familial non-BRCA1/2 tumours were earlier age of diagnosis, negative PgR status and higher grade. These immunohistochemical and pathological characteristics of the tumours, which are available in routine pathological diagnostics, should be of value in evaluating the possibility of mutation and in targeting mutation screening in such families, especially when considering the characteristics of several tumours in the family and combined with the family history of cancer.
Abbreviations
cDNA = complementary DNA; ER = oestrogen receptor; PgR = progesterone receptor.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
We wish to thank families who participated in this study, Minna Merikivi for her help in sample collection, and the Finnish Cancer Registry for cancer diagnosis and identification numbers for archival material from the pathology laboratories. Financial support for this study was provided by grants from the Academy of Finland, from the Clinical Research Fund of Helsinki University Central Hospital, from the Finnish Breast Cancer Group, from the Finnish Cancer Society, from the Finnish–Norwegian Medical Foundation and from the Sigrid Juselius Foundation.
Figures and Tables
Table 1 Mutation spectrum and numbers of families and patients by mutation
Gene Mutation Number of families Number of patients
BRCA1 ex 10 782 del AA 1 1
ex 11 1047 C→T 1 1
ex 11 1731 C→T 1 2
ex 11 1806 C→T 1 1
ex 11 1924 del A 1 1
ex 11 2592 ins A 1 2
ex 11 2803 del AA 1 3
ex 11 3604 del A 3 3
ex 11 3744 del T 2 2
ex 11 3904 C→A 1 1
ex 11 4153 del A 1 1
int 11 4216 (- 2) A→G 4 9
ex 13 4446 C→T 2 14
ex 14 4599 G>T 1 3
ex 17 5145 del 11 1 4
ex 20 5370 C→T 1 1
ex 20 5382 ins C 2 2
Any 25 51
BRCA2 ex 9 999 del TCAAA 5 22
ex 10 1822 G→T 1 1
ex 11 4075 del GT 1 1
ex 11 4081 ins A 1 2
ex 11 5797 G→T 1 1
ex 11 6496 del CA 1 2
ex 11 6503 del TT 1 1
ex 15 7708 C→T 3 12
ex 18 8555 T→G 1 6
int 23 9346 (- 2) A→G 5 11
Any 20 59
Non-BRCA1/2 74 152
Table 2 Analysis of features among BRCA1-, BRCA2- and non-BRCA1/2-associated cancers and in unselected cancers
N (frequency, %) P
Feature BRCA1 BRCA2 Non-BRCA1/2 Unselected
Histology
Ca ductale 37 (72.5) 37 (62.7) 102 (67.1) 561 (65.1)
Ca lobulare 8 (15.7) 17 (28.8) 30 (19.7) 219 (25.4)
Ca medullare 5 (9.8) - 3 (2.0) 20 (2.3) 0.013*, 0.001**
Others 1 (2.0) 5 (8.5) 17 (11.2) 62 (7.2)
Grade
I 3 (6.1) 12 (23.1) 46 (32.4) 152 (23.3)
II 11 (22.4) 26 (50.1) 66 (46.5) 289 (44.4)
III 35 (71.4) 14 (26.9) 30 (21.1) 210 (32.3)
I–II 14 (28.6) 38 (73.1) 112 (78.9) 441 (67.7) ≤ 0.0005*, <0.0005**, 0.009****
Immunohistochemistry
ER- 28 (66.7) 17 (32.1) 40 (27.0) 167 (19.4) ≤ 0.0005*, <0.0005**, 0.026***, 0.035****
ER+ 14 (33.3) 36 (67.9) 108 (73.0) 692 (80.6)
PgR- 37 (84.1) 28 (51.9) 67 (45.3) 312 (36.2) ≤ 0.0005*, <0.0005**, 0.021***, 0.036****
PgR+ 7 (15.9) 26 (48.1) 81 (54.7) 549 (63.8)
p53- 27 (62.8) 42 (82.4) 118 (78.7) 628 (74.0) 0.034*, <0.0005**
p53+ 16 (37.2) 9 (17.6) 32 (21.3) 221 (26.0)
erbB2- 35 (81,4) 45 (84,9) 109 (82,6) NA
erbB2+ 8 (18,6) 8 (15,1) 23 (17,4) NA
*BRCA1 versus non-BRCA1/2 tumours; **BRCA1 versus unselected breast tumours; ***BRCA2 versus unselected breast tumours; ****non-BRCA1/2 tumours versus unselected breast tumours.
Ca, carcinoma; ER, oestrogen receptor; NA, not available; PgR, progesterone receptor.
Table 3 Logistic regression analysis of features of breast cancers among BRCA1- and BRCA2-associated cases in comparison with non-BRCA1/2 cases
Odds ratio (95% confidence interval, P)
Feature BRCA1 BRCA2
First step:
Age (continuous) 0.94 (0.91–0.98, 0.0009) 0.94 (0.91–0.97, 0.0001)
Grade (continuous) 3.78 (1.70–8.39, 0.0011) 1.27 (0.71–2.26, 0.42)
ER+ 1.0 (referent) 1.0 (referent)
ER- 0.91 (0.28–2.90, 0.88) 0.85 (0.32–2.29, 0.75)
PgR+ 1.0 (referent) 1.0 (referent)
PgR- 4.18 (1.26–13.87, 0.019) 1.82 (0.75–4.38, 0.18)
p53+ 1.0 (referent) 1.0 (referent)
p53- 1.96 (0.74–5.18, 0.18) 1.61 (0.63–4.12, 0.34)
Final step after stepwise regression:
Age (continuous) 0.94 (0.92–0.98, 0.0012) 0.94 (0.92–0.97, 0.0001)
Grade (continuous) 3.25 (1.58–6.69, 0.0014)
PgR+ 1.0 (referent)
PgR- 3.45 (1.22–9.80, 0.02)
ER, oestrogen receptor; PgR, progesterone receptor.
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| 15642173 | PMC1064101 | CC BY | 2021-01-04 16:54:48 | no | Breast Cancer Res. 2005 Nov 19; 7(1):R93-R100 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr953 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9541564217210.1186/bcr954Research ArticleBreast cancer oestrogen independence mediated by BCAR1 or BCAR3 genes is transmitted through mechanisms distinct from the oestrogen receptor signalling pathway or the epidermal growth factor receptor signalling pathway Dorssers Lambert CJ [email protected] Agthoven Ton [email protected] Arend [email protected] Jos [email protected] Marcel [email protected] Koen J [email protected] Department of Pathology, Josephine Nefkens Institute, Rotterdam, The Netherlands2 Target Discovery, N.V. Organon, Oss, The Netherlands2005 16 11 2004 7 1 R82 R92 26 1 2004 26 3 2004 2 9 2004 30 9 2004 Copyright © 2004 Dorssers et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Tamoxifen is effective for endocrine treatment of oestrogen receptor-positive breast cancers but ultimately fails due to the development of resistance. A functional screen in human breast cancer cells identified two BCAR genes causing oestrogen-independent proliferation. The BCAR1 and BCAR3 genes both encode components of intracellular signal transduction, but their direct effect on breast cancer cell proliferation is not known. The aim of this study was to investigate the growth control mediated by these BCAR genes by gene expression profiling.
Methods
We have measured the expression changes induced by overexpression of the BCAR1 or BCAR3 gene in ZR-75-1 cells and have made direct comparisons with the expression changes after cell stimulation with oestrogen or epidermal growth factor (EGF). A comparison with published gene expression data of cell models and breast tumours is made.
Results
Relatively few changes in gene expression were detected in the BCAR-transfected cells, in comparison with the extensive and distinct differences in gene expression induced by oestrogen or EGF. Both BCAR1 and BCAR3 regulate discrete sets of genes in these ZR-75-1-derived cells, indicating that the proliferation signalling proceeds along distinct pathways. Oestrogen-regulated genes in our cell model showed general concordance with reported data of cell models and gene expression association with oestrogen receptor status of breast tumours.
Conclusions
The direct comparison of the expression profiles of BCAR transfectants and oestrogen or EGF-stimulated cells strongly suggests that anti-oestrogen-resistant cell proliferation is not caused by alternative activation of the oestrogen receptor or by the epidermal growth factor receptor signalling pathway.
anti-oestrogenbreast cancergene expression profilingsignal transductiontamoxifen
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Introduction
The development and progression of breast cancer is dependent on steroid sex hormones and polypeptide growth factors. Oestrogen action has been implicated in the development of breast cancers and frequently contributes to tumour growth. The action of oestrogen is relayed through its specific nuclear oestrogen receptor (ER), which belongs to the family of ligand-inducible transcription factors [1,2]. Two distinct genes for ER (ERα and ERβ) have been identified. The role of ERα in breast cancer has been studied extensively and this receptor has been the subject of targeted therapies. Less information is available for ERβ [3,4], which exhibits differential tissue distribution and alternative responses to selective ER modulators [5,6]. Epidermal growth factor receptor (EGFR) expression is mainly present in ERα-negative breast tumours and is a marker of poor prognosis [7,8].
The frequent occurrence of ERα in breast tumours (about 75%) has been used as a guide for treatment. Endocrine treatment regimens, which either reduce the endogenous oestrogen levels (for example aromatase inhibitors) or interfere with ERα activation (anti-oestrogen such as tamoxifen), have been shown to block tumour growth and in some cases cause tumour reduction or disappearance [9,10]. However, the resistance of ERα-positive breast tumours is a severe limitation of endocrine treatment. About half of the ERα-positive breast tumours completely fail to respond (intrinsic resistance), whereas all responsive breast cancers ultimately progress and become resistant to the treatment (acquired resistance).
Despite much effort, the basis for the resistance of breast cancer to endocrine treatments is still poorly understood [11]. In general, tamoxifen resistance is not accompanied by loss of ERα expression [12,13]. Previous work has shown that the treatment outcome might be the result of a delicate balance between positive and negative regulators acting in concert with the hormone receptor [2,14]. In addition, alternative growth regulatory pathways might be available to tumour cells, permitting escape from the treatment [15]. We have searched for specific Breast Cancer Anti-oestrogen Resistance (BCAR) genes involved in the progression of oestrogen-dependent breast cancer cells to anti-oestrogen resistance [16]. The BCAR3 gene was shown to control anti-oestrogen-resistant cell growth in two different oestrogen-dependent cell lines and its product exhibits features of a cytoplasmic signalling molecule [17]. The BCAR1 gene causes anti-oestrogen resistance in our cell model and is the human homologue of the rat Crk-associated substrate (p130Cas) gene [18]. This docking protein has been implicated in many types of intracellular signalling processes [19,20]. Moreover, studies of human breast cancer specimens have shown that high BCAR1 expression is associated with poor prognosis and also predicts a poor response of recurrent disease to treatment with tamoxifen [21-24].
Recent developments in global gene expression profiling have elegantly shown their applicability in tumour classification and in predicting the prognosis of the patient [25-30]. Furthermore, studies in model systems have highlighted the use of gene expression profiling for unravelling delicate cellular processes.
The aim of our study was to use gene expression profiling to investigate the anti-oestrogen-resistant growth regulatory process induced by overexpression of the BCAR genes and to establish whether oestrogen or epidermal growth factor (EGF) signalling are involved.
Materials and methods
Breast cancer cell lines cultures and RNA preparation
The oestrogen-dependent human breast cancer cell line ZR-75-1 was maintained in RPMI 1640 medium supplemented with 10% bovine calf serum and 1 nM 17 β-oestradiol (R/BCS/E2) as described previously [16]. The derived EGFR-transfectant cell line ZR/HERc(1A) [31], hereafter referred to as ZR/EGFR, a BCAR1-transfectant cell line (4A12) [18] and a BCAR3-transfectant cell line (B3-10) [17] were also maintained in R/BCS/E2 medium. For short-term induction experiments, cells were cultured for 4 days in regular medium lacking added oestrogen (R/BCS) in 162 cm2 flasks, given fresh R/BCS medium 24 hours before manipulation, and cultured for 6 hours in the presence of 100 nM oestradiol or 1 μM ICI 164,384 (or ethanol vehicle alone) in R/BCS medium. Hormones and anti-hormones were provided by N.V. Organon (Oss, The Netherlands). For long-term induction experiments, cells were grown for 7 days in R/BCS/E2 medium or R/BCS medium containing 10 ng/ml EGF (Roche Diagnostics Nederland B.V., Almere, The Netherlands). Medium was replaced after 3 days and at 24 hours before harvest. After completion of the culture, the medium was removed and cells were lysed directly with 16–20 ml of RNAzol B solution (Campro Scientific, Veenendaal, the Netherlands). RNA was prepared as described by the manufacturer, quantified and checked for integrity on agarose gels. Poly(A)+ mRNA was prepared from pooled total RNA samples of two independent cultures by two cycles of binding to oligo(dT) with the use of OligoTex (Qiagen/Westburg B.V., Leusden, The Netherlands), and checked for integrity and contamination with ribosomal RNA by capillary electrophoresis (Lab-on-a-Chip, Agilent Technologies 2100 bioanalyzer, Amstelveen, The Netherlands).
Expression analysis
Production of Cy5-labelled and Cy3-labelled cDNA from the purified mRNA, hybridisation of the UniGEMV cDNA microarrays and quantification of the signals were performed by Incyte Genomics (Mountain View, CA, USA) as described previously [32]. Two batches of UniGEMV2 microarrays were used for these experiments. All hybridisations (Table 1) were performed in duplicate with a fluor reversal to minimise possible bias caused by the molecular structure of the Cy3 and Cy5 dyes. Data analysis was performed with the Rosetta Resolver software package (v 3.2) with an Incyte/UniGEM microarray error model (Rosetta Inpharmatics Inc., Kirkland, WA, USA). Genes exhibiting at least once a significant difference (P ≤ 0.01) in expression in these experiments were used for further analysis (n = 2373). In addition to the actual measured gene expression ratios we calculated the expression ratios between different experimental conditions from two measurements that contained a common sample [33]. Because the calculated gene expression ratios were in good agreement with available actual measurements, we used these calculations as 'virtual experiments'.
For hierarchical clustering of the measured and calculated expression ratios, we used Resolver software (average linkage agglomerative clustering using Euclidean distance and weighted by error) and Spotfire Decision Site 7.1 analysis package (Spotfire Inc., Somerville, MA, USA) using the Unweighted Paired-Group Method with Arithmetic mean (UPGMA) and Pearson's correlation as a similarity measure. Information on the function of genes has been retrieved from various public databases (for example PubMed, OMIN, GENECARD, KEGG and GO) and from the LifeSeq Gold database (Incyte Genomics). Expression data publicly available from prostate cancer, breast cancer and cell lines were linked to our data by means of the Unigene cluster number.
The strength of the relations between oestrogen and EGF-induced gene expression (log [ratio]) data was tested by Spearman rank correlation. The relations between categorised expression data (differential expression [DE] ≥ 1.60; 1.60 > DE > – 1.60; DE ≤ – 1.60) and gene association with tumour ER status (positive or negative) were tested by Spearman rank correlation. All computations were done with the STATA statistical package, release 8.0 (STATA Corp., College Station, TX, USA). All P values are two-sided.
Quantitative RT–PCR
For quantitative reverse transcriptase-mediated polymerase chain reaction (RT–PCR), 2.5 μg of total RNA (or 100 ng of poly(A)+ mRNA), 0.8 μg of oligo(dT)12–18 (Invitrogen Corporation) and 0.5 μg of random hexamer (Pharmacia) in 20 μl of RNase-free water were heated for 5 min at 65°C and cooled on ice. The final reaction of 40 μl contained 0.4 mM dNTPs (Pharmacia), 60 units of RNAseOUT and 300 units of SuperscriptII Reverse Transcriptase (Invitrogen). Incubations were for 2 min at 0°C, 10 min at 25°C, 50 min at 42°C and 10 min at 55°C; the reaction was stopped by heating for 15 min at 75°C. RNA was destroyed by treatment with 2 units of RNAseH (Promega) for 30 min at 37°C. cDNA products were diluted to 100 μl with 10 mM Tris/HCl pH 7.5; these stocks were stored at -80°C. Forty cycles of amplification of 5 μl of cDNA stocks in distilled water (Invitrogen) diluted 1:19 were performed with an SYBR green PCR mix (Applied Biosystems or Stratagene) and 0.33 μM forward and reverse primers in a volume of 25 μl on a ABI Prism 7700 (Applied Biosystems) in accordance with the recommended protocol. Primer annealing was performed at 60 or 62°C. A dilution series (1:4 to about 1:10,000) of a reference cDNA pool (mixture of cDNA preparations of RNA derived from six different cell lines) was used for normalising gene expression. The intron-spanning gene primers used are listed in Table 2. The cycling conditions were as follows: denaturation for 10 min at 95°C; 40 cycles (15 s at 95°C, 30 s at 60°C (CTSD, TFF1 and MYC) or 62°C, 10 s ramping to 72°C, 20 s at 72°C, 10 s ramping to 79°C, 20 s at 79°C). Data were collected at 72 and 79°C and were analysed at 79°C. At the end of the amplification, the melting curve of the products was determined. PCR products showed discrete melting curves and specific bands of correct lengths on agarose gels after 40 cycles of amplification. We also used Assays-on-Demand™ (Applied Biosystems) for various genes, in accordance with the manufacturer's protocol. cDNA (5 μl, diluted 1:19 or 1:39) was measured in 25 μl reactions with the TaqMan Universal PCR master Mix. All cDNA samples were normalised for HPRT1 levels (four independent measurements) and are presented relative to the gene level in ZR-75-1 cells maintained in R/BCS medium.
Results
Overall gene expression
To evaluate the effects of oestrogen, of EGF and of previously identified BCAR genes involved in oestrogen-independent growth of the human breast cancer cell line ZR-75-1, we determined the global gene expression in these cells by using UniGEMV2 cDNA microarrays. We performed direct comparisons of mRNA samples without the use of a general reference RNA sample (Table 1). Of the approximately 9000 sequences (about 88% represent known genes according to UNIGENE build no. 160) present on the microarray, the expression of 2373 genes (i.e. 26% of total sequences) was significantly (P ≤ 0.01) affected by either the oestrogen treatment or the EGF treatment or the BCAR transfections. Hierarchical clustering distributes the expression profiles according to the experimental culture conditions; that is, profiles of long-term oestrogen-treated cells were separated from short-term oestrogen-treated cells or BCAR-transfected cells (Additional file 1). The majority of the large changes in gene expression were observed in the oestrogen-stimulated or EGF-stimulated cultures. Hybridisation profiles of short-term oestrogen stimulation of ZR-75-1 cells and BCAR3-transfected cells are very similar and distinct from hybridisation profiles of long-term oestrogen-stimulated cell lines (Additional file 1). The EGF-stimulated ZR/EGFR cells as well as the BCAR1- and BCAR3-transfected cells exhibit clearly different profiles in comparison with the oestrogen-stimulated cultures. The comparison of BCAR-transfected cell lines with each other or with non-stimulated parental cells revealed modest changes in gene expression, indicating that gene expression differences in these BCAR cell lines are generally subtle. Below we discuss the expression profiles (average gene expression log(ratios) of the independent experiments) of a selection of 1006 genes exhibiting a |DE| ≥ 1.60 in at least one of the actual or virtual experiments.
Effects of oestrogen and EGF on gene expression of ZR-75-1 human breast cancer cells
ZR-75-1 breast cancer cells are completely dependent on oestrogen for growth. In standard medium without added oestrogen, growth is strongly reduced. Addition of anti-oestrogen completely abolishes the growth of these cells [16]. Oestrogen-induced cell proliferation is mediated by the transcription activation function of the ERα. To identify the early effects (that is, the transcription targets) of oestrogen stimulation, the expression profile was analysed after a 6-hour high-dose pulse of 100 nM oestrogen. From Fig. 1 and Additional file 1 it is clear that limited changes in gene expression (115 genes with |DE| ≥ 1.60) have occurred during this short treatment compared with mock-stimulated cultures. Over 75% of these genes seemed to be induced. Among these genes are well-known oestrogen targets such as TFF1 (PS2), CSTD (cathepsin D), CCND1 (cyclin D1) and PGR (progesterone receptor). These and several novel genes were rapidly induced by oestrogen both in the parental ZR-75-1 cells and in the BCAR3-transfected cells (Fig. 1 and Additional files 123). An extended picture emerges after continuous exposure to the regular dose of 1 nM oestrogen. About 400 genes exhibit consistent changes (at least 1.6-fold) in expression, of which about 60% of the sequences exhibit a significant decrease of gene expression (up to sevenfold) and 40% are increased (up to more than 10-fold). The genes specifically modulated by oestrogen comprise members of all functional compartments and processes in the cell. One-quarter of the early-induced genes remain expressed (DE > 1.60) during continuous exposure to oestrogen (see Fig. 1), whereas the expression of others is turned off (for example AMD1, BCL2, CCND1, GJA1, MEIS3, RIP140, RUNX1 and STC1) or even downregulated (for example HIF1A, IL6ST, MYB, PC4 and UGT2B7). Definitions of these and other and other gene names can be found in Additional file 3. Statistical analysis of all 1006 genes shows a clear positive correlation between early-induced gene expression and genes expressed after 7 days of oestrogen treatment (rs = 0.36, P < 0.0001).
We have previously shown that the addition of EGF to the culture medium does not support growth of ZR-75-1 cells because of the absence of EGF receptors [31,34]. The introduction of EGFR into ZR-75-1 cells (ZR/EGFR) permits a response to EGF and can support proliferation independently of oestrogen [31]. Gene expression of ZR/EGFR cells stimulated with EGF for 7 days was directly compared with that of ZR-75-1 cells stimulated with oestrogen continuously. It is clear from this direct comparison that 247 genes are specifically altered more than 1.6-fold (Additional file 1). From the virtual experiment, which compares the EGF stimulation of ZR/EGFR cells with unstimulated ZR-75-1 cells, we can conclude that EGF modulates a large cohort of genes (707) at least 1.6-fold (Fig. 1). Statistical analysis of all 1006 genes shows a strong positive correlation for gene expression regulated by EGF and long-term oestrogen (rs = 0.67, P < 0.0001), but no significant association with genes induced early by oestrogen (rs = 0.16). As expected, EGFR is one of the most prominently changed genes in our analysis as a consequence of the transgene expression. In addition, the expression of genes implicated in signalling processes, cell adhesion and structure, protein modification, transport and metabolic processes is specifically regulated by treatment with EGF or shows a pronounced alteration in comparison with that in oestrogen-treated cultures (Fig. 1).
Effects of overexpression of BCAR1 or BCAR3 in ZR-75-1 cells
We have previously shown that stable overexpression of BCAR1 or BCAR3 induces cell proliferation independently of oestrogen and anti-oestrogen [17,18]. In an attempt to pinpoint the effects of these cytoplasmic signalling molecules on global gene expression in cultures without added oestrogen, we compared BCAR3 and BCAR1, and BCAR3 and ZR-75-1, directly on microarrays, and calculated the gene expression relation between BCAR1 and ZR-75-1 as a virtual experiment. A total of 79 genes exhibited consistent differences in expression of at least 1.6-fold (Fig. 1). Few genes are modulated solely by the overexpression of a BCAR gene; most are also a target for hormonal and/or EGF stimulation (Fig. 1 and Additional file 1). As expected, the largest observed difference (up to 15-fold) in these comparisons was derived from the BCAR3 transgene expression. No cDNA sequence corresponding to the BCAR1 gene was present on this microarray.
Changes in gene expression specifically caused by BCAR3 overexpression in ZR-75-1 cells were seen for CSTA, DLG7, FMOD, FOLR1, FOXJ1, HSPC242, IGFBP5, LGALS1, LIV1, NEDD4L, NELL2, PCDH7 and UBE2C. In addition, a clear induction of several genes involved in glucose metabolism (PGK1, LDHA, TPI1, and moderate induction levels of ALDOC, ENO1, ENO3 and PFKP; Fig. 1) was observed. This coherent change in gene expression is unlikely to represent a culture artefact because no expression change was observed in these genes in BCAR3-transfected cells after 6 hours of induction with oestrogen (Additional file 1). Specifically altered genes in BCAR1-transfected ZR-75-1 cells include APOD, ASS, CRIP1, ELL2, FOXM1, HSPG2, ID1, IL1R1, IL6ST, LGALS8, PDZK1, TK1, PPP3CA, TOMM20, VIPR1 and various genes encoding ribosomal proteins (Fig. 1). A moderately opposing direction of gene expression change in these two transfectant cell lines compared with ZR-75-1 cells was indicated by TFF3 and MGP (Fig. 1). A set of genes (including BF, BCL2, BIRC5, CDKN1A, INADL, KIAA0101, MAPKAPK2, MYB, P2RY10, PC4, PRCP, OCLN, RAB6KIFL (= KIF20A), SERPINA5, SLC7A2 and UGT2B11) exhibited differential expression in both BCAR-transfected cell lines when compared with the parental cell line ZR-75-1.
Verification of expression differences by quantitative PCR
To establish that the measured differences in gene expression on the microarrays did indeed reflect the concentration of the respective mRNAs, we performed quantitative RT–PCR (Q-PCR) on a selection of 22 genes on the same RNA samples and on additional RNA preparations from different culture conditions. Standard quantities of intact total RNA or mRNA were reverse transcribed and subjected to Q-PCR. To normalise the cDNA samples we used HPRT1 (not present on the microarray) as a reference. Results have been presented relative to non-stimulated ZR-75-1 cells to facilitate direct comparison with microarray data (Table 3). In general, we found good agreement between the levels of HPRT1 and another housekeeping gene (HMBS) in our experimental samples. The expression of MYC was fairly constant in our series, with the exception of a slight decrease in EGF-stimulated ZR/EGFR cells (Table 3). This is in agreement with the results of our microarray hybridisations, which showed MYC expression to be significantly reduced only by EGF (Fig. 1). Oestrogen targets such as TFF1, PGR, PDZK1 and CTSD are indeed increased by treatment of our ZR-75-1 cells and BCAR1-transfected and BCAR3-transfected cells with oestrogen (Table 3). These genes are already elevated after 6 hours of treatment with oestrogen (not by the pure oestrogen antagonist ICI 164,384), but their levels increase further (TFF1 up to 30-fold) after prolonged oestrogen treatment, in agreement with our microarray data. After stimulation of ZR/EGFR cells with EGF, the expression of PGR and PDZK1 was completely abolished, whereas TFF1 and CTSD levels were induced under EGF (Table 3). TFF3 and MGP levels were clearly induced after long-term treatment with oestrogen and reduced after stimulation with EGF. The levels of ERα (not present on the array) show some decrease after treatment of ZR-75-1-derived cell lines with oestrogen (Table 3). A much stronger decrease in ERα levels (10-fold) is achieved after 7 days of treatment of ZR/EGFR cells with EGF. ERβ levels were found to be reduced about 1000-fold compared with ERα in our cells and not strongly affected by the culture conditions. The levels of HER2/Neu (ERBB2) were decreased after treatment with both oestrogen and EGF. The expression levels of BCAR3, EGFR and BCAR1 were not strongly affected in the various cultures, except for the cells containing the introduced transgene. The observed expression modulation of IGFBP5, IL1R1 and CSTA in our microarray experiments is very well reproduced by Q-PCR (Fig. 1 and Table 3). Although oestrogen treatment causes a moderate decrease in these genes in ZR-75-1 cells, treatment of ZR/EGFR cells with EGF causes a marked effect (100-fold decrease in IGFBP5 after 7 days). The Q-PCR data also support the microarray data that BCAR3 cells have decreased levels of IGFBP5 mRNA and increased levels of CSTA mRNA in comparison with the BCAR1 and parental cells. IL1R1 and APOD levels are slightly modulated in BCAR1 cells, in agreement with the hybridisation data. PSAT1 levels were found to be further decreased in BCAR1 cells than suggested by the array experiments.
Discussion
The expression of a large proportion of genes investigated on this cDNA microarray does not alter significantly after stimulation of ZR-75-1 cells with oestrogen, or ZR/EGFR cells with EGF, or transfection of BCAR1 or BCAR3 genes. Furthermore, different ZR-75-1-derived cell clones showed very similar expression profiles after growth manipulation with oestrogen, indicating the stability of the parental cell line and the absence of extensive variation between cell clones. This result is in agreement with previous observations that this human breast cancer cell line is extremely stable and is a suitable target for in vitro insertion mutagenesis with retrovirus [16]. The growth of this cell line is completely dependent on oestrogen, and the proliferation signal is mediated primarily through ERα because ERβ mRNA levels were very low. The ERα mRNA is readily detected in our cells by Q-PCR and is moderately decreased by oestrogen treatment (Table 3). In contrast, ERα mRNA is strongly decreased (about 10-fold) in EGF-treated ZR/EGFR cells, which might relate to the observation of growth interference between signalling by oestrogen and by EGF in these cells [31]. Of the 1006 significantly affected genes in our series of experiments, only few are immediate/early targets of oestrogen-activated ERα (Fig. 1 and Table 3). Most changes in gene expression observed in our cell model are the result of long-term culture with either oestrogen or EGF. Many genes here identified as oestrogen targets have previously been reported to be directly regulated by oestrogen using conventional northern blotting, serial analysis of gene expression (SAGE) [35] or gene expression profiling [36-40] (see also Fig. 1, 'cell lines' column). Good concordance with literature data was observed for the immediate and late targets of oestrogen in our cells (categorised data, rs = 0.42 and 0.41, respectively; P < 0.01). As expected, no association between EGF-regulated gene expression and reported oestrogen targets was seen (categorised data, rs = - 0.07). Undisputed early targets are TFF1, CTSD, CCND1, PGR, PDZK1 and MYB, whereas induction of IGFBP4 by oestrogen was reported to be dependent on the presence of serum in MCF7 cells [37]. Mostly overlapping results for oestrogen-regulated genes in MCF7 cells have been presented recently [40]. Differences between the various cell line models might explain individual differences (see Fig. 1, for example MYC, XBP1 and MGP). STC1 is rapidly induced in our experiments but has been reported not to be regulated in MCF-7 cells with the use of SAGE and microarrays [35,40]. In contrast, its family member STC2 was strongly increased by oestrogen treatment of MCF7 cells [35,37]. On our microarrays we did observe a moderate induction (1.7-fold) in STC2 transcript levels after induction with oestrogen for 6 hours, essentially parallel to STC1 (Fig. 1).
Linking expression databases derived from cell line models and clinical samples provides the opportunity to extract additional information. We have connected our in vitro gene expression data with the public results of gene expression profiling of clinical breast cancer specimens [28,41] through the Unigene cluster number. About one-fifth of the 1006 genes in our selection were reported to be associated with ER status, BRCA mutation status and/or breast cancer prognosis (Fig. 1). Comparison of the categorised data of early oestrogen-regulated gene expression and expression association with ER status reveals a positive correlation (rs = 0.21, P < 0.002). Clear examples of genes showing a positive correlation with ER status in breast carcinoma and oestrogen-induced gene expression in ZR-75-1 cells are CCND1, GFRA1, GJA1, IGFBP4, IL6ST, MEIS3, MYB, PDZK1, PGR, SERPINA5 and TFF1 (Fig. 1). Examination of the genes regulated in our cells by long-term treatment with oestrogen reveals an unexpected inverse relation (rs = - 0.23; P < 0.002) with expression association to ER status of the tumour. About half of the genes regulated by long-term oestrogen and not regulated by EGF exhibit concordance with ER status, whereas most of the genes regulated similarly by both treatments show a discordant relation with ER expression (Fig. 1). A much stronger negative relation exists between EGF-regulated gene expression and expression association with ER status (rs = - 0.43; P < 0.0001), which concurs with the established inverse relation between ER and EGFR in breast cancer [7,8]. The results of this comparison of cell line expression data with profiles of clinical samples indicate that part of the molecular markers for ER status in primary breast tumours might indeed represent genuine ER targets. Various other markers are not linked to oestrogen action but might reflect activation of the EGFR pathway in ER-negative tumour cells. Partial overlap was also reported for genes associated with breast cancer ER status and oestrogen-responsive genes in MCF7 cells [40]. Some genes reported to be associated with the prognosis of node-negative breast cancer (TK1, FBP1 and IGFBP5) or with BRCA mutation-induced disease (CPE and P2RY10) seem to be regulated by BCAR1 and/or BCAR3 (see Fig. 1). In addition, the expression of IL1R1, FOXM1 and PC4 changes markedly during the progression of normal prostate to metastasised prostate cancer [27]. These observed relations of genes regulated by BCAR1 and/or BCAR3 with clinical features of malignancies remain interesting and are targets for further study.
The overall results show that BCAR1-transfected or BCAR3-transfected cells in unsupplemented cultures exhibit only modest changes in gene expression compared with unstimulated ZR-75-1 cells (Fig. 1 and Table 3). The prominent changes in gene expression induced by BCAR3 are upregulation of CSTA, HSPC242 and LGALS1 and downregulation of IGFBP5, NEDD4L and PCDH7. These genes are involved in protein degradation, cell–cell adhesion, the assembly of extracellular matrix and control of cell growth and metabolism. In BCAR1-transfected cells, upregulation of BIRC5, ELL2, FOXM1, ID1, IL1R1, MYB and TK1 and downregulation of APOD, IL6ST and LGALS8 was observed. Several of these genes modulated by BCAR1 have been shown to be important in cell signalling and in the regulation of cell proliferation or possibly in increasing cell survival. These clearly different patterns of gene expression in BCAR1 and BCAR3 transfectants indicate that signalling proceeds along alternative pathways. This contrasts with the co-occurrence of BCAR1 and BCAR3 in a protein complex in some of our cell models (Ton van Agthoven, Arend Brinkman, Lambert CJ Dorssers, unpublished results) and the functional association of BCAR1/p130Cas and BCAR3/AND-34 in cell migration [42]. From the profiles in Fig. 1 and Additional file 1 it is clear that partial overlap exists in the expression profiles of the BCAR1 and BCAR3 transfectants with both oestrogen-induced and EGF-induced cells. Most of these genes are modulated in most experiments and thus might represent expression features of proliferating ZR-75-1 cells. The remaining overlap with either oestrogen-modulated or EGF-modulated genes is limited, making it highly unlikely that the BCAR genes use major parts of these signalling pathways. This observation agrees with previous results showing that BCAR1 and BCAR3 cell lines generated by retroviral insertion mutagenesis had all lost ERα protein expression and did not acquire responsiveness to EGF [16,17]. Furthermore, growth of BCAR1 and BCAR3 transfectants (which are fully responsive to oestrogen; Table 3 and Additional file 1) was not stimulated by anti-oestrogen [17,18], suggesting that there is no role for ERα in the anti-oestrogen-resistant proliferation of these cell models. In clinical specimens, BCAR1 was found to be an independent marker (multivariate analyses also including ERα) for early recurrence of breast cancer and for failure of tamoxifen treatment of recurrent disease [21-24]. Because not all genes are present on this microarray and only a limited set of experimental conditions have currently been analysed in our cell model, we cannot exclude from these microarray experiments the possibility that the BCAR transfectants selectively use components of the hormonal receptor and/or growth factor receptor signalling pathways for proliferation control. In addition, growth control mediated by BCAR1 and BCAR3 might not be reflected in gene expression but could also be supervised at the level of regulatory protein activation.
Our results present an overview of gene expression changes after perturbation of ZR-75-1 breast cancer cells with treatment with oestrogen or EGF or by the overexpression of BCAR genes. The data suggest that oestrogen-independent cell proliferation induced by overexpression of BCAR1 or BCAR3 does not depend merely on the oestrogen-signalling or EGFR-signalling pathways. Because BCAR1 protein levels have been associated with clinical outcome for breast cancer patients, further studies are needed to resolve the underlying mechanism. This study also shows that important cell biological properties such as oestrogen-independent proliferation can be regulated in several subtle ways and thus might be hidden in the excess of gene expression differences observed in profiles of specimens from patients. The combination of expression profiles of relevant cell models, which can be manipulated in vitro, and high-quality specimens from patients might permit the identification and understanding of the important cellular pathways contributing to major clinical features of malignant diseases [43]. This information could ultimately lead to the development of improved or novel treatment strategies for breast cancer.
Abbreviations
BCAR = Breast Cancer Anti-oestrogen Resistance; DE = differential expression; E2 = oestradiol; EGF = epidermal growth factor; EGFR = epidermal growth factor receptor; ER = oestrogen receptor; P130Cas = Crk-associated substrate; Q-PCR = quantitative RT–PCR; R/BCS = RPMI 1640 medium supplemented with 10% bovine calf serum; rs = Spearman rank correlation; RT–PCR = reverse transcriptase-mediated polymerase chain reaction; SAGE = serial analysis of gene expression; ZR/EGFR = EGFR-transfectant cell line ZR/HERc(1A).
Competing interests
The author(s) declare that they have no competing interests.
Supplementary Material
Additional File 1
A TIFF file showing hierarchical clustering of gene ratios. Signal intensity tables were imported into Rosetta Resolver, combined for dye swapping and evaluated using the Incyte/UniGEM error model. A total of 2373 genes displaying at least once a P value of 0.01 or less were used for hierarchical clustering. In this colour picture, increased expression is shown in red and decreased expression is shown in green. Black represents no change and grey indicates missing data. The compared RNA samples are indicated. The gene names and log(ratios) are provided in Additional file 2.
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Additional File 2
An Excel file containing gene expression data, namely Incyte spot ID, accession number, sequence name and description, and the Rosetta Resolver output data columns: log(ratio), P value and log(error).
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Additional File 3
An Excel file containing all data from the actual and virtual experiments meeting the selection criteria (see Fig. 1), Unigene cluster number, gene name and description, and cited literature data with regard to oestrogen-regulated gene expression in cell lines and associations with tumour phenotypes. Ordering is in accordance with the hierarchical clustering of all genes, using the columns depicted in Fig. 1.
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Acknowledgements
The authors thank Anieta Sieuwerts (Department of Medical Oncology) for excellent support with Q-PCR experiments. Furthermore, we acknowledge Dr Guido Jenster (Department of Urology) and Dr Els Berns and Dr John Foekens (Department of Medical Oncology) for suggestions and stimulating discussions. The Biomics Core of the Erasmus MC is acknowledged for support in data management and analysis. The anti-oestrogen-resistance research is supported by grants of the Dutch Cancer Society (KWF) and the Revolving Fund of the Erasmus MC.
Figures and Tables
Figure 1 Expression changes of selected genes in ZR-75-1-derived cell lines, and their relation to reported clinical phenotypes. Average gene expression ratios (log10) in ZR-75-1 cells stimulated with oestrogen for 6 hours (E6hr) or continuously (Econt), of ZR/EGFR cells stimulated with epidermal growth factor for 7 days (EGF), of BCAR1-transfected or BCAR3-transfected ZR-75-1 cells versus unstimulated ZR-75-1 cells are indicated. A selection of 234 sequences was made from the 1006 sequences showing at least once a P value of 0.01 or less and exhibiting at least once a |DE| of more than 1.60. Individual data points of combined swapped and of virtual experiments were only included when either one of the following criteria was met: P < 0.051 or log(error) < 0.1761 or |log(ratio)| > 1.5 × log(error). This procedure eliminates most of the unreliable data points. Genes were hierarchically clustered by using Spotfire. A colour picture was made with TreeView [44]. Increased expression is shown in red and decreased expression is shown in green. Black represents no change and white indicates missing data. In addition, the reported inducing (red) or reducing (green) effects of oestrogen stimulation in cell line models [35–37,39,40] and the correlation (red, positive; green, negative) of individual genes with breast cancer oestrogen receptor (ER) status, BRCA mutation status (BRCA) and prognosis of disease recurrence is indicated [28,41]. Genes investigated with quantitative reverse transcriptase-mediated polymerase chain reaction have been marked with an asterisk. The complete list of 1006 genes with attached information is presented in Additional file 3.
Table 1 Hybridisation experiments
RNA no. Microarray hybridisations
1 BCAR3 (R/BCS) BCAR3 (E2 6 h) BCAR3 (R/BCS) ZR-75-1 (E2 6 h) ZR-75-1 (E2 7 d) ZR-75-1 (E2 7 d) ZR-75-1 (E2 7 d) ZR-75-1 (E2 7 d)
2 BCAR1 (R/BCS) BCAR3 (R/BCS) ZR-75-1 (R/BCS) ZR-75-1 (R/BCS) ZR-75-1 (R/BCS) BCAR1 (R/BCS) BCAR3 (R/BCS) ZR/EGFR (EGF 7 d)
RNA samples were prepared from cell lines pretreated for 5 days in RPMI 1640 medium supplemented with 10% bovine calf serum (R/BCS) and subsequently cultured for the indicated durations in R/BCS medium supplemented with oestrogen (E2), epidermal growth factor (EGF) or R/BCS medium without further additions. All hybridisations were performed in duplicate with swapping of the dyes.
Table 2 Primer information
Gene Sequence 5'→3'
HPRT1 F TATTGTAATGACCAGTCAACAG
R GGTCCTTTTCACCAGCAAG
HMBS (= PBGD) F CATGTCTGGTAACGGCAATG
R GTACGAGGCTTTCAATGTTG
IGFBP5 F GGGTTTGCCTCAACGAAAAG
R TTTCTGCGGTCCTTCTTCAC
ESR1 (ERα) F GAGCACCCAGGGAAGCTAC
R CATCAGGTGGATCAAAGTGTC
CSTD (cathepsin D) F CACGGGCTCCTCCAACCT
R GGACTTGTCGCTGTTGTACTTGTG
TFF1 (PS2) F ATGGCCACCATGGAGAAC
R TTCACACTCCTCTTCTGG
MYC F GAGCCCCTGGTGCTCCAT
R CGATTTCTTCCTCATCTTCTTGTTC
ESR2 (ERβ) F TCAGCCTGTTCGACCAAGTG
R GGCCTTGACACAGAGATATTC
PGR F CAAGTTAGCCAAGAAGAGTTC
R ACTTCGTAGCCCTTCCAAAG
ERBB2 (HER2/Neu) F GTCTACAAGGGCATCTGGAT
R GTGGATGTCAGGCAGATGC
BCAR3 F GCGGTGGAACTGAAGGATTC
R TGGCAGTTTGGGTGTACTGG
BCAR1 F CTGCCCAGGATATTTACCAG
R CGTCATACACCTCCAGCAAC
EGFR F CGGGACATAGTCAGCAGTG
R GCTGGGCACAGATGATTTTG
Gene Product code
CDKN1A HS00355782_m1
PGK1 Hs99999906_m1
IL1R1 Hs00168392_m1
CSTA Hs00193257_m1
TFF3 Hs00173625_m1
FMOD Hs00157619_m1
PDZK1 Hs00420042_m1
APOD Hs00155794_m1
MGP Hs00179899_m1
PSAT1 Hs00253548_m1
F, forward; R, reverse. The gene names are defined in Additional file 3.
Table 3 Relative gene expression in ZR-75-1-derived cell lines
ZR-75-1 cells BCAR1-transfected cells BCAR3-transfected cells EGFR-transfected cells
Addition...a None ICI Oestrogen None Oestrogen None Oestrogen None EGF
6 h 6 h 30 h 96 h Cont. 6 h 30 h 96 h 6 h 30 h 96 h 3 days 7 days
Gene 3 (4)b 1 (1) 2 (4) 1 (1) 1 (3) 1 (3) 2 (6) 2 (3) 1 (1) 1 (1) 2 (4) 2 (3) 1 (1) 1 (1) 1 (3) 1 (2) 1 (4)
HMBS (23.1) 1c 0.58 0.79 0.42 0.49 1.22 0.93 0.75 0.51 0.93 0.59 0.56 0.41 0.64 0.93 0.80 0.99
FMODd (29.4) 1 1.60 1.47 0.79 1.04 0.95 0.58 0.83 0.71 0.81 0.95 1.09 1.00 1.59 1.02 0.94 0.54
PGK1d (22.3) 1 1.33 1.02 1.08 1.18 1.18 1.41 0.94 1.07 1.61 1.46 2.10 0.97 2.11 0.70 2.23 0.94
CDKN1Ad (25.8) 1 0.60 0.72 0.47 0.66 2.03 1.45 1.24 0.61 1.34 1.19 1.37 0.81 1.78 1.26 2.11 3.60
MYC (23.3) 1 0.55 1.21 0.41 0.90 0.97 0.72 0.98 0.47 0.74 0.47 0.60 0.51 0.76 1.00 0.44 0.26
CTSD (18.7) 1 0.68 2.13 2.25 1.94 3.95 0.91 1.89 1.87 3.02 0.77 1.43 1.90 4.21 1.78 2.38 1.95
TFF1 (21.2) 1 0.56 2.07 9.28 20.45 36.67 0.99 2.38 13.37 32.60 0.94 1.35 9.30 21.37 4.71 5.77 2.64
PGR (24.6) 1 0.71 9.08 9.24 12.00 7.52 0.83 7.24 9.29 7.65 0.38 2.22 3.55 5.62 0.56 0.32 0.05
PDKZ1d (26.4) 1 0.92 5.26 14.75 10.12 16.66 0.64 1.85 11.86 11.81 0.52 1.93 7.26 9.10 1.17 0.08 0.03
TFF3d (23.0) 1 0.50 1.03 2.64 6.19 11.56 0.84 1.30 2.60 7.29 1.21 1.23 2.22 4.02 1.09 0.79 0.10
MGPd (22.8) 1 0.83 1.09 1.54 2.99 1.34 0.93 1.00 3.12 3.15 0.89 0.78 2.14 1.11 0.42 0.86 0.23
IGFBP5 (17.6) 1 0.80 0.92 0.24 0.45 0.17 0.91 0.74 0.27 0.49 0.33 0.30 0.22 0.28 1.46 0.04 0.01
APODd (21.5) 1 0.96 0.89 0.53 0.25 0.09 0.37 0.36 0.18 0.08 0.60 0.59 0.36 0.27 0.32 0.02 0.04
CSTAd (27.7) 1 0.57 0.86 0.08 0.06 0.06 0.74 0.53 0.08 0.09 2.95 2.86 0.88 0.44 0.04 0.01 0.02
HER2/Neu (20.4) 1 0.77 0.75 0.18 0.29 0.39 0.73 0.48 0.20 0.25 0.68 0.53 0.29 0.37 1.16 0.51 0.49
IL1R1d (27.5) 1 0.76 0.50 0.10 0.09 0.04 1.54 0.54 0.07 0.15 0.64 0.40 0.14 0.23 0.84 0.12 0.21
ERα (20.2) 1 0.54 0.86 0.36 0.41 0.27 0.50 0.55 0.31 0.29 0.35 0.39 0.28 0.33 0.79 0.12 0.07
ERβ (32.7) 1 1.21 1.93 0.05 1.57 1.62 1.61 1.40 0.63 0.60 0.84 1.78 0.42 3.19 1.58 1.08 1.89
BCAR3 (24.2) 1 0.74 0.51 0.40 0.46 0.45 0.64 0.25 0.14 0.23 18.30 14.36 13.19 24.78 0.35 0.60 0.69
BCAR1 (24.8) 1 0.93 1.17 0.71 0.69 1.31 55.96 31.90 13.67 22.12 0.80 1.01 0.58 0.36 1.01 1.13 1.07
PSAT1d (35.6) 1 0.86 0.67 0.40 0.97 2.64 <0.01 <0.01 <0.01 <0.01 0.87 1.16 0.84 0.76 1.65 3.36 3.64
EGFR (35.0) 1 0.25 0.84 1.62 1.75 1.27 1.47 2.56 2.31 1.25 2.24 2.15 2.75 4.13 8126 11045 10582
All expression levels have been normalised for HPRT1 levels and are presented relative to non-stimulated ZR-75-1 cells. The gene names are defined n Additional file 3.
aThe nature and duration of the culture additions (ICI, oestrogen antagonist ICI 164,384).
bThe number of biological replicates and total number of analyses (in parentheses).
cGene expression level was set at 1.0 for non-stimulated ZR-75-1 cells and the detection threshold cycle (Ct) value for each gene is shown in parentheses (23 for HPRT1).
dQuantitative polymerase chain reaction with assay on demand; others with SYBR green.
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| 15642172 | PMC1064102 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 16; 7(1):R82-R92 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr954 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9561564217010.1186/bcr956Research ArticlePrenylation inhibitors stimulate both estrogen receptor α transcriptional activity through AF-1 and AF-2 and estrogen receptor β transcriptional activity Cestac Philippe [email protected] Guillaume [email protected]édale-Giamarchi Claire [email protected] Philippe [email protected] Patrick [email protected] Gilles [email protected] Jean-Charles [email protected] Sophie [email protected] Département 'Innovation Thérapeutique et Oncologie Moléculaire', Centre de Physiopathologie de Toulouse Purpan, INSERM U563 and Institut Claudius Regaud, Toulouse, France2 INSERM 540, Endocrinologie Moléculaire et Cellulaire des Cancers, Montpellier, France2005 8 11 2004 7 1 R60 R70 23 7 2004 17 9 2004 22 9 2004 4 10 2004 Copyright © 2004 Cestac et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
We showed in a previous study that prenylated proteins play a role in estradiol stimulation of proliferation. However, these proteins antagonize the ability of estrogen receptor (ER) α to stimulate estrogen response element (ERE)-dependent transcriptional activity, potentially through the formation of a co-regulator complex. The present study investigates, in further detail, how prenylated proteins modulate the transcriptional activities mediated by ERα and by ERβ.
Methods
The ERE-β-globin-Luc-SV-Neo plasmid was either stably transfected into MCF-7 cells or HeLa cells (MELN cells and HELN cells, respectively) or transiently transfected into MCF-7 cells using polyethylenimine. Cells deprived of estradiol were analyzed for ERE-dependent luciferase activity 16 hours after estradiol stimulation and treatment with FTI-277 (a farnesyltransferase inhibitor) or with GGTI-298 (a geranylgeranyltransferase I inhibitor). In HELN cells, the effect of prenyltransferase inhibitors on luciferase activity was compared after transient transfection of plasmids coding either the full-length ERα, the full-length ERβ, the AF-1-deleted ERα or the AF-2-deleted ERα. The presence of ERα was then detected by immunocytochemistry in either the nuclei or the cytoplasms of MCF-7 cells. Finally, Clostridium botulinum C3 exoenzyme treatment was used to determine the involvement of Rho proteins in ERE-dependent luciferase activity.
Results
FTI-277 and GGTI-298 only stimulate ERE-dependent luciferase activity in stably transfected MCF-7 cells. They stimulate both ERα-mediated and ERβ-mediated ERE-dependent luciferase activity in HELN cells, in the presence of and in the absence of estradiol. The roles of both AF-1 and AF-2 are significant in this effect. Nuclear ERα is decreased in the presence of prenyltransferase inhibitors in MCF-7 cells, again in the presence of and in the absence of estradiol. By contrast, cytoplasmic ERα is mainly decreased after treatment with FTI-277, in the presence of and in the absence of estradiol. The involvement of Rho proteins in ERE-dependent luciferase activity in MELN cells is clearly established.
Conclusions
Together, these results demonstrate that prenylated proteins (at least RhoA, RhoB and/or RhoC) antagonize the ability of ERα and ERβ to stimulate ERE-dependent transcriptional activity, potentially acting through both AF-1 and AF-2 transcriptional activities.
estrogen receptorfarnesyltransferase inhibitorgeranylgeranyltransferase inhibitorRho proteinstranscription
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Introduction
Both estrogen receptor (ER) subtypes, ERα and ERβ, are ligand-activated transcription factors. ERα is the major ER in mammary epithelium and is an important regulator of cell growth, differentiation and malignant transformation. After binding to estrogen, the receptors associate with specific estrogen response elements (EREs) within the promoters of estrogen-regulated genes or the receptors affect the activity of other transcription factor complexes such as AP-1 (Jun–Fos). The two ER subtypes share affinity for the same ligands and DNA response elements [1]. These nuclear receptors consist of six domains including the A/B domain containing the AF-1 autonomous transcription activation domain, the C domain containing the DNA binding domain, the E domain containing the ligand binding domain, and the AF-2 ligand transcription activation domain located in the C terminus of the receptor. Transcriptional activation by ERα is mediated by the synergistic action of the two distinct activation functions; although AF-1 is constitutively active, it is usually weaker than the AF-2 activity. In contrast, ERβ appears to have no significant AF-1 activity and thus depends entirely on the ligand-dependent AF-2 activity [2].
The current model for ER action suggests that the ER modulates the rate of transcription through interactions with the basal transcription machinery and by altering the recruitment of co-activators that modify chromatin organization at the promoter level of target genes [3-5]. In addition, tissue-specific nuclear receptor co-activators and co-repressors have been described that can modify the transcriptional activity of the ER [6-8].
There is increasing evidence, however, that not all the biological effects of estrogens are mediated by direct control of target gene expression; indeed, some effects are attributed to estrogenic regulation of signaling cascades [9-11]. Several rapid effects suggest that estrogens can interact with receptors that are located in close proximity to the plasma membrane [12,13]. These receptors, which appear to form a subpopulation of the classical ER, are associated with the cell membrane and are responsible for several manifestations of estrogenic signaling [14,15]. Recent data explain how the coordinate interactions between a newly identified scaffold protein, MNAR, the ER and Src lead to Src activation, demonstrating the integration of ER action in Src-mediated signaling [11,13]. These data highlight new evidence for a cross-talk between estradiol (E2) and growth-factor-induced cytoplasmic signaling. Several components of these signaling pathways are low molecular weight GTPases, such as Ras, that require prenylation to function.
Ras belongs to the Ras superfamily of low molecular weight proteins. The activity of such proteins is controlled by a GDP/GTP cycle. Members of the Ras superfamily include the Ras, Rho and Rab subfamilies. The Ras and Rho proteins of this superfamily are modified post-translationally by the isoprenoid lipids farnesylpyrophosphate and geranylgeranylpyrophosphate. Farnesyltransferase and geranylgeranyltransferase I respectively catalyze the covalent attachment of the farnesyl group (C15) and the geranylgeranyl group (C20) to the carboxyl-terminal cysteine of prenylated proteins. Prenylation appears to be essential not only for membrane association, but also for biological activity [16].
In a previous report, we demonstrated the implication of both farnesylated and geranylgeranylated proteins in E2 actions, as prenylation inhibitors block the cell-cycle progression driven by E2 and stimulate the transcriptional activity of the ER [17]. Our data strongly suggest that the transcriptional stimulation of the ER by prenylation inhibitors is due to a shift in the association of a transcription co-regulator with the ERα. Among the numerous co-activators identified to date, one of the best characterized is the p160 family of proteins including SRC-1, GRIP1 and RAC3, which enhance ER transactivation by recruiting other transcriptional regulatory factors such as CREB-binding protein and p300 (reviewed in [18]). We demonstrated that both FTI-277 (a farnesyltransferase inhibitor) and GGTI-298 (a geranylgeranyltransferase I inhibitor) increase the association of the SRC-1 co-activator with ERα and that FTI-277 decreases the association of HDAC1 with ERα, which is essential for transcriptional repression.
In the present report, we assess in further detail the role of prenylated proteins in the estrogenic regulation of transcription in MCF-7 cells and in HeLa cells transiently expressing ERα, ERβ or ERα mutants. Our data clearly outline that prenyltransferase inhibitors (FTI-277 and GGTI-298) only stimulate ERE-dependent luciferase activity in stably transfected MCF-7 cells. They stimulate both ERα-mediated and ERβ-mediated ERE-dependent luciferase activity, without any obvious relocation of the ER from the cytoplasm to the nuclei. By contrast, the roles of both AF-1 and AF-2 of ERα appear to be determining in this effect. These results further establish the involvement of prenylated proteins, and more specifically the Rho-mediated signaling pathway, in the negative regulation of ER transcriptional activity.
Materials and methods
Materials
Materials used for cell culture were from InVitrogen Life Technology (Cergy Pontoise, France). Polyethylenimine was purchased from Sigma-Aldrich S.a.r.l. (St Quentin Fallavier, France). FTI-277 and GGTI-298 were a generous gift from S Sebti (University of South Florida, Tampa, FL, USA). Both FTI-277 and GGTI-298 peptidomimetics were dissolved in a solution of 10 mM dithiothreitol in dimethylsulfoxide.
Cell culture
The MCF-7 human breast adenocarcinoma cell line was obtained from the American Tissue Culture Collection (Rockville, MD, USA). The development of stable transfectants of MCF-7 cells (MELN cells) or HeLa cells (HELN cells) has been described previously [19]. These cells were established by transfecting MCF-7 cells or HeLa cells with the ERE-β-globin-luc-SV-Neo plasmid and therefore expressing luciferase in an estrogen-dependent manner. MCF-7 cells were routinely cultured in RPMI 1640, and MELN cells and HELN cells were cultured in DMEM growth media, supplemented with 5% FCS. Cells were incubated at 37°C in a humidified 5% CO2 incubator.
Cell transfection
Cells were grown for 3 days in phenol red-free medium, containing 5% dextran-coated charcoal-treated fetal calf serum (DCC-FCS). Then 6 × 104cells were seeded per well in 12-well plates and grown for 1 day in phenol red-free medium, containing 5% DCC-FCS. Cells were then transfected (0.4 μg/well DNA, 1 μl/well polyethylenimine in a final volume of 500 μl). Five hours after transfection, and for optimal FTI-277 treatment, cells were pretreated for 24 hours with the farnesyltransferase inhibitor prior to receiving E2 (5 nM) and FTI-277 [17]. For GGTI-298 treatment, no preincubation was performed and the inhibitor was added simultaneously with E2. Control experiments were also carried out; on the one hand in the absence of E2, and on the other hand in the presence of E2 and in the absence of inhibitors.
The plasmids used were the β-glob-Luciferase and the corresponding PGL3 empty vector for MCF-7 transient transfection, pSG5-REβ coding the full-length ERβ, HGO coding the full-length ERα, HG19 coding the AF-1-deleted ERα, HG7 coding the AF-2-deleted ERα, and the pSG5 empty vector for transfection of HELN cells. The HG0, HG19 and HG7 plasmids [20] were provided by Prof Chambon (Institut de Génétique et de Biologie Moléculaire et Cellulaire, Centre National de la Recherche Scientifique, Strasbourg, France). The pRL-CMV Vector (Promega, Charbonnières, France) coding the Renilla Luciferase was co-transfected as a reporter gene.
Luciferase assay in cellular homogenates
Cells were seeded in 12-well plates as already described, and were treated or not with prenyltransferase inhibitors and E2 for 16 hours in a final volume of 500 μl. At the end of the treatment, cells were washed with PBS and lysed in 100 μl lysis buffer (Promega). The luciferase activity from both Luciferase genes was measured using the Dual Luciferase Reporter (Promega), according to the manufacturer's instructions. For MELN cells that were not transfected with the Renilla Luciferase gene, protein concentrations were measured using the Bradford technique to normalize the luciferase activity data. A minimum of 15,000 relative light units were generated for the control conditions, used as the reference onefold induction. For each condition, average luciferase activity was calculated from the data obtained from three independent wells.
ER immunocytochemical detection
For each condition, 5 × 105 MCF-7 cells were seeded onto glass slides in 100 mm Petri dishes and were grown for 6 days in phenol red-free medium, containing 5% DCC-FCS. Cells were then preincubated or not with farnesyltransferase inhibitor and received E2 (5 × 10-9 M) and/or inhibitors 24 hours later. At the end of the treatment period (24 hours), cells were fixed in paraformaldehyde (4% in PBS) for 1 min. The staining was performed by a Techmate Horizon™ slide processor using a one-step peroxidase-conjugated polymer backbone visualization system (En Vision™ DAKO, Glostrup, Denmark) according to the manufacturer's instructions. The chromogenic substrate was 2,3'-diaminobenzidine. The primary antibody used was a monoclonal anti-ERα antibody (clone 1D5; DAKO). Negative controls were performed using the same staining technique without incubation with the primary antibody. Data were quantified using the ImageQuant software (Molecular Dynamics, Inc., Sunnyvale, CA, USA). Results are expressed in arbitrary units for either nuclei or cytoplasms of analyzed cells, in each experimental condition.
Clostridium botulinum C3 exoenzyme treatment
MELN cells were grown for 3 days in phenol red-free medium, containing 5% DCC-FCS. For each condition, 105 cells were seeded per well in 12-well plates and were grown for 1 day in phenol red-free medium, containing 5% DCC-FC. Cells were then treated with 20 μg/ml C3 exoenzyme (produced from the pGEX2T-C3 plasmid; kindly provided by Dr LA Feig, Tufts University School of Medicine, Boston, MA, USA) in a final volume of 0.5 ml [21]. Cells received E2 (5 × 10-9 M) 24 hours later and lysates were obtained after a further 16 hours for the luciferase assay as already described.
Statistical analysis
Statistical analysis of the data was conducted using an unpaired two-sample t test. Significance was defined as P < 0.02 or P < 0.05, as indicated in the text.
Results
Prenyltransferase inhibitors stimulate ERE-dependent luciferase activity in stably transfected MCF-7 cells but not in transiently transfected MCF-7 cells
We previously showed that both FTI-277 and GGTI-298 markedly enhance ER-mediated transcription in MELN cells [17], a clone of MCF-7 cells stably transfected with the ERE-β globin-luciferase reporter gene (Fig. 1a). After 5 days of E2 deprivation, cells were treated or not with 5 nM E2, in the presence of or in the absence of prenyltransferase inhibitors, and the luciferase activity was quantified 16 hours later. For optimal FTI-277 treatments, a 24-hour preincubation was systematically performed before E2 addition, whereas GGTI-298 was added simultaneously with E2 [17]. We confirmed the efficiency and specificity of FTI-277 and GGTI-298 to inhibit protein prenylation in MELN cells in these experimental conditions. Indeed, for this purpose, we checked whether HDJ2, a farnesylated protein, and Rap1A, a geranylgeranylated protein, were indeed not prenylated in the presence of 10 μM FTI-277 and 5 μM GGTI-298, respectively (data not shown).
E2 alone induced a 14.7-fold increase in the luciferase activity in MELN cells. In the absence of E2 (Fig. 1a, white bars), FTI-277 and GGTI-298 stimulated the basal transcriptional activity by 8.4-fold and 3.9-fold, respectively. In the presence of E2 (Fig. 1a, grey bars), FTI-277 and GGTI-298 dramatically enhanced the ability of E2 to stimulate transcription by an additional 4.1-fold for FTI-277 and 2.5-fold for GGTI-298. The ability of E2 to stimulate transcription was statistically enhanced by both FTI-277 and GGTI-298 (P < 0.02).
In parallel to this cell model, in which the Luciferase gene is stably integrated into the genome, we determined the effects of prenylation inhibitors in MCF-7 cells that were transiently transfected with the ERE-β-glob-Luciferase plasmid (Fig. 1b). In vehicle-treated cells, E2 still induced a 6.4-fold induction of luciferase activity. In the absence of E2, FTI-277 and GGTI-298 only stimulated the basal transcriptional activity by 3.2-fold and twofold, respectively. In the presence of E2, the transcription was only enhanced by an additional 1.1-fold for FTI-277 and 1.4-fold for GGTI-298. The ability of E2 to stimulate transcription was not statistically enhanced by FTI-277 or by GGTI-298 (P < 0.05). This result outlines the necessity of having a stably integrated reporter gene in order to elicit the effects of prenylation inhibitors.
This necessity was confirmed by checking the effects of FTI-277 in two other cell lines having a stably integrated reporter system, beside the MELN cells. We used the MVLN cells (MCF-7 cells stably transfected with an ERE-vit-tk-luc plasmid [22]; data not shown) and HELN cells (HeLa cells stably transfected with the ERE-β-globin-luc-SV-Neo plasmid and transiently transfected with the ERα, as described in Fig. 2). The data clearly indicate that in the three models the farnesyltransferase inhibitor does stimulate the ERE-dependent luciferase activity, in the absence of and in the presence of E2.
Prenyltransferase inhibitors stimulate both ERα-mediated or ERβ-mediated ERE-dependent luciferase activity in HELN cells
We then examined whether the effects of prenylation inhibitors were preserved in cells that stably expressed the gene coding the ERE-β glob-Luciferase but only transiently expressed the ER. For this purpose we used HELN cells, which are derived from the HeLa human cervical cancer cell line. HELN cells have no endogenous ER but stably express the gene coding the ERE-β glob-Luciferase [19]. As expected, we observed no induction of luciferase activity by E2 in cells transfected with the empty vector (Fig. 2). For all experiments, luciferase activity was normalized according to the expression of the co-transfected Renilla Luciferase gene so as to take into account the variations due to transfection efficiency.
HELN cells were then transfected with either ERα or ERβ. In both cases, E2 induced the luciferase activity in vehicle-treated cells (3.2-fold and 2.2-fold, for ERα-transfected and ERβ-transfected cells, respectively), which was lower than the activity obtained in MELN cells or MCF-7 cells (14.7-fold and 6.4-fold, respectively). In the absence of E2 (Fig. 2, white bars), FTI-277 and GGTI-298 only weakly stimulated the basal transcriptional activity by 3.6-fold and 2.4-fold, respectively, for ERα and by 1.5-fold and 1.7-fold, respectively, for ERβ. These stimulations were statistically significant (P < 0.05). In the presence of E2 (Fig. 2, grey bars), FTI-277 and GGTI-298 enhanced the basal transcriptional level by an additional 2.2-fold and 1.6-fold, respectively, for ERα and by an additional 2.7-fold and 1.6-fold, respectively, for ERβ. Altogether, the effect of E2 was significantly enhanced by both prenylation inhibitors with either ERα or ERβ (P < 0.05). As previously described for MELN cells [17], these effects were strongly inhibited by ICI 182,780 (data not shown).
Role of AF-1 and AF-2 in the effect of prenyltransferase inhibitors on ERE-dependent luciferase activity in HELN cells
In order to understand how prenylated proteins regulate ER transcription, we investigated whether the effects of prenylation inhibitors on transcription involve the AF-1 and/or AF-2 functions of the ERα. For this purpose, we used HELN cells that stably expressed the ERE-β glob-Luciferase reporter gene and were transiently transfected with plasmids coding either the full-length human ERα (HEG0) or the ERα mutants with defective AF-1 (HEG19) or AF-2 (HEG7) activities [20].
Cells, deprived of E2 for 4 days, were co-transfected with the Renilla luciferase plasmid and the plasmid coding full-length ERα or its mutants (Fig. 3). As expected, E2 alone could induce the luciferase activity only in cells transfected with plasmids coding either the full-length human ERα (HEG0) or the AF-1-defective mutant (HEG19). Indeed, AF-1 is constitutively active, is ligand independent and is mainly induced by growth factors. The absence of induction in cells transfected with the plasmid coding the AF-2 defective mutant (HEG7) is in agreement with the ligand dependence of AF-2.
In the presence of E2 (Fig. 3, grey bars), FTI-277 and GGTI-298 induced a statistically significant increase in the stimulation of transcription by E2 by 2.3-fold and 1.6-fold, respectively, in cells transfected with the full-length ERα (HEG0) and by 1.9-fold and 2.3-fold, respectively, in cells transfected with the AF-1 defective mutant (HEG19) (P < 0.02). In the absence of E2 (Fig. 3, white bars), FTI-277 and GTI-298 statistically increased the transcription of the luciferase gene in cells transfected with plasmids coding either the full-length human ERα or the two defective mutants by twofold to threefold. Cells transfected with the plasmid coding the AF-2 defective mutant (HEG7) exhibited no significant induction of luciferase activity in the presence of the prenylation inhibitors compared with E2 alone. The induction observed in cells transfected with the empty vector in the presence of FTI-277 had no biological significance as the final induction level was very low (1.2-fold).
Decreased cytoplasmic and nuclear presence of ERα due to prenyltransferase inhibitors in MCF-7 cells
To investigate the effects of prenylation inhibitors on ERα localization within the cell, we detected the presence of ERα by immunocytochemistry in MCF-7 cells, treated or not with E2 and prenylation inhibitors for 24 hours. The result of this experiment is shown in Fig. 4a, with the relative quantification presented in Fig. 4b.
As expected, ERα is highly concentrated in the nuclei of the untreated control with a significant staining of the corresponding cytoplasms (indicated by arrows in Fig. 4a). In the presence of E2, the staining intensity of both nuclei and cytoplasms was clearly decreased, with no distinct staining of the cytoplasms. In the absence of E2, similar decreases in labeling intensity were observed in the cytoplasms and nuclei of FTI-277-treated cells. Indeed, the presence of FTI-277 dramatically enhanced the ability of E2 to decrease nuclear staining, with the persistent absence of cytoplasmic staining. GGTI-298 similarly induced a decrease of ERα staining intensity in the cell nuclei, although to a lesser extent than did FTI-277, both in the presence of and in the absence of E2. Unexpectedly, no significant decrease in the staining intensity was observed in the cytoplasms of GGTI-298 treated cells, either in the presence of or in the absence of E2.
It must be outlined that these effects were observed after 24 hours of treatment with the prenylation inhibitors, whereas no effect was observed after short-term incubation (2 hours; data not shown).
Involvement of Rho proteins in ERE-dependent luciferase activity in MELN cells
C. botulinum C3 exoenzyme, a specific inhibitor of RhoA, RhoB and RhoC proteins, was used to determine whether the effects of prenylation inhibitors on ERE-mediated transcription were due to Rho proteins. After 4 days of E2 deprivation, cells were treated or not with C3 exoenzyme. E2 was added after 24 hours, and the luciferase activity was quantified 16 hours later (Fig. 5). The inhibition of ADP-ribosylation had already been examined under these conditions, demonstrating that the C3 exoenzyme does enter the treated cells (data not shown). The results clearly show an induction of luciferase activity by the C3 exoenzyme, in the presence of or in the absence of E2. This demonstrates the involvement of Rho proteins in the negative control of the ERE-dependent luciferase activity.
Discussion
Activation of ERs by estrogens triggers both ER nuclear transcriptional activity and the Src/Ras/Erks pathway-dependent mitogenic activity [9,13]. Prenylated proteins have been implicated in both estrogenic actions [17,23], and the prenylated Rho GTPases are now considered important modulators of ER transcriptional activity [24,25]. We previously demonstrated that prenylated proteins antagonize the ability of ERα to stimulate ERE-dependent transcriptional activity, potentially through the recruitment of a co-regulator [17]. In the present report, we proved that the stable integration of a plasmid coding the ERE-dependent luciferase in MCF-7 cells, or in HeLa cells transfected with the ERα gene, is necessary for FTI-277 and GGTI-298 effects. We did, however, observe a significant but expected stimulation of luciferase activity by E2 alone in the transiently transfected cells.
The need for a stable integration of the reporter system suggests that integration in the cell genome, an optimal chromatin environment of the ERE, and a precise low number of copies of the gene of interest are relevant conditions for the effects of the prenylation inhibitor, and consequently for the prenylated protein role. More insight in the interactions between the co-regulator complex in chromatin and prenylated proteins will be possible by performing chromatin immunoprecipitation experiments.
We chose HELN cells as a model of ER-free cells. These cells are stably transfected with the plasmid coding the ERE-dependent luciferase. Neither E2 nor prenylation inhibitors had any effect on the luciferase activity in these cells. In contrast, the ERE-dependent activation of transcription induced in the presence of both prenyltransferase inhibitors and reversed by the antiestrogen ICI 182,780 (data not shown) could be observed after transient transfection of HELN cells with the ERα gene. The classical consensus ERE is directly bound to ERα and ERβ, and at this site ERβ is a weaker transactivator [3-5]. In the presence of E2 and either ERα or ERβ, the comparison of the effects of prenylation inhibitors revealed that prenylated proteins had similar effects on the transcriptional activities of both ERs. These results are in agreement with the fact that Rho GDIα, a Rho guanine dissociation inhibitor that represses the activity of Rho GTPases including RhoA, Rac1 and Cdc42, has been shown to be a positive regulator of both ERα and ERβ transcriptional activities in human osteosarcoma cells [24]
It must be outlined, however, that there are important DNA binding sites for ERs other than ERE [26,27] where ERβ exerts different, or sometimes even opposite, effects to ERα (e.g. at AP-1 and Sp-1 sites) [28,29]. Such differential signaling between ERα and ERβ may exist with estrogen and prenylated proteins at the AP-1 response element. As it has been shown that AP1-mediated transcription is downregulated by RhoB, then RhoB may to some extent act on E2-mediated effects through this pathway [30]. Moreover, the activity of a number of transcription factors that may determine the overall promoter activity of ERE-containing genes is known to be altered by prenylated proteins (e.g. SRF, NF-κB) [31-34].
Our data strongly suggest the involvement of AF-2 in the transcriptional activation induced by prenylation inhibitors. Indeed, significant luciferase activity in cells expressing the AF-1 deleted mutant is observed in the presence of inhibitors and in both the presence of and the absence of E2, whereas the rates of induction observed in the AF-2-deleted mutant in the presence of inhibitors are similar in the presence of and the absence of E2. This is confirmed by the fact that the effects of prenylation inhibitors are similar on ERα and ERβ, whereas ERβ seems to have no significant AF-1 activity and thus depends entirely on the ligand-dependent AF-2 [2]. Interestingly, prenylated proteins appear to also regulate ER in an AF-2-independent manner, as the AF-2-deleted mutant did not completely abolish the increase in ER transcriptional activity induced by either FTI-277 or GGTI-298, with a significant stimulation in the absence of E2. This is in agreement with the data suggesting that induction of ER transactivation by RhoGDI is mediated largely by an ER AF-2-dependent mechanism, and to a lesser extent by an AF-2-independent mechanism [25].
The process by which a portion of the classical ER relocates to the cell membrane remains undetermined. Steroid receptors rapidly shuttle between the nucleus and cytoplasm by active nuclear import and export mechanisms. In the absence of identified post-translational modifications that could be involved in the attachment of the ER to the cell membrane, it is probable that membrane translocation of the ER is facilitated by another protein such as caveolin-1, which has been reported to interact with the ER [35]. Indeed, proteins of the Rho family (RhoA and Rac1) are targeted to caveolae in fibroblasts and are retained there by an unknown mechanism [36]. Moreover, p122, a GTPase activating protein is localized in the caveolae of both fibroblastic and epithelial cells, and plays an important role in caveolin distribution through the reorganization of the actin cytoskeleton [37].
The intracellular localization of MNAR and the way it traffics around the cell with the ER is still to be determined. Prenylated proteins may then modulate ER signaling through each key step in the various pathways: membrane attachment, translocation to the nucleus, stability, and genomic or nongenomic activities. The mechanisms that are responsible for the effects of prenylation inhibitors could involve modifications in the intracellular localization, or the level of ER protein expression, which would facilitate transcription. As an example, RhoA is a geranylgeranylated protein known to stimulate the degradation of the cyclin-dependent kinase inhibitor, P27kip. We previously demonstrated that the ER protein and RNA expression levels in total cell lysates are decreased by both FTI-277 and GGTI-298 treatment [17], and that ICI 182,780, a pure estrogen antagonist, induces the rapid relocation of ERα to an insoluble nuclear fraction, followed by its degradation [38]. Furthermore, the association of the SRC-1 co-activator to the ER is increased by FTI-277 and GGTI-298 [17], and slows the cytoplasm to nucleus mobility of ligand–ER complexes [39].
Immunocytochemical analysis showed a clear decrease in the intensity of ER staining in the nuclei of MCF-7 cells treated with either E2 or with FTI-277, or both, with a concomitant decrease in cytoplasmic ER staining. It is important to note that GGTI-298 has a much weaker effect, mainly in the cytoplasm. This suggests that farnesylated proteins predominantly maintain a high level of ER expression within the whole cell (either by repressing the degradation of ER or by increasing its expression or stability), whereas geranylgeranylated proteins could only stimulate the nuclear expression of the ER. Such a mechanism must be elucidated; indeed, the degradation of ERα induced by E2 is dependent on the proteasome [40], as has already been suggested for RhoB [41]. Since prenylation inhibitors had no effect on ER localization after a short incubation period in MCF-7 cells (2 hours; data not shown), we speculate that prenylated inhibitors act mainly by inhibiting active regulatory prenylated protein rather than by activating inhibited proteins.
Our data strongly suggest that both farnesylated proteins and geranylgeranylated proteins modulate ER-mediated activities, through either a common pathway or a distinct pathway. A special emphasis has already been placed on the role of Rho GTPases, and especially RhoA, Rac1 and cdc42, as ER-negative modulators in human osteosarcoma and breast cancer cells [24]. RhoA may therefore be the target of GGTI-298, explaining part of the described effects. We evaluated the role of the RhoA, RhoB, or RhoC proteins in the negative regulation of ER transcriptional activity, using the C3 exoenzyme. This toxin inhibits the three small GTPases through ADP-ribosylation. We could therefore implicate Rho proteins, which are generally geranylgeranylated, as negative modulators of ER transcriptional activity in MCF-7 cells. As both farnesylated proteins and geranylgeranylated proteins play a role in the negative control of ER-mediated transcriptional activity, many farnesylated proteins are potential candidates for the stimulatory effects of FTI-277. Due to the absence of specific inhibitors of the GTPase activity of farnesylated proteins, a more progressive screening of the numerous farnesylated proteins is required to identify those that are involved in the negative regulation of ER-mediated transcriptional activity.
It is important to note that RhoB can be both farnesylated and geranylgeranylated, which modifies the cellular localization of the protein. Indeed, farnesylated RhoB is located at the plasma membrane and geranylgeranylated RhoB is endosomal [42]. An increase in RhoB mRNA is observed in some breast cancer cell lines [43]. In rat fibroblasts, it has been shown that in the presence of farnesyltransferase inhibitors, the antiproliferative effect is driven by the geranylgeranylated form of RhoB [44]. Rho proteins and other prenylated proteins may then modulate ER-mediated transcription. Apart from the involvement of RhoGDI as a positive regulator of ER transactivation, it has been shown that Brx, a guanine nucleotide exchange factor for Rho proteins and member of the Dbl oncogene family, contains a nuclear hormone receptor binding region. Brx affects ER-mediated gene activation by a mechanism that is dependent on the Cdc42Hs signaling pathway [45]. Moreover, constitutively active forms of c-Raf and Rac synergistically enhance the CREB-binding protein/p300-mediated increase of transcription in T-cell activation signals [46]. Finally, a constitutively active form of Cdc-42 induces H4 hyperacetylation in chromatin [47].
Altogether, our data strongly suggest that several prenylated proteins, either farnesylated or geranylgeranylated, modulate ER signaling by repressing its transcriptional activity and/or increasing its stability. The determination of the exact Rho protein(s) involved in the effect we described is a major step of the research we are currently performing in the laboratory, in addition to the identification of the potential ER co-regulators.
Conclusion
Targeting the estrogen signaling pathway has proved to be of great value in the treatment of human breast cancer, and current evidence suggests that such targets hold great potential in the prevention of human breast cancer. Evaluation of the multiple pathways that can cross-talk with estrogen signaling pathways should help improve our understanding of some of the possible mechanisms of de novo and acquired tamoxifen resistance. Farnesyltransferase inhibitors are a new class of anticancer drugs that are at present in phase III clinical evaluation [48-50]. We previously showed that the combination of a farnesyltransferase inhibitor and tamoxifen may increase the antitumor effect of either drug alone in breast cancer [23]. Gaining further insight into the involvement of prenylated proteins in the estrogen signaling pathways should allow a more rational approach to treating and/or preventing hormone-resistant phenotypes.
Abbreviations
DCC-FCS = dextran-coated charcoal treated fetal calf serum; DMEM = Dulbecco's modified Eagle's medium; E2 = estradiol; ER = estrogen receptor; ERE = estrogen response element; FCS = fetal calf serum; FTI-277 = farnesyltransferase inhibitor; GGTI-298 = geranylgeranyltransferase inhibitor; MNAR = modulator of non-genomic activity of estrogen receptor; PBS = phosphate-buffered saline.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
The authors' contributions to this research are reflected in the order shown, with the exception of SDS who supervised the research and the preparation of this report. PC, GS, CMG and SDS carried out the experiments and PC drafted the manuscript. PB provided the MELN cells and the HELN cells, and provided technical support to initiate the study. PR carried out the immunocytochemical labeling. SDS and JCF conceived of the study, and participated in its design and coordination. GF, head of the department, gave helpful advice during the whole process. SDS performed the statistical analysis. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by the Association pour la Recherche contre le Cancer and by the Groupe de Recherche de l'Institut Claudius Regaud.
Figures and Tables
Figure 1 Effects of prenyltransferase inhibitors on estrogen response element (ERE)-dependent luciferase activity (a) in stably transfected MELN cells and (b) in transiently transfected MCF-7 cells. Both MELN cells and MCF-7 cells are deprived of estradiol (E2) for 4 days, and the MCF-7 cells are transiently transfected with the ERE-β-glob-Luciferase and the Renilla luciferase plasmids. Three hours after transfection, cells were treated or not with FTI-277 (10 μM or dithiothreitol/dimethylsulfoxide vehicle). Twenty-four hours later, cells were stimulated with E2 (5 nM) or ethanol and were treated or not with FTI-277 or GGTI-298 (10 μM or 5 μM, respectively, or dithiothreitol/dimethylsulfoxide vehicle). Luciferase activity was quantified 16 hours after E2 addition, as described in Materials and methods. Results are expressed in arbitrary units after normalization. Error bars indicate the mean values ± standard deviation from triplicate experiments, and the results are representative of two independent experiments. Results obtained show that prenylation inhibitors statistically increase the luciferase activity induced by E2 compared with the activity induced by E2 alone in MELN cells only (* P < 0.02).
Figure 2 Effects of prenyltransferase inhibitors on estrogen response element-dependent luciferase activity in HELN cells transfected with estrogen receptor (ER) α or ERβ. Cells, deprived of estradiol (E2) for 4 days, were co-transfected with the Renilla luciferase plasmid and either HEG0 (full-length ERα), pSG5-REβ (full-length ERβ) or pSG5 (empty vector). Five hours after transfection, cells were treated or not with FTI-277 (10 μM or dithiothreitol/dimethylsulfoxide vehicle), and 24 hours later they were stimulated with E2 (5 nM) or ethanol and were treated or not with FTI-277 or GGTI-298 (10 μM or 5 μM, respectively, or dithiothreitol/dimethylsulfoxide vehicle). Luciferase activity was quantified 16 hours after E2 addition, as described in Materials and methods. Results are expressed in arbitrary units after normalization. Error bars indicate the mean values ± standard deviation from triplicate experiments, and the results are representative of three independent experiments. Results obtained show that prenylation inhibitors statistically increase the luciferase activity on their own compared with control cells (white bars) and statistically increase the luciferase activity induced by E2 compared with the activity induced by E2 alone (grey bars) in MELN cells transfected with ERα or ERβ (* P < 0.05).
Figure 3 Effects of prenyltransferase inhibitors on estrogen response element-dependent luciferase activity in HELN cells, transfected with estrogen receptor (ER) α or its mutants. Cells, deprived of estradiol (E2) for 4 days, were co-transfected with the Renilla luciferase plasmid and either HEG0 (full-length ERα), HEG19 (AF-1-deleted ERα), HEG7 (AF-2-deleted ERα) or pSG5 (empty vector). Five hours after transfection, cells were treated or not with FTI-277 (10 μM or dithiothreitol/dimethylsulfoxide vehicle), and 24 hours later they were stimulated with E2 (5 nM) or ethanol and treated or not with FTI-277 or GGTI-298 (10 μM or 5 μM, respectively, or dithiothreitol/dimethylsulfoxide vehicle). Luciferase activity was quantified 16 hours after E2 addition, as described in Materials and methods. Results are expressed in arbitrary units after normalization. Error bars indicate the mean values ± standard deviation from triplicate experiments, and the results are representative of three independent experiments. Results obtained show that prenylation inhibitors statistically increase or do not statistically increase the luciferase activity on their own compared with control cells (white bars) and statistically increase the luciferase activity induced by E2 compared with the activity induced by E2 alone (grey bars) in HELN cells transfected with ERα or its mutants (* P < 0.02).
Figure 4 Immunocytochemical analysis of the effects of prenyltransferase inhibitors on estrogen receptor (ER) α distribution in MCF-7 cells. (a) Cells, deprived of estradiol (E2) for 7 days, were treated or not with FTI-277 (10 μM or dithiothreitol/dimethylsulfoxide vehicle), and 24 hours later they were stimulated with E2 (5 nM) or ethanol and treated or not with FTI-277 or GGTI-298 (10 μM or 5 μM, respectively, or vehicle). After 24 hours, cells were fixed and stained. One randomly selected field is presented for each treatment. (b) Data were quantified by determining the grey value of both the nucleus and the cytoplasm for each cell counted, as described in Materials and methods. For each experimental condition, six randomly selected fields were analyzed. The total number of cells present in the fields ranged from 250 to 400. Error bars indicate the mean values ± standard error of the mean, and the results are representative of two independent experiments.
Figure 5 Effect of C3 exoenzyme on estrogen response element-dependent luciferase activity in MCF-7 cells. Cells, deprived of estradiol (E2) for 4 days, were treated with C3 exoenzyme as described in Materials and methods. Twenty-four hours later the cells were stimulated with E2 (5 nM) or ethanol. Luciferase activities were quantified 16 hours after E2 addition, as described in Material and methods. Results are expressed in arbitrary units after protein normalization. Error bars indicate the mean values ± standard deviation from triplicate experiments, and the results are representative of two independent experiments.
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| 15642170 | PMC1064103 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 8; 7(1):R60-R70 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr956 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9581564215810.1186/bcr958Research ArticleMembrane estrogen receptor-α levels in MCF-7 breast cancer cells predict cAMP and proliferation responses Zivadinovic Dragoslava [email protected] Bahiru [email protected] Cheryl S [email protected] Department of Human Biological Chemistry and Genetics, University of Texas Medical Branch, Galveston, Texas, USA2 Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA2005 24 11 2004 7 1 R101 R112 16 4 2004 12 7 2004 18 8 2004 7 10 2004 Copyright © 2004 Zivadinovic et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
17β-estradiol (E2) can rapidly induce cAMP production, but the conditions under which these cAMP levels are best measured and the signaling pathways responsible for the consequent proliferative effects on breast cancer cells are not fully understood. To help resolve these issues, we compared cAMP mechanistic responses in MCF-7 cell lines selected for low (mERlow) and high (mERhigh) expression of the membrane form of estrogen receptor (mER)-α, and thus addressed the receptor subform involved in cAMP signaling.
Methods
MCF-7 cells were immunopanned and subsequently separated by fluorescence activated cell sorting into mERhigh (mER-α-enriched) and mERlow (mER-α-depleted) populations. Unique (compared with previously reported) incubation conditions at 4°C were found to be optimal for demonstrating E2-induced cAMP production. Time-dependent and dose-dependent effects of E2 on cAMP production were determined for both cell subpopulations. The effects of forskolin, 8-CPT cAMP, protein kinase A inhibitor (H-89), and adenylyl cyclase inhibitor (SQ 22,536) on E2-induced cell proliferation were assessed using the crystal violet assay.
Results
We demonstrated a rapid and transient cAMP increase after 1 pmol/l E2 stimulation in mERhigh cells; at 4°C these responses were much more reliable and robust than at 37°C (the condition most often used). The loss of cAMP at 37°C was not due to export. 3-Isobutyl-1-methylxanthine (IBMX; 1 mmol/l) only partially preserved cAMP, suggesting that multiple phosphodiesterases modulate its level. The accumulated cAMP was consistently much higher in mERhigh cells than in mERlow cells, implicating mER-α levels in the process. ICI172,780 blocked the E2-induced response and 17α-estradiol did not elicit the response, also suggesting activity through an estrogen receptor. E2 dose-dependent cAMP production, although biphasic in both cell types, was responsive to 50-fold higher E2 concentrations in mERhigh cells. Proliferation of mERlow cells was stimulated over the whole range of E2concentrations, whereas the number of mERhigh cells was greatly decreased at concentrations above 1 nmol/l, suggesting that estrogen over-stimulation can lead to cell death, as has previously been reported, and that mER-α participates. E2-mediated activation of adenylyl cyclase and downstream participation of protein kinase A were shown to be involved in these responses.
Conclusion
Rapid mER-α-mediated nongenomic signaling cascades generate cAMP and downstream signaling events, which contribute to the regulation of breast cancer cell number.
adenylyl cyclase17β-estradioldose responseprotein kinase A
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Introduction
It has long been recognized that cAMP is an important intracellular messenger that is capable of regulating diverse functions such as steroidogenesis in the ovary, sugar metabolism in the liver, and contractility of the cardiac muscle [1-5]. In addition to these crucial and specific physiological functions, the cAMP pathway plays an important regulatory role in the development of other complex cellular states such as differentiation, proliferation, and synaptic plasticity. Elevated levels of intracellular cAMP can inhibit the growth of some cell types but stimulate the growth of others. Among the cell types whose growth is stimulated by cAMP are thyroid [6], pituitary [7], and normal human breast epithelial cells in culture [8]. Several studies have demonstrated that cAMP inhibits the growth of established breast cancer cell lines and breast cancer cells in primary culture [8-10]. However, such studies have not completely resolved issues such as the effective levels of cAMP in different cell types, the roles played by other participants in the signaling web that may cooperate to bring about different outcomes, and any subpopulations of response-initiating receptors.
The intracellular cAMP level is determined by the rates of its synthesis and clearance. It is synthesized from ATP via a transmembrane adenylyl cyclase (AC), which is activated by stimulatory Gs proteins coupled to cell surface receptors (G-protein-coupled receptors). Clearance is regulated either by cyclic nucleotide phosphodiesterases (PDEs) or by an efflux mechanism that secretes or transports cAMP out of cells [11]. The multiplicity of PDEs was recently established [12], but the regulation and utilization of different isoforms are experimental issues that remain unclear.
The influence of 17β-estradiol (E2) on MCF-7 breast cancer cell proliferation was previously assigned exclusively to genomic mechanisms. However, it has been known for some time that E2 can rapidly (within a few minutes) induce cAMP production, and that this time frame is not compatible with the multiple macromolecular synthetic events necessary for genomic responses [13]. We have an incomplete understanding of the conditions under which cAMP responses are best measured, and of the participation of multiple enzymes in generating and degrading cAMP. There are also reports that some MCF-7 cell sublines either do not respond or do not reliably generate this response (personal communications from many laboratories). Therefore, estrogen receptor (ER)-mediated mechanisms that increase cAMP in MCF-7 and other responsive cell types have not fully been explained. Here, we compare cAMP responses in cell lines selected for low and high expression of the membrane form of ER (mER)-α and thus address the receptor subform that is involved in these mechanisms. Additionally, we further probe the details of mER-α-mediated activation of AC and the contributions of PDEs to final signal levels, as well as subsequent cAMP-dependent protein kinase A (PKA) activation and the regulation of cell proliferation.
Methods
Cell immunoseparation and subculturing
MCF-7 cells originated from the Michigan Cancer Center. We separated them into two subpopulations by immunopanning [14,15] using C-542 carboxyl-terminal ER-α antibody, provided by Drs Dean Edwards and Nancy Weigel. This antibody is now commercially available from Stressgen Biotechnologies (Victoria, Canada). Briefly, sterile antibody on the surface of a petri plate bound cells at 4°C over a 1-hour time period, and cells that attached to the plate (mER+) were propagated separately from those that did not bind (mERlow). Using the same antibody, mER+ cells were then subjected to further separation via a fluorescence-activated cell sorter [16]. Cells selected by both types of sequential separation are highly enriched for mER-α (mERhigh). Upon propagation, the separated cells were not exclusively mER positive or negative, and consequently they were named mERhigh and mERlow. We previously observed this heterogeneity in similarly separated GH3/B6 cells [15,17]. All subpopulations of MCF-7 cells were routinely cultured in phenol-red free Dulbecco's modified eagle medium (Gibco-BRL, Grand Island, NY, USA) supplemented with 10% heat-inactivated DSCS (defined/supplemented bovine calf serum; HyClone Laboratories, Inc., Logan, UT, USA) and 1% antibiotic–antimycotic (Gibco Invitrogen Corporation, #15240-062). Cells used in the experiments were between passage 8 and 14 after separation.
Three days before each experiment, the cell growth medium was replaced with medium containing 4 × dextran-coated charcoal stripped serum (DCSS medium). Stripped serum was produced by incubation with an equivalent volume of packed 0.25% weight/vol charcoal Norit A for 2 hours on a shaker at 4°C. The charcoal preparation had previously been suspended in 0.0025% (weight/vol) dextran T-70 (Sigma, St Louis, MO, USA) in 0.25 mol/l sucrose, 1.5 mmol/l MgCl2, and 10 mmol/l HEPES (pH 7.6). The charcoal was pelleted at 500 g for 10 min and the supernatant (stripped serum) decanted, and the process was repeated four times. In other experiments we deprived cells of serum 2–3 days before the experiment by placing them in a defined medium (DM) consisting of phenol-red free Dulbecco's modified eagle medium with 5 μg/ml insulin, 5 μg/ml transferrin, 5 ng/ml selenium (using the stock preparation from Sigma), and 0.1% bovine serum albumin.
cAMP response measurement
Cells were plated at a density of 0.25 × 106 per well in six-well plates and then incubated in either DM or DCSS for 3 days before the experiment. In experiments in which hormone treatments were performed at 37°C, on the day of the experiment the cells were pretreated for 10 min with 1 mmol/l IBMX (3-isobutyl-1-methylxanthine; Sigma) and then treated for different time intervals (3, 6, 10, 20, 30, 60 or 120 min) with 1 pmol/l E2 or ethanol vehicle (0.1%) in the presence of IBMX. In experiments in which the hormone treatment (as described above) was performed at 4°C, cells were treated either in their growth plates or in suspension. For cell suspensions the cells were scraped from two 150 mm plates, pelleted at 1000 g, and resuspended in 3.5 ml DM. The incubation was then performed in 100 μl of cell suspension with permanent agitation and the reaction was terminated by spinning down the cells and washing them in cold DM. For attached cells, similar incubations were performed in the growth dishes.
The specificity of this response for ER-α was tested with ICI172,780 (Tocris Cookson Inc., Ellisville, MO, USA). The cells were pretreated with ICI172,780 for 30 min (1 μmol/l) and incubated for an additional 15 min after adding 1 pmol/l E2, or they were simultaneously treated for 15 min with E2 and ICI172,780. As a negative control, we also tested the time-dependent production of cAMP with 10 nmol/l 17α-estradiol (Sigma Aldrich, St Louis, MO, USA) – an inactive E2 stereoisomer. Preparation of cell lysates and quantification of intracellular cAMP were performed according to the protocol provided with the cAMP detection kit (Amersham Pharmacia Biotech, Piscataway, NJ, USA). Each value for cAMP was normalized against protein concentration determined using a Bradford (Bio Rad, Hercules, CA, USA) protein detection kit.
Cell proliferation
Cells were plated at a density of 1000 or 2000 cells per well in 96-well plates. The next day the growth medium was replaced with DCSS containing the hormone (E2) or other treatments (forskolin, 8-CPT cAMP, H-89 plus E2, SQ22 536 plus E2). Controls for E2 treatment were done on a separate plate, because we had previously determined that low amounts of volatilized estrogens can affect responses mediated via nongenomic signaling pathways [18]; control treatments were with the vehicle in which test compounds were solubilized (ethanol or dimethyl sulfoxide). In some experiments the growth medium was replenished every second day, and after 5 days the cells were fixed with 2% paraformaldehyde/0.1% glutaraldehyde in PBS. The number of the cells in each well was determined using the crystal violet (CV) assay [19], which we modified previously [17]. Briefly, the cells were incubated in 0.1% CV for 30 min at room temperature, excess dye was removed by three brief rinses with ddH2O, the plates were air dried, and the dye was extracted with 10% acetic acid, which was then read in a plate reader (Wallac 1420; Perkin Elmer, Boston, MA, USA) at 590 nm.
The utility of the CV assay in measuring cell number was verified both in MCF-7 cells and in combination with immunodetection plate assays for GH3/B6 cells [17]. We compared CV results with DNA content measurements and with cell number counts by hemocytometer and both assays correlated very well (data not shown). Additionally, we compared the CV method with the MTT assay (ATCC, Manassas, VA, USA), which is often used to determine cell number and viability. Different numbers of mERhigh and mERlow cells (1000–7000) were plated in 96-well plates, and fixed and treated with CV as described. Both sets of data were approximated with a single linear regression line (Fig. 1a). The number of cells determined by CV versus MTT assays were linearly correlated (Fig. 1b).
Statistical analysis
Statistical differences between two sets of data were determined using two-way analysis of variance. The dose–response curves were fitted with a four-parameter Gaussian distribution (Sigma Plot 8.0; Systat Software Inc., Point Richmond, CA, USA). The differences between the entire curves were tested by comparing the sum of squares of the residuals from each individual curve with the sum of squares of the residuals of the combined curves by applying a Microsoft Excel F test. P < 0.05 was considered statistically significant.
Results
Optimal conditions for E2-induced cAMP accumulation and measurement in MCF-7 cells enriched and depleted for membrane ER-α
MCF-7 cell line enriched for membrane ER-α
We observed that the level and kinetics of cAMP accumulation varied depending on the type of incubation medium, the incubation temperature, and pretreatment with the PDE inhibitor IBMX. In mERhigh MCF-7 cells, 1 pmol/l E2 induced rapid and transient production of cAMP (Fig. 2a,2b,2c). At the reduced temperature of 4°C in a completely defined medium (Fig. 2a), a substantial increase in accumulated cAMP was seen as compared with the levels achieved at 37°C (Fig. 2b). The response peak at 4°C was prolonged, as the accumulated cAMP decreased gradually, even in the absence of the PDE inhibitor IBMX, which is usually included to inhibit the decay of cAMP and enhance the response. The same level of accumulated cAMP was obtained at 5, 15 and 30 min at 4°C, regardless of whether the treatment was performed with cells attached to a plate (Fig. 2a, open circles) or in suspension (Fig 2a, closed circles). However, after 30 min the cAMP level declined abruptly in attached cells. The DCSS medium did not increase cAMP production at 4°C (data not shown).
At 37°C, a low but significant degree of cAMP elevation was achieved by 5 min in attached cells treated in DM (Fig. 2b, triangles); a greater cAMP response was achieved in attached cells pre-incubated in DCSS (Fig. 2b, squares) as compared with DM. All of the experiments conducted at 37°C were performed in the presence of 1 mmol/l IBMX, as in most published protocols.
The impeded ligand E2-peroxidase also triggered production of cAMP in suspended cells treated at 4°C in DM (Fig. 2c). The level of E2 presented by the conjugate in these studies approximated the level applied as free steroid in the experiments shown in the other panels. The resulting maximal level of accumulated cAMP was lower than that with the equivalent amount of free ligand, but the time required for peak accumulation was the same.
MCF-7 cell line depleted for membrane ER-α
When treated at 4°C in suspension, mERlow MCF-7 cells exhibited only a small but significant rise in cAMP (Fig. 2d) in response to E2. Hormone treatment caused a maximal accumulation of cAMP at 6–15 min, but the decay in this second messenger was very rapid. Insignificant changes were observed at 37°C in attached cells, both in serum-containing and in defined media (Fig. 2e). The statistical comparison of cAMP accumulated in mERhigh versus mERlow cells revealed that those differences were significant.
cAMP dynamics
Because our 4°C protocol without any added IBMX is relatively unusual, we wondered about the causes of the cAMP level dynamics. We next asked why accumulated cAMP did not plateau at 37°C. That is, what are the circumstances of cAMP decay in these cells, and does cAMP leave the cells or is it metabolized? To approach these questions we measured cAMP levels at 37°C after 1 pmol/l E2 stimulation, in cells and in their extracellular medium in parallel. We also measured the kinetics of IBMX protection of cAMP by measuring the decrease in cAMP in the cytosolic fraction prepared from cells that had been treated with forskolin. The increase in cAMP in the medium did not match the decrease in intracellular cAMP content of the cell; although the intracellular cAMP of this population of cells decreased by about 80 pmol (from 95 pmol down to 15 pmol), the cAMP in the medium increased only by about 3 pmol (Fig. 3a). As expected, in the absence of IBMX the decrease in cytosolic cAMP was rapid (3.8%/min), whereas in the presence of IBMX it decreased at a slower rate (1.2%/min). Therefore, the addition of IBMX protected cAMP from the action of some PDEs (Fig. 3b), but there was still a significant fraction of cAMP that seemed refractory to this protection.
ICI172,780 and 17α-estradiol effects on cAMP production
ICI172,780 was effective in preventing the 15 min maximal cAMP accumulation (Fig. 4a). Simultaneous application of ICI172,780 (1 μmol/l) and E2 (1 pmol/l) was as effective as the 30 min pretreatment with ICI172,780. The usually inactive E2 stereoisomer 17α-estradiol (10 nmol/l) was not capable of triggering cAMP production in mERhigh MCF-7 cells (Fig. 4b). These studies are consistent with this effect being mediated by known ER proteins.
E2-induced dose-responses of cAMP levels and cell number changes differ between MCF-7 cells enriched and depleted for membrane ER-α
In Fig. 2 we observed that the characteristics of hormonally induced cAMP accumulation differed between mERhigh and mERlow cells. Figure 5a shows that the E2 dose–response curve for this response was biphasic in both cell types, but the sensitivity and upper limit of the responses differed. The mERhigh cells exhibited a higher maximal response level in cAMP levels. In mERlow cells the response peak was achieved with a 50-fold lower E2 concentration; the higher maximal stimulation of cAMP production in mERhigh cells was achieved with 100 pmol/l E2. Therefore, it can be assumed that a larger number of receptors had to be filled to achieve this higher response level in cells that had a larger receptor pool.
E2 also caused different dose effects on the proliferation of mERhigh versus mERlow cells. Cells with low mER levels responded to E2 with stimulated proliferation in the whole range of tested concentrations (from 0.1 fmol/l up to 0.1 mmol/l; Fig. 5b, open circles). However, mERhigh cells showed a biphasic proliferation pattern, with growth stimulation in the lowest range of concentrations of E2 (up to 1 pmol/l) and inhibited proliferation at higher E2 concentrations (from 1 nmol/l up to 1 mmol/l; Fig. 5b, closed circles). Therefore, the higher levels of cAMP production achieved with mERhigh cells result in growth inhibition.
Because we had previously observed an effect of cell density on the level of mER expression and corresponding functional consequences in pituitary tumor cells, we wondered whether cell density would affect functional responses to E2 in this breast cancer model system. Indeed, proliferation of mERhigh cells in the presence of 10 nmol/l E2 was influenced by cell density (Fig. 6). At a low density (such as that used in the preceding experiments) proliferation was inhibited, whereas at higher cell densities this effect was reversed. When the cells were plated at the highest density studied (6000/well), their growth was stimulated at the same level as were the mERlow cells. In contrast, proliferation of mERlow cells was stimulated regardless of the cell plating density.
E2-induced increase in adenylyl cyclase activity correlates with decreased proliferation of MCF-7 cells enriched for membrane ER-α cells via a PKA-activated pathway
An activator of AC (forskolin), as well as the cell-permeable cAMP analog 8-CPT cAMP, inhibited the growth of both mERhigh and mERlow cells (Figs 7 and 8) when provided in the same concentrations to both cell types. In mERhigh cells, both the AC inhibitor SQ22,536 and the PKA inhibitor H-89 abrogated E2-induced inhibition of cell proliferation (Figs 7 and 8). Both inhibitors increased the stimulatory effect of E2 on mERlow cell proliferation (Fig. 8). Therefore, bypassing receptor-mediated signaling mechanisms and controlling cAMP levels with other compounds (which either provided or acted to generate cAMP) made both cell types behave similarly with respect to changes in cell number. Likewise, artificially decreasing cAMP levels or their downstream effects (AC and PKA inhibitors) made cells respond similarly with respect to growth, regardless of their mER levels.
Discussion
Our studies provide evidence that membrane-associated ERs participate in the estrogen-regulated control of MCF-7 breast cancer cell number by changing cAMP levels and downstream cAMP-activated PKA. E2 differentially influenced cell proliferation in mERhigh versus mERlow cells. Measurements of and responses to cAMP manipulated via different culture conditions, assay incubation conditions, and specific response mimetics or inhibitors allowed us to implicate pathways and signal levels in the mechanism of control of cell number, and to suggest possible resolutions between previously reported conflicting results about cAMP and the control of breast cancer cell growth.
Breast cancer cell lines (especially MCF-7 cells) are heterogeneous populations of cells; other investigators have succeeded in separating them into different constituent subpopulations [20,21]. We exploited this characteristic and separated MCF-7 cells into populations enriched and depleted for the membrane subform of ER-α by capturing live cells that bound ER-α-specific antibody to their membranes. These two cell subtypes express the same level of total (membrane plus nuclear) ER [22]. Thus, we have a system in which the consequences of mER levels can be assessed in the same cell line, without other transgenetic manipulations. Because our mERhigh and mERlow cells were not clonally derived (from a single cell), the differences between these populations that we observed can not be attributed to clonal variations.
Attempts to study E2-stimulated cAMP levels using the conditions reported by others for MCF-7 cells [23-25] resulted in variable and marginal responses in our hands. We tested different incubation times from 10 min up to 3 hours at 37°C, and all were relatively ineffective, even in the presence of a PDE inhibitor. When we decreased the incubation temperature to 4°C, the amplitude of maximally accumulated cAMP increased and the slope of its decline decreased considerably. Although this temperature is not physiologic, it is often applied for biochemical assessment of biologic responses. This is done to avoid competition by other biologic responses that could rapidly remove the molecule under study at the physiologic temperature.
We also tested a variety of other conditions, including much shorter incubation times than 10 min and various media, as well as comparing attached versus suspended cell preparations. Both we and others [24] have found an influence of charcoal-stripped serum in the medium on cAMP production. However, whereas Fortunati and coworkers [24] could not identify any cAMP production in serum-free medium, we were able to detect a low but significant E2-induced increase. It is possible that this discrepancy could be due to different incubation times, as we found a very rapid effect (3 min) but the other study tested 15 min as the earliest time point. In more recent analyses it has become common to observe nongenomic responses that are complete and return to baseline by 15 min [26-28].
In our cells 1 mmol/l IBMX was only partially effective, and loss of cAMP was not the consequence of its transport out of the cells. We can assume that some other PDEs that are resistant to IBMX were present, unless our mERhigh cells possess other cAMP effectors (receptors or binding proteins) apart from PKA [29] that could capture cyclic nucleotides and render them undetectable in our cytosol assay. The possibility of multiple PDEs with their own individual patterns of regulation provide significant alternative opportunities for control of this important cell function, and add another level of complexity to its analysis.
The impeded ligand that we used in these studies, E2-peroxidase, was also capable of triggering cAMP accumulation. The attachment of E2 to a large molecule that cannot easily enter cells has previously been used to demonstrate action at the membrane [30-33]. Although this compound stimulated lower but significant levels of cAMP accumulation than did approximately equivalent levels of free steroid, it is likely that steric considerations (such as only partial contact of the large molecule with the cell surface; bound protein obscuring adjacent receptors) may prevent ligand–receptor contacts, or that some other aspect of the ligand–receptor complex configuration is disrupted by ligand tethering. Others have also reported that E2-peroxidase has lower affinity for the receptor compared with free ligand [34]. Although it has been reported that steroid can escape conjugates and elicit a response, we removed free steroid in our studies by incubating an aliquot with dextran-coated charcoal under conditions that remove more than 99% of free hormone [35] just prior to application of the impeded ligand. The release of steroid from conjugates happens over a much longer period of time than a rapid response [36].
We have shown that E2 can play a dual role in breast cancer cells, both stimulating and inhibiting cell proliferation, depending on its concentration, which is consistent with the findings reported by Lippert and coworkers [37]. We have also shown that the same E2 concentration (10 nmol/l) could either prevent or enhance mERhigh MCF-7 cell growth, depending on cell density. We previously showed that increased cell density can dramatically decrease the fraction of ER-α expressed in the cell membrane (see accompanying reports [18] and [22]), and others have observed similar effects on other membrane receptors [18,38]. The estrogenic responses of mERlow cells were not affected by cell density, and their mER levels were already low. We therefore suggest that this type of control can be attributed to effects on the level of mER and thus the signaling cascades elicited by it.
It remains to be determined whether E2-induced apoptosis, necrosis, or cell growth arrest could be part of the negative growth effects caused by high E2 levels in mERhigh cells. Others have shown that very low concentrations of E2 (as low as 1 pmol/l) induce apoptosis in MCF-7 LTED cells [39], selected by long-term growth in the absence of estrogens. The resulting increase in ER-α expression levels in these cells caused hypersensitivity to E2 [40]. In addition, cells in which ER-α has been over-expressed by stable transfection also die in response to physiologic estrogen concentrations [41]. However, measurements in both of these studies failed to distinguish between nuclear and membrane forms of ER-α. Because our mERhigh cells were selected on the basis of their high mER expression levels (see accompanying report [22]), we can assume that their higher sensitivity to E2 is a consequence of their mER levels. Our mERhigh cells exhibited a biphasic pattern of proliferation, with the maximal growth at approximately 10-11 mol/l E2 and the decline in cell number beginning in response to approximately 1 nmol/l E2. Maximal stimulation of LTED cells was achieved at 10-14 mol/l, while stimulation of wild-type MCF-7 cells was maximal at 10-10 mol/l E2. Hence, our mERhigh cells exhibit an intermediary proliferation pattern between wild-type and MCF-7 LTED cells with respect to the E2 dose–response curves reported by Santen and coworkers [21].
Our mERlow cells responded to E2 by dose-dependent enhancement of their cell numbers. These cells have much lower levels of mER but some is present, and they express the intracellular ER at comparable levels to mERhigh cells (see accompanying report [22]). MCF-7 breast cancer cells have also been shown to respond to E2 as an apoptosis survival factor after taxol or ultraviolet exposure [42]. This protective effect was directly dependent on the concentration of E2, and the highest effect was achieved with 10 nmol/l E2. This inhibition of apoptosis was also achieved with E2–bovine serum albumin, which indicated probable mediation through a plasma membrane ER. However, these studies did not directly assess the cellular level of mER (the populations studied were probably mixed, comprising cells with both high and low levels of mER). Therefore, it is hard to correlate their heterogeneous cell population results with our selected mERhigh or mERlow cells. Whether protection from apoptosis plays any role in the survival of our mERlow cells remains to be determined.
Our findings also demonstrated that cAMP-activated PKA activity is associated with inhibition of cell proliferation. In mERhigh cells H-89 blocked the decrease in cell number caused by 10 nmol/l E2, and in mERlow cells it further enhanced the already existing stimulation of proliferation. It is still not clear how cAMP inhibits cell proliferation. Lowe and coworkers [43] suggested that the inhibitory effect of cAMP (and forskolin, which induced it) occurs distal to extracellular signal-regulated kinase (ERK) activation, possibly by the inhibition of an ERK-independent pathway. However, other groups have reported that the cAMP/PKA pathway and mitogen-activated protein kinase (MAPK) pathway are connected in fibroblasts [43,44] and in MCF-7 cells [45,46]. Activated PKA phosphorylates Raf and inhibits phosphorylation of downstream MEK1/2 and Erk1/2, thus preventing proliferation, a counterbalance to E2-stimulated pathways that stimulate proliferation (Fig. 9). Our findings are consistent with the participation of PKA in inhibition of cell proliferation.
In mERlow cells, at both low (1 pmol/l) and high (10 nmol/l) E2 concentrations, a very low amount of cAMP accumulated. Therefore, in these cells cAMP's inhibitory concentration is never achieved, although further inhibition of cAMP and its downstream effects (via inhibitors) could still enhance proliferation. In mERhigh cells, however, both low and high E2 concentrations caused about the same amount of cAMP accumulation, but only at 10 nmol/l E2 did the inhibitory pathway prevail. This finding suggests that the model depicted in Fig. 9 is simplified, and that other pathways participate and integrate at other levels, resulting in modulation of the cell's decision to proliferate, arrest, or die. It was recently shown that E2 simultaneously activates the proliferative MAPK-Erk (via Src-Shc-Ras-Raf-MEK-Erk) and phosphatidylinositol-3 kinase pathways in MCF-7 cells, and that both pathways are required to trigger S-phase entry of cells. In addition, the temporal pattern of their activation is extremely important; a necessary immediate and transient consequence of hormone treatment is MAPK activation, whereas phosphatidylinositol-3 kinase activation must be sustained for at least 3 hours [47]. Our results represent a unique example of the extremely complex effects of steroid on target tissues, and emphasize the importance of a global approach when studying the proliferative effects of estrogens.
Reports from other groups have indicated that there may be other membrane estrogen receptor types, such as GPR30 [48] and SHBG-R [24,49], which participate in E2 signaling from the membrane. Clearly, a detailed analysis of different mER levels on the same cell backgrounds from a variety of responsive tissue types would be a valuable addition to these investigations. Finally, estrogen-dependent breast cancer cells synthesize and secrete growth factors in response to E2 which can stimulate the epidermal growth factor receptor signaling pathway to induce proliferation [50]. Therefore, comparative studies of the contributions of each of these possible players are needed if we are to gain a complete understanding of E2-induced proliferation of breast cancer cells.
Conclusion
Our results indicate that a membrane-associated ER-α is responsible for rapid E2-induced cAMP accumulation and subsequent activation of the downstream PKA pathway. A high level of stimulation through the mER-α results in an interruption to breast cancer cell proliferation. Thus, knowledge of tumor mER-α levels and manipulation of estrogenic compound doses and resulting signaling mechanisms may offer an opportunity to refine treatment strategies for some patients with tumors that have these characteristics.
Abbreviations
AC = adenylyl cyclase; CV = crystal violet; DCSS = medium with 4 × dextran-coated charcoal-stripped serum; DM = defined medium; E2 = 17β-estradiol; ER = estrogen receptor; ERK = extracellular signal-regulated kinase; IBMX = 3-isobutyl-1-methylxanthine; MAPK = mitogen-activated protein kinase; mER = membrane estrogen receptor; PDE = phosphodiesterase; PKA = protein kinase A.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
We thank Dr David Konkel for critical review of our paper. This work was supported by the grant from Department of Defense Breast Cancer Research Program, DAMD17-01-1-0418.
Figures and Tables
Figure 1 Verification of the utility of the crystal violet (CV) assay for measuring cell number. (a) Different numbers of MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh) and MCF-7 cells depleted for membrane estrogen receptor-α (mERlow; 1000–7000/well) were plated, fixed with 2% paraformaldehyde/0.1% glutaraldehyde, and then quantified via the CV assay at 590 nm absorbance. (b) Different numbers of mERhigh cells were assessed in parallel with CV and MTT assays to demonstrate a linear correlation between these assays. The values are means for 28 samples ± standard error.
Figure 2 Optimization of conditions for 17β-estradiol (E2)-induced cAMP accumulation and measurement. (a–c) MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh) and (d, e) MCF-7 cells depleted for membrane estrogen receptor-α (mERlow). All of the cells were stimulated with 1 pmol/l E2, or an equivalent amount of E2 conjugated to peroxidase, for different time intervals, and the intracellular cAMP levels were assessed. (Panels a and d) Cells were incubated at 4°C in defined medium (DM) either attached to a plate (open circles) or in suspension (closed circles). (Panels b and e) Attached cells were incubated at 37°C in DM medium (triangles) or DCSS medium (medium with 4 × dextran-coated charcoal-stripped serum; squares). (Panel c) Cells in suspension were stimulated with E2-peroxidase at 4°C. All experiments were repeated at least three times, and each time point was in triplicate. The data are presented as means ± standard error and the asterisks represent significant differences (P < 0.05) as compared with time 0.
Figure 3 Kinetics of cAMP decrease in MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh) cells. (a) Cells were treated with 1 pmol/l 17β-estradiol (E2) for different time intervals at 37°C in the presence of 1 mmol/l 3-isobutyl-1-methylxanthine (IBMX). The intracellular cAMP (circles) and that in the medium (squares) from the same cells were assessed. (b) cAMP was produced by directly stimulating adenylyl cyclase with 10 μmol/l forskolin for 15 min at 37°C. The decrease in cAMP in the cytosol was tested at 37°C in the absence (open circles) and presence of 1 mmol/l IBMX (closed circles). The entire regression lines were compared by evaluating the differences between the sums of squares of the residuals of individual lines with the sum of squares of the residuals of the combined line using an F-test. The data are presented as means ± standard error. The regression lines were significantly different (P = 0.0001).
Figure 4 Effects of ICI172,780 and 17α-estradiol on cAMP production in MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh) cells. (a) ICI172,780 inhibited 17β-estradiol (E2)-induced cAMP production. Both simultaneous application of 1 μmol/l ICI172,780 and 1 pmol/l E2 (open circle) or 30 min pretreatment with ICI172,780 (open square) was tested. (b) 17α-Estradiol was applied at a 10 nmol/l concentration and the time dependent cAMP production was followed. Closed circles represent E2 induced cAMP production, redrawn for comparison from Fig. 2a.
Figure 5 17β-Estradiol (E2) dose-dependent cAMP responses and proliferation in MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh; closed circles) versus MCF-7 cells depleted for membrane estrogen receptor-α (mERlow; open circles). (a) cAMP response; each experiment was repeated three times with triplicate samples, and the data were approximated with a four-parameters Gaussian equation. The two sets of data were compared by evaluating the differences between the sums of squares of the residuals from each individual curve with the sum of squares of the residuals of the combined curve using the F-test. The curves were significantly different (P = 0.02). (b) E2 dose-dependent proliferation curves vary with mER expression level. mERhigh cells (closed circles) exhibited a biphasic growth pattern, whereas mERlow cells (open circles) responded with a sigmoid growth pattern. The cells were treated on separate plates with different concentrations of E2 or ethanol vehicle (0.1%) for 5 days. The data are percentages of control, and expressed as mean values ± standard error from three separate experiments, each containing 40 replicates.
Figure 6 Effect of cell density on 17β-estradiol (E2)-induced proliferation. Different densities of MCF-7 cells enriched for membrane estrogen receptor-α (mERhigh; shaded bars) and MCF-7 cells depleted for membrane estrogen receptor-α (mERlow; open bars) were treated for 5 days with 10 nmol/l E2. The controls were treated on a separate plate with ethanol vehicle (0.1%). Values are expressed as percentage change from control. All experimental mean values (error bars are ± standard error) were significantly different (P < 0.002) from the control (first bar on the left).
Figure 7 Signaling pathway inhibitors differentially affect cell proliferation. 17β-Estradiol (E2; 10 nmol/l) was introduced on day 0. Adenylyl cyclase stimulator (forskolin; 10 μmol/l) and cAMP analog (8-CPT cAMP; 250 μmol/l) were replenished every second day. PKA inhibitor (H-89; 10 μmol/l) and adenylyl cyclase inhibitor (SQ 22,536; 300 μmol/l) were applied at day 0 together with 10 nmol/l E2. Cells were fixed on days 1, 3 and 5, and their number estimated by crystal violet (CV) assay. The data are averaged values ± standard error (error ranges were very small and are contained within the size of the symbol) of two separate experiments, each containing 24 replicates. mERhigh, MCF-7 cells enriched for membrane estrogen receptor-α; mERlow, MCF-7 cells depleted for membrane estrogen receptor-α.
Figure 8 Proliferation of cells after 5 days of treatment with different compounds. The same incubation conditions apply as for Fig. 7. The asterisks indicate significant differences (P < 0.002) from controls. Data are expresssed as means ± standard error. E2, 17β-estradiol; mERhigh, MCF-7 cells enriched for membrane estrogen receptor-α; mERlow, MCF-7 cells depleted for membrane estrogen receptor-α.
Figure 9 Possible negative feedback effects of estrogen-activated cAMP-PKA (protein kinase A) pathway on the estrogen-activated Ras-Raf-MAPK (mitogen-activated protein kinase) pathway of cell proliferation. The up arrow indicates a cAMP increase. AC, adenylyl cyclase; E2, 17β-estradiol; ERK, extracellular signal-regulated kinase; MEK, MAPK kinase.
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| 15642158 | PMC1064104 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 24; 7(1):R101-R112 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr958 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9591564216210.1186/bcr959Research ArticleMembrane estrogen receptor-α levels predict estrogen-induced ERK1/2 activation in MCF-7 cells Zivadinovic Dragoslava [email protected] Cheryl S [email protected] Department of Human Biological Chemistry and Genetics, University of Texas Medical Branch, Galveston, Texas, USA2005 26 11 2004 7 1 R130 R144 16 4 2004 12 7 2004 18 8 2004 7 10 2004 Copyright © 2004 Zivadinovic and Watson, licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
We examined the participation of a membrane form of estrogen receptor (mER)-α in the activation of mitogen-activated protein kinases (extracellular signal-regulated kinase [ERK]1 and ERK2) related to cell growth responses in MCF-7 cells.
Methods
We immunopanned and subsequently separated MCF-7 cells (using fluorescence-activated cell sorting) into mER-α-enriched (mERhigh) and mER-α-depleted (mERlow) populations. We then measured the expression levels of mER-α on the surface of these separated cell populations by immunocytochemical analysis and by a quantitative 96-well plate immunoassay that distinguished between mER-α and intracellular ER-α. Western analysis was used to determine colocalized estrogen receptor (ER)-α and caveolins in membrane subfractions. The levels of activated ERK1 and ERK2 were determined using a fixed cell-based enzyme-linked immunosorbent assay developed in our laboratory.
Results
Immunocytochemical studies revealed punctate ER-α antibody staining of the surface of nonpermeabilized mERhigh cells, whereas the majority of mERlow cells exhibited little or no staining. Western analysis demonstrated that mERhigh cells expressed caveolin-1 and caveolin-2, and that ER-α was contained in the same gradient-separated membrane fractions. The quantitative immunoassay for ER-α detected a significant difference in mER-α levels between mERhigh and mERlow cells when cells were grown at a sufficiently low cell density, but equivalent levels of total ER-α (membrane plus intracellular receptors). These two separated cell subpopulations also exhibited different kinetics of ERK1/2 activation with 1 pmol/l 17β-estradiol (E2), as well as different patterns of E2 dose-dependent responsiveness. The maximal kinase activation was achieved after 10 min versus 6 min in mERhigh versus mERlow cells, respectively. After a decline in the level of phosphorylated ERKs, a reactivation was seen at 60 min in mERhigh cells but not in mERlow cells. Both 1A and 2B protein phosphatases participated in dephosphorylation of ERKs, as demonstrated by efficient reversal of ERK1/2 inactivation with okadaic acid and cyclosporin A.
Conclusion
Our results suggest that the levels of mER-α play a role in the temporal coordination of phosphorylation/dephosphorylation events for the ERKs in breast cancer cells, and that these signaling differences can be correlated to previously demonstrated differences in E2-induced cell proliferation outcomes in these cell types.
membrane estrogen receptor-αMCF-7 human breast cancer cellsextracellular regulated protein kinase
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Introduction
Estrogen receptor (ER)-α has traditionally been defined as a ligand-dependent transcription factor that regulates its target genes by binding to estrogen response elements present in the promoters of many responsive genes [1]. However, an ever-increasing number of reports indicate that the cellular actions of estrogens can be initiated at the plasma membrane, through membrane versions of estrogen receptors (mERs) [2-4] or via other membrane-resident 17β-estradiol (E2)-binding proteins [5]. There is also evidence that mER-α from vascular endothelium and human MCF-7 breast cancer cells is localized in specialized cholesterol-rich membrane microstructures (caveolae), where it can associate with different signaling molecules and participate in various nongenomic actions [6,7].
A variety of rapid E2-induced signal transduction events can lead to stimulation of calcium flux, cAMP production, phospholipase C activation, and inositol phosphate production [8]. Mitogen-activated protein kinases (MAPKs) such as extracellular signal-regulated kinase (ERK)1 and ERK2 are also rapidly stimulated by estrogens in various cell types (e.g. endothelial [9], osteoblastic [10], neuroblastoma [11], and breast cancer cells [12]). However, the specific relationship of these responses to the levels of antibody-identified ER-α in the membrane has rarely been investigated [13,14]; other rapid estrogen-induced actions were specifically linked to mER-α in pituitary tumor cells in our previous studies [15-18].
The two isoforms of ERK (p42 and p44) play critical roles in the control of cell proliferation, differentiation, homeostasis, and survival. Traditionally, autophosphorylation of receptor tyrosine kinases after ligand binding initiates the cascade of phosphorylation steps that result in dual ERK phosphorylation (on Thr202 and Tyr204 in the human enzyme, or Thr183 and Tyr185 in the rat enzyme [19]). The signaling pathway initiated by E2 at the level of the plasma membrane is not yet completely understood, although recent studies have implicated a cascade of intermediary proteins and signaling steps involving mER-α, G-proteins, Src-induced matrix metalloproteinases that liberate heparin-binding epidermal growth factor (EGF), and EGF receptor [13]; the involvement of many other signaling pathways remains unexamined.
Whether different levels of mER can influence signaling parameters (such as kinetics and final levels of second messengers) that lead to physiological responses remains to be investigated. To address this question we separated MCF-7 cells into two subpopulations based on outer membrane-exposed mER-α levels and confirmed their differential mER-α expression by several methods. We investigated the association of mER-α with caveolin-rich membrane fractions in cells enriched for membrane display of these receptors. We then linked the level of mER-α to the magnitude and patterns of E2-induced ERK1/2 activation. Although activation of kinases was previously demonstrated, those other studies did not address the accompanying inactivation mechanisms for ERKs involving several specific cellular phosphatases.
Methods
Cell immunoseparation and subculturing
Our MCF-7 cells originated from the Michigan Cancer Center. We separated them into two subpopulations by immunopanning [16,20] using C-542 carboxyl-terminal ER-α antibody provided by Drs Dean Edwards and Nancy Weigel; this antibody is now commercially available from Stressgen Biotechnologies (Victoria, Canada). Briefly, sterile antibody on the surface of a petri plate bound cells over a 1-hour time period at 4°C, and cells that attached to the plate (mER+) were propagated separately from those that did not bind (mERlow). The mER+ cells were then subjected to further selection via fluorescence-activated cell sorting (FACS) using the same antibody [21]. Cells undergoing sequential separation were highly enriched for mER-α (mERhigh). All cell subpopulations were routinely cultured in phenol-red free Dulbecco's modified eagle medium (Gibco-BRL, Grand Island, NY, USA) supplemented with 10% heat-inactivated DSCS (defined/supplemented bovine calf serum; HyClone Laboratories Inc., Logan, UT, USA) and 1% of an antibiotic–antimycotic (Gibco Invitrogen Corporation, #15240-062). Cells used in the experiments were between passage 8 and 14 after separation.
Three days before each experiment, the cell growth medium was replaced with medium containing 4 × dextran-coated charcoal stripped serum (DCSS medium) or completely defined medium (DM; phenol-red free Dulbecco's modified eagle medium with 5 μg/ml each of insulin and transferrin, 5 ng/ml selenium, and 0.1% bovine serum albumin). Stripped serum was produced by incubation with an equivalent volume of packed 0.25% weight/vol charcoal Norit A for 2 hours on a shaker at 4°C. The charcoal preparation had previously been suspended in 0.0025% (weight/vol) dextran T-70 (Sigma-Aldrich, St. Louis, MO, USA) in 0.25 mol/l sucrose, 1.5 mmol/l MgCl2, and 10 mmol/l HEPES (pH 7.6). The charcoal was pelleted at 500 g for 10 min, the supernatant (stripped serum) decanted, and the process repeated a total of four times.
Fluorescence immunocytochemical detection of membrane ER-α
The protocol published for immunocytohcemical detection of mER-α in the GH3 rat pituitary cell line [22] was optimized for MCF-7 cells. Briefly, the cells were fixed in 2% paraformaldehyde/0.1% glutaraldehyde in phosphate-buffered saline for 25 min at room temperature; those conditions preserve the integrity of the cell membrane and enable detection of the specific ER-α antibody bound to the cell surface without interference from the usually more intense nuclear signal. Autofluorescence was reduced by blocking the aldehyde groups for 15 min with 1% Na2HPO4 and 0.05% NaBH4. Nonspecific binding sites were blocked with 0.1% fish gelatin in phosphate-buffered saline for 45 min at room temperature, which we successfully used previously in GH6/B6/F10 cells [23,24]. For the present studies with MCF-7 cells, we compared the nonspecific antibody signal blocking abilities of fish gelatin, horse serum, bovine serum albumin and nonfat dry milk, all commonly used methods, and found them to be equally useful.
The plates were incubated with C-542 antibody (5–10 μg/ml) overnight at 4°C in the blocking solution. To detect the bound primary antibody and amplify the signal, we used a biotinylated secondary antibody from the biotin/avidin Vectastain ABC-Alkaline Phosphatase kit and Vector red as a substrate, together with 40 μl of 125 mmol/l levamisol (which inhibits the endogenous alkaline phosphatases). All the components for detection of bound primary antibody were obtained from Vector Laboratories Inc. (Burlingame, CA, USA). Fluorescence photography was performed as previously described [15,22]. The images were photographed with Kodak HC4000 color film and a camera (Model C-35AD-4) with Olympus AHBT microscope and fluorescence attachment (model AH2-RFL) using the FITC filter, under 100 × magnification. Digital deconvolution and pseudo-coloring were performed with Image-Pro Plus software applied to images captured through the FITC filter with a CoolSNAP-Pro digital monochrome camera and a ProScan motorized stage (Meyer Instruments, Houston, TX, USA) attached to a Olympus AHBT microscope equipped with fluorescence attachments (model AH2-RFL).
Quantification of membrane ER-α
The mER was quantified with a protocol modified from one previously developed in our laboratory for GH3 cells [23]. Briefly, cells plated and treated in 96-well plates were fixed as described for immunocytochemistry (see above), and the integrity of the membrane was verified by lack of staining with the anti-clathrin antibody (ICN Biomedicals, Aurora, OH, USA), as clathrin is localized just inside the plasma membrane. Different concentrations of C-542 ER-α antibody were tested (in the range 1–12 μg/ml), and the tagging enzymatic reaction (alkaline phosphatase) was monitored for different time intervals (5, 15 and 30 min) in order to determine optimal conditions for measurement. The specificity of the C-542 antibody was checked by comparing its binding with the nonspecific binding of mouse IgG1k (mIgG1k, of the same immunoglobulin isotype) and by the ability of the peptide representing the C-542 epitope (Genosys, Woodlands, TX, USA) to decrease C-542 binding. Other controls included incubation without any antibody to detect endogenous phosphatase not inhibited by levamisol, and without primary antibody to detect nonspecific binding of secondary antibody. The total cellular ER-α was measured by applying the same procedure to cells permeabilized by including 0.1% of the non-ionic detergent IGEPAL CA-630 (Sigma-Aldrich) during the fixation procedure. The absorbance of the alkaline phosphatase product paranitrophenol (pNp) in each well was measured at 405 nm and normalized against the number of cells determined by the absorbance of crystal violet (CV) at 590 nm, as previously described [23].
Caveolae preparation and Western analysis
To concentrate caveolin-rich membranes, we extended a previously published protocol [25] by introducing a dialysis step to remove sucrose, and a vacuum spin at a low drying rate to concentrate the samples. Specifically, cells were seeded in three 150 mm diameter plates and grown in serum-supplemented medium until 60% confluent. The growth medium was replaced with DCSS medium devoid of antimycotic compound and cultured for an additional 3 days. Cells from all three plates were collected in 1 ml lysis buffer consisting of 50 mmol/l Tris/HCl (pH 7.5), 5 mmol/l EDTA, 100 nmol/l NaCl, 50 mmol/l NaF, 1 mmol/l PMSF, 0.2% TritonX-100 and protease inhibitor cocktail P8340 (100 × diluted; Sigma-Aldrich). Cells in solution were passed through a 25-g syringe needle, then homogenized with 25 strokes using a Dounce B-type pestle. The homogenate was adjusted to 45% sucrose by addition of an equivalent volume of 90% sucrose. A discontinuous sucrose gradient consisting of the sample, 35% sucrose, and a top layer of 5% sucrose was centrifuged for 18 hours at 200,000 g. Fractions of 1 ml were collected and checked for the presence of caveolin-1 and caveolin-2 by Western analysis using antibodies from Biosciences (San Jose, CA, USA). Fractions 5 and 6 (the interface between 5% and 35% sucrose) contained the highest amount of caveolin-1. Those two fractions were pooled and dialyzed overnight against the lysis buffer. The sample was then concentrated by vacuum spin, and 20 μg of these proteins separated by 4–20% SDS-PAGE. The proteins were transferred to nitrocellulose membranes and the mER-α bands were probed with C-542 ER-α antibody. After incubation with secondary antibody (conjugated with horse radish peroxidase) the bands were visualized with an ECL kit (Amersham Pharmacia Biotech, Piscataway, NJ, USA).
Fixed cell-based enzyme-linked immunosorbent assay detection of activated ERK1/2 in 96-well plates
A protocol previously developed in our laboratory for other cells [26] was optimized for MCF-7 cells. Cells were plated at a density of 4000/well in 96-well plates, and after 24 hours the growth medium was replaced with DCSS medium. After 3 more days of culture the cells were treated with E2 (1 pmol/l) for different time intervals (3, 6, 10, 20, 30 and 60 min), or with different E2 concentrations (from 10-16 to 10-7 mol/l) for 10 and 6 min for mERhigh and mERlow cells, respectively. After treatments the cells were fixed in 2% paraphormaldehyde/0.21% picric acid for 2 days at 4°C. The cells were subjected to a 60 min blocking step with 0.1% fish gelatin and 0.1% Triton X-100 at room temperature. Incubation with the antibody raised against the phosphorylated forms of ERK1/2 (Cell Signaling Technology, Beverly, MA, USA) was performed overnight at 4°C (1:400 dilution). To quantify active ERKs, biotinylated secondary antibody (anti-mouse/anti-rabbit) conjugated to alkaline phosphatase was applied. Substrate pNp phosphate was added and incubated for 25 min at 37°C, and the absorbance of the pNp product was determined at 405 nm in a plate reader (Wallac 1420; Perkin Elmer, Boston, MA, USA). The levels of phosphorylate (pERK1/2) were normalized to the cell number in each well (measured using the CV assay).
To confirm the activation of ERK1/2 (after 3, 6 and 10 min in mERhigh or 6 min in mERlow), we pretreated the cells for 15 min with 40 μmol/l U0126 MEK1/2 inhibitor (Cell Signaling Technology). The ER antagonist ICI182,780 (Tocris Cookson Inc., Ellisville, MO, USA) at a concentration of 1 μmol/l was tested with or without E2 by preincubating the cells with ICI182,780 for 30 min followed by the addition of 1 pmol/l E2 or by simultaneous addition of ICI182,780 and E2. MDA-MB-231 cells used to test the requirement of ER-α for these responses were obtained from ATCC (Manassas, VA, USA). We confirmed the absence of ER-α mRNA in these cells by multiple reverse transcription polymerase chain reaction primer pairs representing the ER-α sequence (data not shown).
Cell proliferation
Cells were plated at a density of 1000 cells/well in 96-well plates. The next day the growth medium was replaced with DCSS containing different treatments. The 1 pmol/l E2 was present either for the duration of the experiment or as a short pulse treatment (10 min), whereas 1 pmol/l E2-peroxidase (Sigma-Aldrich; concentration based on the E2 content of the conjugate) was used only as short pulse treatment. The effect of MEK inhibitor (40 μmol/l U0126) on the pulsed E2-induced proliferation of mERhigh cells was tested by a pretreatment for 15 min and an additional treatment for 10 min with E2 together with the inhibitor. To block the effect of E2 in the pulse treatment, we used ER-α antibody (AER315; NeoMarkers Inc., Fremont, CA, USA) raised against the ligand-binding domain. The cells were pretreated with 1 μg/ml AER315 antibody for 1 hour at room temperature, followed by a 10 min incubation at 37°C with 1 pmol/l E2 in the presence of the antibody. Controls for E2 treatment were done on a separate plate, because we previously determined that low amounts of volatilized estrogens can affect responses mediated via nongenomic signaling pathways [24]. After 5 days the cells were fixed with 2% paraformaldehyde/0.1% glutaraldehyde in phosphate-buffered saline in preparation for the CV assay (see below).
Crystal violet assay
The number of the cells in each well was determined with the CV assay [27], which we modified previously [23]. Fixed cells were incubated in 0.1% filtered CV solution for 30 min at room temperature, and excess dye was removed by three brief rinses with ddH2O. The plates were then air dried, the dye was extracted with 10% acetic acid, and the extract (in the same wells) was then read in a plate reader (Wallac 1420, Perkin Elmer) at 590 nm. The utility of this assay was previously verified for GH3B6 cells by comparison with other assays of cell number in combination with the immunoplate assays [23]. In addition, for MCF-7 cells we verified the utility of this assay for measuring cell number by comparison with DNA content measurements and with cell counts by hemocytometer (data not shown). We also compared the CV assay with the MTT assay, which is often used to determine viable cell number (ATCC), and we obtained a linear correlation for the two assays. These latter results are presented in the accompanying paper [28].
Statistical analysis
Statistical differences between two sets of data were determined using two-way analysis of variance. The differences between the entire curves were tested by comparing the sum of squares of the residuals from each individual curve with the sum of squares of the residuals of the combined curve by applying a Microsoft Excel F test. P < 0.05 was considered statistically significant.
Results
Immunoseparated cell characterization of mER-α
Immunopanning and subsequent FACS successfully separated MCF-7 cells into two populations according to the expression of mER-α observed in immunocytochemistry experiments. Punctate staining can be seen on the surface of unpermeabilized mERhigh cells (Fig. 1a), whereas the majority of mERlow cells did not exhibit this staining (Fig. 1b). Whenever occasional staining was present on cells in the mERlow population, its appearance was similar to that seen on mERhigh cells (data not shown). Secondary antibody staining alone was at levels similar to that shown for the mERlow cells in Fig. 1b (not shown). When permeabilized, both subpopulations of cells exhibited plentiful cytoplasmic and nuclear staining (not shown) at similar levels. Digital deconvolution (in 15 separate cell planes) performed on a grouping of three unpermeabilized mERhigh cells clearly demonstrated punctuate staining all along the periphery of these cells (Fig. 2).
To determine whether mER-α is in a submembrane location in our mER-α-enriched cells, we colocalized mER-α with caveolin proteins in gradient-separated membrane fractions. Our mERhigh cells express both caveolin-1 (form α and β represented by doublet bands) and caveolin-2 (Fig. 3a; two different concentrations of protein were loaded in adjacent lanes). In the same density gradient fractions that contained the majority of these caveolar structural proteins (fractions 5 and 6), mER-α was found (Fig. 3b). We detected several prominent bands from both the sucrose gradient fractions (fractions 5 and 6) and the whole cell lysates (Fig. 3b, panel 1). To determine those bands whose signal was specific for C-542 ER-α antibody, we performed an epitope competition (preblocking of antibody with peptide representing the epitope). We identified two ER-α bands competable with peptide: the 67 kDa (the size of classical ER-α) and a prominent lower band at 52 kDa (Fig. 3b, compare panels 1 and 2).
To quantify mER-α with our plate assay, we first determined the lowest optimal C-542 antibody concentration that would saturate binding to the membrane form of the receptor, and then optimized assay conditions to give a reliably detectable signal in the linear range of the fluorescent product assay. All three signal generation incubation times (5, 15 and 30 min) gave acceptable data, but 15 min assays allowed clear delineation of antigen-saturating concentrations of C-542 antibody utilizing lower antibody concentrations (8 μg/ml; Fig. 4a). Those conditions were used for subsequent assays. The same level of mER-α could be detected in cells kept for 3 days in a completely defined medium lacking serum or in the same medium supplemented with 10% DCSS (Fig. 4a, open circles and closed circles, respectively). The background values (obtained in the absence of primary and secondary antibody, or with no primary antibody) were very low (Fig. 4b). At 8 μg/ml the isotype control mIgG1k yielded a value only 10% of that for C-542 antibody; peptide competition resulted in an approximate 50% decrease in C-542 antibody binding, again verifying the specificity of the antibody for this epitope. This verified that the assay was specific, and not subject to interference by endogenous alkaline phosphatases in the presence of levamasole. The fixation conditions that we used preserved the integrity of the cell membrane, as demonstrated by the negligible anti-clathrin antibody binding in nonpermeabilized versus the very high binding in permeabilized cells. Therefore, equivalent protocols differing only in the inclusion of a permeabilization agent during fixation can be used to quantify the total ER (tER) versus the mER.
Because we had previously observed an effect of cell density on the level of ER expression in GH3/B6/F10 cells, which occurred at a density when cells had just begun to make contact by cell processes [24], we wondered whether the same regulation was applicable to breast cancer cells. Considering this effect, plating a single density may not demonstrate the ability of cells to express mER-α. Therefore, we performed a cell density study for mERhigh versus mERlow breast cancer cell types (Fig. 5). The mER-α signal decreased exponentially with increasing cell number (Fig. 5a). At low plating densities, mERhigh cells clearly showed much higher levels of mER than did mERlow cells (Fig. 5, closed circles and open circles, respectively). The two curves approximating the level of mER were significantly different (P = 0.0003). The ER-α-negative cell line MDA-MB-231 did not have mER-α (Fig. 5, diamond), even at relatively low cell density, because their value was at the level of mIgG1k isotype control antibody levels (horizontal hatched line). Increased cell density also decreased tER (Fig. 5b); however, mERhigh and mERlow MCF-7 cells exhibited the same level of tER, because all of these data could be approximated with the same curve. Because mERhigh cells had higher mER-α levels but the same tER-α levels as mERlow cells, then, by subtraction, mERhigh cells have lower intracellular ER-α levels than do mERlow cells.
Kinetics of ERK1/2 activation by 17β-estradiol in MCF-7 cells enriched and MCF-7 cells depleted for membrane ER-α
During the optimization of the fixed cell-based enzyme-linked immunosorbent assay for ERK activation, we established the optimal cell density for a 10 min EGF treatment, which is known to result in substantial phosphorylation of ERK1/2. Both the controls and EGF-treated cells exhibited increased pERK1/2 with increased cell number (Fig. 6a, main graph). As we expected, normalizing pERK1/2 values to the number of cells (CV absorbance at 590 nm) did not significantly change the ratio values except in the case of the highest number of cells plated, verifying that cells plated in the density range of CV values 0.2–0.6 could be used. Partly because others have shown that ERKs can be activated by mechanical stimulus in MCF-7 cells [29], we tested ethanol-treated controls over the same time course (3–60 min). A pronounced decrease in ERK1/2 phosphorylation was seen with time (Fig. 6b), and so appropriate controls were performed for each time point in all subsequent experiments.
In mERhigh MCF-7 cells, ERK1/2 activation with 1 pmol/l E2 was fast and transient (Fig. 7a). The maximal activation was achieved after 10 min, followed by a rapid decline in phosphorylated ERK1/2. However, continued incubation with E2 (1 hour) resulted in the reactivation of ERK1/2. To test whether this signal was initiated at the membrane, the impeded ligand E2-peroxidase was applied to the cells at steroid concentrations that approximated the levels applied as free steroid in the previous experiments (Fig. 7b). To eliminate any free steroid present, just before use we pretreated the E2-peroxidase with dextran-coated charcoal under conditions that remove more than 99% of free hormone [9]. The resulting maximal level of ERK1/2 activation was slightly higher than for treatment with the free ligand, but the peak time of activation was the same. Again, a recurrent later ERK activation was observed.
Cells with lower levels of mER-α (Fig. 7c) also had the capacity for fast and transient activation of ERK1/2, but this smaller activation peak appeared at 6 min after 1 pmol/l E2 treatment. The levels of phosphorylated ERK declined between 10 and 30 min of E2 treatment as they had with mERhigh cells; however, at longer incubations (1 hour) no reactivation was seen but rather a further ERK1/2 apparent dephosphorylation was observed. This implies that higher levels of mER-α associated with more robust early ERK activation are also responsible for the sustained ERK reactivation at the later stage.
The inhibitor of the upstream MEK1/2, namely U0126, was effective in inhibiting ERK1/2 activation in both types of MCF-7 cells (Fig. 6a and 6c, insets), verifying that the values we measured in our plate assay were from MEK-phosphorylated ERK. In the MDA-MB-231 ER-α-negative cell line (Fig. 7d), E2 could not significantly activate ERK1/2, confirming that ER-α is necessary for ERK activation during this 60 min time period.
Dose-dependent activation of ERK1/2 by 17β-estradiol is influenced by the level of membrane ER-α expression
In mERhigh cells, the ability of E2 to induce ERK activation was biphasic with respect to dose at the 10 min time point (early response peak). ERK phosphorylation was stimulated at a wide range of concentrations from 0.1 pmol/l to 100 nmol/l E2, although the highest E2 concentrations resulted in less phosphorylation (Fig. 8a). In mERlow cells a biphasic response was also seen, but the only effective concentrations were 0.1 and 1 pmol/l for the 6 min (early and only) response peak (Fig. 8b).
Physiologic significance of early ERK1/2 activation
Long-term treatment of mERhigh MCF-7 cells with 1 pmol/l E2 resulted in substantial stimulation of proliferation (Fig. 9). A 10 min short pulse treatment also resulted in significant although lower stimulation of proliferation. The same level of stimulation was achieved with both E2 and E2-peroxidase presented for a short pulse. E2-induced proliferation was prevented with MEK inhibitor (U0126) as well as with a specific ER-α antibody recognizing the ligand binding domain. These results are consistent with the participation of mER-α and ERK1/2 in the cell proliferation response.
Phosphatase inhibitors differentially affect ERK activation in MCF-7 cells enriched and depleted for membrane ER-α
We next asked whether the observed decrease in phosphorylated ERK1/2 after 20 min in both subpopulations of cells, and the continued low phosphorylation levels after 60 min in mERlow cells, could successfully be abrogated with specific phosphatase inhibitors (Fig. 10). We tested inhibitors of protein phosphatase (PP)1, PP1A, and PP2B. These phosphatases can be considered principal enzymes of this class, based on their general abundance and broad specificity [30]. Okadaic acid (OA), an inhibitor of PP1 and PP1A, and cyclosporin A, an inhibitor of PP2B, were both able to reverse the ERK inactivation in mERhigh cells (Fig. 10a). In mERlow cells, both the 20 min and continued 60 min dephosphorylation were abrogated only with the PP2B inhibitor (Fig. 10b and 10c). Because of the known apoptotic effect of OA at some concentrations [31], it is important to stress that we applied it at a low concentration (50 nmol/l). In addition, OA does not have toxic effects in short-term incubations [32]. These results suggest that dephosphorylation of ERKs is an important component of their process of action and that coordinated phosphorylation/dephosphorylation is required for strong signaling through this pathway.
Rapid effects of ICI182,780 and 17α-estradiol on ERK1/2 activation
To characterize the pharmacologic properties of ERK activation, we used mERhigh cells to study the effectiveness of the potent antiestrogen ICI182,780 (1 μmol/l) and the inactive E2 stereoisomer 17α-estradiol (10 nmol/l). We used concentrations of these compounds shown by others to be effective in inhibiting the transcriptional activity of E2. We had also previously shown that a 10 nmol/l 17α-estradiol concentration could elicit another type of nongenomic estrogenic effect in our GH3/B6/F10 pituitary tumor cell model – rapid prolactin release [15]. ICI182,780 alone induced an activation pattern very similar to that seen with E2 but with an earlier first peak (Fig. 11a). A 30 min ICI182,780 preincubation before a subsequent 1 pmol/l E2 challenge did not significantly change the E2 activation pattern, although the first peak again appeared at 6 min (the ICI182,780 pattern) and there was a much more pronounced inactivation at 10 and 20 min (compare Fig. 11b with Figs 7a and 11a). However, simultaneous application of ICI182,780 (1 μmol/l) and E2 (1 pmol/l) blunted the response and altered the kinetics of ERK phosphorylation, shifting the now single weak activation to later times (20–60 min; Fig. 11c). The E2 stereoisomer (17α-estradiol) provoked a slightly delayed and blunted response also, but with some other interesting features (Fig. 11d). A significant dephosphorylation occurred before the major activation peak, a return to baseline phosphorylation levels followed the 20 min activation peak, and no second activation peak occurred at 60 min.
Discussion
In the late 1970s, Pietras and Szego [33] reported the presence of high-affinity binding sites for E2 associated with the plasma membranes of the MCF-7 human breast cancer cell line. Since that time few laboratories have followed up on this finding [34,35] until very recently, when more detailed studies started to emerge. However, none of these studies established a correlation between the level of identified mER-α expression and its functions.
To address this issue we used immunopanning followed by FACS to separate MCF-7 cells into two subpopulations that were enriched and depleted for mER-α expression. We then used several approaches to assess the appearance and levels of mER-α in these two subpopulations. These studies demonstrated that MCF-7 cells are heterogeneous with respect to mER-α expression, and that difficulties in reproducibly demonstrating nongenomic estrogenic effects in these cells could in part be due to the dilution of responding cells in the largely nonresponsive total cell population. We were able to obtain two distinct cell subpopulations without applying plasmid-based transfection manipulations to over-express receptor (with probable accompanying altered regulatory parameters). Our experimental model avoids changes in stoichiometry of the multiple interacting proteins that are involved in steroid actions. Because our mERhigh and mERlow cells were not clonally derived (from a single cell), the observed differences between these subpopulations cannot be attributed to clonal variations.
A characteristic punctate staining for mER (earlier reported for GH3/B6/F10 cells [22]) was also detected on the surface of most of our mERhigh MCF-7 cells. MCF-7 mERlow cells also had occasional punctuate staining, but at a much lower level. Because it is difficult and time-consuming to quantify this staining via immunocytochemistry, we modified our previously developed 96-well plate immunoassay [23] to measure both membrane and intracellular receptors in breast cancer cells, and to quantitate the relative amounts in these two receptor subpopulations. This assay must be tailored to specific cell types to ensure preservation of the membranes (which have quite different compositions in different cell types) and to optimize for different antibody labeling systems. Although mER-α levels differed between the two cell types named for these differences (high versus low), we discovered that the two subpopulations had the same quantity of total receptor (measured in permeabilized cells). This finding is consistent with intracellular and membrane fractions of ER-α being from the same ER pool, but with a different balance of subcellular distribution.
There is disagreement in the literature on the expression of caveolin-1 and -2 in MCF-7 cells. To test whether mER is localized in caveolar membranes, we had first to resolve this uncertainty. Some have reported that in MCF-7 cells caveolin-1 is downregulated and only caveolin-2 is expressed [36,37]. Others reported that caveolin-1 could be upregulated and downregulated in MCF-7 cells in concert with the cells' ability to develop drug resistance [38]. Some studies used transient transfection to re-express the missing caveolin-1 and test for its function in signaling [7,39]. Apparently, there are several different subpopulations of MCF-7 cells growing in different laboratories [40,41]. Our MCF-7 cells expressed both caveolin-1 and -2, and ER-α was associated with the same gradient fractions in which most of the caveolin proteins were detected. A 67 kDa ER and a variant of lower molecular weight (52 kDa) were detected in both the cell lysate and the caveolar membrane gradient fractions. Until recently the lower molecular weight ER variants were thought to be proteolytic fragments, but evidence that some of those molecules are truncated products of the full-length ER-α was recently presented. It is well documented that in MCF-7 breast cancer cells ER-α mRNA can undergo alternative splicing, generating transcripts with single, double, or multiple exon deletions [42], and that a 52 kDa protein is translated from the predominant splice variant mRNA that is missing exon 7 [43]. Others have identified a 46 kDa variant in human endothelial cells [44] and in MCF-7 cells [45,46]. Some evidence suggests that the 46 kDa receptor is the major functional membrane form of ER [44]; however, the precise functions of the truncated ER-αs are still under investigation.
The traditional detection and quantitation of phosphorylated MAPKs via Western blot analysis is laborious and somewhat arbitrary in the designation of appropriate densitometric backgrounds for comparison, especially in situations where the significant activation response is not large. We developed an enzyme-linked immunosorbent assay to deal with these experimental problems using fixed cells [47], which allowed convenient testing of large numbers of different conditions. Ours is the first report that different levels of mER in MCF-7 cells influence the different temporal and dose-dependent estrogen-induced phosphorylations of ERKs. The subpopulation of cells with high mER levels exhibited early and more robust activation, peaking at 10 min with a reactivation at 60 min, whereas the subpopulation of cells with low mER levels were only capable of weakly activating ERKs at one early time point (6 min). Furthermore, in the ER-α-negative cell line MDA-MB-231, E2 could not activate these kinases. The physiologic significance of early ERK1/2 activation was confirmed by showing that short pulse E2 treatment also stimulated cell proliferation. We and others [46] have confirmed that mER-α in breast cancer cells is responsible for this effect by showing that E2-peroxidase can stimulate proliferation and that this effect can be abolished by prior blocking of ER-α with ligand-binding domain-specific antibody.
The inability to activate ERKs with E2 in an ER-α-negative cell line, and the ability to do so in MCF-7 ER-α-positive cells, was assigned by others to the orphan G-protein-coupled receptor GPR30 [48]. However, recent studies with antisense GPR30 knockdowns revealed that E2-stimulated MCF-7 cells proliferate as well as cells with normal levels of GPR30 [49,50]. ERK activation associated with the ability of these cells to respond to estrogens by proliferating thus does not appear to be a function of GPR30 levels.
Different levels of mER also determined the E2 dose-dependent phosphorylation of ERKs. Cells with low levels of mER responded to a limited range of concentrations (10-13 to 10-12 mol/l). However, mERhigh cells responded to a much wider range of E2 concentrations (10-13 to 10-7 mol/l) with a declining (but still significant) ERK activation at 10–100 nmol/l E2. This finding corresponds to our observation that 10 nmol/l and higher E2 decreases cell proliferation in mERhigh MCF-7 cells, and is consistent with the idea that E2 induces cAMP-activated protein kinase A inhibition of MAPK pathways at these higher concentrations (see accompanying paper [28]). Activation of ERKs has been linked to the proliferative cellular response [51]. Our accompanying report [28] addresses other rapid estrogen-elicited signaling responses that, in concert with ERK activation, can affect and perhaps balance cell proliferation responses.
The classical antiestrogen ICI182,780 has been well defined as an antagonist of the action of estrogen at the transcriptional level. In our studies this compound was also a potent and rapid ERK activator. With different kinetics, the transcriptionally inactive 17α-estradiol also activated ERKs. It has been demonstrated that pure antiestrogen–ER complex can bind to estrogen response elements, but that the resultant transcriptional unit is inactive [52]. This binding and inactivation is generally used for pharmacological identification of ER-α participation in gene transcription. However, in a yeast reporter system antiestrogens (ICI182,780 and tamoxifen) induce ER dimerization and transcriptional activity [53]. Thus, although the published data disagree, it remains possible that antiestrogen–ER complexes can exert rapid effects such as induction of ERK phosphorylation and other signaling effects [54,55].
Other differences between findings may stem from effects assessed at different time points [56-58]. The length of time between estrogen or antiestrogen administration and kinase assessment can clearly influence outcomes, as evidenced by our time- and compound sequencing-dependent changes in ERK activation. It is also known that lengthy exposures to ICI182,780 can dramatically decrease ER-α protein levels [47,52], which could explain the decreased ERK activation observed in some studies. Long-term estrogen-deprived MCF-7 cells express higher levels of ER than do wild-type MCF-7 cells and are hypersensitive to E2; ICI182,780 did not alter the pattern of response to E2-stimulated growth in these cells [59,60]. To reconcile this observation with the expected inhibitory effect of ICI182,780, the authors suggested that ICI182,780 blocked only the effect of the residual E2 coming from plastic tissue culture flasks, without affecting the added E2. We can speculate that the observed effect of ICI182,780 was genuine and resulted from potentially increased levels of mER in long-term estrogen deprived MCF-7 cells, because our previous studies suggested that serum deprivation elevates mER-α levels [61]. In other cell types ICI182,780 behaved as a potent agonist of ERK1/2 activation (e.g. in rat cerebellar neurons expressing plasmid-generated ERs [60]), or as a partial ER agonist (in the superficial stroma and glandular epithelium of the sheep uterine endometrium [62]). It is likely that subtle differences in the shape of estrogen- and antiestrogen-liganded ERs, whether nuclear or membrane, present different interaction platforms for a variety of co-modulators, and that each signaling pathway must be considered in the context of a particular cell type's repertoire of partnering proteins.
In addition to having positive effects by itself, ICI182,780 was able to alter the E2 response differently, dependent on the timing of its administration. If the cells were pretreated for only 30 min (the time point when ICI182,780 alone did not change basal ERK activation), then the initial E2 activation and the late re-activation at 60 min were preserved, whereas the after-peak deactivation was enhanced. If ICI182,780 and E2 were added simultaneously, then the activation was delayed to 20 min, but weakly persisted up to 1 hour. Similar observations that ICI182,780 can change the time of ERK activation were reported in the case of a human thyroid carcinoma cell line, in which it reduced the 30 min to 1 hour activation, but enhanced later sustained activation at 6 hours [63]. The same authors showed that, in differentiated thyroid gland carcinoma cells (XTC133), addition of ICI182,780 induces a small decrease in the sustained ERK phosphorylation. Thus, in addition to demonstrating that ICI182,780 is not just an antagonist for this response, these studies and ours may point to cell-specific differences in ERK regulation.
Phosphorylations leading to activation and subsequent inactivation of proteins are important regulatory mechanisms for control of cell growth and differentiation [64,65]. Although ERKs are thought to play a key role in cell proliferation, it has been suggested that persistent activation of ERKs might result in cell cycle arrest and differentiation in PC12 cells, or cell growth inhibition and apoptosis in normal rat hepatocytes and several human tumor cell lines [65]. However, a detailed time course and transient ERK activations, interspersed with phosphatase-mediated inactivations, are rarely addressed. We (in the present study) and others [66] showed that the serine/threonine phosphatases PP1 and PP2A participate in the dephosphorylation of ERKs. It is interesting that OA (a PP1 and PP2A inhibitor) was more effective in mERhigh cells, whereas cyclosporin A (a PP2B inhibitor) was almost equally efficient in both cell populations. These data suggest that the level of mER-α expression/activation can be associated with activation of different types of ERK-modulating phosphatases. Other phosphatases, such as tyrosine phosphatase and dual specificity phosphatases (which dephosphorylate tyrosine, threonine, and serine residues), have been implicated in ERK dephosphorylation [67,68] and will be the subject of our future studies.
Additional studies of ERK activations, deactivations, and stability will be needed before we can formulate a more global picture of the post-translational modifications that lead to function of this important group of regulators in proliferation and differentiation. However, it is clear that nongenomic estrogen actions and the membrane receptors through which they act participate in this regulation.
Conclusion
E2-induced changes in breast cancer cell number [28] can be directly related to ERK1/2 activation/deactivation patterns and interacting signaling mechanisms. The differential behavior of cell lines expressing different levels of mER-α suggests a role for this receptor in the temporal coordination of phosphorylation/dephosphorylation events affecting the mitogen-activated kinases ERK1 and ERK2.
Abbreviations
CV = crystal violet; DCSS = medium with 4 × dextran-coated charcoal-stripped serum; E2 = 17β-estradiol; EGF = epidermal growth factor; ER = estrogen receptor; ERK = extracellular signal-regulated kinase; FACS = fluorescence-activated cell sorting; OA = okadaic acid; MAPK = mitogen-activated protein kinase; MEK = mitogen-activated protein kinase kinase; mER = membrane estrogen receptor; pNp = paranitrophenol; PP = protein phosphatase; tER = total estrogen receptor.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
We thank Dr David Konkel for critically reviewing this paper and Dr Bahiru Gametchu for assistance in the FACS separation of cell subpopulations. This work was supported by grants # DAMD17-01-1-0418 and #DAMD17-01-1-0649 from Department of Defense Breast Cancer Research Program.
Figures and Tables
Figure 1 Fluorescence immunocytochemical detection of membrane estrogen receptor (mER)-α in nonpermeabilized MCF-7 cells. Cells were fixed with 2% pararaformaldehyde/0.1% glutaraldehyde, probed with C-542 carboxyl-terminal estrogen receptor-α antibody, and visualized with a biotinylated secondary antibody–avidin conjugated alkaline phosphatase fluorescent Vector red product. Fluorescence images were photographed using the FITC filter and 100 × magnification. (a) mER-α-enriched (mERhigh) MCF-7 cells; the arrows indicate some of the punctate staining. (b) mER-α-depleted (mERlow) MCF-7 cells have no staining. The bar in panel b represents 10 μm.
Figure 2 Digital deconvolution of membrane estrogen receptor (mER)-α fluorescent image in mER-α-enriched (mERhigh) MCF-7 cells. Fifteen different focal planes starting from the top of the cells and moving toward the bottom are displayed. The sequential slices are from left to right in each row of images. These cells are nonpermeabilized and treated as described in the previous figure legend. The corresponding transmitted light image is displayed in the upper left-hand corner. FITC filter images were captured, pseudo-colored, and processed by digital deconvolution.
Figure 3 Identification of membrane estrogen receptor (mER) in caveolae by Western blot analysis. (a) Caveolin-1 (upper panel) and caveolin-2 (lower panel) in MCF-7 cell membranes. Membrane fractions containing caveolae were separated on discontinuous sucrose gradients. Fractions 5 and 6 were combined and 20 and 40 μg/ml protein (shown by adjacent lanes with connecting bar) were resolved on a 4–20% SDS-polyacrylamide gel, transferred to a nitrocellulose membrane, and probed with specific antibodies. Fractions from human endothelial (HE) and mouse fibroblast (RSV-3T3) cells were used as positive controls (50 μg/ml protein). (b) Whole cell lysates (GH3, MCF-7) and pooled membrane fractions 5 and 6 (Fr.5+6) from sucrose gradients for MCF-7 cells were probed with estrogen receptor-α-specific antibody C-542 (panel 1) or the same antibody blocked with the epitope peptide (panel 2). The arrows show the positions of major bands that were identified as receptor by epitope competition (at 67 and 52 kDa).
Figure 4 Optimization of 96-well plate immunoassay for membrane estrogen receptor (mER) quantification in MCF-7 cells. (a) Different concentrations of estrogen receptor-α-specific C-542 antibody (1–12 μg/ml) were tested. Enzymatic paranitrophenol phosphate (pNpp) hydrolysis yielding the paranitrophenol (pNp) product was monitored at 37°C for 5, 15 and 30 min, as shown. Cells pretreated for 72 hours with medium containing dextran-coated charcoal-stripped serum (DCSS) versus defined medium (DM) are represented with closed and open circles, respectively. (b) Antibody binding compared in nonpermeabilized (open bars) and permeabilized (shaded bars) conditions. no1°no2° represents primary and secondary antibodies omitted; no1° represents primary antibody omitted (only secondary antibody applied); mIgG1k represents mouse IgG1k isotype control; peptide comp. represents C-542 epitope peptide blocking of the interaction with primary antibody; anti-clathrin represents our test for cell membrane integrity. Values are expressed as means ± standard error. CV, crystal violet.
Figure 5 Quantification of membrane estrogen receptor (mER) and total estrogen receptor (tER) in mER-α-enriched (mERhigh) and mER-α-depleted (mERlow) MCF-7 cells. (a) Different numbers of cells were fixed in the same manner as described in Fig. 1 and the level of mER was assessed with 8 μg/ml C-542 antibody. Closed and open circles represent mERhigh and mERlow cells, respectively. The data were approximated with an exponential decay curve and compared by evaluating the difference between the sums of squares of the residuals from each curve with the sum of squares of the residuals for the combined curve using the F-test. The curves are significantly different (P = 0.0003). The diamond represents MDA-MB-231 cells and the cross-hatched horizontal bar the level of binding of a mouse IgG1k isotype control antibody. (b) Level of total ER (intracellular estrogen receptor [iER] plus mER) for mERhigh (closed circles) and mERlow (open circles) under the same binding conditions except for permeabilization of the cells. Where error bars for standard error are not visible, they are smaller than the size of the symbols. CV, crystal violet; pNp, paranitrophenol.
Figure 6 Optimization of fixed cell-based enzyme-linked immunosorbent assay for mitogen-activated protein kinase using epidermal growth factor (EGF) stimulation. (a) Different numbers of cells were treated for 10 min with 50 ng/ml EGF, fixed, and the level of extracellular signal-regulated kinase (ERK)1/2 phosphorylation determined using a phospho-specific antibody and the Vectastain ABC alkaline phosphatase kit with paranitrophenol phosphate (pNpp) substrate. Inset: values for paranitrophenol (pNp) normalized against the number of cells determined by the crystal violet (CV) assay. Closed and open circles represent EGF and vehicle treatment, respectively. (b) Time course of ERK stimulation by vehicle. Open circle: zero time point estimated by averaging 20, 30 and 60 min values of vehicle treatment. Asterisks indicate significant differences (P < 0.05) compared with all other values. Values are expressed as means ± standard error.
Figure 7 Time course of extracellular signal-regulated kinase (ERK)1/2 activation with 17β-estradiol (E2) and impeded ligand in membrane estrogen receptor (mER)-α-enriched (mERhigh) and mER-α-depleted (mERlow) MCF-7 cells, and receptor-less cells. After 72 hours in medium with dextran-coated charcoal-stripped serum (DCSS), cells were treated with 1 pmol/l E2, or an equivalent concentration of hormone contained in an E2-peroxidase conjugate, for different time intervals. (a, b) mERhigh MCF-7 cells treated with E2 and E2-peroxidase, respectively. (c) mERlow MCF-7 cells treated with E2. (d) Estrogen receptor-α-negative MDA-MB-231 cells treated with E2. Each experiment was repeated at least three times, each time point represents 16 replicate wells, and the values are expressed as mean ± standard error. Insets: inhibition of ERK1/2 activation with U0126 upstream mitogen-activated protein kinase kinase (MEK)1/2 inhibitor. The cells were pretreated for 15 min with 40 μmol/l inhibitor (gray bars) and E2-induced activation of ERK1/2 was tested at activation times appropriate for each cell type (after 3, 6 and 10 min in the case of mERhigh or 6 min in the case of mERlow cells). The asterisks indicate the significant reduction in ERK1/2 activation compared with cells treated only with E2 (black bars).
Figure 8 Dose-dependent 17β-estradiol (E2) activation of extracellular signal-regulated kinase (ERK)1/2 in membrane estrogen receptor (mER)-α-enriched (mERhigh) versus mER-α-depleted (mERlow) MCF-7 cells. Cells were incubated with different E2 concentrations at peak activation times: (a) 10 min in the case of mERhigh and (b) 6 min in the case of mERlow. The experiments were repeated three times, each with 16 replicates; values are expressed as mean ± standard error. The asterisks indicate significant differences from the vehicle controls (C).
Figure 9 Pulsed effect of 17β-estradiol (E2) and conjugated E2 on cell proliferation. MCF-7 membrane estrogen receptor (mER)-α-enriched (mERhigh) MCF-7 cells were treated for 5 days (bar 1) or 10 min with 1 pmol/l E2 (bar 2) or E2-peroxidase containing the equivalent of 1 pmol/l E2 (bar 3). In bar 4, pretreatment with U0126 (40 μmol/l) for 15 min was followed by 10 min treatment with E2. Bar 5 shows blockage of mER-α by incubating the cells for 1 hour at room temperature with 1 μg/ml of a ligand binding domain-specific antibody and the subsequent effect of 10 min incubation with E2. These experiments were repeated three times, each with 40 well replicates; values are expressed as mean ± standard error. The asterisks indicate significant differences from the controls (C – ethanol vehicle, inhibitor, or antibody treatment alone).
Figure 10 Effects of phosphatase inhibitors on extracellular signal-regulated kinase (ERK)1/2 dephosphorylation. Cells were pretreated for 1 hour with different phosphatase inhibitors: 50 nmol/l okadaic acid (OA) or 3 μmol/l cyclosporin A (CyclA). Then, ERK1/2 phosphorylation was determined after 1 pmol/l E2 stimulation for (a, b) 20 min or (c) 60 min. Black bars are membrane estrogen receptor (mER)-α-enriched (mERhigh) MCF-7 cells; open bars are mER-α-depleted (mERlow) MCF-7 cells. The data are averages of two or three experiments, each with 24 replicates; values are expressed as mean ± standard error. The asterisks indicate significant differences from the vehicle control-treated samples with corresponding inhibitor included.
Figure 11 Effects of ICI182,780 (ICI), ICI + 17β-estradiol (E2), and 17α-estradiol on extracellular signal-regulated kinase (ERK)1/2 activation in membrane estrogen receptor-α-enriched (mERhigh) MCF-7 cells. (a) Time course of ERK1/2 activation by 1 μmol/l ICI. (b) Pretreatment with ICI (1 μmol/l) for 30 min followed by 1 pmol/l E2 stimulation in the continued presence of ICI. (c) ICI (1 μmol/l) and E2 (1 pmol/l) applied simultaneously. (d) Time course of 17α-estradiol (10 nmol/l) activation of ERK1/2. All experiments were repeated at least three times with 24 well replicates/experiment; the averaged values ± standard error are presented. The asterisks indicate significant differences from vehicle controls.
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| 15642162 | PMC1064105 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 26; 7(1):R130-R144 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr959 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9621564216310.1186/bcr962Research ArticleAlphavirus replicon particles containing the gene for HER2/neu inhibit breast cancer growth and tumorigenesis Wang Xiaoyan 12xywang@mdandersonWang Jian-Ping [email protected] Maureen F [email protected] Lawrence B [email protected] Department of Bioimmunotherapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA2 The Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, Texas, USA3 AlphaVax, Inc., Research Triangle Park, North Carolina, USA2005 29 11 2004 7 1 R145 R155 1 7 2004 8 9 2004 9 9 2004 13 10 2004 Copyright © 2004 Wang et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Overexpression of the HER2/neu gene in breast cancer is associated with an increased incidence of metastatic disease and with a poor prognosis. Although passive immunotherapy with the humanized monoclonal antibody trastuzumab (Herceptin) has shown some effect, a vaccine capable of inducing T-cell and humoral immunity could be more effective.
Methods
Virus-like replicon particles (VRP) of Venezuelan equine encephalitis virus containing the gene for HER2/neu (VRP-neu) were tested by an active immunotherapeutic approach in tumor prevention models and in a metastasis prevention model.
Results
VRP-neu prevented or significantly inhibited the growth of HER2/neu-expressing murine breast cancer cells injected either into mammary tissue or intravenously. Vaccination with VRP-neu completely prevented tumor formation in and death of MMTV-c-neu transgenic mice, and resulted in high levels of neu-specific CD8+ T lymphocytes and serum IgG.
Conclusion
On the basis of these findings, clinical testing of this vaccine in patients with HER2/neu+ breast cancer is warranted.
adjuvant treatmentbreast cancergene vaccinesimmunotherapyvirus-like replicon particles
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Introduction
The management of breast cancer currently relies on surgery, chemotherapy and radiotherapy. Despite recent advances in clinical management of breast cancer once metastasis has occurred, the probability of a complete cure is greatly reduced. Of the women who have no detectable lymph node metastases at the time of diagnosis, up to one-third later develop metastases [1]. In patients with metastatic disease that does not respond to radiotherapy or chemotherapy, immunotherapy may offer an additional form of cancer control [2-4]. Clinical trials of trastuzumab, a monoclonal antibody specific for HER2/neu, have demonstrated the utility of an immunologic approach for breast cancers that overexpress this gene [5-7]. A drawback to 'passive' immunotherapy using monoclonal antibodies is that the effect is short-lived. An alternative approach is active vaccination that could induce neu-specific cytotoxic T cells with the ability to control the growth of the primary tumor and metastases. However, unlike passive immunotherapy whose effectiveness quickly wanes, effector and memory T cells induced by vaccination may remain present and be able to respond to any metastatic cells expressing HER2/neu that arise after treatment.
HER2/neu is an excellent target for gene vaccines, and several preclinical studies have shown the effectiveness of plasmid vaccines encoding neu in murine models [8-16]. Using a plasmid markedly different from those previously described [8-16], we created an effective gene vaccine against HER2/neu [17]. The previously described ELVIS plasmid vaccine construct for HER2/neu contained the cDNA of a replicon RNA from the Alphavirus Sindbis [18,19]. The replicon RNA contained the replication/transcription genes of the parent virus, but the structural protein genes were replaced by the gene for rat neu. From this plasmid construct, the replicon RNA is synthesized in the nucleus of the host cell and is transported to the cytoplasm for replication and transcription. The neu gene product is produced at high levels in the cytosol. Since the structural protein genes from the parent virus are not encoded by the replicon, progeny infectious Sindbis virions are not generated [20-22].
Alternatives to Alphavirus replicon plasmid vaccines [17,19,23-25] are Alphavirus-based virus-like replicon particles (VRP). As already mentioned, the replicon RNA does not contain the structural genes from the parent virus. It is therefore a single-cycle, propagation-defective RNA and replicates only within the cell into which it is introduced. To generate VRP from an attenuated strain of the Alphavirus Venezuelan equine encephalitis virus (VEE), the replicon RNA is packaged into particles by co-transfecting the replicon RNA and two separate helper RNAs, which together encode the full complement of VEE structural proteins [26]. Although the VRP can infect target cells in culture or in vivo, and can express the foreign gene to a very high level, the VRP are defective since they lack critical portions of the VEE genome – they lack the VEE structural protein genes necessary to produce infectious virus particles capable of spreading to other cells [21,27].
Several reports have demonstrated that VRP are extremely effective vaccine vectors [28-39]. The VEE VRP vaccine vectors are particularly attractive because the VEE envelope glycoproteins target the VRP to cells of lymphoid tissue [40], because they can be administered multiple times [39], because they induce both cellular and humoral immune responses, and because pre-existing immunity to VEE in humans should not be problematic since the incidence of VEE infection is low.
In the current study, we sought to determine whether vaccination with VEE-derived VRP containing the gene for HER2/neu would inhibit tumor growth in prevention models in which HER2/neu-expressing tumor cells had been injected either into a mammary fat pad or intravenously. We also sought to determine whether vaccination could prevent spontaneous tumorigenesis in HER2/neu transgenic mice. VRP-neu vaccination induced antigen-specific CD8+ T-cell and IgG responses that corresponded with the lack of tumor growth in both tumor models. In light of the clinical benefit of trastuzumab, a safe and effective vaccine that can induce cellular and humoral immunity, VRP-neu warrants clinical evaluation.
Materials and methods
Tumor cell line and reagents
The A2L2 cell line that expresses high levels of rat HER2/neu has been previously described in detail [17]. The A2L2 cell line has consistently expressed high levels of HER2/neu for more than 5 years and consistently induces tumors in Balb/c mice when injected into a mammary fat pad or intravenously. The A2L2 cell line was maintained in Eagle's MEM containing 5% FCS, sodium pyruvate, nonessential amino acids, L-glutamine, and vitamins (GIBCO, Carlsbad, CA, USA). The monolayer cultures were subdivided at approximately 75% confluence by treatment for 1–3 min with 0.25% trypsin and 0.02% EDTA at 37°C.
Virus-like replicon particles
The pSV2-neu plasmid containing the gene for rat HER2/neu was provided by Dr Mien-Chie Hung (The University of Texas MD Anderson Cancer Center, Houston, TX, USA). VEE VRP were constructed at AlphaVax, Inc. (Research Triangle Park, NC, USA) according to the published procedure [26]. Control VRP containing the gene for A/PR/8/34 influenza hemagglutinin (HA) were also prepared following the same procedure. VRP preparations were screened in a sensitive in vitro Vero cell cytopathic effect assay for the detection of replication competent virus. Prior to their use in these studies, the preparations were shown to be devoid of detectable replication competent virus.
Flow cytometry
A2L2 cells were incubated for 1 hour at 37°C with either immune serum or control serum diluted in PBS. FITC-labeled goat anti-mouse IgG diluted 1:1000 in PBS was added to the cells and incubation was continued for 1 hour at 37°C. The cells were washed three times in PBS and were analyzed by flow cytometry using an EPICS Profile Analyzer (Beckman Coulter, Inc., Fullerton, CA, USA).
Mice
Female Balb/c mice, aged 6–8 weeks, were obtained from the Frederick Cancer Research Facility (Frederick, MD, USA). Mice were allowed to acclimate for at least 1 week before use. Six-week-old MMTV-c-neu transgenic mice were obtained from Charles River Laboratories Inc. (Wilmington, MA, USA). This strain of mice is called the 'Oncomouse' by Charles River Laboratories due to the spontaneous generation of breast cancer. These mice are of the FVB/N strain, and the transgene is the 'activated' rat c-neu oncogene preceded by the MMTV promoter [41].
Vaccination with VRP
Mice were vaccinated subcutaneously in the hind foot pad with VRP suspended in 50 μl normal saline. Repeat vaccinations, when administered, were performed using alternate hind feet. Tail vein blood was removed and tumor challenge performed 2 weeks after the final vaccination. Serum was separated from the blood by centrifugation after overnight storage at 4°C.
Mammary fat pad tumor prevention model
A2L2 cells were harvested from subconfluent cultures with trypsin and EDTA as described earlier. The cells were washed once in serum-containing culture medium and were washed once in PBS. Mice were anesthetized by inhalation of isoflurane using a special apparatus developed by the veterinarians at MD Anderson Cancer Center. The fur was shaved over the lateral thorax, and a 5-mm-long incision was made to reveal mammary fat pad 2 as previously described [42]. A 0.1-ml sample containing 2.5 × 104 A2L2 cells in normal saline was injected into the fat pad. The incision was closed with a wound clip. Wound clips that had not already fallen off were removed after 7 days. The mice were then observed daily and their tumors measured in perpendicular directions with a pair of calipers. Mice with tumors 10 mm in the greatest dimension were killed according to our approved Institutional Animal Care and Use protocol. At the termination of the experiment, all mice were killed by CO2 inhalation and all tumors were excised and weighed.
Experimental metastasis prevention model
A 0.1-ml sample containing 2.5 × 104 A2L2 cells in normal saline was injected into the tail vein of each immunized mouse. The mice were killed 30 days later, and the surface lung metastases in each animal were counted.
Tetramer analysis of immune spleen cells
A K(d) tetramer containing the peptide sequence PYVSRLLGI [43] was prepared by the National Institutes of Health Tetramer Facility at Emory University (Atlanta, GA, USA). The incorporated peptide was synthesized at the peptide synthesis facility of MD Anderson Cancer Center. We received a K(d) tetramer specific for A/PR/8/34 influenza HA containing the peptide IYSTVASSL from Linda Sherman at the Scripps Research Institute (La Jolla, CA, USA) [44]. A single-cell suspension of the spleen from a naïve mouse was prepared in R10S medium (RPMI 1640, 10% HI-FCS [Summit Biotechnology, Fort Collins, CO, USA], 1% nonessential amino acids, 100 mM MEM sodium pyruvate, 2 mM L-glutamine, 100 U/ml penicillin, 1 ml streptomycin, and 50 mM 2-mercaptoethanol) by gently swirling the spleen between frosted glass slides.
Debris was removed from the cell suspension by filtration through nylon mesh into a 10-ml centrifuge tube. The cell suspension was centrifuged for 10 min at 700 × g and the pellet resuspended in 5 ml ACK solution (0.15 M NH4Cl, 1.0 mM KHCO3, 0.01 mM NaEDTA, pH adjusted with 1 N HCl to 7.2–7.4) before being gently rocked for 5 min to lyse red blood cells. An additional 5 ml R10S medium was added to the cell suspension, and the cells were washed once with R10S medium. The naïve spleen cells were cultured with either PYVSRLLGI or IYSTVASSL (70 ng/ml) on a rocker for 2 hours at 37°C and were γ-irradiated with 20 Gray.
The mice were vaccinated three times with a 2-week interval with 106 infectious units (IU) VRP-neu or 106 IU VRP-HA, and the immune spleens were harvested 3 weeks after the final vaccination. The immune spleens were prepared to produce a single cell suspension in R10S medium and were co-cultured with the peptide-pulsed stimulator cells at a ratio of one stimulator to eight responders. The cell mixture was cultured for 7 days at 37°C, washed, and was resuspended in FACS buffer (PBS with 1% BSA) at a concentration of 5 × 107 cells/ml. Twenty microliters of the cell mixtures were added to 20 μl PE-conjugated Her2/neu-specific or HA-specific tetramers to make the final dilutions of the tetramers 1:50, 1:100, and 1:200. One microliter of PerCP-conjugated anti-mouseCD3e (PharMingen, San Diego, CA, USA) and FITC-conjugated anti-mouse CD8a (Caltag, Burlingame, CA, USA) was added to the mixture and incubated for 1 hour at 4°C in the dark. The cells were then suspended in 150 μl FACS buffer and transferred to a polystyrene round-bottomed tube. The cells were washed twice with FACS buffer and suspended in 200 μl of 1% paraformaldehyde in PBS.
List mode data were acquired with a FACScan (BD Biosciences, San Jose, CA, USA). Dead cells and monocytes were excluded from the analysis by forward scatter and side scatter gating. A total of 10,000–30,000 events were typically acquired, and compensation was optimized using unstained cells, cells stained with only PerCP-conjugated anti-mouse CD3e, cells stained with only FITC-conjugated anti-mouseCD8a and cells stained with only PE-conjugated anti-mouse CD4. The CD3+ cells were gated from the total population of live cells, and the CD8+ cells were gated from the CD3+ cells. From the CD3+ cells and the CD8 double-positive cells, the percentages of PE-tetramer-positive cells were calculated. Isotype controls for the anti-CD3e and anti-CD8a were subtracted from the acquired data. List mode files were analyzed using CELLQUEST software (BD Biosciences, San Jose, CA, USA).
Intracellular interferon-γ analysis of immune spleen cells
The procedure for this technique was obtained from PharMingen, whose reagents were used whenever possible. Spleens from VRP-neu-immunized and VRP-HA-immunized mice were prepared as already described for tetramer analysis. The spleen cells were stimulated with PYVSRLLGI at a concentration of 70 ng/ml at 37°C in R10S medium for 5 days as described. On the sixth day, the cells were suspended at 2 × 106/ml in R10S medium containing 10 ng/ml phorbol-12-myristate acetate (Sigma-Aldrich, St Louis, MO, USA) and 250 ng/ml ionomycin (Sigma-Aldrich). Brefeldin (1 μl/ml; Sigma-Aldrich) was added at the same time to block cytokine secretion. The cells were washed after 5 hours and resuspended at 107 cells/ml in staining buffer (Dulbecco's PBS without Mg2+or Ca2+, 1% heat-inactivated FCS, w/v 0.09% sodium azide; pH adjusted to 7.4–7.6). The cells were then incubated for 15 min with purified 2.4 G2 antibody 10 μg/ml (PharMingen) to block nonspecific staining by fluorochrome-conjugated antibodies via Fc receptors. The cells were washed twice with staining buffer, and 106 cells were stained with 1 μg FITC-labeled anti-mouse CD8a and PerCP-labeled anti-mouse CD3e (PharMingen) in 50 μl staining buffer at 4°C for 30 min. The cells were again washed twice with staining buffer, followed by fixation and permeabilization with Perm/Wash (Cytofix/Cytoperm Kits; PharMingen) for 20 min at 4°C. The cells were washed twice and resuspended in 50 μl of the same solution. PE-conjugated anti-interferon (IFN)-γ monoclonal antibody (0.5 μg/106 cells; PharMingen) was added and the suspension was incubated for 30 min at 4°C. The cells were then washed twice with Perm/Wash solution and resuspended in staining buffer.
The FACScan was used to analyze the percentage of intracellular IFN-γ-containing cells among the CD3+ and CD8+ cells. Isotype controls for anti-CD3a, anti-CD8e, and anti-IFN-γ (PharMingen) were subtracted from the acquired data.
ELISPOT analysis of IFN-γ production by immune spleen cells
The procedure for the ELISPOT technique was obtained from PharMingen, whose reagents were used whenever possible. The wells of an ELISPOT plate (CTL Immunospot plate; Cellular Technology Ltd, Cleveland, OH, USA) were coated overnight at 4°C with 100 μl anti-mouse IFN-γ (2 μg/ml; PharMingen) diluted at 1:200 in coating buffer (PBS, pH 7.2). The coated wells were washed with blocking solution (R10S medium), fresh blocking solution was added to each well, and then the plate was incubated for 2 hours at room temperature. The blocking solution was then discarded and 100 μl A2L2 cells (2 × 105-1 × 106) was added to each well of the ELISPOT plate.
Immune spleens were dissected from vaccinated mice and were prepared as already described for tetramer analysis. Increasing numbers of spleen cells (5 × 106 - 2 × 107) in 100 μl R10S medium were added to the wells, and the plate was incubated for 24 hours at 37°C in a 5% CO2 atmosphere at 99% humidity. Wells containing only spleen cells served as negative controls, and spleen cells from VRP-neu-vaccinated mice cultured overnight with 5 μg/ml concanavalin A (Sigma-Aldrich) served as a positive control. The cell suspension was aspirated, and the wells were washed twice with deionized water and were then soaked with deionized water for 5 min. The wells were washed three times with wash buffer I (PBS containing 0.05% Tween-20).
The detection antibody, biotinylated anti-mouse IFN-γ (PharMingen), was diluted to 2 μg/ml in dilution buffer (PBS containing 10% FBS), and 100 μl was added to each well. The plate was incubated at room temperature for 2 hours, and the wells were washed three times with buffer I. Avidin–horseradish peroxidase reagent (PharMingen) was diluted to 1:100 in dilution buffer, and 100 μl was added to each well, which was then incubated at room temperature for 1 hour. The wells were washed four times with wash buffer I and twice with wash buffer II (PBS). A stock solution containing 100 mg 3-amino-9-ethyl-carbazole (Sigma) dissolved in 10 ml N, N-dimethylformamide (Sigma-Aldrich) was prepared. The final substrate solution was made by adding 333 μl 3-amino-9-ethyl-carbazole stock solution to 10 ml of 0.1 M sodium acetate (pH 5.0), followed by filtering through a 0.45-μm filter. Five microliters of 30% H2O2 was added to the substrate solution immediately before use.
One hundred microliters of the final substrate solution was added to each well, and the plate was incubated in the dark for 5–60 min at room temperature. The reaction was stopped by washing the wells with deionized water. The plate was air-dried overnight at room temperature in the dark and sent to ZellNet Consulting, Inc. , where the spots were enumerated automatically using an ImmunoSpot Series I analyzer (BD Biosciences). If overlapping spots (confluence) were present in the wells, the number of spots in a nonconfluent area of that well was determined. To estimate the total number of spots in each well with confluence, the following equation was used: total spot number = spot count + 2 × (spot count × % confluence / [100% - % confluence]).
Statistical analysis
Student's t test was performed using Prism 4.0 Graphpad Software (San Diego, CA, USA). Statistical analysis, power analysis and the sample size per group were evaluated and found to be statistically acceptable by Dr Lyle Broemling (Associated Professor of Biostatistics, The University of Texas MD Anderson Cancer Center).
Results
Induction of antigen-specific IgG by vaccination with VRP-neu
Groups of Balb/c mice (n = 5 per group) were vaccinated once subcutaneously in one hind leg footpad with either 106 IU VRP-neu or 106 IU VRP-HA suspended in PBS. The HER2/neu-specific humoral response of serum pooled from mice in each group was measured 14 days later by flow cytometry using A2L2 cells. Compared with the mice vaccinated with VRP-HA, the mice vaccinated with VRP-neu had a strong IgG response (Fig. 1). Pre-immune sera for both groups were nonreactive with A2L2 cells, and immune sera from both vaccinated groups were nonreactive with 66.3 cells, the parental cell line from which A2L2 was derived by transfection with neu (data not shown).
Protection from tumor challenge in a mammary fat pad prevention model following vaccination with VRP-neu
Groups of mice (n = 7 per group) were vaccinated subcutaneously with 105 IU or 106 IU VRP-neu or with 106 IU VRP-HA three times at 14-day intervals. Two weeks after the final vaccination, the mice were challenged with 2.5 × 104 A2L2 cells injected into a mammary fat pad. Five weeks after tumor challenge, the largest tumor dimension was measured and the mice were killed. If a tumor was present, its mass was determined. All of the mice vaccinated with VRP-HA had a measurable tumor, whereas only one mouse in each group vaccinated with 106 IU VRP-neu or 105 IU VRP-neu had a measurable tumor (Fig. 2a,2b). These findings clearly demonstrate that vaccination three times with either 105 IU or 106 IU VRP-neu protected mice from challenge with A2L2 cells. VRP-HA failed to provide protection for any of the mice, and therefore the protective effect was specific for the vaccine containing the gene for HER2/neu.
Determination of the minimal effective vaccine dose in two tumor prevention models
Because vaccination three times with 105 IU VRP-neu prevented tumor growth in a mammary fat pad, we next determined the minimum number of VRP-neu particles and the minimum number of vaccinations necessary to significantly inhibit tumor growth. In the mammary fat pad prevention model, vaccination twice with 105 IU VRP-neu or vaccination three times with 104 IU VRP-neu completely prevented tumor growth in many mice and significantly reduced the tumor mass in the entire group compared with the tumor mass of the mice vaccinated three times with VRP-HA (Fig. 3a). Identical results were obtained in the experimental lung metastasis prevention model, in which mice were injected with A2L2 cells intravenously in the tail vein after vaccination (Fig. 3b). These results demonstrate that, in both tumor models, vaccination three times with 104 IU VRP-neu or twice with 105 IU VRP-neu significantly reduced the tumor mass and lung metastasis. In addition, several mice in each vaccinated group were tumor free in mammary tissue or lungs.
Vaccination of MMTV-c-neu transgenic mice
MMTV-c-neu transgenic mice contain the activated rat neu gene under the control of the MMTV promoter and spontaneously develop neu+ breast tumors within 110–120 days [45]. Without intervention, all of the mice die of breast cancer. We vaccinated groups of mice (n = 10 per group) three times at 14-day intervals with 106 IU VRP-neu or 106 IU VRP-HA and determined the effect on survival. Eight of the 10 mice vaccinated with VRP-HA were killed by 140 days owing to moribundity, and the remaining two mice were killed on day 195 (Fig. 4). None of the mice vaccinated with VRP-neu showed any sign of illness at 240 days, and breast tumors were not evident on palpation. The mice in the VRP-neu-vaccinated group were killed at this time, and gross pathologic examination of the breasts following retraction of the skin did not reveal any tumors. These results demonstrate that vaccination with VRP-neu prevented spontaneous formation of tumor in the breasts of neu transgenic mice and that tolerance to the neu transgene was broken by vaccination with VRP-neu.
Induction of antigen-specific IgG by vaccination of MMTV-c-neu transgenic mice with VRP-neu
Immune serum was drawn from mice described at 133 days or 55 days after the third vaccination with VRP-neu or VRP-HA (Fig. 4). Flow cytometric analysis of the immune sera using A2L2 cells demonstrated that vaccination with VRP-neu induced a strong IgG response that was evident at dilutions of 1:25 (Fig. 5a) and 1:100 (Fig. 5b). Vaccination with the control VRP-HA failed to induce a neu-specific antibody response (Fig. 5a,5b). These findings indicate that vaccination with VRP-neu induced humoral immunity to the protein product of the neu-transgene in neu-transgenic mice, thus breaking any existing tolerance to p185.
Tetramer analysis of CD8+ T cells following vaccination with VRP-neu or VRP-HA
Ikuta and colleagues [43] identified a K(d)-restricted peptide for mouse HER2/neu. The identical sequence (PYVSRLLGI) is present in rat HER2/neu. We therefore ordered a tetramer containing this sequence from the National Institutes of Health Tetramer Facility at Emory University. A K(d) tetramer specific for A/PR/8/34 influenza HA was used as a positive control for VRP-HA-vaccinated mice. Balb/c mice were vaccinated three times at 2-week intervals with VRP-neu or VRP-HA, and the spleens from two mice were collected 3 weeks after the third injection. We found that 2.38% of the pooled spleen cells from the mice that had been vaccinated three times with VRP-neu were stained by both the anti-CD8 antibody and the HER2/neu tetramer (Table 1). This value is in excellent agreement with the percentage of dual-positive cells from mice that had been vaccinated with VRP-HA and stained with the HA tetramer (2.79%). In contrast, the percentage of dual-positive cells from the mice that had been vaccinated with VRP-HA and stained with the HER2/neu tetramer was only 0.22%, and the percentage from the mice that had been vaccinated with VRP-neu and stained with the HA tetramer was only 0.37%. These results clearly demonstrate that vaccination with VRP-neu produced antigen-specific CD8+ T cells.
Intracellular IFN-γ analysis of CD8+ T cells following vaccination with VRP-neu or VRP-HA
An alternative assay to tetramer analysis is measurement of the percentage of CD8+ T cells that also contain intracellular IFN-γ after in vitro stimulation with an antigenic peptide. We therefore vaccinated Balb/c mice three times at 2-week intervals with VRP-neu or VRP-HA, and then removed the spleens 3 weeks after the third injection. The in vitro culture procedure was similar to that used for tetramer analysis except that the peptide PYVSRLLGI was cultured directly with the immune spleen cells rather than with naïve spleen cells. We found that 2.80% of the spleen cells from the mice that had been vaccinated three times with VRP-neu stained positive for intracellular IFN-γ (Fig. 6a and Table 2) compared with only 0.27% for spleen cells from mice vaccinated with VRP-HA (Fig. 6b and Table 2). This percentage (2.80%) is in excellent agreement with that of tetramer-positive cells after vaccination with VRP-neu (2.38%) (Table 1).
Analysis of immune spleen cells by IFN-γ ELISPOT after vaccination with VRP-neu or VRP-HA
We vaccinated Balb/c mice three times at 2-week intervals with VRP-neu or VRP-HA and removed the spleens 3 weeks after the third injection. The number of spleen cells secreting IFN-γ in response to overnight co-culture with A2L2 cells in an ELISPOT assay were then determined. At all three effector-to-stimulator ratios, vaccination with VRP-neu resulted in a statistically significant increase in the number of spots per 106 spleen cells (Table 3). This finding demonstrates that secretion of IFN-γ in response to co-culture with A2L2 cells was dependent on vaccination with VRP-neu and did not result from vaccination with the control VRP-HA.
Discussion
Although more than a decade has elapsed since the original descriptions of gene vaccines [46,47], a clinically approved gene vaccine for either an infectious disease or for cancer has yet to be developed. Gene vaccines containing elements of the Alphaviruses VEE, Sindbis virus and Semliki Forest virus offer substantial clinical potential and safety [22,27]. Vaccine vectors incorporating genetic elements of Alphaviruses can be divided into two major categories [48]: expression plasmids containing viral genes that are essential for replication and transcription of the positive-strand RNA genome of the viruses [18,24], and infectious but replication-incompetent viral particles in which the genes for the viral structural proteins have been replaced by the gene for a protein antigen [20-22]. We have previously described a gene vaccine for HER2/neu of the first category [17]. In the present article, we describe results of a gene vaccine for HER2/neu of the second category.
We report here that VRP vaccine vectors derived from an attenuated strain of VEE containing the gene for rat HER2/neu were highly immunogenic when used to vaccinate both conventional mice and mice transgenic for the rat neu gene. Immune serum from mice vaccinated once with VRP-neu was reactive with A2L2 cells, demonstrating induction of an antigen-specific IgG response, whereas serum from mice that had been vaccinated with VRP-HA was nonreactive (Fig. 1).
To determine whether vaccination with VRP-neu could protect mice from challenge with a breast cancer cell line engineered to overexpress HER2/neu, mice that had been vaccinated three times with either VRP-neu or VRP-HA were injected in a mammary fat pad with A2L2 cells. Vaccination with either 105 IU or 106 IU VRP-neu protected all but one mouse in each group from developing tumors (Fig. 2). Because our previous experiment demonstrated that a single vaccination with 106 IU VRP-neu induced an IgG response, we next vaccinated mice once, twice or three times with 104 IU or 105 IU VRP-neu or the control VRP-HA. Vaccination three times with 104 IU VRP-neu (P = 0.0265) or twice with 105 IU VRP-neu (P = 0.0079) was sufficient to prevent tumor growth of A2L2 cells injected into a mammary fat pad (Fig. 3a). Mice similarly vaccinated were also challenged with A2L2 cells injected intravenously in the tail vein. Vaccination three times with 104 IU VRP-neu (P = 0.0317) or twice with 105 IU VRP-neu (P = 0.0159) prevented tumor growth in the lungs (Fig. 3b). Some mice in the vaccine groups had no visible lung metastases 5 weeks after the tumor challenge. This finding clearly demonstrates that vaccination with VRP-neu prevented tumor growth in two tumor prevention models.
There is an important point to be considered regarding our experimental models. We are vaccinating mice with the gene for rat neu and we are challenging mice with a cell line also overexpressing the gene for rat neu. We must therefore consider the possibility that mice recognized rat p185 (the protein product of the HER2/neu gene) as a xenoantigen and that vaccination with VRP-neu merely boosted, but did not initiate, an immune response. We addressed this question by vaccinating rat neu transgenic mice, which are immunologically tolerant to rat p185. MMTV-c-neu transgenic mice express the rat neu transgene under the control of the murine mammary tumor virus promoter and spontaneously develop neu+ breast cancer. Vaccination of these transgenic mice with VRP-neu very clearly demonstrated (P < 0.0001) that the mice survived to 240 days old (Fig. 4), at which time the experiment was concluded. On postmortem examination, no tumor was detected in the breast of any mouse that had been vaccinated with VRP-neu. All mice in the control group that had been vaccinated with VRP-HA were moribund owing to extensive breast cancer by 200 days. This result is much more dramatic than our previous finding that vaccination of MMTV-c-neu transgenic mice with the Sindbis/DNA plasmid-based ELVIS replicon vector increased survival of the transgenic mice but failed to protect against tumor formation and death [17]. To determine whether vaccination of the MMTV-c-neu transgenic mice with VRP-neu induced an IgG response, as we found in Balb/c mice (Fig. 1), we tested the serum of the vaccinated neu-transgenic mice at 133 days old. Vaccination with VRP-neu induced a strong, antigen-specific IgG response in these transgenic mice (Fig. 5). Vaccination with VRP-neu therefore overcame tolerance to p185 (Fig. 5).
In our present study, we performed three separate in vitro T-cell assays to determine whether vaccination with VRP-neu induces antigen-specific T cells. In the first assay we used tetramers containing an antigenic peptide of HER2/neu to analyze spleen cells from mice vaccinated with VRP-neu. As a positive control we used a tetramer containing an antigenic peptide of HA. We found that 2.38% of the CD8+ spleen cells resulting from vaccination with VRP-neu were positive for tetramer binding (Table 1), a value in excellent agreement with the 2.79% of the spleen cells from VRP-HA-vaccinated mice that were positive for an HA-specific tetramer. We further analyzed the induction of antigen-specific CD8+ T cells resulting from VRP-neu vaccination by performing intracellular IFN-γ analysis. We found that 2.80% of CD8+ cells from VRP-neu-vaccinated mice were positive for intracellular IFN-γ (Table 2), a value in excellent agreement with the 2.38% tetramer-binding cells (Table 1). In the third assay we measured the number of IFN-γ secreting cells by ELISPOT analysis (Table 3). At all three effector:stimulator ratios, the number of IFN-γ secreting cells in the VRP-neu-vaccinated spleen cells was significantly greater than that in the VRP-HA-vaccinated spleen cells.
These three independent assays clearly demonstrate that vaccination with VRP-neu induced antigen-specific CD8+ T lymphocytes. The tetramer and intracellular IFN-γ assays further indicate that immune spleen cells were able to recognize an antigenic peptide of p185 presented on the A2L2 cells used for the ELISPOT assay.
The in vivo and in vitro experiments described demonstrate that vaccination induced antigen-specific cell-mediated and humoral immunity, but they do not indicate the role that each type of immunity may have played in the resulting antitumor effect. Therefore, although vaccination with VRP-neu produced antigen-specific IgG in both conventional and transgenic mice, whether this antibody played a role in the antitumor effect remains unclear. Pilon and colleagues [49] reported that antibody was not required for the antitumor effect of a plasmid vaccine against HER2/neu. Lindencrona and colleagues [50] similarly induced antitumor immunity against HER2/neu in B cell-deficient mice. Although T-cell immunity alone may be sufficient in tumor prevention models in mice, a clinical trial showed that trastuzumab clearly benefited patients and increased the antitumor effect of a whole-cell vaccine to HER2/neu [51].
Conclusion
Our findings demonstrate that vaccination with VRP-neu inhibited or eliminated tumor growth in prevention models in which breast tumor cells had been injected either in the mammary fat pad or intravenously. Vaccination with VRP-neu also prevented tumorigenesis in transgenic mice in which the neu gene was expressed in the breasts under the control of an MMTV promoter. Vaccination with VRP-neu induced antigen-specific CD8+, and this finding corresponded with the absence or inhibition of tumor growth. Furthermore, vaccination with VRP-neu induced antigen-specific IgG in both conventional and transgenic mice, and tolerance to HER2/neu in neu-transgenic mice was broken by vaccination with VRP-neu. These findings suggest that VRP-neu constitutes a powerful gene vaccine that induces both cellular and humoral immunity against HER2/neu. We speculate that such vaccination could be more effective than passive immunotherapy using monoclonal antibodies such as trastuzumab in patients with breast cancer.
Abbreviations
BSA = bovine serum albumin; FACS = fluorescence-activated cell sorting; FCS = fetal calf serum; FITC = fluorescein isothiocyanate; HA = hemagglutinin; IFN = interferon; IU = infectious units; MEM = modified Eagle's medium; MMTV = mouse mammary tumor virus; PBS = phosphate-buffered saline; VEE = Venezuelan equine encephalitis virus; VRP = virus-like replicon particles.
Competing interests
MFM is an employee of AlphaVax, Inc. and has options to purchase the company's stock. None of the other authors have any financial interest in AlphaVax, Inc. or in any products that could result from this research. All of the research was conducted independently at the University of Texas MD Anderson Cancer Center and was not influenced in any way by employees or the management of AlphaVax, Inc.
Authors' contributions
The authors' contributions to this research are reflected in the order shown with the exception of LBL, who supervised all aspects of this research and the preparation of this report.
Acknowledgements
This research was supported by the US Army Medical Research and Materiel Command, Department of Defense Breast Cancer Research Program, BC980071, the WM Keck Center for Cancer Gene Therapy, and the Cancer Center Core Grant CA16672. The authors thank Galina M Kiriakova in the Department of Cancer Biology for her cooperation and outstanding technical assistance. They also thank Kate Ó Súilleabháin of the Department of Scientific Publications for expert editing of the manuscript.
Figures and Tables
Figure 1 Flow cytometric analysis of A2L2 cells with serum from mice vaccinated with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA). Serum was collected 2 weeks after a single vaccination of Balb/c mice with 106 IU VRP-neu (filled curve) or 106 IU VRP-HA (open curve). The primary serum was diluted with PBS (1:100), and FITC-labeled goat anti-mouse IgG diluted in PBS (1:1000) was used as the secondary antibody.
Figure 2 Protection from tumor challenge after vaccination with virus-like replicon particles (VRP)-neu. Groups of seven or eight mice were vaccinated three times at 2-week intervals with VRP-neu or VRP-hemagglutinin (HA). Two weeks after the final vaccination, the mice were challenged with A2L2 cells injected into a mammary fat pad. Five weeks after the tumor challenge, (a) the largest dimension of each tumor was measured and (b) the mice were killed and the mass of the tumor in each animal was determined.
Figure 3 Minimal effective vaccine dose in two tumor prevention models. Groups of four or five mice were vaccinated once, twice or three times (1X, 2X, or 3X) at 2-week intervals with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA) (104 IU or 105 IU). Two weeks after the final vaccination, the mice were challenged with A2L2 cells injected into a mammary fat pad or intravensouly. The mice were killed 5 weeks after the tumor challenge. (a) The mass of the mammary tumors, if present, was determined in the mammary fat pad prevention model group, and (b) the number of surface metastases on the lungs was determined in the experimental lung metastasis prevention model group.
Figure 4 Inhibition of spontaneous tumor formation by vaccination with virus-like replicon particles (VRP)-neu. Groups of MMTV-c-neu transgenic mice (n = 10 per group) were vaccinated three times (▼) at 2-week intervals with 106 IU VRP-neu (▲) or 106 IU VRP-hemagglutinin (HA) (■). The mice vaccinated with VRP-HA were killed when moribund, and the mice vaccinated with VRP-neu were killed at 240 days.
Figure 5 Flow cytometric analysis of A2L2 cells with sera from MMTV-c-neu mice vaccinated with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA). Immune serum from the mice (described in Fig. 4) was drawn at 133 days. The serum was analyzed at dilutions of (a) 1:25 and (b) 1:100. Filled curve, sera from VRP-neu-vaccinated mice; open curve, sera from VRP-HA-vaccinated mice. The secondary antibody, FITC-labeled goat anti-mouse IgG, was diluted in PBS (1:1000).
Figure 6 Intracellular interferon (IFN)-γ analysis of spleen cells from vaccinated mice. Spleen cells from mice vaccinated three times with (a) virus-like replicon particles (VRP)-neu or (b) VRP-hemagglutinin (HA) were analyzed for the presence of intracellular IFN-γ after in vitro culture with a neu-specific peptide.
Table 1 Tetramer analysis of CD8+ T cells after vaccination three times with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA)
Vaccination CD8+ cells (%) Tetramer-positive CD8+ cells (%) Tetramer-positive CD8+/CD8+ (%)
neu-specific tetramer
VRP-neu 15.98 0.38 2.38
VRP-HA 23.06 0.05 0.22
HA-specific tetramer
VRP-neu 16.01 0.06 0.37
VRP-HA 17.46 0.50 2.79
Table 2 Intracellular interferon-γ analysis of CD8+ T cells after vaccination three times with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA)
Vaccination Isotype control (%) Interferon-γ+/CD8+ (%)
VRP-neu 0.10 2.80
VRP-HA 0.14 0.27
Naïve 0.03 0.39
Table 3 Interferon-γ ELISPOT assay after vaccination with virus-like replicon particles (VRP)-neu or VRP-hemagglutinin (HA)
Effector:stimulator VRP-neu (spots/106 cells) VRP-HA (spots/106 cells)
100:1 99a 11
50:1 126a 17
25:1 129a 25
Effector only 2 5
Concanavalin A 256a,b Not determined
aP = 0.05 by Student's t test.
bSpleen cells stimulated with concanavalin A as a positive control for the assay.
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| 15642163 | PMC1064108 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 29; 7(1):R145-R155 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr962 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9631564216010.1186/bcr963Research ArticleImmunohistochemical expression of insulin-like growth factor binding protein-3 in invasive breast cancers and ductal carcinoma in situ: implications for clinicopathology and patient outcome Vestey Sarah B [email protected] Claire M [email protected] Chandan [email protected] Caroline J [email protected] Jeff MP [email protected] Zoe E [email protected] University of Bristol, Department of Clinical Sciences at South Bristol – Surgery, Bristol Royal Infirmary, Bristol, UK2 Department of Histopathology, United Bristol Healthcare NHS Trust, Bristol Royal Infirmary, Bristol, UK2005 23 11 2004 7 1 R119 R129 17 5 2004 22 6 2004 8 10 2004 14 10 2004 Copyright © 2004 Vestey et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Insulin-like growth factor binding protein-3 (IGFBP-3) differentially modulates breast epithelial cell growth through insulin-like growth factor (IGF)-dependent and IGF-independent pathways and is a direct (IGF-independent) growth inhibitor as well as a mitogen that potentiates EGF (epidermal growth factor) and interacts with HER-2. Previously, high IGFBP-3 levels in breast cancers have been determined by enzyme-linked immunosorbent assay and immunoradiometric assay methods. In vitro, IGFBP-3's mechanisms of action may involve cell membrane binding and nuclear translocation. To evaluate tumour-specific IGFBP-3 expression and its subcellular localisation, this study examined immunohistochemical IGFBP-3 expression in a series of invasive ductal breast cancers (IDCs) with synchronous ductal carcinomas in situ (DCIS) in relation to clinicopathological variables and patient outcome.
Methods
Immunohistochemical expression of IGFBP-3 was evaluated with the sheep polyclonal antiserum (developed in house) with staining performed as described previously.
Results
IGFBP-3 was evaluable in 101 patients with a variable pattern of cytoplasmic expression (positivity of 1+/2+ score) in 85% of invasive and 90% of DCIS components. Strong (2+) IGFBP-3 expression was evident in 32 IDCs and 40 cases of DCIS. A minority of invasive tumours (15%) and DCIS (10%) lacked IGFBP-3 expression. Nuclear IGFBP-3 expression was not detectable in either invasive cancers or DCIS, with a consistent similarity in IGFBP-3 immunoreactivity in IDCs and DCIS. Positive IGFBP-3 expression showed a possible trend in association with increased proliferation (P = 0.096), oestrogen receptor (ER) negativity (P = 0.06) and HER-2 overexpression (P = 0.065) in invasive tumours and a strong association with ER negativity (P = 0.037) in DCIS. Although IGFBP-3 expression was not an independent prognosticator, IGFBP-3-positive breast cancers may have shorter disease-free and overall survivals, although these did not reach statistical significance.
Conclusions
Increased breast epithelial IGFBP-3 expression is a feature of tumorigenesis with cytoplasmic immunoreactivity in the absence of significant nuclear localisation in IDCs and DCIS. There are trends between high levels of IGFBP-3 and poor prognostic features, suggesting that IGFBP-3 is a potential mitogen. IGFBP-3 is not an independent prognosticator for overall survival or disease-free survival, to reflect its dual effects on breast cancer growth regulated by complex pathways in vivo that may relate to its interactions with other growth factors.
clinicopathologyductal carcinoma in situIGFBP-3immunohistochemistryprognosis
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Introduction
Insulin-like growth factors (IGF-I and IGF-II) regulate the cellular growth of normal and malignant breast epithelial cells with a role in malignant transformation [1-3]. IGFs are potent mitogens and are synergistic with oestrogen and epidermal growth factor (EGF) to stimulate cellular proliferation[B4]. The IGFs are modulated by a family of six high-affinity IGF binding proteins, of which IGFBP-3 predominates in serum and is upregulated in breast cancer cell lines, including breast epithelium [1,5,6]. Both IGFs (IGF-I and IGF-II) have a preferential stromal expression and together with epithelial IGFBP-3 have a significant paracrine influence on breast epithelial growth [1,7]. IGFBPs have multiple and complex functions that can be either IGF dependent or IGF independent. With respect to IGF-dependent function, IGFBP-3 preferentially binds IGFs to either inhibit or activate IGF mitogenic effects in vitro, through blocking the IGF-receptor interaction, in contrast to a prolongation of IGFs' half-life and their protection from degradation [1-3]. IGFBP-3 is pro-apoptotic in an IGF-dependent manner, as well as an IGF-independent manner, and enhances the p53 DNA damage response in vitro [8,9]. The particular significance of IGFBP-3 in regulating epithelial cell growth has been highlighted because the actions of many growth inhibitors, apoptotic agents and anti-cancer treatments (transforming growth factor-β, retinoids, p53 and anti-oestrogens) are, at least in part, mediated by their ability to stimulate local IGFBP-3 production [1-3].
Through complex and as yet poorly understood mechanisms, IGFBP-3 is a direct (IGF-independent) growth inhibitor as well as a mitogen on breast epithelial cells[B10,11]. Further, IGFBP-3 inhibits oestradiol-stimulated cell proliferation in breast cancer cell lines, with the potential to accentuate ceramide and paclitaxel-induced apoptosis directly [12-14]. By contrast, accumulating evidence in vitro suggests the potential mitogenicity of IGFBP-3 through its interactions with EGF receptor (EGFR) and Ras-p44/42 mitogen-activated protein kinase (MAPK) signalling in breast epithelial cells [15-18]. Postulated mechanisms for IGFBP-3's direct intrinsic actions on cell growth and apoptosis have not as yet characterised a definitive cell membrane receptor or the necessity for IGFBP-3 cell surface interaction or nuclear translocation[B19]. Intracellular trafficking of IGFBP-3 with nuclear localisation in T47D breast cancer cells is explicable through a carboxy-terminal nuclear localisation signal and importin-β-mediated nuclear transport[B15,20,21]. At present, the functional implications of nuclear IGFBP-3 are unknown[B22].
Complex IGFBP-3 modulation of breast cancer growth has prompted several studies to examine levels of IGFBP-3 in breast cancer tissues in relation to clinicopathological characteristics and patient outcome [23-26]. Circulating IGFBP-3 may avert breast cancer development, with the clinical paradox that increasing IGFBP-3 levels in breast tumours may indicate adverse prognostic cancers [2,23-26]. This reflects the complexity of IGFBP-3 effects on cell proliferation and its potential role as a mitogen as well as a growth inhibitor. Poor prognostic tumours with increasing IGFBP-3 expression may relate to recent evidence in vitro in which the pro-apoptotic action of IGFBP-3 is reversed by the extracellular matrix protein fibronectin[B27,28]. In keeping with these findings, high levels of fibronectin expression are associated with poor prognostic breast cancers [6,29]. IGFBP-3 interacts with integrin-receptor signalling with modulation by fibronectin to increase cell attachment and possible resistance to apoptosis[B27].
The aim of this first immunohistochemical study was to evaluate breast epithelial IGFBP-3 expression in relation to clinicopathological parameters and prognosis in breast cancer. IGFBPs are upregulated in malignant breast epithelial cells with evidence in vivo that IGFBP-5 is overexpressed in the cytoplasm of breast cancers and their lymph node metastases on tissue microassay immunohistochemistry (IHC)[B6]. Accumulating evidence in vitro supports the dual effects of IGFBP-3 on the cellular growth of breast epithelial cells to emphasise the importance of selectively analysing breast epithelial IGFBP-3 expression in comparison with the stroma. By contrast, previous studies have collectively assessed breast epithelial and stromal IGFBP-3 levels by using immunoassay and immunoblot or ligand blot methods [23-26]. Moreover, both the histological location of IGFBP-3 and variations in its immunoreactivity have been compared in this series of invasive ductal breast cancers with concomitant evaluation of IGFBP-3 expression in synchronous ductal carcinoma in situ (DCIS) within the same tumour specimens. Increasing IGFBP-3 levels in breast tumours may indicate adverse prognostic cancers, with contradictory implications on patient outcomes[B24,25] to reflect the complexity of IGFBP-3 effects on cell proliferation[B27,28,30]. We have shown a varying pattern of epithelial IGFBP-3 cytoplasmic expression (1+/2+ score) in 85% of invasive ductal cancers (IDCs) and 90% of DCIS components, without detectable nuclear immunoreactivity. Increasing levels of IGFBP-3 expression showed a trend with increased proliferation, oestrogen receptor (ER) negativity and HER-2 overexpression to suggest its association with poor prognostic tumours.
Methods
Patients
The study included 103 patients aged from 26 to 88 years (median 59 years) with IDC of the breast, in association with concomitant DCIS diagnosed between 1996 and 2000 at the Bristol Royal Infirmary, Bristol, UK (Table 1). Patient numbers in clinicopathological subgroups reflect those in whom IGFBP-3 was evaluable. Regional Ethics Committee approval was granted before the start of the study. Axillary lymphadenopathy was evaluable in 88 patients; 37 (42%) were lymph-node-negative and 51 (58%) were lymph-node-positive (N1, mobile ipsilateral, or N2, fixed ipsilateral) patients. No axillary surgery was undertaken in the remaining 13 patients because of age-related co-morbidity. Clinicopathological subgroups were analysed in accordance with the Nottingham Prognostic Index (NPI) and divided into good (GPG), moderate (MPG) and poor (PPG) prognostic groups as described, with a modification that included no assessment of the internal mammary lymph nodes[B31]. Evaluation of the NPI was precluded in 15 patients because of non-evaluable regional lymphadenopathy and tumour size. Similarly, subgroups of DCIS were analysed in accordance with the Van Nuys Pathologic Classification (VNPC) (Table 1), which was not assessable in five patients [32]. The design of the study to include tumour representative samples of synchronous IDC and DCIS precluded the analysis of the Van Nuys Prognostic Index[B32]. Adjuvant treatment groups comprised the following: tamoxifen in 60 patients (27 GPG, 16 MPG, 4 PPG and 13 no NPI), and CMF-containing and anthracycline-containing regimes in 17 and 21 patients, respectively (4 GPG, 19 MPG, 13 PPG and 2 no NPI). Five patients received no adjuvant treatment. The median follow-up duration was 51 months (range 4–120 months). All were primary tumours with the exception of six local tumour recurrences, which were excluded from the analysis of patient outcome (see Table 3 and Fig. 2).
Tumour samples were collected and freshly fixed in buffered formalin in accordance with a standardised protocol at a single institution. Tumours were classified in accordance with NHSBSP guidelines [33]. Invasive ductal carcinomas were graded by the modified Bloom's grading system described by Elston and Ellis[B34]. ER immunostaining was performed with a standard three-layered streptavidin-avidin-biotin horseradish peroxidase method with a mouse anti-human ER primary antibody (M0747, 1:100 dilution; DAKO, Ely, Cambridgeshire, UK) and a biotinylated rabbit anti-mouse secondary antibody (E354, 1:350 dilution; DAKO). Expression of ER was assessed with the quick-score (0–8) and classified as positive (4–8; >3) or negative (0–3; ≤ 3) in five high-power fields (HPFs) [35]. Tumour proliferation was assessed with nuclear Ki67 immunostaining (polyclonal rabbit anti-human Ki67 antigen; A0047, 1:100 dilution; DAKO). A goat anti-rabbit biotin-labelled polypeptide (E432, 1:400 dilution; DAKO, Glostrup, Denmark) was used as a secondary antibody.
Tonsillar tissue was used as a positive control and primary antibody was replaced with Tris-buffered saline (TBS) as a negative control. Ki67 staining was evaluated as percentage of positive tumour cells, with low proliferation indicative of <10% of positive-staining cells, compared with high proliferation with ≥ 10% positivity [36]. HER-2 immunostaining was performed with the mouse monoclonal anti-HER-2 antibody (RTU-CB11; Novocastra/Vector, Newcastle upon Tyne, UK), and the Envision Plus HRP system (K4006; DAKO). HER-2 expression was scored according to the degree and proportion of membrane staining, with a score of 0 or 1+ defined as negative, and 2+ or 3+ as HER-2 positive[B37]. Lymphovascular invasion was assessed as present or not, and together with ER, HER-2 and Ki67 was analysed in the Department of Pathology (by CS and CC).
Immunohistochemistry
IGFBP-3 immunoreactivity was evaluated with the in-house sheep polyclonal antiserum (Professor JMP Holly, IGF Research Group, University of Bristol, Bristol, UK) at 1:800 dilution[B9]. IGFBP-3 immunostaining of IDC and DCIS was compared with formalin-fixed normal liver tissue as a positive control for IGFBP-3, and a replacement of the primary antibody with TBS as a negative control. Validation and specificity of the sheep polyclonal in-house antiserum has previously been demonstrated with an IGFBP-3 peptide on immunocytochemistry of Hs578T breast cancer cells[B38].
Formalin-fixed paraffin sections of breast cancer tissue and normal liver tissue were mounted on glass slides coated with 3-aminopropyl-triethoxysilane (APES; Sigma, Poole, Dorset, UK) and were baked for 30 min at 56–60°C, before being dewaxed in Clearene (Surgipath Europe, Peterborough, UK). The tissue was rehydrated by sequential immersion in 100% and 50% ethanol to distilled water. Tissue sections were subjected to heat antigen retrieval for 3 min in citrate buffer (pH 6) in a pressure cooker, and after cooling were incubated for 5 min in 0.3% (v/v) hydrogen peroxide. Subsequently, sections were washed in tap water and TBS (pH 7.45). Before incubation with IGFBP-3 primary antibody, sections were exposed to avidin and biotin blocking solutions (Vector Laboratories, Burlingame, CA, USA) for 15 min, respectively. Further blocking was achieved through exposure to normal rabbit serum (diluted with TBS) for 30 min at room temperature (20–22°C). Primary antibody was applied and incubated overnight at 4°C (18 hours). After washing with TBS, biotinylated rabbit anti-goat secondary antibody, together with the Strept-AB Complex/HRP (0377, DAKO, Glostrup, Denmark) was applied for 30 min at room temperature. Staining was revealed by development in the chromogen 3,3-diaminobenzidine tetrahydrochloride (DAB) for 5–10 min before counterstaining with haematoxylin in preparation for mounting.
Immunostaining was assessed with a Zeiss Axioskop microscope with a 40× Achrostigmat lens (× 400 overall magnification) and a field diameter of 0.46 mm. In the neoplastic cell population for IDC and DCIS, the degree of staining intensity and the proportion of cells with IGFBP-3 immunoreactivity in the nucleus and cytoplasm were graded semi-quantitatively to produce an intensity distribution score for each localisation, with invasive and pre-invasive components given separate scores. Initial scoring was of 10 HPFs; however, in view of the homogeneous staining, this was reduced to 5 HPFs. Sections were scored independently by two observers and were scored as follows: negative (0), weak/moderately positive (1+) or strongly positive (2+), with DCIS and invasive components scored independently. Scores were assessed as a continuum for the purposes of statistical correlation, unless otherwise stated.
Statistical analysis
Data were analysed with the SPSS 10.0 for Windows statistics software and summarised with descriptive statistics. The associations between IGFBP-3 and patient characteristics were assessed with the Spearman non-parametric test for continuous variables and the χ2 test for categorical factors. Analyses of survival data were performed with the log-rank test and the Cox regression model, and survival curves were computed with the Kaplan-Meier method. For IGFBP-3, univariate and multivariate analyses were performed, the latter adjusting for NPI score and treatment received (tamoxifen/chemotherapy/none). Because the NPI is based on nodal involvement, on tumour size and on grade, patients (n = 11) with non-evaluable lymphadenopathy and tumour size were excluded from the multivariate regression analyses (see Table 3).
Results
IGFBP-3 expression in IDCs and DCIS and their relationships to clinicopathological factors
IGFBP-3 immunoreactivity was evaluable in 101 (98%) cases of IDCs and in 102 cases of DCIS, and scored positively (1+/2+) as a homogeneous cytoplasmic expression in 86 (85%) of invasive and 92 (90%) of DCIS components (Fig. 1; Tables 1 and 2). Strong (2+) IGFBP-3 expression was seen in 32 invasive breast cancers and 40 cases of DCIS. A weak/moderate (1+) expression of IGFBP-3 was evident in the majority of invasive (n = 54) and DCIS (n = 52) components. There was no clear evidence of nuclear IGFBP-3 expression on IHC in IDC or DCIS. Consistently low levels of IGFBP-3 were evident throughout the stroma, without strong expression except in vascular endothelial cells. A comparison showed that the levels of IGFBP-3 expression in DCIS were similar to those in invasive disease (Table 2).
We investigated the relationships between the levels of IGFBP-3 expression and clinicopathological parameters in IDCs and DCIS. IGFBP-3 scores were analysed as a continuum for the purposes of statistical analysis. There were no significant associations (where data was assessed as a continuum) between IGFBP-3 and established prognostic indicators in invasive disease (lymph node involvement, increasing tumour size, increasing tumour histological grade, ER negativity [quick-score 0–8], lymphovascular invasion and NPI). There was a possible trend between increasing IGFBP-3 levels and increasing cellular proliferation (Ki67) in invasive disease (Spearman correlation coefficient [cc] 0.166, P = 0.096) (where assessed as a continuum) that was not observed in DCIS (P = 0.8) (data not shown), or where Ki67 was categorised as <10% versus ≥ 10% (Table 1). A comparison of categorical IGFBP-3 expression with ER-positive (quick-score 4–8) and ER-negative (quick-score 0–3) IDCs showed a trend (P = 0.06) with ER-negative tumours and HER-2-positive invasive tumours (P = 0.065) (Table 1). In Table 1, 94% (33 of 35) of ER-negative cancers expressed either 1+ or 2+ IGFBP-3 versus 80% of ER-positive tumours (χ2 test, P = 0.06). Similarly, 94% (15 of 16) of HER-2-positive tumours expressed IGFBP-3 (1+/2+) versus 84% of HER-2-negative cancers (P = 0.065).
There were no associations between IGFBP-3 expression and pathological variables on logistic regression in DCIS (VNPC, HER-2 expression and increased proliferation/Ki67). A categorical analysis of individual IGFBP-3 scores (negative versus 1+ versus 2+) in DCIS showed that 97% (32 of 33) of IGFBP-3-positive DCIS were ER-negative versus 80% of ER-positive DCIS (P = 0.037) (Table 1). We demonstrated an inverse correlation between local IGFBP-3 expression and patient age, in which IGFBP-3 immunoreactivity analysed on a continuum decreased with age (cc -0.214, P = 0.03) (data not shown); Table 1 shows a significant association with age (P = 0.04) when IGFBP-3 and age are analysed categorically in the invasive components, with a possible trend in DCIS (cc -0.174, P = 0.08) (data not shown).
Relationships of clinicopathological factors to prognosis and the predictive potential of IGFBP-3 expression
Overall survival (OS) and disease-free survival (DFS) were determined in 87 and 95 patients, respectively, with a median follow-up of 51 months (range 4–120 months). Disease relapses (local or distant recurrences) occurred in 30 women; of these, deaths were confirmed in 23 patients, with 8 suspected deaths in the absence of a recorded mortality date. Locoregional recurrence occurred at a median duration of 28.5 months (range 3–156 months) from diagnosis. Breast cancer-related mortality occurred at a median of 26 months (range 8–98 months) from presentation. The mean durations of OS and DFS were 93 months and 87 months, respectively. Four-year DFS and OS were 70% and 77%, respectively. The relationship of established clinicopathological features with OS and DFS were analysed with Cox's regression analysis (Table 3). Generally poor prognostic factors such as large tumour size, high tumour grade, lymphovascular invasion, lymph node metastases, ER negativity, HER-2 overexpression and NPI were significantly associated with decreased OS and DFS. High tumour proliferation (Ki67 on IHC), although associated with a lower percentage of patients remaining disease-free (DFS) and alive (OS) at 4 years, did not reach statistical significance.
Univariate and multivariate analysis with the continuous score variables were used to investigate possible relationships between patient outcome data and levels of expression for IGFBP-3. IGFBP-3 scores (assessed as a continuum) were not predictive for OS or DFS after univariate or multivariate analysis (Table 3), adjusted for NPI and adjuvant treatment (tamoxifen/chemotherapy/none) with respect to the multivariate analysis. Where IGFBP-3 was categorised as positive (1+/2+) or negative (0), the Kaplan-Meier survival curves (Fig. 2) demonstrate a possible trend towards a more favourable outcome for IGFBP-3-negative tumours, although this failed to reach statistical significance for OS (P = 0.168) or DFS (P = 0.269), perhaps related in part to the small numbers of IGFBP-3-negative tumours observed in this study.
Discussion
IGFBP-3 has direct intrinsic actions, as well as regulating IGFs to influence cellular growth, survival and apoptosis of breast epithelial cells. Breast cancer cells typically express several IGFBPs, with IGFBP-2, IGFBP-3, IGFBP-4 and IGFBP-5 being observed most often [5,6,22]. Although previous studies in vivo have suggested a tumour-related upregulation of IGFBP-3 levels, clinical studies have yet to determine IGFBP-3 epithelial protein expression in breast cancers; this is the first IHC study. IHC of tissue microassays confirms a tumour-specific upregulation of IGFBP-5 and IGFBP-2 in primary breast cancers and their lymph node metastases [6]. The significance of these findings in the context of a decreased mRNA expression for IGFBP-5 and IGFBP-2, respectively, highlights the value and clinical contribution of an immunohistochemical evaluation[B6]. Furthermore, a tumour-specific pathway is implicated, with negligible levels of IGFBP-5 and IGFBP-2 in normal breast epithelial cells[B6,39,40]. Increasing levels of IGFBP-3 in breast cancer tissues correlate with poor prognostic features, as demonstrated in four studies in vivo through enzyme-linked immunosorbent assay and immunoradiometric assay [23-26]. The admixture of tumour with normal and stromal breast tissue is a feature of both quantitative methods with possible limitations regarding the evaluation of a predominant tumour-specific epithelial protein.
No studies have yet clarified IGFBP-3 expression on IHC in invasive breast cancers in comparison with DCIS, although there has been a limited review of colorectal carcinomas and normal colonic mucosa[B9]. This study suggests that most breast cancers express epithelial IGFBP-3 (1+/2+), mostly with a weak/moderate immunoreactivity (1+). Fewer tumours expressed IGFBP-3 strongly (2+) and it was evident more frequently in DCIS. Similar genomic aberrations may occur in DCIS and IDC to highlight possible similarities in protein expression [41,42]. This study, in large part, shows a consistent similarity in the levels of IGFBP-3 expression in DCIS and IDC (cc 0.789; P < 0.001) (Table 2).
Nuclear localisation of IGFBP-3 has been described in several cell lines in vitro, including breast cancer cells[20,21]. This observation is supported by the direct interaction of IGFBP-3 with the nuclear receptor retinoid X receptor and the ability of IGFBP-3 to act as a nuclear-import carrier for IGF-I[B20,22,43]. This has raised considerable interest in the potential nuclear actions of IGFBP-3 and its functional implications. There have been very few observations of nuclear localisation of IGFBPs in vivo, with cytoplasmic IGFBP-5 expression in breast tumours and, similarly, cytoplasmic IGFBP-3 in normal colonic crypts[B6,9]. Moreover, there is further evidence in vitro suggesting that IGFBP-3 growth modulation might be independent of nuclear translocation[B19]. In the 101 samples of IDC and DCIS analysed in the study, we observed no evidence of nuclear staining for IGFBP-3.
IGFBP-3 modulates cellular proliferation with dual actions that either enhance IGFs or inhibit their actions. By contrast, in IGF-unresponsive Hs578T breast cancer cells, IGFBP-3 is predominantly growth-inhibitory and pro-apoptotic[B11,27]. Similarly, there is a potential for IGFBP-3 to switch its action on cell survival in Hs578T cells through changes in the extracellular matrix, with a clear reversal of IGFBP-3 accentuation of apoptosis when cells are changed from being grown on either plastic, collagen or laminin to fibronectin[B27]. This suggests that IGFBP-3 might be preferentially activating integrin receptors that bind fibronectin in a pro-survival growth stimulatory pathway[B44,45]. Upregulation of fibronectin expression is a feature of breast cancer metastases and poor prognostic tumours, with evidence of IGFBP-3 binding to this mesenchymal extracellular matrix glycoprotein[B29,46,47]. Tumour-related upregulation of IGFBP-3 levels in aggressive breast cancers is only partly explicable by the described findings in vitro.
Evidence is accumulating that IGFBP-3 is a potential mitogen that interacts with EGFR and HER-2 signalling pathways [16-18,28]. The potential to switch the IGFBP-3 action on cell growth suggests that IGFBP-3 has a role in malignant progression of breast cancer cells with insensitivity to IGFBP-3 growth inhibition through the expression of oncogenic Ras [17]. This is further supported by four clinical studies of breast tumours, including the present study demonstrating a spectrum of increased IGFBP-3 levels, with the highest expression indicative of a more malignant phenotype [23-26]. Increasing passages of T47D cells switch their response to IGFBP-3 with increasing tumorigenicity, although initially growth-inhibited by this binding protein [18]. Dual growth modulation by IGFBP-3 is demonstrated in MCF10A cells, in which IGFBP-3 changes from a growth inhibitor to a mitogen through Ras-induced malignant transformation and activation of Ras-p44/42 MAPK [16,17]. Normal breast epithelial MCF10A cells exposed to increasing doses of IGFBP-3 show a similar biphasic response, with preliminary growth inhibition followed by IGFBP-3 mitogenicity in the context of an IGF-I receptor antagonist, or a serine phosphorylation domain peptide (SPD, a non-IGF binding peptide), to verify these IGF-independent effects[B11,48]. LNCaP prostate cancer cells are similarly growth-stimulated by IGFBP-3 independently of IGFs [49].
In this study we found a possible association with increased proliferation, together with other adverse prognostic features such as ER negativity and HER-2 overexpression. Elevated IGFBP-3 expression might therefore suggest an abundance of IGFs sequestered by the binding protein, with further implications of an IGF-independent mitogenic role. IGFBP-3 mitogenicity may also relate to candidate proteases, such as cathepsin D, prostate-specific antigen and matrix metalloproteinases, with IGFBP-3 proteolysis potentially releasing IGFs to enhance their mitogenicity. The precise mechanism for this regulation remains unknown because studies so far have shown no clear correlations between IGFBP-3 and these proteins in tissue extracts.
This study suggests an association between IGFBP-3 expression and ER negativity and confirms previous findings in vivo [24,25]. Clearly, there is a significant synergism between the ER and the IGFs in breast cancer cells [1-3]. Many members of the IGF system are under transcriptional control of the ER, with IGF-I similarly enhancing the transcriptional activity of ER[B1,2]. Oestrogens transcriptionally downregulate IGFBPs in breast tissue and increase IGFBP-3 proteases, which may in part explain the inverse association between IGFBP-3 and ER expression [24,25], as well as perhaps reflecting the disruption of common pathways characteristic of poor prognostic tumours [2,50,51]. HER-2 overexpression predicts aggressive and poor prognostic breast tumours that are likely to be ER negative and tamoxifen resistant[B52]. Recent evidence in vitro suggests a functional interaction between the IGF-I receptor and HER-2, with the potential for IGFBP-3 to modulate this response[B53,54]. Our finding of an association between IGFBP-3 and HER-2 expression is preliminary and, although based on a limited number of HER-2-positive tumours, may suggest a role for HER-2 in IGFBP-3 growth modulation. In vitro, IGFBP-3 promotes EGF in HER-2-overexpressing T47D and Hs578T breast cancer cells [18,28,30]. Ageing is associated with profound changes in the growth hormone/IGF regulatory pathways, with diminished circulating IGFBP-3, as well as decreased tissue levels previously observed in vivo and confirmed in this study [2,25].
Although high levels of IGFBP-3 in breast cancers are associated with poor prognostic features of the tumour, few studies have substantiated any significant implications on patient outcome[B24,25]. Tissue IGFBP-3 concentrations have been reported to predict a reduced OS, but this was not associated with breast cancer recurrence [25]. This study suggests that IGFBP-3 expression in breast cancers might be associated with a shorter OS and DFS, although few patients were negative for IGFBP-3 expression. Other studies, including the present one, show a consistent negative association between IGFBP-3 expression and favourable prognostic markers underlining the potential importance of this pathway in tumorigenesis [23-26]. IGFBP-3 regulates breast cancer epithelial growth through IGF-dependent and IGF-independent pathways that involve both growth inhibition and enhanced apoptosis with the potential to switch to growth stimulatory pathways interacting with EGFR, HER-2 and fibronectin. Defining these mechanisms merits further studies in vitro and in vivo.
Conclusion
IGFBP-3 is important in tumorigenesis because of its effects on cellular proliferation, survival and apoptosis. We have demonstrated a tumour-associated upregulation of cytoplasmic IGFBP-3 epithelial expression in invasive and non-invasive breast cancers, with similar patterns of immunoreactivity. Despite evidence in vitro and functional implications, no nuclear IGFBP-3 expression was detectable on IHC. Invasive breast cancers expressing IGFBP-3 showed an association with poor prognostic features including increased proliferation, ER negativity and HER-2 overexpression, with possible implications for patient outcome. IGFBP-3 is a growth modulator with the potential to switch from a growth inhibitor to a mitogen that interacts with the EGFR family.
Abbreviations
cc = Spearman correlation coefficient; DCIS = ductal carcinoma in situ; DFS = disease-free survival; ER = oestrogen receptor; GPG = good prognostic group; HPF = high-power field; IDC = invasive ductal cancer; IGF = insulin-like growth factor; IGFBP-3 = insulin-like growth factor binding protein-3; IHC = immunohistochemistry; MAPK = mitogen-activated protein kinase; MPG = moderate prognostic group; NPI = Nottingham Prognostic Index; OS = overall survival; PPG = poor prognostic group; TBS = Tris-buffered saline; VNPC = Van Nuys Pathologic Classification.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SBV carried out the immunohistochemistry for IGFBP-3 as well as the clinical and statistical analyses. CMP established conditions for testing the IGFBP-3 antibody and together with JMPH has contributed to the development of the in-house antibody. CS evaluated all scoring of IGFBP-3 expression and performed all histopathology review. CJC evaluated the IGFBP-3 scoring and re-reviewed all histopathology. JMPH developed the IGFBP-3 antibody and conditions for its application, as well as conceiving the study. ZEW conceived the study, and participated in the study design, statistical analyses and wrote the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Rosemary Greenwood (Statistician, Department of Research and Development, United Bristol Healthcare NHS Trust) for her contribution to statistical analyses, and Mr Paul Newcomb (Department of Medicine, University of Bristol) for his technical assistance. This work was funded by the Association of International Cancer Research.
Figures and Tables
Figure 1 Examples of IGFBP-3 immunoreactivity in infiltrating ductal carcinoma of the breast with concomitant DCIS. Immunostaining was performed as described in the Methods section, and nuclei were counterstained with haematoxylin. (a) A tumour showing cytoplasmic IGFBP-3 expression in invasive ductal cancer. (b) A similar cytoplasmic IGFBP-3 positivity in concomitant DCIS. High-power magnification (original magnification × 400).
Figure 2 The relationship of IGFBP-3 to disease-free survival (DFS) and overall survival (OS). DFS (a) and OS (b) curves according to IGFBP-3-positive (1+/2+) and IGFBP-3-negative (0) breast cancers. The patient numbers reflect the exclusion of six local tumour recurrences as described in the Methods section, and include only recorded deaths in 23 patients. The P values are given for the log ranks.
Table 1 Relationship between IGFBP-3 expression and clinicopathology
Characteristic IGFBP-3 score P No. of patients
Negative Weak (1+) Strong (2+)
Invasive tumours 15 54 32 101*
Age (years)
≤ 50 1 13 13 0.04 27
> 50 14 41 19 74
Lymph node status
Negative 7 17 13 0.216 37
Positive 4 31 16 51
Not assessed 13
IDC grade
I 2 9 5 0.836 16
II 9 24 17 50
III 4 21 10 35
IDC size
≤ 2 cm 9 26 17 0.720 52
>2 cm 6 27 14 47
Multifocal 2
LV invasion
Absent 9 27 12 0.411 48
Present 6 26 18 50
Not assessed 3
NPI
GPG (< 3.4) 6 17 11 0.662 34
MPG (3.4–5.4) 4 19 13 36
PPG (> 5.4) 1 11 4 16
Not calculable 15
ER (quick-score)
Positive (4–8) 12 28 21 0.06 61
Negative (0–3) 2 24 9 35
Not assessed 7
HER-2 IHC
Negative (0/1+) 14 48 23 0.065 85
Positive (2+/3+) 1 6 9 16
Ki67 IHC
Low proliferation < 10% 10 26 13 0.249 49
High proliferation ≥ 10% 5 28 19 52
DCIS 102*
VNPC
Grade I 1 14 8 0.586 23
Grade II 5 17 14 36
Grade III 2 21 15 38
Not assessed 5
ER (quick-score)
Positive (4–8) 11 26 19 0.037 56
Negative (0–3) 1 23 9 33
Not assessed 13
HER-2 IHC
Negative (0/1+) 7 41 28 0.876 76
Positive (2+/3+) 2 9 8 19
Not assessed 7
Ki67 IHC
Low proliferation < 10% 6 30 24 0.972 60
High proliferation ≥ 10% 4 22 16 42
DCIS, ductal carcinoma in situ; ER, oestrogen receptor; GPG, good prognostic group; IHC, immunohistochemistry; MPG, moderate prognostic group; NPI, Nottingham Prognostic Index; PPG, poor prognostic group; VNPC, Van Nuys Pathologic classification. Significant P values (P < 0.05) are indicated in bold.
*Patient numbers reflect those in whom IGFBP-3 was evaluable.
Table 2 Immunohistochemical (IHC) scores (0–2) for IGFBP-3 expression in the cytoplasm of invasive ductal cancers and ductal carcinoma in situ
Cancer type Total no. of tumours IGFBP-3 IHC score
Negative (0) Weak/moderate (1+) Strong (2+)
Invasive 101 15 54 32
DCIS 102 10 52 40
Tumour numbers scored (0–2) on immunohistochemistry (IHC) for IGFBP-3 expression in the cytoplasm of invasive cancers and ductal carcinoma in situ (DCIS). IHC expression was individually assessed in the nucleus and cytoplasm, and defined as negative (0), weak/moderate (1+) or strong (2+). Positivity for IGFBP-3 expression was defined as an IHC score of (1+/2+).
Table 3 Relationships between clinicopathological criteria, cytoplasmic IGFBP-3 and patient outcome
Variable Univariate analysis Multivariate analysis
95% CI P 95% CI P
Overall survival
Age 0.99–1.04 0.4 0.98–2.90 0.009
Lymph node status (+/-) 2.79–153.9 0.004 2.65–161 0.003
Invasive tumour grade (I, II, III) 1.27–4.93 0.002 1.14–4.62 0.002
Tumour size 1.02–1.05 <0.001 1.02–1.05 <0.001
NPI 1.48–3.36 <0.001 1.39–3.32 <0.001
ER (quick-score) 0.66–0.92 0.002 0.68–0.98 0.03
HER-2 IHC score 0.94–2.02 0.04 0.76–1.95 0.56
Ki67 proliferative index 0.99–1.00 0.4 0.99–1.00 0.65
Lymphovascular invasion (+/-) 1.46–9.38 0.006 0.89–6.69 0.08
IGFBP-3 IHC score 0.62–2.01 0.719 0.59–2.50 0.592
Disease-free survival
Age 0.97–1.02 0.9 0.99–1.06 0.06
Lymph node status (+/-) 2.29–25.3 <0.001 2.06–25.6 0.002
Invasive tumour grade (I, II, III) 1.48–5.38 0.005 1.36–5.11 0.006
Tumour size 1.02–1.04 <0.001 1.02–1.04 <0.001
NPI 1.46–3.17 <0.001 1.38–3.15 <0.001
ER (quick-score) 0.66–0.89 0.001 0.68–0.95 0.01
HER-2 IHC score 1.04–1.92 0.02 0.95–1.87 0.2
Ki67 proliferative index 0.99–1.00 0.2 0.99–1.00 0.3
Lymphovascular invasion (+/-) 1.63–6.95 0.002 1.09–6.65 0.03
IGFBP-3 IHC score 0.61–1.71 0.922 0.57–1.90 0.896
Confidence intervals (CI) and P values are given for the results of both the univariate and multivariate analyses. The multivariate analysis is adjusted for Nottingham Prognostic Index (NPI) (nodes, grade and size) and treatment (tamoxifen/chemotherapy/none). Data for univariate analysis were evaluable in 95 patients (reflecting the exclusion of six local tumour recurrences as described in the Methods section) and included a multivariate analysis on 84 cases that excluded non-evaluable NPI in 11 patients. All clinicopathological variables and cytoplasmic IGFBP-3 immunohistochemistry (IHC) scores were analysed as a continuum, with lymph node status and lymphovascular invasion assessed as present or absent. Significant P values (P < 0.05) are indicated in bold.
ER, oestrogen receptor.
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Hollowood AD Stewart CE Perks CM Pell JM Lai T Alderson D Holly JM Evidence implicating amid-region sequence of IGFBP-3 in its specific IGF-independent actions J Cell Biochem 2002 86 583 589 12210764 10.1002/jcb.10223
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Mathieu M Vignon F Capony F Rochefort H Estradiol down-regulates the mannose-6-phosphate/insulin-like growth factor-II receptor gene and induces cathepsin-D in breast cancer cells: a receptor saturation mechanism to increase the secretion of lysosomal proenzymes Mol Endocrinol 1991 5 815 822 1656243
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| 15642160 | PMC1064109 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 23; 7(1):R119-R129 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr963 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9651564215910.1186/bcr965Research ArticleHypersensitive K303R oestrogen receptor-α variant not found in invasive carcinomas Davies Michael PA [email protected]'Neill Penny A [email protected] Helen [email protected] D Ross [email protected] Clatterbridge Cancer Research Trust, JK Douglas Laboratories, Clatterbridge Hospital, Bebington, Wirral, UK2 Clatterbridge Centre for Oncology, Clatterbridge Hospital, Bebington, Wirral, UK2005 23 11 2004 7 1 R113 R118 2 2 2004 25 3 2004 31 8 2004 18 10 2004 Copyright © 2004 Davies et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Genetic abnormalities or mutations in premalignant breast lesions may have a role in progression toward malignancy or influence the behaviour of subsequent disease. The A908G (Lys303→Arg) change in the gene encoding oestrogen receptor-α (ER-α) creates a hypersensitivity to oestradiol and would have significant consequences if present in breast carcinoma, especially those treated with endocrine therapy. We have therefore examined a panel of endocrine-treated invasive carcinomas for the presence of this mutation.
Methods
Sequencing of control DNA was shown to detect mutation present in as little as 15% of the starting material. Enrichment for the mutation by using MboII restriction digestion allowed the detection of mutant present at 1% or less. We applied these techniques to genomic DNA and cDNA from 136 invasive breast carcinomas.
Results
No evidence of the A908G mutation was found with either technique. The incidence of this mutation in our panel of tumours is therefore significantly less than previously reported.
Conclusion
The fact that the mutation was not found leads us to believe that this mutation is absent from most cells in invasive carcinomas and furthermore that the major expression product of the ER-α gene in cancers does not contain the K303R mutation. It is therefore unlikely to influence the effectiveness of endocrine treatment.
breastcarcinomahyperplasiamutationoestrogen receptor
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Introduction
Most breast cancers overexpress oestrogen receptor-α (ER-α), and this molecule is the major target of the anti-estrogens, such as tamoxifen, used to treat the disease. Not all patients respond to anti-oestrogen treatment and many who do respond later relapse. The reasons for treatment failure are still unclear. The suggestion that mutation of ER-α might have a role in the formation of breast cancer and subsequent response to treatment was raised by the detection of a somatic A908G (Lys303→Arg; K303R) mutation in the gene encoding ER-α. This mutation was reported in a significant proportion of breast hyperplasia [1] and also in the majority of invasive cancers and all metastases tested [2,3]. The K303R ER-α variant apparently exhibits a hypersensitivity to oestradiol [1], a characteristic that might allow breast cancers to respond to much lower levels of oestrogenic stimulation with a subsequent impact on malignant progression and the effectiveness of anti-oestrogen treatment. This would be of particular importance in post-menopausal women and in women receiving anti-oestrogen therapy. We have therefore studied a cohort of post-menopausal, endocrine-treated breast cancers for the presence of this mutation. Furthermore, because expression of the hypersensitive mutant would be required for activity, we also examined mRNA.
The detection of mutations in breast cancers is not a trivial task, owing in part to the heterogeneity of tumour tissue and also to the reporting of false positive and false negative results. Although microdissection allows the purity of selected cellular populations to be enhanced, the sensitivity and specificity of standard DNA sequencing approaches still limit our ability to assess mutation status accurately. We now show that by performing a second round of sequencing after enrichment for the mutation, we were able to increase the sensitivity and specificity of our assay and apply it to invasive carcinomas without the need for microdissection.
Methods
Determination of sensitivity for mutation detection
To confirm the sensitivity of this technique for detection of the mutant allele in the presence of wild-type DNA, experiments were performed with the use of mixtures of cloned wild-type and mutant polymerase chain reaction (PCR) products, generated with primers described by Fuqua and colleagues [1] (ERmut1, 5'-CAA GCG CCA GAG AGA TGA TG-3'; ERmut2, 5'-ACA AGG CAC TGA CCA TCT GG-3').
Plasmid clones of PCR products with either an A or a G at position 908 of ER-α were mixed in various ratios (100% to 0% mutant) and diluted to the equivalent of 1000 copies per PCR reaction before being amplified and sequenced. A two-stage PCR was performed on 2 μl of mixed DNA in the 20 μl first-round reaction (20 cycles) and 4 μl of first-round product in the 40 μl second round (50 cycles). For each PCR an initial 13 min 95°C denaturation step was followed by cycles of 95°C for 30 s, 68°C for 60 s and a single 3 min final extension at 72°C. Other PCR conditions were also the same in both rounds: 0.2 mM dNTPs, each primer at 0.5 μM, 0.5 units of HotstarTaq DNA polymerase (Qiagen) and 1× PCR Buffer (containing 1.5 mM MgCl2; Qiagen).
PCR products were sequenced (Fig. 1) by using primers ERmut1 and ERmut2 and subjected to MboII digestion, reamplification and further sequencing as described below.
Digestion with MboII and reamplification by PCR
The MboII restriction enzyme has a GAAGA recognition site; this sequence is present at the K303R mutation with the second A being mutated to G. Digestion with MboII can therefore be used as an assay for the presence of mutation, with non-digested DNA indicating mutation within the recognition site [4]. PCR product (5–10 μl) was digested in a total volume of 20 μl including 1× Buffer2 (New England Biolabs) and 5 units of MboII restriction enzyme (New England Biolabs) by incubation at 37°C for 90 min. PCR products were separated by electrophoresis on gels containing 3% Seakem Agarose (Flowgen) and TAE buffer (40 mM Tris-acetate, 1 mM EDTA, pH 7.6). Molecular mass markers (φX174/HaeIII; Abgene) were included on each gel and DNA was revealed by the inclusion of 0.5 μg/ml ethidium bromide, scanning with a Molecular Dynamics FluorimagerSI and analysis with ImageQuant version 4.1 software (Molecular Dynamics). After digestion the non-mutant 158-base-pair (bp) ERmut1–ERmut2 PCR product gives rise to 118-bp and 40-bp bands, detectable by agarose gel electrophoresis (Fig. 2), whereas 158-bp PCR products containing mutant remain undigested. Similarly ERADNA1–ERADNA2 genomic DNA and ERARNA1–ERARNA2 cDNA PCR products give different patterns of digestion products depending on the presence of a mutation affecting the MboII recognition site.
Rather than relying on the detection of non-digested PCR products by agarose gel electrophoresis, we further increased the sensitivity and specificity of this assay by sequencing amplified non-digested DNA after digestion. PCR reamplification of non-digested DNA (20 cycles) was performed as above for primers ERmut1 and ERmut2 with 2 μl of a 1:10 dilution of the restriction digest. Reamplified PCR products were sequenced with ERmut1 primer. In all cases with any evidence of a mutant G at the relevant position, alternative PCR reactions were examined and reverse sequencing with primer ERmut2 was performed, to rule out PCR or sequencing anomalies.
ER-α PCR
For ESR1 genomic DNA (RefSeq NM_000125) from breast cancer cases, primers were designed that were specific for genomic DNA (because they included intronic sequence) and that better allowed sequence determination in both directions (ERADNA1, 5'-AAG TGG CCT GAA GTT TGT TA-3'; ERADNA2, 5'-CTT ACC TGG CAC CCT CTT-3'). PCR reactions contained 1 ng of genomic DNA, 0.2 mM dNTPs, each primer at 1 μM, 0.5 units of HotstarTaq DNA polymerase (Qiagen), 1× PCR Buffer (containing 1.5 mM MgCl2; Qiagen) and additional MgCl2 to a final concentration of 2.5 mM. An initial 13 min 95°C denaturation step was followed by 40 cycles of 94°C for 40 s, 60°C for 40 s, 72°C for 60 s and a single 10 min final extension at 72°C. The 609-bp genomic PCR product is digested by MboII to give a 296-bp product, a 6-bp product and products of 130 and 177 bp; the last two of these fail to digest if a mutation is present and instead result in a 307-bp product that can be reamplified by ERmut1 and ERmut2.
For RNA analysis from breast cancer cases, reverse transcription (RT) was performed in duplicate on 0.5 μg of RNA in accordance with the manufacturer's instructions (Invitrogen), after a DNAaseI digestion step (Invitrogen). RT reactions incorporated Superscript II Reverse Transcriptase (Invitrogen), 0.5 μg of oligo (dT)17 and 0.5 μl of Prime Recombinant Ribonuclease Inhibitor (Eppendorf). Parallel reactions were performed in which the RT enzyme was omitted; these acted as controls for genomic DNA contamination. RT–PCR reactions were performed with cDNA-specific primers designed to cross intron–exon boundaries (ERARNA1, 5'-AAG TGG GAA TGA TGA AAG GT-3'; ERARNA2, 5'-CAA GAG CAA GTT AGG AGC AA-3') and that better allowed sequence determination in both directions. These primers are located in exons 4 and 6 of ER-α and generate a 491-bp RT–PCR product. The same primers occasionally amplify additional, minor, shorter products arising from splice variants in which exon 5 is absent. PCR reactions contained 2 μl of a 1:20 dilution of the RT reaction, 0.2 mM dNTPs, each primer at 1 μM, 0.5 units of HotstarTaq DNA polymerase (Qiagen) and 1× PCR Buffer (containing 1.5 mM MgCl2; Qiagen). PCR cycling conditions were the same as for genomic DNA from breast cancers. The 491-bp genomic PCR product is digested by MboII to give a 135-bp product, a 49-bp product and products of 130 bp and 177 bp; the last two of these fail to digest if a mutation is present and instead result in a 307-bp product that can be reamplified by ERmut1 and ERmut2.
DNA sequencing
PCR products were treated with ExoSAP (Amersham Biosciences) before sequencing. Fluorescent DNA sequencing (Fig. 1) was performed with DYEnamic ET Dye Terminator Cycle Sequencing Kit for MegaBACE (Amersham Biosciences) and analysed on a MegaBACE 1000 (Amersham Biosciences). For RT–PCR products, the primers used for PCR were also used for sequencing. When sequencing the genomic DNA from breast cancers the reverse primer ERADNA2 and an additional forward sequencing primer (ERADNA3, 5'-TAC GAA AAG ACC GAA GAG-3') were used. Primer ERADNA3 is located in exon 5 of ER-α and overcomes difficulties in sequencing through an A and T rich tract in intron 4 of ER-α; the same primer can also be used to sequence RT–PCR products and overcomes any difficulties caused by exon-5-deleted PCR products. DNA sequencing of genomic and cDNA PCR products was performed in both directions and the position of the putative mutation was analysed individually for each PCR product. To call a mutation we adopted the standard practice that it should be observed in at least two independent PCR reactions (avoiding possible PCR misincorporation due to Taq polymerase infidelity) and in both sequencing directions (avoiding sequencing anomalies due to misincorporation of dideoxy terminators).
Patients and samples
Post-menopausal patients undergoing treatment for invasive breast cancer during the period between 1993 and 1999 were identified within the database of the Cancer Tissue Bank Research Centre (CTBRC) at the Royal Liverpool University Hospital. The diagnoses of invasive cancers were made in accordance with the pathology guidelines of the NHS Breast Screening Program [5].
DNA and/or RNA from 136 cases were selected for analysis and provided by CTBRC along with further details as given in Table 1. All tissue donated to CTBRC is fully consented for research purposes and permission was granted by all required local research ethics committees.
Results
Our ability to detect variable amounts of mutant 908G ER-α in the presence of wild-type 908A ER-α was confirmed by sequencing PCR products from mixtures of these two variants (Fig. 1). Direct sequencing was routinely sensitive enough to detect mutant ER-α when present in as little as 15% of the starting DNA and was frequently able to detect the mutation at even lower levels (for example 10%). This was true of sequencing in either direction, although for the original primers described by Fuqua and colleagues [1] the reverse sequence was more difficult to read because the mutation was closer to the reverse PCR primer (ERmut2). The new primers designed for use on cDNA overcame this limitation, while ensuring that only cDNA was amplified. PCR primers for genomic DNA were similarly specific for genomic DNA and, when used in conjunction with a sequencing primer (ERADNA3), again gave sequence in both directions.
Digestion with MboII restriction enzyme allowed the detection of as little as 1% mutant DNA by agarose gel electrophoresis in control reactions (Fig. 2). However, no evidence of undigested PCR product was visible for any breast tumour assayed in this way. When sequencing control DNA reamplified with ERmut1 and ERmut2 after enrichment for mutant DNA by MboII digestion (Fig. 1), the mutant G base was clearly detected even when only present in 1% of the original DNA. The wild-type A was also detected, either because of inefficient digestion or because of heterodimer formation in the PCR reaction. Notably in control reactions containing only wild-type ER-α, any post-digest reamplified DNA was clearly wild type. Therefore, despite a failure to detect non-digested PCR product on gels stained with ethidium bromide, reamplification and sequencing confirms that restriction digestion is seldom 100% efficient and that by PCR we are able to reamplify products originating from 1% or less of the starting DNA. It is therefore possible to apply this reamplification and sequencing technique to all samples, because the PCR used routinely amplifies non-digested DNA enriched for mutant PCR product.
The enhanced sensitivity after enrichment using digestion with MboII and reamplification is not without drawbacks, in that we detected a greater number of PCR and sequencing anomalies with this technique. For non-enriched sequence analysis, evidence of a very minor G in genomic DNA from two breast cancers was subsequently shown to be due to sequencing anomalies because they were not present either in repeat PCR products from the same cases or in sequence generated in the reverse direction. After enrichment, similar errors were noted in eight genomic DNA PCR products and six cDNA PCR products. Ten of these anomalies were apparently due to Taq polymerase infidelity (that is, they were identified in reverse sequencing but not repeatable in replicate PCR) and four were sequencing anomalies (that is, they were not identifiable in reverse sequencing). Notably, errors due to Taq polymerase infidelity were seen only with the more sensitive assay based on enrichment with MboII, and no such anomalies of either type were seen in any non-mutant controls at the relevant base position.
We were unable to confirm that the proposed ER-α A→G mutation leading to a K303R amino acid substitution was present in any case of invasive cancer examined. In all, we examined 136 cases of invasive cancer (Table 1) with our MboII-enriched sequencing assay, having also sequenced 130 of these before enrichment. With the exception of the 14 cases noted previously, in every sequenced PCR product the A at the base position of the variant was clearly an A with no evidence of any G substitution. For 60 of these cases we examined both genomic DNA and cDNA, for 71 cases genomic DNA only was examined (for example, for ER-α-negative cancers) and for 5 cases only cDNA was examined. All 100 cases of invasive cancer for which a pathologist assessed the proportion of tumour cells contained at least 50% tumour, and 63% contained at least 90% tumour cells. Given the proven sensitivity of our techniques we would therefore have expected to detect the hypersensitive ER-α mutation even if present in only a minority of cancer cells or contaminating normal cells.
Discussion
The abundant expression of ER-α in most breast cancers is fundamental to both our understanding of this disease and its treatment. The observations that ER-α is overexpressed in a proportion of premalignant lesions [6,7] and is possibly related to an increased risk of progression [8] further raise the importance of oestrogenic activity in the establishment and behaviour of breast carcinoma. It is therefore important to understand whether other mechanisms for increased ER-α activity are also present in breast tumours or their putative precursors. One such mechanism is the hypersensitivity apparently inferred by a K303R mutant ER-α reported to be present in a significant proportion of breast hyperplasia [1].
We have been unable to detect the reported A908G mutation of ER-α in genomic DNA from any case of invasive carcinoma in our study, despite applying a sensitive and robust assay capable of detecting mutant if present in as little as 1% of the starting DNA. The absence of this hypersensitive ER-α variant suggests that this mutation is not present in the majority of cells in such lesions, or indeed even in a significant minority. Furthermore, the absence of the mutation in RT–PCR products confirms that non-mutant ER-α is the major expression product in the breast cancers. Zhang and colleagues also failed to detect the mutation in a variety of breast lesions from Japanese women [4], and while this manuscript was under review two further papers have reported a lack of evidence for the K303R mutation [9,10]. We have previously been unable to detect the mutation by sequencing a series of 56 microdissected ductal carcinomas in situ from an unrelated cohort, confirming that this mutation is also apparently absent from these premalignant lesions.
Case selection can influence detection rates, and the cohort of patients tested here was selected by virtue of being post-menopausal and treated with endocrine therapies but not chemotherapy, so as to allow any effect on endocrine treatment to be more easily defined. Although it is possible that the mutation occurs in other groups of breast cancers, our cohort is broadly representative of subgroups defined by ER-α status, nodal status, grade, tumour size and histology. Other studies reporting an inability to detect the mutation [4,9,10] have applied no obvious selection criteria and it therefore seems unlikely that any selection bias is to blame for the lack of detectable mutation.
The low level of risk of subsequent cancer attached to hyperplasia implies that many of these lesions fail to contribute to disease progression and it is perhaps therefore unsurprising that a mutation so far reported in only about one-third of hyperplasias by a single laboratory is found to be absent from more advanced lesions in the present study. Preliminary reports of the K303R mutation in a higher proportion of breast cancers [2,3] from the same group as reported the original observations in hyperplasia are perplexing. Although confirmatory data have not so far been published, these results continue to be reported at scientific meetings and referenced in reviews [11]. Many previous studies of mutation in breast cancers have failed to report this mutation, and more recent reports [4,9,10] have also produced evidence that if it exists it does so below the level of detection of the techniques used. It is therefore important to consider the sensitivity and specificity of the different assays used to detect the K303R mutant. The fact that Zhang and colleagues analysed 7 breast hyperplasias [4] and Tebbit and colleagues analysed 25 hyperplasias [9] and found no mutation rules out the use of hyperplasia as 'positive' controls and challenges the original observations.
Here the sequencing of PCR products with fluorescent dideoxy terminators is shown to be sensitive enough to detect a 908G mutant when present at 15% or less of the starting DNA, in keeping with the results of Tebbit and colleagues [9]. This is probably suitable for use with microdissected lesions as used by Tebbit and colleagues for 36 invasive cancers, or lesions with a relatively high tumour content as used here (Table 1), but might not be sensitive enough for use with non-microdissected cancers used elsewhere [4,10] or if the mutation is present in only a small minority of cancer cells. We therefore also used gel-based PCR with restriction-fragment-length polymorphism and were able to detect as little as 1% mutant by agarose gel electrophoresis of PCR products from control mixing experiments. Although the same technique was used by Zhang and colleagues for 215 cancers [4], they did not report their detection sensitivity or confirm their specificity. Given that this technique potentially detects very low levels of mutation at any position in the MboII recognition site, it is important to confirm sensitivity and specificity by a further round of DNA sequencing. A very significant enhancement of sequencing sensitivity (to 1% mutant or less) was seen with this MboII digestion and reamplification technique. We can therefore state that this mutation is not present in the vast majority of tumour cells of any cancer tested, even without the benefit of enrichment for tumour cells by microdissection.
Although assay sensitivity is important, specificity is equally crucial. We never reported any mutant in either assay used for our non-mutant control DNA, indicating that our specificity is high. However, in our experience, false positive mutations by DNA sequencing, as seen here in 16 of the 136 cancers tested, are to be expected when PCR reactions are performed on very low levels of DNA as found in many microdissected samples, or after digestion with restriction enzyme. It is therefore important to perform rigorous confirmatory assays. To this end, we always sought to confirm mutations by sequencing in both directions to avoid anomalies that often occur as a result of the misincorporation of dideoxy terminators in unidirectional sequencing. We also sequenced multiple PCR products to avoid anomalies due to Taq polymerase infidelity. Originally, the K303R mutation was reported in 34% of microdissected hyperplasias tested by unidirectional, non-fluorescent sequencing [1]. This assay is prone to false positives and it is unclear whether replicate PCR or other confirmatory assays were performed. The fact that the mutation was always found in addition to the wild type is also to be expected with artefacts due to dideoxy terminator misincorporation. The possibility that observations of the K303R mutation in hyperplasia result from a lack of specificity must therefore be considered and some doubt cast over other positive results in cancers.
Conclusion
We have developed and validated a highly sensitive and specific assay for the detection of the A908G (K303R) mutation of ER-α. Having tested 136 different breast tumours, we find no evidence to support the hypothesis that the A908G mutation of ER-α is present or expressed in breast carcinoma cells from post-menopausal, endocrine-treated patients. It is therefore unlikely to have an impact on the effectiveness of such treatments.
Abbreviations
ER-α = oestrogen receptor-α; PCR = polymerase chain reaction; RT = reverse transcription.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors listed contributed to the production of this manuscript. PAO and HI provided clinical review of cases studied; PAO prepared the cDNA used; HI provided genomic DNA; mutation detection was performed by MPAD, and DRS provided both technical insight and critical review. The manuscript was produced by MPAD. All authors read and approved the final manuscript.
Acknowledgements
We thank the Cancer Tissue Bank Research Centre for providing DNA and RNA samples and patient information, all medical professionals who contribute towards tissue bank donations and all patients who kindly donated their tissue for research use. We also thank Dr Balvinder Shoker for invaluable pathology advice, and both Dr Shoker and Dr Musswar Iqbal for providing microdissected breast samples used for initial assay optimisation. This work was supported by Clatterbridge Cancer Research Trust (MPAD, HI, PO, DRS) with help from the Cancer & Polio Research Fund and the Ward Blenkinsop Trust.
Figures and Tables
Figure 1 Sample electrophoretograms of sequence data with and without enrichment by digestion with MboII. The sequence of the region of interest is shown: coding residues 903–913 (residues 1195–1205 of ESR1; GenBank accession number M12674). The position of the detected A908G mutation is marked with asterisks.
Figure 2 Agarose gel electrophoresis of control PCR products with and without digestion with MboII.
Table 1 Case selection
Parameter Detail Value
Age (years) Range 48–88
Histology Invasive ductal 115
Invasive lobular 11
Other 10
ER-α Positive 88
Negative 42
Grade 1 21
2 53
3 62
Nodal status Positive 51
Negative 62
Size <2 cm 55
≥ 2 cm 79
Stage 1 33
2 76
3 5
Tumour (%) ≥ 50 100
≥ 75 91
≥ 90 63
ER-α, oestrogen receptor-α.
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Hopp TA Allred DC Mohsin S Hilsenbeck SG Nawaz Z Pestell RG Fuqua SAW A hypersensitive estrogen receptor a protein in breast cancer Proceedings of the 92nd Annual Meeting of the American Association of Cancer Research: 24–28 March 2001; New Orleans 2001 Philadelphia: AACR 4806
Zhang Z Yamashita H Toyama T Omoto Y Sugiura H Hara Y Haruki N Kobayashi S Iwase H Estrogen receptor alpha mutation (A-to-G transition at nucleotide 908) is not found in different types of breast lesions from Japanese women Breast Cancer 2003 10 70 73 12525766
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Shaaban AM Sloane JP West CR Foster CS Breast cancer risk in usual ductal hyperplasia is defined by estrogen receptor-alpha and Ki-67 expression Am J Pathol 2002 160 597 604 11839580
Tebbit CL Bentley RC Olson JA JrMarks JR Estrogen receptor alpha (ESR1) mutant A908G is not a common feature in benign and malignant proliferations of the breast Genes Chromosomes Cancer 2004 40 51 54 15034868 10.1002/gcc.20017
Tokunaga E Kimura Y Maehara Y No hypersensitive estrogen receptor-alpha mutation (K303R) in Japanese breast carcinomas Breast Cancer Res Treat 2004 84 289 292 15026626 10.1023/B:BREA.0000019963.67754.93
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| 15642159 | PMC1064111 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Nov 23; 7(1):R113-R118 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr965 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9681564216510.1186/bcr968Research ArticleAtm heterozygous deficiency enhances development of mammary carcinomas in p53 heterozygous knockout mice Umesako Seiichi [email protected] Kae [email protected] Sayoko [email protected] Nobuko [email protected] Masahiro [email protected] Doo-Pyo [email protected] Chang-Woo [email protected] Satomi [email protected] Syunsuke [email protected] Otsura [email protected] Masaaki [email protected] Graduate school of Agriculture and Biological Sciences, Osaka Prefecture University, Osaka, Japan2 Research Institute of New Medicines, Shionogi Pharmaceutical Co., Osaka, Japan3 Research Institute for Advanced Science and Technology, Osaka Prefecture University, Osaka, Japan4 Korea Research Institute of Chemical Technology, Taejon, Korea5 Department of Anatomy, Nara Medical University, Nara, Japan6 Nara Prefecture Institute for Hygiene and Environment, Nara, Japan7 Radiation Biology Center, Kyoto University, Kyoto, Japan2005 10 12 2004 7 1 R164 R170 27 7 2004 17 9 2004 15 10 2004 15 10 2004 Copyright © 2004 Umesako et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License () which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Ataxia-telangiectasia is an autosomal-recessive disease that affects neuro-immunological functions, associated with increased susceptibility to malignancy, chromosomal instability and hypersensitivity to ionizing radiation. Although ataxia-telangiectasia mutated (ATM) heterozygous deficiency has been proposed to increase susceptibility to breast cancer, some studies have not found excess risk. In experimental animals, increased susceptibility to breast cancer is not observed in the Atm heterozygous deficient mice (Atm+/-) carrying a knockout null allele. In order to determine the effect of Atm heterozygous deficiency on mammary tumourigenesis, we generated a series of Atm+/- mice on the p53+/- background with a certain predisposition to spontaneous development of mammary carcinomas, and we examined the development of the tumours after X-irradiation.
Methods
BALB/cHeA-p53+/- mice were crossed with MSM/Ms-Atm+/- mice, and females of the F1 progeny ([BALB/cHeA × MSM/Ms]F1) with four genotypes were used in the experiments. The mice were exposed to X-rays (5 Gy; 0.5 Gy/min) at age 5 weeks.
Results
We tested the effect of haploinsufficiency of the Atm gene on mammary tumourigenesis after X-irradiation in the p53+/- mice of the BALB/cHeA × MSM/Ms background. The singly heterozygous p53+/- mice subjected to X-irradiation developed mammary carcinomas at around 25 weeks of age, and the final incidence of mammary carcinomas at 39 weeks was 31% (19 out of 61). The introduction of the heterozygous Atm knockout alleles into the background of the p53+/- genotype significantly increased the incidence of mammary carcinoma to 58% (32 out of 55) and increased the average number of mammary carcinomas per mouse. However, introduction of Atm alleles did not change the latency of development of mammary carcinoma.
Conclusion
Our results indicate a strong enhancement in mammary carcinogenesis by Atm heterozygous deficiency in p53+/- mice. Thus, doubly heterozygous mice represent a useful model system with which to analyze the interaction of heterozygous genotypes for p53, Atm and other genes, and their effects on mammary carcinogenesis.
Atmmammary carcinomamousep53radiation
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Introduction
Ataxia-telangiectasia is an autosomal-recessive disease that affects neuro-immunologic functions, and is associated with increased susceptibility to malignancy, chromosomal instability and hypersensitivity to ionizing radiation [1,2]. ATM (ataxia-telangiectasia mutated) heterozygous deficiency has been proposed to increase susceptibility to breast cancer [3-7]. However, those early studies were limited by the lack of reliable assays with which to identify carriers [8]. In fact, in a later study a lack of association of heterozygous ATM mutations with early onset of breast cancer was found [9]. More recent epidemiological studies suggested that missense mutation in the ATM gene, rather than a protein-truncating mutation, which accounts for the majority of mutations in patients with ataxia-telangiectasia, confers increased risk for breast cancer [10]. Thus, cancer risk in ATM heterozygotes varies depending on the mutation type (i.e. some missense-type mutations are associated with early onset of breast carcinoma whereas truncation-type mutations are not) [11,12]. Recently, epidemiological studies on excess risk for breast cancer in ATM heterozygosity were reported [13].
In experimental animals, no tumours were observed in Atm heterozygous mice carrying a knockout null allele of Atm [14]. In contrast, Atm knock-in heterozygous mice harbouring an in-frame deletion corresponding to the human mutation exhibit increased susceptibility to a wide variety of tumours [14]. Thus, data in humans and mice suggest that the type of Atm mutation determines susceptibility to cancer in heterozygous individuals. Heterozygosity for a null knockout allele of Atm in mice and protein-truncating alleles of ATM in humans was thought not to increase susceptibility to mammary cancer. On the other hand, haploinsufficiency at the Atm gene has a phenotype of increased sensitivity to ionizing radiation in mice [15].
In contrast to the Atm gene, the p53 null allele exhibits haploinsufficiency for the development of tumours in mice, mainly lympho-haematopoietic malignancies [16,17]. The p53 heterozygotes of BALB/c genetic background develop mammary tumours [18-20]. Mice doubly null for the p53 and Atm genes were reported to exhibit a dramatic acceleration in tumour formation relative to singly null mice, indicating that the genes cooperate in a significant manner to prevent tumourigenesis [21]. However, the authors noted no mammary carcinoma in any of the four genotypes studied (p53+/+ Atm-/-, p53+/- Atm-/-, p53-/- Atm+/-, and p53-/- Atm-/-). Thus, the significance of haploinsufficiency of the Atm null allele in mammary carcinogenesis is obscure at present.
In order to determine the effect of Atm heterozygous deficiency on mammary tumourigenesis, we generated a series of Atm+/- mice on the background of p53+/- mice with a certain predisposition to spontaneous development of mammary carcinomas, and we examined the development of tumours after X-irradiation. Our results indicate a strong enhancement of mammary carcinogenesis in the Atm heterozygous deficient mice under the p53 heterozygous deficiency.
Materials and Methods
Mice
The p53 targeted allele generated by Donehower and coworkers [22] was introduced into the BALB/cHeA mouse at The Netherlands Cancer Institute (Amsterdam). The p53 heterozygous deficient mice (p53+/-) were repeatedly backcrossed to BALB/cHeA mice more than 30 times, and maintained at the animal facility of Osaka Prefecture University. The Atm targeted mouse (129/SvEv-Atmtm1Awb/+ mouse) was originally generated in the Jackson Laboratory [23]. The Atm heterozygous deficient mice (Atm+/-) were repeatedly backcrossed more than 10 times to MSM/Ms mice. The BALB/cHeA-p53+/- mice were crossed with MSM/Ms-Atm+/- mice, and females of the F1 progeny ([BALB/cHeA × MSM/Ms]F1) with four genotypes (i.e. p53+/- Atm+/-, p53+/- Atm+/+, p53+/+ Atm+/- and p53+/+ Atm+/+) were used in the experiments. The conditions for breeding were described previously [24].
X-irradiation
Mice were exposed at 5 weeks of age to X-rays (5 Gy; 260 kV, 12.0 mA, 0.3 mm Cu + 0.5 mm Al filter; 0.5 Gy/min) from an X-ray generator (Radioflex 350; Rigaku Industrial Co., Takatsuki, Japan). All animal experiments were carried out in accordance with the standards relating to the care and management of experimental animals (Japan) and Osaka Prefecture University's guidelines for animal care and use.
Histopathological examination
Moribund mice were killed by cervical dislocation for autopsy. In cases of thymic lymphoma, the enlarged thymuses were examined as previously described [25]. In tumour-bearing mice the tumours were fixed in 10% buffered formalin, processed histologically, and stained with haematoxylin and eosin. The processed tumour specimens were evaluated by medical and veterinary pathologists using the Annapolis guidelines established by Cardiff and coworkers [26].
DNA isolation and genotyping
Normal and mammary carcinoma tissues were removed. Isolation of DNA, PCR amplification, electrophoresis of PCR products and assessment of allelic losses were performed according to a procedure described previously [24]. Genotypes for the wild-type and targeted alleles of p53 and Atm genes were determined by analyzing the PCR products for these alleles. Amplification for the p53 alleles was done as described elsewhere [27]. The wild-type and the targeted alleles of the p53 gene were amplified by PCR using primers p53-4F (5'-CGACCTCCGTTCTCTCTCCTCTCTT-3') and p53-6R (5'-AGACGCACAAACCAAAACAAAATTACA-3'), and primers p53-NF (5'-GCCTTCTATCGCCTTCTTGACGAGT-3') and p53-6R, respectively. Similarly, amplification of the wild-type and the targeted allele of the Atm gene were performed by using primers IMR0640F (5'-GCTGCCATACTTGATCCATG-3') and IMR0641R (5'-TCCGAATTTGCAGGAGTTG-3'), and primers IMR0640F and AtmNeo410R (5'-CGGTGGATGTGGAATGTGTG-3'), respectively.
Statistical analysis
Statistical significance was evaluated for the incidence of mammary carcinoma and number of carcinomas per mouse by χ2 analysis and Mann–Whitney U-test, respectively. Comparison of latency in mammary carcinoma development was examined by unpaired Student's t-test.
Results and discussion
Histopathological features of tumours developed in F1 mice doubly heterozygous for p53 and Atm null alleles
Mammary carcinomas occurred in BALB/c mice of the p53+/- genotype, and tumours of similar macroscopic morphologies were also observed in the (BALB/c × MSM/Ms)F1–p53+/- mice. The histological features of these tumours in the mammary glands are shown in Fig. 1a,1b,1c,1d,1e,1f, which indicates that they are adenocarcinomas. The histopathology of the tumours in nonirradiated mice (Fig. 1a,1b) exhibited more hyperplastic lesions than did those in irradiated mice (Fig. 1c,1d,1e,1f), but basically there were no marked differences between the two groups. There was also no remarkable difference in histopathological features between Atm+/- (Fig. 1c,1d) and Atm+/+ mice (Fig. 1e,1f) in the irradiated groups. These tumours formed glands lined by highly pleomorphic cells exhibiting frequent mitosis, and were classified as glandular adenocarcinomas, high grade according to the Annapolis Pathology Classification [26].
Lymphomas (Fig. 1g), mainly thymic lymphomas, were efficiently induced by exposure to X-rays, regardless of p53 and Atm genotype, although only a few lymphomas were observed in nonirradiated mice. Ovarian carcinomas, osteosarcomas (Fig. 1h) and hepatomas developed spontaneously. Squamous cell carcinomas, basal cell carcinomas, histiocytic sarcomas and granulocytic leukaemias were also observed in irradiated mice.
Spontaneous tumour development in mice with four genotypes for p53 and Atm
Twenty-eight p53+/- Atm+/-, 22 p53+/- Atm+/+, 11 p53+/+ Atm+/- and 25 p53+/+ Atm+/+ mice were examined for spontaneous development of tumours until age 26 months (113 weeks). Fourteen out of 28 p53+/- Atm+/- mice (50%) and seven out of 22 p53+/- Atm+/+ mice (32%) developed mammary carcinomas during the period of observation (Table 1, Fig. 2). The incidence of mammary carcinomas in p53+/- Atm+/- mice appeared to be higher than that in p53+/- Atm+/+ mice, but the incidences did not differ significantly between Atm+/- and Atm+/+ genotypes (P = 0.32, by Fisher's exact probability test). Among the tumours that developed in the p53+/- mice, mammary carcinoma were the most common. These mammary carcinomas were mainly observed at 41–77 weeks after birth (Fig. 2). No significant difference in latency in the nonirradiated groups was observed between doubly heterozygous mice and p53 singly heterozygous mice (P = 0.47, by unpaired Student's t-test). None of 11 p53+/+ Atm+/- mice and 25 p53+/+ Atm+/+ mice developed mammary carcinomas. Several lymphomas and a few other tumours developed in the four genotypes (Table 1). Thus, mammary carcinoma development depended strongly on p53 heterozygous deficiency in (BALB/c × MSM/Ms)F1 mice, and p53+/- mice of both Atm+/+ and Atm+/- genotypes developed mammary carcinoma.
Enhancement of mammary carcinogenesis in Atm heterozygous deficient mice by X-irradiation
To test the effect of haploinsufficiency of the Atm gene on mammary carcinogenesis after X-irradiation in p53+/- mice, 55 p53+/- Atm+/-, 61 p53+/- Atm+/+, 47 p53+/+ Atm+/- and 53 p53+/+ Atm+/+ mice (for a total of 216 mice) were exposed to X-rays (5 Gy) at age 5 weeks. Only one out of 53 p53+/+ Atm+/+ mice and none of 47 p53+/+ Atm+/- mice developed mammary carcinoma, indicating that almost all p53+/+ mice fail to develop mammary carcinomas, despite X-irradiation and irrespective of Atm gene status. In contrast, 32 out of 55 p53+/- Atm+/- mice (58%) and 19 out of 61 p53+/- Atm+/+ mice (31%) developed mammary carcinomas (Table 2, Fig. 2). The proportion of mice developing mammary carcinomas in the p53+/- Atm+/- group was significantly greater than that in the p53+/- Atm+/+ group (P = 0.0034, by χ2 test). A total of 52 mammary carcinomas developed in 55 p53+/- Atm+/- mice (average number of mammary carcinomas/mouse = 0.95), whereas 28 mammary carcinoma developed in 61 p53+/- Atm+/+ mice (average number of mammary carcinomas/mouse = 0.46; Table 2). The average number of mammary carcinomas per mouse in the p53+/- Atm+/- group was significantly greater than that in p53+/- Atm+/+ mice (P = 0.0052, by Mann–Whitney U-test). Thus, Atm heterozygous deficiency enhanced development of mammary carcinoma in irradiated p53 heterozygous knockout mice. Spring and coworkers [14] observed no tumours in Atm knockout (Atm+/-) heterozygous mice. Mice bearing a knockout allele of Atm and humans carrying a mutant allele of truncated type in the ATM gene have been shown not to have obviously elevated susceptibility to mammary carcinogenesis. Our findings show that heterozygosity for a null knockout allele of Atm enhances mammary carcinogenesis under p53+/- status, although the Atm mutation is not a dominant-negative type. Heterozygous deficiency of p53 might make clear the effect on mammary carcinogenesis of haploinsufficiency in the Atm gene.
These mammary carcinomas were observed significantly earlier (at 18–38 weeks after irradiation; i.e. 23–43 weeks of age) than in the nonirradiated group (age 41–75 weeks; Fig. 2). In particular, mammary carcinomas frequently developed 23–28 weeks after irradiation. The mean (± standard deviation) latency periods were 32.6 ± 4.8 and 29.8 ± 3.6 weeks in p53+/- Atm+/- and p53+/- Atm+/+ mice, respectively. Thus, X-irradiation at 5 Gy at age 5 weeks considerably shortened the latency period of mammary carcinoma development in these two groups with different genotypes. As shown in Tables 1 and 2, the incidences of mammary carcinoma in p53+/- Atm+/- mice were 58% (32 out of 55) and 50% (14 out of 28) in irradiated and nonirradiated groups, respectively; in p53+/- Atm+/+ mice the incidence in the irradiated group was 31% (19 out of 61) and that in the nonirradiated group was 32% (7 out of 22). The incidence of mammary carcinoma for each genotype was similar between irradiated and non-irradiated groups. Thus, irradiation may not elevate the incidence of the tumours.
Altogether, irradiation markedly hastened mammary carcinoma development in the p53+/- mice, in which mammary carcinomas developed spontaneously. Furthermore, irradiation also induced lymphomas, mainly thymic lymphomas, in all four genotypes of mice. The incidence of the lymphomas did not differ significantly among the four groups, with different genotypes for p53 and Atm genes. A high incidence of thymic lymphoma was observed in previous studies performed using p53 heterozygous deficient F1 mice [17,28]. In the present study an extremely high incidence of tumours, most of which were mammary carcinomas and thymic lymphomas, was observed in irradiated p53+/- Atm+/- mice.
Status of wild-type alleles of p53 and Atm in mammary carcinoma
The wild-type alleles of the p53 and Atm genes were examined in mammary carcinoma tissue from heterozygous mice. The wild-type p53 allele was lost in 25 (96%) out of 26 mammary carcinomas from irradiated p53+/- mice and in all of 15 mammary carcinomas from nonirradiated p53+/- mice, regardless of Atm gene status. Only one mammary carcinoma in irradiated p53+/+ Atm+/+ mice (Table 2) was found to retain the p53 wild-type allele. On the other hand, wild-type Atm allele was preserved in all of 17 mammary carcinomas from irradiated Atm+/- mice and in all of 10 mammary carcinomas from irradiated Atm+/+ mice, regardless of p53 gene status. Wild-type Atm allele was also retained in nine out of 10 mammary carcinomas from nonirradiated Atm+/- mice and in all of five mammary carcinomas from nonirradiated Atm+/+ mice. These results suggest that the homozygous loss of the p53 allele was a necessary condition for the development of mammary carcinomas, whereas the Atm null allele exhibited haploinsufficiency.
Haploinsufficiency for tumour development was reported for the Nbn knockout mice, the mouse homologue of NBS1. Heterozygosity for the Nbn knockout allele rendered the mice susceptible to tumour development, yet the Nbn wild-type allele was fully retained in all 12 tumours examined [29]. p27 heterozygotes are also predisposed to tumours in multiple tissues when challenged with irradiation or a chemical carcinogen, and in the developed tumours the remaining wild-type allele is neither mutated nor silenced, indicating that p27 is haploinsufficient for tumour suppression [30]. In that study, heterozygous Atm knockout enhanced mammary carcinoma development in p53-heterozygous deficient mice, but the effect of Atm deficiency was not as profound. A previous study showed that heterozygosity for the Atm knockout allele did not enhance tumour development but that dominant-negative type missense mutations in the Atm gene did [14]. In humans, heterozygosity of the truncation-type mutations, which represent the majority of Atm mutations that occur in humans, had no effect on carcinogenesis, suggesting that enhancement in tumourigenesis depends strongly on mutation type. However, in the present study we demonstrated that haploinsufficiency does occur for the Atm null allele in combination with heterozygous deficiency in the p53 gene. The p53 heterozygous deficient BALB/c mice, which developed mammary carcinomas early and efficiently, may represent a useful model for the study of effects of genes other than Atm on mammary carcinogenesis. F1 mice between different subspecies may also provide an experimental system for precise genome-wide allelotype analysis of genes that cooperate with p53.
Conclusion
Tumourigenesis is strongly enhanced in mice with homogeneous deficiency in the p53 or Atm gene. In the present study we tested the effect of haploinsufficiency of the Atm gene on mammary tumourigenesis after X-irradiation in p53+/- mice of the BALB/cHeA × MSM/Ms background. Singly heterozygous p53+/- mice X-irradiated (5 Gy) at age 5 weeks developed mammary carcinomas at around 25 weeks of age, and the final incidence of mammary carcinoma at 39 weeks was 31% (19 out of 61). Introduction of the heterozygous Atm alleles into the background of the p53+/- genotype significantly increased the incidence of mammary carcinomas to 58% (32 out of 55) and increased the average number of mammary carcinomas per mouse. However, it apparently did not change the latency of mammary carcinoma development. In nonirradiated mice, introduction of the Atm+/- allele into p53+/- mice also tended to increase spontaneous incidence of mammary carcinoma. In contrast, almost none of the p53+/+ mice developed mammary carcinoma, regardless of the Atm gene status and whether mice were subjected to irradiation. In almost all of the spontaneous and radiation-induced mammary carcinomas, the wild-type p53 allele was found to be lost whereas the wild-type Atm allele was retained, suggesting haploinsufficiency of the latter gene in mammary carcinoma development. Thus, doubly heterozygous mice represent a useful model system with which to analyze the interaction of heterozygous genotypes for p53, Atm and other genes and their effect on mammary carcinogenesis.
Abbreviations
PCR = polymerase chain reaction.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SU carried out design of the study, observed mammary carcinoma development and other diseases, and drafted a manuscript. KF prepared tissue specimens and conducted histopathological examinations (veterinarian). SI carried out DNA isolation and genotyping of mice. NM carried out X-irradiation of mice and statistical analysis. MT performed DNA isolation and genotyping of mice. DH carried out production of heterozygous deficient mice. CS participated in designing the study and discussion of data on mammary carcinoma development. SH contributed to histopathological examination. SI conducted histopathological examinations (medical doctor). ON participated in discussion of data and contributed to preparation of the final manuscript. MO carried out design of the study and X-irradiation, and wrote the final manuscript. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to Mr M Ikeda and Mr U Ujihara for their skilful animal care. This study was performed in part by Grants-in-Aid for scientific research No. 13480172 (to MO) and No. 14580571 (to NM) from the Ministry of Education, Science, Sports and Culture of Japan, and a Grant-in-Aid from the Japan Atomic Energy Research Institute under contract to the Nuclear Safety Research Association.
Figures and Tables
Figure 1 Histopathology of tumours in (BALB/cHeA × MSM/Ms)F1 mice with genotypes p53+/- Atm+/-, p53+/- Atm+/+ or p53+/+ Atm+/-. (a, b) Spontaneously developing mammary adenocarcinoma from p53+/- Atm+/- mouse. (c, d) Mammary adenocarcinoma from X-irradiated p53+/- Atm+/- mouse. (e, f) Mammary adenocarcinoma from X-irradiated p53+/- Atm+/+ mouse. (g) Osteosarcoma observed in nonirradiated p53+/- Atm+/- mouse. (h) Thymic lymphoma from p53+/+ Atm+/- mouse exposed to X-rays. Panels a, c and e: 40×. Panels b, d, f, g and h: 100×.
Figure 2 Cumulative incidence of mammary carcinomas in irradiated and nonirradiated (BALB/cHeA × MSM/Ms)F1 mice with p53+/- Atm+/- or p53+/- Atm+/+ genotype.
Table 1 Incidence of tumours developing spontaneously in (BALB/cHeA × MSM/Ms)F1 female mice that were heterozygously deficient for p53 and/or Atm genes
Genotypes p53+/- p53+/+
Atm+/- (n = 28) Atm+/+ (n = 22) Atm+/- (n = 11) Atm+/+ (n = 25)
Mammary carcinoma 14 (50%)a 7 (32%) 0 (0%) 0 (0%)
Lymphoma 3 (11%) 3 (14%) 2 (18%) 4 (16%)
Ovarian carcinoma 1 (4%) 1 (5%) 0 (0%) 1 (4%)
Osteosarcoma 1 (4%) 0(0%) 0 (0%) 0 (0%)
Hepatoma 1 (4%) 0(0%) 0 (0%) 0 (0%)
Number of mammary carcinomas/mouseb 14/28 (0.50) 7/22 (0.32) 0/11 (0.00) 0/25 (0.00)
aNumber of mice with tumours. bTotal number of mammary carconomas developing in mice with given genotypes/number of mice examined; numbers in parentheses are average numbers of tumours per mouse.
Table 2 Incidence of tumours in irradiated (BALB/cHeA × MSM/Ms) F1 female mice that were heterozygously deficient for p53 and/or Atm genes
Genotypes p53+/- p53+/+
Atm+/- (n = 55) Atm+/+ (n = 61) Atm+/- (n = 47) Atm+/+ (n = 53)
Mammary carcinoma 32 (58%)a* 19 (31%) 0 (0%) 1 (2%)
Lymphoma 24 (44%)b 29 (48%) 26 (55%) 22 (42%)
Squamous cell carcinoma 1 (2%) 1 (2%) 0 (0%) 0 (0%)
Histiocytic sarcoma 1 (2%)c 0 (0%) 0 (0%) 0 (0%)
Basal cell carcinoma 0 (0%) 1 (2%) 0 (0%) 0 (0%)
Granulocytic leukaemia 0 (0%) 1 (2%) 0 (0%) 0 (0%)
Ovarian carcinoma 0 (0%) 1 (2%) 0 (0%) 0 (0%)
Nonthymic lymphoma (NOS) 0 (0%) 1 (2%) 3 (6%) 0 (0%)
Solid tumour (NOS) 1 (2%)d 1 (2%)e 1 (2%) 1 (2%)
Number of mammary carcinomas/mousee 52/55 (0.95)** 28/61 (0.46) 0/47 (0.00) 1/53 (0.02)
aNumber of mice with tumours; percentage in parenthesis is the proportion of mice developing mammary carcinoma. bThree animals developed both lymphomas and mammary carcinoma. cOne animal developed both histiocytic sarcoma and mammary carcinoma. dTumour in abdomen. eTotal number of mammary carcinomas developing in mice with given genotypes/number of mice examined; numbers in parentheses are average numbers of tumours per mouse. *The proportion of mice developing tumours is significantly greater than that in p53+/- Atm+/+ mice (P = 0.0034 by c2 test). **The average number of tumours/mouse is significantly greater than that in p53+/- Atm+/+ mice (P = 0.0052 by Mann–Whitney U-test). NOS, not otherwise specified.
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| 15642165 | PMC1064114 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Dec 10; 7(1):R164-R170 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr968 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9691564216410.1186/bcr969Research ArticleLetrozole sensitizes breast cancer cells to ionizing radiation Azria David [email protected] Christel [email protected] Severine [email protected] Mahmut [email protected] Sophie [email protected] Pierre [email protected] Dean B [email protected] Gilles [email protected] Pascal [email protected]èlegrin Andre [email protected] Department of Radiation Oncology, CRLC Val d'Aurelle, Montpellier, France2 INSERM EMI0227, CRLC Val d'Aurelle, Montpellier, France3 INSERM U 540, Montpellier, France4 Department of Radiation Oncology, Lausanne, Switzerland5 Biostatistics Unit, CRLC Val d'Aurelle, Montpellier, France6 Centre for Pharmacology and Health Biotechnology, CNRS, Montpellier, France7 Novartis Pharma AG, Basel, Switzerland8 Department of Medical Oncology, CRLC Val d'Aurelle, Montpellier, France2005 7 12 2004 7 1 R156 R163 16 3 2004 28 6 2004 15 9 2004 2 11 2004 Copyright © 2004 Azria et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Radiotherapy (RT) is considered a standard treatment option after surgery for breast cancer. Letrozole, an aromatase inhibitor, is being evaluated in the adjuvant setting. We determined the effects of the combination of RT and letrozole in the aromatase-expressing breast tumour cell line MCF-7CA, stably transfected with the CYP19 gene.
Methods
Irradiations were performed using a cobalt-60 source with doses ranging from 0 to 4 Gy. Cells were incubated with androstenedione in the presence or absence of letrozole. Effects of treatment were evaluated using clonogenic assays, tetrazolium salt colorimetric (MTT) assays, and cell number determinations. Cell-cycle analyses were conducted using flow cytometry.
Results
The survival fraction at 2 Gy was 0.66 for RT alone and was 0.44 for RT plus letrozole (P = 0.02). Growth of MCF-7CA cells as measured by the cell number 6 days after radiotherapy (2 and 4 Gy) was decreased by 76% in those cells treated additionally with letrozole (0.7 μM) compared with those receiving radiotherapy alone (P = 0.009). Growth inhibition, assessed either by cell number (P = 0.009) or by the MTT assay (P = 0.02), was increased after 12 days of the combination treatment. Compared with radiation alone, the combination of radiation and letrozole produced a significant decrease in radiation-induced G2 phase arrest and a decrease of cells in the S phase, with cell redistribution in the G1 phase.
Conclusions
These radiobiological results may form the basis for concurrent use of letrozole and radiation as postsurgical adjuvant therapy for breast cancer.
breast cancerconcurrent treatmentletrozoleradiotherapy
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Introduction
The aromatase inhibitor letrozole has been shown to be superior to tamoxifen (TAM) in the first-line treatment of metastatic breast cancer [1,2]. A randomized clinical trial comparing these two drugs in the adjuvant setting is currently ongoing, and should soon provide insight into the relative efficacy of letrozole and TAM. Another adjuvant trial, the MA.17 study, evaluated patients from the National Cancer Institute of Canada Clinical Trials Group who were disease-free after initially being treated with 5 years of TAM and then being randomized to receive either 5 years of placebo or 5 years of letrozole treatment. As compared with placebo, letrozole therapy after the completion of TAM treatment significantly improved disease-free survival [3].
Postoperative radiotherapy (RT) decreases the risk of locoregional recurrence. RT is also associated with improved survival in high-risk premenopausal and postmenopausal women with breast cancer given either adjuvant chemotherapy [4] or TAM [5], respectively. Whether letrozole sensitizes breast cancer cells to RT has not been determined. The present study has examined this question in vitro, using a human breast cancer cell line stably transfected with the human aromatase gene. We present herein the capacity of letrozole to sensitize these cells to RT using clonogenic, MTT, and cell-count assays. Cell-cycle analyses showed a G1 phase arrest and a decrease of the S phase with letrozole as compared with control cells. Moreover, compared with RT alone, combined RT and letrozole produced a significant decrease in radiation-induced G2 phase arrest and in the number of cells in the S phase, with cell redistribution in the G1 phase.
Materials and methods
Cell line, culture, and letrozole
MCF-7 human breast cancer cells stably transfected with the human aromatase/CYP19 gene (MCF-7CA) were kindly provided by Dr S Chen (City of Hope, Duarte, CA, USA [6]). These cells were maintained in DMEM/NUT MIX F-12 containing 10% heat-inactivated FCS (Gibco Laboratories, Cergy Pontoise, France), 500 μg/ml geneticin (G418), 300 μg/ml glutamine, 0.25 μg/ml fungizone, 100 μg/ml streptomycin, and 100 units/ml penicillin G. The two cell lines, MCF-7 wild type and MCF-7CA, were adherent and grew as monolayers at 37°C in a humidified 5% CO2 incubator. The cells were harvested with 0.5 g/l trypsin (Gibco Laboratories) and 0.2 g/l EDTA (Gibco Laboratories) for 3 min. Cultures were checked for the absence of mycoplasma every month.
Steroids were removed from the FCS by two treatments with dextran-coated charcoal (DCC) [7]. Cells were cultured as monolayers in 60-mm Petri dishes with phenol-red-free medium and DCC FCS.
Letrozole used in the study was synthesized in the laboratories of Novartis Pharma AG (Basel, Switzerland). In all experiments, letrozole was added to cultures that were nonconfluent at concentrations of 7 nM–0.7 μM in 10 μl ethanol 6 days before RT.
Aromatase tritiated water assay
Aromatase activity was determined using the 3H2O release method reported by Zhou and colleagues [6], based on the procedure described by Thompson and Siiteri [8]. Cells were maintained for 5 days in 10% FCS phenol-red-free medium and then seeded at 700,000 cells per well in six-well dishes with fresh DCC-treated medium. Three days later, culture plates were washed twice with PBS. One millitre of serum-free medium containing 40 nM 1β-3H(N)-androst-4-ene-3,17-dione (NET-926; NEN PerkinElmer Life Sciences, Inc., Courtaboeuf, France) as substrate (specific activity, 25.9 Ci/mmol) with or without 100 nM letrozole was then added to each well. After 6 hours of incubation at 37°C, the reaction mixture was removed and extracted with two volumes of chloroform to terminate the reaction and to extract unused substrate and steroids. After 5 min centrifugation at 100 × g, the aqueous phase was then treated with an equal volume of 5% charcoal suspension to eliminate residual steroids. After 5 min centrifugation at 15,000 × g, radioactivity was assessed by liquid scintillation counting. The protein concentration was determined by the Bradford method [9] after cell extraction with Reporter Lysis 5X Buffer according to the manufacturer's instructions (PROMEGA Corp., Lyon, France). Aromatase activity was then calculated from the disintegrations per minute as picomoles of oestrogen produced by milligrams of protein per hour
Radiation modalities
Cells were plated in 10 ml DCC and phenol-red-free medium (to ensure homogeneous energy deposition within each dish using 60-mm Petri dishes) and irradiated with a cobalt-60 source (γ irradiation, ELITE 100; Theratronics, Ottawa, ON, Canada) in the Radiation Department. The radiation was delivered as a single dose ranging from 0 to 4 Gy in an 11 cm × 11 cm field size at a dose rate of 0.5 Gy/min. A 3-cm polystyrene block was used under the Petri dishes during each irradiation to allow homogeneous backscattering γ-rays. The source-half-depth distance was initially calculated to obtain a constant dose rate of 0.5 Gy/min, and was adapted monthly to the cobalt-60 source radioactivity decay. Control cells were removed from the incubator and were placed for the same period of time under the cobalt-60 source but without RT. In the combined treatment modality studies, letrozole was added 6 days prior to RT.
Cytotoxicity assays
Cultures were trypsinized and washed, and cells were plated in quintuplicate at a density of 100 cells per 60-mm Petri dish after dilution. Letrozole was added at concentrations ranging from 7 nM to 0.7 μM 24 hours after the cells were plated to allow for cell attachment. Cells were incubated at 37°C in a humidified chamber containing 5% CO2 for 12 days. The colonies were then fixed with a 1:3 (v/v) acetic acid methanol solution and were stained with 10% Giemsa (Sigma Chemical Co., St Louis, MO, USA); colonies of greater than 50 cells were scored. The plating efficiency was calculated with and without letrozole.
The cytotoxicity of RT against asynchronous, exponentially growing cells was also determined from colony formation assays. Before irradiation, the cell density was determined using appropriate dilutions (100, 100, 200, 300, and 400 cells for 0, 1, 2, 3, and 4 Gy, respectively), and cells were plated onto five replicate 60-mm Petri dishes. Cells were irradiated as already described 24 hours after plating to allow for cell attachment before the administration of RT. The letrozole-containing medium was given at a concentration of 0.7 μM 6 days before RT. Cultures were irradiated with the drug present in the medium, and were immediately returned to the incubator after irradiation. Colonies were counted after 12 days.
Experimentally derived data points are the mean of five experiments. The dose–response curves were fitted to a four-parameter model, where the response R varies with the dose D according to the equation: R = a/[1 + (D/b)c] + R∞, where a is the difference between the maximum and minimum response, b is the concentration of drug needed to obtain 50% of the maximal effect, c is a slope factor, and R∞ is the maximal effect. The multitarget model survival curves were fitted to the data using a least-squares regression to the linear-quadratic model: S = S0 exp(-αD1 - βD12), where D1 is the radiation dose, S is the surviving fraction, and S0 is a normalizing parameter.
MTT and cell number assays
The antiproliferative effect of letrozole with or without RT on the MCF-7CA cell line was evaluated using the MTT assay as described by Mosmann [10]. Briefly, exponentially growing cells were seeded into 96-well plates and were incubated in medium containing letrozole from 7 nM to 0.7 μM for 48 hours at 37°C. Duplicate plates containing six replicate wells/assay condition were seeded in 0.1 ml medium at densities of 5000, 15,000, and 35,000 cells per well, and were then treated with letrozole alone, with RT (2 Gy) ± letrozole (0.7 μM), and with RT (4 Gy) ± letrozole (0.7 μM), respectively. Twelve days after letrozole ± RT (2 or 4 Gy) treatment, the viability of the cells was analysed using the tetrazolium salt (Sigma) MTT colorimetric assay. Briefly, 50 μl of a 0.5% MTT solution was added to each well, followed by incubation for 4 hours at 37°C to allow MTT metabolization. The crystals formed were dissolved by adding 100 μl/well isopropylic alcohol, 1 N HCl. The absorbance at 540 nm was measured on a Microtiter® Plate Reader MRX (Dynatech Laboratories, Chantilly, VA, USA). The results were expressed with respect to control values (cells without any treatment).
The antiproliferative effect of letrozole ± RT on the MCF-7CA cell line was also evaluated using a cell-count assay. MCF-7CA cells were allowed to grow for 24 hours, at which time letrozole was added at a concentration of 0.7 μM. RT (2 and 4 Gy) was delivered to those cells receiving the combination treatment 6 days after the addition of letrozole to the medium. Cells were counted every 6 days (days 6, 12, and 18) over an 18-day period after removing the cells from the plates with 0.5 g/l trypsin.
Flow cytometry
Cells were plated in 60-mm Petri dishes at a density of 2 × 106 cells/dish. Treatment consisted of letrozole (0.7 μM) alone, RT (2 and 4 Gy) alone, or letrozole (0.7 μM) plus RT (2 and 4 Gy). Cells were collected 15 days after plating, and were processed for cell-cycle analysis. Cells were harvested by trypsinization, were washed with PBS, and then 1 × 106 cells/dish of treated cells were fixed in 70% ethanol for 2 min. After removal of ethanol by centrifugation, cells were then stained with a solution containing 40 μg/ml propidium iodide (Sigma) and 0.1 mg/ml RNase A (Roche, Indianapolis, IN, USA). Stained nuclei were analysed for DNA-PI fluorescence using a Becton-Dickinson FACScan flow cytometer (Mountain View, CA, USA). Resulting DNA distributions were analysed using the CellQUEST software (Becton Dickinson) for the proportion of cells in the sub-G0 phase, the G1 phase, the S phase, and the G2–M phase of the cell cycle.
In a second series of experiments, cells were treated with letrozole (0.7 μM) alone and were then cultured for 21 days. Cells were stained at different time points up to 21 days and were analysed for DNA content on a FACScan as already described.
Statistical analysis
The nonparametric Wilcoxon signed-rank test was used to compare the surviving fraction for each dose between the two groups (RT alone and RT plus letrozole). All experiments were performed three times and the results are expressed as the mean ± standard error. All statistical tests were two-sided with an alpha level of 0.05. Data were analysed using the STATA 7.0 software (Stata Corporation, College Station, TX, USA).
Results
Letrozole inhibits MCF-7CA aromatase activity
The aromatase activity was induced by the androgenic substrate Δ4 androstenedione at a 40 nM concentration. The effects of letrozole on aromatase activity in cultured breast cancer cells are shown in Fig. 1. In the MCF-7CA transfected cell line, letrozole (100 nM) markedly inhibited aromatase activity.
Letrozole inhibits MCF-7CA proliferation
The cytotoxic effects of several concentrations of letrozole (7 nM–0.7 μM) on asynchronous, exponentially growing MCF-7CA cells were determined by cell-count assay. Letrozole at a concentration of 7 nM did not inhibit the growth of MCF-7CA cells during 18 days of incubation, whereas 0.7 μM resulted in about 50 ± 10% inhibition after 6, 12, and 18 days of incubation (Fig. 2). No effect of letrozole was seen on MCF-7 wild-type cells.
Letrozole enhances radiosensitivity
Cell survival following RT in aerated medium fits a linear quadratic model, as described in Materials and methods. Figure 3 depicts normalized results from clonogenic experiments. The survival fraction at 2 Gy was 0.66 ± 0.05, and the D0 value (dose of radiation producing a 37% survival rate) was 3.2 Gy when irradiation was used alone. Letrozole added 6 days before RT led to a greater decrease of the surviving fractions as compared with those obtained when letrozole was added 3 days before RT, when letrozole was added 3 days after RT or when RT was delivered alone. Treatment at 6 days before RT led to a survival fraction at 2 Gy and a D0 value of 0.46 ± 0.05 and 2.2 Gy, respectively. The survival fraction at 2 Gy was thus nearly 30% (±4%) lower for the combination treatment, with a significant test result (P = 0.02). When the data were analysed according to the linear quadratic model, the α and β components were, respectively, nearly 0/Gy and 0.098 ± 0.0038/Gy2 without letrozole, and were nearly 0/Gy and 0.154 ± 0.014/Gy2 for the combination treatment. These data indicate that treatment with letrozole results in a steeper decline in cell survival due both to a higher initial slope of the dose–response curve and to a major decrease of the quadratic parameter. These results thus show possible additive effects for the combined treatment.
Growth of MCF-7CA cells measured by MTT assay was inhibited to a 40 ± 6% greater extent with letrozole plus 2 Gy RT, and to a 76 ± 3% greater extent with letrozole plus 4 Gy RT, compared with radiation alone (P = 0.02) (Fig. 4).
Growth of MCF-7CA cells measured by cell-count assay was determined for an 18-day period after initial treatment. It was inhibited to a 76 ± 2% greater extent after 12 days, and to an 85 ± 4% greater extent after 18 days, for letrozole treatment plus 2 or 4 Gy RT compared with RT alone (P = 0.009) (Fig. 5).
Letrozole induces G1 cell-cycle arrest
The effect of letrozole treatment on cell-cycle phase distribution in the MCF-7CA cell line was evaluated using flow cytometry (Fig. 6). Treatment with 0.7 μM letrozole for 6 days induced accumulation of cells in the G1 phase (77.5 ± 1.5%), with a significant decrease in the percentage of cells in the S phase (9.0 ± 1%) relative to controls (20.4 ± 0.7%). No cells with subdiploid DNA content were observed, demonstrating that letrozole does not induce apoptosis in these cell lines.
After 6 days of treatment, cells were further cultured for 21 days in the presence of letrozole. A G1 cell-cycle arrest (nearly 70%) was observed at days 12 and 21. One day after RT alone, we observed a cell-cycle arrest in the G2 phase (32 ± 0.3%) with a decrease in the percentage of cells in the G0/G1 phase and the S phase as compared with the control (59 ± 0.8% versus 63 ± 1.2%, and 9 ± 0.4% versus 20.4 ± 0.7%, respectively). When letrozole was added 6 days before RT, the radiation-induced G2/M phase arrest decreased compared with RT alone (21 ± 0.6% versus 32 ± 0.3%, respectively), with a letrozole-induced G0/G1 phase blockade and a very low proportion of cells in the S phase.
Discussion
TAM is the most widely used endocrine agent for the adjuvant therapy of early breast cancer in postmenopausal women [11]. TAM use nevertheless remains limited, most notably by its recognized pharmacological properties and side effects. For instance, TAM has been associated with an increased risk of both thromboembolic events and endometrial changes, including endometrial cancer [12,13]. It is possible, therefore, that other endocrine drugs may provide at least equivalent, if not superior, efficacy together with improved tolerability in patients with early disease. In this context, the third-generation aromatase inhibitors are now under investigation, and the initial results may substantially change our current clinical practice patterns [3,14,15].
The efficacy of breast-conserving surgery with axillary dissection and breast radiation has been known for a long time [16]. Overgaard and colleagues more recently demonstrated that postoperative radiotherapy decreases the risk of locoregional recurrence, and that it is associated with improved survival in high-risk patients with breast cancer given either adjuvant chemotherapy (in premenopausal women) [4] or tamoxifen (in postmenopausal women) [5].
In the present study, we wanted to evaluate in vitro the effects of the combination of ionizing radiation and letrozole, one of the third-generation aromatase inhibitors. To our knowledge, our results demonstrate for the first time that letrozole could act as a potent radiosensitizer. A significant effect was observed in all three assays used in our experiments (clonogenic, MTT, and cell count). The effects of associating other hormonotherapies, such as TAM, with radiotherapy have been rarely described and remain poorly defined. TAM appears to exert its cytostatic activity at least partly through competitive inhibition of oestrogen binding at the oestrogen receptor, resulting in segregation of cells into the G0/G1 phase of the cell cycle [17]. Because relatively less radiosensitivity was observed in the early G1 phase [18], a hypothetical concern was raised in several studies that the use of TAM during RT might result in the radioprotection of tumour clonogens of both hormonally responsive and unresponsive breast carcinoma cells at dose levels typically used in clinical practice [19]. In these studies, however, cell cultures were grown in a medium containing phenol red and foetal bovine serum, two sources of exogenous estrogenic compounds [20-23]. This fact complicates the interpretation of the resultant radiation survival curves [24]. In contrast to these reports, no significant differences were observed in radiosensitivity for oestradiol-stimulated or 4-hydroxytamoxifen-inhibited cultures plated into growth-stimulating conditions immediately after irradiation or following an additional 24 hours in oestrogen-free conditions [25]. Clearly, under defined hormonal conditions [26], no protective effect of the active TAM metabolite 4-hydroxytamoxifen was observed [25].
Our data suggest that letrozole inhibits cell proliferation by invoking a transition delay or by blocking in the G1 phase of the cell cycle. This delay may require more than one complete passage of some cells through the cell cycle, since cell numbers more than doubled before the plateau growth phase was reached. In addition, two to three generation times were required for maximal accumulation of cells in the G1 phase. We therefore added letrozole 6 days before RT. Under defined hormonal conditions, we found more than an additive effect [27] using the combination of letrozole and RT at each dose tested. This result was reinforced by the poor effect of letrozole used alone at high concentration (7 μM) and tested in all combination treatments. Isobologram analyses are planned to confirm the existence of either an additive effect or a supra-additive effect for the association of these two treatment modalities in a future study, as proposed by Steel [28].
A small percentage of cells was insensitive to letrozole, and remained in the proliferative fraction (9% in the S phase). The mechanism of unresponsiveness of these cells to letrozole will require further study. As shown with anti-oestrogens [17], these data are consistent with the hypothesis that aromatase inhibitors such as letrozole induce regression of breast cancer in vivo by simply blocking progression of cells through the cell cycle rather than by a direct cytotoxic effect. With cell replication inhibited, tumours might then regress because of ongoing cell shedding or death, or because of interaction with normal host defences.
In the RT–letrozole combination treatment, we observed a 50% decrease of MCF-7CA cells arrested in the G2 phase as compared with RT alone, a proportional redistribution in the G1 phase, and an interrupted synthesis phase for an 18-day period. This cell-cycle redistribution phenomenon may also explain the decrease in the surviving fraction in the combination treatment presented in the present study (Fig. 3).
In addition, associating irradiation and letrozole may modify the cytosolic oestrogen and progesterone receptor content in breast cancer cells, which might explain the observed changes in these cells' radiation sensitivity during hormonal treatment, as published with TAM [29].
These results may have important clinical implications for the treatment of breast cancer. Since the adjuvant use of aromatase inhibitors is still an emerging treatment option [3,14,15], the most effective time at which to commence the use of these drugs after surgery is not yet known. Also, if letrozole is simply placing hormone-responsive breast cancer cells into a 'resting' phase of the cell cycle (G0–G1 phase), then treatment of patients after surgery for primary breast cancer with adjuvant letrozole may have to be continued at least long enough for host defences or normal cell attrition to eradicate residual tumour cells. Premature discontinuation of therapy might lead to tumour regrowth. Finally, where letrozole is used concurrently with RT, we recommend starting letrozole at least 3 weeks before the start of RT in an attempt to obtain an optimal effect, consistent with the biological rationale described earlier. Furthermore, and in so far as the preclinical data with breast cancer cells can be extrapolated to the clinical situation, a radiosensitizing effect may be observed in the healthy breast. Nevertheless, Miller and colleagues showed a rising aromatase activity level in adipose tissue obtained from patients with breast cancer and within the quadrant involved with the tumour [30-32].
Conclusions
These results are the first preclinical findings demonstrating the radiosensitization effect of letrozole, and thus may provide a basis for the use of combined letrozole and radiation for the adjuvant therapy in breast cancer patients.
Abbreviations
DCC = dextran-coated charcoal; DMEM = Dulbecco's modified Eagle's medium; FCS = foetal calf serum; MTT = 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; PBS = phosphate-buffered saline; RT = radiotherapy; TAM = tamoxifen.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DA initiated the project and developed all first experiments. CL confirmed all previous results and performed all FACS analyses. SC and PP performed the aromatase activity assay. MO and SG made the statistical analyses. PM constructed survival curves of the clonogenic assay. DA, DBE, GR, and AP managed scientific experiments and contributed to the elaboration of the discussion.
Acknowledgements
The authors thank Ms G. Heinz and Mr P. Delard for their excellent technical assistance and Mr M. Betz for his excellent editorial assistance.
Figures and Tables
Figure 1 Aromatase activity in MCF-7 wild-type (wt) and MCF-7CA transfected cell lines. Aromatase activity was measured by the tritiated water assay. Treatment with letrozole inhibited the 40 nM 1β-3H-androstenedione substrate conversion. Open bars, filled bars, and grey bars represent background radioactivity, aromatase activity without treatment, and aromatase activity after treatment with letrozole (100 nM), respectively.
Figure 2 Growth inhibition with letrozole alone. (a) Seven nanomolar letrozole did not inhibit the growth of MCF-7CA cells during 18 days of incubation, whereas (b) 0.7 μM letrozole resulted in about 50% inhibition after 6, 12, and 18 days of incubation. Results are from cell-count assays.
Figure 3 Potentiation of radiation-induced growth inhibition by letrozole measured by clonogenic assay. With radiation alone the MCF-7CA cell survival fraction decreased in a dose-dependent manner, which was significantly potentiated by the addition of 0.7 μM letrozole. For 2 Gy radiation, the surviving fraction was 0.66 with radiation alone and was 0.46 with the addition of letrozole (P = 0.02). For 3 Gy radiation, the corresponding surviving fractions were 0.4 and 0.18, respectively (P = 0.02).
Figure 4 Potentiation of radiation-induced growth inhibition by letrozole measured by the MTT assay. Growth of MCF-7CA cells, measured 6 days after treatment, was inhibited to a 40% greater extent with letrozole plus 2 Gy radiation, and to a 76% greater extent with letrozole plus 4 Gy radiation, compared with radiation alone.
Figure 5 Potentiation of radiation-induced growth inhibition by letrozole measured by cell-count assay. Growth of MCF-7CA cells, measured for 18 days after treatment, was inhibited to a 76% greater extent with letrozole plus 4 Gy radiation after 12 days, and to an 85% greater extent after 18 days, compared with radiation alone. Solid lines, ■ and ◆ represent radiation alone at 2 Gy and 4 Gy, respectively; dotted lines, ■ and ◆ represent combination of radiation plus letrozole (0.7 μM) at 2 Gy and 4 Gy, respectively.
Figure 6 Effect of letrozole (0.7 μM) or/and radiotherapy (RT) on MCF-7CA cell-cycle progression. (a) Control cells were compared with (b) MCF-7CA cells harvested after exposure to letrozole. In the case of RT treatment, cells were harvested after RT (c) without or (d) with letrozole. Cells were fixed and stained with propidium iodide for flow cytometry analysis as described in Materials and methods. Percentages of the G0/G1 phase, the S phase, and the G2/M phase were determined by CellQUEST analysis software on the basis of DNA content of the histogram. Data represent mean values of duplicate samples. Similar results were obtained in replicate experiments.
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| 15642164 | PMC1064115 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Dec 7; 7(1):R156-R163 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr969 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9711574349710.1186/bcr971Research ArticleThe androgen receptor CAG repeat polymorphism and modification of breast cancer risk in BRCA1 and BRCA2 mutation carriers Spurdle Amanda B [email protected] Antonis C [email protected] David L [email protected] Nirmala [email protected] Livia [email protected] Xiaoqing [email protected] Susan [email protected] Margaret R [email protected] Paula L [email protected] David M [email protected] Beth [email protected] Gillian S [email protected] Carmel [email protected] Melissa C [email protected] Graham G [email protected] John L [email protected], EMBRACE Study Collaborators, ABCFS, AJBCS Chenevix-Trench Georgia [email protected] Douglas F [email protected] Queensland Institute of Medical Research, Brisbane, Australia2 Cancer Research UK Genetic Epidemiology Unit, University of Cambridge, Cambridge, UK3 School of Public Health, Queensland University of Technology, Brisbane, Australia4 University of Melbourne, Melbourne, Australia5 The Cancer Council Victoria, Carlton, Australia2005 16 12 2004 7 2 R176 R183 24 6 2004 29 7 2004 5 10 2004 11 11 2004 Copyright © 2004 Spurdle et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
The androgen receptor (AR) gene exon 1 CAG repeat polymorphism encodes a string of 9–32 glutamines. Women with germline BRCA1 mutations who carry at least one AR allele with 28 or more repeats have been reported to have an earlier age at onset of breast cancer.
Methods
A total of 604 living female Australian and British BRCA1 and/or BRCA2 mutation carriers from 376 families were genotyped for the AR CAG repeat polymorphism. The association between AR genotype and disease risk was assessed using Cox regression. AR genotype was analyzed as a dichotomous covariate using cut-points previously reported to be associated with increased risk among BRCA1 mutation carriers, and as a continuous variable considering smaller allele, larger allele and average allele size.
Results
There was no evidence that the AR CAG repeat polymorphism modified disease risk in the 376 BRCA1 or 219 BRCA2 mutation carriers screened successfully. The rate ratio associated with possession of at least one allele with 28 or more CAG repeats was 0.74 (95% confidence interval 0.42–1.29; P = 0.3) for BRCA1 carriers, and 1.12 (95% confidence interval 0.55–2.25; P = 0.8) for BRCA2 carriers.
Conclusion
The AR exon 1 CAG repeat polymorphism does not appear to have an effect on breast cancer risk in BRCA1 or BRCA2 mutation carriers.
ARBRCA1BRCA2modifier
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Introduction
A CAG length polymorphism within exon 1 of the androgen receptor (AR) gene encodes a string of 9–32 glutamines. Even within this normal range, CAG repeat number is inversely associated with AR-mediated transcriptional activation in vitro [1,2]. Involvement of the AR in breast tumourigenesis is suggested by the existence of inactivating germline mutations in the hormone-binding domain in male breast cancer patients [3,4], and by splice variants that disrupt the transactivation domain in female breast tumours and tumour cell lines [5].
There is evidence that suggests an association between longer AR CAG repeat length – representative of less active AR – and breast cancer risk at the population level (for review, see Lillie and coworkers [6]). Using slightly variable definitions of shorter and longer allele size across studies, one study reported a significant twofold increased risk for breast cancer, another three studies reported a slightly increased risk for breast cancer (1.2- to 1.4-fold), and another reported a 1.7-fold increased risk limited to individuals with a first-degree family history of breast cancer [6].
Several studies have been undertaken to assess the role of the CAG repeat polymorphism as a modifier of breast cancer risk in BRCA1 and BRCA2 mutation carriers. The hypothesis-generating study [7] examined AR CAG length in 304 female BRCA1 mutation carriers (54% with breast cancer), and assessed breast cancer risk associated with CAG length as a continuous variable, and at a number of different cut-points. That study reported a 1.8-fold relative risk (95% confidence interval [CI] 1.1–3.1; P = 0.03) among the small subgroup of women with at least one AR allele of 28 or more CAG repeats in length, with relative risks of 2.6 (95% CI 1.5–4.7; P < 0.001) and 4.5 (95% CI 1.3–15.2; P = 0.02) for the cut-points ≥ 29 CAG repeats and ≥ 30 CAG repeats, respectively. Subsequent studies have reported conflicting results. A relative risk of 1.1 (95% CI 0.5–2.6) was observed for women with 28 or more CAG repeats from a pooled analysis of 188 BRCA1 and BRCA2 mutation carriers [8], whereas a study of 227 BRCA1 and BRCA2 mutation carriers reported shorter mean CAG repeat number in women with breast cancer diagnosed before age 42 years as compared with those diagnosed after this age [9].
It has been hypothesized that AR alleles with decreased transactivation might act directly to result in decreased breast cell proliferation, or possibly indirectly via an endocrine or paracrine mechanism whereby altered levels of circulating or stromal hormones might affect mammary epithelial growth [7]. Subsequent biochemical studies reported a protein–protein interaction between the AR protein and particular regions of both the BRCA2 and BRCA1 proteins. BRCA2 has been reported to enhance androgen-dependent AR activity [10]. Although that study also reported that an expressed truncated BRCA2 protein encoded by the BRCA2 L1042X mutation failed to enhance AR transactivation [10], it did not examine the effect of BRCA2 mutation position in general, or which domains of BRCA2 are required for the BRCA2–AR interaction.
BRCA1 has also been reported to enhance androgen-dependent AR transactivation [11,12]. Mammalian two-hybrid assays have shown that BRCA1 amino acids 231–1314 and 1560–1863 are responsible for this direct interaction, with BRCA1 region 758–1064 observed to bind specifically to the AR amino-terminal domain containing the glutamine repeat [11]. Androgen response assays indicated that, although BRCA1 mutations across the gene all reduce AR activity enhancement as compared with the wild-type, the effect was more marked for mutations up to amino acid 1365 [11]. This was particularly true for a mutation that caused truncation at amino acid 772 within the BRCA1–AR amino-terminus interaction domain, which had 20% activity relative to the wild-type BRCA1. The effect of the AR CAG repeat length on BRCA1–AR interactions has not yet been investigated. However, molecular studies provide some support for a biochemical interaction between AR CAG repeat length and BRCA1 mutation status, in that the decreased AR transactivation observed in vitro with increasing glutamine length was only observed in the absence of coexpressed BRCA1 [12].
These data suggest that an association of AR CAG repeat length with increased breast cancer risk may be found only in BRCA1 or BRCA2 mutation carriers (and not individual germline without BRCA1 or BRCA2 mutations), and that AR-dependent modification of cancer risk in BRCA1 and BRCA2 mutation carriers may differ according to which gene is mutated, and the mutation position relative to AR-binding site. To evaluate further the evidence for an association between AR CAG repeat length and breast cancer risk in BRCA1 and BRCA2 mutation carriers, we genotyped the polymorphism in a large series of female mutation carriers.
Methods
Subjects
The distribution of samples according to source, gene and cancer status is shown in Table 1.
A total of 604 living female Australian and British carriers of pathogenic BRCA1 or BRCA2 mutations were identified in 376 families from the following sources: the Epidemiological study of BRCA1 and BRCA2 Mutation Carriers (EMBRACE; ), the Kathleen Cuningham Consortium for Research into Familial Breast Cancer (kConFaB; ), the Australian Jewish Breast Cancer Study (AJBCS) [13], and the Australian Breast Cancer Family Study (ABCFS) [14,15]. EMBRACE recruits participants from among women and men referred for genetic testing at clinical genetics centres in the UK and Eire. kConFaB recruits participants from multiple-case breast and ovarian cancer families referred for genetic testing at family cancer clinics in Australia and New Zealand. AJBCS recruits Ashkenazi Jewish women reporting a personal or family history of breast or ovarian cancer in a first- or second-degree relative, and living in Melbourne or Sydney, Australia. Finally, ABCFS is a population-based case–control-family study that includes women with a first primary breast cancer recruited through the Victorian and New South Wales cancer registries, and their affected and unaffected relatives. Apart from index cases recruited through cancer registries for the ABCFS, the cancer status of participants was based on self-report.
For samples recruited through EMBRACE, a pathogenic mutation was defined as an established disease-causing mutation under the classification scheme used by Breast Cancer Information Core . For samples recruited through kConFaB, AJBCS and ABCFS, mutations were classified as pathogenic according to the criteria established by kConfab . Specifically, the criteria specify the following as being pathogenic: all truncating mutations, unless there is clear evidence that the mutation is a single nucleotide polymorphism (e.g. terminal BRCA2 variant); and any variant that is well characterized in family studies of multiple generations, and not found in control individuals, that results in a nonconservative amino acid substitution, and occurs in a residue conserved across species and in a functional domain. All mutations included in the study that were shared across sites were classified as pathogenic according to both routes of definition.
Within Australia, ethical approvals were obtained from the ethics committees of the Peter MacCallum Cancer Institute, The Prince of Wales Hospital, The University of Melbourne, The Cancer Council New South Wales, The Cancer Council Victoria and the Queensland Institute of Medical Research. Ethical approval for the EMBRACE study was obtained from the Eastern Multicentre Research Ethics Committee and the relevant local ethics committees. Written informed consent was obtained from each participant.
Molecular methods
The AR exon 1 CAG repeat length was measured by fluorescent polymerase chain reaction PAGE methodology, using the ABI Prism 373 Genescan (Applied Biosystems, Foster City, CA, USA) and Genotyper systems (Applied Biosystems). Details of this method were previously reported [16]. Genotyping was successful for 375 out of 382 (98%) BRCA1 carriers, 218 out of 221 (99%) BRCA2 carriers, and the single carrier of both a BRCA1 and a BRCA2 mutation.
Statistical methods
Individuals with a first diagnosis of primary invasive breast cancer were considered to be affected, whereas individuals with no reported breast or ovarian cancer were censored at age at interview. Individuals with a first diagnosis of primary ovarian cancer were censored as unaffected at age at onset of ovarian cancer, and selected analyses were also performed in which individuals with a first primary diagnosis of ovarian cancer were excluded. All individuals were censored at age of prophylactic mastectomy. Individuals reporting prophylactic surgery included a single BRCA2 carrier who was subsequently diagnosed with multiple breast cancers 4 and 5 years after surgery, and an additional 12 unaffected individuals (7 BRCA1 and 5 BRCA2 carriers) with surgery 1–11 years before interview (average 3 years). Prophylactic oophorectomy of affected and unaffected individuals was controlled for by adjustment as a time-dependent covariate, as described below.
Linear regression was used to assess the association of AR CAG repeat length (smaller allele size, larger allele size and average allele size) with potential confounders within the subset of 364 BRCA1 carriers and 209 BRCA2 carriers for whom information was available. The potential confounders included year of birth (categorized into subgroups 1910–1949, 1950–1959 and 1960–1979), age at menarche (categorized as ≤ 11, 11.5–12, 12.5–13, 13.5–14 or ≥ 14.5 years), oral contraceptive pill use (ever/never) and parity (categorized as 0 or ≥ 1 live births before censored age). Questionnaire information was available from participants on age at first and last live birth, but not age at each live birth. Hence, it was not possible to assess association with parity as an absolute number of live births before censored age, but rather only as a never/ever variable. Associations were assessed separately for affected and unaffected women.
The primary analyses of association between AR genotype and disease risk were performed using Cox regression with time to breast cancer onset as the end-point. AR CAG repeat length was defined as follows: a binary variable, defined by cut-points investigated in the hypothesis-generating study conducted by Rebbeck and coworkers [7] (namely one or more allele of ≥ 28 CAG repeats, ≥ 29 CAG repeats, or ≥ 30 CAG repeats); or a continuous variable, using the length of the smaller of the two alleles (AR small CAG), the larger of the two alleles (AR large CAG), and the average length of a participant's two alleles (AR average CAG). Rate ratios (RRs) and 95% CIs were estimated with adjustment for source group (as indicated in Table 1) and ethnicity (non-Jewish Caucasian, Jewish, other). Analyses were complicated by the fact that more than one mutation carrier could come from the same family and could not therefore be considered independent. Standard Cox regression provides unbiased RR estimates but their standard errors and CIs are incorrect. This was rectified by computing the confidence limits for the RRs using Huber's sandwich estimator of the covariance matrix [17]. This allows for variation between carriers from the same family without modelling their dependence explicitly. Further analyses adjusted for oophorectomy and parity as time-dependent covariates, and for age at menarche, oral contraceptive pill use and year of birth. Oophorectomy before censored age at interview or diagnosis of breast cancer was reported by 39 BRCA1 carriers (10 with primary breast cancer) and 21 BRCA2 carriers (9 with primary breast cancer) with genotype information available.
RRs were estimated separately for BRCA1 and BRCA2 carriers, including in both analyses the single individual with a mutation in both genes. Models adjusting for only group and ethnicity included all individuals with genotype information, namely 376 BRCA1 carriers (with 200 events) and 219 BRCA2 carriers (with 122 events). The sample size for BRCA1 carriers with a putative risk allele was 28 (14 events) for the ≥ 28 CAG cut-point, 26 (13 events) for the ≥ 29 CAG cut-point, and 11 (4 events) for the ≥ 30 CAG cut-point. Similarly, for BRCA2 carriers it was 17 (10 events) for the ≥ 28 CAG cut-point, 14 (7 events) for the ≥ 29 CAG cutpoint, and 11 (5 events) for the ≥ 30 CAG cut-point. Full models adjusting for year of birth and additional hormonal variables included the 364 BRCA1 and 219 BRCA2 carriers with full information on potential confounders, comprising 193 and 116 events, respectively.
In addition, analyses were carried out separately for subgroups of BRCA1 and BRCA2 carriers defined by mutation position in relation to proposed AR-binding domains, and/or in vitro data regarding mutation effect on AR transactivation [10,11]. For BRCA1, subgroups were defined by the mutation position either relative to amino acid 1365 (< or ≥ nucleotide 4213), because mutations 5' of amino acid 1365 have been shown to have a markedly decreased effect on AR transactivation [11]. This created subgroups including 314 and 62 individuals. In addition, BRCA1 subgroups were defined by mutation relative to amino acid 1065 (< or ≥ nucleotide 3311), because this defines the 3' end of the BRCA1 fragment shown in vitro to bind the AR amino-terminal domain containing the CAG-encoded polyglutamine tract [11], creating subgroups of 210 and 166 individuals. BRCA2 subgroups were defined by mutation relative to amino acid 1042 (< or ≥ nucleotide 3352), because it has been shown that the BRCA2 L1042X mutation does not enhance AR transactivation [10]. Subgroup sample sizes were 42 and 177. For both BRCA1 and BRCA2 subgroup analyses, the 3' and 5' subgroups were termed domain 1 and domain 2, respectively. Protein truncating and splice mutations were stratified into domain 1 or domain 2 according to their nucleotide/amino acid position, whereas all missense mutations were included in domain 2 because these nontruncating mutations may act in a dominant-negative manner.
Although the primary analysis provides a valid test of the association between a genotype and disease risk, it may not provide a consistent estimate of the RR because the disease status of the individuals may have affected the likelihood of ascertainment (for the non-population-based studies). Oversampling of affected individuals is likely, as is presentation of affected carriers at later mean age than unaffected carriers. To correct for this potential bias, we also conducted secondary analyses using the weighted Cox regression approach as described by Antoniou and coworkers (unpublished data), in which individuals are weighted such that the observed breast cancer incidence rates in the study sample are consistent with established breast cancer risk estimates for BRCA1 and BRCA2 mutation carriers. Antoniou and coworkers (unpublished data) have shown that this approach gives estimates that are close to unbiased, but with some loss of power as compared with the standard unweighted approach. Weights were computed separately for BRCA1 and BRCA2 mutation carriers using the breast cancer incidence rate estimates reported in the meta-analysis conducted by Antoniou et al. [18]. A global set of weights was computed because the number of mutation carriers by study was too small to compute reliable study-specific weights. Moreover, Antoniou et al. [18] found no significant differences in the BRCA1 and BRCA2 cancer risks by country or study centre. As for unweighted analyses, confidence limits for the risk ratio were calculated using a robust variance approach to allow for the dependence among individuals.
We evaluated the power of detecting the effects reported by Rebbeck and coworkers [7] in our samples of BRCA1 and BRCA2 mutation carriers using simulations. For this purpose, we assumed the age distribution of the affected and unaffected carriers in our sample (Table 1) and simulated among them risk factors with risk ratios 1.8 and 2.6 and the frequencies for the ≥ 28 and ≥ 29 CAG cut-points observed in our sample. The data were then analyzed using unweighted Cox regression. We conducted 1000 simulations per model. More details about the simulations are available from the authors of the present report. The power of detecting risk ratios of 1.8 and 2.6 was estimated to be 51% and 92%, respectively, for the sample of BRCA1 mutation carriers and 28% and 78% for the sample of BRCA2 mutation carriers.
R version 1.9.0 (R Foundation for Statistical Computing, Vienna, Austria) was used to perform the unweighted Cox regression, and STATA version 7 (Stata Corporation, College Station, TX, USA) was used for the weighted analyses.
Results
The AR CAG length ranged from 8 to 36 repeats. There was little power to assess potential confounding or risk associated with the cut-point ≥ 30 CAG repeats, because only 11 BRCA1 and 11 BRCA2 carriers had at least one allele of this size. There was no evidence for an association between AR CAG repeat length and year of birth, age at menarche, or parity. There was marginal evidence for a preponderance of smaller alleles among affected BRCA2 carriers who reported using the oral contraceptive pill (P = 0.03), but this association was not seen in unaffected individuals or in BRCA1 affected or unaffected carriers (P > 0.2).
The estimated rate ratios associated with AR CAG repeat length are given in Tables 2 and 3. Risk estimates using the weighted Cox regression approach were very similar to the unweighted estimates, and for simplicity the unweighted estimates are shown. No associations were observed for alleles ≥ 28 CAG repeats or ≥ 29 CAG repeats – cut-points previously reported to be associated with risk. Estimated RRs were close to and not significantly different from 1 (all P > 0.3) and were in most instances less than 1. The number of individuals with ≥ 30 CAG repeats was too small to provide reliable risk estimates, but point estimates (0.49 [P = 0.1] for BRCA1, 0.69 [P = 0.5] for BRCA2) provided no evidence for increased risk associated with these large alleles. When AR CAG repeat length was considered as a continuous variable, there was no association either with average repeat length or with the length of the shorter or longer allele (P ≥ 0.2). There was little difference between the estimates adjusted only for source group and ethnicity, and those adjusted also for year of birth, and hormonal variables oophorectomy, parity, age at menarche and contraceptive pill use. Risk estimates were not markedly different when women with a first primary diagnosis of ovarian cancer were excluded, with a RR (95% CI) for the ≥ 28 CAG cut-point of 0.85 (0.49–1.47) for BRCA1 mutation carriers (P = 0.6), and 1.12 (0.0.55–2.27) for BRCA2 mutation carriers (P = 0.8).
Results using the weighted Cox regression approach indicated that RR estimates were not materially affected by possible ascertainment biases. For example, for the ≥ 28 CAG cut-point, the RR (95% CI) adjusted for group and ethnicity was 0.74 (0.33–1.66) for BRCA1 carriers (P = 0.5) and 0.94 (0.32–2.77) for BRCA2 carriers (P = 0.9). For average CAG length, the RR (95% CI) adjusted for group and ethnicity was 1.01 (0.94–1.10) for BRCA1 carriers (P = 0.8) and 0.96 (0.82–1.11) for BRCA2 carriers (P = 0.6). These results are consistent with the findings of Antoniou and coworkers (unpublished data), in which both weighted and unweighted Cox regression analyses give similar estimates when the true RR is 1.0.
There was also no compelling evidence for an effect of mutation position on risk associated with AR CAG repeat length. BRCA1 mutations were firstly divided by position relative to amino acid 1365 (nucleotide 4213), because mutations 5' of this have been reported to exhibit markedly decreased AR transactivation ability [11]. The RR (95% CI) for the ≥ 28 CAG cut-point analyses were 0.93 (0.53–1.64) for domain 1 (P = 0.8) and 0.35 (0.12–1.02) for domain 2 (P = 0.06), with marginal evidence for an interaction (P = 0.1). BRCA1 mutations were also divided by position relative to amino acid 1065 (nucleotide 3311), because the 3' end of the BRCA1 fragment has been shown in vitro to bind the AR amino-terminal domain containing the CAG-encoded polyglutamine tract [11]. The RR (95% CI) for the ≥ 28 CAG cut-point analyses were 0.98 (0.54–1.78) for domain 1 (P = 0.9) and 0.45 (0.16–1.23) for domain 2 (P = 0.1), with no evidence for an interaction (P = 0.3)
There was no convincing rationale for stratification of the BRCA2 mutation carriers by mutation position, in that there is no published information available with regard to the domains of BRCA2 required for BRCA2–AR interaction or to the effect of BRCA2 mutation position on the BRCA2–AR interaction. However, because a single report has shown that the BRCA2 L1042X mutation does not enhance AR transactivation [10], mutations 5' to this mutation site might be expected to have similar drastic consequences, and to convey increased risk among carriers with large AR CAG alleles. Stratification by mutation position relative to amino acid 1042 (nucleotide 4213) divided the BRCA2 carrier sample into two subgroups of 42 and 177 individuals. None of the 42 individuals with mutations in domain 1 had alleles with repeat length ≥ 28 CAG, precluding estimates of risk for this subgroup. However, there was no evidence for increased risk among BRCA2 carriers with mutations in the domain 5' to amino acid L1024, with a RR (95% CI) for the ≥ 28 CAG cut-point of 1.11 (0.54–2.30) for the 5' domain (P = 0.8).
Discussion
Our study found no evidence to support the previously reported association of AR allele length with increased breast cancer risk in BRCA1 carriers [7]. The hypothesis-generating study estimated a 1.8-fold risk (95% CI 1.1–3.1) in BRCA1 carriers with at least one AR allele length ≥ 28 CAG repeats, and increasing risks of 2.7-fold and 4.5-fold for the ≥ 29 CAG repeat and ≥ 30 CAG repeat cut-points, respectively [7]. The RR estimated by our study for ≥ 28 repeats was 0.74, and the upper 95% confidence limit (1.28) excludes the effect size reported by Rebbeck and coworkers [7]. Moreover, there was no evidence for increased RRs at the ≥ 29 or ≥ 30 cut-points. Given that we had approximately 80% or more power to detect risk estimates previously reported for the ≥ 29 cut-point [7], these results suggest that if there is any increased risk for large number of CAG repeats among BRCA1 mutation carriers, then it is of much lower magnitude than was first reported. Rebbeck and coworkers [7] found no effect of AR CAG repeat length when considered as a continuous variable, and our study likewise found no evidence for an association with shorter, larger, or average CAG repeat length.
Other published studies have found no evidence to support an association between AR CAG repeat length and breast cancer risk in BRCA2 mutation carriers [8,9]. Our study also found no evidence in support of an association. Although our sample of 220 BRCA2 carriers was not sufficiently large to provide precise estimates of risk, the upper 95% confidence limits imply that a 2.5-fold risk associated with ≥ 28 or ≥ 29 repeat lengths is unlikely.
Conclusion
Our analyses provide no support for an association between AR CAG repeat length and breast cancer risk in either BRCA1 or BRCA2 mutation carriers. An increased risk associated with ≥ 28 or ≥ 29 repeat lengths is not compatible with our data. Weak associations cannot be excluded, but analyses involving much larger numbers of carriers would be required to evaluate this possibility.
Abbreviations
AR = androgen receptor; CI = confidence interval; RR = rate ratio.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ABS was responsible for supervising genotyping design and laboratory work, for data cleaning and analysis, and leading the manuscript preparation. ACA, DLD, NP, DMP and BN assisted in the analytical design and analysis of this study. XC and LK were responsible for assay design and optimization, laboratory work and genotype data cleaning. SP and MRC were responsible for the coordination of the EMBRACE study. PLS assisted with the data management and analysis of this study. GSD, CA and MCS assisted with management and provision of data and DNA from the ABCFS and AJBCS. JLH and GGG initiated the ABCFS, and have been instrumental in the ongoing execution of this study. GC-T initiated and obtained funding for this study, initiated collaborative efforts and was involved in the supervision of laboratory work. DFE initiated the EMBRACE study, and assisted in the development of the analytical design of this study and the manuscript preparation. All authors reviewed the manuscript and offered comments.
Acknowledgements
We thank all the laboratories that contributed to the identification of mutation carriers. We thank Renee McIlroy for her role in initiating this study, and Heather Thorne, Sandra Picken, Eveline Niedermayer, Jenny Leary, Tracey Davis, Lesley Andrews, Kathy Tucker, Andrea Tesoriero, Sarah Steinborner and Deon Venter for data supply and DNA preparation for this project. We would specifically like to thank Margaret McCredie for her role in the establishment of the ABCFS. We are grateful to the physicians, surgeons and oncologists who endorsed this project, the interviewing staff, and the many women who participated in this research. KConFaB has been funded by the Kathleen Cuningham Foundation, National Breast Cancer Foundation, National Health and Medical Research Council (NHMRC), Cancer Council of Victoria, Cancer Council of South Australia, Queensland Cancer Fund, Cancer Council of New South Wales, Cancer Foundation of Western Australian and Cancer Council of Tasmania. The ABCFS and AJBCS were funded by the National Health and Medical Research Council, the Victorian Health Promotion Foundation, the New South Wales Cancer Council, the Peter MacCallum Cancer Institute, the Inkster-Ross Memorial Fund and the National Institute of Health, as part of the Cancer Family Registry for Breast Cancer Study (CA 69638). The EMBRACE study was funded by a project grant from Cancer Research UK. This work is supported by a grant from the NHMRC. ABS is funded by an NHMRC Career Development Award, and GC-T and JLH are NHMRC Senior and Senior Principle Research Fellows, respectively. PLS was funded by a grant from the INHERIT BRCAs programme from the Canadian Institute for Health Research. DFE is a Cancer Research UK Principal Research Fellow.
Figures and Tables
Table 1 Characteristics of study subjects
Sample sources BRCA1 BRCA2 BRCA1 and BRCA2a
n (% of total) n (% of total) n
EMBRACE 247 (64) 92 (42) 0
kConFaB 96 (25) 84 (38) 0
AJBCS 19 (5) 22 (10) 0
ABCFS 20 (5) 23 (10) 1
Total 382 221 1
Affected breast cancerb 205 (54) 125 (57) 1
Affected ovarian cancerb 24 (6) 8 (4) 0
Number of families 257 118 1
Questionnaire information on potential confounders was available for 344 BRCA1 carriers (239 EMBRACE, 80 kConFaB, 10 AJBCS and 15 ABCFS) and 200 BRCA2 carriers (92 EMBRACE, 76 kConFaB, 12 AJBCS, and 20 ABCFS). aOne individual was found to carry a deleterious mutation in both BRCA1 and BRCA2 [19]. bCancer type refers to first primary cancer diagnosis. One BRCA2 carrier with breast cancer was censored as unaffected at age of prior mastectomy. ABCFS, Australian Breast Cancer Family Study; AJBCS, Australian Jewish Breast Cancer study; EMBRACE, Evaluation of Mutant BRCA Carrier Epidemiology study; kConFaB, Kathleen Cuningham Consortium for Research into Familial Breast Cancer.
Table 2 Risk associated with AR CAG repeat length amongst BRCA1 mutation carriers
Risk allele Adjusted group, ethnicity Adjusted group, ethnicity and additional variables
P RR (95% CI) P RR (95% CI)
AR ≥ 28 CAG 0.3 0.74 (0.42–1.29) 0.6 0.88 (0.53–1.46)
AR ≥ 29 CAG 0.3 0.76 (0.43–1.33) 0.7 0.89 (0.54–1.48)
AR average CAG 0.5 1.02 (0.96–1.09) 0.3 1.03 (0.97–1.10)
AR small CAG 0.3 1.03 (0.97–1.09) 0.2 1.04 (0.98–1.10)
AR large CAG 0.9 1.01 (0.96–1.06) 0.6 1.01 (0.97–1.06)
First primary breast cancer diagnosis was considered an event (status affected), whereas first primary ovarian cancers were censored as unaffected at age of diagnosis, and individuals without breast or ovarian cancer were censored as unaffected at age at interview. All individuals were censored as unaffected at age of prophylactic mastectomy prior to diagnosis/interview. Analyses were adjusted for source group, ethnicity, year of birth and hormonal variables oophorectomy, parity, age at menarche and contraceptive pill use. Oophorectomy and parity were treated as time-dependent variables from age at first variable event. Analyses were conducted using unweighted Cox regression.
Table 3 Risk associated with AR CAG repeat length amongst BRCA2 mutation carriers
Risk allele Adjusted group, ethnicity Adjusted group, ethnicity and additional variables
P RR (95% CI) P RR (95% CI)
AR ≥ 28 CAG 0.8 1.12 (0.55–2.25) 0.9 1.04 (0.47–2.32)
AR ≥ 29 CAG 0.8 0.88 (0.37–2.09) 0.8 0.90 (0.32–2.52)
AR average CAG 0.7 0.98 (0.91–1.06) 1.0 1.00 (0.92–1.09)
AR small CAG 0.6 0.98 (0.90–1.06) 1.0 1.00 (0.92–1.09)
AR large CAG 0.9 0.99 (0.93–1.06) 1.0 1.00 (0.93–1.07)
First primary breast cancer diagnosis was considered an event (status affected), whereas first primary ovarian cancers were censored as unaffected at age of diagnosis, and individuals without breast or ovarian cancer were censored as unaffected at age at interview. All individuals were censored as unaffected at age of prophylactic mastectomy before diagnosis/interview. Analyses were adjusted for source group, ethnicity, year of birth, and hormonal variables oophorectomy, parity, age at menarche, and contraceptive pill use. Oophorectomy and parity were treated as time-dependent variables from age at first variable event. Analyses were conducted using unweighted Cox regression.
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Antoniou A Pharoah PD Narod S Risch HA Eyfjord JE Hopper JL Loman N Olsson H Johannsson O Borg A Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies Am J Hum Genet 2003 72 1117 1130 12677558 10.1086/375033
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| 15743497 | PMC1064126 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Dec 16; 7(2):R176-R183 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr971 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9731574349610.1186/bcr973Research ArticleHaplotype analysis of common variants in the BRCA1 gene and risk of sporadic breast cancer Cox David G [email protected] Peter [email protected] Susan E [email protected] David J [email protected] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA2 Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA3 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA2005 16 12 2004 7 2 R171 R175 12 8 2004 15 10 2004 12 11 2004 15 11 2004 Copyright © 2004 Cox et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Truncation mutations in the BRCA1 gene cause a substantial increase in risk of breast cancer. However, these mutations are rare in the general population and account for little of the overall incidence of sporadic breast cancer.
Method
We used whole-gene resequencing data to select haplotype tagging single nucleotide polymorphisms, and examined the association between common haplotypes of BRCA1 and breast cancer in a nested case-control study in the Nurses' Health Study (1323 cases and 1910 controls).
Results
One haplotype was associated with a slight increase in risk (odds ratio 1.18, 95% confidence interval 1.02–1.37). A significant interaction (P = 0.05) was seen between this haplotype, positive family history of breast cancer, and breast cancer risk. Although not statistically significant, similar interactions were observed with age at diagnosis and with menopausal status at diagnosis; risk tended to be higher among younger, pre-menopausal women.
Conclusions
We have described a haplotype in the BRCA1 gene that was associated with an approximately 20% increase in risk of sporadic breast cancer in the general population. However, the functional variant(s) responsible for the association are unclear.
breast cancerBRCA1haplotypesingle nucleotide polymorphism
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Introduction
Truncation mutations in the BRCA1 gene are high-penetrance, low-prevalence factors in risk of breast cancer. BRCA1 is hypothesized to be a locus under recombinational inhibition, and very few haplotypes have been described. In fact, only one haplotype block and two major haplotypes have been shown to exist in Caucasians. Because of the size of the gene (more than 80 kilobases), polymorphism discovery screenings have focused on exons. Although many non-synonymous polymorphisms are known in the gene, the degree of linkage disequilibrium (LD) across the entire region limits genetic variability. This limited variability has led to inconclusive results in the risk of sporadic breast cancer associated with variants in the BRCA1 gene [1,2].
The high degree of LD at BRCA1 led Huttley and colleagues [3] to investigate the possibility of recent selective pressure being exerted on this gene. They found that whereas the ratio of non-synonymous to synonymous nucleotide substitutions is the same between the chimpanzee and humans, this ratio is different from other primates, and greater than 1. They also note that these differences occur in the region of BRCA1 that interacts with Rad51, suggesting that it is the role of BRCA1 in maintaining genome integrity that has driven this selection.
Paradoxically, BRCA1 has a large number of Alu repeat sequences. These are repetitive elements that are thought to be involved in recombination and evolution of the genome [4,5]. Given that knocking out brca1 in mice is embryonic lethal [6], it can be hypothesized that the apparent suppression of recombination at BRCA1 in the human is due to the non-viability of recombinants.
Recently, resequencing information over the entire region of the gene, including most introns, has become publicly available [7]. We used these data to select haplotype tagging single nucleotide polymorphisms (htSNPs), to test the association of these haplotypes with breast cancer risk in a nested case-control study within the Nurses' Health Study.
Method
Resequencing information from the Environmental Genome Project of the National Institute of Environmental Health Sciences (NIEHS) at the University of Washington was used to generate haplotypes for the selection of htSNPs [7]. There were 90 individuals with 301 SNPs in the whole data set. SNPs were excluded from analysis if they were out of Hardy–Weinberg equilibrium (P < 0.05), had a minor allele frequency of less than 5%, or had more than 25% missing data. Haplotypes were reconstructed with PHASE [8], and htSNPs were determined with BEST [9]. Four htSNPs were selected, at positions 33,420 (rs799917, P871L), 38,085 (rs8176166), 44,059 (rs3737559), and 64,646 (rs8176267, base pairs reported as on GenBank sequence AY273801).
These htSNPs were genotyped in cases and controls using the TaqMan system (Applied Biosystems, Foster City, CA). Primer and probe sequences are available from the authors on request. Our study consisted of 1323 breast cancer cases and 1910 controls, nested within the prospective Nurses' Health Study. The Nurses' Health study was initiated in 1976, when 121,700 United States registered nurses between the ages of 30 and 55 years returned an initial questionnaire reporting medical histories and baseline health-related exposures. Updated information has been obtained by questionnaire every 2 years. Incident breast cancers were identified by self-report and confirmed by medical record review. Between 1989 and 1990, blood samples were collected from 32,826 women. Follow-up has been about 98% in all subsequent questionnaire cycles for this subcohort. Eligible cases in this study consisted of women with incident breast cancer from the subcohort who gave a blood specimen. Cases with a diagnosis any time after blood collection up to 1 June 2000 with no previously diagnosed cancer except for nonmelanoma skin cancer were included. Controls were randomly selected participants who gave a blood sample and were free of diagnosed cancer (except nonmelanoma skin cancer), and were matched to cases on the basis of age, menopausal status, recent post-menopausal hormone use, and time, day, and month of blood collection. Table 1 shows basic characteristics of cases and controls.
Haplotype frequencies were estimated with the EM algorithm, as implemented in SAS PROC Haplotype (SAS Institute, Cary, NC). Omnibus tests of haplotype association and haplotype-specific odds ratios (ORs) were calculated by haplotype replacement regression [10], assuming an additive model using the probability of carrying each pair of haplotypes provided by PROC Haplotype. The most common haplotype was used as the reference, and rare haplotypes (combined frequency less than 0.5%) were dropped from analysis. Unconditional logistic regression analyses were used to determine relative risk, controlling for age, family history of breast cancer, history of benign breast disease, post-menopausal hormone use, parity, age at first birth, and age of menopause. We assumed an additive model, where haplotype-specific parameters represent the per-haplotype increase in log odds of disease. Departures from a multiplicative gene × environment interaction model were tested by means of likelihood ratio tests.
A fifth SNP (Q356R, rs1799950), previously described as being associated with a reduced risk of breast cancer [1], was also examined with a TaqMan assay. This SNP was not present at more than 5% in the resequencing data and therefore was not included in our haplotype analysis. All P values reported are two sided.
Sequence alignments were performed with base pairs 64,601–64,700 on GenBank sequence AY273801. Blast alignments were performed over the web at using the blastn program against the alu_repeats database. No filtering was used, and expected values were set at 10-20 to limit the number of hits. All other default values were used. AluSp repeats on contig NT_010755 were selected from the AluGene database . These sequences were aligned with ClustalW at , using all default values.
Results and Discussion
The polymorphism at codon 356 in the BRCA1 gene had previously been described as being inversely associated with breast cancer risk (Gln356→ Arg, OR 0.88, 95% confidence interval [CI] 0.63–1.23; Arg356→ Arg, OR 0.00, 95% CI 0.00–0.56) [1]. We were unable to reproduce these results in our data set. Dunning and colleagues did not observe any homozygotes of the Arg allele at this codon among cases (n = 765). In contrast, we observed homozygotes among both cases and controls, and did not detect any association (Table 2). We had about 80% power to detect a relative risk of 0.73 assuming a log additive model. This polymorphism was not detected above the 5% threshold for inclusion as a htSNP in the NIEHS database, and was not included in our haplotype analyses. We did explore its inclusion in the haplotype analyses, and it did not materially alter the risk estimates for other haplotypes.
Five haplotypes of more than 5% frequency were described from the 39 polymorphisms meeting the selection criteria. BRCA1 exists as one haplotype block, with significant LD along the entire gene. Only four SNPs were needed to tag these haplotypes. To test the hypothesis that a difference in haplotype frequencies is seen between cases and controls, a global test was performed (P = 0.08). This test is not formally significant; this should be kept in mind while interpreting results based on haplotype analysis. Table 3 shows the results of the regression trend test of haplotypes.
Haplotype 2 (C A G G) was associated with a small, though significant, increase in risk (OR 1.18, 95% CI 1.02–1.37; Table 3). When considering the diplotype of haplotype 2, a significant increase in risk was observed among the homozygous carriers (OR 1.62, 95% CI 1.05–2.48; Table 4). A nearly significant interaction was seen between haplotype 2 and family history of breast cancer (P = 0.05). A large increase in risk (OR 10.83, 95% CI 2.39–49.2) was observed in women homozygous for haplotype 2 and having a positive family history of breast cancer (Table 5). Similar, although not statistically significant, interactions were seen for age of diagnosis (less than 50 or more than 50, interaction P = 0.36) and menopausal status at diagnosis (pre-menopausal or post-menopausal, interaction P = 0.19, data not shown). Additional studies focusing on breast cancer incidence in younger, pre-menopausal women would be of interest, to improve the definition of risk associated with this haplotype.
Little is known about the actual effects on the expression or function of these polymorphisms in BRCA1. Because of the low complexity of the gene at the haplotype level, we can describe haplotype 2 by using just one SNP, at base pair 64,646. This is in the intron between exons 19 and 20, in the middle of an Alu repeat sequence. This is a rather long intron, spanning 6 kilobases (63,044–69,242). The Alu repeat surrounding base pair 64,646 is a member of the AluSp family. Aligning this sequence against the Alu database at NCBI shows that the consensus nucleotide for this family at this position is G, which is the risk allele. Alignment with other AluSp repeats on the same contig as BRCA1 shows that those most similar to this region also have a G at this position. This implies that the G allele might recombine more readily than the A allele with other Alu repeats in this region. It could therefore be hypothesized that this SNP is influential in Alu-mediated non-homologous recombination and other rearrangements of the BRCA1 gene. These sorts of aberrations are responsible for roughly 10% of BRCA1 disease-causing mutations, and could be involved in somatic alteration of the structure of the BRCA1 gene [11]. However, it is important to note that this SNP was selected not because of any prior knowledge of potential function but because it tags a common haplotype.
This risk haplotype is a subset of the wild-type haplotype, and no coding or potential splice-site SNPs in the NIEHS Environmental Genome Project database are in LD with the SNP defining this haplotype. It should be noted that the sequencing data reported by the NIEHS Environmental Genome Project are limited to about 1 kilobase of sequence 5' to the start of transcription, and although the entire 3' untranslated region has been sequenced, only about 800 base pairs beyond the poly(A) site are included. Additionally, 13,403 of 82,899 base pairs (16%) of the genomic region of BRCA1 was not sequenced. However, all the unsequenced regions are intronic. This leaves the possibility that a potentially functional SNP in the unsequenced regions of the BRCA1 gene resides on haplotype 2.
Osorio and colleagues [12] examined the occurrence of mutations in BRCA1 among the index cases of familial breast and ovarian cancers. They found that mutations occur more readily on the rarer of the two common haplotypes of BRCA1 (their haplotype II). These haplotypes are the third, fourth and fifth listed in Table 3, not the haplotype for which we observed an increase in risk, so the relevance of their observation for our findings is unclear.
Although we cannot rule out the possibility that these results are spurious or due to population stratification, the Nurses' Health Study consists almost entirely of Caucasian women; population stratification should therefore be minimal. Two additional hypotheses that need further examination are that there are functional polymorphisms in the BRCA1 gene that are not in the coding sequence, and/or that variants in BRCA1 are in LD with functional variants in neighboring genes. The LD block around BRCA1 is quite extensive [13], and includes a BRCA1 pseudogene, as well as the genes NBR1 and NBR2. Potentially functional variation in these genes also needs to be described.
Conclusions
We have described a haplotype associated with the BRCA1 gene that is associated with an approximately 20% increase in risk of sporadic breast cancer in the general population. However, the functional variant(s) responsible for the association are unclear.
Abbreviations
CI = confidence interval; htSNPs = haplotype tagging single nucleotide polymorphisms; NIEHS = National Institute of Environmental Health Sciences; LD = linkage disequilibrium; OR = odds ratio.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DGC carried out analyses and wrote the manuscript, SEH provided funding for the Nurses' Health Study blood cohort and participated in manuscript editing, PK provided statistical support and participated in manuscript editing, DJH provided funding for genotyping and participated in manuscript editing. All authors read and approved the final manuscript.
Acknowledgements
We are indebted to the participants in the Nurses' Health Study for their continuing dedication and commitment. We thank Patrice Soule and her laboratory for DNA extraction and sample preparation, and Dr Hardeep Ranu and her laboratory for genotyping and data archiving. This work was supported by National Institutes of Health research grants CA87969, CA49449 and CA65725. DGC is supported by training grant CA 09001-27 from the National Institutes of Health.
Figures and Tables
Table 1 Basic characteristics of cases and controls
Characteristic Cases (n = 1322) Controls (n = 1908)
First-degree family history of breast cancer (%) 256 (19) 242 (13)
History of benign breast disease (%) 853 (65) 972 (51)
Post-menopausal status (%) 1133 (91) 1691 (93)
Ever used post-menopausal hormone (%) 902 (76) 1195 (68)
Age at diagnosis (selection in controls, SD) 62.9 (7.4) 63.4 (7.1)
Age at menopause (SD) 48.2 (5.8) 47.8 (6.2)
Table 2 Relation of Q356R and breast cancer risk in the Nurses' Health Study
Genotype Case (frequency)a Control (frequency)a OR (95% CI) ORb (95% CI)
Q356Q 1065 (86.23) 1413 (87.01) 1.00 (reference) 1.00 (reference)
Q356R 165 (13.36) 206 (12.68) 1.06 (0.85–1.32) 1.05 (0.84–1.32)
R356R 5 (0.4) 5 (0.31) 1.33 (0.38–4.59) 1.67 (0.44–6.35)
CI, confidence interval; OR, odds ratio.
aSamples lacking genotype information were removed from analysis.
bLogistic regression controlling for age, age of menopause, post-menopausal hormone use, age at first birth, parity, family history of breast cancer, and history of benign breast disease.
Table 3 Relation of common BRCA1 haplotypes to risk of breast cancer in the Nurses' Health Study
Haplotypea Caseb (frequency) Controlb (frequency) OR (95% CI) ORc (95% CI)
C A G A 1195(46) 1637 (48) 1.0 (reference) 1.0 (reference)
C A G G 536 (20) 623 (18) 1.19 (1.03–1.37) 1.18 (1.02–1.37)
T A A A 233 (9) 281 (8) 1.11 (0.95–1.29) 1.13 (0.96–1.32)
T A G A 273 (10) 403 (12) 0.92 (0.77–1.10) 0.94 (0.78–1.13)
T G G A 384 (15) 475 (14) 1.15 (0.95–1.39) 1.13 (0.93–1.37)
CI, confidence interval; OR, odds ratio.
aOrder of SNPs: 33420 (rs799917, P871L), 38085 (rs8176166), 44059 (rs3737559), 64646 (rs8176267).
bSamples lacking genotype information at all four SNPs were removed from haplotype analysis.
cLogistic regression controlling for age, age of menopause, post-menopausal hormone use, age at first birth, parity, family history of breast cancer, and history of benign breast disease. ORs represent risk increase per copy of each haplotype carried. P for global test = 0.08.
Table 4 Relative risk of breast cancer by haplotype 2 status in the Nurses' Health Study
Diplotype Case (frequency) Control (frequency)a OR (95% CI) ORb (95% CI)
Other/other 832 (63) 1137 (66) 1.00 (reference) 1.00 (reference)
Hap2/other 429 (33) 531 (31) 1.11 (0.95–1.30) 1.09 (0.92–1.28)
Hap2/Hap2 53 (4.0) 47 (2.7) 1.62 (1.07–2.49) 1.62 (1.05–2.48)
CI, confidence interval; Hap2, haplotype 2; OR, odds ratio.
aSamples lacking genotype information at all four SNPs were removed from haplotype analysis.
bLogistic regression controlling for age, age of menopause, post-menopausal hormone use, age at first birth, parity, family history of breast cancer, and history of benign breast disease. P value for trend = 0.05.
Table 5 Haplotype 2 and risk of breast cancer by family history of breast cancer in the Nurses' Health Study
Family history Case (frequency)a Control (frequency)a OR (95% CI) ORb (95% CI)
None
Other/other 668 (51) 995 (58) 1.00 (reference) 1.00 (reference)
Hap2/other 351 (27) 456 (27) 1.16 (0.97–1.38) 1.13 (0.95–1.35)
Hap2/Hap2 39 (2.9) 43(2.5) 1.36 (0.86–2.15) 1.34 (0.84–2.15)
Present
Other/other 165 (12) 145 (8) 1.69 (1.33–2.17) 1.78 (1.38–2.29)
Hap2/other 77 (5.8) 75 (4.4) 1.51 (1.07–2.12) 1.60 (1.13–2.27)
Hap2/Hap2 14 (1.1) 3 (0.2) 10.06 (2.25–45.0) 10.83 (2.39–49.2)
CI, confidence interval; Hap2, haplotype 2; OR, odds ratio.
aSamples lacking genotype information at all four SNPs were removed from haplotype analysis.
bLogistic regression controlling for age, age of menopause, post-menopausal hormone use, age at first birth, parity, and history of benign breast disease. P interaction = 0.05.
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| 15743496 | PMC1064127 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Dec 16; 7(2):R171-R175 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr973 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9741574349810.1186/bcr974Research ArticleBreast implants following mastectomy in women with early-stage breast cancer: prevalence and impact on survival Le Gem M [email protected]'Malley Cynthia D [email protected] Sally L [email protected] Charles F [email protected] Janet L [email protected] Theresa HM [email protected] Dee W [email protected] Northern California Cancer Center, Fremont, California, USA2 Iowa Cancer Registry, University of Iowa, Iowa City, Iowa, USA3 Fred Hutchison Cancer Research Center, Division of Public Health Sciences, Seattle, Washington, USA2005 23 12 2004 7 2 R184 R193 16 7 2004 10 9 2004 25 10 2004 16 11 2004 Copyright © 2004 Le et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Background
Few studies have examined the effect of breast implants after mastectomy on long-term survival in breast cancer patients, despite growing public health concern over potential long-term adverse health effects.
Methods
We analyzed data from the Surveillance, Epidemiology and End Results Breast Implant Surveillance Study conducted in San Francisco–Oakland, in Seattle–Puget Sound, and in Iowa. This population-based, retrospective cohort included women younger than 65 years when diagnosed with early or unstaged first primary breast cancer between 1983 and 1989, treated with mastectomy. The women were followed for a median of 12.4 years (n = 4968). Breast implant usage was validated by medical record review. Cox proportional hazards models were used to estimate hazard rate ratios for survival time until death due to breast cancer or other causes for women with and without breast implants, adjusted for relevant patient and tumor characteristics.
Results
Twenty percent of cases received postmastectomy breast implants, with silicone gel-filled implants comprising the most common type. Patients with implants were younger and more likely to have in situ disease than patients not receiving implants. Risks of breast cancer mortality (hazard ratio, 0.54; 95% confidence interval, 0.43–0.67) and nonbreast cancer mortality (hazard ratio, 0.59; 95% confidence interval, 0.41–0.85) were lower in patients with implants than in those patients without implants, following adjustment for age and year of diagnosis, race/ethnicity, stage, tumor grade, histology, and radiation therapy. Implant type did not appear to influence long-term survival.
Conclusions
In a large, population-representative sample, breast implants following mastectomy do not appear to confer any survival disadvantage following early-stage breast cancer in women younger than 65 years old.
breast implantsepidemiologymastectomySurveillanceEpidemiologyand End Resultssurvival
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Introduction
Over the past 30 years, an estimated 1.5–2 million women have received breast implants in the United States [1]. Starting in the 1980s, widespread public health concern arose regarding their potential adverse health effects [2]. Numerous epidemiologic investigations have focused on systemic complications, particularly cancer and connective tissue disease, but have found no significantly increased short-term risk for these diseases [3,4].
Approximately 20% of breast implants are used for reconstruction in breast cancer patients following mastectomy [1]. In this population, however, the use of breast implants, while increasing, has not been well documented [5]. Furthermore, little research has addressed long-term survival, although the few studies conducted suggest that use of implants for breast reconstruction does not impact patient survival [6-9]. However, these studies were limited by comprising clinic-based samples not necessarily representative of all patients, by small sample sizes, by lack of information on type of implant, and by short durations of follow-up.
In 1993, the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute implemented the Breast Implant Surveillance Study to document and validate postmastectomy breast implant usage in a population-based series of young, early-stage breast cancer cases. Since the SEER program also monitors patient vital status for life, survival up to 17 years exists in the study cohort. The purpose of the current analysis was to describe the use of breast implants and to examine the impact of breast implants on survival after breast cancer in this cohort.
Methods
Breast Implant Surveillance Study
The Breast Implant Surveillance Study was conducted during the period 1993–1994 in the United States. Patients were identified through the population-based SEER cancer registries in San Francisco–Oakland, CA, in Seattle–Puget Sound, WA, and in the state of Iowa. Eligible patients included the 5862 females diagnosed younger than age 65 with early-stage or unstaged first primary breast cancer in 1983, 1985, 1987, or 1989 and treated with mastectomy during their first course of therapy.
Participation involved completing a standardized questionnaire inquiring about implant status (right breast only, left breast only, both breasts, no implant, or unknown) and implant type (silicone gel, saline, double lumen [consisting of silicone gel and saline], other, and unknown type), and providing signed consent for release of medical records for validation of implant usage and implant type. Patients in the Seattle–Puget Sound region and the state of Iowa were mailed a self-administered questionnaire, and nonrespondents were contacted for a telephone interview. Women from the San Francisco–Oakland region were administered questionnaires by telephone.
Next-of-kin were asked about the deceased patient's implant status and for consent for medical record review. For women reporting breast implants, a medical record review of breast implant usage (including the date and type of implant received, and removal and replacement status) was conducted by trained abstractors for women who reported having a breast implant. Patient and tumor characteristics at diagnosis, including age, race/ethnicity, year, stage, histology, grade, radiation treatment, vital status, and cause of death, were obtained from the SEER database.
Although socioeconomic status (SES) is not routinely collected by SEER, we were able to assign census block group level measures of SES to a subset of subjects (1989 San Francisco Bay Area patients). Using data from the 1990 US Census, we examined the impact of living in census block groups characterized by low education, by poverty, and by occupation (median income, < 20% below poverty versus ≥ 20% below poverty; education, no high school diploma versus high school graduate; and occupation, blue collar versus nonblue collar [10]).
Patients who reported a breast implant were classified as having a particular type of implant if they had a unilateral implant, or if they had bilateral implants of the same type. Nineteen women with bilateral implants and discordant information about implant type were excluded from analyses stratified by the type of implant. An additional 133 women reporting implants but lacking implant information were excluded from all analyses requiring detailed implant information. The vital status (obtained annually through patient contact, death records, motor vehicle departments, voters' registration records, and Social Security files) was determined from the December 1999 SEER Public Use Tape. The outcome variables were death due to breast cancer and death due to nonbreast cancer causes, as routinely ascertained by SEER and as defined by the International Classification of Disease, Ninth Revision, codes 174.0–174.9 for deaths occurring between 1983 through 1998, and by the International Classification of Disease, 10th Revision, code C509 for deaths occurring in 1999. Survival time was calculated from the date of diagnosis to the earliest date: death, last known to be alive, or 31 December 1999 (study cutoff date).
Statistical analysis
For descriptive analyses, women with breast implants were compared with those without implants on characteristics at diagnosis (SEER region of residence, age, year, race/ethnicity, marital status, stage, grade, and histology), using chi-square tests and Fisher's exact test to assess differences. Two-sided P < 0.05 was considered statistically significant.
Of the 5862 patients eligible for the study, 4968 (84.7% of those eligible) patients participated in the study. After restricting the sample to women with primary invasive breast cancer and known survival time, a final sample size of 4385 patients were used for all survival analyses. Survival estimates were computed using the Kaplan–Meier method, and differences in survival were compared using the two-sided log-rank test. To adjust for patient and tumor characteristics, the risks of nonbreast cancer mortality and death due to breast cancer were modeled using Cox proportional hazards regression after censoring deaths from other or unknown causes. The assumption of proportionality was tested and met for all covariates used in the Cox analysis. All analyses were performed using SAS Version 8.02 (SAS Institute, Inc., Cary, NC, USA).
Results
Study population
Eighty-five percent of study-eligible women completed the interview. Responders were slightly older compared with nonresponders. They also were more often non-Hispanic white, were diagnosed with in situ cancers, were less likely to have more than one primary tumor and were more likely to have lived until the end of the study period (Table 1).
Among the responders, 20% received a breast implant (Table 1). Their mean age at diagnosis was 47 years, and they were younger, on average, than women without implants. Somewhat higher proportions of women with implants were of non-Hispanic white race/ethnicity, resided in the San Francisco–Oakland region, were diagnosed in the late 1980s, and had tumors of lobular histology than women without implants. The percentage of women with in situ breast cancer was twice as high in women with breast implants as in nonimplanted women. In addition, women with implants were less likely to receive radiation therapy or to be diagnosed with more than one primary tumor.
Implant characteristics and usage
Implant information was obtained for 866 women; these women were slightly older and more likely to be living than women with unknown implant information. Among the 1143 breast implants received (Table 2), the most common types were silicone gel and double lumen. The majority of women (67%) received a unilateral implant a median of 9.6 months after breast cancer diagnosis. Approximately one-third of the women had an implant removed; the majority of these women chose to have the implant replaced. Saline-filled implants were removed for 48% of women, although saline-filled tissue expanders (temporary implants) may be incorrectly included in this category. Fifty-four percent of women with 'other' implants had them removed, and 49% had them replaced.
Survival
At the end of the follow-up period, 231 (5.3%) patients did not have complete follow-up. Twenty-eight percent of all patients died, with nearly two-thirds of these deaths due to breast cancer (Table 3). Women with implants had a similar distribution of causes of death to those without implants (Table 3), except for a significantly larger proportion of deaths due to suicide – although this was based on a small number of deaths (0.4% versus 0.03% of all patients, respectively; P = 0.02). Among the 4385 patients with invasive breast cancer and known survival time, the 817 (19%) receiving a breast implant experienced better survival than women without implants, after adjustment for age at diagnosis (Fig. 1).
The multivariate Cox proportional hazards model showed that implant status was a significant factor associated with improved survival for deaths due to breast cancer and for nonbreast cancer mortality (Table 4). Risk of breast cancer death in women with implants was approximately one-half of that for women without implants, after adjustment for multiple clinical and sociodemographic factors. Age at diagnosis, stage, grade, histology, and radiation therapy were significant predictors of breast cancer death in this cohort, as they were for women without implants. With the exception of age, results were similar when modeled for nonbreast cancer mortality, although hazard rate ratios for women with implants were slightly higher overall. Among women with implants, the type of breast implant did not significantly impact survival, although women receiving saline implants had marginally lower risks than women receiving silicone gel implants.
For the subsample of 384 San Francisco Bay Area study subjects for whom we were able to assign census-level SES indicators, risk of death among women with breast implants compared with that among women without implants continued to be reduced (hazard ratio, 0.50; 95% confidence interval, 0.20–1.25) after adjustment for census block-group level SES variables.
Discussion
In this large population-based study of breast cancer patients treated with mastectomy, risks of breast cancer death and nonbreast cancer mortality were lower in women with implants than in women without implants, after adjustment for potential confounders. Postmastectomy breast implants were used by one-fifth of patients who were slightly younger at diagnosis and were more likely to be of white race/ethnicity and to have in situ disease than women without implants. The silicone gel-filled implant was the most common type of implant received.
Although breast reconstruction has been shown to provide psychosocial benefits to breast cancer survivors [11-13], concerns have been raised that breast implants may increase the risk of local complications and systemic diseases, including certain cancers and autoimmune diseases [2,3,14,15]. Breast implants have been suggested to interfere with mammography, thereby facilitating delayed detection of breast tumors, and, consequently, decreased survival [16]. Despite recent Institute of Medicine recommendations to continue monitoring women with breast implants and to evaluate the potential long-term health effects [1], few research studies have addressed long-term health outcomes in this group. Moreover, these studies were often conducted on small, nonrepresentative samples without detailed information on implant type and history of use.
Georgiade and colleagues found that the survival time for 101 women undergoing breast reconstruction with breast implants was nonsignificantly better than that for 377 women without reconstruction, after adjustment for tumor grade, histology, lymph node involvement, and age at diagnosis, and after a median of 3 years of follow-up [6]. With a median of 13 years of follow-up, Petit and colleagues found that the risk of breast cancer death was marginally lower in 146 women who underwent breast reconstruction with silicone gel-filled implants than in a matched group without implants (relative risk, 0.6; 95% confidence interval, 0.3–1.1) [7]. Vandeweyer and colleagues compared 49 women who received saline-filled breast implants following mastectomy with a matched group of women who did not. They found no difference in the number of breast cancer deaths between the two groups [8]. In a matched analysis of 176 women with a mean of almost 6 years of follow-up, Park and colleagues found that women with breast implants after mastectomy had approximately a 70% reduced risk of death compared with women without implants (relative risk, 0.33; 95% confidence interval, 0.11–0.92) [9].
Our finding of better survival in women with breast implants is consistent with most of this research [6-8]. However, our study has the substantial advantages of being population-based, being large, having a long follow-up (median, 12.4 years), and including information on implant type and the implant removal and replacement. It is thus well suited to address public health concerns regarding the long-term survival and use of breast implants in women with early-stage, mastectomy-treated breast cancer. Such concerns have recently been re-evaluated in conjunction with the Food and Drug Administration hearings regarding the safety of silicone gel breast implants and their availability for the general market [17,18].
One explanation for our finding of reduced mortality in patients with breast implants may relate to self-selection rather than to a causal role of implants. Although most women who receive mastectomy are eligible to receive breast implants as part of breast reconstruction, surgeons may not recommend this surgery to women with health conditions such as obesity or a recent history of smoking that may contribute to postoperative complications, and may thus impact on survival [19,20]. In our data, the possibility of self-selection based on smoking is supported by the higher proportions of deaths from respiratory cancers and chronic obstructive pulmonary diseases in women without breast implants (Table 3). Further investigation is warranted for lifestyle factors (e.g. smoking, diet) and for comorbidities that may account for the survival advantage seen in women with breast implants.
In the present study, women with breast implants had a significant excess proportion of deaths due to suicide compared with women without implants. This finding, albeit based on small numbers, is consistent with observations from studies conducted in cosmetic breast implant patients [21,22] and suggests psychiatric consultation should also be considered for breast cancer patients seeking reconstructive surgery with breast implants. In any case, future studies with larger sample sizes are needed to confirm this finding in the breast reconstruction population.
An important bias of common concern in retrospective cohort studies is loss to follow-up. A total 231 (5.3%) of the 4385 patients included in the survival analysis did not have complete follow-up at the end of the study period. However, because of the relatively small percentage of patients lost to follow-up, we know that bias due to loss to follow-up has little impact on our survival findings since we found no substantial change in hazards ratios when we assumed the worst-case scenario that all patients lost to follow-up had all died or assumed that all patients lost to follow-up all lived until the end of the study period.
Furthermore, although our response rate was relatively high, differences between nonresponders and responders on several patient and tumor characteristics could have biased our findings. Although we were able to adjust for reported patient and tumor characteristics in our multivariate analyses, 356 women (nearly 40% of study-eligible patients) who did not participate in the study were deceased. In the unlikely event that all 356 deceased women had received breast implants, it is possible that the exclusion of these cases from our analysis could bias our results towards and beyond the null, and thereby overestimate the protective effects.
Although our survival analyses were adjusted for various demographic and clinical characteristics, our finding of better survival in women with breast implants could reflect uncontrolled confounding by social class, medical care, and psychological factors related to implant usage and survival. Among breast cancer patients treated with mastectomy, those choosing to have breast reconstruction have been shown to differ from women without breast reconstruction on SES, which may be an important factor affecting survival [23,24]. In a convenience sample of more than 200,000 breast cancer patients undergoing mastectomy between 1985 and 1995, Morrow and colleagues found that patients with a family income of $40,000 or more were twice as likely as patients with a family income of less than $40,000 to receive postmastectomy breast reconstruction [25]. Higher income may be a predictor of better survival after breast cancer, as women with higher incomes may have better access to cancer care and treatment. In the present study, SES did not alter the effect of implants on survival in the subset of women for whom SES measures were available. Differences in these area-level measures of SES are thus not likely to contribute substantially to the survival differences between breast cancer patients with and without implants in this study.
Additional unmeasured confounders related to the increased medical care of women with breast implants could explain the protective association of breast implants with cancer survival. Because women with breast implants may be more closely followed in their medical care, they may have recurrences diagnosed and treated earlier; thus they may experience better survival than women without implants. Although our study lacked information on breast cancer recurrence, we were able to examine the impact of subsequently diagnosed primary breast tumors. We observed that the proportion of women with two or more primary breast tumors was lower in women with breast implants than in women without (15% and 21%, respectively).
To address the possibility that a higher incidence of subsequently diagnosed primary breast tumors impacted survival in women without breast implants, we limited survival analyses to women with only one primary tumor (n = 3535) and found a consistently reduced risk of breast cancer death associated with breast implant usage (hazard ratio, 0.54; 95% confidence interval, 0.42–0.68), after adjusting for similar prognostic factors. Our findings also are consistent with results from studies showing a reduced risk of death in augmentation mammoplasty patients with at least 10 years of follow-up compared with the general population [26-29]. Furthermore, psychological factors underlying a woman's decision to obtain breast implants [30,31], including body image concerns and self-esteem, may play a role in lifestyle behaviors relevant to survival, although the extent to which they directly impact survival is unclear.
Several biological mechanisms have been proposed to explain how breast implants may influence survival outcomes [16,26,32,33]. Breast implants may stimulate a local immune response in which cancer cells are more likely to be destroyed [34]. Breast implants may compress breast tissue, reducing the flow of blood and thereby slowing the rate of cell or tumor growth. Breast implants may decrease the temperature of the breast by separating the breast tissue from the body, thereby decreasing the metabolic rate and slowing the growth rate of residual breast cancer cells [35]. These mechanisms may provide important clues in cancer prevention and warrant further investigation.
Conclusions
With a median of more than 12 years of follow-up on patients, our population-based study shows that the risk of breast cancer mortality in patients with breast implants following mastectomy is about one-half of that for patients without implants, after adjustment for prognostic characteristics. Although patients in our study received implants in the 1980s and early 1990s, breast implants continue to be an integral part of breast reconstruction in breast cancer patients and have not changed dramatically in design. Thus, despite an overall decrease in implant use among breast cancer patients, breast implants remain an important and commonly used option for women considering reconstruction. Certainly, further research is needed to explain the survival differential in women with breast implants and those without, by examining potentially explanatory factors such as SES, comorbidity, smoking, or other lifestyle factors. However, based on this large, representative sample of breast cancer patients with extensive follow-up, we found that breast implants following mastectomy do not appear to confer any survival disadvantage following early-stage breast cancer in women younger than 65 years old.
Abbreviations
SEER = Surveillance, Epidemiology, and End Results; SES = socioeconomic status.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SLG, DWW, JLS, and CFL implemented the study and acquired the data. GML, SLG, and CDO participated in the design and conceptualization of the study. GML performed the statistical analysis and drafted the manuscript. GML, SLG, CDO, and THMK participated in the analysis and interpretation of the data. All authors read and approved the final manuscript.
Acknowledgements
Data for the SEER Breast Implant Surveillance Study were collected by the Northern California Cancer Center (contract N01-CN-05224 Modification Number 12), Seattle-Puget Sound Cancer Registry (contract N01-CN-05230), and Iowa Cancer Registry (contract N01-CN-05229 Modification Number 11) with support of the National Cancer Institute, National Institutes of Health. The content of this publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.
Figures and Tables
Figure 1 Age-adjusted breast cancer survival curve by breast implant status, 1983–1989.
Table 1 Characteristics of mastectomy-treated breast cancer patients in the Surveillance, Epidemiology, and End Results (SEER) Breast Implant Surveillance Study, 1983–1989
Characteristic All eligible patients (n = 5862) Study responders only (n = 4968)
Nonresponders (n = 894) Responders (n = 4968) P value Women with breast implants (n = 1018) Women without breast implants (n = 3950) P value
n % n % n % n %
SEER region
San Francisco–Oakland 468 52.4 1853 37.3 0.0001 416 40.9 1437 36.4 0.0001
Seattle–Puget Sound 190 21.3 1663 33.5 374 36.7 1078 27.3
Iowa 236 26.4 1452 29.2 228 22.4 1435 36.3
Year of diagnosis
1983 246 27.5 1102 22.2 0.0001 176 17.3 926 23.4 0.0003
1985 241 27.0 1152 23.2 244 24.0 908 23.0
1987 230 25.7 1405 28.3 319 31.3 1086 27.5
1989 177 19.8 1309 26.3 279 27.4 1030 26.1
Age at diagnosis
< 35 years 59 6.6 175 3.5 0.0001 88 8.6 87 2.2 0.0001
35–44 years 231 25.8 919 18.4 334 32.8 585 14.8
45–54 years 256 28.6 1545 31.1 370 36.4 1175 29.8
55–64 years 348 38.9 2329 46.9 226 22.2 2103 53.2
Mean age at diagnosis (years) 49.9 - 52.1 - 0.0001 46.7 - 53.4 - 0.0001
Race/ethnicity
Non-Hispanic White 670 74.9 4444 89.5 0.0001 959 94.2 3485 88.2 0.0001
Non-Hispanic Black 78 8.7 150 3.0 13 1.3 137 3.5
Hispanic 38 4.3 131 2.6 20 2.0 111 2.8
Asian/Pacific Islander 86 9.6 179 3.6 16 1.6 163 4.1
Other/unknown 22 2.5 64 1.3 10 1.0 54 1.4
Stage at diagnosis
In situ 78 8.7 567 11.4 0.04 199 19.6 368 9.3 0.0001
Localized 562 62.9 3141 63.2 601 59.0 2540 64.3
Regional 241 27.0 1209 24.3 211 20.7 998 25.3
Unstaged 13 1.5 51 1.0 7 0.7 44 1.1
Histology
Infiltrating ductal 687 76.9 3638 73.2 0.04 695 68.3 2943 74.5 0.0001
Lobular 44 4.9 364 7.3 106 10.4 258 6.5
Infiltrating ductal and lobular 35 3.9 195 3.9 54 5.3 141 3.6
Not otherwise specified/other 128 14.3 771 15.5 163 16.0 608 15.4
Grade
Well differentiated 17 1.9 150 3.0 0.02 29 2.9 121 3.1 0.91
Moderately differentiated 128 14.3 712 14.3 137 13.5 575 14.6
Poorly differentiated 144 16.1 824 6.6 169 16.6 655 16.6
Undifferentiated 17 1.9 185 3.7 39 3.8 146 3.7
Unknown grade 588 65.8 3097 62.3 644 63.3 2453 62.1
Number of primary tumors
One primary only 771 86.2 4001 80.5 0.0001 867 85.2 3134 79.3 0.0001
Two or more primaries 123 13.8 967 19.5 151 14.8 816 20.7
Radiation therapy during first course of treatment
No 814 91.1 4626 93.1 0.03 969 95.2 3657 92.6 0.01
Yes 80 9.0 333 6.7 48 4.7 285 7.2
Unknown 0 0 9 0.2 1 0.1 8 0.2
Vital statusa
Alive 538 60.2 3696 74.4 0.0001 867 85.2 2828 71.6 0.0001
Dead 356 39.8 1272 25.6 151 14.8 1122 28.4
aFinal date of follow-up, December 1999.
Table 2 Characteristics of breast implants in mastectomy-treated breast cancer patients in the Surveillance, Epidemiology, and End Results Breast Implant Surveillance Study, 1983–1989 (n = 866)a
Silicone gel (n = 333) Saline (n = 149) Double lumen (n = 314) Other (n = 33) Unknown (n = 37) P value
n % n % n % n % n %
Prevalence of type 38.4 17.2 36.3 3.8 4.3
Implant removal status
No known removal 244 73.3 77 51.7 216 64.9 15 45.5 25 67.6 0.0001
Removed 89 26.7 72 48.3 98 31.2 18 54.5 12 32.4
Implant replacement status
No known replacement 262 78.7 87 58.4 237 75.4 17 51.5 26 70.3 0.0001
Replaced 71 21.3 62 41.6 77 24.5 16 48.5 11 29.7
aExcludes 133 patients with missing implant information and 19 patients with bilateral implants of disordant types
Table 3 Distribution of living and deceased patients by breast implant status in the Surveillance, Epidemiology, and End Results Breast Implant Surveillance Study, 1983–1989 (n = 4385)
Women with breast implants (n = 817) Women without breast implants (n = 3568) Total (n = 4385) P valuea
n % n % n %
Alive 676 82.3 2498 70.0 3174 72.4 < 0.0001
Deceased
All malignant cancers
Digestive system 2 0.2 32 0.9 34 0.8 0.08
Respiratory 3 0.4 51 1.4 54 1.2 0.01
Breast 101 12.4 703 19.7 804 18.3 < 0.0001
Female genital 4 0.5 20 0.6 24 0.5 0.80
Kidney - - 2 0.1 2 0.2 -
Brain - - 7 0.2 7 0.2 -
Melanoma 1 0.1 2 0.1 3 0.1 0.46
Multiple myeloma - - 3 0.1 3 0.1 -
Non-Hodgkin's lymphoma - - 3 0.1 3 0.1 -
Leukemia 2 0.2 2 0.1 4 0.1 0.16
Diabetes mellitus 2 0.2 13 0.4 15 0.3 0.60
Circulatory system
Heart disease 4 0.5 65 1.8 69 1.6 0.01
Cerebrovascular disease 2 0.2 14 0.4 16 0.4 0.75
Atherosclerosis - - 3 0.1 3 0.1 -
Respiratory disease
Pneumonia/influenza 1 0.1 10 0.3 11 0.3 -
Chronic obstructive pulmonary disease 2 0.2 23 0.6 25 0.6 0.21
Digestive system disease
Stomach/duodenal ulcers - - 3 0.1 3 0.1 -
Chronic liver disease 1 0.1 7 0.2 8 0.2 -
Nephritis - - 7 0.2 7 0.2 -
All external causes
Accidents - - 4 0.1 4 0.3 -
Suicides 3 0.4 1 0.03 4 0.3 0.02
Other cause of death 5 0.6 38 1.1 43 3.6 0.26
Unknown cause of death 7 0.9 32 0.9 39 3.2 0.91
Sum of deaths will not add to overall total since data are only shown for causes with at least two deaths in either group of women.
aP value for chi-square test or, where appropriate, Fisher's Exact Test.
Table 4 Proportional hazards regression model: hazard ratio and 95% confidence interval (CI) for mastectomy-treated breast cancer patients in the Surveillance, Epidemiology, and End Results (SEER) Breast Implant Surveillance Study, 1983–1989 (n = 4385)a
Covariate Distribution of sample Breast cancer mortality Non-breast cancer mortality
n % Hazard ratio 95% confidence interval Hazard ratio 95% confidence interval
Implant status
No (referent) 3568 81.4 1.00 - 1.00 -
Yes 817 18.6 0.54 0.43–0.67 0.59 0.41–0.85
Age at diagnosis
< 35 years 163 3.7 1.95 1.42–2.69 0.24 0.09–0.66
35–44 years 805 18.4 1.29 1.07–1.56 0.19 0.12–0.30
45–54 years 1332 30.4 1.02 0.86–1.20 0.35 0.26–0.46
55–64 years (referent) 2085 47.6 1.00 - - -
Race/ethnicity
Non-Hispanic White (referent) 3915 89.3 1.00 - 1.00 -
Non-Hispanic Black 131 3.0 0.90 0.58–1.39 1.58 0.93–2.68
Hispanic 120 2.7 1.37 0.93–2.04 1.22 0.64–2.33
Non-Hispanic Asian 160 3.7 0.86 0.57–1.31 0.68 0.33–1.39
Other/unknown 59 1.4 0.75 0.37–1.50 1.36 0.60–3.07
Stage at diagnosis
Local (referent) 3126 71.3 1.00 - 1.00 -
Regional 1208 27.6 2.28 1.97–2.63 1.86 1.65–2.09
Unstaged 51 1.2 3.80 2.41–5.99 3.35 2.28–4.92
SEER region
San Francisco–Oakland (referent) 1624 37.0 1.00 - 1.00 -
Seattle–Puget Sound 1423 34.1 1.10 0.91–1.32 1.17 0.91–1.51
Iowa 1268 28.9 1.15 0.96–1.38 0.86 0.66–1.13
Year of diagnosis
1983 (referent) 1037 23.7 1.00 - 1.00 -
1985 1003 22.9 1.06 0.88–1.29 0.96 0.73–1.27
1987 1232 28.1 0.97 0.80–1.18 1.05 0.78–1.40
1989 1113 25.4 0.84 0.68–1.04 1.12 0.80–1.56
Grade
Well differentiated (referent) 142 3.2 1.00 - 1.00 -
Moderately differentiated 695 15.9 2.16 1.13–4.13 0.75 0.42–1.34
Poorly differentiated 817 18.6 3.45 1.82–6.53 0.79 0.44–1.40
Undifferentiated 182 4.2 4.97 2.52–9.78 1.23 0.60–2.52
Unknown 2549 58.1 2.41 1.28–4.52 0.85 0.50–1.45
Histology
Ductal (referent) 3253 74.2 1.00 - 1.00 -
Lobular 279 6.4 1.09 0.82–1.44 1.26 0.88–1.81
Mixed ductal and lobular 167 3.8 1.06 0.74–1.53 0.66 0.32–1.33
Other 686 15.6 0.71 0.57–0.89 1.01 0.76–1.34
Radiation therapy
No (referent) 4047 92.3 1.00 - 1.00 -
Yes 329 7.5 1.43 1.15–1.78 0.84 0.54–1.30
Unknown 9 0.2 4.68 1.74–12.60 - -
Type of breast implantb
Silicone gel (referent) 274 39.8 1.00 - 1.00 -
Saline 118 17.1 1.01 0.44–2.34 1.75 0.29–10.39
Double lumen 240 34.8 1.49 0.83–2.70 3.13 0.91–10.78
Other 26 3.8 1.11 0.24–5.03 - -
Unknown 31 4.5 2.03 0.71–5.79 6.59 1.17–37.06
aAdjusted for age, year, stage at diagnosis, race/ethnicity, SEER region, tumor grade, histology, and radiation therapy.
bModel (n = 689) restricted to women with implant-specific information and concordant bilateral implants
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| 15743498 | PMC1064128 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Dec 23; 7(2):R184-R193 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr974 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9751574349910.1186/bcr975Research ArticleKnockdown of c-Myc expression by RNAi inhibits MCF-7 breast tumor cells growth in vitro and in vivo Wang Yi-hua [email protected] Shuang [email protected] Guo [email protected] Cui-qi [email protected] Hong-xia [email protected] Xiao-bo [email protected] Lan-ping [email protected] Jin-feng [email protected] Ning-zhi [email protected] Laboratory of Cell and Molecular Biology, Cancer Institute & Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China2005 17 12 2004 7 2 R220 R228 24 5 2004 10 8 2004 26 9 2004 24 11 2004 Copyright © 2004 Wang et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Breast cancer is the leading cause of cancer death in women worldwide. Elevated expression of c-Myc is a frequent genetic abnormality seen in this malignancy. For a better understanding of its role in maintaining the malignant phenotype, we used RNA interference (RNAi) directed against c-Myc in our study. RNAi provides a new, reliable method to investigate gene function and has the potential for gene therapy. The aim of the study was to examine the anti-tumor effects elicited by a decrease in the protein level of c-Myc by RNAi and its possible mechanism of effects in MCF-7 cells.
Method
A plasmid-based polymerase III promoter system was used to deliver and express short interfering RNA (siRNA) targeting c-myc to reduce its expression in MCF-7 cells. Western blot analysis was used to measure the protein level of c-Myc. We assessed the effects of c-Myc silencing on tumor growth by a growth curve, by soft agar assay and by nude mice experiments in vivo. Standard fluorescence-activated cell sorter analysis and TdT-mediated dUTP nick end labelling assay were used to determine apoptosis of the cells.
Results
Our data showed that plasmids expressing siRNA against c-myc markedly and durably reduced its expression in MCF-7 cells by up to 80%, decreased the growth rate of MCF-7 cells, inhibited colony formation in soft agar and significantly reduced tumor growth in nude mice. We also found that depletion of c-Myc in this manner promoted apoptosis of MCF-7 cells upon serum withdrawal.
Conclusion
c-Myc has a pivotal function in the development of breast cancer. Our data show that decreasing the c-Myc protein level in MCF-7 cells by RNAi could significantly inhibit tumor growth both in vitro and in vivo, and imply the therapeutic potential of RNAi on the treatment of breast cancer by targeting overexpression oncogenes such as c-myc, and c-myc might be a potential therapeutic target for human breast cancer.
c-Mycgene therapyMCF-7RNA interference
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Introduction
Breast cancer is the leading cause of cancer death in women worldwide. Despite advances in detection and chemotherapy, many women with breast cancer continue to die of this malignancy [1]. Therefore, an understanding of the molecular mechanisms involved in breast cancer formation and progression should be helpful in developing more effective treatments for breast cancer.
c-Myc is believed to participate in most aspects of cellular function, including replication, growth, metabolism, differentiation, and apoptosis [2]. Previous studies indicate that c-Myc activates a variety of known genes as part of a heterodimeric complex with Max [2]. A frequent genetic abnormality seen in breast cancer is the elevated expression of c-Myc [3,4]. The importance of c-Myc expression in breast cancer is demonstrated both by studies of transgenic mice and by clinical research [3,5]. Abnormal expression of c-myc transgenes in the mouse mammary gland is associated with an increased incidence of breast carcinomas [5]. Moreover, clinical studies have indicated that c-Myc is important in the development and progression of breast cancer, in that overexpression of c-Myc was found in most breast cancer patients and was correlated with poor prognosis in those patients [3].
The role of c-Myc in breast cancer has been extensively examined in many studies for the past decade [6]; however, specifically reducing its level by genetic means in established breast cancer cell lines is still helpful for a better understanding of its role in maintaining the malignant phenotype. Thus, in this study, we investigated whether specifically decreasing the protein level of c-Myc in a breast cancer cell line in which this protein was overexpressed might result in the inhibition of cell growth in vitro and in vivo. For this purpose, RNA interference (RNAi) directed against c-myc was used.
RNAi is the sequence-specific gene silencing induced by double-stranded RNA (dsRNA). This phenomenon is conserved in a variety of organisms: Caenorhabditis elegans, Drosophila, plants, and mammals. RNAi is mediated by short interfering RNAs (siRNAs) that are produced from long dsRNAs of exogenous or endogenous origin by an endonuclease of the ribonuclease-III type, called Dicer. The resulting siRNAs are about 21–23 nucleotides (nt) long and are then incorporated into a nuclease complex, the RNA-inducing silencing complex, which then targets and cleaves mRNA containing a sequence identical to that of the siRNA [7]. Rapid progress has been made in the use of RNAi [8]. More recently, a technical breakthrough came from the demonstration that dsRNA of 19–29 nt expressed endogenously with RNA polymerase III promoter induced target gene silencing in mammalian cells [9]. The expression of siRNA from DNA templates offers several advantages over chemically synthesized siRNA delivery. Hairpin siRNAs transcribed from a vector are thought to suppress the expression of targeted genes more efficiently, less expensively and more easily than synthesized siRNA [10].
Here we used a plasmid-based polymerase III promoter system to deliver and express siRNA targeting c-myc to determine whether this technique could be used for the specific inhibition of oncogene overexpression and whether this inhibition resulted in antitumor effects. We showed in our study that specific downregulation of c-Myc by RNAi was sufficient to inhibit the growth of MCF-7 cells in vitro and in vivo, and that c-myc might serve as a therapeutic target for human breast cancer.
Method
Plasmid construction
To generate c-Myc knockdown vector, one annealed set of oligonucleotides encoding short hairpin transcripts corresponding to nt 1906–1926 of c-myc mRNA (GenBank accession no. NM-002467) [11] were cloned into pSilencer 1.0_U6 (Ambion; hereafter abbreviated to pSilencer). In brief, the short-hairpin-RNA-encoding complementary single-stranded oligonucleotides, which hybridized to give overhangs compatible with ApaI and EcoRI, were designed with a computer program available on the Internet . Oligonucleotides encoding short hairpin RNAs were then ligated into pSilencer. Bacterial colonies were pooled and used for plasmid preparation. The positive clones were confirmed by sequencing. The resulting plasmid was designated as pSilencer–c-Myc.
Cell line and cell culture
The breast cancer adenocarcinoma cell line MCF-7 was obtained from the American Type Culture Collection (Manassas, VA). The cells were grown in Dulbecco's modified Eagle's medium (Invitrogen) supplemented with 10% fetal bovine serum (Gibco BRL), 50 units/ml penicillin, and 50 μg/ml streptomycin. The MCF-7 cells were maintained in a humidified 37°C incubator with 5% CO2, fed every 3 days with complete medium, and subcultured when confluence was reached.
Transfection of cells
A total of 2 × 105 cells were seeded into each well of a six-well tissue culture plate (Costar). The next day (when the cells were 70–80% confluent), the culture medium was aspirated and the cell monolayer was washed with prewarmed sterile phosphate-buffered saline (PBS). Cells were transfected with the appropriate plasmids by using LipofectAMINE reagent (Invitrogen) in accordance with the manufacturer's protocol. The cells were harvested at different time points. Western blot analysis or other experiments were performed.
Western blot analysis
Cells were harvested at different time points and lysed in mammalian cell lysis buffer, then western blot analysis was performed with the use of conventional protocols as described previously [12]. In brief, the protein concentration was determined with a bicinchoninic acid kit with bovine serum albumin as a standard (Pierce). Equal amounts of total protein were then separated on 12% polyacrylamide gels by using standard sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) techniques, then transferred to nitrocellulose membranes (PROTRAN). The antibodies and dilutions used included anti-c-Myc (9E10; 1:1000 dilution; Santa Cruz) and anti-β-actin (AC-15; 1:5000 dilution; Sigma), and after being washed extensively the membranes were incubated with anti-mouse IgG–horseradish peroxidase conjugate antibody (Zhongshan Company) for 1 hour at room temperature and developed with a Luminol chemiluminescence detection kit (Santa Cruz). Membranes probed for c-Myc were reprobed for β-actin to normalize for loading and/or quantification errors and to allow comparisons of target protein expression to be made. Protein expression was quantified with a Gel EDAS analysis system (Cold Spring USA Corporation) and Gel-Pro Analyzer 3.1 software (Media Cybernetics).
Cell growth assay
At 2 days after transfection, MCF-7 cells transfected with indicated plasmids were harvested and replated at a density of 50 cells/mm2 in triplicate. The total cell number was quantified every 2 days with a hematocytometer and an Olympus inverted microscope. Cell viability was assessed by using trypan blue.
Soft agar colony assay
At 2 days after transfection, MCF-7 cells (300 cells per well) transfected with indicated plasmids were mixed with tissue culture medium containing 0.7% agar to result in a final agar concentration of 0.35%. Then 1 ml samples of this cell suspension were immediately plated in six-well plates coated with 0.6% agar in tissue culture medium (2 ml per well) and cultured at 37°C with 5% CO2. After 2 weeks the top layer of the culture was stained with 0.2% p-iodonitrotetrazolium violet (Sigma). The culture was analyzed in triplicate, and colonies larger than 100 μm in diameter were counted.
Tumor growth in nude mice
Equal numbers (106 or 2 × 106) of MCF-7 cells transfected with pSilencer–c-Myc or pSilencer were harvested by trypsinization 2 days after transfection, washed twice with 1 × PBS, and resuspended in 0.2 ml of saline. Two groups of five 4–6-week-old female nude mice were then given bilateral subcutaneous injections with control cells or cells transfected with plasmids against c-Myc. The mice were kept in pathogen-free environments and checked every 2 days. The date at which a palpable tumor first arose and the weight of the tumor were recorded.
Cell cycle analysis
Standard fluorescence-activated cell sorting analysis was used to determine apoptosis of the cells. In brief, MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer; 24 hours later, cells were deprived of serum for 36 hours. Then cells were harvested, washed once in PBS and stained with propidium iodide (BD Biosciences). The apoptotic cells were assessed by flow cytometric detection of sub-G1 DNA content.
TdT-mediated dUTP nick end labelling assay
Apoptotic cells were confirmed with the in situ cell death detection kit, Alkaline Phosphatase (Roche Applied Science), in accordance with the manufacturer's instructions. In brief, MCF-7 cells were grown on coverslips. The next day, cells were transfected with pSilencer–c-Myc or pSilencer. At 24 hours after transfection, cells were deprived of serum for 36 hours. Coverslips with adherent cells were fixed in 4% paraformaldehyde for 1 hour at room temperature and permeabilized with 0.1% Triton X-100 for 2 min on ice. DNA fragments were labeled with the TdT-mediated dUTP nick end labelling (TUNEL) reaction mixture for 60 min at 37°C in a humidified atmosphere in the dark. The coverslips were then incubated with Converter alkaline phosphatase for 30 min at 37°C in a humidified chamber, rinsed in PBS, and incubated with nitro blue tetrazolium/5-bromo-4-chloroindol-3-yl phosphate (Roche Applied Science) for 10 min. Cells were mounted cell side downward on a microscope slide, and the apoptotic cells (dark blue staining) were counted under a microscope. Three fields were randomly counted for each sample.
Statistical analysis
SPSS for Windows (SPSS Inc.) was used to analyze the data and plot curves. A two-tailed unpaired t-test was used to compare the statistical significance of the differences in data from the two groups.
Results
Suppression of c-Myc overexpression in MCF-7 cells by RNAi
We designed and synthesized one pair of oligonucleotides encoding short hairpin transcripts directed against a portion of the c-myc mRNA. This pair of oligonucleotides was then ligated into pSilencer. The plasmid of pSilencer–c-Myc contains a U6 promoter that directs the synthesis of oligonucleotides in an inverted repeat with 9 nt for its loop, with six T bases added at the end to serve as a termination signal for RNA polymerase III. The RNA is expected to fold back to form a hairpin loop structure after being transcribed; the hairpin dsRNA can then be further cleaved by Dicer to generate a 21-nucleotide siRNA, the active form for the RNAi effect, which will form dsRNA–endonuclease complexes and will bind and destroy c-myc mRNA inside cells [8] (Fig. 1).
pSilencer–c-Myc was transfected into MCF-7 cells and its effects on c-Myc protein levels were determined by comparison with pSilencer-transfected cells by western blot at the time points indicated. We found that the c-Myc expression levels were suppressed by up to 80% in MCF-7 cells at day 5 after transfection. Actually the levels of c-Myc were decreased as early as 24 hours after transfection and remained at low levels until 12 days after transfection (Fig. 2). The inhibitory effect was shown to be specific because transfection with pSilencer did not alter c-Myc levels. In addition, RNAi did not cause a nonspecific downregulation of gene expression, as determined by the β-actin control (Fig. 2). Similar results with c-Myc RNAi were also seen in other cell lines including HCT116 and HepG2 (data not shown). These data indicated that vector-based RNAi could effectively suppress c-Myc overexpression and resulted in prolonged decreases in specific cellular gene expression without marked effects on other cellular proteins.
Decreased levels of c-Myc significantly alter the growth rate of MCF-7 cells
Previous studies have shown that c-Myc is important in cellular proliferation and cell growth [6]. Thus, increased levels of c-Myc might have a function in the growth advantage seen in breast tumors. In our study, MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer. The number of MCF-7 cells was then counted every 2 days after transfection. Our data showed that RNAi directed against c-myc significantly decreased the growth rate of MCF-7 cells, with a 50–60% decrease at different time points repeatedly in three separate experiments (Fig. 3).
Decreases in c-Myc protein inhibit colony formation
We then tested whether RNAi-mediated reductions in c-Myc levels could influence the ability of MCF-7 cells to form colonies in soft agar. MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer. At 48 hours after transfection, the cells were placed into medium with soft agar, and colonies were counted after 2 weeks. RNAi directed against c-myc resulted in a significant decrease (about 65%) in colony formation in MCF-7 cells (Fig. 4). The smaller number of colonies in the pSilencer–c-Myc group than in the control group was statistically significant (P < 0.001). These results showed that the reduction in c-Myc protein level decreased the ability of breast cancer cells to form colonies in soft agar.
RNAi directed against c-Myc reduces tumor growth in nude mice
To address the potential effects of RNAi in vivo on inhibiting the growth of breast cancer cells, equal numbers (106 or 2 × 106) of MCF-7 cells transfected with pSilencer–c-Myc or pSilencer were injected into female nude mice (five animals for each treatment). At 5 or 8 weeks after injection of these cells, the mice were killed and the weights of the tumors were recorded. As seen in Table 1, when 106 cells were injected into nude mice, tumors were seen 8 weeks later in three of five at the left lateral where MCF-7 cells transfected with pSilencer were injected, whereas none were seen at the right lateral where MCF-7 cells transfected with pSilencer–c-Myc were injected. In addition, 5 weeks after 2 × 106 cells were injected into nude mice, tumors were seen in four of five at the left lateral where MCF-7 cells transfected with pSilencer were injected, whereas none were seen at the right lateral where MCF-7 cells transfected with pSilencer–c-Myc were injected (Table 1). Thus, c-Myc RNAi significantly suppressed tumor growth in nude mice in comparison with control, indicating that targeting c-myc by RNAi could exert a strong antitumor effect in vivo on MCF-7 cells.
Induction of apoptosis in MCF-7 cells by RNAi depletion of c-Myc upon serum deprivation
The above data demonstrated that knockdown of c-Myc in MCF-7 cells could significantly inhibit the growth of tumor cells both in vitro and in vivo. To determine whether depletion of c-Myc could promote the death of tumor cells, flow cytometry and TUNEL assays were performed. At 24 hours after transfection with pSilencer–c-Myc or pSilencer, MCF-7 cells were deprived of serum for 36 hours. These cells were then analyzed by flow cytometry or TUNEL assay. Significant sub-G1 (apoptotic) populations were observed in the flow cytometry assay. We found that 31.1% of MCF-7 cells transfected with pSilencer–c-Myc underwent apoptosis after serum starvation, compared with 5.8% in the control group (Fig. 5a). We also confirmed the apoptosis of MCF-7 cells by TUNEL assay (Fig. 5b). About 40% cells were TUNEL-positive in the pSilencer–c-Myc group, compared with 6% in the control group (P < 0.01). These data suggested that depletion of c-Myc by RNAi in MCF-7 cells made the cells more sensitive to apoptosis after serum deprivation.
Discussion
Cancer cells often show alteration in the signal-transduction pathways, leading to proliferation in response to external signals. Oncogene overexpression is a common phenomenon in the development and progression of many human cancers. Oncogenes therefore provide a potential target for cancer gene therapy [13].
The important oncogene c-myc is expressed in a high proportion of most human cancers, including breast, prostate, gastrointestinal cancer, lymphoma, melanoma, and myeloid leukemia [14]. In its physiological role, c-Myc is broadly expressed during embryogenesis and in tissue compartments of the adult that possess high proliferative capacity. Altered expression of c-Myc seems to define a common event associated with the pathogenesis of most human cancers [6]. Previous studies demonstrated that the continued presence of c-Myc was required for cancer development and not just for initiation, and inactivation of c-Myc resulted in the sustained regression of tumors [15-17]. Similar results were also observed in breast cancer. D'Cruz and colleagues demonstrated that overexpression of c-Myc by an inducible system in the mammary epithelium of transgenic mice resulted in the formation of invasive mammary adenocarcinomas, many of which regressed fully after c-Myc deinduction [18].
Therefore, specific downregulation of c-Myc might be a potential therapeutic strategy against human cancers, including breast cancer. In fact, the antagonists of c-Myc, including full-length antisense mRNA [19], oligonucleotides against c-myc mRNA [20] or a dominant-negative mutant [21], were previously reported to inhibit proliferation of cancer cell lines in vitro. However, it was only successful in some situations; these technologies have been difficult to apply universally [22]. Recently the advent of RNAi-directed 'knock-down' has sparked a revolution in somatic cell genetics, allowing the inexpensive, rapid analysis of gene function in mammals, and might be exploited for gene therapy [7,8]. Some studies directly compared RNAi with antisense RNA and found that RNAi seemed to be quantitatively more efficient and durable in cell culture and in nude mice [23].
By means of the RNAi method, in the present study, cellular growth assays, both in vitro and in vivo, were used to determine the functional consequences of RNAi-mediated decreases in of c-Myc in established breast cancer cells. Our results demonstrated that RNAi can effectively downregulate oncogene overexpression with great specificity. We showed that the plasmids endogenously expressing siRNA could successfully deplete up to 80% of c-Myc expression in MCF-7 cells at day 5 after transfection. Furthermore, the tumor inhibition effects persisted for at least 12 days after transfection in dishes and for 2 months in nude mice as shown by experiments in vitro and in vivo, even though the protein level of c-Myc in silenced clones expressing siRNA was back to almost the same level as in the control cells by day 12 after transfection. Our data were consistent with the results of Jain and colleagues [17].
They showed that within 24 hours of c-myc inactivation, the osteogenic sarcoma cells flattened and showed less cell division. And even after reactivation of c-myc expression in these cultured cells, total cell numbers continued to be lower. Less than 1% of the tumor cells regained their neoplastic growth properties [17]. All of these data indicated that brief inactivation of c-Myc could induce a sustained loss of neoplastic phenotypes. Moreover, other groups using chemically synthesized siRNAs to knock down their favored oncogenes also found that a transient decrease in oncogene expression could inhibit the growth of tumor cells in vitro and/or in vivo [8]. Nevertheless, the underlying mechanism of this phenomenon in MCF-7 cells should be further investigated.
Although some studies previously revealed that the effects of inactivation of c-Myc in some cell lines were modest [6], other groups using different approaches to reduce the protein level of c-Myc found that a decrease in c-Myc expression could inhibit the growth of these tumor cells, including breast tumor cells [19-21]. Nevertheless there were still conflicting results on whether c-Myc expression was necessary to maintain tumorigenesis in different animal models from different laboratories [15-18,24,25]. For example, some studies showed that the role of oncogenic c-Myc in tumor maintenance was essential and that all effects of c-Myc in vivo were reversible, in that without continuous c-Myc activation there would even be regression of established tumors back to phenotypically normal in transgenic mouse models [15,16,24]. Similar phenomena were observed by other groups focusing on other oncogenes, such as bcr/abl [26] and H-ras [27]. However, there were also conflicting reports, mainly showing that brief inactivation of c-Myc could induce sustained loss of neoplastic phenotypes in certain animal models [17,18,25]. It was notable that without secondary oncogenic mutation, spontaneously or selectively, such as Kras2 oncogene mutation, in nearly all breast tumors induced by conditionally expressing the human c-Myc in the mammary epithelium of a transgenic mouse model, deinduction of c-Myc protein could lead to full regression of tumors [18]. Similar data were also obtained by Karlsson and colleagues that the inactivation of c-Myc alone was found to be sufficient to cause sustained tumor regression in c-Myc-induced hematopoietic tumors; in contrast, tumor cells that acquired novel chromosomal translocations relapsed independently of Myc to maintain their neoplastic phenotype [25]. It was therefore not surprising for us to show here that a transient reduction of c-Myc protein level by RNAi could significantly inhibit the growth rate of MCF-7 cells and its ability to form colonies in soft agar. Additionally, the remarkable effect in nude mice supported the effectiveness of this treatment.
Our data also suggested that knockdown of c-Myc by RNAi in MCF-7 cells could increase the sensitivity of these cells to apoptotic stimuli, such as serum starvation. This was most probably one of the reasons for the anti-tumor effects. There have previously been conflicting reports about the role of c-Myc in apoptosis [3]. Constant overexpression of c-Myc might induce apoptosis [2,6], and a decrease in c-Myc levels by techniques brought about by, for example, an antisense approach might also cause apoptosis of certain tumor cells [28-30] or might increase the sensitivity of the cells to apoptotic stimuli [31]. These conflicting observations suggested that c-Myc was capable of both inducing and suppressing apoptosis in different types of tumor cell, under different conditions, and in different systems. In MCF-7 cells, suppression of c-Myc expression in response to aromatase inhibitors or topoisomerase α inhibitors could induce these cells to apoptosis [32,33]. However, the pathways that c-Myc controls and/or that are involved in the observed apoptosis remain obscure. D'Agnano and colleagues suggested that in melanoma cells the downregulation of c-Myc by an antisense approach could activate apoptosis by increasing the levels of p27Kip1 [28]. Overexpression of the cyclin-dependent kinase inhibitor p27Kip1 was able to promote apoptosis in several mammalian tumor cell lines [34]. However, understanding the precise pathway by which a decrease in c-Myc in MCF-7 cells by RNAi was able to induce apoptosis upon serum deprivation needs further study.
So far, RNAi has been used to inhibit virus-induced diseases (for example HIV [35] and influenza [36]), oncogenic K-ras, H-ras-induced tumorigenesis [37,38], activation of oncogenes resulting from chromosomal translocations (for example bcr/abl in chromic myeloid leukemia [39]), cancers caused by viral infections [40], and so on. Recently, retroviral-based approaches to deliver siRNA into tissue-cultured mammalian cells have been proved to be powerful [37,38], and doxycycline-regulated inducible knockdown of gene expression by RNAi has been shown to be particularly useful for the analysis of genes that are essential for cellular survival [41,42]. These studies have marked a new era in the genetic manipulation of human cancer development by allowing oncogenes to be downregulated by RNAi.
Conclusion
In summary, RNAi has been used in this study to demonstrate that decreases in c-Myc levels can inhibit tumor growth in assays both in vivo and in vitro. In addition, these data indicate that RNAi provides a useful method with which to study the role of genes that control the growth of cancer cells; and, given its specificity and the lower doses needed to inhibit gene expression than those required for antisense oligonucleotides, RNAi might have potential therapeutic utility in a variety of disease states, including cancers. Future studies could further test whether c-Myc overexpression can be efficiently depleted by siRNA expressed from a DNA-based expression vector combined with a tumor-specific promoter, such that RNAi can specifically target oncogenes in cancer cells without affecting normal cells.
Abbreviations
dsRNA = double-stranded RNA; nt = nucleotides; PBS = phosphate-buffered saline; RNAi = RNA interference; SDS–PAGE = sodium dodecyl sulfate–polyacrylamide gel electrophoresis; siRNA = short interfering RNA.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
YW designed the experiments, constructed the plasmids, performed transfections, and wrote the manuscript. SL constructed the growth curve and performed the nude mice experiments. GZ performed the western blots and the soft agar assay. CZ, HZ, and XZ performed the flow cytometry and TUNEL experiments. LQ and JB conducted the cell culturing. NX is the corresponding author. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by National Natural Science Foundation 39925020 and State Key Basic Research Program G1998051204, PR China.
Figures and Tables
Figure 1 Schematic drawing of the pSilencer1.0_U6 vector. The U6-RNA promoter was cloned in front of the gene-specific targeting sequence (19-nucleotide sequences from the target transcript separated by a short spacer from the reverse complement of the same sequence) and six thymidines (T6) as a termination signal. The predicted secondary structure of the pSilencer–c-Myc transcript target c-Myc is shown. The transcript, a short hairpin double-stranded RNA (dsRNA), is believed to be further cleaved by Dicer to generate a 21-nucleotide siRNA that forms dsRNA–endonuclease complexes and will bind and destroy c-myc mRNA.
Figure 2 Time course of the reduction in c-Myc protein levels by pSilencer–c-Myc. Exponentially proliferating MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer and whole cell lysates were prepared at the time points indicated. Total cell lysates were separated by SDS–polyacrylamide-gel electrophoresis and immunoblotted with an antibody against c-Myc; expression levels were normalized for loading by probing for β-actin.
Figure 3 RNAi directed against c-Myc leads to a reduced cellular growth rate. MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer. After 48 hours the cells were trypsinized and replated at a density of 50 cells/mm2 in triplicate. Cells were counted every 2 days. The data shown are means and SD from three independent experiments. **P < 0.01.
Figure 4 Knockdown of c-Myc by RNAi reduces colony formation in soft agar. MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer as controls, and seeded in 0.35% agarose containing Dulbecco's modified Eagle's medium with 10% fetal bovine serum. The colony numbers were counted 2 weeks later. (a) Representative wells demonstrating the total number of colonies formed by MCF-7 transfected with the indicated plasmids. (b) The numbers of colonies of pSilencer–c-Myc-treated cells standardized against the control cells (set at 100%). The data shown are means and SD from two independent triplicate experiments. The difference between treatments is statistically significant (P < 0.001).
Figure 5 Apoptosis induced after serum deprivation by depletion of c-Myc in MCF-7 cells. (a) Downregulation of c-Myc promoted apoptosis of MCF-7 cells after serum deprivation. MCF-7 cells were transfected with pSilencer–c-Myc or pSilencer. After 24 hours, cells were deprived of serum for 36 hours. Cells were then collected by trypsinization. The apoptotic cells were determined by flow cytometry. The cell population in sub-G1 is shown. The x and y axes show DNA content and cell number, respectively. (b) TUNEL assay to detect apoptotic cells in situ. MCF-7 cells were grown on coverslips and transfected with pSilencer–c-Myc or pSilencer. After 24 hours, cells were deprived of serum for 36 hours. Cells were then analyzed for apoptosis with the TUNEL assay. Dark-blue staining of nuclei indicates apoptosis. Arrows indicate representative TUNEL-positive cells.
Table 1 RNAi directed against c-Myc significantly inhibits MCF-7 cell growth in nude mice
No. of cellsa pSilencer pSilencer–c-Myc
Tumor weight (mg) Tumors Injections Latencyb (day) Tumor weight (mg) Tumors Injections Latencyb (day)
106 45–210 3 5 42–49 0 0 5 >56
2 × 106 145–2248 4 5 21–28 0 0 5 >35
aNumber of cells transfected with the indicated plasmids that were injected subcutaneously, bilaterally into five nude mice.
bThe time interval required for a palpable tumor to arise.
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| 15743499 | PMC1064129 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Dec 17; 7(2):R220-R228 | utf-8 | Breast Cancer Res | 2,004 | 10.1186/bcr975 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9761574350310.1186/bcr976Research ArticleSULT1A1 genotype, active and passive smoking, and breast cancer risk by age 50 years in a German case–control study Lilla Carmen [email protected] Angela [email protected] Silke [email protected] Jenny [email protected] Division of Clinical Epidemiology, German Cancer Research Center, Heidelberg, Germany2 Division of Toxicology and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany2005 14 1 2005 7 2 R229 R237 11 8 2004 24 9 2004 22 11 2004 25 11 2004 Copyright © 2005 Lilla et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Sulfotransferase 1A1 (encoded by SULT1A1) is involved in the metabolism of procarcinogens such as heterocyclic amines and polycyclic aromatic hydrocarbons, both of which are present in tobacco smoke. We recently reported a differential effect of N-acetyltransferase (NAT) 2 genotype on the association between active and passive smoking and breast cancer. Additional investigation of a common SULT1A1 genetic polymorphism associated with reduced enzyme activity and stability might therefore provide deeper insight into the modification of breast cancer susceptibility.
Methods
We conducted a population-based case–control study in Germany. A total of 419 patients who had developed breast cancer by age 50 years and 884 age-matched control individuals, for whom risk factor information and detailed smoking history were available, were included in the analysis. Genotyping was performed using a fluorescence-based melting curve analysis method. Multivariate logistic regression analysis was used to estimate breast cancer risk associated with the SULT1A1 Arg213His polymorphism alone and in combination with NAT2 genotype in relation to smoking.
Results
The overall risk for breast cancer in women who were carriers of at least one SULT1A1*2 allele was not significantly different from that for women with the SULT1A1*1/*1 genotype (adjusted odds ratio 0.83, 95% confidence interval 0.66–1.06). Risk for breast cancer with respect to several smoking variables did not differ substantially between carriers of the *2 allele and noncarriers. However, among NAT2 fast acetylators, the odds ratio associated with passive smoking only (3.23, 95% confidence interval 1.05–9.92) was elevated in homozygous carriers of the SULT1A1*1 allele but not in carriers of the SULT1A1*2 allele (odds ratio 1.28, 95% confidence interval 0.50–3.31).
Conclusion
We found no evidence that the SULT1A1 genotype in itself modifies breast cancer risk associated with smoking in women up to age 50 years. In combination with NAT2 fast acetylator status, however, the SULT1A1*1/*1 genotype might increase breast cancer risk in women exposed to tobacco smoke.
breast cancerpolymorphismsmokingsulfotransferase
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Introduction
Epidemiologic evidence linking cigarette smoking to increased risk for development of breast cancer is mounting (for review [1,2]). In addition, findings from both epidemiology and molecular biology indicate that there is differential susceptibility within the population to development of malignant neoplasms following exposure to certain xenobiotics because of polymorphisms in genes that encode metabolizing enzymes.
Previously, we reported a differential effect of N-acetyltransferase (NAT) 2 genotype on the association between active and passive smoking and breast cancer risk [3]. The identification of passive smoking as a breast cancer risk factor, particularly for fast acetylators, implied that heterocyclic aromatic amines (HCAs) are among the responsible carcinogens. HCAs are particularly abundant in side-stream tobacco smoke [4] and are activated by O-acetylation catalyzed by NATs [5]. Because sulfotransferase (SULT)1A1 (encoded by SULT1A1) is also involved in the metabolism of pro-carcinogens from tobacco smoke, the additional investigation of a common polymorphism in the SULT1A1 gene might provide deeper insight into the modification of susceptibility to breast cancer.
The SULT1A1 enzyme is generally associated with detoxification of xenobiotic compounds and has been implicated in oestrogen metabolism. However, Glatt and coworkers [6,7] showed that several substances can be activated by the conjugation reaction with SULT1A1, among which are pro-carcinogens such as polycyclic aromatic hydrocarbons and HCAs, both of which are present in tobacco smoke [8,9].
In contrast to earlier assumptions [10], there is increasing evidence that the SULT1A1 enzyme apparently does not play an important role in oestrogen metabolism in vivo. Results from in vitro studies showed that only the SULT1E1 enzyme is capable of the sulfonation of oestradiol, oestrone and catecholestrogens at physiologically relevant concentrations [11,12]. For instance, Adjei and Weinshilboum [11] showed that Km values for the sulfonation of oestradiol with SULT1A1 were in the micromolar range, which is clearly above physiological concentrations, whereas the Km value for SULT1E1 was considerably lower (0.029 ± 0.01 μmol/l).
Large interindividual variations in the biochemical and metabolic properties of the SULT1A1 enzyme have been observed that can partly be explained by a G to A polymorphism at nucleotide 638 (Arg213His), referred to as the *2 allele. The *2 allele has been associated with lower activity and lower thermal stability of the SULT1A1 enzyme [13,14], and thus reduced bioactivation of mutagens [6].
Thus far, three case–control studies that investigated the association between SULT1A1 genotype and breast cancer risk [15-17] have been reported. Results were not consistent and the effect of smoking was not considered in any of those case–control studies. Saintot and coworkers [18] recently reported a positive interaction between smoking and the variant allele for SULT1A1 with respect to breast cancer risk in a case-only study.
We conducted the present study to elucidate the potential role of SULT1A1 genotype alone and in combination with NAT2 genotype as a modifier of susceptibility to breast cancer associated with exposure to tobacco smoke among predominantly premenopausal women.
Methods
Study population
The present study is based on a case–control study that is described in greater detail elsewhere [19,20]. In brief, between January 1992 and December 1995 a population-based case–control study on breast cancer was conducted in two regions (Rhein-Neckar-Odenwald and Freiburg regions) in southern Germany. Women with a diagnosis of in situ or invasive breast cancer were identified by surveying all of the hospitals that serve the two study regions. Women were eligible for inclusion in the study if they spoke German, if they lived in the study region and if the neoplasm was diagnosed before their 51st birthday. During the period of study 1020 women were identified, of whom 1005 were alive at the time of identification. Of the living, eligible patients, 706 (70.2%) completed a self-administered questionnaire. For every patient, two controls were selected randomly from lists of female residents obtained by the population registries of the study regions and matched according to exact age and residence. Of the 2257 eligible control individuals who were contacted by letter, 1381 (61.2%) participated in the study. After giving written informed consent, all participants completed a self-administered questionnaire and were asked to provide a blood sample. The study is in compliance with the Declaration of Helsinki and was reviewed and approved by the ethics committee of the University of Heidelberg.
The study participants were re-contacted in August 1999 and were invited to participate in a computer-assisted telephone interview to assess comprehensively their history of active and passive smoking [20]. Of the original study population, 66.3% of cases and 78.9% of controls took part in this additional investigation. In short, women were asked when they began smoking, the type of product, the amount and frequency of tobacco usage, the intensity of inhalation, and the date of cessation or changes in their smoking habits. Exposure to passive smoking was assessed in childhood, in the adult household and at work. For passive smoking in adult life, women who had lived with a smoking partner were asked the onset, end, or changes to smoking exposure, daily amount and type of product smoked, and number of hours and days of passive exposure. For childhood exposure as well as exposure at work and that due to other household members, questions pertained to number of smokers living in the household, onset of exposure, and the number of hours and days of smoke exposure that the participant experienced in the presence of each smoking person. All information was truncated at the reference date, which was the date of diagnosis for patients and the date of recruitment for control individuals.
Menopausal status was defined as the reported state at half a year before the reference date. The status of women with previous hysterectomy not accompanied by bilateral oophorectomy was not ascertainable and therefore classified as unknown. Because the study participants were all aged 50 years or younger, these women were included in the analysis restricted to the subgroup with premenopausal status.
Blood samples were available for 95% of cases and 82% of controls in the original study population. This analysis was restricted to women who had either both (97.8%) or at least one parent of German nationality (2.2%) in order to achieve ethnic homogeneity of the study population. In total, 419 patients with breast cancer and 884 control individuals, for whom full genotype information and detailed history of tobacco smoke exposure were available, were included in the analysis.
Genotyping
DNA was extracted from EDTA blood samples using a standard method based on salt precipitation. SULT1A1-specific primers and hybridization probes were used to detect G638A in exon 7. The primers for DNA amplification were previously described by Coughtrie and coworkers [21]. As sensor and anchor probes, we used LCRed640-CAgggAgCgCCCCACAA-p and gAACCATgAAgTCCACggTCTCCTCT-x, respectively. PCR and melting curve analyses were performed in 10 μl volumes in glass capillaries (Roche Diagnostics, Mannheim, Germany) using the following: 1× PCR buffer, 2.5 mmol/l MgCl2, 200 μmol/l dNTPs, 0.1% bovine serum albumin, 0.5 U Taq polymerase, 0.15 μmol/l of each probe (TIB MOLBIOL, Berlin), 1 μmol/l of the sense primer (CF) and 0.1 μmol/l of the reverse primer (CR; asymmetric PCR). Approximately 10 ng gDNA was used as a template. The cycling conditions were as follows: initial denaturation at 95°C for 2 min followed by 45 cycles of denaturation at 95°C for 0 s, annealing at 63°C for 5 s and elongation at 72°C for 10 s, with a ramping rate of 20°C/s.
Melting curve analyses were performed with an initial denaturation at 95°C for 10 s, 20 s at 40°C, followed by slow heating of the samples to 80°C with a ramping rate of 0.1°C/s and continuous fluorescence detection. The melting curves were converted to melting peaks by plotting the negative derivatives of fluorescence against temperature (–dF/dT). Melting peaks were mostly unambiguous, but certain samples exhibited abnormal peaks due to two rare silent genetic polymorphisms, one covered by the sensor (G645A [Leu215]) and another covered by the anchor probe (G654A [Glu218]). Forty-eight such samples were additionally digested with HhaI [15], allowing unambiguous genotyping at position 638. A further 160 samples selected randomly for quality control exhibited no discrepancies between genotyping results obtained with both methods. Two additional rare genetic variants of the SULT1A1 gene (i.e. the *3 [Met223Val] and *4 [Arg37Gly] alleles), which have been observed in Caucasian populations with allele frequencies of 0.01 and 0.003, respectively, were not accounted for in the present study [22].
Detection of polymorphic sites in the NAT2 gene was also carried out by capillary-based PCR followed by melting curve analysis. The method was described in detail previously [3].
Statistical analysis
The association between active/passive smoking and breast cancer by SULT1A1 genotype was assessed by multivariate conditional logistic regression analysis. We computed maximum likelihood estimates for the odds ratios (ORs) and their 95% confidence intervals (CIs) using the PHREG procedure of the statistical software package SAS release 8.2 (SAS Institute, Cary, NC, USA).
'Ever active smoking' was defined as having smoked more than 100 cigarettes in one's life. Among ever active smokers, women were termed current smokers if they had smoked regularly within the year preceding the interview; otherwise, they were classified as former smokers. If women were on average exposed to passive smoke for more than 1 hour/day for at least 1 year, then they were defined as ever passive smokers. The average exposure was obtained by multiplying the average hours/day by the duration in years for each exposure phase and dividing the sum over all phases separately for childhood and adulthood by the total years of passive exposure. Missing data on hours/day for 7.7% of cases and 5.7% of controls were replaced with the mean hours/day of exposed controls for the particular source of exposure. A detailed description of the quantification of lifetime exposure to passive smoke can be found elsewhere [20].
In the multivariate model, we included several relevant variables that influence breast cancer risk, such as first-degree family history of breast cancer, total duration of breastfeeding, body mass index, average daily alcohol intake, education level, number of full-term pregnancies and menopausal status. Variables that did not change the estimates substantially, such as study region or age at menarche, were not adjusted for in the analyses presented here. Statistical interaction between genotype and smoking variables was tested by using multiplicative interaction terms and evaluated using the likelihood ratio test. We performed the multivariate analyses with stratification in 5-year age groups to ensure sufficient numbers of subjects in the subgroups for genotypes and smoking characteristics.
Results
The women included in the present study, for whom a comprehensive history of active and passive smoking was available, closely resemble the original study population with respect to the distributions of several sociodemographic characteristics and putative risk factors, such as age, family history of breast cancer, body mass index, education level, parity, menopausal status, alcohol consumption, smoking and breastfeeding (data not shown).
Selected characteristics of the present study population are summarized in Table 1. The mean (± standard deviation) age for breast cancer patients was 42.9 ± 5.5 years and that for control individuals was 42.7 ± 5.6 years. The frequency of the SULT1A1*2 allele was 0.33 among cases and 0.35 among controls (0.32 and 0.34 in the original population). Of cases and control individuals, 52.7% and 57.7%, respectively, were carriers of at least one SULT1A1*2 allele. The distribution of SULT1A1 genotypes was in Hardy–Weinberg equilibrium (P = 0.92 for control individuals, P = 0.09 for cases).
The overall risk for breast cancer among carriers of the SULT1A1*2 allele was not significantly different from that in women with the SULT1A1*1/*1 genotype (adjusted OR 0.83, 95% CI 0.66–1.06). The distributions of potential risk factors, such as first-degree family history of breast cancer, body mass index, alcohol consumption, menopausal status, parity and breastfeeding, were similar in carriers and noncarriers of the SULT1A1*2 allele. There was also no major effect of SULT1A1 genotype in combination with NAT2 acetylator status on breast cancer risk (data not shown).
We assessed the effect of SULT1A1 genotype on the association between smoking and breast cancer risk, initially comparing ever active smokers with nonsmokers (i.e. passive-only smokers were included in the reference group). The ORs for variables such as smoking status (current or former active smoker), duration and pack-years of smoking did not differ by SULT1A1 genotype (data not shown). We then considered a separate category of only passively exposed women, with a reference group comprising women with neither active nor passive cigarette smoke exposure (Table 2). Associations of breast cancer risk with smoking variables were apparent, but the risk estimates were similar for carriers and for noncarriers of the SULT1A1*2 allele. In the analysis of passive smoking among never active smokers, we observed a tendency toward higher ORs in women with the SULT1A1*1/*1 genotype compared with carriers of the SULT1A1*2 allele (Table 2). The test for interaction between SULT1A1 genotype and passive smoking was not statistically significant (P = 0.6).
We investigated the combined effect of SULT1A1 and NAT2 genotype with respect to smoking, and observed elevated ORs associated with passive smoking only (OR 3.23, 95% CI 1.05–9.92) in NAT2 fast acetylators with the SULT1A1*1/*1 genotype but not in NAT2 fast acetylators carrying the SULT1A1*2 allele (Table 3). There was also a difference in OR for 11 or more pack-years of active smoking by SULT1A1 genotype, but the risk estimates were not significant. The test for interaction between SULT1A1 genotype and active/passive smoking among NAT2 fast acetylators did not reach statistical significance (P = 0.4). Among NAT2 slow acetylators, risk estimates for active and passive smoking did not differ by SULT1A1 genotype.
The results were generally similar when the analysis was restricted to the subgroup of women with premenopausal status, although the confidence intervals were wider. With regard to the differential effect of SULT1A1 and NAT2 genotype, the OR for passive smoking was 2.24 (95% CI 0.68–7.35) in NAT2 fast acetylators with the SULT1A1*1/*1 genotype and 1.03 (95% CI 0.39–2.71) in fast acetylators carrying the SULT1A1*2 allele.
Discussion
Our data do not suggest a strong influence of SULT1A1 genotype alone or in combination with NAT2 on the risk for breast cancer. There is no clear evidence that the SULT1A1 Arg213His single nucleotide polymorphism investigated in this study in itself is an important effect modifier of breast cancer risk associated with active/passive smoking among women up to age 50 years.
Differences in risk estimates for carriers and noncarriers of the SULT1A1*2 allele associated with smoking were apparent among NAT2 fast acetylators but not among slow acetylators. The observed estimates indicated that fast acetylators with the SULT1A1*1/*1 genotype were at higher risk for breast cancer than were carriers of the SULT1A1*2 allele when exposed to tobacco smoke, with a particularly prominent increase in risk for passive smokers versus never active/passive smokers.
We cannot rule out the possibility that the observed risk elevation for SULT1A1*1/*1 among fast acetylators was due to chance, because confidence intervals were wide for the combined analysis of genotypes. However, it seems biologically plausible that the combination of NAT2 and SULT1A1 'fast' genotypes is unfavourable. Both enzymes have been shown to be capable of bioactivating several pro-carcinogens. NAT2 is thought to play a major role in the activation of N-hydroxy derivatives of HCAs by O-acetylation [5]. The sulfonation of a variety of xenobiotics or their metabolites, such as polycyclic aromatic hydrocarbons, HCAs and aromatic amines, can lead to short-lived conjugates that may react with DNA and other cellular nucleophiles [23]. Studies that investigated the genotype–phenotype correlation for SULT1A1 clearly indicate that the SULT1A1*2 allele is associated with decreased catalytic activity of the respective allozyme as compared with the sulfonation activity of the wild-type SULT1A1 enzyme [6,13,14]. Consequently, in individuals with this genotype combination, reactive metabolites from acetylation and sulfonation might accumulate and lead to greater DNA damage and increase tumourigenesis.
For instance, DNA adducts of 2-amino-3-methylimidazo [4,5-f]quinoline (IQ) and 2-amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP) have been detected in human breast milk [24]. These HCAs, which are also present in tobacco smoke, have been classified as probably and possibly carcinogenic to humans, respectively [25]. In mutagenicity assays after heterologous expression of NAT2 and SULT1A1 in Salmonella typhimurium, N-hydroxy-IQ was found to be efficiently activated by NAT2 whereas N-hydroxy-PhIP was specifically activated by the SULT1A1 enzyme [26]. Likewise, mutagenicity of 2-amino-3-methyl-9H-pyrido [2,3-b]indole, another abundant HCA, was strongly enhanced in a Salmonella typhimurium strain expressing SULT1A1 [27].
Consistent with a role of greater bioactivation of HCAs associated with SULT1A1*1 rather than SULT1A1*2 in carcinogenesis are previous reports of greater risk for breast cancer and prostate cancer associated with intake of well done red meat [14,15]. In accord with this notion are the findings of three studies that investigated the formation of DNA adducts of heterocyclic and aromatic amines [28-30]. All of them found a tendency toward a higher capacity for adduct formation for the SULT1A1*1 enzyme as compared with the *2 allozyme, which is in agreement with a more efficient activation of several pro-mutagens by SULT1A1*1 than by the *2 allelic variant reported by Glatt and coworkers [6].
Two previous studies [15,17] reported an increased risk for breast cancer associated with the SULT1A1*2 allele per se. Risk estimates were statistically significant only in the study conducted by Zheng and coworkers [15], a nested case–control study in postmenopausal women, which found an 80% elevated risk for homozygous carriers of the variant allele. We found no association with the SULT1A1*2 variant although our study had 93% power to detect an OR of equivalent magnitude at a significance level of α = 0.05.
In a recent case-only study, an interaction between the SULT1A1 polymorphism and tobacco smoke exposure with an OR for interaction of 2.55 (95% CI 1.21–5.36) for current smokers carrying the *2 allele was found [18]. Results from our case–control study did not provide an indication for such a strong interaction between SULT1A1 genotype and smoking. Accordingly, we failed to detect a significant interaction in a case-only analysis of our data, although the power of our study is similar and the precondition of independence between genotype and exposure in the general population was fulfilled. The ORs (95% CIs) for interaction were 1.04 (0.50–2.14) for passive smoking only, 1.34 (0.63–2.86) for former smoking, and 1.15 (0.55–2.39) for current smoking.
We feel confident that our study population is representative of the general German population. The observed allele frequencies for SULT1A1 are in accordance with previous studies conducted in Caucasian populations [31] and the SULT1A1 genotype distribution did not deviate from Hardy–Weinberg equilibrium. Re-contacting the study participants for the telephone interview might have introduced selection bias. However, the participants in the present study closely resemble the original study population with regard to the distributions of relevant characteristics. Also, we do not believe that recall bias is a major concern because smoking was not known to be associated with breast cancer at the time of the interviews, and the correlation of reported active smoking between the original study and the present study is high [20]. Moreover, previous studies showed that the validity for self-reported active smoking, as well as passive smoke exposure, is high and nondifferential in cases and controls [32,33].
Although in vitro data suggest that SULT1A1 may not play an important role at physiologically relevant oestrogen concentrations [11,12], the question regarding whether SULT1A1 genotype actually has an effect on oestrogen metabolism in vivo deserves further study. Concerning the expression of SULT1A1 and SULT1E1 enzymes, for instance, there is some controversy in the literature. Falany and coworkers [34,35] observed SULT1E1 expression in normal breast epithelial cells, whereas Williams and coworkers [36] reported that only SULT1A1 was expressed at detectable levels. Because we cannot definitely rule out a potential role of SULT1A1 in the metabolism of oestrogens, we analyzed our data also with respect to use of oral contraceptives and various reproductive factors that may alter exposure to oestrogens. The results did not provide any indication for a modification of breast cancer risk related to oestrogens by SULT1A1 genotype (data not shown) and corroborate recent evidence indicating that sulfonation of oestrogens catalyzed by SULT1A1 is less relevant in normal breast tissue in physiological conditions [11,12].
The inconsistent findings of previous studies, which also considered the possible involvement of the SULT1A1 gene in other cancer sites (summarized by Glatt and Meinl [23]), and the broad substrate specificity of the SULT1A1 enzyme indicate the complexity of the issue. Elucidation of the potential effects of SULT1A1 genotype on a hormone-related cancer, such as breast cancer, is rendered more complicated by the fact that smoking may alter oestrogen levels in the body [37-40]. Moreover, and as suggested by our findings, it is possible that SULT1A1 genotype only exerts a detectable effect in combination with other genes, not to mention several polymorphic genes that are involved in oestrogen metabolism. Further determinants such as varying levels of enzyme expression or enzyme induction, which cannot easily be assessed in epidemiological studies, might also be of importance. Nevertheless, we cannot exclude that, because of limitations in statistical power, we were unable to detect a potential weak or moderate association or interaction between SULT1A1 genotype, smoking and breast cancer, independent of NAT2 genotype.
Conclusion
In summary, the results of our study do not suggest that there is a strong association between the SULT1A1 Arg213His genetic polymorphism and risk for breast cancer in women who had developed breast cancer by age 50 years. We did not find any evidence for a significant interaction of SULT1A1 with smoking. The SULT1A1*1/*1 genotype in combination with NAT2 fast acetylator status, however, appeared to increase breast cancer risk in women exposed to tobacco smoke. Hence, further biochemical investigations and large molecular epidemiologic studies are required to evaluate the effects of multiple genes and exposures on susceptibility to breast cancer.
Abbreviations
CI = confidence interval; HCA = heterocyclic aromatic amine; IQ = 2-amino-3-methylimidazo [4,5–f]quinoline; NAT = N-acetyltransferase; OR = odds ratio; PCR = polymerase chain reaction; PhIP = 2-amino-1-methyl-6-phenylimidazo [4,5–b]pyridine; SULT = sulfotransferase.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CL performed the statistical analysis and drafted the manuscript. AR was responsible for the genotyping assays, and contributed to study design and manuscript preparation. SK conducted the re-contacting of study participants in 1999 and participated in the statistical analyses. JCC conceived the study and supervised the project. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to thank the many gynaecologists and oncologists of the 38 clinics of the study region; ZUMA (Zentrum für Umfragen, Methoden und Analysen) Mannheim for the conduct of the telephone interviews; U Eilber, D Bodemer and B Jäger for excellent technical assistance; and S Nowell for helpful discussion. Sample collection was made possible by funding from the 'Deutsche Krebshilfe e.V.'; Dr A Risch was supported by the 'Verein zur Förderung der Krebshilfe in Deutschland e.V.'. C Lilla has a scholarship from the Deutsche Forschungsgemeinschaft Graduiertenkolleg 793.
Figures and Tables
Table 1 Selected characteristics of breast cancer patients and controls
Characteristic Cases (n = 419) Controls (n = 884)
Mean age (years) 42.9 42.7
Mean body mass index 23.9 24.3
First-degree family history of breast cancer 62 (14.8%) 53 (6.0%)
Menopausal status
Premenopausal 325 (77.6%) 718 (81.2%)
Postmenopausal 25 (6.0%) 49 (5.5%)
Unknown 69 (16.5%) 117 (13.2%)
SULT1A1 genotype
*1/*1 198 (47.3%) 374 (42.3%)
*1/*2 169 (40.3%) 403 (45.6%)
*2/*2 52 (12.4%) 107 (12.1%)
NAT2 genotypea
Fast acetylator 177 (42.2%) 351 (39.7%)
Slow acetylator 242 (57.8%) 533 (60.3%)
aNAT2 fast acetylators are carriers of at least one *4 (wild-type) allele, based on detection of known genetic polymorphisms at nucleotide positions 481, 590, 803 and 857; slow acetylators are carriers of two variant alleles [3].
Table 2 Odds ratios for breast cancer associated with smoking variables stratified by SULT1A1 genotype
SULT1A1*1/*1 genotype SULT1A1*2 allele carriera
Variable Cases (n = 198) Controls (n = 374) ORb (95% CI) Cases (n = 221) Controls (n = 510) ORb (95% CI)
Never active 86 159 1 (ref) 88 209 1 (ref)
Ever active 112 215 0.92 (0.63–1.34) 133 301 1.01 (0.73–1.42)
Never active/passivec, d 20 56 1 (ref) 19 57 1 (ref)
Former active 44 94 1.23 (0.64–2.36) 59 157 1.11 (0.60–2.06)
Current active 68 121 1.44 (0.76–2.71) 74 144 1.51 (0.82–2.78)
Average cigarettes per dayc, e
>0–9 42 106 1.06 (0.55–2.03) 52 154 0.99 (0.54–1.84)
10–19 56 73 2.12 (1.10–4.07) 60 99 1.75 (0.94–3.25)
20+ 14 34 1.05 (0.45–2.46) 20 48 1.09 (0.51–2.32)
Duration of smokingc, d
1–15 years 36 80 1.19 (0.61–2.34) 46 145 0.97 (0.52–1.82)
16+ years 76 135 1.44 (0.77–2.69) 87 156 1.62 (0.89–2.95)
Age at first cigarettec, d
9–15 years 21 47 1.09 (0.51–2.35) 20 63 0.84 (0.40–1.79)
16–18 years 56 109 1.33 (0.70–2.52) 70 145 1.42 (0.77–2.62)
19+ years 35 59 1.51 (0.75–3.05) 43 93 1.42 (0.74–2.72)
Pack-yearsc, e
>0–10 59 129 1.22 (0.65–2.27) 76 191 1.15 (0.64–2.07)
11+ 53 84 1.77 (0.91–3.42) 56 110 1.43 (0.77–2.67)
Passive smokingf
No 20 57 1 (ref) 19 57 1 (ref)
Yes 66 102 1.69 (0.89–3.21) 69 152 1.40 (0.74–2.64)
Duration of passive exposure in adulthoodf, g, h
1–13 years 29 40 1.91 (0.91–4.00) 33 70 1.47 (0.72–2.98)
14+ years 32 47 1.66 (0.79–3.48) 29 61 1.47 (0.70–3.10)
Cumulative lifetime exposure (in hours/day-years)f, g, i
1–55 22 49 1.13 (0.52–2.38) 37 76 1.76 (0.89–3.50)
56+ 43 52 2.12 (1.06–4.25) 32 71 1.39 (0.68–2.83)
aIncludes SULT1A1*1/*2 and SULT1A1*2/*2 genotypes. bOdds ratios (ORs) stratified for age by 5-year intervals; additional adjustment was made for first-degree family history (yes/no), breastfeeding (total number of months) and body mass index (weight [kg]/ height [m]2) as continuous variables, parity (0, 1–2, 3+ children), alcohol consumption (0, 1–18, 19+ g/day), menopausal status (premenopausal, postmenopausal, unknown) and education (low, intermediate, high) as categorical variables. cReference group comprises never active/never passive smokers; category of passive smokers included in the models. dData missing for one control individual. eData missing for three control individuals and one case. fEver active smokers are excluded from the analysis; reference group consists of women not exposed to passive smoke. gDichotomization according to median of nonsmoking control individuals. hCategory of subjects only exposed during childhood is included in the model. iSum of hours/day-years for the sources partner, work and childhood, whereby childhood hours/day-years were divided by the number of smokers to avoid overlapping of exposures; data missing for one case and six control individuals.
Table 3 Odds ratios for breast cancer associated with smoking stratified by NAT2 acetylator status and SULT1A1 genotype
SULT1A1*1/*1 genotype SULT1A1*2 allele carriera Total
Cases Controls ORb (95% CI) Cases Controls ORb (95% CI) Cases Controls ORb (95% CI)
NAT2 fast acetylatorc
Never active/passive 6 20 1 (ref) 8 18 1 (ref) 14 38 1 (ref)
Passive only 30 37 3.23 (1.05–9.92) 37 59 1.28 (0.50–3.31) 67 96 1.95 (0.97–3.91)
1–10 pack-years 22 53 1.26 (0.40–3.95) 30 74 0.76 (0.29–1.96) 52 127 0.98 (0.48–1.98)
11+ pack-years 23 38 2.21 (0.68–7.12) 20 50 0.75 (0.27–2.06) 43 88 1.22 (0.59–2.54)
NAT2 slow acetylator
Never active/passive 14 33 1 (ref) 11 39 1 (ref) 25 75 1 (ref)
Passive only 36 65 1.35 (0.62–2.91) 32 93 1.18 (0.53–2.66) 68 158 1.25 (0.72–2.16)
1–10 pack-years 37 76 1.24 (0.57–2.69) 46 117 1.34 (0.62–2.92) 83 193 1.24 (0.72–2.12)
11+ pack-years 30 46 1.83 (0.79–4.26) 36 60 2.07 (0.91–4.72) 66 106 1.85 (1.04–3.30)
aIncludes SULT1A1*1/*2 and SULT1A1*2/*2 genotypes. bOdds ratios (ORs) stratified for age by 5-year intervals; additional adjustment was made for first-degree family history (yes/no), breastfeeding (total number of months) and body mass index (weight [kg]/ height [m]2) as continuous variables, parity (0, 1–2, 3+ children), alcohol consumption (0, 1–18, ≥19 g/day), menopausal status (premenopausal, postmenopausal, unknown) and education (low, intermediate, high) as categorical variables. Data missing for one case and three controls. cNAT2 fast acetylators are carriers of at least one *4 (wild-type) allele.
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| 15743503 | PMC1064130 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Jan 14; 7(2):R229-R237 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr976 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9771574350010.1186/bcr977Research ArticleHigh expression of focal adhesion kinase (p125FAK) in node-negative breast cancer is related to overexpression of HER-2/neu and activated Akt kinase but does not predict outcome Schmitz Klaus Jürgen [email protected] Florian [email protected] Rainer [email protected] Friedrich [email protected] Jeremias [email protected] Bodo [email protected] Rainer [email protected] Kurt Werner [email protected] Hideo Andreas [email protected] Institute of Pathology, University Hospital of Essen-Duisburg, Essen, Germany2 Department of Obstetrics and Gynecology, University Hospital of Essen-Duisburg, Essen, Germany3 West German Cancer Centre, Essen, Germany4 Institute of Pathophysiology, University Hospital of Essen-Duisburg, Essen, Germany2005 7 1 2005 7 2 R194 R203 17 8 2004 9 11 2004 22 11 2004 26 11 2004 Copyright © 2005 Schmitz et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
Focal adhesion kinase (FAK) regulates multiple cellular processes including growth, differentiation, adhesion, motility and apoptosis. In breast carcinoma, FAK overexpression has been linked to cancer progression but the prognostic relevance remains unknown. In particular, with regard to lymph node-negative breast cancer it is important to identify high-risk patients who would benefit from further adjuvant therapy.
Methods
We analyzed 162 node-negative breast cancer cases to determine the prognostic relevance of FAK expression, and we investigated the relationship of FAK with major associated signaling pathways (HER2, Src, Akt and extracellular regulated kinases) by immunohistochemistry and western blot analysis.
Results
Elevated FAK expression did not predict patient outcome, in contrast to tumor grading (P = 0.005), Akt activation (P = 0.0383) and estrogen receptor status (P = 0.0033). Significant positive correlations were observed between elevated FAK expression and HER2 overexpression (P = 0.001), as well as phospho-Src Tyr-215 (P = 0.021) and phospho-Akt (P < 0.001), but not with phospho-ERK1/2 (P = 0.108). Western blot analysis showed a significant correlation of FAK Tyr-861 activation and HER2 overexpression (P = 0.01).
Conclusions
Immunohistochemical detection of FAK expression is of no prognostic significance in node-negative breast cancer but provides evidence that HER2 is involved in tumor malignancy and metastatic ability of breast cancer through a novel signaling pathway participating FAK and Src.
Aktbreast cancerfocal adhesion kinaseHER2prognosis
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Introduction
Breast cancer is a major cause of death among women. Adjuvant systemic therapy may considerably improve survival rates, but it is associated with severe toxic side effects. Especially in patients with node-negative breast cancer, the pros and cons of adjuvant systemic therapy are always critically weighted out. The identification of novel markers for node-negative high-risk patients who would benefit from adjuvant therapy is therefore of major importance. The aim of the present study was to assess the prognostic relevance of focal adhesion kinase (FAK) expression in node-negative breast cancer. In addition, the present report was designed to investigate the consequence of FAK expression on two major downstream targets (namely, Akt and Erk1/2) and to elucidate its connection with the HER2 signaling pathway.
The invasion and metastasis of cancer is a complex process including changes in cell adhesion and motility that allow tumor cells to invade and migrate through the extracellular matrix. Some of these alterations may occur at focal adhesions, which are cell/extracellular matrix contact points containing membrane-associated, cytoskeletal and intracellular signaling molecules [1]. The survival of normal epithelial cells critically depends on cell–cell and cell–matrix contact. Without these contacts epithelial cells die through the controlled process of apoptosis, termed anoikis [2]. FAK is a tyrosine kinase considered a central molecule in integrin-mediated signaling, and it is involved in cellular motility and protection against apoptosis [3-7]. The importance of FAK in epithelial cell biology is underlined by the findings that epithelial cells lines expressing constitutively active FAK survive in suspension and that cells derived from FAK-/- mouse embryos exhibit reduced migration [8,9].
High levels of FAK have been found in a variety of tumors, including head and neck carcinomas, ovarian carcinomas, thyroid carcinomas and colon carcinomas [10-12]. Only two studies have so far described elevated FAK expression in human breast cancer and preinvasive lesions in contrast to benign breast lesions in small cohorts [13,14]. The prognostic value of FAK expression in breast cancer remains unclear. Regarding other cancer types, three studies demonstrated varying results in colon cancer, squamous cancer and hepatocellular carcinoma [15-17].
Recent studies have identified HER2, a prognostic marker for aggressiveness in breast cancer, to influence the migratory behavior of breast cancer cells in vitro through a novel signaling pathway involving phosphorylation of FAK at tyrosine 861 by its upstream kinase c-Src [18,19]. These findings suggest that the Her-2/neu signaling pathway may influence migration and metastasis of breast cancer cells through activating the Src/FAK signaling pathway. There are several downstream targets of FAK such as the Ras-ERK signaling cascade, which is activated by extracellular, frequently mitogenic ligands and results in increased cellular proliferation and malignancy in vitro and in vivo [20,21]. FAK also impacts on the phosphotidylinositol-3-kinase (PI3K)/Akt-signaling pathway that plays a central role in tumorigenesis [22,23]. In node-negative breast cancer, we have previously shown that activated Akt is a prognostic parameter [24].
The aim of the present study was to assess the prognostic relevance of FAK expression in node-negative breast cancer. In addition, the present report was designed to investigate the correlation of FAK expression with two major downstream signaling pathways, Akt and Erk1/2, and to elucidate its connection with the HER2 signaling pathway.
Materials and methods
Patients
This study comprised 162 female breast cancer patients (mean age, 59 years) from the Department of Gynecology, University of Essen-Duisburg, Germany, who underwent surgery and further adjuvant therapy between 1989 and 1996. Complete clinical records and follow-up information were available in all cases. The negative lymph node status was confirmed by axillary dissection. All surgical material was fixed in 4% formalin and routinely processed. The tumors were classified according to the pTNM system (sixth edition) and were graded according to Elston and Ellis [25]. Twenty-seven of the 162 patients died during follow-up, the cause of death being unknown in two cases. Altogether 19 patients died of breast cancer, and six patients were excluded from survival analysis having died from either benign or other cancer diseases. Statistical analysis was based on a mean follow-up period of 7.48 years. Table 1 summarizes the clinicopathological parameters of this study.
Immunohistochemistry
The primary antibodies used in this study are presented in Table 2. Immunohistochemistry was performed on paraffin sections 5 μm thick. The alkaline phosphatase anti-alkaline phosphatase method was used for antibody demonstration. Antigen retrieval was carried out with 0.01 M citrate buffer at pH 6.1 for 20 min (both phospho-Akt antibodies), for 40 min (phospho-ERK1/2) and for 20 min (FAK, phospho-Src Tyr-215 and phospho-Src Tyr-416) in a hot water bath (95°C). Antibodies were incubated overnight in a humidified chamber at 4°C. Positive controls were included in each staining series. No significant staining was observed in the negative controls using mouse immunoglobin replacing the primary antibody. The DAKO HerceptTest™ (DakoCytomation, Glostrup, Denmark) and a HER2/ErbB2 polyclonal antibody at a 1:50 dilution (rabbit polyclonal antibody; Cell Signaling Technology, Beverly, MA, USA) were used for the detection of HER2 protein expression. The estrogen receptor (ER) status of the tumors was determined using a monoclonal anti-human antibody as previously described [24].
Evaluation of immunostaining
FAK immunostaining
The constant positive staining of smooth muscle cells of tumor vessels served as the positive internal control as previously described [26]. If more than 20% of carcinoma cells in a given specimen were stained more intensely than smooth muscle cells, the sample was classified as strong FAK overexpression (3+). In the case of equal FAK immunostaining compared with that of vascular smooth muscle cells, the sample was classified as intermediate expression (2+). When FAK immunostaining was weaker than that of the internal control, the tumor was classified as low FAK expression (1+). Tumors lacking FAK immunostaining were classified as negative (0). For statistical analysis, negative (0), intermediate (1+ and 2+) and strong (3+) groups were created.
Phospho-Akt and phospho-ERK1/2 immunostaining
The number of positive immunoreactive tumor nuclei was counted within 300 cells at the invasive tumor front, and is expressed as a percentage. Cytoplasmatic phospho-Akt staining was noticed but was not part of the scoring system. Tumor cells with easily detectable specific phospho-ERK1/2 immunostaining, independent of the amount of stained cells, were scored as strongly positive (2+). Tumors exhibiting a detectable but faint immunostaining were scored as weak (1+), whereas tumors with a minimal, hardly detectable or missing staining pattern were classified as phospho-ERK1/2-negative (0).
Phospho-Src Tyr-215 and phospho-Src Tyr-416 immunostaining
Most tumor samples showed a membranous staining with varying intensity. Four samples revealed a strong cytoplasmatic staining. Nuclear staining was absent. Tumor samples with a readily detectable membranous staining or with a strong cytoplasmatic staining were classified as positive. Samples lacking or with a weak membranous staining or with a weak cytoplasmatic staining were classified as negative.
HER2 protein and ER status
The Her-2/neu status was determined according to the evaluation system of the HerceptTest™. For statistical analysis, negative (DAKO score 0 and 1+) and positive (DAKO score 2+ and 3+) groups were created. A tumor was regarded as ER-negative if none or less than 10% of the tumor cells showed weak or missing nuclear immunostaining.
Western blot analysis
Total cell protein of seven representative frozen human breast cancer samples was extracted in lysis buffer and the protein concentration was measured to load equal protein amounts. Each lane consisted of 50 μg protein, and was size fractioned by electrophoresis on 10% polyacrylamide-SDS gels and electrotransferred to a polyvinylidene fluoride membrane using a tank-blotting system. After staining the membranes with reversible Ponceau red solution to confirm equal protein loading, the membranes were blocked with 5% non-fat dry milk in Tris-buffered saline for 1 hour at room temperature. The membranes were then incubated with the anti-HER2/ErbB2 antibody (polyclonal rabbit HER2/erbB2 antibody, 1:1000; Cell Signaling Technology, Inc.), anti-phospho-FAK antibody (Tyr-861, polyclonal goat antibody, 1:50; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA) and anti-phospho-Src antibody (Tyr 215, polyclonal rabbit antibody, 1:2000; Sigma-Aldrich, Inc., St Louis, MO, USA) for 16 hours at room temperature. After incubating the membrane for 1 hour with goat anti-rabbit/donkey anti-goat antibody conjugated to horseradish peroxidase, the antigen–antibody complex was visualized by chemiluminescence (Amersham Biosciences, Piscataway, NJ, USA).
Statistical analysis
FAK, phospho-Erk1/2, Her-2/neu, phosph-Src Tyr-215/416 and ER immunostainings were assessed by a set of pathologists (KJS, FG, JWO, FO) in a blind-trial fashion without knowledge of the clinical outcome. In cases of disagreement, the slides were evaluated by two pathologists and a final decision was made. Two of the authors assessed the percentage of phospho-AKT-positive tumor cells and the mean percentage of both counts was used for statistical analysis. Staining scores of both phospho-Akt antibodies (Santa Cruz Biotechnology Inc. and Cell Signaling Technology Inc.) revealed a high, significant interobserver concordance (Pearson's r = 0.671, P < 0.001).
Results obtained from phospho-Akt immunostaining performed with the antibody from Santa Cruz showed less background staining, and were therefore used for statistical analysis. All data were converted to a PC and statistically analyzed using SPSS Version 10 for Windows (SPSS Inc., Chicago, IL, USA). Relationships between ordinal parameters were investigated using two-tailed chi-squared analysis (or Fisher's exact test where patient numbers were small). The relationship between FAK expression and the percentage of phospho-Akt-positive tumor cells was determined using the Kruskal–Wallis test. Overall survival curves were estimated using the Kaplan–Meier method, and any differences in the survival curves were compared by the log-rank test. Western blot results were digitized with Image Master software from Amersham Biosciences.
Results
Immunohistochemical analysis of FAK
Staining results were obtained from all 162 cases. In normal breast epithelium, a weak to missing FAK expression was detected at the cell membrane and in the cytoplasm. Strong FAK overexpression was detected in 30 patients (18.5%), intermediate staining in 119 patients (73.5%) and negative staining in 13 patients (8%). Higher FAK expression was significantly associated with a poorer differentiation grade. No statistically significant differences were found among cases with different tumor sizes or different tumor types (Table 1). The staining intensity of an adjacent in situ component was equal to or stronger than that of the invasive tumor, with only one sample exhibiting a weaker FAK staining in the in situ component than in the corresponding invasive carcinoma (Fig. 1).
Correlation between FAK expression and survival
The impact of FAK expression and clinicopathological features on patient survival was assessed using univariate Kaplan–Meier survival analysis. Except for histological grading (P = 0.005), ER status (P = 0.0033) and activation of Akt (P = 0.0383), none of the other investigated parameters – including FAK expression (irrespective of ER expression) or HER-2/neu status – proved to be of prognostic significance in the node-negative breast cancers examined.
Correlation of FAK protein expression with HER-2/neu status, ER status and phospho-Src Tyr-215 and phospho-Src Tyr-416 overexpression
A total of 159 tumors were analyzed for HER-2/neu protein expression: 59 tumors (37.1%) were classified as negative (0), 56 tumors (35.2%) as negative (1+), 21 tumors (13.2%) as weakly positive (2+) and 23 tumors (14.5%) as strongly positive (3+). Tumors with a score of 2+ and 3+ were defined as Her-2/neu-positive. A total 51.7% of tumors classified as strongly FAK-positive exhibited Her-2/neu protein overexpression, but intermediate classified tumors totaled only 24.5% of Her-2/neu overexpressing tumors. All of the FAK-negative tumors were Her-2/neu-negative. The ER status was obtained from 152 tumors. Ninety tumors (59.2%) showed a significant ER expression whereas 62 tumors (40.8%) were classified as negative.
Statistical analysis revealed a significant relationship of elevated FAK expression (P = 0.001) with Her-2/neu overexpression (2+ and 3+; Table 3) but not with the ER status. Phospho-Src Tyr-215 staining was obtained from 137 patients and phospho-Src Tyr-416 staining was obtained from 140 samples. Normal breast tissue exhibited no phospho-Src Tyr-215 immunostaining. Membranous phospho-Src Tyr-416 immunostaining was noticed in sporadical epithelial cells. Myoepithelial cells lacked both phospho-Src Tyr-215 and phospho-Src Tyr-416 immunostaining. Tumor tissue of the remaining samples was not available due to lack of paraffin material. Statistical analysis revealed a significant direct correlation of FAK overexpression with phospho-Src Tyr-215 staining results (P = 0.021) but not with phospho-Src Tyr-416 staining scores (Table 3). On the other hand, positive phospho-Src Tyr-215 staining was significantly associated with HER2 overexpression (P = 0.011) whereas phospho-Src Tyr-416 was not. Representative immunohistochemical stainings are shown in Figs 2 and 3.
Correlation between FAK expression and Akt/ERK activation
Normal breast tissue revealed mostly a cytoplasmatic Akt-staining pattern, with a low mean percentage of positive nuclear stained cells in normal breast tissue of 28%. Prominent nuclear and partly cytoplasmatic phospho-Akt immunoreactivity was noticed in tumor cells. Staining heterogeneity was occasionally apparent at the invasive tumor front. The percentage of phospho-AKT-positive nuclei in the tumor samples ranged from 0% to 85%. Tumors with more than 45% of positive nuclei were classified as phospho-Akt-positive.
Erk1/2 immunohistochemistry of tumor samples revealed strong nuclear immunostaining and partly cytoplasmatic immunostaining, while non-neoplastic tissue only occasionally revealed a weak staining. Similar to phospho-Akt, heterogeneous immunostaining for phospho-ERK1/2 was detected at the invasive tumor front. Phospho-Akt-positive classified tumors exhibited stronger FAK immunostaining intensities more frequently (P < 0.001) than phospho-Akt-negative classified tumors.
No correlation was observed between different FAK immunostaining scores and phospho-ERK1/2 immunostaining scores (P = 0.108) (Table 3). Statistical analysis revealed a significant direct correlation of different FAK immunostaining intensities with the percentage of phospho-Akt-positive stained tumor cells (P < 0.001).
Correlation between HER2 protein overexpression and phosphorylation of FAK at Tyr-861 in western blot analysis
We next analyzed the relationship of HER2 overexpression with the levels of phospho-Src Tyr-215, phospho-FAK Tyr-861 and total FAK using seven samples of fresh-frozen breast cancer tissue. Of the seven samples analyzed, four tumors with high HER2 levels exhibited increased levels of phosphorylation of FAK on tyrosine 861 compared with tumors without HER2 overexpression (P = 0.01; Fig. 4). An increase of total FAK and phospho-Src Tyr-215 was noticed within the HER2 overexpressing tumors but reached not statistical significance. These results provide evidence that the HER/FAK Tyr-861 pathway is activated in human breast cancer.
Discussion
Several studies have demonstrated upregulation of FAK expression in malignant tumors, including breast cancer, but the prognostic value of FAK expression in human cancer remains unclear. From the three studies that have previously analyzed this, one study denies the prognostic value of FAK in adenocarcinomas of the colon [16], whereas the other two studies have provided evidence for the prognostic value of immunohistochemically detected FAK expression in esophageal squamous carcinoma and in hepatocellular carcinoma, respectively [15,17]. The present study is the first to clarify the prognostic relevance of FAK expression in a cohort of 162 invasive node-negative breast cancer cases with long-term follow-up. Our findings demonstrate that FAK overexpression does not predict patient outcome in this setting; although, similar to esophageal squamous cancers, levels of FAK expression were strongly correlated with poorer tumor differentiation [17].
FAK is a nonreceptor protein kinase involved in integrin-mediated signaling that has a profound impact on cell proliferation, survival and migration. One downstream target is the PI3K/Akt signaling pathway. In the present study we found a significant association of elevated FAK expression with Akt phosphorylation in support of the notion that Akt may be a downstream target of FAK-mediated signaling [27]. However, while Akt activation is associated with a worse prognosis in our study cohort, FAK expression levels are not. This may be due to the central role of Akt in tumorigenesis, where a number of stimuli and pathways besides FAK contribute to the common end point of increased Akt activity.
Akt, also known as protein kinase B, is a serine/threonine protein kinase that has been shown to regulate cell survival signals in response to a diversity of signals generated by growth factors, cytokines and oncogenic ras. Alternative activation mechanisms for Akt besides growth factor-mediated PI3K stimulation are mutational inactivations of PTEN or activation via integrin-linked kinase [28-30]. The difference in prognostic relevance between Akt and FAK may therefore not be surprising. Several studies have demonstrated upregulation of FAK in human cancer including breast cancer and have suggested that FAK overexpression is an early event in tumorigenesis [13,14]. Our data support this hypothesis as all (but one) of the in situ carcinomas adjacent to the invasive cancer exhibited FAK overexpression. Interestingly, in every cancer specimen analyzed the adjacent preinvasive component exhibited an equal or even higher FAK immunostaining than the corresponding invasive carcinoma.
In recent studies, Vadlamudi and colleagues utilized human breast cancer cell lines in vitro to establish a novel signaling pathway involving HER2, phospho-Src Tyr-215 and phospho-FAK Tyr-861 leading to increased cellular motility [18,19]. The authors showed that heregulin-induced HER2 activation resulted in phosphorylation of FAK at tyrosine 861, while six breast tumor samples exhibited increased level of phospho-Src Tyr-215 in HER2 neu-overexpressing breast cancers. From these in vitro data it was tempting to hypothesize that HER2 influences metastasis of breast cancer in vivo via a similar pathway.
To test for the existence of this novel pathway in a large cohort of human breast cancer tissue we evaluated total FAK protein and phospho-Src Tyr-215 expression as well as phospho-Src Tyr-416 expression by immunohistochemistry. Additionally, we investigated the levels of phospho-FAK Tyr-861, phospho-Src Tyr-215 and Her-2/neu in a set of seven of freshly frozen breast cancer tissues using western blot analysis. Our findings support the hypothesis that HER2-overexpressing tumors exhibit higher levels of phospho-FAK Tyr-861 but also of total FAK levels. This is also supported by the immunohistochemical data where Her-2/neu protein expression levels coincide with FAK protein expression. Interestingly, increased levels of phospho-Src Tyr-215 but not of phospho-Src Tyr-416 were significantly associated with HER2 overexpression, supporting the role of an activatory Src Tyr-215 phosphorylation as an intermediate between HER2 and FAK signaling.
Although Src comprises several phosphorylation sites, phosphorylation of tyrosine 215 and not tyrosine 416, the 'classical' activatory phosphorylation site, appears to be essential for FAK activation via HER2 [19]. Our data confirm the specificity of this pathway as src phosphorylation at tyrosine 416 was not associated neither with HER2 overexpression. The present study was designed to investigate this novel signaling pathway in vivo via an immunohistochemical approach allowing the determination of HER2 and FAK protein expression.
A recent study identified frequent polysomic patterns for chromosome 1, chromosome 8 and chromosome 17 that are indicative for increased tumor malignancy in breast cancer [31]. As the focal adhesion kinase and the Her-2/neu gene are located on chromosome 8 and chromosome 17, respectively, one might conclude that such a polysomic pattern causes a combined HER2/FAK protein overexpression. However, the identification of potential underlying causative genetic alterations should be a topic of further investigation.
Our results propose that among the many biological effects of Her-2/neu overexpression the activation of the Akt survival pathway via the FAK Tyr-861–Src Tyr-215 pathway might contribute to the more aggressive behavior of Her-2/neu-overexpressing breast cancers.
In summary, FAK overexpression is not a prognostic marker in this series of 162 node-negative breast cancers. This might be due to the composition of our cohort, which mainly contains early-stage cancers without lymph node metastasis. Recent studies demonstrated a higher level of FAK expression in colorectal liver metastasis than in the primary tumor, and provided evidence for an upregulation of FAK protein in hepatocellular cancers with portal invasion [15]. Nevertheless, FAK protein overexpression coincides with Her-2/neu overexpression and may mediate HER2 signaling via Src, resulting in PI3K/Akt activation. These data might contribute to the understanding of how Her-2/neu overexpression in human breast cancer affects tumor malignancy and metastasis (Fig. 5). Owing to the relatively small number of breast cancer samples exhibiting FAK protein overexpression in this cohort, however, these promising results need to be confirmed in larger cohorts.
Abbreviations
ER = estrogen receptor; ERK = extracellular regulated kinase; FAK = focal adhesion kinase; PI3K = phosphotidylinositol-3-kinase.
Competing interests
There has been no prior publication of the content of this study. This paper has not been submitted to any other journal. No financial relationship exists that might lead to conflict concerning the content of this study. The paper has been read and approved by all of the authors. The authors declare that they have no competing interests.
Authors' contributions
KJS carried out the primary immunohistochemical staining evaluation, performed statistical analysis and wrote the article. FG, FO and JW carried out the secondary immunohistochemical staining evaluation to rule out interindividual observer discrepancies. RC and RK provided the clinical data. BL contributed substantially to the conception and design of the study and to the drafting of the manuscript. KWS performed critical revision of the manuscript for important intellectual content. HAB provided administrative, technical and material support and supervision, and analyzed western blot data. All authors read and approved the final manuscript.
Acknowledgements
The technical assistance of Dorothe Möllmann and Caroline Stang is gratefully acknowledged. This work was supported by the local research fund (IFORES) of the University of Essen.
Figures and Tables
Figure 1 Immunohistochemistry for focal adhesion kinase (FAK) in ductal carcinoma in situ, invasive breast carcinonoma and normal breast tissue. (a) Strongest FAK expression was frequently seen in ductal carcinoma in situ (arrowhead). Notice the missing staining in normal breast tissue (arrow). (b) Invasive breast carcinoma cases (arrowhead) exhibited an equal or less intense FAK immunostaining then adjacent ductal carcinoma in situ (arrow) in all but one case. Original magnification, × 400.
Figure 2 Representative immunohistochemical staining of a Her-2/neu and focal adhesion kinase (FAK)-overexpressing tumor for phospho-Src Tyr-215 and phospho-Src Tyr-416. Light micrograph displaying (a) strong phospho-Src Tyr-215 and (b) missing phospho-Src Tyr-416 staining in serial sections of a Her-2/neu-overexpressing and FAK-overexpressing invasive breast cancer sample as analyzed by immunohistochemistry. Inset: immunohistochemical staining showing FAK overexpression of the same sample. Original magnification, × 400.
Figure 3 Representative immunohistochemical staining of serial sections of three tumors for phospho-Akt, focal adhesion kinase (FAK) and HER2. (A1)–(A3) HER2: tumors with strong HER2 staining intensity (DAKO score 3+) (A1), weak HER2 staining (DAKO score 1+) (A2) and missing HER2 staining (DAKO score 0) (A3). (B1)–(B3) Tumors showing strong HER2 immunostaining (A1) more frequently exhibited strong FAK immunostaining (B1), whereas tumors expressing weak or no HER2 immunostaining were more often associated with weak or no FAK immunostaining (B2, B3). (C1)–(C3) Phospho-Akt: the percentage of phospho-Akt positively stained tumor cells ranged from more than 80% (C1) to 45% (C2) or was lower than 45% (C3). Higher HER2 (A1) and FAK (B1) staining intensites are correlated with higher percentage of phospho-Akt positively stained tumor cells (C1, C2). Original magnification, × 200.
Figure 4 Upregulation of tyrosine phosphorylation of focal adhesion kinase (FAK) at tyrosine 861 in HER2-overexpressing breast tumors. Western blot analysis: lysates of seven breast cancer samples were analyzed by immunoblotting using antibodies that recognize total FAK and distinct tyrosine sites on Src and FAK kinases. Tumor samples were put in order according to the HER2 expression status (HerceptTest™) and the blot was probed with anti-HER2 antibody to analyze the exact amount of HER2 protein.
Figure 5 Scheme depicting signalling transduction in breast cancer involving HER2, Src, focal adhesion kinase (FAK), phosphatidylinositol 3-kinase (PI3K), Akt and Erk.
Table 1 Clinicopathological data and focal adhesion kinase expression in 162 node-negative breast cancers
Focal adhesion kinase expression P value*
Negative Strong Intermediate
Total adenocarcinoma 13 (8%) 30 (18.5%) 119 (73.5%)
Tumor type Not significant
Invasive ductal 5 (4.1%) 27 (22.5%) 88 (72.7%)
Invasive lobular 3 (13%) 2 (8.7%) 18 (78.3%)
Mucinous 5 (62.5%) / 3 (37.5%)
Tubulo/tubulo-lobular / / 5 (100%)
Others / 1 (16.7%) 5 (88.3%)
Histological grade P = 0.038*
Well differentiated 4 (11.4%) 3 (8.6%) 28 (80%)
Moderately differentiated 6 (7%) 13 (15.1%) 67 (77.9%)
Poorly differentiated 3 (7.3%) 14 (34.1%) 24 (58.5%)
Tumor size Not significant
pT1(a,b,c) 7 (7.2%) 21 (21.6%) 69 (71.1%)
pT2 5 (7.9%) 9 (14.3%) 49 (77.8%)
pT3 and pT4 1 (50%) 1 (50%)
* P value of chi-squared analysis.
Table 2 Primary antibodies used for immunohistochemistry
Antibody Dilution Source
Phospho-Akt (1/2/3) Serin 473 Polyclonal 1:200 Cell Signaling Technology
Phospho-Akt (1/2/3) Serin 473 Polyclonal 1:2000 Santa Cruz Biotechnology
Focal adhesion kinase (C-20) Polyclonal 1:100 Santa Cruz Biotechnology
Phospho-focal adhesion kinase tyrosine 861 Polyclonal 1:50 Santa Cruz Biotechnology
Phospho-Src tyrosine 215 Polyclonal 1:200 Sigma-Aldrich
Phospho-Src tyrosine 416 Polyclonal 1:200 Cell Signaling Technology
Phospho p44/42 Map kinase (Erk1/2) threonine 202;tyrosine 204 Monoclonal 1:100 Cell Signaling Technology
Estrogen receptor Monoclonal 1:25 DAKO
Table 3 Correlation between focal adhesion kinase, HER2, estrogen receptor, phospho-Src Tyr-215 and phospho-Src Tyr-416, phospho-Akt and phospho-ERK1/2 immunohistochemistry
Focal adhesion kinase expression P value*
Negative Intermediate Strong
Her-2/neu status (n = 159) 0.001
Negative (0, 1+) 13 88 14
Positive (2+, 3+) 0 29 15
Estrogen receptor status (n = 152) Not significant
Negative 4 48 10
Positive 6 67 17
Phospho-Src Tyr-215 (n = 137) 0.021
Negative 9 87 17
Positive 15 9
Phospho-Src Tyr-416 (n = 140) Not significant
Negative 8 86 21
Positive 20 5
Phospho-Akta (n = 162) < 0.001
Negative 7 10 1
Positive 6 109 29
Phospho-ERK1/2 (n = 161) Not significant
Negative 5 28 9
Intermediate 8 74 13
Strong 16 8
* P value of chi-squared analysis.
aNegative, percentage of phospho-Akt-positive cells ≤ 45%; positive, percentage of phospho-Akt-positive cells > 45%.
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| 15743500 | PMC1064131 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Jan 7; 7(2):R194-R203 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr977 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9781574350410.1186/bcr978Research ArticleDownregulation of the anaphase-promoting complex (APC)7 in invasive ductal carcinomas of the breast and its clinicopathologic relationships Park Kwang-Hwa [email protected] Sung-E [email protected] Minseob [email protected] Yup [email protected] Department of Pathology, Yonsei University, Wonju, Korea2 Institute for Medical Science, Ajou University School of Medicine, Suwon, Korea2005 14 1 2005 7 2 R238 R247 1 9 2004 10 10 2004 30 11 2004 Copyright © 2005 Park et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
The anaphase-promoting complex (APC) is a multiprotein complex with E3 ubiquitin ligase activity, which is required for the ubiquitination of securin and cyclin-B. Moreover, the mitotic spindle checkpoint is activated if APC activation is prevented. In addition, several APC-targeting molecules such as securin, polo-like kinase, aurora kinase, and SnoN have been reported to be oncogenes. Therefore, dysregulation of APC may be associated with tumorigenesis. However, the clinical significance and the involvement of APC in tumorigenesis have not been investigated.
Methods
The expression of APC7 was immunohistochemically investigated in 108 invasive ductal carcinomas of the breast and its relationship with clinicopathologic parameters was examined. The expression of APC7 was defined as positive when the summed scores of staining intensities (0 to 3+) and stained proportions (0 to 3+) exceeded 3+.
Results
Positive APC7 expression was less frequent than its negative expression when histologic (P = 0.009) or nuclear grade (P = 0.009), or mitotic number (P = 0.0016) was elevated. The frequency of APC7 negative expression was higher in high Ki-67 or aneuploid groups than in low Ki-67 or diploid groups.
Conclusion
These data show that loss of APC7 expression is more common in breast carcinoma cases with poor prognostic parameters or malignant characteristics. They therefore suggest that dysregulation of APC activity, possibly through downregulation of APC7, may be associated with tumorigenesis in breast cancer.
anaphase-promoting complexaneuploidybreast cancerhistologic gradeKi-67
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Introduction
The anaphase-promoting complex (APC) is an E3 ubiquitin ligase that controls mitotic progression [1,2]. APC is a polymeric protein complex composed of at least 11 subunits, which contains tetratricopeptide repeat proteins (APC3, 5, 6, 7, and 8), a cullin homolog (APC2), and a ring-H2 finger domain (APC11). APC requires two WD40 repeat-containing coactivators, Cdc20 and Cdh1, to recruit and select various substrates at different stages of the cell cycle, and it was recently suggested that APC3 and APC7 interact with these APC activators [3]. APC promotes metaphase/anaphase transition by ubiquitizing and degrading securin, an inhibitor of separase that participates in the degradation of the chromatic cohesion complex. APC also ubiquitinates cyclin-B and accelerates its degradation during the late mitotic to the G1 phase, which results in mitotic exit. In addition, APC is known to target various cell cycle regulatory molecules, including spindle-associated protein, DNA replication inhibitors, and mitotic kinases.
Several molecules targeted by APC have been reported to promote transformation. Pituitary tumor-transforming gene (PTTG), a vertebrate analog of securin, has been reported to be an oncogene [4], and cancerous tissues from patients with leukemia, lymphoma, or testicular, ovarian, breast, or pituitary cancer were found to over-express PTTG [5-7]. It was further reported that the constitutive expression of polo-like kinase (PLK), a serine/threonine kinase that is involved in spindle formation, centrosome cycles, and chromosome segregation [8], may induce tumor formation [9]. Several reports have suggested a role for PLK in the progression and/or malignancy of human cancers, such as glioma, and endometrial carcinoma, breast, ovarian, and esophageal carcinoma [10-13]. Aurora kinase, another serine/threonine kinase that is involved in chromosome segregation and centrosome maturation [14], has also been reported to be amplified in bladder, gastric, breast, and colorectal cancers [15-18] and to have the ability to transform NIH3T3 cells [19]. Recently, SnoN, a negative regulator of Smad that is involved in the transforming growth factor-β signaling pathway, was shown to be a target molecule for the APC [20,21] and to have transforming potential [22]. It was also found that SnoN is amplified in stomach, thyroid, and lung carcinoma and lymphoma [23]. APC-regulating molecules have also been reported to be involved in transformation. RASSF-1A and Mad2, which inhibit APC activity, were reported to be tumor suppressors [24,25].
Chromosome instability is believed to contribute to malignant transformation because the majority of malignant human cancers exhibit chromosomal gain or loss [26] and because mitotic defects including chromosome aberrations are frequently found in malignant cancers [27-29]. Because of the roles played by APC in mitotic cell cycle progression, the timely activation of APC is thought to be important for maintaining accurate chromosome separation. In addition, a report indicating that the mitotic spindle checkpoint was reached by preventing APC activation [30] suggests that the dysregulation of APC may give rise to abnormal chromosome segregation, resulting in aneuploidy. The recent finding that APC5 deficiency in Drosophila is accompanied by a mitotic defect, which included aneuploidy, suggests a role for APC in the maintenance of chromosome stability [31]. It can therefore be hypothesized that the abnormal regulation of APC may be involved in malignant transformation through chromosome instability.
However, it is not known whether the abnormal regulation of APC, possibly through genomic mutation or the modulation of APC components, is related to tumorigenesis. Furthermore, whether dysregulation of APC is related to clinical parameters in various human cancers is yet to be determined. Thus, we investigated immunohistochemically the levels of APC7 in various cancer tissues and found weak APC7 expression in high-grade ductal carcinomas of breast. Therefore, we were encouraged to investigate the expression of APC7 in 108 breast carcinomas and to examine the relationship between the expression of APC7 and clinicopathologic parameters.
Methods
Production of polyclonal antibodies against APC7
Polyclonal antibodies against mouse APC7 were raised in a NZW rabbit by immunization with recombinant APC7 protein. Briefly, recombinant mouse APC7 proteins were produced in Escherichia coli using a pET32 expression vector system (Novagen, Madison, WI, USA). The resulting 6× histidine-tagged APC7 proteins were purified by Ni-NTA affinity chromatography (Qiagen, Hilden, Germany). A NZW rabbit was then immunized with the purified APC7 protein and boosted twice. Blood was collected from the auricular artery, and serum was prepared by clotting and differential centrifugal separation (10,000 g for 10 min). APC7-specific antibodies were further purified by binding serum to APC7-coupled nitrocellulose and eluting with 100 mmol/l glycine–HCl buffer (pH 2.5).
Immunoblotting and immunoprecipitation
Protein extracts were prepared by solubilizing cells in RIPA buffer (150 mmol/l NaCl, 1% NP40, 0.5% deoxycholate, 0.1% sodium dodecylsulfate, 50 mmol/l Tris.Cl, pH 7.5, protease inhibitors) and differential centrifugation (10,000 g for 10 min). Of the protein fractions obtained, 30 μg was resolved by 12% SDS-PAGE, and then the separated proteins were electrotransferred onto Immobilon membranes (Millipore, Bedford, MA, USA). After preblocking these membranes with 5% skimmed milk, they were treated with anti-mouse APC7 or human APC7 (Santa Cruz Biotechnology, Santa Cruz, CA, USA) antibodies as primary antibody and horseradish peroxidase-conjugated anti-rabbit antibodies as secondary antibody. Immunoreactive bands were developed using an electrogenerated chemiluminescence system (Amersham Pharmacia Biotech, Uppsala, Sweden). Immunoprecipitation was carried out with anti-human APC3 antibodies (Transduction Laboratory, San Diego, CA, USA) or anti-mouse APC7 antibodies. Subconfluent cells were collected and then lysed by incubation on ice for 15 min in EBC buffer (0.5% Nonidet P-40, 40 mmol/l Tris.HCl, pH 8.0, 120 mmol/l NaCl, 10 μg/ml aprotinin, 10 μg/ml leupeptin, 10 μg/ml PMSF). Cell lysates (1 mg) collected by differential centrifugation (12,000 g for 15 min) were mixed with 1 μg anti-human APC3 antibodies or anti-APC7 antibodies, and these mixtures were then further incubated for 1 hour at 4°C. Immune complexes were collected by incubating with 30 μl of 50% protein A-sepharose slurry for 1 hour and centrifugation (10,000 g for 15 s). After washing three times with ice-cold EBC buffer, the pellets were suspended in 2× loading buffer (125 mmol/l Tris.HCl, pH 6.8, 4% SDS, 20% glycerol, 14.4 mmol/l 2-mercaptoethanol) and boiled for 5 min. Immunoblotting was carried out with anti-human APC3, anti-human APC6 (Santa Cruz Biotechnology), or anti-mouse APC7 antibodies.
Tissue samples
Paraffin wax embedded blocks containing breast tumor tissues resected from 108 patients diagnosed as having invasive ductal carcinoma of breast at Wonju Christian Hospital (Wonju, Korea) between January 1996 and May 2001 were used in this study. Patient ages ranged from 24 to 81 years (mean 47.5 years). All procedures were performed in accordance with our hospital's ethical guidelines, and approval for the study was granted by the university hospital's ethics committee. All patients provided informed consent.
Pathologic examination
Hematoxylin–eosin stained slides were reviewed, and histologic grade was determined in terms of tubule formation, nuclear pleomorphism and mitosis, using the criteria described by Bloom and Richardson [32]. Tumor size, lymphatic metastasis, and clinical stage were determined according to the American Joint Committee on Cancer criteria [33].
Immunohistochemistry and evaluation
Specimens were fixed in 10% buffered formaldehyde and embedded in paraffin using routine methods. Sections 5 μm thick were placed on silane-coated glass slides, dried at 50°C for 2 hours, deparaffinized in xylene, rehydrated in graded ethanol, and then washed in distilled water. To retrieve antigenicity, the sections were dipped in citrate buffer (10 mmol/l, pH 6.0) in a tender cooker (Nordic Ware, Minneapolis, MN, USA) and then warmed for 15 min in a microwave oven. Endogenous peroxidase activity was blocked by pretreating with 0.3% hydrogen peroxide for 10 min. After washing with 50 mmol/l Tris buffer (pH 7.5), primary antibodies, namely anti-mouse APC7, human APC7, human Ki-67 (DAKO, Copenhagen, Denmark), or estrogen receptor (ER) antibodies (Novocastra, Newcastle, UK), were applied overnight at a dilution of 1:50 or 1:100. The sections were then further incubated for 20 min in a 1:50 dilution of biotinylated goat anti-rabbit or rabbit anti-mouse antibody (DAKO) as secondary antibody. Color was developed by incubating with streptavidin peroxidase (DAKO) for 20 min and staining with 3-amino-9-ethylcarazole. Counter-staining was carried out with hematoxylin before mounting. To obtain relevant staining equivalence of APC7 in different carcinoma tissues, an unstained tissue sample and a strongly stained tissue sample were used as negative and positive control, respectively. Whenever a staining procedure was performed, negative and positive control tissues were simultaneously stained with new battery of tissues and then the control tissues were used as a staining reference.
All slides were examined by three pathologists and scores were determined by consensus. The immunohistochemical intensity of APC7 was awarded an 'intensity' score of 0 to 3+, with 0 represented an unstained nucleus and 3+ the strongest staining intensity. The 'proportion' score represented the estimated percentage of stained cells as a fraction of all tumor cells in the microscopic field (i.e. 0 = 0%, 1+ = 0–25%, 2+ = 25–50%, and 3+ = >50%). Because true negative expression with a staining intensity of 0 was very rare (six cases/108 tissue samples), and therefore the statistical significance between APC7 expression and clinicopathologic parameters could not obtained, we included a weak APC7 expression group (34 cases/108 tissue samples) with staining intensity of 1 and proportion score of 1 in the negative group. Therefore, a summed intensity and proportion score of ≥ 3+ was defined as positive APC7 expression whereas a score of ≤ 2+ was defined as negative. The Ki-67 labeling index was defined as the percentage of positively stained cells in five to seven high power fields (×400). At least 1000 cells per field were counted. Nuclear ER staining was also examined at ×400 and compared with a strong positive control. ER staining intensity was designated weak, moderate, or strong. Positive reactivity was defined when the proportion of cells exhibiting moderate to strong staining exceeded 10%.
DNA analysis
Two 50-μm sections were cut from each paraffin block, deparaffinized in xylene, rehydrated in a descending ethanol series, and then washed in phosphate-buffered saline. The sections were then placed in 10 mmol/l citrate solution (pH 6.0) and incubated for 2 hours at 80°C. After cooling, 1 mg/ml pepsin in 0.1 N HCl was added and the sections were digested for 30 min. The resulting suspension (2 × 106 nuclei) was filtered through 50 μ-mesh and further suspended in 500 μl of 1% bovine serum albumin solution. DNAs were stained using a Cycle TEST PLUS DNA Reagent Kit™ (Becton Dickinson, Ontario, Canada). Stained cells were analyzed using a FACscan (Becton Dickinson, San Jose, CA, USA) and the fraction of aneuploid cells was calculated using Cell Fit software (Becton Dickinson).
Statistical analysis
Statistical analysis was performed using the SPSS ver. 10.0 program (SPSS Inc., Chicago, IL, USA). The association between APC7 expression and clinicopathologic parameters was analyzed using χ2 tests. P ≤ 0.05 was considered statistically significant.
Results
Characterization of polyclonal antibodies against APC7
In this study we isolated a novel gene (GenBank Accession Number: AF076607) and identified it as the mouse APC7 gene (GenBank Accession Number: BC006635). It was found to have 97.7% homology with its human counterpart (GenBank Accession Number: AF191340). Polyclonal antibodies were raised by immunizing a NZW rabbit with recombinant mouse APC7 proteins (amino acids 88–565), and the APC7-specific antibodies so obtained were then purified by affinity binding to APC7-coupled nitrocellulose. Immunoblotting analysis of MCF-7 human breast carcinoma extracts showed that these purified antibodies and human APC7 antibodies (sc-20987; Santa Cruz) recognized a distinct 63-kDa band, and that this immune reactivity was APC7-specific (Fig. 1A, panels a and b). The antibodies recognized same sized antigens from mouse and human cells (Fig. 1A, panel c). Moreover, APC7 antibodies precipitated both APC3 and APC6 components in mouse and human derived cells, whereas human CDC27 (APC3) antibodies precipitated APC6 and APC7 components (Fig. 1B), thus demonstrating that the antigen recognized by our purified antibody is the APC7 component of the APC. Immunohistochemical studies on the paraffin-embedded sections of normal breast tissues showed that the purified APC7 antibodies recognized antigens located in the nucleus (Fig. 1C, panel a), which is in accordance with the finding that most APC antigens are localized in nucleus during the interphase [33]. Immune reactivity, as shown by immunohistochemistry, was also APC7 specific (Fig. 1C, panel b).
Expression of APC7 in various human tissues
To search for differentially expressed APC7 in normal and cancerous tissues, we performed immunohistochemical analyses with the purified mouse APC7 antibodies using tissue array slides containing 50 normal or 50 tumor tissue cores. We compared the APC7 expressions of the cores by assessing the averaged staining intensities (0 to 3+). Staining of ≥ 2+ was defined as positive expression and of ≤ 1+ as negative expression. Table 1 lists the APC7 expression of 17 normal and 22 tumor tissues with multiple cores. Positive staining was observed in rapid growing normal epithelial tissues. In contrast, slow growing but more differentiated tissues such as skeletal muscle, adipocytes, spinal cord, brain, and basal stromal tissues near epithelial cells exhibited no or weak immune reactivity to APC7. In addition, slowly growing tumors such as chondrosarcomas, lipomas, low-grade urothelial carcinomas, and renal cell carcinomas tended to show weak reactivity to APC7, whereas most tumor tissues with high proliferation rate were positive. Interestingly, some ductal carcinomas of the breast with an undifferentiated high histologic grade exhibited weak reactivity to APC7.
Relationship between APC7 expression and clinicopathologic parameters in breast carcinomas
To determine whether the loss of APC7 expression is related to tumorigenesis in breast cancer, we scrutinized the expression level of APC7 immunohistochemically in 108 invasive ductal carcinomas of the breast and then searched for correlations with clinocopathologic parameters. Figure 2A shows the representative features of APC7 staining scores of 0 to 3+, and Fig. 2B shows the immunoblotting results for three representative tissues with different intensity scores. These data show that APC7 staining intensity was proportional to band intensity by immunoblotting, demonstrating that immunohistochemical staining intensities represent APC7 expression. Typical negative and positive APC7 expressions in breast carcinoma are shown in Fig. 2C panels a and b. The ratios of APC7-positive to APC-negative expression and their relationships with various clinicopathologic parameters are summarized in Table 2. Of breast carcinomas, 63% exhibited positive APC7 expression and 37% were negative. APC7 expression did not correlate with tumor size (P = 0.8180) or metastasis (P = 0.9703). No statistical significance was found between clinical stage (0.2798) and APC7 expression, or between ER expression (0.1031) and APC7 expression. Nevertheless, the frequency of positive APC7 expression tended to be lower in clinical stage III (58.3%) than in stage I (78.9%) tumors, and in patients who were ER negative (52.9%) than in those who were ER positive (70.6%). In contrast, negative APC7 expression was highest in stage III tumors and in those who were ER negative.
On the other hand, the negative expression of APC7 was positively correlated with higher histologic grade (P = 0.001), nuclear grade (P = 0.0090), mitotic number (P = 0.0016), Ki-67 index (P = 0.0078), and aneuploidy (P = 0.0095). Moreover, APC7 expression was more frequent in those with a low histologic grade (84.8%) than in those with a high grade (35.0%). Because histologic grade is determined by nuclear pleomorphism, mitotic number, and tubule formation, those with a high nuclear grade and a high mitotic number also exhibited a similar negative correlation with APC7 expression. The frequency of positive APC7 expression was lower in the high Ki-67 group than in the low Ki-67 group. About 82% (77/94) of tissue samples were classified as aneuploid. Nearly half of the aneuploid group exhibited a low level of APC7 expression (42.9%), whereas most of the diploid group showed positive APC7 expression (94.1%), indicating that breast carcinomas with normal ploidy express higher levels of APC7.
Immunoblotting analysis of APC expression in breast carcinomas
To determine whether the expression of the APC7 component is exclusively modulated in breast carcinoma, we investigated the expression levels of other APC components. We first performed immunohistochemic analysis using anti-human APC3 antibodies or anti-human APC6 antibodies. However, we could not obtain meaningful data because nuclei in all breast carcinoma tissues were strongly stained by these antibodies, probably because of nonspecific cross-reactivity. Next, we compared the expression levels of APC3 and APC6 components by immunoblotting. However, immunoblotting analysis with anti-human APC6 antibodies also failed to exhibit a distinct band in tumor tissues because of the weak immune reactivity and the nonspecific reactivity of the APC6 antibody. Thus, we were able to obtain expression data on APC3 in various breast carcinoma tissues, in conjunction with APC7 expression.
Figure 3 shows immunoblotting results for APC3 and APC7 in 24 representative breast carcinoma tissues. The expression levels of APC3 and APC7 in these tissues was variable, which might have been due to variable APC expression in these tissues. Some tissues (lanes 1, 2, 3, 8, 9, 13, 14, and 15) exhibited relatively high levels of expression of both APC3 and APC7, whereas other tissues (lanes 6, 7, 16, and 22) showed no expression of APC3 and APC7. These data suggest that the expression levels of APC3 and APC7 are simultaneously regulated in some breast carcinoma tissues. On the other hand, several carcinomas (lanes 4, 11, 12, 23, and 24) showed relatively high APC3 expression but low APC7, suggesting that selective downregulation of APC7 is unique to some breast carcinomas.
Discussion
This work was undertaken to determine whether the expressional modulation of APC7 is related to tumorigenesis in human cancers. We first used immunohistochemistry to investigate the expression of APC7 in tissue array slides mounted with various cancer tissues, and we observed strong immune reactivity to APC7 in the most rapidly growing tumor tissues. However, some breast cancer tissues with a high histologic grade exhibited weak immune reactivity to APC7. Therefore, we scrutinized APC7 expression in 108 invasive ductal carcinomas of the breast and compared these findings with clinicopathologic parameters. Although positive immune reactivity to APC7 was observed in more than 60% of breast carcinomas, negative APC7 expression was frequently observed in breast carcinomas with more aggressive characteristics (i.e. a higher histologic grade, a higher nuclear grade, a higher proliferation rate, or aneuploidy). These findings suggest a possible association between the expression of APC7 and breast cancer tumorigenesis.
Most components of APC have been reported to be expressed in growing tissues at fairly constant levels [34]. On the other hand, Gieffers and coworkers [35] reported that components of APC (i.e. APC2, APC3, and APC7) are expressed in postmitotic adult brain tissue. However, it is not known how the expressions of APC components are modulated according to growth or cell differentiation. We observed twofold APC7 modulation in mouse NIH3T3 cells according to cell cycle (data not shown). In the present study, immunohistochemical studies using normal and cancer tissue arrays showed that APC7 is highly expressed in most proliferating cells. Strong immunoreactivity to APC7 was restricted to normal epithelial tissues and proliferating cancer tissues, whereas low APC7 immunoreactivity was observed in slow growing and differentiated tissues, such as adipocytes, hepatocytes, muscle cells, brain, and spinal cord, and in slowly growing tumor tissues such as lipoma, pleomorphic adenoma of the salivary gland, adenoid cystic carcinoma, chondrosarcoma, low-grade urothelial carcinoma, and renal cell carcinoma.
Interestingly, we found a negative correlation between APC7 expression and some high-grade breast carcinoma tissues, and especially in those with aneuploidy. This negative correlation seems to be unique to some malignant breast carcinomas because we did not observe significant loss of APC7 expression in other aggressive carcinomas. In fact, we further investigated APC7 expression in two representative carcinomas, namely lung and renal carcinomas (i.e. rapid and slow growing carcinomas, respectively; data not shown), and obtained the same result as that obtained using the tissue array. All rapidly growing carcinoma tissues examined showed positive APC7 expression, whereas over 90% of slow growing renal carcinomas showed negative APC7 expression. Reports that the level of APC1 expression in breast cancer tissue is lower than that in other cancer tissues, and that expressed sequence tags of APC7 are reduced in breast tissues [36] support our observation that the loss of APC7 expression seems to be restricted in some high-grade breast carcinomas. Immunoblotting analysis showed that the expression of APC7 was variable in different breast carcinomas, which appears to be due to differences in expression of APC7 between individual breast carcinomas. However, the possibility of epithelial cell contamination during tissue extraction cannot be excluded. In several breast carcinomas, the loss of APC7 expression was accompanied by the loss of another APC component, such as APC3, but other tissues exhibited selective APC7 downregulation. These observations demonstrate that a unique loss of APC7 expression occurs in some breast carcinomas, and suggest that regulation of APC components in breast carcinomas is heterogeneous. Our data agree with reports that the expressional patterns of APC components are not simultaneously modulated [37] and that APC components can be individually modulated by environmental stimuli [38].
It has been reported that chromosome instability through abnormal mitotic progression plays a critical role in tumor malignancy [38,39]. Therefore, the dysregulation of APC activation, which probably perturbs mitotic progression, may affect malignant transformation or tumor progression. Moreover, the finding that APC is required for the G2 and mitotic checkpoints suggests that malignant transformation can be caused by chromosome instability through the dysregulation of APC activation [40]. Recently, Wang and coworkers [41] reported a genetic alteration in APC6 and APC8 in human colon cancer cells, and suggested their involvement in colon carcinogensis. Aneuploidy is frequently observed in breast cancer tissues [42], and APC target molecules such as PTTG, PLK, and aurora kinase are often upregulated in the same tissues [7,12,17], supporting the notion that dysregulation of APC may play a role in the tumorigenesis of breast cancer.
Our data concerning the negative correlation between APC7 expression and a high histologic grade with aneuploidy supports a possible linkage between the downregulation of APC7 and malignant transformation in breast cancer. As several papers have reported lethality induced by the loss of APC components [43,44], our observation raises the question as to how the cell cycle can progress in the absence of APC7. Although defects in the spindle checkpoint could elicit cell death, cancer cells in conjunction with p53 mutation could override the mitotic checkpoint and the cell lethality elicited by abnormal mitosis [41]. In this situation, it is believed that an abnormality in APC regulation could induce unscheduled mitotic progression [31]. One interesting observation was that the cell cycle of mutant APC8-harboring cells progressed, even with a disturbed pattern [45]. A recent report that the expression of cyclin-B1 in breast cancer cells was sustained in the G1 phase suggests that the cell cycle can progress in the presence of an abnormal mitotic cell cycle machinery [46]. Therefore, we hypothesize that cells with a functional APC defect can drive cell cycle progression in a situation in which cyclin-B has accumulated. However, the mechanism that allows breast cancer cells to progress through mitosis with functionally defective APC remains unknown.
We did not determine whether APC7 expression is correlated with disease-free survival because most of the patients had recently been diagnosed. However, our data demonstrate that the downregulation of APC7 is more common in those who exhibit markers of poor prognosis. A number of parameters, namely lymph node metastases, tumor size, histologic grade, ER and progesterone receptor expression, lymphovascular invasion, proliferation rate, DNA content, and expression of oncogenes, have been reported to influence the prognosis of women with breast cancer [47]. On the other hand, in the present study poor prognostic indications such as high histologic grade, high proliferation rate, and aneuploidy were found to be related to downregulation of APC7, which suggests that breast cancer patients exhibiting weak APC7 expression would have poor survival. Yoshimoto and coworkers [48] reported that histologic grade and clinical stage, including lymph node metastasis, are important prognostic markers in breast cancer patients, which supports the notion that the downregulation of APC7 can be a viable prognostic marker. An earlier report that the Ki-67 index is related to histologic grade also supports the prognostic significance of the negative correlation between APC7 and proliferation [49].
Conclusion
We found that downregulation of APC7 in breast carcinoma is more common in those with high histologic grade and high proliferation rate, and in those showing aneuploidy. Therefore, downregulation of APC7 may be a marker of a poor prognosis and may contribute to breast cancer tumorigenesis via chromosome instability and/or accelerated oncogenic signaling.
Abbreviations
APC = the anaphase-promoting complex; ER = estrogen receptor; PLK = polo-like kinase; PTTG = pituitary tumor transforming gene.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KP carried out immunohistological studies and statistical analysis. SC produced mouse APC7 proteins and prepared APC7 antibodies. She also performed immunoblotting studies. ME participated in pathologic and immunohistologic studies. YK designed and coordinated this research and prepared the manuscript.
Acknowledgements
This study was supported by a grant (R05-2002-000-00101-0) from the Basic Research Program of the Korea Science & Engineering Foundation.
Figures and Tables
Figure 1 Recognition of anaphase-promoting complex (APC)7 antigen with anti-mouse APC7 antibodies. (A) Panel a shows immunoblotting of human MCF-7 breast carcinoma cell extracts with affinity-purified anti-mouse APC7 polyclonal antibodies before (-) and after (+) prebinding APC7 antibodies to recombinant mouse APC7-coupled nitrocellulose. Panel b shows immunoblotting with human APC7 polyclonal antibodies (Santa Cruz; sc-20987). Panel c shows immunoblotting of human Hela cervical carcinoma, mouse NIH 3T3 fibroblast, and human breast carcinomas (MCF, MCF-7; MDA, MDA-MB-231; SK, SK-BR-3; HS, HS 578T) with purified mouse APC7 antibodies. Cell extracts were resolved on 12% SDS-PAGE and the separated proteins were electrotransferred onto Immobilon membranes (Millipore). The membranes were then treated with anti-APC7 antibodies and horseradish peroxidase-conjugated anti-rabbit IgG antibodies as primary and secondary antibodies, respectively. Immune reactive bands were developed using an electrogenerated chemiluminescence (ECL) system. (B) Immunoprecipitation and immunoblotting of mouse 3T3 and human Hela cell extracts with anti-human APC3 (CDC27; Transduction Laboratory) or purified anti-mouse APC7 antibodies. After mixing cytosolic extracts (1 mg) with pre-immune rabbit sera (2 μl), anti-APC3 (1 μg), or anti-mouse APC7 antibodies (1 μg), precipitates were fractionated on 12% SDS-PAGE. After electrotransfer, immunoblotting was performed with anti-APC3, anti-human APC6 (Santa Cruz Biotech), or anti-mouse APC7 antibodies as primary antibodies. Peroxidase-conjugated anti-mouse, anti-goat, or anti-rabbit IgG antibodies were bound as secondary antibodies and developed using the ECL system. (C) Immunohistochemistry of normal breast tissues with purified anti-mouse APC7 antibodies before (panel a) and after (panel b) prebinding APC7 antibodies to recombinant mouse APC7-coupled nitrocellulose (original magnification ×200). After binding the purified anti-mouse APC7 antibodies and the biotinylated goat anti-rabbit antibodies as primary and secondary antibodies, respectively, a streptavidin-peroxidase kit (DAKO) was used to detect APC7 antigen. Staining and counter-staining were then performed using 3-amino-9-ethylcarazole and hematoxylin, respectively.
Figure 2 Representative immunohistochemical and immunoblotting analysis of anaphase-promoting complex (APC)7 in invasive ductal carcinomas of the breast. (A) Invasive ductal carcinomas stained by APC7 antibody with intensity scores of 0 to 3+ (original magnification, ×400). After binding with anti-mouse APC7 antibodies as primary antibodies and with biotinylated anti-rabbit antibodies as secondary antibodies, streptavidin peroxidase was bound to the biotinylated antibodies. Staining was conducted with 3-amino-9-ethylcarazole and hematoxylin. (B) Immunoblotting of three breast tissues with APC7 intensities ranging from 0 to 3+. Immunoblotting was carried out using affinity-purified APC7 antibodies and peroxidase-conjugated anti-rabbit IgG antibodies as primary and secondary antibodies, respectively. Electrogenerated chemiluminescence was used to detect positive bands. (C) Representative negative (panel a) and positive (panel b) APC7 immunostaining of invasive ductal breast carcinoma with histologic grades of 3 and 2, and staining intensities of 0 and 3+, respectively. Immunohistochemical staining was also performed using a streptavidin peroxidase kit.
Figure 3 Immunoblotting analysis of breast carcinoma using anti-anaphase-promoting complex (APC)3 and APC7 antibodies. Protein extracts were prepared from 24 ductal breast carcinoma tissues by homogenizing tissues in EBC buffer for 1 min and centrifuging (12,000 g for 15 min). Of protein from each tissue, 30 μg was fractionated by 12% SDS-PAGE and transferred to Immobilon membranes. Immunoblotting analysis was also carried out using anti-APC3 or APC7 antibodies as primary antibodies, and horseradish peroxidase-conjugated anti-mouse or anti-rabbit IgG antibodies as secondary antibodies, and bands were then detected by electrogenerated chemiluminescence.
Table 1 Expression of APC7 in various human tissues
Expression of APC7a Tissue type
Normal tissue Tumor tissue
Negative Adipocytes, brain, hepatocytes, skeletal muscle cells, spinal cord Lipoma, pleomorphic adenoma of salivary gland, low grade urothelial carcinoma, chondrosarcoma, adenoid cystic carcinoma, renal cell carcinoma, high grade ductal carcinoma of breast
Positive Basal cells of epidermis, bronchial epithelium, ductal cells of breast, ductal cells of pancreas, ductal cells of salivary glands, endometrial glands, kidney epithelium, gastric mucosa, prostate glands and ducts, urothelial epithelium, fibroblasts, germinal center cells Adenocarcinoma of colon, adenocarcinoma of endometrium, adenocarcinoma of pancreas, adenocarcinoma of prostate, adenocarcinoma of stomach, ductal carcinoma of pancreas, hepatocellular carcinoma, high grade urothelial carcinoma, papillary serous carcinoma of ovary, squamous cell carcinoma of esophagus, squamous carcinoma of cervix, leiomyosarcoma of uterus, malignant lymphoma, melanoma, seminoma of testis
aImmunohistochemical data from tissue arrays (Tissue-Array Co., Seoul, Korea) stained with anti-anaphase-promoting complex (APC)7 antibodies. Tissue-array slides mounted with 50 normal or 50 tumor cores contained 17 normal or 22 tumor tissues, respectively, in triplicate or duplicate. Negative APC7 expression is represented by staining intensities of 0 or 1+, whereas positive expression is represented by staining intensities of 2+ or 3+, as designated in Fig. 2A. Average staining intensities of several cores were used to determine tissue expression.
Table 2 Relationship between APC7 expression and various clinicopathological parameters in invasive ductal carcinomas of the breast
Characteristics All cases APC7 positive APC7 negative Pa (significance)
All carcinomas 108 (100%) 68 (63.0%) 40 (37.0%)
Tumor size 0.8180 (NSb)
Small (<5 cm) 81 (100%) 51 (63.0%) 30 (37.0%)
Large (≥ 5 cm) 27 (100%) 17 (63.0%) 10 (37.0%)
Metastasis 0.9703 (NS)
Negative 47 (100%) 29 (61.7%) 18 (38.3%)
Positive 61 (100%) 39 (63.9%) 22 (36.1%)
Clinical stage 0.2798 (NS)
I 19 (100%) 15 (78.9%) 4 (21.1%)
II 65 (100%) 39 (60.0%) 26 (40.0%)
III 24 (100%) 14 (58.3%) 10 (41.7%)
ERc 0.1031 (NS)
Positive 51 (100%) 36 (70.6%) 15 (29.4%)
Negative 51 (100%) 27 (52.9%) 24 (47.1%)
Histologic grade 0.001
I 33 (100%) 28 (84.8%) 5 (15.2%)
II 55 (100%) 33 (60.0%) 22 (40.0%)
III 20 (100%) 7 (35.0%) 13 (65.0%)
Nuclear grade 0.0090
1 6 (100%) 6 (100%) 0 (0%)
2 87 (100%) 57 (65.5%) 30 (34.5%)
3 15 (100%) 5 (33.3%) 10 (66.7%)
Mitotic number (n/10 hpfd) 0.0016
<10 49 (100%) 37 (75.5%) 12 (24.5%)
10–19 41 (100%) 26 (63.4%) 15 (36.6%)
≥20 18 (100%) 5 (27.8%) 13 (72.2%)
Ki-67 labeling indexe 0.0078
L (<25.5) 63 (100%) 58 (76.2%) 15 (23.8%)
H (≥ 25.5) 41 (100%) 20 (48.8%) 21 (51.2%)
DNA ploidyf 0.0095
Diploidy 17 (100%) 16 (94.1%) 1 (5.9%)
Aneuploidy 77 (100%) 44 (57.1%) 33 (42.9%)
aχ2 test. bNot significant (P > 0.05). cEstrogen receptor (ER): because of an insufficient amount of some carcinoma tissues, we could not obtain meaningful ER staining data from six of the 108 breast carcinoma tissues. dhpf, high power field. eKi-67 (%): labeling index of Ki-67. The H and L groups were categorized in terms of being higher or lower than the mean index (25.5) in all cases. The mean indices of the L and H groups were 22.17 and 31.79, respectively. Because of an insufficient amount of some carcinoma tissues, we could not obtain meaningful Ki-67 staining data from four of the 108 breast carcinoma tissues. fDNA ploidy: in some cases we did not perform flow cytometry because of insufficient tissues. In another case, we could not interpret DNA ploidy data because of a high coefficient of variance (over 8.0). We obtained meaningful data for 94 tissue samples.
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| 15743504 | PMC1064132 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Jan 14; 7(2):R238-R247 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr978 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9801574350210.1186/bcr980Research ArticleRelative microvessel area of the primary tumour, and not lymph node status, predicts the presence of bone marrow micrometastases detected by reverse transcriptase polymerase chain reaction in patients with clinically non-metastatic breast cancer Benoy Ina H [email protected] Roberto 1Elst Hilde 1Van Dam Peter 1Weyler Joost 2Van Marck Eric 1Scharpé Simon 3Vermeulen Peter B [email protected] Luc Y [email protected] Translational Cancer Research Group Antwerp (Laboratory of Pathology, University of Antwerp/University Hospital Antwerp, Edegem, Belgium, and Oncology Centre, General Hospital Sint-Augustinus, Wilrijk, Belgium)2 Department of Epidemiology and Social Medicine, University of Antwerp, Wilrijk, Belgium3 Medical Biochemistry, University of Antwerp, Wilrijk, Belgium2005 10 1 2005 7 2 R210 R219 5 10 2004 2 12 2004 Copyright © 2005 Benoy et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About 50% of patients with breast cancer have no involvement of axillary lymph nodes at diagnosis and can be considered cured after primary locoregional treatment. However, about 20–30% will experience distant relapse. The group of patients at risk is not well characterised: recurrence is probably due to the establishment of micrometastases before treatment. Given the early steps of metastasis in which tumour cells interact with endothelial cells of blood vessels, and, given the independent prognostic value in breast cancer of both the quantification of tumour vascularisation and the detection of micrometastases in the bone marrow, the aim of this study was to determine the relationship between vascularisation, measured by Chalkley morphometry, and the bone marrow content of cytokeratin-19 (CK-19) mRNA, quantified by real-time reverse transcriptase polymerase chain reaction, in a series of 68 patients with localised untreated breast cancer. The blood concentration of factors involved in angiogenesis (interleukin-6 and vascular endothelial growth factor) and of factors involved in coagulation (D-dimer, fibrinogen, platelets) was also measured. When bone marrow CK-19 relative gene expression (RGE) was categorised according to the cut-off value of 0.77 (95th centile of control patients), 53% of the patients had an elevated CK-19 RGE. Patients with bone marrow micrometastases, on the basis of an elevated CK-19 RGE, had a mean Chalkley count of 7.5 ± 1.7 (median 7, standard error [SE] 0.30) compared with a mean Chalkley count of 6.5 ± 1.7 in other patients (median 6, SE 0.3) (Mann–Whitney U-test; P = 0.04). Multiple regression analysis revealed that Chalkley count, not lymph node status, independently predicted CK-19 RGE status (P = 0.04; odds ratio 1.38; 95% confidence interval 1.009–1.882). Blood parameters reflecting angiogenesis and coagulation were positively correlated with Chalkley count and/or CK-19 RGE. Our data are in support of an association between elevated relative microvessel area of the primary tumour and the presence of bone marrow micrometastases in breast cancer patients with operable disease, and corroborate the paracrine and endocrine role of interleukin-6 and the involvement of coagulation in breast cancer growth and metastasis.
angiogenesisbone marrowbreast cancerChalkleymicrometastasis.
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Introduction
The development of distant metastases is the primary cause of death in breast cancer patients. The involvement of the axillary lymph nodes, tumour size, histopathological grade and hormone receptor status determine prognosis and treatment options at initial diagnosis [1]. Nevertheless, these parameters do not accurately predict which patients will relapse after primary treatment, and they give limited information about the effectiveness of adjuvant treatment.
About 50% of patients have no involvement of the axillary lymph nodes at diagnosis and can therefore be considered cured after primary locoregional treatment. However, about 20–30% will experience distant relapse within 5–10 years, suggesting outgrowth of disseminated tumour cells present at diagnosis and undetectable by the current diagnostics [2]. This prompted the refinement of methods able to detect subclinical tumour deposits in various body compartments.
Tumour cells residing in bone marrow are considered to mirror the efficacy of the metastatic process throughout the body. Several prospectively designed clinical trials have confirmed the independent prognostic significance of the lodging of tumour cells in the bone marrow [3], suggesting that this minimal disease is indeed the progenitor of manifest metastasis. It is not clear whether the tumour cells that are part of subclinical metastases have arisen early during progression of the primary tumour or whether they are late and rare metastatic variants as a result of the cumulative acquisition of malignant phenotypic traits such as self-sufficiency in growth signals, insensitivity to anti-growth signals, evasion of apoptosis, limitless replicative potential, genomic instability, tissue invasion and sustained angiogenesis [4-6].
Bone marrow micrometastasis can be detected by immunocytochemical analyses with antibodies directed at epithelial markers. Polymerase chain reaction (PCR)-based techniques that amplify epithelial mRNA are more sensitive but need the introduction of cut-off values for positivity to correct for the inevitable loss of specificity.
The concept of dependence on vascularisation of growth, invasion and metastasis of malignant tumours has been challenged by the description of angiogenesis-independent mechanisms [7-10]. Nevertheless, the growth of most primary tumours needs angiogenesis. There is accumulating evidence that angiogenesis is intrinsically linked with the process of haemostasis [11]. Both angiogenesis and haemostasis are tightly regulated in physiological circumstances, for example during wound healing, but are deregulated when involved in tumour growth, invasion and metastasis. We have previously demonstrated the prognostic importance of the angiogenic cytokine interleukin (IL)-6, and the fibrin degradation product D-dimer, in patients with metastatic breast cancer [12,13].
A reproducible method of quantifying vascularisation, by assessing the relative microvessel area, is Chalkley point overlap morphometry [14]. Significant associations between the Chalkley count and axillary lymph node metastasis, increasing tumour size, high grade and histological subtype have been reported in patients with breast cancer. Moreover, an independent prognostic value has been demonstrated in patients with breast cancer by a meta-analysis of 87 published studies [15]. Chalkley counting is done in selected areas of high microvessel density, so-called 'hot spots'. The hypothetical rationale for counting in these highly vascular areas is that they predict the presence of more angiogenic subclones of the tumour [16]. These subclones might therefore have a higher metastatic efficiency.
The primary aim of this study was to assess the association of tumour vascularity, quantified by a well-standardised morphometrical method, and the presence of bone marrow micrometastases, detected by a sensitive and quantitative real-time reverse transcriptase PCR (RT–PCR) technique, in patients with operable breast cancer before treatment, to evaluate the relative microvessel area as a surrogate marker of bone marrow micrometastasis, as suggested by Fox and colleagues [17].
Materials and methods
Patients
Blood and bone marrow samples were collected preoperatively in 68 consecutive patients with localised, untreated, operable breast cancer. Eleven patients with a haematological malignancy who underwent bone marrow sampling for diagnostic purposes were entered as control patients for cytokeratin-19 (CK-19) relative gene expression (RGE).
Tissue
A representative, full cross-section of the tumour sample surrounded by adjacent normal breast tissue was taken from all tumours. The tissue was fixed in buffered formalin and was paraffin-embedded. Sections 5 μm thick were cut and mounted on poly-L-lysine-coated slides.
Blood coagulation tests
Plasma collection and measurement of fibrinogen, platelet counts and D-dimer levels were performed as described previously [12].
Chalkley morphometry
Tumour vascularisation was evaluated by the Chalkley method, a morphometric point overlap counting system using a microscope eyepiece graticule. It has been shown to be both a rapid and a reproducible method that gives independent prognostic information and has been suggested as a standard method in an international consensus report on the quantification of angiogenesis [14]. In brief, a representative paraffin section including the tumour border was immunostained with a monoclonal anti-CD34 antibody (clone QBEnd/10; Biogenex, San Ramon, CA, USA) diluted 1:5 with overnight incubation at 4°C and without antigen retrieval procedure. The areas of highest vascular density ('hot spots') were identified at low magnification (× 10 ocular and × 10 objective). On a higher magnification (× 10 ocular and × 20 objective), a 25-point Chalkley eyepiece graticule (Chalkley grid area 0.22 mm2) was applied to each hot spot and orientated to permit the maximum number of points to hit on or in a microvessel. The Chalkley count in the hot spot with highest vascular density (the largest number of hits) was used for further analyses.
Enzyme-linked immunosorbent assay (ELISA) of angiogenic cytokines
Serum samples were collected in serum separator tubes (type Vacutainer; Becton-Dickinson) and centrifuged at 2000 g for 5 min. Measurements of serum vascular endothelial growth factor (VEGF)-A165 and IL-6 were performed as described previously [18]. Plasma (Becton-Dickinson Vacutainer tubes type 9NC 0.129 M, 4.5 ml) VEGF-A165 was measured with an ELISA kit (R&D Systems, Minneapolis, MN, USA) on trisodium citrate-anticoagulated collected blood. For the ELISA assay of VEGF-A165, no cross-reactivity with other VEGF-A isoforms is documented. No cross-reactivity and no interference was found with IL-6-related (for example leukaemia inhibitory factor and oncostatin M) and growth factors not related to IL-6 (for example human platelet-derived growth factor and IL-7) (Quantikine human IL-6; R&D Systems, Minneapolis, MN, USA). Samples were assayed in duplicate. Within-assay variability had been tested before and was limited [18].
Bone marrow sampling
Bone marrow (9 ml) was aspirated from the posterior iliac crest under local anaesthesia into syringes containing heparin as anticoagulant (Becton-Dickinson Vacutainer tubes type NH 170 IU 10 ml). Mononuclear cells were isolated by density-gradient centrifugation through Ficoll-Paque (Amersham Pharmacia Biotech) and washed twice with phosphate-buffered saline. After final centrifugation, the cell pellets were resuspended in a guanidine-containing buffer and stored at -70°C.
Cell line
The MDA-MB 361 cell line (American Type Culture Collection [ATCC] catalogue code HTB-27) was cultured in Leibovitz's L-15 medium with a free gas exchange with atmospheric air, and supplemented with 20% fetal bovine serum and antibiotics. The medium was replaced every 3–4 days. Cells were harvested in accordance with the ATCC guidelines.
RNA isolation and cDNA synthesis
Total RNA was extracted from the mononuclear cell fraction with the RNeasy kit (Qiagen). The amount of RNA was measured spectrophotometrically. Only samples with an A260/A280 ratio of more than 1.8, indicating high purity, were used. The RNA integrity was tested with the Agilent Bioanalyzer (Agilent Technologies). Only samples with a lack of degradation on the electrophoretogram and with a 28S/18S ratio of at least 1.9 were analysed. For first cDNA strand generation, 2 μg of total RNA was reverse-transcribed with the High-Capacity cDNA Archive kit (Applied Biosystems) in a total volume of 100 μl.
Primers and probe design
Primers and probe for CK-19 were designed with the aid of Primer Express software (Applied Biosystems). To avoid amplification of contaminating genomic DNA, primers and probe were located on different exons.
The forward primer of CK-19 (5'-CCCGCGACTACAGCCACTA-3') is situated on exon 1, the probe (5'-ACCATTGAGAACTCCAGGATTGTCCTGCA-3', labelled with 6-carboxyfluorescein [FAM] at the 5' end and 6-carboxy-tetramethylrhodamine [TAMRA] at the 3' end) on exon 2 and the reverse primer (5'-CTCATGCGCAGAGCCTGTT-3') on exon 3. RT-PCR with this primer set resulted in a 163-base-pair fragment. The nucleotide sequences of the primers and probe were checked for their specificity in the NCI BLAST® database. The amplification product was sequenced and was comparable with the predicted nucleotide sequence.
PCR amplification
All PCR reactions were performed on the ABI Prism 7700 Sequence Detection System (Applied Biosystems) with the fluorescent Taqman method. Mispriming of processed pseudogenes was excluded.
The CK-19 mRNA quantities were analysed in triplicate, normalised against β-actin as a control gene and were expressed in relation to a calibrator sample. The calibrator was produced from the blood of a healthy volunteer spiked with five MDA-MB 361 cells per 106 mononuclear cells. The calibrator was given a RGE of 100. As described by Livak and colleagues [19], results are expressed as RGE with the ΔΔCT method.
Ethical considerations
The Ethical Committee of both institutions approved this study. Written informed consent was obtained from all patients.
Statistical analysis
Statistical analysis was performed with Graphpad Prism (version 2.0; Graphpad Software, Inc.). For IL-6 and VEGF-A ELISA assays, half of the detection limit value of the patient samples was used for statistical analysis in case the measured values did not reach the detection limit of the assay. Detection limits were 0.7 and 9 pg/ml, respectively. Comparisons of continuous variables were performed with the Mann–Whitney U-test. Relationship between categorical variables was validated with a χ2 test. The correlation of continuous variables was analysed with a Spearman rank test. A multiple regression analysis was performed with a stepwise-backward likelihood procedure. P < 0.05 was necessary for statistical significance.
Results
Patients
Age, T status, N status, oestrogen and progesterone receptor-status, tumour differentiation, menopausal status and tumour histology are given in Table 1.
CK-19 RGE
Quantification of the mRNA transcripts of CK-19 in the bone marrow aspirates could be performed in all control samples and in 62 bone marrow samples of patients with breast cancer. In six patients, the volume of bone marrow or the quality of RNA was insufficient. A median RGE of 0.57 (range 0.22–0.78) was found for the control samples. Patients with primary breast cancer had a mean RGE of 3.85 ± 20.95 (median 0.79, standard error [SE] 2.66). Values above the 95th centile of the control values (0.77) were considered to indicate the presence of bone marrow micrometastasis. With this cut-off, 33 of 62 patients (53%) had bone marrow micrometastasis. In 49% of patients there was a concordance between lymph node status and bone marrow status. Fifty-five percent of patients with bone marrow micrometastasis had negative lymph nodes. Forty-six percent of patients without bone marrow micrometastasis had positive lymph nodes. Fifty-five percent of patients without lymph node metastasis had bone marrow micrometastasis. Forty-six percent of patients with lymph node metastasis had no bone marrow micrometastases.
Chalkley count
Quantification of relative microvessel area by Chalkley morphometry was performed in all (n = 68) breast cancer samples. A mean value of 7.1 ± 1.9 (median 7, SE 0.23) was found. The median value of 7 was taken as a cut-off for categorisation into 'high' (7 or more) and 'low' Chalkley count. There was a positive association of Chalkley count with T-stage, but not with lymph node status (Table 2).
CK-19 RGE and Chalkley count
Considering both Chalkley counts and CK-19 RGE as continuous variables, a significant correlation was found (r = 0.26, P = 0.04; Fig. 1a).
Sixty-two percent (23 of 37 patients) of breast tumours with a high Chalkley count were related to a bone marrow sample with positive CK-19 RGE. This was true for only 40% (10 of 25 patients) of breast tumours with a low Chalkley count (χ2 test P = 0.04).
Patients with a CK-19 RGE of 0.77 or more had a tumour with a mean Chalkley count of 7.5 ± 1.7 (median 7.0, SE 0.3) compared with 6.5 ± 1.7 (median 6.0, SE 0.3) in patients with a CK-19 RGE of less than 0.77 (Mann–Whitney U-test P = 0.04; Fig. 1b).
A stepwise-backward likelihood multiple regression analysis for the prediction of the presence of bone marrow micrometastases was performed, including Chalkley count, serum IL-6, D-dimers, fibrinogen, platelet count, serum VEGF-A, plasma VEGF-A, T-stage, N-stage, oestrogen receptor status, progesterone receptor status, tumour type and differentiation status. Only Chalkley count, as a continuous variable, independently predicted CK-19 RGE status (P = 0.04; odds ratio 1.38; 95% confidence interval 1.009–1.882).
Coagulation parameters and angiogenesis parameters in blood
Values and associations are given in Tables 3 and 4. In general, parameters reflecting coagulation and angiogenesis in blood had positive significant associations.
Chalkley count, coagulation parameters and angiogenesis parameters in blood
Serum IL-6 levels were correlated with Chalkley count (r = 0.2, P = 0.08; Fig. 2a). The mean IL-6 concentration in the serum of patients with a high Chalkley count was 2.57 ± 3.75 pg/ml (median 1.10, SE 0.57), and 1.52 ± 4.21 pg/ml (median 0.35, SE 0.84; Mann–Whitney U-test P = 0.018; Fig. 2b) in patients with a low Chalkley count. Platelet count in patients with a high Chalkley count was (282.8 ± 64.6) × 103/μl (median 272, SE 9.8), and (240 ± 73.9) × 103/μl (median 243, SE 14.8; Mann–Whitney U-test P = 0.02; Fig. 3) in patients with a low Chalkley count.
CK-19 RGE, coagulation parameters and angiogenesis parameters in blood
D-dimer levels were correlated (r = 0.22, P = 0.08; Fig. 4a) with CK-19 RGE. Forty-one patients had elevated (more than 250 ng/ml) D-dimer levels. A mean D-dimer concentration of 477 ± 362 ng/ml (median 319, SE 67) was found in patients with CK-19 RGE values of less than 0.77, whereas the mean D-dimer level was 674 ± 634 ng/ml in patients with a CK-19 RGE value of 0.77 or more (median 458.5, SE 115.8; Mann–Whitney U-test P = 0.09; Fig. 4b). No associations between CK-19 RGE and other variables were found (data not shown).
Discussion
The relation between the relative microvessel area, quantified by Chalkley counting of immunostained blood vessels in sections of primary breast cancer, and the pre-operative quantification of micrometastases in bone marrow by Taqman real-time RT–PCR was investigated in a group of patients with primary breast cancer before the start of treatment. Every increase in Chalkley count independently predicted a higher likelihood of bone marrow epithelial cells (odds ratio of 1.4) in patients with non-metastatic breast cancer. Patients with RGE of CK-19 in the bone marrow above the 95th centile of a control population had a primary tumour with a Chalkley count of 7.5, compared with 6.5 in patients without PCR-detected bone marrow micrometastases (P = 0.04).
Our work confirms the study by Fox and colleagues [17]. In 214 patients with primary breast cancer, tumour cells were detected through the examination of epithelial membrane antigen expression and the analysis of cell morphology. In immunostained breast cancer sections, a semi-quantitative vascular grade, expressed as being low or high, was determined in so-called vascular hot spots. Absolute concordance by kappa statistics of this vascular grade and Chalkley count was reported in a small subset of 22 patients. High vascular grade and the presence of vascular invasion independently predicted the presence of bone marrow micrometastases (respective odds ratios 2.7 and 2.7). In our study, Chalkley counts were determined in all 68 patients. However, the main difference lies in the higher sensitivity of Taqman quantitative real-time RT–PCR than that of immunocytochemistry on bone marrow cytospins used by Fox and colleagues [17].
This study corroborates the well-documented prognostic value of the Chalkley count in breast cancer [20]. Hansen and colleagues [20] categorised Chalkley counts by using cut-off points of 5 and 7. Patients with Chalkley counts of 7 or more had a hazard ratio for death of 1.46 (95% confidence interval 1.14–1.87) compared with the group of patients with Chalkley counts between 5 and 7. Bone marrow micrometastases are predicted by a high Chalkley count ([17] and this study) and have been linked to adverse outcome in patients with breast cancer: currently, about 2500 patients with breast cancer have been analysed in five prospectively designed clinical trials to verify associations between the presence of immunostained tumour cells in bone marrow and prognosis [3]. Braun and colleagues [21] have included 552 patients with stage I, II and III breast cancer in a prospective study: the presence of tumour cells detected with the antibody A45-B/B3 independently predicted distant metastasis but not locoregional recurrence. The association of a high Chalkley count with the presence of tumour cells in the bone marrow explains at least partly the prognostic value of the former parameter.
The presence or absence of lymph node metastases did not predict the bone marrow CK-19 RGE status by multiple regression analysis in this study. Concordance between bone marrow status and lymph node status was present in only 49% of all patients. This is in accord with results of cDNA array analyses of primary breast tumours of bone marrow-positive and bone marrow-negative patients [22]. Distinct profiles were reported, indicating that bone marrow metastasis is a selective process with a specific molecular signature of the primary breast tumour. In addition, in a prognostic study of patients with operable breast cancer, the bone marrow status provided independent information in addition to tumour size and lymph node status in a multivariate analysis [23]. The presence of bone marrow micrometastases was related to earlier recurrence.
Although we did not compare the CK-19 RGE with the number of immunostained tumour cells in bone marrow cytospins in this study, the Taqman quantitative real-time RT–PCR methodology has been validated by us in a group of patients with metastatic breast cancer [24]. RT–PCR quantification of mRNA transcripts of epithelial cells has a high sensitivity but a rather poor specificity for the detection of tumour cells in bone marrow or blood. The main reason for this is the expression of some epithelial markers, such as CK-19, by haematopoietic and blood cells. A stringent cut-off for CK-19 RGE positivity has therefore been set in this study. Methodological adaptations to avoid mispriming due to pseudogenes and to avoid amplification of contaminating DNA have also been made. Expression of the CK-19 RGE as an absolute number of tumour cells was not performed, because of unpredictable variations of the expression levels of CK-19 mRNA in tumour cells from individual patients.
What are the putative tumour-biological mechanisms to explain the positive association between the extent of tumour vascularisation and the amount of disseminated epithelial cells, and thus probably to a large extent 'tumour' cells, in the bone marrow of breast cancer patients? A first explanation might be that intravasation of breast cancer cells, one of the initial steps of metastasis, is facilitated in tumours with extensive vascularisation [25-27]. McCulloch and colleagues [28] indeed demonstrated a positive association between, on the one hand, the number of tumour cells in the blood of the veins draining the breast gland during surgery and, on the other, the vascularity of the primary tumour [28,29]. Although mechanical stress due to diagnostic procedures, such as mammography, or due to surgery itself can augment tumour cell shedding, this study links high vascularity to increased intravasation of breast cancer cells in a peri-operative setting. A comparable positive relation between breast tumour vascularity and CK-19 mRNA in the blood was found in patients 2–3 weeks after surgery [30]. An increased endothelial surface area might provide easier access to the bloodstream. A complementary effect of high vascularity is the more pronounced paracrine interaction between endothelial cells and tumour cells. Activated endothelial cells can indeed produce tumour cell growth factors, for example IL-6 in the vertical growth phase of melanoma [31], or matrix remodelling enzymes that facilitate tumour cell motility and invasion, as demonstrated in skin carcinogenesis models [32]. The survival and growth of tumour cells can also be influenced by endocrine interactions with a primary tumour present. In cervical, ovarian and colorectal cancer IL-6 is shed in the blood by the tumour, creating a 2.5-fold arteriovenous gradient [33]. In this study, patients with a Chalkley count of 7 or more had concentrations of circulating IL-6 that were about 2.5-fold higher than patients with a Chalkley count of less than 7 (P = 0.018). This is in accord with the angiogenic potential of IL-6. In metastatic breast cancer, high levels of circulating IL-6 predict a shortened survival time [13].
Unlimited haemostasis is intimately linked to tumour growth and angiogenesis, and tumours can therefore be regarded as non-healing wounds [34]. Immunohistochemical analyses with antibodies directed at fibrin demonstrated abundant stromal fibrin depositions in inflammatory breast cancer, a highly aggressive and highly angiogenic type of breast cancer, and less or no fibrin in non-inflammatory breast cancer [35]. A relation of high Chalkley counts and the presence of fibrin in the tumour was also reported in cutaneous breast cancer deposits [36]. In this study, levels of D-dimers in plasma, as a reflection of active fibrin remodelling, were correlated with CK-19 RGE values (r = 0.23; P = 0.08). Levels of D-dimers in patients with CK-19 RGE values of 0.77 or more were 1.4-fold those in patients with CK-19 RGE values of less than 0.77 (P = 0.09). Significantly higher levels of plasma D-dimers have been found in veins draining colorectal adenocarcinomas than in arteries [33], suggesting that plasma D-dimer levels are a measure of matrix remodelling in the tumour. Elevated levels of circulating D-dimers have been correlated with enhanced progression kinetics and reduced overall survival in metastatic breast cancer [12]. Taken together, these results strongly suggest interactions between angiogenesis and haemostasis to facilitate metastasis in breast cancer.
Conclusion
In conclusion, our data show that tumour vascularity, and not lymph node status, independently predicts the presence of PCR-detected bone marrow micrometastases. This is consistent with direct haematogenous spread from the primary breast cancer without orderly progression from local to nodal to distant disease.
Abbreviations
CK = cytokeratin; ELISA = enzyme-linked immunosorbent assay; IL = interleukin; RGE = relative gene expression; RT–PCR = reverse transcriptase polymerase chain reaction; SE = standard error; VEGF = vascular endothelial growth factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
IB performed the RT–PCR reactions, the blood coagulation tests and the ELISA analysis of the angiogenic cytokines. She made substantial contributions to analysis and interpretation of data, and drafted the manuscript. RS performed the Chalkley morphometry and made substantial contributions to analysis and interpretation of data, and drafted the manuscript. HE performed the cell line culture, the primers and probe design and optimised the RT–PCR reaction. PvD, EVM and SS made substantial contributions to conception and design. JW performed the statistical analysis. PV conceived of the study and has been involved in drafting the article and revising it critically for important intellectual content. LD conceived of the study and has given final approval of the version to be published. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to Hilde Hellemans, Liliane Schelfhout and the personnel of the Department of Pathology of the University Hospital of Antwerp, of the University of Antwerp and of the General Hospital St Augustinus for the excellent technical assistance. IB is a research fellow of the Fund for Scientific Research (FWO), Flanders. This work was supported by a grant from FWO Vlaanderen 'Kom op tegen Kanker', grant no. G 0330-02.
Figures and Tables
Figure 1 Chalkley count and relative gene expression of cytokeratin-19 (CK-19 RGE). (a) Correlation analysis of Chalkley count and CK-19 RGE. (b) Chalkley counts of patients with and without bone marrow metastases (according to the cut-off value of 0.77 for CK-19 RGE). Box-and-whisker plot limits depict 75th and 25th centiles and median value (box), and upper/lower quantile ± 1.5 × (interquantile range) (upper and lower whiskers, respectively).
Figure 2 Chalkley count and interleukin-6 (IL-6). (a) Correlation of IL-6 serum levels and Chalkley count. (b) IL-6 serum levels of patients with a tumour with high and low Chalkley count (according to the cut-off value of 7).
Figure 3 Analysis of platelet count in relation to high and low Chalkley count (according to the cut-off value of 7).
Figure 4 Cytokeratin-19 relative gene expression (CK-19 RGE) and D-dimers. (a) Correlation analysis of CK-19 RGE and D-dimers. (b) Analysis of D-dimers in relation to categorised CK-19 RGE.
Table 1 Clinicopathological characteristics of patients and tumours (N = 68)
Characteristic Value
Mean age in years (SD) 60.6 (12.3)
T status
T1 30
T2 15
T3 13
T4 10
N status
N0 36
N1 28
N2 4
Hormone receptor status
ER+ and/or PgR+ 59
ER-/PgR- 9
Tumour differentiation
I 27
II 27
III 14
Menopausal status
Pre- and perimenopausal 11
Postmenopausal 57
Histology
IDA 59
ILA 8
Medullar 1
ER, oestrogen receptor; IDA, invasive ductal adenocarcinoma; ILA, invasive lobular adenocarcinoma; PR, progesteronereceptor; SD, standard deviation.
Table 2 Contingency table comparing vascularisation (Chalkley count: low [less than 7] and high [7 or more]) with the clinicopathological characteristics of the patients with breast cancer (χ2 test P value)
Characteristic Vascularisation P
Low High
Age in years
<50 4 (40%) 6 (60%) 0.97
50–64 12 (36%) 21 (64%)
>65 9 (36%) 16 (64%)
Lymph node status
Negative 16 (44%) 20 (56%) 0.26
Positive 10 (31%) 22 (69%)
Grade
I 10 (37%) 17 (63%) 0.79
II 9 (33%) 18 (67%)
III 6 (43%) 8 (57%)
Oestrogen receptor
Negative 17 (34%) 33 (66%) 0.43
Positive 8 (44%) 10 (56%)
Micrometastases (CK-19 RGE)
≥ 0.77 10 (30%) 23 (70%) 0.04
<0.77 15 (52%) 14 (48%)
T-stage
T1/T2 21 (47%) 24 (53%) 0.05
T3/T4 5 (22%) 18 (78%)
CK-19 RGE, cytokeratin-19 relative gene expression.
Table 3 Coagulation and angiogenesis factors
Factor N Mean (median) SD (SE) 95% CI
sIL-6 68 2.18 (0.35) 3.92 (0.48) 1.21–3.13
sVEGF 68 356.4 (285.9) 259.5 (31.47) 293.5–419.2
pVEGF 67 34.81 (22.2) 52.56 (6.42) 21.99–47.63
D-D 63 570.1 (419) 504.9 (63.61) 442.9–697.2
Platelets 68 267.2 (266) 70.66 (8.57) 250.1–284.3
Fibrinogen 63 320.9 (306) 73.47 (9.26) 302.4–339.4
sVEGF/pl 68 1.37 (1.15) 0.92 (0.11) 1.15–1.60
Abbreviations and units: D-D, D-dimers (ng/ml); platelets, 103 platelets/μl; pVEGF, plasma vascular endothelial growth factor (pg/ml); sIL-6, serum interleukin-6 (pg/ml); sVEGF, serum VEGF (pg/ml); sVEGF/pl, serum VEGF (pg/ml) per 106 platelets. Fibrinogen is shown in mg/dl. CI, confidence interval; SD, standard deviation; SE, standard error.
Table 4 Correlation of coagulation and angiogenesis markers
r/P
sIL-6 sVEGF pVEGF D-D Platelets Fibrinogen sVEGF/pl
sIL-6 0.25/0.03 0.33/0.005 0.41/0.006 0.23/0.06 0.22/0.06
sVEGF 0.48/<0.001 0.25/0.04 0.22/0.06 0.88/<0.0001
pVEGF 0.38/0.0019 0.23/0.05 0.26/0.0339 0.38/0.001
D-D 0.26/0.03 0.34/0.0061
D-D, D-dimers; pVEGF, plasma vascular endothelial growth factor; sIL-6, serum interleukin-6; sVEGF, serum VEGF; sVEGF/pl, serum VEGF per 106 platelets. Spearman correlation test r values and P values (r/P) are given if there was a statistical trend or a significant difference.
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| 15743502 | PMC1064134 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Jan 10; 7(2):R210-R219 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr980 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9821574350110.1186/bcr982Research ArticleCommon ERBB2 polymorphisms and risk of breast cancer in a white British population: a case–control study Benusiglio Patrick R [email protected] Fabienne [email protected] Craig [email protected] Donald M [email protected] Mitul [email protected] Douglas F [email protected] Nick E [email protected] Alison M [email protected] Paul D [email protected] Bruce AJ [email protected] Department of Oncology, University of Cambridge, Strangeways Research Laboratories, Cambridge, UK2 Department of Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratories, Cambridge, UK3 EPIC, University of Cambridge, Strangeways Research Laboratories, Cambridge, UK2005 12 1 2005 7 2 R204 R209 17 8 2004 15 10 2004 29 11 2004 1 12 2004 Copyright © 2005 Benusiglio et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
About two-thirds of the excess familial risk associated with breast cancer is still unaccounted for and may be explained by multiple weakly predisposing alleles. A gene thought to be involved in low-level predisposition to the disease is ERBB2 (HER2). This gene is involved in cell division, differentiation, and apoptosis and is frequently amplified in breast tumours. Its amplification correlates with poor prognosis. Moreover, the coding polymorphism I655V has previously been associated with an increased risk of breast cancer.
Methods
We aimed to determine if common polymorphisms (frequency ≥ 5%) in ERBB2 were associated with breast cancer risk in a white British population. Five single-nucleotide polymorphisms (SNPs) were selected for study: SNP 1 near the promoter, SNP 2 in intron 1, SNP 3 in intron 4, SNP 4 in exon 17 (I655V), and SNP 5 in exon 27 (A1170P). We tested their association with breast cancer in a large case–control study (n = 2192 cases and 2257 controls).
Results
There were no differences in genotype frequencies between cases and controls for any of the SNPs examined. To investigate the possibility that a common polymorphism not included in our study might be involved in breast cancer predisposition, we also constructed multilocus haplotypes. Our set of SNPs generated all existing (n = 6) common haplotypes and no differences were seen in haplotype frequencies between cases and controls (P = 0.44).
Conclusions
In our population, common ERBB2 polymorphisms are not involved in predisposition to breast cancer.
breast cancercase–control studyERBB2haplotypesingle-nucleotide polymorphism
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Introduction
Breast cancer is the most common cause of cancer in women in the United Kingdom and is, after lung cancer, the most common cause of cancer death (Office for National Statistics). Positive family history is a well-established risk factor for the disease: the risk to first-degree relatives of a breast cancer case is about twice the population risk [1]. Most of the excess familial risk associated with breast cancer is likely to be genetic in origin [2,3]. However, only about a third of this risk is accounted for by known genes, the most important being BRCA1 and BRCA2, while the remainder might be explained by a combination of weakly predisposing alleles [2-4]. A gene thought to be involved in low-level susceptibility to breast cancer is ERBB2 (HER2). This gene is located on chromosome 17q12–q21, spans 38 kilobases, and comprises 27 coding exons. It is a member of the ERBB family, a family of protein tyrosine kinases involved in cell division, migration, adhesion, differentiation, and apoptosis and consisting of EGFR (ERBB1), ERBB2, ERBB3, and ERBB4 [5]. ERBB2 amplification or overexpression is seen in about 25% of breast cancers and has been associated with metastatic phenotype, endocrine therapy unresponsiveness, and poor prognosis [6]. ERBB2 is polymorphic in the transmembrane region of the protein at codon 655 (ATC/isoleucine to GTC/valine [I655V]). The amino acid change could result in increased protein tyrosine kinase activity [7]. Several association studies of I655V and breast cancer risk have yielded conflicting results. In a study on 700 Han Chinese women, Xie and colleagues first reported a significantly increased risk for carriers of the rare allele (odds ratio [OR] = 1.4) [8]. Only one of seven subsequent studies showed an overall effect of I655V on breast cancer risk [9-15]. However, of the negative studies, all but one had limited power to detect a risk of this magnitude [13]. Three groups did report associations in specific subgroup analyses in the absence of overall effect: Wang-Gohrke and Chang-Claude showed an association in women with a positive family history of breast cancer and McKean-Cowdin and colleagues showed an association with localized breast cancer, whereas Millikan and colleagues showed an association in women with a positive family history who were aged 45 years or younger as well as an increased risk of carcinoma in situ [12,13,15].
I655V has usually been selected for study because of the possible functional consequences of the amino acid change in the transmembrane region of the protein. Many more single-nucleotide polymorphisms (SNPs) in ERBB2 are known but only one negative study has reported on any of these [10]. A selected set of sequence polymorphisms can serve as genetic markers to detect association between a particular region and the disease, whether or not the markers themselves have a functional effect [16]. It is therefore not necessary to test each polymorphism individually. Because most SNPs are correlated with nearby polymorphisms, genotypes at unsassayed, risk-related SNPs will be correlated with one or more assayed SNPs [17]. If the set of selected markers provides enough information about the remainder of the common polymorphisms in that gene, any susceptibility allele within or close to the gene should be uncovered through the evaluation of the underlying haplotypes [18]. To clarify the role of ERBB2 in the predisposition to breast cancer, we tested the association of five common polymorphisms (including I655V) with the disease in a large case–control study of white British women. We aimed to identify sufficient SNPs to tag all the common haplotypes across the gene.
Materials and methods
Patients and controls
Cases were drawn from the Anglian Breast Cancer Study, an ongoing population-based study with cases ascertained through the East Anglian Cancer Registry [4]. All women diagnosed with invasive breast cancer under the age of 55 years between 1 January 1991 and 30 June 1996 and who were alive at the start of the study (prevalent cases) as well as women under the age of 70 who were diagnosed from 1996 onwards (incident cases) were eligible for inclusion. We used prevalent and incident cases in order to maximize sample size; approximately 65% of eligible patients have enrolled in the study. Women taking part in the study were asked to provide a 20-ml blood sample for DNA analysis and to complete a comprehensive epidemiological questionnaire. We carried out genotyping on a subset consisting of the first 2192 (1438 incident and 754 prevalent) enrolled cases. Controls (2257) were randomly drawn from the Norfolk component of the European Prospective Investigation of Cancer (EPIC) [19]. The ethnic background of both cases and controls is similar, with over 98% being white Anglo-Saxon. Ethical approval was obtained from the Anglia and Oxford Multicentre Research Committee and informed consent was obtained from each patient.
SNP identification and selection
SNPs with validated frequency data were identified in January 2004 through the dbSNP database . If these data were from a non-Caucasian population, we confirmed the presence of the polymorphism in our population by performing denaturing high-performance liquid chromatography on a set of 48 genomic DNA samples from UK breast cancer patients. We selected all nonsynonymous coding SNPs (n = 2), SNPs located in the promoter region (n = 1), and two randomly chosen intronic SNPs [20]. A total of five SNPs were thus selected for study (Table 1). In order to have good power to detect small relative risks, we restricted our attention to SNPs with a frequency of 5% or more.
Genotyping
Genotyping was carried out using Taqman® (Applied Biosystems, Warrington, UK) according to the manufacturer's instructions. Primers and probes were either supplied directly by Applied Biosystems in case of Assays-by-Design™ (SNP 1 and SNP 2) and Assays-on-Demand™ (SNP 3) or designed using Primer Express Oligo Design Software v2.0 (Applied Biosystems) (SNP 4 and SNP 5). Sequences are available on request. Reactions were carried out at 54°C (SNP 4) or 60°C (SNP 1, SNP 2, SNP 3, and SNP 5). All assays were carried out in 384-well plates. Each plate contained 384 samples including 2 negative controls with no DNA and 12 samples duplicated on a separate quality-control plate. Plates were read on the ABI Prism 7900 using the Sequence Detection Software (Applied Biosystems). Failed genotypes were not repeated.
Statistical methods
The characteristics of cases and controls were explored with SPSS© v12.0.1 (SPSS Inc, Chicago, IL, USA). For each SNP, deviation of genotype frequencies in controls from the Hardy–Weinberg equilibrium was assessed by χ2 test with one degree of freedom (df). Genotype frequencies in cases and controls or within cases stratified by disease stage (stage I vs stages II–IV) or age group (≤ 45 vs >45) were compared by χ2 test for heterogeneity (2df). Genotype-specific risks were estimated as ORs using standard cross-product ratio. Confidence intervals were calculated using the variance of the log (OR), which was estimated by the standard Taylor expansion. Power was determined using standard statistical methods [21]. We have over 90% power at the 1% significance level to detect a dominant allele with a frequency of 0.05, which confers a relative risk of 1.5, or a dominant allele with a frequency of 0.2 that confers a relative risk of 1.3. Power to detect recessive alleles at the 1% significance level is more limited: 59% for an allele with a frequency of 0.2 that confers a relative risk of 1.5 or 77% for an allele with a frequency of 0.3 that confers a relative risk of 1.4. The LDA program [22] was used to calculate pairwise linkage disequilibrium (LD) for each SNP pair in the whole case–control set. The haplo.score program [23] was used to test for association between haplotypes and breast cancer risk. Haplo.score uses a likelihood that depends on estimated haplotype frequencies to test the statistical association between haplotypes and phenotype. It is based on score statistics, which provide both global tests and haplotype-specific tests [23].
Results
The median age was 48 years (range 25–54) for prevalent cases, 52 years (26–55) for incident cases, and 56 years (25–81) for controls. Incident and prevalent cases were similar regarding breast cancer stage (P = 0.12) and histological grade (P = 0.41). Table 2 shows the genotype frequencies in cases and controls as well as genotype-specific risks for the five SNPs assayed. The genotype frequencies were similar in the prevalent and incident cases for all polymorphisms (data not shown). None of the genotype distributions for the controls differed significantly from those expected under Hardy–Weinberg equilibrium. There was no evidence that any of the SNPs is associated with breast cancer; genotype-specific ORs were all close to unity with narrow confidence intervals. We also compared genotype frequencies within cases stratified by disease stage and age group for SNP 4 (I655V). No differences were seen (P [stage] = 0.61, P [age group] = 0.33). LD was strong (D' > 0.7) across pairs involving SNPs 1, 2, 3, and 5, whereas SNP 4 was in weak LD (D' < 0.3) with all other polymorphisms except SNP 1 (D' [SNP 1-SNP 4] = 0.98) (Fig. 1). SNPs 3 and 5 were in nearly perfect LD (r2 = 0.92). Of 32 possible haplotypes, only 6 were observed with a frequency greater than 5% (Table 3). For the whole case–control set, common haplotypes constituted 98% of all the observed haplotypes. Two haplotypes (haplotypes 3 and 5) contained the SNP 4 (I655V) minor allele. The global test was not significant (P = 0.44), nor were there any differences between cases and controls for individual haplotypes. Similarly, no differences in haplotype frequencies were seen within cases stratified by disease stage (P = 0.37) or age group (P = 0.48).
Discussion
Our study is the largest case–control study reported on ERBB2 genetic variation. To our knowledge, this is also the first study on ERBB2 reporting results for more than two polymorphisms and looking for involvement of haplotypes in breast cancer predisposition. We performed a study of five common SNPs and found no evidence for association with breast cancer risk. Four of the polymorphisms may be functional: SNP 1 near the promoter region and SNP 2 in intron 1 could be involved in regulatory processes whereas SNP 4 and SNP 5 are nonsynonymous coding SNPs that could affect tyrosine kinase activity or protein structure [7]. Two association studies have previously reported a positive association between SNP 4 (I655V) and breast cancer risk [8,14]. Both genotyped about 700 individuals and showed a similarly increased risk for carriers of the Val allele (OR = 1.4). We were not able to replicate these findings. We have over 90% power to detect a risk of this magnitude at the 10-4 level of significance. This suggests that previous positive findings may have been due to type I statistical errors. Neither could we replicate findings associating I655V with low-stage breast cancer or with breast cancer in younger women [12,13]. Positive results from stratified analyses should be treated with caution; very large sample sizes are required to obtain reliable results, the number of possible analyses that can be undertaken is large, and there is a strong possibility that one or more tests will be statistically significant simply by chance [24]. We could not carry out analyses within cases stratified by family history, because we only had incomplete family history data [15]. To investigate the possibility that a common polymorphism not included in our study might be involved in breast cancer predisposition, we constructed multilocus haplotypes and observed similar frequencies in cases and controls. We found six common haplotypes. Recently, the NIEHS Environmental Genome Project at the University of Washington released resequencing data based on 90 individuals (the PDR90 population; individual genotypes are available on line: ) and identified nine common SNPs (frequency ≥ 5%) in ERBB2. All the common haplotypes (frequency ≥ 5%) were tagged by our set of five SNPs, even though, as expected given the multiethnicity of PDR90, differences in frequencies were seen between the two populations (data not shown). Crawford and colleagues resequenced 100 candidate genes involved in inflammation, lipid metabolism, and blood pressure regulation and showed that in a population of European descent the average number of common haplotypes per gene was 4.5, with a maximum number of 8 observed in only two genes [25]. We are therefore confident that we have detected all common ERBB2 haplotypes present in our population. We limited our study to common polymorphisms. A larger study set would be needed to identify a rarer polymorphism involved in disease predisposition. For example, dominant alleles with a frequency of 2% would require more than 4000 cases and 4000 controls to detect a relative risk of 1.5 significant at the 1% level with 90% power. We cannot exclude the possibility that a common SNP might have a differential effect in another ethnic group via gene–gene or gene–environment interactions, or that a predisposing SNP might be present exclusively in another population [26]. In summary, we conducted a large case–control study of ERBB2 and breast cancer. We genotyped five common SNPs, including the much-studied I655V polymorphism, and saw no association with the disease. Our set of SNPs generated all common haplotypes, and no differences in haplotype frequencies were seen between cases and controls.
Conclusion
In our population, common ERBB2 polymorphisms are not involved in predisposition to breast cancer.
Abbreviations
df = degree of freedom; LD = linkage disequilibrium.; OR = odds ratio; SNP = single-nucleotide polymorphism.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PRB performed the experiments, carried out the analyses, and wrote the manuscript under the supervision of FL, PDP, and BAJP. CL and DMC managed the genotyping process. MS and NED supervised DNA samples collection. DFE was the statistical advisor and AMD was the laboratory manager. All authors read and approved the final manuscript.
Acknowledgements
Paula Smith contributed to the haplotype analysis. We thank the EPIC management team (K-T Thaw, S Oakes, S Bingham, R Luben, and J Russell) for access to control DNA. Patrick R Benusiglio is supported by the Ligue Genevoise contre le Cancer (N/Ref 0208). This work was funded by Cancer Research United Kingdom. BAJP is a Gibb Fellow of Cancer Research UK.
Figures and Tables
Figure 1 Pairwise linkage disequilibrium (LD) measures of D' (left bottom half) and r2 (right top half) for the five SNPs.
Table 1 Single-nucleotide polymorphisms (SNPs) selected for genotyping
SNP name dbSNP id Location Base change
SNP 1 rs4252596 START -657a C>Ab
SNP 2 rs2952155 intron 1 C>T
SNP 3 rs1810132 intron 4 T>C
SNP 4 rs1801200 exon 17 (I655V) A>G
SNP 5 rs1058808 exon 27 (A1170P) G>C
a657 base pairs upstream first translated base. bCommon allele given first.
Table 2 Genotype frequencies and genotype-specific risks in 2192 women with breast cancer and 2257 controls
SNP Series Rare-allele frequency Common homozygote No. (%) Heterozygote No. (%) Rare homozygote No. (%) Number genotyped P (2df)
SNP 1 Cases 0.13 1618 (77) 447 (21) 42 (2) 2107
Controls 0.13 1645 (75) 511 (23) 33 (2) 2189 0.13
OR (95% CI) 1 (ref) 0.89 (0.77–1.03) 1.29 (0.82–2.05)
SNP 2 Cases 0.25 1151 (57) 734 (36) 140 (7) 2025
Controls 0.26 1219 (55) 839 (38) 147 (7) 2205 0.48
OR (95% CI) 1 (ref) 0.93 (0.82–1.05) 1.01 (0.79–1.29)
SNP 3 Cases 0.32 962 (47) 855 (42) 219 (11) 2036
Controls 0.32 1022 (46) 956 (43) 230 (11) 2208 0.69
OR (95% CI) 1 (ref) 0.95 (0.84–1.08) 1.01 (0.82–1.24)
SNP 4 Cases 0.25 1128 (57) 748 (37) 113 (6) 1989
Controls 0.25 1230 (57) 791 (37) 134 (6) 2155 0.69
OR (95% CI) 1 (ref) 1.03 (0.91–1.17) 0.92 (0.71–1.20)
SNP 5 Cases 0.33 911 (45) 870 (43) 233 (12) 2014
Controls 0.33 960 (44) 983 (45) 238 (11) 2181 0.45
OR (95% CI) 1 (ref) 0.93 (0.82–1.06) 1.03 (0.84–1.26)
OR, odds ratio; SNP, single-nucleotide polymorphism.
Table 3 Estimated haplotype frequencies in cases and controls
Haplotype Allelea Frequency (cases) Frequency (controls) Pb
(1) cctIA 0.35 0.36 0.80
(2) ctcIP 0.19 0.18 0.88
(3) cctVA 0.18 0.16 0.15
(4) actIA 0.13 0.13 0.49
(5) ctcVP 0.06 0.08 0.07
(6) cccIP 0.07 0.07 0.50
(7) cctIP 0.02 0.01 0.93
aPolymorphisms in 5' to 3' order as indicated in Table 1. bGlobal score statistic = 6.91, df = 7, P = 0.44
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| 15743501 | PMC1064135 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Jan 12; 7(2):R204-R209 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr982 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9831574350810.1186/bcr983Research ArticleClaudin-1, -3 and -4 proteins and mRNA expression in benign and malignant breast lesions: a research study Tőkés Anna-Mária [email protected] Janina [email protected] Sándor [email protected] Ágnes [email protected]áska Csilla [email protected]ák Pál Kaposi [email protected]ák László [email protected] András [email protected]ögi Krisztina [email protected] Zsuzsa [email protected] 2nd Department of Pathology, Semmelweis University, Budapest, Hungary2 Department of Molecular Pathology, Joint Research Organization of the Hungarian Academy of Sciences, Budapest, Hungary2005 31 1 2005 7 2 R296 R305 23 1 2004 15 3 2004 1 10 2004 2 12 2004 Copyright © 2005 Tőkés et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Introduction
We compared levels of protein and mRNA expression of three members of the claudin (CLDN) family in malignant breast tumours and benign lesions.
Methods
Altogether, 56 sections from 52 surgically resected breast specimens were analyzed for CLDN1, CLDN3 and CLDN4 expression by immunohistochemistry. mRNA was also analyzed using real-time PCR in 17 of the 52 cases.
Results
CLDNs were rarely observed exclusively at tight junction structures. CLDN1 was present in the membrane of normal duct cells and in some of the cell membranes from ductal carcinoma in situ, and was frequently observed in eight out of nine areas of apocrine metaplasia, whereas invasive tumours were negative for CLDN1 or it was present in a scattered distribution among such tumour cells (in 36/39 malignant tumours). CLDN3 was present in 49 of the 56 sections and CLDN4 was present in all 56 tissue sections. However, CLDN4 was highly positive in normal epithelial cells and was decreased or absent in 17 out of 21 ductal carcinoma grade 1, in special types of breast carcinoma (mucinous, papillary, tubular) and in areas of apocrine metaplasia. CLDN1 mRNA was downregulated by 12-fold in the sample (tumour) group as compared with the control group using GAPDH as the reference gene. CLDN3 and CLDN4 mRNA exhibited no difference in expression between invasive tumours and surrounding tissue.
Conclusions
The significant loss of CLDN1 protein in breast cancer cells suggests that CLDN1 may play a role in invasion and metastasis. The loss of CLDN4 expression in areas of apocrine metaplasia and in the majority of grade 1 invasive carcinomas also suggests a particular role for this protein in mammary glandular cell differentiation and carcinogenesis.
breastclaudinimmunohistochemistryreal-time PCRtight junction
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Introduction
In epithelial cells, tight junctions (TJs) are the most apical intercellular junctions, and function as selective barriers to macromolecules and prevent diffusion of lipids and proteins between apical and basolateral membrane domains. TJs appear as a network of continuous and anastomosing filaments on the protoplasmic face of the plasma membrane [1]. The molecular architecture of TJ strands has been described in recent years. The proteins involved in the formation of TJs have been divided into two categories: integral membrane proteins, such as claudins (CLDNs), occludins and JAM (junctional adhesion molecule) [2]; and peripheral membrane proteins, such as zonulae occludens (ZO)-1, ZO-2 and ZO-3, cingulin, symplekin, pilt, MAGI-1 (MAGUK [membrane associated guanylate kinase] inverted protein 1) and AF-6 (afadin) [3].
CLDNs, a family comprising 24 members, are the main family of proteins that make up the TJs [2,4]. The tightness of individual TJ strands in situ is determined by the various combinations of CLDN family members. CLDNs have a membrane topology similar to that of occludin, even though they are smaller proteins of about 22 kDa. Their protein structure, which is uniform among family members, contains four transmembrane domains with amino- and carboxyl-termini in the cytoplasm and two extracellular loops [5].
Until now only few data have been reported regarding the role played by CLDNs in breast lesions, and findings on their function remain controversial [6,7]. There is 38% homology between human CLDN1 and CLDN2 at the amino acid sequence level [8,9]. Expression of the four-exon CLDN1 gene, formerly named senescence associated epithelial membrane protein, was identified in testis [10], colorectal carcinoma [11] and lung [12]. The direct involvement of CLDN1 in the barrier function of TJs has been demonstrated in CLDN1-over-expressing MDCK cells [13].
In a study conducted in breast carcinomas by Kramer and coworkers in 2000 [7] it was shown that there are no genetic alterations in the promoter or coding sequences in the CLDN1 gene that could account for the loss of CLDN1 protein expression in tumour cells. Of the relatively few publications concerning CLDN expression in the breast, one study [14] found that CLDN7 protein and mRNA are downregulated in primary breast cancers as compared with normal breast epithelium. Taken together, these observations indicate that there is a need to study the possible role played by CLDNs in carcinogenesis and tumour differentiation.
CLDN3 and CLDN4 can function as receptors for Clostridium perfringens enterotoxin [15]. The one-exon genes of CLDN3 and CLDN4 are closely linked (CLDN3 7q11 and CLDN4 7q11.23) and share extended homology of approximately 80% at the DNA level. SAGE (serial analysis of gene expression) demonstrated that, of differentially upregulated mRNAs, CLDN3 and CLDN4 were among the six most upregulated in primary ovarian carcinoma cells [16]. Van Itallie and coworkers [17] demonstrated the close correlation between the level of CLDN4 expression and change in both conductance and ionic selectivity.
Thus far there is no evidence on how CLDN3 and CLDN4 are expressed in breast carcinomas. The present study was designed to determine whether human CLDN1, CLDN3 and CLDN4 are expressed in different types of breast lesions, what differences there are between different CLDNs in breast, and how the protein expression correlates with CLDN mRNA expression.
Methods
Materials
For immunohistochemistry, paraffin-embedded sections were used. For immunofluorescence, paraffin-embedded sections and frozen resected breast tissue samples were analyzed. For real-time PCR RNA isolated from frozen surgical breast tissues were analyzed.
Materials used for immunohistochemistry
Altogether, 56 paraffin-embedded sections from 52 surgically resected breast specimens were analyzed for expression of CLDN1, CLDN3 and CLDN4 using immunohistochemistry. Tissue samples were collected with the patient's written consent and conforming with national law (23/2002.V.9 EüM) regarding human studies. The age of the 52 female patients ranged between 31 and 80 years. Carcinomas were graded according to the Elston-Bloom and Richardson system [18].
The histopathological diagnoses of the selected breast lesions were as follows: fibrocystic changes/breast lesions (n = 12), ductal carcinoma in situ (DCIS; n = 5), invasive ductal carcinoma (IDC) ± DCIS (n = 27), invasive lobular carcinoma (ILC; n = 5) and special types of breast carcinoma (papillary, mucinous, tubular; n = 7).
Materials used for immunofluorescene
Immunofluorescence detection of CLDN1, CLDN3 and CLDN4 was performed on frozen sections (IDC and surrounding breast) as well as on tissue microarray of IDCs obtained from paraffin-embedded blocks.
Materials used for CLDN1, CLDN3, CLDN4 and GAPDH mRNA expression with real-time PCR
In 17 out of the 52 cases analyzed by immunohistochemistry, the level of mRNA expression of CLDN1, CLDN3 and CLDN4 were also analyzed using real-time PCR. The histopathological diagnoses of these 17 breast lesions are as follows: fibrocystic changes/breast lesions (n = 3), IDC ± DCIS (n = 12), ILC (n = 1) and intraductal papilloma (n = 1). In the following text only the real-time PCR results for the 13 invasive carcinomas (12 IDC ± DCIS and 1 ILC) are discussed in detail.
Because relative quantification of the real-time PCR results was used for data analyses in all 17 breast samples, the normal component of the breast tissue was simultaneously investigated for mRNA expression of CLDN1, CLDN3, CLDN4 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA expression. GAPDH was used as the reference gene, and so data are normalized to GAPDH.
Breast tissue samples, obtained in accordance with current local and ethical recommendations, were snap frozen in liquid nitrogen within 30 min of surgical removal and stored at -80°C.
Immunohistochemistry for CLDN1, CLDN3 and CLDN4 detection
Immunohistochemical detection of CLDNs was performed in formalin-fixed, paraffin-embedded sections. Sections (5 μm thick) were cut from each paraffin block, deparaffinized and rehydrated. Immunohistochemical staining for CLDN1, CLDN3 and CLDN4 was performed using the streptavidin-peroxidase procedure.
For CLDN1 and CLDN3 proteins, antigen retrieval was performed with two different antigen retrieval solutions in parallel sections: 20 min in Vector (Burlingame, CA, USA) antigen retrieval solution in a microwave oven; and 40 min in DAKO (Carpinteria, CA, USA) antigen retrieval solution in a microwave oven. For CLDN4, antigen retrieval was performed for 20 min in Vector antigen retrieval solution in a microwave oven. Endogenous peroxidase was blocked with 1% H2O2 for 10 min at room temperature.
Sections were incubated with primary antibodies (Zymed, San Francisco, CA, USA) to CLDN1, CLDN3 and CLDN4 (concentration 1:100) overnight. The antibodies used were polyclonal rabbit anti-CLDN-1 (Zymed; reactivity: human, rat and dog), polyclonal rabbit anti-CLDN-3 (Zymed; reactivity: human, mouse and dog) and monoclonal mouse anti-CLDN-4 (Zymed; reactivity: human).
Antigen-bound primary antibody was detected using standard avidin–biotin immunoperoxidase complex (DAKO LSAB2 Kit). The chromogen substrate was aminoethylcarbazol. In each case, for counter-staining Mayer's haemalaun was used. For each CLDN a negative control with omission of the primary antibody was included. Colon carcinoma was used as positive control. In each case, two independent observers (A-MT and JK) recorded the distribution of staining, intensity and localization. Staining of CLDNs was expressed relative to adjacent normal mammary epithelium.
Immunofluorescence microscopy for CLDN1, CLDN3 and CLDN4 detection
Sections (10 μm thick) were cut from frozen tissue and from breast tissue array paraffin blocks. The tissue arrays were constructed in our laboratory. The selected breast tumour samples were obtained from paraffin blocks from our files. In brief, the study involved breast tumours from 30 T1N0 and 30 T1N1 cases. Histopathological data included tumour stage according to the UICC TNM system. With the guidance of an experienced pathologist (JK), representative core tissue biopsies (2 mm in diameter) were taken from archival paraffin-embedded primary breast carcinomas and seeded in new paraffin blocks using an instrument provided by Histopathology Company (Pécs, Hungary). Two biopsies were taken from each paraffin block. For paraffin-embedded sections antigen retrieval was performed as described above (under Immunohistochemistry for CLDN1, CLDN3 and CLDN4 detection).
Sections were incubated with goat antiserum for 1 hour at room temperature and then incubated with the primary antibodies (Zymed) to CLDN1, CLDN3 and CLDN4 (1:50) overnight. CLDN1 and CLDN3 antigen-bound primary antibody was detected using anti-rabbit IgG conjugated to Alexafluor 488 (Molecular Probes, Eugene, OR, USA) and CLDN4 stained sections was detected using anti-mouse IgG conjugated to Alexafluor 488 (Molecular Probes) for 30 min at room temperature. Cell nuclei were counter-stained with propidium iodide (Molecular Probes) for 10 min at room temperature. For each CLDN a negative control with omission of the primary antibody was included. Coverslips were mounted with fluorescent mounting medium (DAKO). Stained specimens were analyzed by laser scanning confocal microscopy (MRC 1024; Bio-Rad, Hercules, CA, USA).
Western blotting
The monospecificity of CLDN1, CLDN3 and CLDN4 antibodies was tested by our group in whole tumour cell lysate by Western blot analyses, as described previously [14]. In brief, from snap frozen tissues of IDC of the breast and normal tissue surrounding the tumour, total protein was extracted using lysis buffer (100 mmol/l NaCl, 1% NP-40, 2 mmol/l EDTA, 50 mmol/l TRIS, 20 μg/ml aprotinin, 20 μg/ml leupeptin, 1 mmol/l PMSF, pH 7.5). Subsequently, the samples were mixed with 2× Laemmli sample buffer and boiled for 10 min, followed by centrifugation at 13,000 rpm for 15 min. Similar amounts of protein were loaded in each lane and run on 10% SDS-PAGE under reducing conditions. Then, gel electrophoresis proteins were transferred to nitrocellulose membranes. For immunodetection, blots were incubated with CLDN1 polyclonal antibody (1:400), CLDN3 polyclonal antibody (1:800) and CLDN4 monoclonal antibody (1:200) overnight. The biotinylated secondary antibody, goat anti-mouse IgG (diluted 1:2000, E433; DAKO), was applied for 1 hour at room temperature. Antibody detection was conducted using an enhanced chemiluminescent reaction system.
Real-time PCR for CLDN1, CLDN3, CLDN4 and GAPDH mRNA detection
RNA isolation
Breast tissue (50–100 mg) was homogenized and RNA was isolated with Trizol (Invitrogen, Carlsbad, CA, USA) [19], in accordance with the manufacturer's instructions, using a Polytron homogenizer (Kinematica AG, Littau/Lucerne, Switzerland). Briefly, the RNA was precipitated with 0.5 ml isopropyl alcohol in the aqueous phase. The RNA pellet was washed once in 70% ethanol, dried and resuspended in 50 μl of RNAse-free water and kept at -80°C until use. Total RNA integrity was verified by electrophoresis using ethidium bromide staining as well as by measuring optical density (OD). OD 260 nm/OD 280 nm absorption ratio was accepted between 1.70 and 2.8.
Reverse transcription of RNA
Total RNA (700 ng) was reverse transcribed for 40 min at 48°C in 30 μl with 2.5 unit M-MuLV reverse transcriptase (Applied Biosystems, Foster City, CA, USA) in the presence of RNAse inhibitor (Applied Biosystems) using Random Hexamers (Applied Biosystems).
Real-time PCR
Conditions for real-time PCRs were optimized in a gradient cycler (Mastercycler Gradient, Eppendorf, Hamburg, Germany). Optimized conditions were transferred to the Bio-Rad real time PCR detection system as follows. Typical real-time PCR reaction was conducted with 2 μl cDNA template in a total volume of 25 μl, using the Bio-Rad iCycler Real Time PCR detection system (BioRad 1708740). For CLDNs and GAPDH the SYBR Green based real-time PCR method was used. Each PCR was conducted in 25 μl volume of 1 × PCR puffer (BioRad 1708882) with 300 nmol/l of each primer (Table 1) for 2 min at 95°C for initial denaturing, then 40 cycles of 95°C for 20 s, 63°C for 30 s, and 72°C for 30 s, followed by melting analyses from 55 to 95°C. Melting curve analyses resulted in single product specific melting temperatures for the analyzed CLDN1, CLDN3, CLDN4 and GAPDH genes. No primer–dimer formations were observed during the 40 real-time PCR amplification cycles. PCR reaction for each sample was done in duplicate in 96-well plates. In addition, the resulting real-time PCR product (10 μl) was loaded onto 2% agarose gel prepared in Tris-borate EDTA puffer and stained with ethidium bromide (Bio-Rad). Each PCR was optimized to ensure that no bands corresponding to genomic DNA amplification or primer–dimer pairs were present. The relative quantification method (described in detail by Pfaffl and coworkers [20] from the Technical University of Munich) was used for data analysis of real-time PCR. The program included a statistical evaluation. Data were analyzed for significant differences by analysis of variance using approximate tests [20]. The relative expression is based on the expression ratio of a target gene versus a reference gene (GAPDH).
Electron microscopy
In two cases both the normal breast tissue and the tumour sample were investigated by transmission electron microscopy in order to analyze the presence of TJs in tumours compared with nontumourous tissue, as described previously [21].
Results
Immunohistochemical localization of CLDNs indicated that CLDNs were expressed along the entire length of the lateral plasma membranes between epithelial cells, including apical areas containing TJ structures. Using electron microscopy, TJ structures were identified in normal breast as well as in breast tumour tissue. Cell types in breast tissue are heterogeneous, and it is therefore noteworthy that CLDNs were expressed only in epithelial cells. Surrounding fibroblasts and adipocytes served as negative controls because these cells were negative for CLDN antibodies.
Immunohistochemistry of CLDN1
The two antigen retrieval methods used yielded similar results. The monospecificity of the CLDN antibodies was tested by Western blot analyses, and a single band was found at approximately 20 kDa for CLDN1, CLDN3 and CLDN4 (Fig. 1).
Immunohistochemical localization of CLDN1 indicated that CLDN1 was present in the membranes of normal duct cells in the majority of cases (Fig. 2a). CLDN1 positivity was also observed in some cell membranes in pure DCIS cases or in the DCIS component of invasive carcinomas. On the other hand, major differences in CLDN1 expression were observed among tumours.
Two major differences were noticed. First, in 36 of the 39 carcinomas examined, invasive tumour cells were either negative for CLDN1 or it was present in a scattered distribution among such tumour cells (Fig. 2b). These findings were confirmed by confocal microscopy images (Fig. 2c). There was no difference immunohistochemically in CLDN1 expression between different types of invasive carcinomas or between ductal carcinomas of different grade. The second major difference from normal tissue was that CLDN1 was frequently present (eight out of nine samples) in areas of apocrine metaplasia (Fig. 2d). In these cases positivity was observed only in cells of apocrine metaplasia, and the neighbouring tumour tissue was negative for CLDN1.
Immunohistochemistry of CLDN3 and CLDN4
CLDN3 positivity was observed in the majority (49/56) of cases examined (12 fibrocystic breasts, 5 DCIS, 23 IDC, 4 ILC, 2 mucinous, 2 tubular and 1 papillary breast carcinomas) and in all cases analyzed by confocal microscopy. There were no remarkable differences in CLDN3 expression in different types of breast lesions or between normal and tumour components (Fig. 3a,b). CLDN3 expression is evaluable by standard immunohistochemistry and by comparing two antigen retrieval methods; in the majority of cases CLDN3, similar to CLDN1, was observed along the entire length of the epithelial cell membrane, but in a few cases, especially in normal tissues and in tissue with fibrocystic changes, CLDN3 appeared to be localized close to the apical end of the cells or at the basolateral membrane of the epithelial cells. These findings were seen on both confocal and light microscopy (Fig. 3c,d).
CLDN4 positivity was present in all 56 tissue sections in epithelial cell membranes as well as in the frozen section and breast tumour array analyzed by confocal microscopy. Intense positivity was observed in normal epithelial cells (Fig. 4a) and in tumours of grade 2 and grade 3 (Fig. 4b), and was greatly reduced in the majority of tumours of grade 1.
CLDN4 was absent or its expression was greatly reduced in tumour cells of 17 out of 21 grade 1 tumours (Fig. 4c), but it was present in the normal epithelial components within the same blocks (14 IDC NST [no special type], 5 special types, 2 ILC). Positivity was seen in 8 out of 11 grade 2 tumours and in 5 out of 7 grade 3 invasive carcinomas. In the normal tissue surrounding grade 2 and 3 tumours, the CLDNs were consistently present. CLDN4 was absent or its expression was decreased in the seven special-type breast carcinomas (mucinous, tubular, papillary) but it was present in the normal tissue surrounding these tumours. In contrast to CLDN1, CLDN4 was consistently absent in regions of apocrine metaplasia (Fig. 4d).
Real time-PCR results
Real-time PCR using CLDN1, CLDN3, CLDN4 and GAPDH specific primers was performed in the 17 samples detailed above. mRNA was expressed in all of the analyzed cases. Relative quantification using GAPDH as the reference gene was applied [20]. mRNA expression in 13 invasive tumours out of the 17 samples was analyzed as the sample group, and the surrounding breast tissue was used as the control group. During the real-time PCR reaction, CLDN1 mRNA was detected in 12 out of 13 analyzed tumours and in 12 out of 13 corresponding surrounding nontumourous breast tissue samples. In normal, surrounding breast tissue, the mean value of crossing points (cycle threshold) was 34,02 whereas in the tumour tissue samples it was 36,22 (indicating low levels of CLDN1 mRNA). CLDN1 was downregulated by 12-fold in the sample (tumour) group in comparison with the control group using GAPDH as the reference gene. CLDN3 mRNA was present in all tumours and surrounding tissues. CLDN3 exhibited no expressional difference between invasive tumours and nontumourous tissue samples.
CLDN4 mRNA was also found to be present in these 13 invasive tumours and surrounding mammary tissues using real time-PCR. According to relative quantification, CLDN4 mRNA expression did not appear to be changed in the invasive tumours in comparison with the surrounding normal tissue. It is important to emphasize that there were only two grade 1 invasive tumour samples among the 13 investigated invasive tumours.
The program used by us and described by Pfaffl and coworkers [20] (Relative Expression Software Tool [REST] for group-wise comparison and statistical analysis of relative expression results in real-time PCR) includes a statistical analysis (analysis of variance). Using this program, we found no statistically significant differences in the distribution of data between the sample and control groups in terms of CLDN expression (the P values for CLDN1, CLDN3 and CLDN 4 were 0.166, 0.928 and 0.996, respectively). However, only 13 tumours were analyzed for RNA expression using real-time PCR because fresh tumour tissue and corresponding normal tissue surrounding the tumour were available in only 13 cases. In these cases the results of immunohistochemistry and real-time PCR appeared to correlate; specifically, CLDN1 appeared to be downregulated in tumours as compared with normal breast, whereas CLDN3 was expressed at similar levels in tumours and normal breast.
In the case of CLDN4 expression the differences observed by immunohistochemistry between tumours of different grade were not observed by real-time PCR. CLDN4 mRNA expression did not appear to be changed in the invasive tumours in comparison with the surrounding normal tissue. Real-time PCR remains to be performed in a greater number of cases, including tumours of different grade.
Apart from melting analysis, the specificity of real-time PCR results was confirmed on 2% agarose gel electrophoresis. The PCR product was seen at the appropriate size and nonspecific bands were not visualized. The specificity of real-time PCR of CLDN4 was also analyzed by sequencing (data not shown).
Discussion
In the present study we examined the expression in breast lesions of three members of the CLDN family, namely CLDN1, CLDN3 and CLDN4. The majority of studies published to date have described roles for CLDNs in forming TJs [22], conferring ionic selectivity [17] and functioning as a barrier [23], but only few data have been published on the expression of CLDNs in the breast [14]. CLDN1 and CLDN2 were the first described CLDN components of TJs, and so the majority of data are related to these proteins. It was demonstrated that CLDN1 and CLDN2 function as major structural components of TJ strands, and that occludin is an accessory protein within this structure [8,24].
Well developed TJs found in the lung contain CLDN3, CLDN4 and CLDN5, which are abundant, whereas levels of expression of CLDN1 and CLDN2 are reduced [12]. These findings suggest that different members of the CLDN family are involved in the formation of TJ strands in different tissues. The extent to which CLDN expression is tissue dependent remains a subject of research interest, as does the physiological relevance of the existence of multiple CLDN species. In CLDN1-, CLDN2- and CLDN3-transfected mouse L-cells, Kubota and coworkers [25] showed that CLDNs may act as calcium-independent adhesion molecules, although the adhesion strength is low. It was hypothesized by Rangel and coworkers [26] that the precise ratio of different CLDNs determined the permeability of TJs. In ovarian tumours, the same group suggested that CLDN3 and CLDN4 are required for signalling through survival or proliferative pathways.
In the present study, immunohistochemistry revealed expression of CLDNs in TJs along the lateral plasma membranes of epithelial cells and along the basal plasma membrane of the epithelium, suggesting that CLDNs are not exclusively localized to TJs. Localization of these three CLDNs together in breast tissue sections has not previously been reported, but based on the observations presented here CLDN1, CLDN3 and CLDN4 are not exclusively localized to areas of TJs. Gregory and coworkers [27] described similar observations for CLDN1 in the rat epididymis.
We demonstrated that immunohistochemically detectable CLDN1 protein is absent or its expression is markedly decreased in the majority of different types of invasive breast carcinomas as compared with normal ducts. Of 39 malignant tumours examined by immunohistochemistry, 36 were negative for CLDN1 or it was present in a scattered distribution among the tumour cells. CLDN1 mRNA expression investigated by real-time PCR confirmed this finding. The mRNA level in tumour is considerably lower than that in normal breast tissue. Theoretically, several regulatory pathways could influence CLDN1 protein synthesis and expression. How these regulatory pathways are involved in cellular transformation and oncogenic progression in the breast remains to be elucidated. Previous studies reported that CLDN1 mRNA expression is absent or downregulated in the majority of breast cancer cell lines [28]. Kramer and coworkers [7] found no evidence for alterations in the CLDN1 gene being responsible for the loss of expression of CLDN1 protein. The same group found a similar loss of CLDN1 expression in five primary breast tumours. This finding is in contrast to the broad spectrum of CLDN1 expression found in other human tissues, such as liver and pancreas (our unpublished data). This may suggest that CLDN1 might be involved in the development of breast cancer or other epithelial tumours [7]. Whether TJs are simply lost in breast neoplasia and whether downregulation of CLDN1 is among the causative factors in this process are unknown. Alterations in the number, appearance and permeability of TJs have been described in various tumour types [21,29]. Abnormalities in TJ permeability and number greatly influence the transepithelial flux of growth factors [30] and hence the development of epithelial tumours [31]. In two cases, both normal breast tissue and tumour sample were investigated by transmission electron microscopy. Membrane densities corresponding to TJ structures were identified in both normal and neoplastic epithelial cells. The eventual abnormalities of TJs in breast tumours must be analyzed in a large series of breast tumours.
Although the similar CLDN3 and CLDN4 TJ proteins are highly expressed in breast neoplasia, we hypothesize that mechanisms other than alteration in TJs are involved in the loss of CLDN1 expression in breast tumours. The role played by CLDN1 positivity in apocrine cells observed in this study requires further investigation. In the present study, CLDN3 appeared to be expressed in a similar manner in primary breast cancers as in normal epithelium. Immunohistochemistry and real-time PCR yielded similar findings without notable upregulation or downregulation in different groups of tumours or compared with normal epithelium. In the majority of cases, CLDN3 was found to be localized along the entire membrane of epithelial cells, but in some cases it was only detected close to the apical end or at the basolateral membrane of the epithelial cells. Although this observation was confirmed by both light and confocal microscopy, in the breast this staining pattern has not previously been reported. Interestingly, Rangel and coworkers [26] did not find CLDN3 exclusively at the membrane in any of the samples studied; the majority of samples of primary ovarian tissue exhibited a combination of cytoplasmic and membrane staining. In gut, Rahner and coworkers [32] found CLDN3 to be strongly expressed in the surface epithelial cells of the stomach fundus, and have found it to be distributed predominantly along the basolateral membrane and not at the TJ. That group concluded that CLDNs have different patterns of expression in different gastrointestinal tissues – patterns that should account for differences in paracellular permeability.
We found that CLDN4 protein expression was downregulated in grade 1 IDCs. Further sample collection and analysis are required to confirm this result at the mRNA level, because only two grade 1 IDCs were available for PCR examination in our series. However, mRNA and protein were expressed in grade 2 and 3 IDCs to degrees similar to expression in surrounding breast tissue. In a study conducted in ovarian tissue, Hough and coworkers [16] found that expression of CLDN3 and CLDN4 protein was undetectable in normal ovarian cells and highly expressed on the membranes of ovarian carcinoma cells. Contrary to the findings in ovarian tumours reported by Rangel and coworkers [26] that CLDN3 and CLDN4 exhibited cytoplasmic staining and that over-expression of these proteins were associated with malignancy, in our study in breast we did not observe clear positivity of CLDNs in cytoplasm. A close relation between CLDN4 expression and changes in both conductance and ionic selectivity has been described. Further important observations were reported by van Itallie and coworkers [17]; they found that the number of junction strands was increased by over-expression of CLDN4, and that the levels of several other strand proteins were unaffected. Loss of CLDN4 from the cell surface coincides with dramatic changes in the morphology of TJ fibrils. Removal of CLDN4 disrupts fibril organization and increases junction permeability [15]. A recent study [33] identified CLDN4 as a potent inhibitor of the invasiveness and metastatic phenotype of pancreatic carcinoma cells by playing role in the transforming growth factor-β and Ras/Raf signal regulated kinase pathway. No studies to date have reported decreased expression of CLDN4 in grade 1 breast carcinomas, in special types of breast tumour, or in apocrine metaplasia compared with normal epithelium, as were observed in our study. The mRNA expression of different CLDNs in a large number of special-type breast carcinomas and tumours of different grades requires investigation so that the decrease in CLDN4 expression in special-type breast carcinomas and IDCs of grade 1 can be unequivocally confirmed.
Conclusion
Our observations suggest that, in breast tissue, CLDN3 expression is similar in tumours and surrounding normal tissue, as demonstrated by immunohistochemistry and real-time PCR. In contrast, absent or decreased expression of CLDN1 in invasive breast carcinomas was demonstrated at both mRNA and protein levels. These data suggest that CLDN1 is involved in invasion and metastasis, and in mammary carcinogenesis. Furthermore, loss of or decrease in CLDN4 expression in grade 1 IDCs and the absence of CLDN4 in special-type breast carcinomas and in areas of apocrine metaplasia in benign breast tissue, as compared with normal epithelium, suggest a role for CLDN4 in cellular differentiation. The implications of this unique feature require further investigation. The important issue is whether loss of CLDN1 and CLDN4 plays a significant role in cell–cell adhesion and tumour differentiation.
Abbreviations
CLDN = claudin; DCIS = ductal carcinoma in situ; GAPDH = glyceraldehyde-3-phosphate dehydrogenase; IDC = invasive ductal carcinoma; ILC = invasive lobular carcinoma; OD = optical density; PCR = polymerase chain reaction; TJ = tight junction; ZO = zonulae occludens.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
A-MT and JK selected the cases, performed the pathological analysis (mainly JK), evaluated the immunohistochemical slides, performed the RT-PCR reactions (mainly A-MT) and wrote the manuscript. SP participated in the confocal microscopic investigation of the immunofluorescence slides. ÁS carried out the immunohistochemical reactions. CP participated in the evaluation of the RT-PCR reactions, the statistical analysis of the data and the writing of the manuscript. PKN participated in designing the CLDN primers. LS was the designer of the CLDN primers. AK participated in the evaluation of the RT-PCR reactions and the statistical analysis of the data and in the writing of the manuscript. KB participated in the evaluation of the immunohistochemical reactions and in the writing of the manuscript. ZS participated in the evaluation of the immunohistochemical reactions and in the writing of the manuscript in its final stage.
Acknowledgements
We thank Prof. Anna Kádár and Rigóné Káli Elvira for careful reading of our manuscript and valuable comments. This study was supported by grants FKFP 129/2001 and NKFP-1A/0023/2002.
Figures and Tables
Figure 1 The monospecificity of claudin (CLDN)4 antibody using Western blot analysis. CLDN4 protein expression in invasive breast carcinoma, surrounding normal breast and human liver. Western blot analysis was performed on equal amounts of protein from total cell lysates using CLDN4 monoclonal antibody. A single band was shown at approximately 22 kDa for CLDN4. IDC, invasive ductal carcinoma.
Figure 2 Claudin (CLDN)1 positivity in normal breast epithelium, tumour cell membranes and intraductal papilloma. (a) CLDN1 positivity in normal breast epithelium. Normal epithelial cells exhibit intensive CLDN1 positivity in the cell membrane. Image obtained using laser scanning confocal microscopy (MRC 1024; Bio-Rad). Primary antibody was CLDN1 polyclonal antibody (Zymed); secondary antibody was IgG conjugated to Alexafluor 488 (Molecular Probes). Cell nuclei were counter-stained with propidium iodide. Green fluorescence along the cell membranes corresponds to CLDN1; the nuclei are seen in red. (Original magnification: 400×.) (b) CLDN1 positivity in tumour cell membranes. Shown is the immunohistochemical reaction of invasive ductal carcinoma of the breast using polyclonal CLDN-1 antibody. Note the membrane staining only in some scattered tumour cells, as compared with normal epithelial cells (panel a). Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 600×.) (c) CLDN1 positivity in tumour cell membranes. Shown is the immunohistochemical reaction of invasive ductal carcinoma using polyclonal CLDN1 antibody. There is scattered membrane staining in some tumour cells. The secondary antibody is IgG conjugated to Alexafluor 488. Cell nuclei were counter-stained with propidium iodide. The image was obtained using laser scanning confocal microscopy (MRC 1024; Bio-Rad). (Original magnification: 400×.) (d) CLDN1 positivity in intraductal papilloma. Shown is the immunohistochemical reaction of a breast papilloma with an area of apocrine metaplasia. There is increased CLDN1 positivity in apocrine cells and negative staining in the surrounding papilloma. Sections were counter-stained with Mayer's hemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 200×.)
Figure 3 Claudin (CLDN)3 positivity in benign breast tissue and invasive ductal breast carcinoma. (a) CLDN3 positivity in benign breast tissue. Regular immunohistochemical reaction of benign breast epithelial cells using polyclonal CLDN3 antibody. Continous membrane staining characterizes most of the luminal epithelial cells. Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 400×.) (b) CLDN3 positivity in invasive ductal breast carcinoma. CLDN3 positivity is apparent in the membranes of some carcinoma cells. Sections were counter-stained with Mayer's hemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 600×.) (c) CLDN3 positivity in benign breast epithelium. CLDN3 positivity is seen in the majority of the membranes of benign epithelial cells. CLDN3 appeared to be localized in normal breast close to the apical end of the cell membranes or in the basolateral membranes of the epithelial cells. The primary antibody was CLDN3 polyclonal antibody (Zymed); the secondary antibody was IgG conjugated to Alexafluor 488 (Molecular Probes). Cell nuclei were counter-stained with propidium iodide. The image was obtained using laser scanning confocal microscopy (MRC 1024; Bio-Rad). (Original magnification: 400×.) (d) CLDN3 positivity in benign breast epithelium. CLDN3 positivity is seen in the membranes of the majority of breast epithelial cells. The positive membrane reaction is incomplete in most of the cells. The primary antibody was CLDN3 polyclonal antibody (Zymed). Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 400×.)
Figure 4 Claudin (CLDN)4 expression in benign breast epithelium, in invasive ductal carcinomas and in intraductal papilloma. (a) Intense CLDN4 positivity in benign breast epithelium. Shown is the immunohistochemical reaction of benign breast using monoclonal CLDN4 antibody (Zymed). Strong positivity is seen in epithelial cell membranes. Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 400×.) (b) CLDN4 positivity in invasive ductal breast carcinoma. CLDN4 is expressed in invasive ductal breast carcinoma of grade 3. Positive reaction is evident in the membranes of the tumour cells. Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 600×.) (c) CLDN4 expression in invasive ductal carcinoma of the breast. Shown is complete loss of CLDN4 expression in invasive ductal breast carcinoma of grade 1 as compared with grade 3 (panel b). Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 400×.) (d) CLDN4 positivity in intraductal papilloma. Shown is the immunohistochemical reaction of breast papilloma with an area of apocrine metaplasia. The loss of CLDN4 positivity in apocrine cells and the positive staining of other epithelial cells is a 'mirror image' of that seen in the CLDN1 reaction (Fig. 2d). Sections were counter-stained with Mayer's haemalaun. The chromogen substrate was aminoethylcarbazol. (Original magnification: 400×.)
Table 1 The CLDN1, CLDN3, CLDN4 and GAPDH primer sequences, position and product size
Primer Forward Reverse Product size (bp) GI number
Sequence 5'–3' Size (bp) Sequence 5'–3' Size (bp)
CLDN1 ttcgtacctggcattgactgg 378–399 22 ttc gta cct ggc att gac tgg 470–490 21 112 4559277
CLDN3 ctgctctgctgctcgtgtcc 732–751 20 ttagacgtagtccttgcggtcgtag 836–860 25 128 21536298
CLDN4 ggctgctttgctgcaactgtc 741–761 21 gagccgtggcaccttacacg 829–848 20 107 14790131
GAPDH gaagatggtgatgggatttc 81–98 20 gaaggtgaaggtcggagt 287–306 18 225 7669491
bp, base pairs; CLDN, claudin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.
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| 15743508 | PMC1064136 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Jan 31; 7(2):R296-R305 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr983 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9881574350510.1186/bcr988Research ArticleClaudin 7 expression and localization in the normal murine mammary gland and murine mammary tumors Blackman Brigitte [email protected] Tanya [email protected] Steven K [email protected] Daniel [email protected] Margaret C [email protected] University of Colorado Health Sciences Center, Denver, Colorado, USA2 Baylor College of Medicine, Houston, Texas, USA2005 17 1 2005 7 2 R248 R255 3 8 2004 14 9 2004 29 11 2004 10 12 2004 Copyright © 2005 Blackman et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
Claudins, membrane-associated tetraspanin proteins, are normally associated with the tight junctions of epithelial cells where they confer a variety of permeability properties to the transepithelial barrier. One member of this family, claudin 7, has been shown to be expressed in the human mammary epithelium and some breast tumors. To set the stage for functional experiments on this molecule, we examined the developmental expression and localization of claudin 7 in the murine mammary epithelium and in a selection of murine mammary tumors.
Method
We used real-time polymerase chain reaction, in situ mRNA localization, and immunohistochemistry (IHC) to examine the expression and localization of claudin 7. Frozen sections were examined by digital confocal microscopy for colocalization with the tight-junction protein ZO1.
Results
Claudin 7 was expressed constitutively in the mammary epithelium at all developmental stages, and the ratio of its mRNA to that of keratin 19 was nearly constant through development. By IHC, claudin 7 was located in the basolateral part of the cell where it seemed to be localized to discrete vesicles. Scant colocalization with the tight-junction scaffolding protein ZO1 was observed. Similar results were obtained from IHC of the airway epithelium and some renal tubules; however, claudin 7 did partly colocalize with ZO1 in EPH4 cells, a normal murine mammary cell line, and in the epididymis. The molecule was localized in the cytoplasm of MMTV-neu and the transplantable murine tumor cell lines TM4, TM10, and TM40A, in which its ratio to cytokeratin was higher than in the normal mammary epithelium.
Conclusion
Claudin 7 is expressed constitutively in the mammary epithelium at approximately equal levels throughout development as well as in the murine tumors examined. Although it is capable of localizing to tight junctions, in the epithelia of mammary gland, airway, and kidney it is mostly or entirely confined to punctate cytoplasmic structures, often near the basolateral surfaces of the cells and possibly associated with basolateral membranes. These observations suggest that claudin 7 might be involved in vesicle trafficking to the basolateral membrane, possibly stabilizing cytoplasmic vesicles or participating in cell–matrix interactions.
claudinEPH4 cellsmammary developmentmammary tumorstight junction
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Introduction
The claudins comprise a large family of tetraspanin membrane proteins thought to be the major barrier-forming proteins of tight junctions, the cell–cell contacts at the apical border of epithelial cells that control the paracellular movement of solutes. These proteins are highly conserved, with four transmembrane domains and two hydrophobic extracellular loops; the latter are thought to mediate cell–cell adhesion [1] and to confer specific paracellular permeability properties on cell monolayers [2,3]. Claudin 7 shares the general structural characteristics of the family, differing primarily in its amino-terminal cytoplasmic tail [4]. The molecule has been shown to be associated with epithelial cells in the human breast [5], and its loss is associated with some breast and head and neck malignancies [5,6]. It has been shown to be expressed in parts of the renal tubule [7] and the airway epithelium [8], where it is localized to the basolateral aspects of the cells. Here we show that claudin 7 is constitutively present in the epithelium of the murine mammary gland, again localized, not to tight junctions but to punctate structures at or near the basolateral surfaces of the cells. It was present at all cell borders of several murine mammary tumors. Nonetheless, the protein can localize to tight junctions, as shown by its partial colocalization with ZO1 in cultured mammary epithelial cells and epididymis, suggesting a possible dual function depending on tissue type.
Method
Animals and tissue preparation
CD-1 mice, purchased from Charles River Breeding Laboratory (Wilmington, DE), were maintained in the USDA-approved Animal Resource Center of the University of Colorado Health Sciences Center. All procedures were approved by the Institutional Animal Care and Use Committee. The fourth mammary glands of virgin female mice at 3, 6, and 12 weeks of age, of female mice during early gestation (5–7 days), mid-gestation (12 days) and late gestation (18 days), at days 2 and 10 of lactation, and at days 21 and 29 of involution were collected after killing with a lethal dose of pentobarbital. Liver, lung, and kidneys were obtained from virgin female mice and epididymis from male mice. The day on which vaginal plugs were observed was counted as day one of pregnancy. Mice overexpressing the Her2/neu oncogene [9] were obtained from Jackson Laboratories, and mammary tumors were dissected and frozen when they reached about 0.5 cm in diameter. The transplantation tumor models TM4, TM10L, and TM40 are maintained in the Medina laboratory as described [10]. Tissues were flash-frozen in liquid nitrogen in Tri-Zol reagent (Gibco BRL, Life Technologies) for total RNA purification. For protein extractions, the tissue was washed twice in 1 ml of phosphate-buffered saline (PBS) and then placed in 3 volumes of 1% Nonidet P40 buffer (25 mM Hepes-NaOH, pH 7.4; 150 mM NaCl, 4 mM EDTA, 1% Nonidet P40) containing protease inhibitors (10 μg/μl leupeptin, 10 μg/μl aprotinin, 10 μM pepstatin A, 1 mM phenylmethylsulphonyl fluoride, in dimethyl sulphoxide). For hybridizations and immunohistochemistry of frozen tissue in situ, tissues were placed in Tissue-Tek OCT 4583 Compound (Sakura, Torrance, CA) and flash-frozen in isopentane and liquid nitrogen. All samples were stored at – 70°C until ready for use.
Characterization of an antibody against claudin 7
A rabbit antibody was custom-made against the carboxy-terminal peptide of murine claudin 7, APRSYPKSNSSKEYV, by Zymed Laboratories (San Francisco, CA). This affinity-purified antibody bound only a 23 kDa peptide by western blotting, did not cross-react with claudin 1, its closest congener, in claudin-1-transfected Cos cells or at the apical border of the mammary epithelium where claudin 1 is present at junctional complexes, and stained only epithelia where its mRNA has been demonstrated. Staining was blocked by preincubation of the antibody with the peptide. Cos cells transfected with full-length claudin 7 were stained by this antibody but not by an antibody against claudin 1.
Protocol for real-time polymerase chain reaction
For real-time reverse transcriptase polymerase chain reaction, RNA was purified with the Qiagen system: 1 μg of DNase-treated RNA was used for each reaction. Triplicate tissue samples were assayed in duplicate with the ABI Prism 7700 sequence detection system, with lung as the positive control. Primer sequences were as follows: murine claudin 7, forward 5' -CGAAGAAGGCCCGAATAGCT-3' (338-337), reverse 5' -GCTACCAAGGCAGCAAGACC-3' (407–388), probe5' -GCCACAATGAAAACAATGCCTCCAGTCA-3' (359–386); murine cytokeratin 19, forward 5' -TTTAAGACCATCGAGGAC-3', reverse 5' -TCATACTGACTTCTCATCTCAC-3'.
Immunohistochemistry and digital confocal microscopy
Rat anti-mouse ZO1 antibody was obtained from Chemicon International Inc. (Temecula, CA). Tissue sections were cut at 10 μm thickness from frozen blocks with a Damon/IEC division minotome set at – 18 to -20°C. Sections were collected onto Cell-Tak coated coverslips and were further vapor-fixed with paraformaldehyde for 15 min. Sections were never allowed to dry. PBS was added carefully so as to not disrupt the sections. Tissue was permeabilized with 1% Triton X-100 for 15 min, rinsed well with PBS and blocked with sterifiltered 10% normal donkey serum for 20 min. All antibody solutions were microfuged for 20 min before use. The claudin antibody was diluted 1:1000. Primary incubations were for 1 hour at 21–22°C, followed by extensive washes in PBS, generally six times for 5 min each. Secondary antibodies were diluted in accordance with the manufacturer's instructions, in PBS alone. The host species of all secondary antibodies was donkey and all secondary antibodies were cross-adsorbed against mouse serum proteins. Antibodies used were conjugated to fluorescein, CY3 or CY5. Secondary antibodies were combined with 0.6 μg/ml 4,6-diamidino-2-phenylindole, and incubated on the tissue for 1 hour. Coverslips were rinsed briefly and permitted to soak overnight in PBS.
Images were collected with SlideBook software (Intelligent Imaging Innovations, Inc., Denver, CO) on a Nikon Diaphot TMD microscope equipped for fluorescence with a xenon lamp and filter wheels (Sutter Instruments, Novato, CA), fluorescent filters (Chroma, Brattleboro, VT), cooled charge-coupled device camera (Cooke, Tonawanda, NY) and stepper motor (Intelligent Imaging Innovations, Inc., Denver, CO). Multi-fluor images were merged, deconvolved, and renormalized with SlideBook software.
Results
Developmental expression of claudin 7 mRNA in the mammary gland
Figure 1a shows claudin 7 gene expression as a function of developmental stage in the mouse mammary gland from the virgin animal (non-pregnant non-lactating) through pregnancy (days P5, P12, and P18), lactation (days L2, L10, and L19), and involution (days L22 and L29). Gene expression increased more than 1000-fold between the virgin and early lactating gland, leveling out through lactation and decreasing at late involution (day L29) with the loss of epithelial cells. To determine whether expression of claudin 7 was a function of developmental stage or epithelial cell number, we also examined the expression of keratin 19, found only in luminal epithelial cells [11]. As can be seen, claudin 7 expression parallels that of keratin 19. Further, the ratio of claudin 7 to keratin 19 (Fig. 1a, dotted line) is relatively constant through pregnancy and early lactation, increasing only during later lactation when the expression of keratin 19 mRNA decreases significantly.
At late involution, day L29, claudin 7 expression was lower than that of keratin 19, for reasons that are not clear but probably have to do with the types and characteristics of epithelial cells present during late glandular remodeling. Similar results were obtained from microarray analysis (data not shown). In situ hybridization with 35S-labeled RNA probes to claudin 7 showed that the mRNA was localized to the epithelium in virgin, pregnant, and lactating animals (Fig. 1b). We conclude that, in the normal mammary gland, claudin 7 is an epithelial cell marker expressed at approximately constant levels through development. The very large changes in expression levels during pregnancy most probably reflect an increase in cell number as the mammary epithelium expands from a system of sparse ducts in the virgin gland, to a dense mass of lactating cells in a gland in which the adipose tissue is largely obliterated.
Immunolocalization of claudin 7 in the mammary gland
We made and characterized an affinity-purified antibody against the cytoplasmic tail of claudin 7 (see Methods) that proved quite satisfactory for both western blots (Fig. 2a) and immunohistochemistry on both frozen and paraffin sections. Given the well-known association of claudins with tight junctions, we were surprised to find that claudin stained the basolateral region of mammary cells in all stages of development (Fig. 2b,c). A ductal structure surrounded by adipose stroma is shown in the virgin; alveoli from the pregnant and lactating glands are also shown (Fig. 2b). Staining was blocked when the antibody was preabsorbed with the claudin 7 peptide against which it was made (Fig. 2b, lower right). As with the in situ analysis, stain was observed only in luminal epithelial cells. Although there seems to be some colocalization of claudin 7 with stain for the tight-junction scaffolding protein ZO1 in the lower-power images of Fig. 2b, at higher magnification (Fig. 2c) claudin 7 was entirely localized to the basal and lateral cytoplasmic regions, where it often appeared punctate in nature, particularly when it was not closely apposed to the cell border. Stain was excluded from nuclei (blue) and largely from tight junctions (green), as shown by the minimal overlap between the green and red stain in Fig. 2c. It seems likely that there is basolateral membrane staining in this tissue, but the basal and lateral membranes of the mammary epithelial cells are deeply infolded and membrane localization cannot be assessed at the magnifications possible with the light microscope.
When immunohistochemistry is used in the characterization of claudins, there is always a concern that the antibody cross-reacts with another claudin species. We have found claudins 1, 3, and 8 in the murine mammary epithelium (MC Neville, unpublished data); however, claudin 7 stain shows entirely different patterns from these claudins. As shown in Fig. 2a, claudin 1 is localized at the position of the junctional complexes; claudin 3 is present in epithelium from virgin and pregnant glands, whereas claudin 8 is present only during lactation. These experiments will be described in detail elsewhere.
Claudin 7 localization in other cell types
Sukumar and colleagues [5] reported that claudin 7 was localized to tight junctions of the human mammary carcinoma cell line MCF-7 by using an antibody directed toward the cytoplasmic tail of the human protein, which differs by one amino acid from the mouse protein. We therefore examined claudin 7 staining in the normal mouse mammary cell line EPH4 [12,13]. Figure 3a shows the distribution of claudin and ZO-1 in EPH4 cells. Claudin 7 distinctly colocalizes with ZO-1 with a slightly less compact distribution, as shown by the zy image at the top of the figure as well as the xy image just below. A scattering of cytoplasmic vesicles containing claudin 7 can also be seen. This finding suggests that claudin 7 is capable of localizing to tight junctions, a conclusion supported by the distribution of claudin 7 in the epididymis (Fig. 3b), where claudin 7 colocalized with ZO-1 at the apical borders of some cells but not others. Punctate cytoplasmic stain for claudin 7 could also be observed in some cells.
Claudin 7 mRNA was reported in lung and kidney, with liver being negative [4]. We therefore examined these tissues and found that claudin 7 was present in cells lining bronchiolar structures of the lung, where its distribution was mainly cytoplasmic as observed previously [8] (Fig. 3c). However, careful examination of a black-and-white image at the highest magnification in the luminal cells of this structure shows some claudin 7 to be colocalized with ZO-1, suggesting that it might also be associated with tight junctions in these cells. There was no stain in the pulmonary alveoli. In the renal cortex we found claudin 7 localized only to certain segments, identified as connecting tubules and cortical collecting ducts by Li and colleagues [7], where stain was distinctly basolateral (Fig. 3d). Because our antibody was different from that used by Li and colleagues, our finding confirms this localization. At high magnification the stain is punctate, as in the mammary and airway epithelia. No specific claudin stain was observed in the liver (Fig. 3e). These findings indicate that claudin 7 is capable of localizing to tight junctions, as in cultured mammary epithelial cells and epididymis; however, in mammary gland, airway, and kidney it is mostly or entirely confined to punctate cytoplasmic structures, often near the basolateral surfaces of the cells and possibly associated with the basolateral membranes. In no tissue was the protein observed in nuclei.
Claudin 7 localization in mammary tumors
We examined four types of mammary tumor: tumors arising in the transgenic mouse expressing the Erb2 receptor under the control of the mammary tumor virus promoter [9], and three transplantable tumors obtained from the laboratory of DM [10]. All tumors expressed claudin 7 mRNA at levels no more than twice that in the lactating mammary gland when normalized to ribosomal RNA (Fig. 4a). Although tumors themselves are more closely related to the pregnant gland, we chose the lactating gland for comparison because, like the tumors, it is composed largely of epithelial cells. Interestingly, the ratio of claudin 7 to keratin 19 in the tumors ranged from 4 to 60 times that in the lactating gland, reflecting a lower expression of keratin 19, and possibly a loss of differentiation, in the tumors. In all murine tumors examined here, claudin 7 was localized to the perimembrane region, as illustrated by a section of the TM4 tumor (Fig. 4b). None of these tumors showed ZO1 staining, indicating that they lacked tight junctions, so that the claudin 7 observed here was probably associated with membrane vesicles and possibly with basolateral membranes, as in the normal cells of the mammary gland. Sukumar and colleagues [5] observed a loss of claudin 7 expression in some human tumors, particularly lobular tumors. However, this finding was not true of the mouse mammary tumors examined.
Discussion
We find claudin 7 to be a constitutive component of the mammary epithelium, where it is localized to the basolateral regions of the cell. The finding that the ratio of its mRNA to that of keratin 19 is relatively constant throughout the developmental cycle suggests that the molecule might be an alternative marker to keratin for the proportion of epithelial cells in the mammary gland. It is not clear whether the more than 1000-fold increase in expression between the virgin gland and early lactation indicates that the number of epithelial cells increases in this proportion, because part of the increase could be a function of an increase in cell size as the cells differentiate. The rapid increase between the virgin gland and pregnancy day 5 is consistent with studies showing a peak of thymidine incorporation between days 2 and 5 of pregnancy, when about 25% of the cells were labeled [14,15]. In both studies, labeling decreased thereafter but remained about 10% almost to the end of pregnancy, consistent with the continued increase in both claudin 7 and keratin mRNA up to day P18.
Our findings are consistent with images of claudin staining in the human mammary gland, where a diffuse diaminobenzidene stain from alkaline phosphatase localization was present throughout the cytoplasm [5]. However, in that study no attempt was made to localize claudin 7 stain with tight-junction components. Basolateral localization of other claudins has been observed. Claudin 1 was localized to the cytoplasm in the epididymis [16], intestine [17], and cornea [18]. Rahner and colleagues [19] observed claudins 3, 4, and 5 to be laterally distributed in various portions of the gastrointestinal track. The finding that claudin 7 is exclusively located in non-tight-junction regions of mammary and renal epithelial cells [7] suggests that claudins might have functions other than the regulation of tight-junction permeability. Our images seem to be the first to show claudin 7 stain at sufficiently high resolution to show punctate cytoplasmic stain in mammary, airway, and renal epithelial cells. Even at this resolution, obtained with digital confocal imaging with a resolution of about 200 nm, it is not possible to discern with certainty whether claudin 7 is inserted into the convoluted basolateral membranes of these cell types. If so, it is possible that the cytoplasmic spots represent vesicles en route to and from to the basolateral membranes, where claudin 7 might interact with components of the extracellular matrix. As a precedent, claudin 11, an oligodendrocyte protein, has been shown to interact with α1-integrin and to regulate the proliferation and migration of oligodendrocytes in culture [20]. Other possibilities are that vesicular claudins could regulate tight-junction permeability by sequestering tight-junction regulatory molecules away from tight junctions, or they could be involved in the stabilization of specialized vesicle compartments within the cytoplasm. Matsuda and colleagues, using time-lapse photography, have shown that claudin-containing cytoplasmic vesicles can originate from the tight junctions as the epithelial layer remodels [13]. However, because claudin 7 is never observed in association with tight junctions in mammary epithelial cells, it seems unlikely that these vesicles have an origin in the junctional complex. Our mammary epithelial cell model, EPH4 cells, does possess a complement of cytoplasmic vesicles that could allow live-cell imaging studies to determine the origin and disposition of claudin 7-containing vesicles. It is likely that the function of the vesicles can be better assessed after analysis of the composition of the claudin 7-containing cytoplasmic vesicles.
Claudin 7 expression was inversely correlated with histological grade in a large series of breast tumors [5]. These same authors found, similarly to our observations with EPH4 cells, that claudin 7 colocalized with ZO1 in MCF7 breast cancer cells and could also be observed in cytoplasmic spots. It was present in luminal cells of the human breast, where the diaminobenzidine staining seemed to be localized to basolateral membranes, although it is difficult to draw a firm conclusion in the absence of an apical marker like ZO1. An image of ductal carcinoma in situ in that paper shows a distribution of stain remarkably similar to that of the TM10 tumor shown in Fig. 4b. This tumor line has been shown to have a ductal morphology [10].
Thus, we conclude that low-grade breast carcinomas show a cellular distribution of stain similar to that observed in the murine tumors. Interestingly, the murine tumors showed claudin 7 expression at the mRNA level equal to or higher than that of the lactating mammary gland, where the largest proportion of the cells are the luminal epithelial cells that give rise to mammary tumors. TM4, the most tumorigenic of these lines, had the highest claudin 7 expression, although the level was quite variable and not significantly different from the other lines examined. Interestingly, TM4 and the MMTV-neu tumor had ratios of claudin 7 to cytokeratin less than one-eighth of that in the slower-growing TM10 line. All of the tumors had claudin 7 to cytokeratin ratios significantly higher than those in the pregnant or lactating mammary gland. These observations suggest that loss of the cytoplasmic architectural stability conferred by keratin might be an early event in tumorigenesis. Together with the data from the Sukumar laboratory [5], we might speculate that loss of claudin 7, as occurs in high-grade tumors, alters cell–matrix interactions, allowing a greater degree of cell mobility and contributing to metastasis.
Conclusion
Claudin 7 is a constitutive component of mammary epithelium at all stages of development, maintaining a level of gene expression that is approximately proportional to the amount of epithelial tissue in the gland. The protein is not associated with tight junctions but is found in particulate structures in the cytoplasm, generally denser at basolateral borders of the cells. Its distribution is similar in mammary tumors, at least in more well-differentiated ones. Claudin 7 function in the mammary epithelium is currently unknown but might be related to vesicle trafficking or cell-matrix interactions.
Abbreviations
IHC = immunohistochemistry; PBS = phosphate-buffered saline.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
BB performed the real-time polymerase chain reaction and in situ experiments and was involved in antibody development and immunocytochemistry. She wrote the initial draft of the manuscript. TR assisted with the immunocytochemistry. SKN provided general molecular knowledge for experimental design and provided valuable input into the manuscript. DM provided the TM series of tumors. MCN conceived the study, performed the high-resolution immunocytochemical analysis and completed the manuscript for submission. All authors read and approved the final manuscript.
Acknowledgement
This research was supported by DOD grant DAMC17-01-1-0211 and NIH grants R37-HD19547 and P01-HD38129 to MCN.
Figures and Tables
Figure 1 Developmental expression of claudin 7 mRNA in the murine mammary gland. (a) Real-time reverse transcriptase polymerase chain reaction measurement of claudin 7 and keratin 19 from total mammary glands of non-pregnant non-lactating (NPNL) mice, mice at 5, 12, and 18 days of pregnancy (P5, P12, and P18), and mice at 2, 10, 19, 22, and 29 days post partum (L2, L10, L19, L22, and L29). By L29 the pups have weaned themselves, and mammary gland involution is nearly complete. Dotted line, ratio of claudin 7 RNA to keratin 19 at the same time points. Three mice analyzed at each time point. Error bars represent SEM. Where no bar is visible, the SEM falls within the symbol. Asterisks indicate points that differ significantly from values at L2, P < 0.05. (b) In situ hybridization of claudin 7 probes to sections from virgin, pregnant, and lactating mammary glands. Sections labeled A were hybridized to the anti-sense probe; the lower right-hand section labeled S is the sense control. Scale bar, 20 μm.
Figure 2 Claudin 7 immunohistochemistry in the mammary gland. (a) Western blot on 50 μg of protein from mammary gland at pregnancy days 7 and 18 (P7 and P18) and lactation days 2 and 9 (L2 and L9). Because gene expression is 20-fold lower at P7, as can be seen from Fig. 1a, protein would not be expected to be visible at this exposure. Right-hand side: section of gland from virgin mouse stained with antibody (Zymed) to Claudin 1. (b) Lower-power views of epithelial structures from the developmental stages indicated with fluorescein isothiocyanate-labeled ZO-1 (green), Cy3-labeled claudin 7 (red), and 4,6-diamidino-2-phenylindole-labeled nuclei (blue). Lu, lumen; adip, adipocyte. Lower right: anticlaudin antibody blocked with claudin 7 peptide. Scale bar, 30 μm. (c) High-power views of claudin 7 localization. The right-hand images show the merged views as in (b). The left-hand black-and-white images show only the claudin 7 stain, demonstrating clearly the punctate nature of the claudin 7 distribution near the basolateral membranes; claudin 7 is excluded from both nuclei and tight junctions. Scale bar, 10 μm. Arrows in all figures point to FITC-stained ZO-1 (green).
Figure 3 Immunolocalization of claudin 7 (Cy3, red) in cultured mammary epithelial cells, epididymis, lung, kidney, and liver. ZO-1 is stained with fluorescein isothiocyanate to demonstrate location of the tight junctions. (a) Eph4 cells. Claudin 7 is found with a patchy distribution at the tight junctions with ZO1. Vesicular stain can also be seen in the cytoplasm. The inset at the top shows the z axis across the xy view of the section at the level of the arrow. The overlap of ZO1 and claudin indicates colocalization at the level of resolution of the light microscope. (b) Mouse epididymis. Claudin 7 stain colocalizes with ZO-1 in most, but not all, tight junctions. Lu, lumen. Small arrowheads in this and subsequent panels denote the apical border of the epithelium. (c) Lung. The luminal cells of the bronchiole show heavy staining for claudin 7 in a punctate distribution at the basolateral surface of the cell; however, some claudin 7 colocalizes with ZO-1 (see black-and-white inset). (d) Kidney. Some of the tubules of the renal cortex stain as observed by Li and colleagues [7]. Claudin 7 shows a punctate distribution that is heaviest in the basal portion of the cells. (e) Liver. Tight junctions at the bile canaliculi are stained with ZO-1. However, no stain for claudin 7 is apparent. The magnification for each figure is indicated by the label (u, μm) above the scale bar.
Figure 4 Claudin 7 in mouse mammary tumors. (a) Black bars, mRNA levels by real-time reverse transcriptase polymerase chain reaction in the four murine mammary tumors and the lactating gland, chosen for comparison because it, like the tumors, contains a large proportion of epithelial cells. All values are normalized to ribosomal RNA. Gray bars, Ratio of claudin 7 mRNA to keratin 19 mRNA. Error bars represent two SEM. (b) Claudin 7 distribution in paraffin section of the transplantable tumor TM4. The stain is localized to the perimembrane cytoplasm in all cells and excluded from nuclei. The arrowheads mark the point in which tight-junction stain would be expected if tight junctions were to form; however, no ZO1 stain was detected in the section. Scale bar, 20 μm.
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| 15743505 | PMC1064138 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Jan 17; 7(2):R248-R255 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr988 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9911574350610.1186/bcr991Research ArticleLong-term prognostic significance of HER-2/neu in untreated node-negative breast cancer depends on the method of testing Schmidt Marcus [email protected] Barbara 1Kohlschmidt Nikolai 2Glawatz Christiane 1Steiner Erik 1Tanner Berno 1Pilch Henryk 1Weikel Wolfgang 1Kölbl Heinz 1Lehr Hans-Anton 21 Department of Obstetrics and Gynecology, Johannes Gutenberg University, Medical School, Mainz, Germany2 Department of Pathology, Johannes Gutenberg University, Medical School, Mainz, Germany2005 26 1 2005 7 2 R256 R266 8 9 2004 3 11 2004 21 11 2004 15 12 2004 Copyright © 2005 Schmidt et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
The prognostic significance of HER-2/neu in breast cancer is a matter of controversy. We have performed a study in 101 node-negative breast cancer patients with long-term follow-up not treated in the adjuvant setting, and analysed the prognostic significance of immunohistochemistry (IHC) and fluorescence in situ hybridisation (FISH), both separately and in combination, in comparison with traditional prognostic factors.
Methods
Overexpression was classified semiquantitatively according to a score (0 to 3+) (HER-2_SCO). FISH was used to analyse HER2/neu amplification (HER-2_AMP). Patients classified 2+ by IHC were examined with FISH for amplification (HER-2_ALG). Patients with 3+ overexpression as well as amplification of HER-2/neu were positive for the combined variable HER2_COM. These variables were compared with tumour size, histological grade and hormone receptor status.
Results
HER-2_SCO was 3+ in 20% of all tumours. HER-2_ALG was positive in 22% and amplification (HER-2_AMP) was found in 17% of all tumours. Eleven percent of the tumours showed simultaneous 3+ overexpression and amplification. Only histological grade (relative risk [RR] 3.22, 95% confidence interval [CI] 1.73–5.99, P = 0.0002) and HER-2_AMP (RR 2.47, 95% CI 1.12–5.48, P = 0.026) were significant for disease-free survival in multivariate analysis. For overall survival, both histological grade (RR 3.89, 95% CI 1.77–8.55, P = 0.0007) and HER-2_AMP (RR 3.08, 95% CI 1.24–7.66, P = 0.016) retained their independent significance.
Conclusion
The prognostic significance of HER-2/neu in node-negative breast cancer depends on the method of testing: only the amplification of HER-2/neu is an independent prognostic factor for the long-term prognosis of untreated node-negative breast cancer.
breast cancerFISHHER-2/neunode-negativeprognosis
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Introduction
Human epidermal growth factor receptor-2 is a proto-oncogene that encodes a cell-surface receptor designated HER-2/neu or c-erbB-2. Gene amplification and/or protein overexpression occurs in 14–30% of all breast cancers [1-3]. Initially, the adverse prognostic impact of HER-2/neu in breast cancer was the main focus of research. However, results from different study groups were not entirely consistent. Studies that supported the initially reported adverse prognosis in breast cancer [1,3] were later followed by reports that failed to show any association with prognosis [4,5]. Although no consensus exists concerning the prognostic value of HER-2/neu, an increasing quantity of data indicates a predictive value for the efficacy of certain adjuvant therapies. The response of HER-2/neu-positive breast cancer patients to tamoxifen is significantly worse than for HER-2/neu-negative patients [6,7] even though this point of view is not unopposed [8,9]. More recently, it was shown that aromatase inhibitors might provide more benefit than tamoxifen in patients with tumours positive for erbB-1 and/or erbB-2 [10].
However, the strongest evidence for a predictive role for HER-2/neu comes from several retrospective trials that consistently showed that HER-2/neu-positive patients responded better to an anthracycline-based therapy than to treatment with cyclophosphamide, methotrexate and fluorouracil [11-13]. Because of this predictive impact of HER-2/neu, many of the studies on the prognostic role of HER-2/neu cannot be reliably interpreted when the patients enrolled were treated in an adjuvant setting. To avoid this potential bias, we have performed a retrospective study on the prognostic impact of HER-2/neu in a historical cohort of node-negative T1/T2 breast cancer patients who were at that time not being treated in an adjuvant setting.
A point of utmost importance when assessing the utility of HER-2/neu as a prognostic factor is the technique of HER-2/neu testing. Principally, gene-based assays such as Southern blot analysis or fluorecence in situ hybridisation (FISH) have to be distinguished from assays that assess the level of protein expression, such as western blot analysis or immunohistochemistry (IHC). When analysing the published studies of HER-2/neu as a prognostic factor with regard to the technique used, Ross and colleagues [14,15] found that those studies that applied FISH to the assessment of gene amplification found an association of HER-2/neu with the prognosis of the patients, whereas studies that used IHC for the assessment of protein expression gave rather ambiguous results. Reasons for the apparently worse performance of IHC than that of FISH could be differences in the sensitivity of the applied antibodies [16] or the lack of a uniform scoring system in most of the older studies that used IHC. Standardisation of HER-2/neu testing has received considerably more attention in recent years owing to the advent of trastuzumab, a monoclonal antibody that affords prolonged survival in patients with metastatic breast cancer [17]. To improve the quality of IHC when testing for HER-2/neu status before starting a therapy with trastuzumab, a standardised scoring protocol was developed. On the basis of these findings, an algorithm incorporating FISH only in doubtful cases (2+ in IHC) was introduced.
The aim of our present study on 101 node-negative breast cancer cases was to compare gene amplification by FISH with protein expression by IHC with the standardised scoring system, the above-mentioned algorithm and the combination of HER-2/neu overexpression and amplification in the prognostic value of HER-2/neu.
Methods
Patients
The study cohort consisted of 101 lymph-node-negative breast cancer patients who were treated at the Department of Obstetrics and Gynecology of the Johannes Gutenberg University Mainz between 1988 and 1993. Patients were all treated with surgery and did not receive any systemic therapy in the adjuvant setting. The established prognostic factors (tumour size, histological grade and steroid receptor status) were collected from the original pathology reports of the gynaecological pathology division within our department.
Patients were treated either with modified radical mastectomy (n = 58) or breast-conserving surgery followed by irradiation (n = 43). Because the administration of adjuvant systemic therapy was not allowed in this study, we focused on node-negative breast cancer patients with pT1 and pT2 tumours and without any evidence of metastasis at the time of surgery. The median age of the patients at surgery was 56 years (range 29–86 years).
The median time of follow-up was 131 months for the patients still alive at the time of analysis. Within this follow-up period, 31 patients relapsed, 20 patients died of breast cancer and 10 patients died of unrelated causes. The patients dying of causes other than breast cancer were censored for the survival analyses at their date of death.
HER-2/neu amplification determined by FISH
FISH for HER-2/neu gene amplification was performed with the Appligene Oncor HER-2/neu gene amplification system (Ventana Medical Systems, Tucson, AZ, USA), following the supplier's instructions. In brief, fresh frozen slides were first treated with a protein-digesting enzyme at 37°C for 10 min, washed in 2× sodium chloride/sodium citrate (SSC) at 22°C, dehydrated in an 75–100% ethanol series and air dried. Tissue sections were than denatured for 5 min in 70% formamide, pH 7.5, at 75°C, followed by rinsing with 100% ethanol and air drying. Appligene Oncor HER-2/neu DNA probe was prewarmed for 5 min at 37°C before application. Slides were than incubated for 24 hours at 37°C in a humidified chamber. After hybridisation, the slides were washed in 2× SSC for 5 min at 72°C; this was then followed by a wash in phosphate-buffered detergent (Oncor, Gaithersburg, MD, USA) at room temperature for 5 min. Detection was achieved with the Appligene Oncor fluorescein-labelled anti-digoxigenin antibody (Ventana Medical Systems). The slides were incubated with this antibody for 5 min in a humidified chamber at 37°C. Slides were then subjected to three washes (2 min each) in phosphate-buffered detergent at room temperature and were stored in the dark at -20°C for up to 5 days before analysis. The nuclei were counterstained with a propidium iodide/antifade solution (Oncor, Gaithersburg, MD, USA). Appropriate positive controls were included in each staining run. A serial section of each slide used for FISH was stained with haematoxylin and eosin to control for the presence of invasive tumour formations.
Additionally, serial sections (6 μm thick) of formalin-fixed paraffin-embedded blocks from five randomly selected amplified and five randomly selected non-amplified tumours were deparaffinised and then subjected to the staining protocol outlined above.
Interpretation of FISH results
Analysis was performed with an Axioskop fluorescence microscope (Zeiss, Jena, Germany). Images were captured with an analogue camera (Leica, Bensheim, Germany). In each quadrant of the slide, the number of fluorescein signals was counted in 20 nuclei of invasive tumour cells (that is, a total of 80 tumour nuclei). Cases were considered amplified if the mean number of fluorescence signals was greater than four (HER2_AMP) [18]. Additionally, we compared tumours with low-level amplification (five or six signals per nucleus) against tumours with a higher level of amplification (more than six signals per nucleus).
HER-2/neu expression determined by IHC
The immunohistochemical staining with a monoclonal antibody against HER-2/neu was performed as described previously [19]. In brief, serial sections (4 μm thick) of formalin-fixed, paraffin-embedded blocks were first deparaffinised. They were then microwaved in 10 mM citrate buffer, pH 6.0, to unmask epitopes and treated for 10 min with 1% hydrogen peroxide to block endogenous peroxidase. The sections were incubated for 30 min at 37°C with monoclonal HER-2/neu antibodies (clone CB-11; Novocastra, Newcastle upon Tyne, UK) diluted 1:50. The sections were then incubated with a biotin-labelled secondary antibody and streptavidin–peroxidase for 20 min each. Tissue was subsequently treated for 5 min with 0.05% 3',3-diaminobenzidine tetrahydrochloride and lightly counterstained with haematoxylin. All series included appropriate positive and negative controls. All controls gave adequate results.
Interpretation of IHC results
Only cases showing unequivocal staining of membranes were regarded as positive for HER-2/neu overexpression [2]. A score was determined in accordance with the criteria used in the approval trials for trastuzumab [17,20]. In brief, cases showing no staining were scored 0, cases with less than 10% membrane staining 1+, cases with more than 10% weak to moderate complete membrane staining 2+, and cases with more than 10% strong complete membrane staining 3+ (HER2_SCO). Only 3+ cases were considered positive for survival analyses.
Interpretation of combined IHC and FISH results
Cases that were scored 2+ by IHC were considered positive for the HER-2/neu algorithm [21] only if they showed an amplification of HER-2/neu (HER2_ALG).
Finally, cases showing HER-2/neu amplification as well as overexpression with an immunohistochemical score of 3+ were considered positive for the combined HER-2/neu evaluation (HER2_COM).
Statistical analysis
The concordance between two methods was assessed by using the kappa test. The sensitivity and specificity of IHC (HER2_SCO) were evaluated, with the use of FISH as reference method. Life tables were calculated in accordance with the Kaplan–Meier method. Disease-free survival (DFS) was computed from the date of diagnosis to the date of recurrence of disease. Overall survival (OS) was computed from the date of diagnosis to the date of death from breast cancer. Patients who died of an unrelated cause were censored at the date of death. Survival curves were compared with the log-rank test. Univariate Cox survival analyses were performed and multivariate analyses were done in a backward stepwise fashion with the Cox proportional hazards model. All tests were performed at a significance level of α = 0.05. All P values are two-sided.
Results
Distribution of traditional and HER-2/neu-related factors
In a group of 101 node-negative patients with primary breast cancer of sizes T1 and T2 without systemic treatment in the adjuvant setting, established pathological and clinical parameters (tumour size, histological grade, steroid hormone receptor status, age and menopausal status) and HER-2/neu-related parameters (HER2_AMP, HER2_SCO, HER2_ALG and HER2_COM) were assessed and are presented in Table 1; 36% of the patients were premenopausal and perimenopausal. Tumour size was T1 in 57% and T2 in the remaining 43%. A total of 64% of the patients had tumours with a positive steroid hormone receptor status; that is, they were oestrogen receptor and/or progesterone receptor positive. A favourable histological grade (G I) was present in 17%, G II in 58% and G III in 20%. Five percent had medullary carcinomas and were therefore not assigned a histological grade.
HER-2/neu overexpression classified as 3+ as assessed by IHC was found in 20% (HER2_SCO); 17% of tumours showed an amplification of HER-2/neu by FISH (HER2_AMP). Of the amplified cases, five tumours showed five or six signals per nucleus, indicating low-level amplification, and 12 tumours showed more than six signals per nucleus (evidence of a higher level of amplification). Two of the patients who were scored as 2+ by IHC also exhibited an amplification of HER-2/neu. This resulted in a total of 22% of the patients being positive for HER2_ALG. Finally, in 11% of the patients an amplification as well as a 3+ overexpression of HER-2/neu was found (HER2_COM).
The estimated DFS was 70% and the breast cancer-specific OS was 80% at 10 years for the whole group of patients.
Concordance of amplification status in formalin-fixed paraffin-embedded tumour samples with fresh-frozen tumour samples
All five tumours amplified for HER-2/neu in frozen tumour samples were also amplified when formalin-fixed paraffin-embedded tumour samples were used. Similarly, complete concordance was found between frozen and formalin-fixed tissue samples when five tumours without amplification were compared pair by pair.
Concordance of HER2_AMP and HER2_SCO
A concordance between amplification and HER2_SCO was detected in 86 cases (85%) when only 3+ cases were considered positive for HER2_SCO. Six cases with amplification did not score 3+, whereas nine 3+ cases failed to show an amplification. This resulted in a degree of concordance (kappa) of 0.50 (95% CI 0.29–0.72).
Sensitivity and specificity of HER2_SCO
The sensitivity of HER2_SCO 3+ with FISH as reference method was 65% (11 of 17 amplified cases) and specificity was 89% (75 of 84). Considering also 2+ cases as positive resulted in an increase in sensitivity to 76% (13 of 17 amplified cases) with a decreased specificity of 70% (59 of 84).
Breast cancer-specific DFS
In univariate analysis (Table 2) neither age at diagnosis nor menopausal status, tumour size or steroid hormone receptor status had a significant influence on the DFS. From the classical prognostic factors only histological grade turned out to be significantly related to DFS (P = 0.0002; RR 3.26, 95% CI 1.77–5.99) for DFS. Among the HER-2/neu-related variables, HER2_ALG did not show an influence on DFS, whereas HER2_SCO had a borderline significance (P = 0.059, RR 1.46, 95% CI 0.99–2.16). In contrast, both HER2_AMP (P = 0.004, RR 3.07, 95% CI 1.44–6.57) and HER2_COM (P = 0.006, RR 3.27, 95% CI 1.40–7.65) had a significant influence on the DFS. There was no significant difference in DFS between tumours with low-level amplification (five or six copies per nucleus) and tumours with a higher level of amplification. Three of five and 7 of 12 patients relapsed, respectively. The Kaplan–Meier estimates that yielded significant results are shown in Fig. 1. We then conducted a multivariate Cox regression in a backward fashion. In this Cox regression only histological grade (P = 0.0002, RR 3.22, 95% CI 1.73–5.99) and HER2_AMP (P = 0.026, RR 2.47, 95% CI 1.12–5.48) retained an independent prognostic significance (Table 3).
Breast cancer-specific OS
In univariate analysis (Table 4) neither age at diagnosis nor menopausal status or tumour size had a significant influence on the OS. From the classical prognostic factors only histological grade (P = 0.0003, RR 4.27, 95% CI 1.96–9.29) and the steroid hormone receptor status (P = 0.012, RR 0.32, 95% CI 0.13–9.78) were significant in univariate analysis for OS. Among the HER-2/neu-related variables, HER2_ALG did not show any influence on OS whatsoever. However, HER2_SCO (P = 0.047, RR 1.60, 95% CI 1.01–2.53) as well as HER2_AMP (P = 0.004, RR 3.78, 95% CI 1.54–9.26) and HER2_COM (P = 0.004, RR 4.18, 95% CI 1.60–10.90) had a significant influence on the OS. There was no significant difference in OS between tumours with low-level amplification (five or six copies per nucleus) and tumours with a higher level of amplification. Three of five and 5 of 12 patients died of breast cancer, respectively. The Kaplan–Meier estimates that yielded significant results are shown in Fig. 2. In a multivariate Cox regression analysis only histological grade (P = 0.0007, RR 3.89, 95% CI 1.77–8.55) and HER2_AMP (P = 0.016, RR 3.08, 95% CI 1.24–7.67) retained an independent prognostic significance (Table 5).
Discussion
The prognostic value of HER-2/neu has always been controversial. Studies showing a shorter DFS and/or OS for HER-2/neu-positive patients [3,18,22,23] were opposed by studies which failed to find such an association [4,5]. In an earlier series from our department [24] an association with survival was shown only for node-positive, but not for node-negative, breast cancer patients. This association with prognosis in node-positive patients, who are almost uniformly treated with systemic therapy, might be markedly influenced by the ability of HER-2/neu status to affect responsiveness to systemic treatment [11-13]. In any case, the actual role of HER-2/neu as a predictive marker is still a matter of debate [25]. In our present study, we could rule out any such interference of purely prognostic with treatment-related predictive effects because we examined only node-negative patients without any systemic therapy in the adjuvant setting.
When examining the techniques used for the assessment of the HER-2/neu status, gene-based assays almost uniformly confirmed the negative prognostic impact of HER-2/neu in node-negative patients. Especially in the past few years, FISH has gained considerable interest as a reliable and valid method for determining the HER-2/neu status, confirming its prognostic utility [18,26]. Compared with other gene-based assays such as Southern blotting or polymerase chain reaction, FISH is not hampered by dilutional artefacts possibly resulting from a mixture of different cell populations. Similarly to IHC it allows for the specific detection of the alteration in individual cells within the important architectural context. However, in comparison with IHC, FISH is rather time-consuming and leads to substantial costs [27]. It is nevertheless considered the gold standard for assessing HER-2/neu status. A potential advance in the practicability of in situ hybridisation could be the more recently described chromogenic in situ hybridisation, which has shown a good correlation with FISH [28] and an independent prognostic importance in patients with node-negative breast cancer [23].
When determining the amplification of HER-2/neu with FISH in our cohort of 101 untreated node-negative breast cancer patients, we found 17% with an amplification of the gene locus. This fraction is largely in line with the literature [18,26,29-31]. The amplification of HER-2/neu measured by FISH showed a significant correlation with the DFS and OS. This strong association between amplification of HER-2/neu and survival of lymph-node-negative breast cancer patients is consistent with previous studies [18,23,26,30]. Only one group could document this association only for OS but not for DFS [32]. Searching for an explanation for their deviating results, the authors speculated that HER-2/neu might be more a predictive factor for treatment response than a prognostic factor per se because all patients in their study had been treated systemically after relapse.
In contrast with HER-2/neu gene amplification, overexpression of HER-2/neu was not associated with survival in several studies [4,5,33] even though others found a prognostic impact [22,23,34]. More recently, Volpi and colleagues [35] found a prognostic significance of HER-2/neu overexpression only for a subgroup of patients with high proliferative activity, whereas they failed to show any prognostic significance of HER-2/neu overexpression in the overall series of node-negative breast cancer patients. These apparent discrepancies have largely prevented the widespread acceptance of HER-2/neu as a prognostic factor in node-negative breast cancer up to now [36].
Several possible reasons could account for these controversial findings. One frequently quoted, simple but nonetheless unsatisfactory reason could be the rather small sample size of several studies. However, a strong and biologically relevant prognostic factor should eventually become evident even within a small sample size. Another possible reason is the remarkable discrepancy in sensitivity between the numerous antibodies used [16] and differences in tissue fixation and processing [37]. However, perhaps the most important reason is the lack of a standardised evaluation protocol in many of the older studies. The standardisation of IHC has become increasingly important since the successful use of trastuzumab (Herceptin™) in the treatment of HER-2/neu-overexpressing metastatic breast cancer [17,20,38]. The United States Food and Drug Administration has approved a standardised IHC kit (HercepTest™; Dako) with a detailed published scoring system ranging from 0 to 3+. However, the HercepTest has a rather low specificity, as outlined by Jacobs and colleagues [39]. The studies mentioned above that led to the approval of trastuzumab for HER-2/neu-positive metastatic breast cancer used a cocktail of different antibodies, one of which, the monoclonal antibody CB11, was used in our study.
When we adapted the scoring system defined for the HercepTest to the CB11 antibody we used in our study, we found a borderline significant correlation with survival.
To our opinion, the concordance between FISH and IHC was only moderate at best with a rate of 85%, even though we regarded only 3+ cases as overexpressing HER-2/neu. This level of concordance is in line with previously published results [40]. However, others found higher levels of concordance between FISH and IHC, ranging from 92% up to 98% [39,41,42].
When FISH results were compared with IHC data by using computer-assisted image analysis, we showed previously that concordance rates can be significantly improved when the IHC signal is corrected by the subtraction of non-specific cytoplasmic chromogen deposition [43], suggesting that only the strictly membrane-confined IHC signal can be considered truly positive when estimating HER-2/neu IHC slides. However, similarly to our findings others have failed to detect oncogene amplification by FISH in as many as 51% of tumours with strong 3+ staining [44]. Because overexpression of HER-2/neu is not necessarily caused only by amplification, polysomy of chromosome 17 has to be taken into account [45]. This polysomy of chromosome 17 could well lead to an incorrect diagnosis of a low-level amplification using single-colored FISH. However, in our study tumours with five or six copies of HER-2/neu had a survival comparable to that of tumours with a higher level of amplification. As a consequence of the moderate concordance, sensitivity and specificity for IHC in our study were lower than described by others [41,42]. However, our findings with the frequently used antibody CB11 should not be generalised to IHC as a whole because different antibodies show well-documented differences in terms of sensitivity and specificity [16].
Cases reported as 2+ by IHC should be reassessed with FISH in accordance with the test algorithm used in metastatic breast cancer before the start of a therapy with trastuzumab [21]. To the best of our knowledge we are the first group to use this algorithm to assess the prognosis in node-negative breast cancer patients. The use of this algorithm found 2 of 18 2+ tumours amplified and hence increased the percentage of tumours positive for HER-2/neu to 22%. A similar percentage of HER-2/neu-positive cases has previously been reported when performing FISH only in uncertain cases after IHC with the HercepTest [46]. For these authors, decreases in time and costs were quoted as strong arguments in favour of using this same testing algorithm for the determination of HER-2/neu status.
Because overexpression of HER-2/neu does not necessarily mirror amplification and vice versa, and because both parameters have been correlated with a poor outcome, we investigated whether the combination of amplification and overexpression could be useful in identifying a subgroup of patients with a particularly dismal prognosis. Altogether, 11 tumours showed amplification and overexpression (defined by a score of 3+). Indeed, these patients had a significantly worse prognosis than the remainder of the study cohort. These results are comparable to the data of Sauer and colleagues [31], who also found a markedly adverse prognostic effect in this dual-positive subgroup. Nonetheless, in our multivariate analysis, the combination of both techniques did not add additional prognostic information to that obtained by the amplification alone. However, owing to the relatively small number of events, especially for HER2_COM, subtle differences might be difficult to address adequately.
To assess the clinical relevance of these findings, we included the HER-2/neu-dependent variables (HER2_SCO, HER2_AMP, HER2_ALG and HER2_COM) into a multivariate model with the variables tumour size, histological grade and steroid receptor status. These last three variables are commonly accepted for risk assessment in node-negative breast cancer [36]. The factor age (not more than 35 years at diagnosis) was ignored because only one patient in our cohort was younger than 35. In this multivariate analysis, only histological grade and HER-2/neu amplification were identified as independent prognostic factors for DFS and OS, respectively.
We therefore conclude that HER-2/neu is an independent prognostic factor in node-negative breast cancer, even though its prognostic utility is largely influenced by the method of testing.
To further validate these observations, a prospective study in patients not treated in an adjuvant setting would be desirable. However, this is largely precluded in practice, given the recent consensus recommendations for the treatment of primary breast cancer [36]. For this reason we are currently engaged in a formal meta-analysis of all published studies that have used FISH or chromogenic in situ hybridisation to determine the HER-2/neu status and hope to clarify once and for all the controversial status of HER-2/neu as a prognostic factor in breast cancer.
Conclusions
The prognostic significance of HER-2/neu in node-negative breast cancer depends strongly on the method of testing: only the amplification of HER-2/neu is an independent prognostic factor for the long-term prognosis of untreated node-negative breast cancer.
Abbreviations
CI = confidence interval; DFS = disease-free survival; FISH = fluorescence in situ hybridisation; IHC = immunohistochemistry; OS = overall survival; RR = relative risk.
Competing interests
The author(s) declare that they have no competing interests.
Authors'contributions
MS planned the study, performed the IHC and drafted the manuscript. BL performed the in situ hybridisation. CG participated in the statistical analysis. NK participated in the in situ hybridisation. ES, HP, BT and WW participated in defining the study cohort and participated in the IHC. HAL and HK participated in finally drafting the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Mrs Christa Krebs and Mrs Christa Reue for helpful assistance and valuable support.
Figures and Tables
Figure 1 Effect of histological grade, HER2_SCO, HER2_AMP and HER2_COM on disease-free survival. (a) Grade; (b) HER2_SCO; (c) HER2_AMP; (d) HER2_COM.
Figure 2 Effect of histological grade, steroid hormone receptor status, HER2_SCO, HER2_AMP and HER2_COM on overall survival. (a) Grade; (b) steroid hormone receptor status; (c) HER2_SCO; (d) HER2_AMP; (e) HER2_COM.
Table 1 Clinicopathological characteristics of 101 patients with node-negative primary breast cancer
Characteristic n %
Age at diagnosis
Less than 50 36 36
More than 50 65 64
Menopausal status
Pre/perimenopausal 36 36
Postmenopausal 65 64
pT stage
pT1a,b 16 16
pT1c 42 42
pT2 43 43
Histological grade
GI 17 17
GII 59 58
GIII 20 20
Not done (medullary carcinomas) 5 5
Steroid hormone receptor status
Positive 65 64
Negative 36 36
HER2_SCO
0, 1+ 63 62
2+ 18 18
3+ 20 20
HER2_AMP
Positive 17 17
Negative 84 83
HER2_ALG
Positive 22 22
Negative 79 78
HER2_COM
Positive 11 11
Negative 90 89
Table 2 Univariate analysis for breast cancer-specific disease-free survival
Prognostic factor P RR 95% CI
Age n.s. - -
Menopausal status n.s. - -
pT stage n.s. - -
Grade 0.0002 3.35 1.77–5.99
Steroid hormone receptor status n.s. - -
HER2_SCO n.s.
HER2_AMP 0.004 3.07 1.44–6.57
HER2_ALG n.s. - -
HER2_COM 0.006 3.27 1.40–7.65
CI, confidence interval; n.s., not significant; RR, relative risk.
Table 3 Multivariate analysis for breast cancer-specific disease-free survival
Prognostic factor P RR 95% CI
Age n.s. - -
Menopausal status n.s. - -
pT stage n.s. - -
Grade 0.0002 3.22 1.73–5.99
Steroid hormone receptor status n.s. - -
HER2_SCO n.s. - -
HER2_AMP 0.026 2.47 1.12–5.48
HER2_ALG n.s. - -
HER2_COM n.s. - -
CI, confidence interval; n.s., not significant; RR, relative risk.
Table 4 Univariate analysis for breast cancer-specific overall survival
Prognostic factor P RR 95% CI
Age n.s. - -
Menopausal status n.s. - -
pT stage n.s. - -
Grade 0.0003 4.26 1.96–9.29
Steroid hormone receptor status 0.012 0.32 0.13–0.78
HER2_SCO 0.047 1.60 1.01–2.53
HER2_AMP 0.004 3.78 1.54–9.26
HER2_ALG n.s. - -
HER2_COM 0.004 4.18 1.60–10.90
CI, confidence interval; n.s., not significant; RR, relative risk.
Table 5 Multivariate analysis for breast cancer-specific overall survival
Prognostic factor P RR 95% CI
Age n.s. - -
Menopausal status n.s. - -
pT stage n.s. - -
Grade 0.0007 3.89 1.77–8.55
Steroid hormone receptor status n.s. - -
HER2_SCO n.s. - -
HER2_AMP 0.016 3.08 1.24–7.66
HER2_ALG n.s. - -
HER2_COM n.s. - -
CI, confidence interval; n.s., not significant; RR, relative risk.
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| 15743506 | PMC1064139 | CC BY | 2021-01-04 16:04:32 | no | Breast Cancer Res. 2005 Jan 26; 7(2):R256-R266 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr991 | oa_comm |
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Breast Cancer ResBreast Cancer Research1465-54111465-542XBioMed Central London bcr9961574350710.1186/bcr996Research ArticlePolysomy of chromosome 17 in breast cancer tumors showing an overexpression of ERBB2: a study of 175 cases using fluorescence in situ hybridization and immunohistochemistry Salido Marta [email protected] Ignasi [email protected] Josep M [email protected] Marta [email protected] Blanca [email protected] Cristina [email protected] Meritxell [email protected] Xavier [email protected] Sergi [email protected]é Francesc [email protected] Laboratori de Citogenètica i Biologia Molecular, Servei de Patologia, Hospital del Mar, IMAS, Barcelona, Spain2 Escola de Citologia Hematològica S Woessner-IMAS, Hospital del Mar, IMAS-IMIM, Barcelona, Spain3 Unitat de Recerca translacional en tumors sòlids-IMAS, Barcelona, Spain4 Servei d'Oncologia Mèdica, Hospital del Mar, IMAS, Barcelona, Spain5 Universitat Autònoma de Barcelona, Barcelona, Spain6 Servei de Patologia, Hospital del Mar, IMAS, Barcelona, Spain2005 26 1 2005 7 2 R267 R273 19 8 2004 19 10 2004 8 11 2004 5 1 2005 Copyright © 2005 Salido et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Introduction
One of the most common genetic aberrations associated with breast cancer is the amplification and overexpression of the ERBB2 proto-oncogene located at chromosome 17, bands q12-21. The amplification/overexpression occurs in 25 to 30% of all breast cancers. In breast cancer, aneusomy of chromosome 17, either monosomy or polysomy, is frequently observed by conventional cytogenetics and fluorescence in situ hybridization (FISH). The aim of this study was to discover whether or not numerical aberrations on chromosome 17 have a correlation to the amplification or overexpression of the ERBB2 gene and to analyze their clinical implications in subgroups showing 2+ or 3+ positive scores by immunohistochemistry (IHC).
Methods
We used FISH on a series of 175 formalin-fixed paraffin-embedded breast carcinomas to detect ERBB2 amplification, using a dual-probe system for the simultaneous enumeration of the ERBB2 gene and the centromeric region of chromosome 17, as well as using IHC to detect overexpression. We analyzed clinical and pathological variables in a subgroup of patients with 2+ and 3+ IHC scores (147 patients), to describe any differences in clinicopathological characteristics between polysomic and non-polysomic cases with the use of the χ2 test.
Results
We found 13% of cases presenting polysomy, and three cases presented monosomy 17 (2%). According to the status of the ERBB2 gene, instances of polysomy 17 were more frequently observed in non-amplified cases than in FISH-amplified cases, suggesting that the mechanism for ERBB2 amplification is independent of polysomy 17. Polysomy 17 was detected in patients with 2+ and 3+ IHC scores. We found that nodal involvement was more frequent in polysomic than in non-polysomic cases (P = 0.046).
Conclusions
The determination of the copy number of chromosome 17 should be incorporated into the assesment of ERBB2 status. It might also be helpful to differentiate a subgroup of breast cancer patients with polysomy of chromosome 17 and overexpression of ERBB2 protein that probably have genetic and clinical differences.
breast cancerERBB2 genefluorescence in situ hybridizationimmunohistochemistrypolysomy 17
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Introduction
Proto-oncogenes and tumor suppressor genes are two classes of genes with central roles in the regulation of cell growth. One of the most common genetic alterations associated with human breast cancer is the amplification of the ERBB2 proto-oncogene [1]. The ERBB2 gene located on 17q12-q21 encodes a 185 kDa transmembrane tyrosine kinase receptor [2,3]. This protein is a member of the epidermal growth factor receptor family [4] that comprises four homologous receptors: HER1 (ERBB1), ERBB2 (ERBB2), HER3 and HER4. These receptors are involved in the activation of complex signaling pathways, essential for cell survival and for the regulation of normal breast growth and development [5-7]. Several studies performed in various laboratories have demonstrated that 25 to 30% of all breast and ovarian malignancies show the amplification and overexpression of this gene [8,9]. Amplification of the ERBB2 gene is found in more than 90% of cases that have ERBB2 protein overexpression [10,11], but in normal breast the expression of ERBB2 is due to a transcriptional activation [12]. ERBB2 overexpression in women with both node-positive [8,9] and node-negative [13] breast cancer is associated with a poor prognosis, and several studies have found a correlation between ERBB2 overexpression and a shorter disease-free period and shorter overall survival [14,15].
ERBB2 overexpression and/or gene amplification is an indication for trastuzumab (Herceptin; Genentech, South San Francisco, CA, USA) therapy in patients with metastatic breast cancer [16,17]. Clinical trials combining trastuzumab and chemotherapy have been initiated, based on preclinical data about potentially enhanced anti-tumor activity when anti-ERBB2 antibodies were combined with chemotherapeutic agents [18-20].
There are different methods available to evaluate ERBB2 status [21], although immunohistochemistry (IHC; for protein overexpression) and fluorescence in situ hybridization (FISH; for gene amplification) offer several advantages, because the aberration can be evaluated directly in malignant cells taken from archival breast cancer specimens. Reports of false-positive Herceptest cases led to suggestions that Herceptests yielding a 2+ score should also be studied by FISH [22]. Tubbs and colleagues [23], called for the US Food and Drug Administration (FDA) to mandate the retraction of the earlier accepted criteria for trastuzumab therapy, namely that of Herceptests yielding a 2+ score, unless those cases were also confirmed by FISH.
The FDA-approved FISH assay, PathVysion (Vysis, Inc., Downers Grove, IL, USA), is a dual-probe system for the simultaneous enumeration of the ERBB2 gene and the centromeric region of chromosome 17, defining ERBB2 amplification as a ratio of ERBB2 gene copies per chromosome 17 centromere [24]. Another FDA-approved FISH assay, INFORM (Ventana Medical systems, Tucson, AZ, USA), defines ERBB2 amplification as a mean absolute ERBB2 gene copy number of more than four spots per nucleus, without centromere 17 correction. FISH with the PathVysion probe and immunohistochemical assay with Herceptest are highly concordant for cases showing 3+ IHC scores and for negative cases, although the 2+ IHC score group includes both ERBB2 amplified and non-amplified tumors, showing a relatively high rate of discordance [25,26]. The mechanisms for ERBB2 expression in non-amplified tumors scored 2+ by IHC are unclear and may involve increased gene dosage by chromosome 17 polysomy [27].
In breast carcinoma, chromosomal aneusomy – either monosomy or polysomy – is frequently observed by conventional cytogenetics and FISH [28,29]. Sauer and colleagues [30,31], found that an abnormal number of copies of chromosome 17 have a low impact on ERBB2 gene and its expression, but more studies are necessary to confirm these results.
The aim of our study was to analyze the role of polysomy 17 in ERBB2 protein expression and its implication in ERBB2 gene status in a series of patients with tumors scoring 2+/3+ by IHC.
Materials and methods
Patients' characteristics
This is a prospective analysis of 175 breast cancer patients, consecutively treated at the Hospital del Mar of Barcelona between August 2000 and August 2003.
Specimens
We studied specimens taken from 175 prospective cases of human breast cancer. Overexpression was determined by IHC, and amplification was studied by FISH.
The breast cancer specimens used in this study were fixed with 4% buffered formalin, and the tissue was then embedded in paraffin. Sections 4 to 6 μm thick were cut from the tissue, mounted on silanized slides and then deparaffinized in a xylene series, followed by immersion in 100% ethanol. Then we serially sectioned a hematoxylin/eosin (H&E)-stained tissue section, an IHC tissue section and a FISH tissue section from each of the patient samples.
IHC assay
This method involved the application of primary ERBB2 antibody (rabbit anti-human ERBB2 oncoprotein; DakoCytomation, Glostrup, Denmark) at 1:200 dilution. This antibody was evaluated with the dextran/peroxidase technique (Dako Envision). We used negative and positive controls for ERBB2 overexpression in all samples. The IHC technique was applied with an automated system (Tech Mate 500) after antigen retrieval at 110°C for 1 min in a wet autoclave. When FDA approved the Herceptest kit (DakoCytomation) as a diagnostic method to detect ERBB2 overexpression, we began to use it. As a single institution, we observed an excellent correlation between these two IHC detection systems.
Membrane staining was interpreted as ERBB2 oncoprotein expression. To evaluate the immunostaining for ERBB2 antibody, we considered the intensity and type of membrane expression. Expression was recorded as follows: score 0, negative staining; score 1, local positivity and incomplete membrane staining; score 2, moderate, complete membrane staining; score 3, strong, complete membrane staining.
FISH assay
The Pathvysion probe was used. This probe consists of two different probes, one with the centromeric α-satellite probe, specific for chromosome 17 (Spectrum green), and a locus-specific probe from the ERBB2 gene (Spectrum orange). The probe was provided denatured, in single-stranded DNA.
Deparaffinized tissue sections were treated with 0.2 M HCl and then with sodium thiocyanate, to eliminate salt precipitates. Pretreated slides were incubated for 10 min in a solution of proteinase K at 37°C. The slides were then postfixed in buffered formalin.
Pretreated tissue sections and probes were denatured at 78°C for 5 min and hybridized overnight at 37°C on a hotplate (Hybrite chamber; Vysis). Washes were performed for 2 min at 72°C in a solution of 2 × SSC/0.3% Nonidet P40. Tissue sections were counterstained with 10 μl of 4,6-diamino-2-phenylindole (DAPI counterstain; Vysis).
Results were analyzed in a fluorescent microscope (Olympus BX51) with the Cytovysion software (Applied Imaging, Santa Clara, CA, USA). Tissue sections were scanned at low magnification (×100) with DAPI excitation to localize those areas where histopathological characteristics had been established by examining a serially sectioned H&E-stained tissue section from the same patient.
ERBB2 amplification was calculated by a ratio dividing the most frequent value for ERBB2 spots per nucleus by the most frequent value of chromosome 17 centromere spots per nucleus. A minimum of 60 nuclei were scored. Amplification of ERBB2 gene was considered when the ratio was 2 or more, in accordance with the manufacturer's recommended scoring system. We considered polysomy 17 when the cells had three or more copy numbers of centromeres for chromosome 17 per cell.
Statistical analysis
A total of 147 patients with 2+ and 3+ IHC scores out of 175 of the complete cohort were analyzed, ensuring that all the polysomies of chromosome 17 were detected in this subgroup of patients. Table 1 describes the following clinicopathological parameters: clinical stage, nodal status, histological grade, estrogen receptor (ER), progesterone receptor (PgR), p53 protein and relapse of the disease, and age description. We performed a concordance analysis using the χ2 test between prognostic variables and the presence or absence of polysomy 17 to detect any differences between polysomic and non-polysomic subgroups.
Results
To validate the FISH technique, in a first screening step we performed FISH on 50 specimens that represented all of the immunohistochemical subgroups (0, 1+, 2+ and 3+). For subsequent specimens we performed FISH on 2+ and 3+ IHC subgroups. The exclusion of the subgroups with scores of 0 and 1+ was based on our previous finding that none of those cases had ERBB2 gene amplification [25]. This selection process resulted in the under-representation of IHC-negative and 1+ specimens for the FISH assay and a high proportion of 2+/3+ cases.
Among the 175 specimens studied by IHC and FISH, 22 cases showed polysomy of chromosome 17 (13%), and three cases presented monosomy (2%) (Table 2). Of the 175 cases, 147 showed an IHC score of 2+ or 3+. The distribution of chromosome 17 copy numbers in this 2+/3+ subgroup is illustrated in Fig. 1. Most of the cases with polysomy 17 had four copies of chromosome 17 per cell.
We observed that all cases with polysomy of chromosome 17 had an IHC score of 2+ or 3+. Thirteen of 22 cases with polysomy revealed an IHC score of 2+, and the remaining 9 cases had an IHC score of 3+. In a subgroup of 78 patients having IHC scores of 2+, 13 of 78 (17%) showed polysomy of chromosome 17 with increased ERBB2 gene copies, but without gene amplification. In a subgroup having IHC scores of 3+, 9 of 69 cases (15%) presented polysomy 17. In patients with monosomy of chromosome 17, two cases presented IHC scores of 2+, and one presented an IHC score of 3+, without ERBB2 gene amplification.
According to ERBB2 gene status, all cases with polysomy and 2+ IHC scores were considered to be normal for ERBB2 gene amplification (the ratio was 2 or less). Of the cases considered normal by FISH, 15% presented polysomy 17, and 10% of the amplified cases showed amplification and polysomy 17 simultaneously (Table 2). Within the subgroup having scores of 3+ and polysomy, only one case presented non-amplification of the ERBB2 gene.
We compared the polysomic subgroup with the non-polysomic subgroup, to determine whether there were differences in the clinicopathological features. After clinical analysis of the 147 patients who were 2+/3+ by IHC, we found that nodal involvement was significantly associated with polysomy 17 (P = 0.046). Furthermore, patients with polysomy 17 also showed a non-statistical trend toward relapse (P = 0.181). No statistically significant correlation was observed between histological grade, ER, PgR and p53 variables and the polysomy of chromosome 17 (Table 3).
Discussion
In our study, 175 cases diagnosed as invasive breast cancer were examined to determine the frequency of chromosome 17 polysomy in different ERBB2 IHC subgroups. In our series we found 22 specimens with polysomy 17 (13%) and three cases with monosomy 17 (2%). In a series from Wang and colleagues [31], aneusomy was very common (more than 50%), but only 10 of 189 cases (5%) showed high polysomy (at least 3.76 signals of centromere 17 per cell). Series from Watters and colleagues [32] presented a high proportion of aneusomy 17 (54%); most of those presented polysomy, but the highest proportion fell into the 2.00 to 3.00 chromosome 17 copy numbers category. In those two series [31,32], they considered polysomy with a mean chromosome copy number near to disomy. The reason for the discordance with our series (13% versus 50%) could be explained by the consideration of polysomy 17 when the cells had three or more copy numbers of centromeres for chromosome 17 per cell, in agreement with other authors [22,26,27,29].
Cases with polysomy 17 could be considered to be amplified at a low level by absolute criteria, but they had to be classified as non-amplified when the number of ERBB2 copies was corrected for the number of chromosome 17 centromeres. FISH, using the dual probe for the ERBB2 gene and the centromere of chromosome 17, offers the greatest resolution in detecting alterations of the ERBB2 gene in breast tumors because it allows true amplification to be differentiated from polysomy 17. The FISH method is less affected by tissue variables than the IHC method, and it has emerged as the gold standard for the assessment of ERBB2 status in breast cancer.
We observed polysomy 17 in non-amplified cases more frequently than in FISH amplified cases (15% versus 10%), suggesting that the mechanism for amplification of the ERBB2 gene is independent of polysomy.
In our series, polysomy 17 was frequently associated with a 2+ and 3+ IHC score. We had 13 of 22 polysomic cases that showed IHC 2+ scores with FISH-negative results. In contrast, other authors [33,34] found that the incidence of polysomy 17 in 2+ non-amplified cases was similar to the incidence of polysomy 17 in IHC-negative cases, suggesting that weak overexpression (2+) without gene amplification is not secondary to chromosome 17 polysomy. Our findings support the concept that polysomy 17 is a genetic aberration independent of IHC status but that it might have a role in ERBB2 expression in weakly positive (2+) and in strongly positive (3+) cases. It is probable that there are non-amplified cases analyzed by FISH with false positive IHC (2+) scores as a result of polysomy 17. Polysomy 17, in the absence of ERBB2 amplification, would result in an increase in protein production to such level that it might be IHC stained as a positive result (2+).
We identified only one such case, IHC scored at 3+, non-amplified by FISH, and showing polysomy 17. It is reasonable that the copy number of chromosome 17 was associated with an increased level of ERBB2 protein but not with ERBB2 gene amplification. With regard to this case, we are in agreement with Lal and colleagues [33] and Varshney and colleagues [34], who suggested that the findings of strongly positive IHC staining (3+) without gene amplification might be due, in part, to the increased copy numbers of chromosome 17, resulting in ERBB2 overexpression.
In our study of 147 patients having tumors with 2+/3+ IHC scores, we found a statistical association between polysomy 17 and nodal involvement, and we observed that this subgroup of patients had a trend towards relapse more frequently than the non-polysomic cases. Only a few studies have focused on the analysis of the prognostic value of chromosome 17 aneusomy, and specifically on polysomy 17. In a large series from Watters and colleagues [32], abnormalities in chromosome 17 copy numbers were associated with grade III carcinomas and ER negativity, but they were without significant impact on survival. Other studies proposed that aneusomy of chromosome 17 is associated with poor prognostic factors in breast cancer [30].
Conclusions
Breast tumors scoring at 2+ or 3+ by IHC should be analyzed by FISH for ERBB2, preferably with a dual-probe system capable of detecting aberrations in chromosome 17 that could affect ERBB2 expression. The accurate measurement of chromosome 17 polysomy might also be helpful in determining subgroups of patients with genetic and clinical differences, which would permit their inclusion in future clinical trials.
Abbreviations
ER = estrogen receptor; FDA = US Food and Drug Administration; FISH = fluorescence in situ hybridization; H&E = hematoxylin/eosin; IHC = immunohistochemistry; PgR = progesterone receptor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MS conceived and designed the study, performed FISH analysis, interpretated the data and drafted the article. IT coordinated the study and was significantly involved in patient recruitment and the correlation of clinical data with experimental findings, also taking a role in supervising and final approval of the article. JMC contributed to the design of the study, analysed the immunohistochemistry experiments providing several data presented in the publication, and took a role in supervising and final approval of the article. MS contributed to the analysis of the data and was involved in patient recruitment. CC contributed to analysis and interpretation of the data and revised the article. BE contributed to interpretation of the data and revised the article. MB contributed to the recruitment of patients and revised the paper. XF is the head of the Oncology Department of Hospital del Mar de Barcelona. FS revised the paper, giving final approval of the version to be submitted, and is the head of the Cytogenetics Laboratory of Hospital del Mar de Barcelona. SS is the head of the Pathology Department of Hospital del Mar de Barcelona. All authors read and approved the final manuscript.
Acknowledgments
We thank MC Vela, C Melero, T Baró and P Garcia for their excellent technical assistance, and Juan Vila, who performed statistical analysis. This study was made possible by grant FIS/02/0002 from the Ministerio de Sanidad y Consumo, grant C3/10, a grant from the Instituto de Salud Carlos III and support from Roche-Pharma (Spain).
Figures and Tables
Figure 1 Distribution pattern of polysomy 17 in our series of 147 patients studied by fluorescence in situ hybridization.
Table 1 Correlation of chromosomal aneusomy with clinicopathological parameters
Parameter Total Disomy Polysomy Monosomy
Stage
I 39 36 3 0
II 46 35 11 0
III 35 28 5 2
IV 7 6 0 1
Nodal status
pN- 35 29 5 1
pN+ 113 99 12 2
HG
I 16 15 1 0
II 55 42 12 1
III 47 39 6 2
ER
- 60 50 9 1
+ 85 71 12 2
PgR
- 87 72 12 3
+ 58 48 10 0
p53
- 87 72 14 1
+ 56 8 46 2
Relapse
No 110 94 14 2
Yes 36 27 8 1
The mean age of patients was 62 years (range 30 to 94). ER, estrogen receptor; HG, histological grade; PgR, progesterone receptor.
Table 2 Polysomy 17 according to immunohistochemical scoring and fluorescence in situ hybridization analysis
Polysomy 17 Non-polysomy 17
IHC score FISH normal FISH amplified Polysomy 17 (%) FISH normal FISH amplified
0 0/8 0/0 0 8/8 0/0
1+ 0/20 0/0 0 20/20 0/0
2+ 13/66 0/12 17 53/66 12/12
3+ 1/1 8/68 15 0/1 60/68
Total 14/95 (15%) 8/80 (10%) 13 81/95 (85%) 72/80 (90%)
FISH, fluorescence in situ hybridization; IHC, immunohistochemistry.
Table 3 Concordance analysis using the χ2 test between the presence and absence of polysomy 17 and clinicopathological variables in a subgroup of 2+/3+ IHC patients
Parameter Non-polysomy (%) Polysomy (%) P
HG
I 15.6 5.3
II 43.8 63.2 0.243
III 40.6 31.6
Nodal status
pN- 61.3 35.3 0.046
pN+ 38.7 64.7
ER
- 41.7 40.9 0.947
+ 58.3 59.1
PgR
- 60 54.5 0.632
+ 40 45.5
p53
- 61 63.6 0.817
+ 39 36.4
Relapse
No 77 63.6 0.181
Yes 23 36.4
ER, estrogen receptor; HG, histological grade; PgR, progesterone receptor.
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| 15743507 | PMC1064140 | CC BY | 2021-01-04 16:54:49 | no | Breast Cancer Res. 2005 Jan 26; 7(2):R267-R273 | utf-8 | Breast Cancer Res | 2,005 | 10.1186/bcr996 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1575736510.1371/journal.pbio.0030087Research ArticleCell BiologyDevelopmentCardiology/Cardiac SurgeryMus (Mouse)Adult Murine Skeletal Muscle Contains Cells That Can Differentiate into Beating Cardiomyocytes In Vitro Cardiomyocytes from Skeletal Stem CellsWinitsky Steve O
1
Gopal Thiru V
1
Hassanzadeh Shahin
1
Takahashi Hiroshi
1
Gryder Divina
2
Rogawski Michael A
2
Takeda Kazuyo
3
Yu Zu X
4
Xu Yu H
5
Epstein Neal D [email protected]
1
1Molecular Physiology Section, Laboratory of Molecular CardiologyNational Heart, Lung, and Blood Institute, Bethesda, MarylandUnited States of America2Epilepsy Research Section, National Institute of Neurological Disorders and StrokeBethesda, MarylandUnited States of America3Laboratory of Molecular Cardiology, National HeartLung, and Blood Institute, Bethesda, MarylandUnited States of America4Pathology Section, National HeartLung, and Blood Institute, Bethesda, MarylandUnited States of America5Electron Microscopy Core Facility, National HeartLung, and Blood Institute, Bethesda, MarylandUnited States of AmericaChien Kenneth R. Academic EditorUniversity of California at San DiegoUnited States of America4 2005 15 3 2005 15 3 2005 3 4 e8723 12 2004 6 1 2005 Copyright: © 2005 Winitsky et al.2005This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Heart Repair Gets New Muscle
Alchemy and the New Age of Cardiac Muscle Cell Biology
It has long been held as scientific fact that soon after birth, cardiomyocytes cease dividing, thus explaining the limited restoration of cardiac function after a heart attack. Recent demonstrations of cardiac myocyte differentiation observed in vitro or after in vivo transplantation of adult stem cells from blood, fat, skeletal muscle, or heart have challenged this view. Analysis of these studies has been complicated by the large disparity in the magnitude of effects seen by different groups and obscured by the recently appreciated process of in vivo stem-cell fusion. We now show a novel population of nonsatellite cells in adult murine skeletal muscle that progress under standard primary cell-culture conditions to autonomously beating cardiomyocytes. Their differentiation into beating cardiomyocytes is characterized here by video microscopy, confocal-detected calcium transients, electron microscopy, immunofluorescent cardiac-specific markers, and single-cell patch recordings of cardiac action potentials. Within 2 d after tail-vein injection of these marked cells into a mouse model of acute infarction, the marked cells are visible in the heart. By 6 d they begin to differentiate without fusing to recipient cardiac cells. Three months later, the tagged cells are visible as striated heart muscle restricted to the region of the cardiac infarct.
A population of primitive cells from adult murine skeletal muscle can develop into beating cardiomyocytes in vitro and can contribute to the repair of damaged heart in vivo
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Introduction
The difficulty in recovery of cardiac function after cardiomyocyte death, such as occurs with a heart attack, contrasts with injury to skeletal muscle in which myocyte numbers can increase through the recruitment of new myocytes from a local stem-cell pool called satellite cells. At present, cardiac transplant, with the intrinsic limitations of supply, immunosuppression, and organ rejection, remains the only long-term treatment for irreversible cardiac failure. Injection of fetal or embryonic stem cells into infarcted hearts holds some promise [1,2,3] but is complicated by the potential for immunologic rejection as well as by political and ethical considerations. Cell plasticity observed after in vivo transplantation of a variety of cell lineages [4,5,6,7] or after in vitro transformation with 5-azacytidine [8] has encouraged the study of cell-based therapy. Investigators have identified endogenous cardiomyocyte proliferation [9] and have experimented with skeletal myoblasts as well as with adult stem cells isolated from blood or heart to try to repair cardiac damage [9,10,11,12,13,14,15].
In vivo analysis of stem-cell transplantation studies has become complicated in part because of a growing understanding of the process of in vivo donor stem-cell and recipient mature-cell fusion [16,17,18]. Recent studies have shown a small population of cells in murine fat that progress to beating cells in vitro [19]. Three independent groups have isolated stem cells from the heart that show a capacity to differentiate into cardiomyocytes in vivo [14,15,20]. All three cell types are extensively passaged, after which two of these can be pushed to differentiate in vitro.
In evaluating a variety of primary cell cultures of different lineages, we noticed nonadherent cells isolated from adult murine skeletal muscle that become beating floating cells. We named them “Spoc” cells (skeletal-based precursors of cardiomyocytes) for their ability to generate beating cardiomyocytes in vitro. These cells, easily obtained from skeletal muscle, provide new options for cardiac research and therapeutic interventions. When injected systemically into an acutely infarcted mouse, they migrate to the cardiac infarct region and differentiate into cardiac myocytes.
Results/Discussion
Characterization of Spoc Cells
At initial isolation, Spoc cells are CD34− and CD45− (data not shown). Dissociation of 10 g of leg muscle yields approximately 2 × 106 Spoc cells. C-kit+ cells, comprising less than 1% of Spoc cells, are present after isolation but can be removed by affinity purification without any change in the subsequent results (data not shown). Electron microscopy (EM) shows that on the day of isolation, Spoc cells are 4–8 μm in diameter, often smaller than a red blood cell, and display copious vesicles suggestive of active transport. Many lamellipodia and filopodia are present, which may in part explain the typical Velcro-like clumping of the cells that makes chemical and physical dispersion more difficult (Figure 1). The CD34−/CD45−/c-kit− phenotype differentiates Spoc cells from other nonadherent cells previously described as derived from skeletal muscle [5]. Spoc cells are distinguished from satellite cells by the following criteria: (1) Spoc cells do not express Pax-7 [21] or the surface marker c-met [22]; (2) approximately the same number of Spoc cells are isolated from both young (less than 4-wk-old) and older (12- to 16-wk-old) mice, whereas satellite cells are difficult to isolate from normal mouse muscle after 8 wk of age without first inducing muscle injury [21]; (3) at isolation, Spoc cells remain round floating cells approximately 4 μm in diameter, whereas isolated satellite cells soon become adherent; (4) Spoc cells are CD34− and Myf-5−, excluding them from the class of quiescent satellite cells [23]; and (5) the three skeletal muscle super-regulatory genes myf5, myoD, and myogenin are known to be present at some time in satellite cells [22], but in Spoc cells, all three markers remain negative from the day of isolation throughout long-term culture (data not shown). Thus, Spoc cells are neither satellite cells nor further-differentiated skeletal muscle cells.
Figure 1 EMs of Day 0 Spoc Cells
Electron micrograph of a freshly isolated Spoc cell (A) shows its small size relative to the typical biconcave profile of the red blood cell above it (arrow). (B) shows a typical Spoc cell with a large number of vesicular structures, lamellipodia, and filopodia. (C) displays three Spoc cells so tightly clumped that their borders are difficult to distinguish. This typical clumping makes fluorescence-activated cell sorting (FACS) analysis difficult.
During the first 7 d in medium containing epidermal growth factor (EGF) and fibroblast growth factor (FGF), Spoc cells undergo several rounds of division, begin to express
GATA-4 (a mostly cardiac-specific transcription factor), and become clusters of floating round cardiac precursors from Spoc (CPS) cells with an increased diameter of 10–14 μm. The pattern of
GATA-4 staining in these cells is unusual in that it is predominantly cytoplasmic, which is distinct from the expected nuclear staining of a transcription factor. Although rare, many transcription factors are well known to have a cytoplasmic phase.
GATA-4 has been described similarly and in this case has been shown to move into the nucleus after the addition of the beta-adrenergic drug isoproterenol [24,25]. For these reasons, isoproterenol was added to a culture of CPS cells for 1 h, after which
GATA-4 staining was observed in the nucleus of many of the cells (Figure 2). These cells go on to express other cardiac-specific markers, including cardiac troponin-T, Nkx-2.5, MLC-2v, and a cardiac L-type calcium channel, detected either by immunostaining or real-time PCR (Figure 3A–3G; real-time PCR data not shown). Although Nkx-2.5 may be present in vascular smooth muscle, neither alpha- nor beta-myosin is present in smooth muscle. Beating cardiomyocytes derived from Spoc cells express both alpha- and beta-myosin at day 28, as shown by staining with polyclonal antibodies against alpha- and beta-myosin (Figure S1). In later stages, connexin 43 is expressed in cell clusters (Figure 3H–3I). Even before they become adherent, some isolated cells begin to beat (Video S1).
Figure 2 Sublocalization of
GATA-4 in Spoc Cells
(A)
GATA-4 is detected in the cytoplasm of day 10 Spoc cells (cytospin).
(B) Nomarski image of (A), showing DAPI-stained blue nuclei.
(C) Merge image showing nuclei and
GATA-4 staining.
(D) When Spoc cells are incubated with 20 μM isoproterenol for 1 h, the
GATA-4 nuclear staining is seen.
(E) Nomarski image of (D).
(F) Merge of (D) and (E), showing sublocalization of
GATA-4 to nuclei. A weaker
GATA-4 signal is present in the cytoplasm.
Figure 3 CPS Cells Stain Positive for Cardiac-Specific Proteins
(A)
GATA-4 in day 7 CPS cells.
(B) Nuclear staining with DAPI.
(C) Overlay of (A) and (B).
(D) Nkx-2.5 is detected in the nuclei of round, day 21 beating cells (green).
(E) Noncardiac cells (red arrowheads) do not show nuclear staining for Nkx-2.5.
(F) Overlay of (D) and (E).
(G) Beating cells, after 28 d in culture, stain positive for cardiac L-type Ca++ channel.
(H) Connexin 43 (green) in cluster of uninucleate day 21 beating cells in culture.
(I) Nomarski light micrograph (differential interference contrast) of cell cluster in (H).
Sca-1 Separation of Spoc Cells
Further fractionation of Spoc cells by sorting for the Sca-1 marker (a cell-surface antigen found on murine hematopoietic stem cells [HSCs]) shows that the beating cells develop out of the Sca-1− pool, which comprises 20%–40% of the total isolated cells. This is in contrast to recent studies in which an isolated Sca-1+ population from the heart homed to the heart after vascular injection and showed signs of cardiac differentiation [14,26]. When the skeletal muscle–derived Sca-1+ fraction of Spoc cells are plated under our conditions (see Materials and Methods) separately from the Sca-1− fraction, the former cells rapidly adhere to the plate and, with the exception of a few putative contaminating Sca-1− cells, do not develop into beating cells. With the Sca-1+ population removed, the remaining Sca-1− population undergoes additional divisions before beginning to differentiate. Occasionally, a small, round beating cell with no organized sarcomere is seen after 3 d, but approximately 80% of the total population of cells differentiates into beating cells after a 7- to 10-d proliferative period. These Sca-1−–derived CPS cells remain in this immature state, i.e., round, loosely attached, and spontaneously beating, for more than 2 mo of culture. When a green fluorescent protein (GFP)–tagged Sca-1− population of CPS cells is replated onto a nontagged adherent monolayer of the purified Sca-1+ cells, the fluorescent Sca-1− cells adhere to the monolayer within 24 h, stretch out, and mature into beating cells. As the stretched-out GFP+ cells develop a sarcomeric structure, the GFP signal diminishes. This phenomenon, which is possibly due to a down-regulation of the beta-actin promoter-driven GFP, or to the exclusion of GFP protein from the developing sarcoplasm, may compromise the detection of GFP-tagged donor cells in the mouse infarct model.
Spoc Cells Are Not Derived from Bone Marrow or Adipose Tissue
Spoc cells do not appear to be bone marrow cells sequestered in skeletal muscle, and because they are c-kit−, they are distinguished from the c-kit+ bone marrow cells and side population cells that have been used directly or indirectly in experiments to reconstitute infarcted heart [11,13]. When approximately 50 mg of whole marrow or dissociated total heart is co-cultured in permeable-membrane-separated compartments in a 1:1 ratio with Spoc cells, only the Spoc cells develop into beating cardiomyocytes. Thus, bone marrow and heart do not contain a cell population that is isolatable in this manner and phenotypically similar to Spoc cells.
We also performed experiments to determine whether Spoc cells are similar to adipocyte-derived stroma vascular fraction cells (SVFs), which have been derived from a mixture of murine inguinal and intrascapular fat pads [19]. Spoc cells were cultured in parallel with SVFs in our EGF/FGF–containing medium, as well as in methocult supplemented with beta-mercaptoethanol, erythropoietin, Interleukin (IL)–3, IL-6, and Stem Cell Factor, as outlined in [19], except that inguinal- and intrascapular-derived cells were kept separate from each other. In the supplemented methocult culture, Spoc cells developed into CPS cells, which subsequently fused into large (up to 1 mm in length) beating myocytes as described earlier [19]. We were not able to generate CPS cells, or any beating cells, from SVFs derived from inguinal fat alone. A result similar to culturing Spoc cells was observed with intrascapular-derived SVFs cultured in supplemented methocult, but only in cultures that had microscopic contamination with skeletal muscle fibers. Neither SVFs from the inguinal nor from the intrascapular fat pads yielded any beating cells when cultured under standard EGF/FGF Spoc cell conditions. Planat-Benard et al. [19] report an efficiency of conversion to cardiomyocytes of 0.02%–0.07%, whereas 1%–10% of total Spoc cells become beating cardiomyocytes. When pure Sca-1− Spoc cells are plated, after the proliferation phase, approximately 80% of the cells are beating CPS cells.
Unlike the case with HSCs, no colony-forming units are generated when Spoc cells are cultured in methylcellulose in the presence of erythropoietin, IL-3, IL-6, and Stem Cell Factor (data not shown). To evaluate the ability of Spoc cells to reconstitute bone marrow, standard and competitive bone marrow transplantations with Spoc cells were performed. In four mice, 3 × 106 bone marrow cells and 3 × 104 GFP +/Sca-1− Spoc cells were injected into each of the lethally irradiated mice. All four mice survived; however, only a rare GFP+ donor–derived cell was seen in the peripheral blood or bone marrow (data not shown). The marrow of six lethally irradiated mice, injected with either 1.5 × 105 Sca-1− Spoc cells or 2 × 105 Spoc cells unfractionated for Sca-1, could not be rescued, and all six mice died within 2 wk. Thus, because only a single HSC is required to reconstitute bone marrow, no HSCs are contained in 2 × 105 Spoc cells.
Transmission EM of CPS Cells as They Progress to Beating Cells
Figure 4 shows the post-replating progression of CPS cells. Three days after the replating of floating cells (see Materials and Methods), when increasing numbers of CPS cells show rhythmic beating, EM shows round cells with large central nuclei surrounded by copious mitochondria and myosin thick filaments (Figure 4A and 4D). As the cells attach to the tissue culture plate, the central nucleus elongates and electron-dense structures are visible (Figure 4B; arrowhead in Figure 4E), with myosin filaments radiating outward (box/inset, Figure 4B). By 2 wk, the electron-dense bodies have begun to align with filaments coursing between them (Figure 4C). These structures are nearly identical to those seen in embryoid body–derived developing cardiomyocytes [27]. By 8 wk in culture, the electron-dense structures in EMs of beating cells have progressed to become the Z-lines of organized sarcomeres (Figure 4G). The beating cells also have one or two large, centrally located nuclei surrounded by mitochondria, distinguishing them from skeletal myotubes, which have many small, subsarcolemmal nuclei.
Figure 4 Transmission EMs Show the Post-Replating Progression of CPS Cells
(A) Round, day 3 cells contain disordered myosin filaments. Some of these cells beat while still floating (see Video S1) and typically have APs as shown in Figure 6A.
(B) Upper box is a blowup taken from lower panel, showing myosin filaments of characteristic 1.6-μm length radiating outward from dense body.
(C) Day 14 cell with a single, central nucleus shows a stretching out of the dense bodies into an organizing sarcomere.
(D) Day 3 round cells containing copious mitochondria (inset).
(E) Elongated day 7 cell containing a dense body (arrowhead).
(F) Uninucleate day 14 cell, same cell as in (C).
(G) By day 56, a well-defined sarcomere is present, with identifiable A- and I-bands and M- and Z-lines.
(H) Sarcomere from a fetal cardiomyocyte is shown for comparison.
Calcium Transients and Cardiac Action Potentials in CPS Cells
As the CPS cells progress to spontaneously beating cardiomyocytes in vitro, the frequency of beating ranges from 1 to 8 Hz. Cells continue to beat even after culture for 3 mo. Despite the absence of beating initially, some clusters of cells adherent to an extracellular matrix display calcium transients detected by fluo-4 (4 μM concentration) visualized with confocal microscopy (Video S2). This suggests that the excitation portion of “excitation–contraction” develops before the contractile apparatus of the cell is mature enough to overcome the minimal resistance offered by an extracellular matrix. The round beating cells shown in Video S1 progress to the elliptical beating cells shown in Video S3 and subsequently to the more mature cardiomyocytes shown in Video S4. These last cells also display calcium transients detected through the use of fluo-3 (4 μM concentration) as shown in Video S5 and Figure 5. By day 14 after replating, 10% of the cells in a confluent dish beat spontaneously. The calcium transients indicate the existence of action potentials (APs), which have been characterized by patch recordings of single cells in culture (Figure 6). A variety of cardiac APs are observed in beating and nonbeating cells (Figure 6A and 6B), which both show a resting membrane potential of approximately −60 mV with a robust overshoot of 50–90 mV. The form and duration of the APs match the descriptions of adult murine cardiomyocyte APs, which lack the plateau phase seen in cardiomyocytes of other species [28].
Figure 5 Measuring Calcium Transient Frequency
Graphical representation of the calcium transient in a beating CPS cell–derived cardiomyocyte (A). Fluorescent intensity is proportional to the amount of calcium binding to fluo-3 dye upon release of calcium from the sarcoplasmic reticulum. Peak intensity (B) and baseline (C) are shown.
Figure 6 Whole-Cell Voltage Recordings from Spoc Cell–Derived Cardiomyocytes
(A) Spontaneous AP firing in a nonbeating, teardrop-shaped cell.
(B) Representative AP from recording in (A) on an expanded time scale; AP threshold is –60 mV.
(C) Action potential firing in another cell is blocked upon bath perfusion with 0.5 mM cadmium chloride (horizontal bar).
(D) Acceleration of AP firing upon perfusion with 25 nM isoproterenol (horizontal bar) is demonstrated, indicating the presence of adrenergic receptors on these cells.
(E) Skeletal myotube APs, if present, differ in that their frequency is unaffected by Cd++.
(F) Isoproterenol also does not affect skeletal muscle AP frequency.
The beating in Spoc cell–derived cardiomyocytes differs from the sporadic twitches that have been observed in skeletal muscle because the addition of 0.5 mM cadmium chloride, a non-specific blocker of L-type Ca++ and Na+ channels, abolishes the cardiac AP [29,30], while having no effect on skeletal myotubes (Figure 6C and 6E) [31,32]. Likewise, both beating and nonbeating cardiac cells have an intact adrenergic pathway [33,34,35], as shown by the increase in AP frequency with the addition of 25 nM isoproterenol (Figure 6D). The expected lack of effect on skeletal myotubes [36] is shown in Figure 6F. Uninucleate myoblasts with spontaneous calcium transients and APs under standard culture conditions have not been described [37], and even when they are contrived by arresting cell fusion, they retain the electrical activity characteristic of skeletal muscle cells, such as the lack of response to cadmium chloride [38].
Transplantation of Spoc Cells into Murine Myocardial Infarction Models
In order to determine whether Spoc cells engraft within a myocardial infarct (MI) and differentiate into mature cardiomyocytes, an acute infarct model in C57Bl/6J mice was created by ligation of the left coronary artery. We injected 1 × 105 GFP+ total Spoc cells (unfractionated for Sca-1) into the peripheral circulation. After 14 wk, many donor-derived GFP+ cells had engrafted (Figure 7B). Of donor cells that had migrated to the infarct, 8% (63/782) had developed into cardiomyocytes (arrows, Figure 7A) displaying a phosphorylated cardiac myosin regulatory light chain (RLCP), shown by co-labeling with an antibody against GFP (green) and RLCP (red) [39]. All samples were scanned on green and red channels simultaneously. Cells were considered GFP+ only if their cytoplasm fluoresced green and not red. This prevents mistaking the anti–GFP signal (green) in Figure 7A for autofluorescence. Autofluorescence in an infarct will characteristically “bleed” through both green and red channels in the absence of exogenous fluorescent markers, causing all autofluorescence to show up as a generalized yellow, i.e., the combination of red and green [40]. In this case, only the myosin striations, which are co-stained, appear as both red and green, serving as an internal control for the technique. No labeled cells, differentiated or undifferentiated, were observed in the normal portion of the infarcted heart (data not shown). These findings suggest that Spoc cells either actively home to or are passively delivered to an area of cardiac damage where they begin to differentiate into cardiomyocytes, as observed in vitro. In order to determine if Sca-1− Spoc cells engraft and differentiate within a MI as efficiently as Spoc cells unfractionated for Sca-1, the same acute anterior MI model described earlier was used. When 1 × 105 Sca-1−/GFP+ donor cells were injected via the tail vein immediately after infarction, a low level of engraftment occurred with only an occasional donor-derived cardiomyocyte found at the periphery of the infarct zone (Figure 7C–7E). This finding is consistent with our in vitro findings described earlier, in which purified Sca-1− cells remained immature, loosely attached, round beating cells for months until co-culture with a feeder layer of immobile Sca-1+ cells allowed the Sca-1− fraction to adhere and mature.
Figure 7 In Vivo Myocardial Infarction Transplantation Studies
(A) The GFP-tagged Spoc cells (green), unfractionated for Sca-1, injected into the peripheral blood of a murine acute infarct model have developed after 14 wk into cardiomyocytes (arrows) within the infarct region. Donor Spoc cell–derived GFP cardiomyocytes are characterized by single central nuclei and striations staining for RLCP (red).
(B) Longitudinal fresh-frozen tissue slice showing the region of infarct from which (A) was taken (orange box) and adjacent normal endogenous cardiac tissue (RLCP, red).
(C) GFP+/Sca-1− Spoc cells were injected into the peripheral circulation of a murine acute MI model and are detected in the infarct after 4 wk by expression of GFP (green).
(D) RLCP (red) is also expressed.
(E) Overlay of (C) and (D).
To evaluate for a similar effect in an older infarct, the same number of Spoc cells, unfractionated for Sca-1, was injected via the tail vein into two mice (8 and 14 wk after MI). Two weeks after injection, the heart of the 8-wk-old infarct model showed co-localization of GFP and
GATA-4 in 3% (4/136) of donor cells that migrated to the peripheral region of the infarct. Five weeks after injection into the 14-wk-old infarct model, an increased number of GFP+/RLCP+ cells (7/102 donor-derived cells) in the infarct region were evident (data not shown). Spoc cells that were partially differentiated by culturing for 7 d were injected into the hearts and tail veins of three mice with acute infarcts. No labeled cells were identified in the hearts at 7 d and 1 mo later, compared to two control mice injected with saline at the time of infarct (data not shown). This suggests that the undifferentiated cells more easily actively home to or are delivered passively to the heart.
We used a Cre-recombinase (Cre) expressor/beta-galactosidase reporter system to rule out donor stem-cell fusion with host cardiomyocytes as an explanation for apparent in vivo differentiation. In the event of fusion, Cre from the donor cells will excise the floxed stop codon present in the host DNA and thus de-repress the beta-galactosidase reporter gene. Within hours of producing an acute infarct by surgical ligation of the left coronary artery in R26Rh reporter mice, tail-vein injection of 3 × 105 Cre+ Spoc cells from an EIIa promoter/Cre donor mouse was performed. Cre+ donor cells are seen in the heart as early as 2 d post infarct (data not shown). By day 7, isolated nests of donor cells that have not fused with host cells can be detected, as shown by the lack of X-gal staining in Cre+ cells (Figure 8A–8F). Also by day 7,
GATA-4 is co-expressed with Cre in some of the cells in these clusters (Figure 8G–8I). In order to further isolate and track donor Spoc cells, a monoclonal antibody library was generated by inoculating a rat with live Spoc cells via injection into the peripheral circulation. The library was screened for the ability to detect cell-surface antigens on total Spoc cells. One such antibody that has been generated (MSC 21) detects live Spoc cells in culture and can be used to isolate a positive and negative subpopulation. MACS separation using MSC 21 gives two populations, with the 21+ subset containing cells that develop into beating cardiomyocytes (Video S6). In the Cre donor/R26Rh reporter infarct mice, some of the injected Cre+ cells are co-stained by MSC 21, further supporting the donor origin of these transplanted cells (Figure 8J–8L). MSC 21 does not detect cells in normal controls or infarcted heart (Figure 8M).
Figure 8 Cre Expressor/Beta-Galactosidase Reporter Myocardial Infarction Studies
(A) Nests of Cre+ cells (green) are detected 1 wk after tail-vein injection into an acute infarct model.
(B) Nomarski image of (A).
(C) Merged image of (A) and (B). The clusters are located near a blood vessel (arrow).
(D) Infarcted tissue in a control MI model (infarction surgery but no donor-cell injection) showing a lack of staining for Cre (no green) and
GATA-4 (no red).
(E) Control X-gal staining of ROSA mouse heart.
(F) In a sequential series of tissue sections, odd-numbered sections were immunostained for Cre, yielding the results seen in (A). These two clusters of cells were seen on five sections (sections 1, 3, 5, 7, and 9). Even-numbered sections (sections 2, 4, 6, and 8) were stained for X-gal. No X-gal+ cells were found. One slide was immunostained, showing the Cre+ cells present. This slide was then stained for X-gal and was found to be X-gal−. The lack of X-gal staining of the serial sections indicates that at 1 wk no fusion of donor and host cells has occurred in the infarct.
(G) Cluster of Cre+ donor cells detected in infarcted heart tissue of a 1-wk-old acute infarct model. Arrowheads indicate cells that also express
GATA-4, as shown in (H).
(H)
GATA-4 (red) is mostly present in some cells in the margin of the cluster. Arrowheads indicate cells that also express Cre, as shown in (G)
(I) Merged image showing co-localization (arrowheads) of Cre (green) with
GATA-4 (red) in some cells of the cluster of Cre+ cells.
(J–L) Co-staining of donor cells with anti–Cre antibody (green) and MSC 21 (red) is apparent after 7 d in an acute infarct model.
(M) There is a lack of staining with MSC 21 (no red) in the infracted tissue of mice that have not received Spoc cell injections.
Concluding Remarks
Our isolation and description of what we call Spoc cells was initially surprising to us in that a few simple variations in the usual technique of skeletal muscle cell culture yielded a novel finding. These adaptations included avoiding trypsin, substituting FGF and EGF for commonly used complex supplements like chick embryo extract, and discarding adherent cells while continuing to culture the nonadherent cells. The observation of isolated nonadherent beating cells in a skeletal muscle culture has probably been observed previously by others in the course of experimenting with skeletal muscle culture conditions but has anecdotally been considered to be some variant of primitive skeletal muscle cells and never been carefully investigated . This report is, to our knowledge, the first time that the phenotype of these cells has been methodically studied with single-cell patch recordings, Ca++ transient recordings, EM, longitudinal video microscopy, and immunostaining to detect the presence of skeletal and cardiac histologic markers. The absence of skeletal and satellite cell markers in the face of the histologic and physiologic evidence of the cardiac phenotype supports our conclusion that these cells are closer to true cardiac myocytes than to some variant of skeletal muscle cells. The homing of these freshly isolated cells to the injured heart and their subsequent differentiation into a cardiac phenotype in vivo that matches the in vitro experiments is corroborating evidence.
Recently, three independent groups have isolated a stem-cell population from the heart. In two studies, the stem cells were cultured extensively [15,20] before reintroduction directly into the heart, whereas in the other study, freshly isolated cells were injected into the vascular circulation [14]. Beltrami et al. [15] did not observe beating cells in vitro, whereas the cells isolated by Oh et al. [14] required treatment with the demethylating agent 5-azacytidine to produce beating cells, similar to the treatment that was previously used to generate beating cell lines from bone marrow stromal cells [41]. Messina et al. [20] used cardiotropin to treat round cells released from a 3-wk explant culture, generating a sphere of cells that remain primitive and proliferating on the inside while they differentiate into beating cardiac myocytes at the margins. They observed that this growth pattern is reminiscent of neurospheres. All three cardiac-derived populations appear to be distinct from one another with respect to parameters such as the presence of c-kit or the ability to beat in vitro; however, the diverse conditions make direct comparison difficult.
Spoc cells do not emerge from cardiac tissue grown in parallel with the skeletal cultures. However, freshly isolated Spoc cells are observed in infarcted heart within 2 d of their systemic injection into a murine model of acute MI. Within 7 d of systemic injection, Spoc cells form a sphere-like body at the borders of the infarction near blood vessels. The Cre signal is strongly present in the center of these spheres but diminished at the borders where the
GATA-4 signal emerges. This is likely due to EIIa promoter suppression in the differentiating cells. This pattern of the more proliferative cells in the center and the differentiating cells at the borders is reminiscent of neurospheres or the in vitro culture of cardiac-derived stem cells [20]. By 3 mo, many more of the cells are distributed throughout the border areas of the infarct and can be detected with an antibody to ventricular RLCP.
The pattern of
GATA-4 staining in early Spoc cells is particularly interesting in its unusual cytoplasmic distribution, but the shift to a nuclear distribution after the addition of the beta-adrenergic agonist isoproterenol to the cell culture illustrates the staining specificity. The importance of beta-adrenergic stimulation to the processes of differentiation and cardiac myocyte hypertrophy makes these cells particularly useful to study the Akt–GSK3 beta regulation of nuclear-expressed
GATA-4 [25] as well as its beta-adrenergic–mediated interaction with the calcineurin pathway [24].
Although the beating cells in vitro contract asynchronously, local regions of confluent plates appear to beat at distinct rates. This fact, coupled with the late expression of connexin 43 in vitro (see Figure 3H), suggests the potential for coordinated beating, a requirement for efficient cardiac contraction. Although Spoc cells appear more efficient at homing to the heart in an acute MI model, their ability to differentiate in vivo at the border of an old infarct may aid research focused on chronic heart failure or dilated cardiomyopathy. Generation of cardiomyocytes from Spoc cells derived from transgenic models may be an important tool in studying cardiac development, because the progression from undifferentiated cells to mature cardiomyocytes may be observed in vitro.
Why these cardiac stem cells are sequestered in skeletal muscle and why clinically significant numbers of them are not normally recruited to salvage an infarcted heart remains a mystery. Perhaps additional cells or factors present in skeletal muscle suppress the cardiac differentiation of Spoc cells in situ or inhibit migration. Certainly, Spoc cells do not differentiate into cardiac cells while they reside in skeletal muscle. Along these lines, fractionation of Spoc cells based on Sca-1 greatly enriches for a population of cells in the Sca-1− fraction that in vitro become beating cardiomyocytes; however, engraftment of these Sca-1− cells in MI models seems less efficient than that of unfractionated Spoc cells. This is consistent with the increased magnitude and kinetics of cardiomyocyte differentiation observed when the Sca-1− fraction is plated over a monolayer of the Sca-1+ fraction. Thus, there is likely a role for the Sca-1+ fraction in engraftment efficiency through any number of mechanisms, including migration, tropism, and entrapment. It remains to be determined whether Spoc-cell transplants contribute to mouse survival or electrically couple to endogenous cardiac myocytes. There is evidence, however, that Spoc cells survive more than 3 mo post transplant, during which time they mature into striated cardiomyocytes.
Materials and Methods
Isolation of Spoc cells
To isolate cardiomyocyte precursor cells from adult mouse skeletal muscle, skeletal muscle tissue from the hind legs of 6- to 14-wk-old male C57Bl/6J mice is cut into small pieces and digested with collagenase type-2 (5 mg/ml) for 45 min at 37 oC, and then for another 45 min at 37 oC in a shaking rotator. The digested tissue is cleared of cell debris and other undigested tissue fragments by passage first through 100 μm and then through 40 μm filters. Cells are incubated in DMEM/F12 medium containing 5% FBS for 2 h at 37 oC. The cell suspension is then centrifuged at 1,500 RPM for 15 min. The pellet consists mostly of small cells, 4–10 μm in diameter. These cells are dispersed by trituration and are then sorted using anti–Sca-1 antibody on Miltenyi Biotech (Bergisch Gladbach, Germany) magnetic columns so that the Sca-1− cells from total hind leg muscles of five to eight mice are passed sequentially through three to four columns to obtain a high degree of Sca-1− purity (>95% by fluorescent staining).
Cells are plated at a density of approximately 105 cells/cm2 in regular tissue culture dishes in 50:50 DMEM/F12 supplemented with 5% FBS, 10 ng/ml each of human EGF and bFGF (Peprotech, Rocky Hill, New Jersey, United States), insulin, transferrin, selenium, ethanolamine (ITS-X; Invitrogen, Carlsbad, California, United States ), and antibiotics (gentamicin and fungizone). Within 24 h, it is clear that the Spoc cells eluted in the Sca-1− fraction consist of mostly nonadherent round cells, whereas the Sca-1+ fraction consists of mainly adherent cells. Within 48–72 h, the Sca-1− culture consists of a floating population of round cells and scant adherent fibroblasts. The round cells enlarge as they divide in this medium, undergoing a few rounds of cell division. The floating population of round cell clusters is gently collected after 7 d of growth in complete growth medium and replated in the same medium, minus EGF and bFGF. The cells, which have enlarged to 10–14 μm in diameter, are by now
GATA-4 positive. Spontaneously beating cells are visible while they are still floating or loosely attached, but the number of beating cells increases within a few days of replating as the cells elongate and attach to the floor of the tissue culture plate. The beating cells do not undergo any more cell divisions and can be maintained in this medium for several weeks, displaying a spontaneous beating phenotype.
Alternatively, the total population of Spoc cells, unfractionated for Sca-1, can be plated in growth-factor-containing medium for 3–5 d and then the nonadherent cells removed carefully and replated without growth factors. This will result in a greater population of what will become adherent cells in the replated culture. This allows the nonadherent Spoc cells to adhere and stretch out into more mature beating cardiomyocytes. The isolation and differentiation of Spoc cells from skeletal muscle is extremely reproducible and has been performed successfully in our laboratory hundreds of times. These cells have been used for characterization, as described in the text, at different times after the initiation of the culture.
Transmission EM
For routine transmission EM, cells were fixed in situ on petri dishes with 1.25% glutaraldehyde in 0.1M cacodylate buffer containing 1% cadmium chloride at 4 oC for 2 h. After fixation, cells were washed three times in Sabatini's solution (0.1 M cacodylate buffer containing 6.8% sucrose), and post-fixed with 1% osmium tetroxide in cacodylate buffer for 1 h. After three washes in Sabatini's solution, samples were dehydrated in alcohol and embedded in Scipoxy 812 (Energy Beam Sciences. Agawarm, Massachusetts, United States). Polymerization was carried out at 37 oC for 24 h and then at 60 oC overnight. Ultra-thin sections were cut with a Leica Ultracut UCT ultramicrotome (Leica, Wetzslar, Germany), stained with uranyl acetate and Reynolds lead citrate, and examined with a JEOL 1200 CXII transmission electron microscope (JEOL, Peabody, Massachusetts, United States).
Confocal imaging and detection of calcium transients
The images were collected with a Zeiss LSM-510 laser scanning confocal system and a Zeiss C-Apochromat 63x objective (1.2 NA). Fluo-3 and fluo-4 were excited at 488 nm with an argon laser, and the emission light was collected using an LP 505 filter. The pinhole was adjusted to produce a 5-μm slice to minimize axial movements with contraction that may affect viewing the calcium transients. All transmitted light images were collected simultaneously using a transmitted light detector in conjunction with the 488-nm excitation light. Data depth for the images was 8 bit. The size of the images varied from 512 × 512 pixels to 128 × 128.
Whole-cell clamp recordings
Current clamp recordings were carried out using the tight-seal whole-cell patch technique at room temperature in Tyrode solution containing 136 mM sodium chloride, 5.4 mM potassium chloride, 1 mM magnesium chloride, 1.8 mM cadmium chloride, 0.33 mM sodium phosphate monobasic monohydrate, 10 mM glucose, and 10 mM HEPES (adjusted to pH 7.4 with sodium hydroxide). The pipette solution contained 20 mM potassium chloride, 110 mM potassium aspartate, 1 mM magnesium chloride, 10 mM HEPES, 5 mM EGTA, 0.1 mM GTP, and 5 mM Mg2+/ATP (adjusted to pH 7.2 with potassium hydroxide). Voltages were filtered at 2 kHz (–3 dB; four-pole, low-pass Bessel filter). The resting membrane potential upon breaking in was –39.8 ± 1.6 mV (n = 9), but it generally improved by 10 mV or more. In some cases, cells were hyperpolarized slightly to AP threshold.
Immunostaining
Cells were fixed in either 4% paraformaldehyde at 4 oC, neutral buffered 10% formalin at 4 oC, or acetone at –20 oC for 10 min and then washed in PBS for a total of 15 min (three times). Cells were permeabilized with 0.2% Triton X-100 for 10 min. Blocking was performed for 30 min at room temperature with either 3% BSA in PBS, 5% goat serum in PBS, or 10% goat serum in PBS. Incubation with primary antibody was performed at 4 oC overnight. Cells were then washed for a total of 15 min (three times) in 1X PBS. Incubation with secondary antibody was done at room temperature for 1 h. Afterwards, cells were washed three times with PBS and mounted in a fluorescent mounting medium with 4′,6-diamidino-2-phenylindole (DAPI) (Vector Laboratories, Burlingame, California, United States). A cover slip was then placed over the sample. Images of cells were obtained with a laser scanning confocal fluorescence microscope (Leica TCS-4D DMIRBE) equipped with argon and argon–krypton laser sources. Excitation wavelengths of 365 nm (DAPI), 488 nm (FITC), and 568 nm (rhodamine) were used to generate fluorescence emissions in blue, green, and red, respectively. The primary antibodies used were against MyoD (Novocastra Laboratories, Newcastle-upon-Tyne, United Kingdom), GFP (Abcam, Cambridge, United Kingdom), cardiac L-type channel (United States Biological, Swampscott, Massachusetts, United States), Sca-1 (Cedarlane Laboratories, Hornby, Ontario, Canada), and CD34 and CD45 (PharMingen, San Diego, California, United States).
GATA-4, Myf-5, Myogenin, connexin 43, Nkx-2.5 Pax-3/7, c-met, and c-kit antibodies were from Santa Cruz Biotechnology (Santa Cruz, California, United States). The antibody to RLCP was produced in our laboratory and has previously been described [39]. Alpha-myosin antibody has been produced and characterized by our laboratory and is unpublished. Beta-myosin antibody was produced by our laboratory and previously published [42]. Antibody MSC 21 was generated after peripheral blood injection of Spoc cells into rats (performed by Antibody Solutions, Palo Alto, California, United States). Figure S1 was taken using a Zeiss Axiovert 200M microscope in conjunction with Compix (Lake Oswego, Oregon, United States) deconvolution software. To perform
GATA-4 sublocalization studies, CPS cells in culture dishes (day 10) were incubated with 20 μM isoproterenol at 37 oC for 1 h. Cells were then collected by trituration, cytospins were made, and cells were stained for
GATA-4, as described earlier.
Bone marrow transplantation studies
Eight 1-wk-old C57Bl/6J mice were irradiated with one total dose of 850 cGy. Four hours after irradiation, mice were injected via the tail vein with either 1.5 × 105 GFP+/Sca-1− Spoc cells or 2 × 105 unfractionated GFP+/Spoc cells. A competitive assay was performed by injecting mice with 3 × 106 whole bone marrow cells and 3 × 104 GFP+/Sca-1− Spoc cells. Mice were evaluated for GFP+ cells in the peripheral blood and bone marrow.
MI studies
Sixteen 1-wk-old male C57Bl/6J mice were administered Avertin (1.25%, tribromoethanol) at 0.015 ml/g body weight or pentobarbital diluted to 0.5% and administered at 100 mg/kg IP. Animals were intubated by direct visualization, and ventilation was performed. Once anesthetized, hair in the surgical field was removed, and a thoracotomy was performed. Heart was visualized after retractors were used to enlarge the incision. The left anterior descending artery was permanently ligated using 5-O silk suture. The chest was closed with sutures. For acute infarct models, mice were monitored for recovery from anesthesia and then injected (2 h post surgery) with 1 × 105 C57Bl/6J Spoc cells (either fractionated or unfractionated) via the tail vein. Mice were sacrificed at 14 wk; the hearts were excised and fresh frozen sections made. For chronic infarct models, infarction protocol was performed as described earlier. The mice were maintained for either 8 or 14 wk before injection of 1 × 105 Spoc cells into the tail vein. Mice were sacrificed at either 2 or 5 wk post injection, and the hearts were harvested for frozen sections. For Cre expressor/beta-galactosidase reporter MI studies, the infarct procedures described earlier were performed in 16-wk-old R26Rh mice. We obtained 3 × 105 donor cells from 8-wk-old EIIa/Cre mice and injected the cells via the tail vein on the day of surgery.
Animal studies were created under National Institutes of Health protocols 2-MC-31(R) and 1-CB-2, in accordance with the guidelines set forth by the National Heart, Lung, and Blood Institute Animal Care and Use Committee.
Supporting Information
Figure S1 Day 28 Spoc-Derived Cardiomyocytes Express Alpha- and Beta-Myosin
(A) Day 28 Spoc cell–derived cardiomyocytes express alpha-myosin, as shown by immunostaining using anti–alpha-myosin antibody.
(B) Beta-myosin is also present in these cells.
(531 KB TIF).
Click here for additional data file.
Video S1 Round Beating Cell in Culture
Video microscopy of round, spontaneously beating cell about 10 d from isolation (diameter = 15 μm), at approximately the stage shown in Figure 4D.
(5.1 MB MPG).
Click here for additional data file.
Video S2 Calcium Transients in Beating and Nonbeating CPS Cells
Calcium transients are present in a cluster of day 14 beating cells in culture as detected by fluo-4 fluorescent dye. Only some of the cells with detectable calcium transients are visibly contracting, which shows an uncoupling of excitation–contraction, presumably because of an immature contractile apparatus or decreased movement caused by a dense extracellular matrix.
(7.0 MB MPG).
Click here for additional data file.
Video S3 More Mature Beating Cells Appear Elliptical
The round beating cell shown in Video S1 has progressed by day 14 after replating to an elliptical cell (length = 30 μm) that continues to display small contractions as shown by video microscopy.
(7.4 MB MPG).
Click here for additional data file.
Video S4 Mature Beating CPS Cell–Derived Cardiac Myocyte in Culture
Beating cell in culture (length = 55 μm). Spontaneous beating is continuous (frequency 1–6 Hz) and appears to be indefinite. Cells kept at room temperature have been noted to beat continuously for at least 3 h, and 3-mo-old cultures contain beating cells.
(7.6 MB MPG).
Click here for additional data file.
Video S5 Beating Cells Exhibit Calcium Transients
Calcium transients are seen in a day 21 beating cell, as detected by fluo-3 fluorescent dye. Flashes indicate binding of calcium to fluo-3 upon release of calcium from the sarcoplasmic reticulum.
(3.5 MB MPG).
Click here for additional data file.
Video S6 MSC 21 Selects for a Subset of Cells That Develop into Beating Cardiomyocytes
A cluster of beating cardiomyocytes derived from MACS-separated MSC 21+ cells is shown here after 14 d in culture.
(9.2 MB AVI).
Click here for additional data file.
We would like to acknowledge Matt Daniels for thoughtful discussion, Chris Combs for his help with the confocal video microscopy, Robert Hoyt and Randy Clevenger for their surgical expertise in creating the mouse infarct models, the LAMS animal care staff for technical support, and Julien Davis and Robert Adelstein for their careful reading of the manuscript. The EIIa/Cre and R26Rh mice were a generous gift of Heiner Westphal.
Competing interests. A US patent application entitled “Stem Cells that Transform to Beating Cardiomyocytes” (PTC #33860) has been filed by the US government, with SOW, TVG, SH, and NDE listed as co-inventors.
Author contributions. SOW, TVG, SH, HT, MAR, and NDE conceived and designed the experiments. SOW, TVG, SH, HT, DG, MAR, KT, ZXY, and YHX performed the experiments. SOW, TVG, SH, HT, DG, KT, ZHY, YHX, and NDE analyzed the data. SOW and NDE wrote the paper.
Citation: Winitsky SO, Gopal TV, Hassanzadeh S, Takahashi H, Gryder D, et al. (2005) Adult murine skeletal muscle contains cells that can differentiate into beating cardiomyocytes in vitro. PLoS Biol 3(4): e87.
Abbreviations
APaction potential
CPScardiac precursors from Spoc
CreCre recombinase
DAPI4′,6-diamidino-2-phenylindole
EGFepidermal growth factor
EMelectron microscopy
FGFfibroblast growth factor
GFPgreen fluorescent protein
HSChematopoietic stem cell
ILinterleukin
MImyocardial infarct
RLCPphosphorylated cardiac myosin regulatory light chain
Spocskeletal-based precursor of cardiomyocytes
SVFstroma vascular fraction cell
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| 15757365 | PMC1064849 | CC0 | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e87 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030087 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1575736310.1371/journal.pbio.0030092Research ArticleEcologyZoologyMammalsBirdsGray Wolves as Climate Change Buffers in Yellowstone Climate Change BuffersWilmers Christopher C [email protected]
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Getz Wayne M
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1Department of Environmental Science, Policy and ManagementUniversity of California, Berkeley, CaliforniaUnited States of America2Mammal Research Institute, Department of Zoology and EntomologyUniversity of PretoriaSouth AfricaDobson Andy P. Academic EditorPrinceton UniversityUnited States of America4 2005 15 3 2005 15 3 2005 3 4 e9211 7 2004 13 1 2004 Copyright: © 2005 Wilmers and Getz.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Gray Wolves Help Scavengers Ride Out Climate Change
Understanding the mechanisms by which climate and predation patterns by top predators co-vary to affect community structure accrues added importance as humans exert growing influence over both climate and regional predator assemblages. In Yellowstone National Park, winter conditions and reintroduced gray wolves (Canis lupus) together determine the availability of winter carrion on which numerous scavenger species depend for survival and reproduction. As climate changes in Yellowstone, therefore, scavenger species may experience a dramatic reshuffling of food resources. As such, we analyzed 55 y of weather data from Yellowstone in order to determine trends in winter conditions. We found that winters are getting shorter, as measured by the number of days with snow on the ground, due to decreased snowfall and increased number of days with temperatures above freezing. To investigate synergistic effects of human and climatic alterations of species interactions, we used an empirically derived model to show that in the absence of wolves, early snow thaw leads to a substantial reduction in late-winter carrion, causing potential food bottlenecks for scavengers. In addition, by narrowing the window of time over which carrion is available and thereby creating a resource pulse, climate change likely favors scavengers that can quickly track food sources over great distances. Wolves, however, largely mitigate late-winter reduction in carrion due to earlier snow thaws. By buffering the effects of climate change on carrion availability, wolves allow scavengers to adapt to a changing environment over a longer time scale more commensurate with natural processes. This study illustrates the importance of restoring and maintaining intact food chains in the face of large-scale environmental perturbations such as climate change.
Reintroducing wolves can help ameliorate the negative effects of warmer winters on other species and reveals the importance of maintaining intact food chains in the face of climate change
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Introduction
Average earth temperatures have increased by 0.6 °C over the last 100 years [1] and are predicted to increase by 1.4–5.8 °C over the next century [2]. Commensurate with rising global temperatures are regional changes in weather patterns affecting the quantity and timing of precipitation and moisture levels. A challenge facing ecologists is to understand how these changes in the abiotic environment will impact populations and communities of organisms. Already, studies have documented the effect of a changing climate on the phenology, range, reproductive success, and synchrony of certain plants and animals (see [1] for a comprehensive review). In addition, climate-caused community-level changes have been documented when range shifts lead to the transfer of an entire assemblage of species [3].
Given such responses by individual species, we can expect consequent shifts in trophic structure and competitive hierarchies at the community scale [4]. Studies addressing this problem have focused primarily on how species-specific responses in phenology and geographic range alter competitive balances and the timing of food availability for neonates [5,6,7,8]. In Britain, for instance, winter warming has precipitated disparate responses in the breeding phenology of different amphibian species, exposing frog larvae (Rana temporaria), which have shown no phenological response, to higher levels of predation from newts (Triturus spp.) that are entering ponds earlier than before [5].
As predicted by community stability theory, the impact of climate change on communities may vary in relation to levels of species diversity [9,10,11,12]. Depauperate communities or those lacking keystone species [13,14] may be more vulnerable to the perturbing effects of climate change than more speciose communities. As such, understanding the mechanisms or pathways that confer community resistance to climate change will be important to conservationists and managers in mitigating the effects of a changing climate on shifting community patterns and local extinctions.
The reintroduction of gray wolves (Canis lupus) to Yellowstone National Park (NP) in 1995 [15] provides a research opportunity for comparing the response of an ecosystem to climate change in scenarios with and without direct human alteration of species composition. Wolf restoration is already realizing a change on the Yellowstone ecosystem by altering the quantity and timing of carrion availability to scavengers [16]. Ravens (Corvus corax), bald eagles (Haliaeetus leucocephalus), golden eagles (Aquila chrysaetos), magpies (Pica pica), coyotes (Canis latrans), grizzly bears (Ursus arctos), and black bears (Ursus americanus) are each frequent visitors at wolf kills [17] and are highly reliant on winter carrion for survival and reproductive success [16,18,19,20,21,22].
Prior to wolf reintroduction, winter mortality of elk (Cervus elaphus), the most abundant ungulate in Yellowstone, was largely dependent on snow depth (SDTH) [23]. Deep snows lead to increased metabolic activity [24] and decreased access to food resources, thereby causing elk to weaken and die [25]. In the absence of wolves, carrion was plentiful both during severe winters and at the end of moderate winters, but more scarce in early winter or during mild winters [23]. Reintroduced wolves are now the primary cause of elk mortality throughout the year [26]. Scavengers that once relied on winter-killed elk for food now depend on kleptoparasitizing wolf-killed elk [16]. Hence carrion availability has become primarily a function of wolf pack size, with SDTH an important but secondary factor.
As global temperatures rise, evidence suggests that northern latitude and high elevation areas will experience shorter winters and earlier snow melts [27]. Given the overwhelming influence of gray wolves on scavenger food webs, community-level responses to climatic changes in the absence of wolves may differ substantially from those in the presence of Yellowstone's newly restored top carnivore. As such, we analyzed over 50 y of weather data from Yellowstone's northern range for trends in winter conditions, and constructed empirically and dynamically grounded scenarios to investigate how changes in SDTH and seasonality differentially affect scavengers in the presence and absence of wolves.
Results
Weather Data Analysis
Over the past 55 y, average monthly SDTH at the Mammoth Hot Springs weather site show a steady decline in all winter months except November [the effect is significant at p ≤ 0.05 for February through April and nearly significant for December and January (Figure 1)]. Furthermore, the slope of the line relating SDTH to year becomes more negative with each month, indicating a more pronounced effect of climate change in late winter. The result for April, however, is confounded by a number of zeros, which created a violation of the normality assumption for the linear regression. Average monthly SDTH at the Tower Falls weather site (Figure 2) did not indicate a strong pattern in the early winter, but showed a significant decline in the late-winter months of March and April (Figure 2E and 2F).
Figure 1 Winter Snow Depths 1948–2003 at Mammoth Hot Springs
Average monthly SDTH for November (A), December (B), January (C), February (D), March (E), and April (F) 1948–2003 at the Mammoth Hot Springs weather site.
Figure 2 Winter Snow Depths 1948–2003 at Tower Falls
Average monthly SDTH for November (A), December (B), January (C), February (D), March (E), and April (F) 1948–2003 at the Tower Falls weather site.
Winters in Yellowstone are getting shorter. While we did not detect a difference in the date of the arrival of the first snow, we did detect a declining trend in the date of last snow on the ground (Figure 3A and 3B).
Figure 3 Changes in the Last Day of Snow Cover over the Last 55 Years at Mammoth Hot Springs and Tower Falls
Last day of snow cover is reported as the number of days from January 1 of that year until the first day of bare ground. Changes in last day of snow cover over the last 55 y are shown for Mammoth Hot Springs (A) and Tower falls (B). The number of days from January through March that temperatures exceeded freezing at Mammoth (C) and Tower (D) are increasing with time.
At both the Tower and Mammoth weather sites, the number of days that maximum temperature (TMAX) exceeded freezing for the period of January through March increased significantly (Figure 3C and 3D). Furthermore, midwinter snowfall is decreasing, and late-winter minimum temperature (TMIN) and TMAX show signs of increasing in certain months (Table 1).
Table 1 Regression Analyses Predicting Mean Monthly SNFL, and Average Late-Winter TMIN and TMAX
Included are results from regression analyses using year as the independent variable to predict dependent variables SNFL, TMIN, and TMAX for given winter months. We present results for p < 0.10
Wolf Effects
Statistical model.
The presence of wolves in Yellowstone significantly mitigates the reduction in late-winter carrion expected under climate change (Figure 4). In the scenario without wolves, late-winter carrion availability is reduced by 27% in March and by 66% in April. In contrast, the scenario with wolves reveals a reduction in carrion availability of only 4% in March and 11% in April. There was not a significant difference in the reduction of early- to midwinter carrion (December through February) between the two scenarios.
Figure 4 Reduction in Winter Carrion Available to Scavengers due to Climate Change 1950–2000: Statistical Model
Shown are percent reductions (± standard error) in winter carrion available to scavengers due to climate change from 1950 to 2000 with and without wolves in our statistical model. * Significant difference between the two scenarios.
Dynamic model.
Percent change, z, in late-winter carrion from 1950 to 2000 was not sensitive to changes in any of the parameters in either scenario with or without wolves. Specifically, r
2 values did not exceed 0.02 for any of the parameters regressed upon z. Mean monthly percent change in carrion availability from 1950 to 2000 under scenarios with and without wolves reveals a relative reduction in late-winter carrion from 1950 to 2000 and an increase in early-winter carrion (Figure 5). Note that this change in carrion availability is much less pronounced in the presence than in the absence of wolves.
Figure 5 Change in Carrion Available to Scavengers due to Climate Change 1950–2000: Dynamic Model
Shown is the mean monthly change (± standard error) in carrion available to scavengers due to climate change from 1950 to 2000 with and without wolves in our dynamic model.
Discussion
The winter period on the northern range of Yellowstone NP is shortening. Both late-winter SDTHs and the overall duration of snow cover have decreased significantly since 1948 (see Figures 1–3). There are several potential causes of reduced snow pack. Average TMIN and TMAX values are increasing in late winter, while midwinter snowfall appears to be declining (Table 1). Compounding the effects of declining snowfalls on SDTH is an increase in the number of winter days with temperatures above freezing (see Figure 3C and 3D).
Decreases in late-winter snow pack and in the date of last snow cover imply that elk will recover sooner from the detrimental stresses of winter: Smaller snow packs allow elk easier access to food and decrease energy expenditures required for movement. In addition, herbaceous plant growth usually begins within a few days to weeks of last snow cover [28], so elk may increase the quality and quantity of food intake earlier in the year, thus shortening the physiologically stressful winter period. These factors are likely to influence the timing and abundance of carrion as late-winter elk mortality declines. As we demonstrate here, climate change serves to sharply reduce the amount of late-winter carrion available to Yellowstone's scavengers (see Figure 4). According to our statistical and dynamic models, however, this reduction is much less pronounced in the presence of wolves. In our statistical model, for instance, we found an 11% reduction with wolves versus a 66% reduction without wolves in April (see Figure 4). Our dynamic model, which incorporates wolf and elk population growth, also reveals a decline in late-winter carrion, especially in the absence of wolves (Figure 5). In contrast to the statistical model, our dynamic model predicts an increase in early winter carrion, but less so with wolves. As the winter period shortens, elk that normally would die in March and April will increasingly die in the early winter months, November through February. This will lead to an increasingly pulsed or seasonal carrion resource. It is important to note that our model has more detailed elk than wolf dynamics. As suitable data become available, future work can attempt to tease out such factors as the effects of SDTH and territoriality on wolf kill-rate. In both our dynamic and statistical models we find that wolves buffer the effects of climate change on carrion abundance and timing.
This effect will be crucial to scavenger species in the Yellowstone area that are highly dependent on winter and spring carrion for overwinter survival and reproduction. Under scenarios without wolves, these species could face food bottlenecks in the absence of late-winter carrion. The magnitude of this effect will depend on how quickly these species adapt to a changing environment and how their other food resources respond to a shortening of the winter period.
Asynchrony of organismal responses to climate change has been prevalent in other areas, leading to changes in the competitive balance between species and to food shortages at important times of year [1]. Yellowstone should prove no exception. Species that respond to weather cues, such as many herbaceous plants, will simply start growing earlier in the year in response to earlier snow melt. Species that respond primarily to day length cues, such as some hibernating species, may change less. Coyotes, for instance, are highly dependent on late-winter and early-spring carrion to carry them over until late spring, when elk calves and ground squirrels become abundant. If late-winter carrion were to disappear without a corresponding change in the timing of elk calving or ground squirrel emergence, a serious food bottleneck could develop.
As carrion becomes more concentrated over a shorter window of the year, the relative access to carrion among different scavenger species may change. Highly aggregated or pulsed resources saturate local communities of scavengers, allowing species with better recruitment abilities (animals capable of covering large distances and communicating about the location of resources such as ravens and bald eagles) to dominate consumption at carcasses [17]. Resources that are more dispersed, conversely, do not saturate local scavenger communities, so that a competitive dominance hierarchy (with grizzly bears and coyotes at the top) determines which species consume the bulk of available scavenge. Our analysis suggests that winter carrion in the absence of wolves will become increasingly pulsed during winter. Consequently, areas without wolves may experience an increase in scavengers with high recruitment abilities. Actual numerical responses by scavenger species to wolf-provided carrion can now be tested in field studies by comparing areas with wolves to those without wolves in order to determine if changes in scavenger population sizes following wolf reintroduction are consistent with the predicted magnitude of the temporal subsidy due to wolves.
As the climate warms, those species will persist that are able to adapt to differences in the environment. Late-winter carrion in Yellowstone will decline with or without wolves, but by buffering this reduction, wolves extend the timescale over which scavenger species can adapt to the changing environment. It is important to note that under present-day climatic conditions, we expect wolves to decrease the long-term average elk population in Yellowstone [29]. This will lead to a corresponding decrease in average yearly carrion levels, which is expected to be small, however, because declines in carrion due to a drop in elk numbers will be partly offset by a higher turnover in the elk population due to wolf predation on old animals [29]. Scenarios both with and without wolves therefore provide a meaningful and roughly equivalent (see Figure 4 in [29]) amount of carrion to scavengers. What we demonstrate here is that scavengers in areas without wolves will experience carrion as an increasingly pulsed resource under climate change, whereas in areas with wolves carrion will remain spread out over the winter months.
The primary objective of this study is to understand the influence of winter climate and predation on trophic dynamics. Our analysis is retrospective, examining what would have happened to scavenge availability in scenarios with and without wolves over the last fifty years of climate change. One may ask, however, what these results imply in light of predictions for continuing global warming into the future. Elk population numbers in Yellowstone are currently constrained by the availability of winter range, where snow levels are low enough to allow for elk movement and cratering through the snow to access food resources. If snow levels in Yellowstone continue to decline in the future, winter range expansion and thus higher elk densities are likely to occur. We expect, therefore, that the wolf-elk-scavenger complex will accrue added importance in the years to come. Future studies examining climate change impacts on spring and summer rainfall, which sets forage levels for elk, will be crucial to further deciphering the effects of global change on trophic relationships in Yellowstone.
We are just beginning to understand the interaction between top predators, such as wolves, and global climate patterns. On Isle Royale, trophic effects have recently been shown to be mediated by behavioral responses to climate. There, gray wolf pack size is partly controlled by climatic conditions that, in turn, affect wolf kill-rates on moose (Alces alces) and consequent herbivory levels on balsam fir (Abies balsamea) [30]. In Yellowstone, our scenarios demonstrate that wolves act to retard the effects of a changing climate on scavenger species. Together these results begin to elucidate the expected changes that may occur to boreal ecosystems as a result of climate change effects on top predators.
Materials and Methods
The northern range of Yellowstone NP is the wintering area of the park's largest elk herd and home to 4–6 gray wolf packs. Elevations range from 1,500 to 3,400 m, with 87% of the area between 1,500 and 2,400 m [25]. The climate is characterized by short, cool summers and long, cold winters, with most annual precipitation falling as snow. Mean annual temperature is 1.8 °C, and mean annual precipitation is 31.7 cm [25]. Large, open valleys of grass meadows and shrub steppe dominate the landscape, with coniferous forests occurring at higher elevations and on north-facing slopes.
Weather data analysis
Since 1948, meteorological data has been collected daily from two permanent weather stations on the northern range of Yellowstone NP. One is located in Mammoth Hot Springs at park headquarters near the northern entrance to the park. The other is located at the Tower Falls ranger station about 29 km east of Mammoth. Data for the period 01 August 1948 to 01 June 2003 were made available to us by the Western Regional Climate Center in Reno, Nevada, United States.
Using linear regression, we investigated multiannual trends in monthly average SDTH over the 55 y provided in the data set. SDTH is treated as the response variable and regressed upon year. We also examined trends in the timing of the date of first bare ground. This was defined as the first day of the year for which SDTH was zero. In order to understand changing patterns in SDTH, we analyzed average monthly snowfall (SNFL), average TMIN and TMAX, and the number of days per winter that TMAX exceeded freezing.
Wolf effects: Statistical model
In order to compare the effects of carrion availability to scavengers under climate change in scenarios with and without wolves, we used previously published regression equations [23] relating SDTH, S, to monthly carrion availability, Cp, prior to wolf reintroduction given by
and relating SDTH and wolf pack size to carrion availability, Ca, after wolf reintroduction [16] obtained using
where K is the wolf kill-rate per wolf, P is the wolf pack size, 30 is the number of days in a month, and Q is the percent of the edible biomass of a carcass consumed by a wolf pack given by Wilmers et al. [16]. We used Monte Carlo methods, as elaborated below, to reconstruct how much carrion would have been available to scavengers during each of the winter months (November through April) in the years 1950 and 2000 under scenarios with and without wolves. Specifically, for each scenario [1950 without wolves, 2000 without wolves, 1950 with wolves, and 2000 with wolves], we drew 100 random SDTH values for each of the months, where SDTH was assumed to be normally distributed with mean and standard error for the years 1950 and 2000 given by the regression analyses of the Tower Falls weather data (see Figure 2). This incorporated uncertainty into our estimate of SDTH for the years 1950 and 2000, allowing us to draw random SDTH values from those years for our Monte Carlo simulation. In the scenarios without wolves, we inserted our randomly chosen monthly SDTH values for each year and each run into equation 1 to yield the amount of carrion available per month without wolves. We used the same procedure for selecting SDTH in our scenario with wolves. In order to select wolf pack size, we assumed that wolf pack sizes were normally distributed, with a mean (± standard deviation) pack size of 10.6 (± 5) representing the current distribution of Yellowstone wolves [31]. We then inserted our randomly chosen monthly SDTH values and wolf pack sizes into equation 2 to yield the amount of carrion available per month with wolves. For each run of each scenario, we recorded the reduction in monthly winter biomass available to scavengers in 2000 as a proportion of what was available in 1950.
Our statistical modeling approach, although rooted empirically, is limited by the fact that it does not take into account the possible effects of wolf and elk population dynamics on carrion availability. In order to explore these effects, therefore, we used a previously published model [29] that was originally built to explore the effects of wolf and elk population dynamics on monthly carrion flow to scavengers.
Wolf effects: Dynamic model
The details of the model are exactly the same as in Wilmers and Getz [29], except for the following changes. In the original model, SDTH was incorporated into the elk population dynamics but was treated as a random variable. In the present study, we modified the model so that the actual progression of winter weather from 1950 to 2000 was used. We ran the model for 51 y, from 1950 to 2001. We selected SDTH, V, for the year and month in question from the Tower Falls regression equations in exactly the same manner that we describe above in the statistical model. Since the distribution of elk among age classes from 1950 is not known, we performed, as a baseline, a 50-y run of the model under average 1950 weather conditions. This is long enough for the effects of initial conditions to dissipate. We then used the numbers and age structure of the final month of the baseline run as the initial conditions of the run using observed weather data from 1950 to 2000.
Sensitivity analyses were conducted using Monte Carlo methods to assess the relative effects of different parameter values on model output [29,32]. Since the primary goal of using the dynamic model is to assess whether late-winter carrion will be affected by elk and wolf population dynamics in the context of a changing climate, we defined an output variable, z, as the percent change in late-winter carrion from 1950 to 2000. We assigned March and April to late winter for comparison to Figure 4, since these are the two months showing a significant effect between scenarios with and without wolves. For each scenario, we conducted 1,000 runs of the model, choosing a different set of parameter values at random from the ranges provided in Table 1 of Wilmers and Getz [29]. Each model parameter was then regressed against z to determine its effect.
We thank Dan Stahler, Doug Smith, Blake Suttle, Mary Power, Dale McCullough, and George Wittemeyer for helpful discussions and reviews of the manuscript. Reviews by Bob Holt, Andy Dobson, and David Macdonald also greatly improved the manuscript. Funding was provided by an Environmental Protection Agency Science to Achieve Results fellowship to CCW and by the James S. McDonnell Foundation Twenty-First Century Science Initiative Study of Complex Systems award to WMG.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CCW conceived and designed the experiments, performed the experiments, and analyzed the data. CCW and WMG contributed reagents/materials/analysis tools and wrote the paper.
Citation: Wilmers CC, Getz WM (2005) Gray wolves as climate change buffers in Yellowstone. PLoS Biol 3(4): e92.
Abbreviations
NPNational Park
SDTHsnow depth
SNFLsnowfall
TMAXmaximum temperature
TMINminimum temperature
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| 15757363 | PMC1064850 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Apr 15; 3(4):e92 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030092 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1575736410.1371/journal.pbio.0030093Research ArticleEvolutionGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyDrosophilaFunctional Evolution of a cis-Regulatory Module Evolution of the S2E cis-ElementLudwig Michael Z [email protected]
1
Palsson Arnar
1
Alekseeva Elena
1
Bergman Casey M
2
Nathan Janaki
1
Kreitman Martin
1
1Department of Ecology and Evolution, University of ChicagoIllinoisUnited States of America2Department of GeneticsUniversity of CambridgeUnited KingdomLevine Michael Academic EditorUniversity of California, BerkeleyUnited States of America4 2005 15 3 2005 15 3 2005 3 4 e9323 8 2004 13 1 2005 Copyright: © 2005 Ludwig et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Plus Ca Change: Gene Enhancers Upset Evolutionary Assumption
Lack of knowledge about how regulatory regions evolve in relation to their structure–function may limit the utility of comparative sequence analysis in deciphering cis-regulatory sequences. To address this we applied reverse genetics to carry out a functional genetic complementation analysis of a eukaryotic cis-regulatory module—the even-skipped stripe 2 enhancer—from four Drosophila species. The evolution of this enhancer is non-clock-like, with important functional differences between closely related species and functional convergence between distantly related species. Functional divergence is attributable to differences in activation levels rather than spatiotemporal control of gene expression. Our findings have implications for understanding enhancer structure–function, mechanisms of speciation and computational identification of regulatory modules.
Analysis of the even-skipped stripe 2 enhancer from four Drosophila species revealed that functional divergence is attributable to differences in activation levels rather than spatiotemporal control of gene expression
==== Body
Introduction
The annotation of genes from comparative sequence data rests on a fundamental evolutionary dictum, first elaborated by M. Kimura, that the rate of molecular evolution will be inversely related to the level of functional constraint. But the application of this principle would not be interpretable without a corresponding understanding of gene structure and organization (i.e., the genetic code and its degeneracy, the signals for initiation and termination of translation, intron/exon junction sequences, etc.). Knowledge of equivalent scope and depth does not exist for cis-regulatory sequences. These sequences often contain docking sites for transcription factors (TFs), but the number of binding sites and the spacing between them vary, and binding-site sequences are often degenerate to the point that they can only be characterized probabilistically. Even more striking is the lack of data relating functional evolution of gene expression to cis-regulatory sequence evolution. There are good reasons to expect the two may be only weakly correlated [1,2]: De novo binding sites can readily evolve [3]; individual TFs often bind at multiple locations and may be exchangeable, and the spacing between binding sites can rapidly evolve. Thus, despite recent progress [4,5], rules have yet to be elucidated for the functional molecular evolution of this critically important component of the genome.
The Drosophila gene even-skipped (eve) produces seven transverse stripes along the anterior–posterior (A–P) axis of a blastoderm embryo (Figure 1). Expression of these early stripes is regulated by five distinct cis-elements (Figure 2A). The best studied of them, the stripe 2 enhancer (S2E), contains multiple binding sites for five TFs, the activators bicoid and hunchback, and the repressors giant, Kruppel, and sloppy-paired [6,7,8]. Maternal deposition of bicoid mRNA in the anterior pole of the egg regulates expression of the other gap genes, which are expressed in broad A–P diffusion gradients. Spatiotemporal control of eve stripe 2 expression is brought about through the integration of these graded signals by the S2E.
Figure 1 Expression of eve
(A–D) Embryos of four Drosophila species at early cellular blastoderm stage. EVE stained with immunoperoxidase DAB reaction enhanced by nickel.
(E–H) Df(eve) D. melanogaster embryos with two copies of transgenes containing eve S2E from four species fused to D. melanogaster eve coding region (−0.9 to +1.85 kb) at blastoderm stage. Immunofluorescence-labeled EVE. The S2Eere-EVE (G) produces consistently weaker stripes than lines carrying S2Es from the other three species. (A and E) D. melanogaster, (B and F) D. yakuba, (C and G) D. erecta, and (D and H) D. pseudoobscura.
Figure 2 Genetic Constructs and Rescue Scheme
(A) Summary map of the eve locus and eve S2E deletion transgene (EVEΔS2E). Adam and Apple are adjacent open reading frames [40]. The late element (Auto) and early stripe enhancers are shown.
(B) S2E-EVE transgenes used to rescue eve function. The rescue EVE locus used is the D. melanogaster eve flanked by 0.9 kb of 5′ and approximately 0.6 kb of 3′ of endogenous sequence. The S2E
o
-EVE does not have any S2E sequences and is a negative control. The known trans-factor-binding sites in the S2E from D. melanogaster: five bicoid (circles), three hunchback (ovals), six Kruppel (squares), three giant (rectangles), and one sloppy-paired (triangle) binding site. Symbols representing sites 100% conserved compared to D. melanogaster are open, while those diverged are shaded gray. Note the evolutionary gain of novel but functionally necessary [6] activator (bicoid and hunchback) binding sites (red) in D. melanogaster lineage. Full sequences are shown in Figures S1 and S2.
(C) Example of a cross between independent rescue lines and relevant offspring genotypes for the viability assay (see Materials and Methods for details). Genetic notation b: mutant black; yellow box: native eve; R13 and X'd out yellow box: eveR13 lethal mutant; P(S2EΔEVE): eve −6.4 to 8.4 kb without S2E; P(S2E
A1
-EVE) and P(S2E
A2
-EVE) are two independent rescue-transgene inserts with S2E from species A.
We previously used a reporter transgene assay to investigate eve S2E functional evolution in three Drosophila species in addition to D. melanogaster. The sister taxa D. yakuba and D. erecta [9] are separated by approximately 5 million years ago (MYA), while the ancestor they share with D. melanogaster existed approximately 10–12 MYA. In contrast, D. pseudoobscura is a member of a different group and is believed to have split from the melanogaster clade approximately 40—60 MYA. As expected for a trait as ontogenetically important as primary pair-rule stripe formation, the temporal progression of eve stripe expression is nearly identical among the species (see Figure 1A–1D). This functional conservation of gene expression, however, is not reflected in patterns of sequence conservation (see Figures 2B, S1, and S2). Instead, S2E sequences from these species are substantially diverged, including large insertions and deletions in the spacers between known factor-binding sites, single nucleotide substitutions in binding sites, and even gains or losses of binding sites for the activators bicoid and hunchback.
Yet despite these evolved differences, reporter transgene analysis showed that spatiotemporal patterns of gene expression driven by S2Es of all four species are indistinguishable when placed in D. melanogaster [10], indicating that evolved changes in the enhancer have had little or undetectable impact on spatiotemporal control of gene expression. But further experiments with native and chimeric S2Es of D. melanogaster and D. pseudoobscura showed that this functional conservation required coevolved changes in the 5′ and 3′ halves of the enhancer [11], suggesting compensatory (i.e., adaptive) evolution. This functional evidence for adaptive substitution, together with indications that levels of gene expression might also differ among the four species' S2Es, raises questions about whether these orthologous enhancers are indeed functionally identical. To overcome limitations inherent in functionally interpreting the overlap of a reporter and native gene expression, here we report results of an in vivo complementation assay to investigate S2E performance. This approach allows us to put the functional equivalency hypothesis to a rigorous test.
Results
Strategy and Proof of Principle
First, we created a fly line, EVEΔS2E, in which the native eve S2E was deleted (see Figure 2A). We then attempted to complement, that is, rescue this lethal mutation with the introduction of a transgene, denoted S2E-EVE, containing an eve S2E from one of the four species (D. melanogaster, D. yakuba, D. erecta, or D. pseudoobscura) linked to a functional eve promoter and coding region (Figure 2B). This allowed us to compare both viabilities and developmental consequences among lines differing only in the evolutionary source of their S2E. By genetically manipulating rescue-transgene copy number (Figure 2C), effects of EVE abundance on viability and development could also be investigated.
We created the eve S2E deficiency mutant by removing a 480-bp fragment corresponding to the minimal stripe 2 element (MSE; see Figure S1) from a 15-kb cloned copy of the eve locus [12]. A transgene containing the complete fragment is capable of rescuing eve null mutant flies to fertile adulthood [12]. EVEΔS2E is functionally a null allele for stripe 2, as evidenced by the expression of the segment polarity gene, engrailed (en). Establishment of en 14-stripe pattern is a complex process that includes involvement by eve early stripes [13,14]. Eve stripe 2 corresponds to parasegment 3, which is bordered by en stripes 3 and 4. We hypothesized that these en stripes might be developmental indicators of early eve stripe 2 expression. Indeed EVEΔS2E embryos lacking a functional S2E (Figure 3A–3F) produce a short parasegment 3 and vestigial en stripe 4 (Figure 3F). This defect alone is almost certainly a lethal condition.
Figure 3 Developmental Series of EVE Abundance
(A–E) Immunofluorescence labeling of time-staged early EVEΔS2E homozygous embryos. This developmental sequence, which corresponds roughly from the initialization of cellularization (A) to its completion (E), takes approximately 45 min at 25 oC in wild-type flies [41].
(F) Expression of en in same genotype at stage 10. Arrows mark third and fourth en stripes. Note the short interval between en stripes 3 and 4 (parasegment 3) and the reduced fourth stripe.
(G) EVE expression in stripe 2 during the developmental series around cellularization, where times 1–5 correspond to pictures in A–E. Stage 1 is early cellularization, while the process has been completed for embryos in class 5. The series is comparable to time classes 4–8 on the FlyEx Web site (http://flyex.ams.sunysb.edu/flyex/) [34]. Estimated least square means (± SE) for EVEΔS2E/Cy stock and wild-type line w1118; note the Cy/Cy homozygote is essentially wild-type. Early eve pair-rule expression is not known to be autoregulated (as occurs in postcellularization stages), and we observe a 2-fold difference in early stripe expression, with an additive component (a) of 0.62 and negligible dominance deviation (d/a) = 0.01, for the first two stages. This dosage dependency is lost after the cellularization stage (3), presumably because all embryos carry two copies of the autoregulatory element.
Transgenes containing precisely orthologous S2Es from each of the four species linked to the D. melanogaster eve promoter and coding region were introduced onto the third chromosome. The fragment we chose to investigate is 692 bp in length in D. melanogaster (see Figure S1). It contains the central MSE, and every other previously identified TF-binding site in the S2E region. Notably, this fragment contains completely conserved sequences at its 5′ and 3′ ends in all four species, thus ensuring that we could compare precisely orthologous fragments. As expected, all four S2E-EVE transgenes express a single early eve stripe in the expected spatial location (see Figure 1E–1H).
Having created the EVEΔS2E chromosome line and the S2E-EVE rescue third chromosome lines, we could then produce flies carrying EVEΔS2E; S2E-EVE in a doubly balanced configuration (see Figure 2C). Crossing this line with itself or with another line carrying an independent copy of the same S2E allowed us to estimate relative survival to adulthood of offspring carrying one or two copies of the rescue transgene. EVEΔS2E homozygotes are embryonic lethal, whereas flies carrying two copies of the D. melanogaster S2Emel-EVE transgene in an EVEΔS2E genetic background rescue approximately 34% of flies to adulthood (Figure 4). This is approximately the same rescue percentages found for the same genotype (P[EVEG84], R13), which contains the wild-type eve locus (including the native S2E) [12]. This implies that the fragment we used to drive stripe 2 eve expression is complete and that it can function normally when removed from its native context. Importantly, our negative control, S2E0-EVE, does not rescue, indicating that the rescue transgene requires this enhancer to drive eve stripe 2 expression.
Figure 4 Rescue to Adulthood of eve Null Mutants
Rescue percentages to adulthood of EVEΔS2E homozygotes with one or two copies of rescue construct from the four species, and the negative control, denoted on x-axis. Each bar represents percentages summarized over sexes and reciprocal crosses (full data in Table S1).
Functional Equivalence of the D. melanogaster and D. pseudoobscura S2Es
We evaluated the ability of S2E-EVE rescue constructs to complement the embryonic lethal EVEΔS2E deletion by estimating survival to adulthood, based on a genetic design used extensively in Drosophila evolutionary genetics [15]. Viability measurements were made by crossing two independent lines of each rescue transgene to reduce potential recessive fitness effects caused by the site of rescue-transgene insertion. Offspring with two copies of the transgene are doubly hemizygous; few deleterious effects of transgene insertion were observed in these flies (compare, for example, EVEΔS2E, R13/CyO; S2E-EVE/S2E-EVE versus EVEΔS2E, R13/CyO; S2E-EVE/TM3 survivors in Table S1). Rescue abilities of S2Es from different species can be compared quantitatively because the viability of each S2E-EVE transgene is calculated relative to a standard genotype present in every cross.
S2Es from the four species exhibited large differences in rescue abilities that follow neither a phylogenetic trend nor net sequence divergence (Figure 4). The S2E of the most distantly related species, D. pseudoobscura, is completely conserved at only three of 18 TF-binding sites identified in D. melanogaster and is missing two of them entirely (see Figures 2B and S2). It is also nearly 25% longer due to insertions and deletions in the spacers between binding sites. Yet in terms of rescue ability it is indistinguishable from the D. melanogaster S2E.
Functional Divergence of S2Es from Closely Related Species
Given the complete functional conservation of the D. pseudoobscura S2E, we were surprised to discover the failure of the D. erecta transgene to restore viability in EVEΔS2E homozygotes (see Figure 4). The inability of the doubly hemizygous S2Eere-EVE genotype to rescue cannot be due to deleterious effects of transgene insertion, because the presence of each single transgene has minimal impact on viability (see Table S1). Two additional independent transformants were also investigated, neither of which produced viable adult flies. We conclude, therefore, that the D. erecta sequence, although precisely orthologous to the D. melanogaster and D. pseudoobscura S2E fragments, is nonfunctional when placed in a D. melanogaster embryonic context.
The D. yakuba's S2E also exhibits a rescue defect in that two copies of the rescue transgene are required for robust rescue. Flies carrying a single copy of the D. yakuba rescue transgene are less than half as viable as flies carrying one copy of either the D. melanogaster or D. pseudoobscura rescue transgene. A smaller dosage effect on viability of approximately 20% is seen with the S2Es of D. melanogaster and D. pseudoobscura. Since the spatiotemporal expression of eve stripe 2 must be the same for flies carrying one or two copies of a transgene, eve stripe 2 expression level alone must have a measurable influence on fitness.
As expected, embryos carrying one or two copies of either the D. melanogaster or the functionally equivalent D. pseudoobscura S2E rescue transgene exhibit a wild-type en staining pattern, indicating a normal parasegment 3 (Figure 5A–5I). In contrast, the D. erecta S2E exhibits an en pattern defect similar to the one produced in embryos lacking eve stripe 2 expression (i.e., EVEΔS2E homozygote). The inability to drive normal en expression provides further evidence that the D. erecta S2E is a weak (or nonfunctional) enhancer in the D. melanogaster genetic background.
Figure 5 Effects on en Expression
(A, C–I) The en pattern in homozygous EVEΔS2E and (B) wild-type (w1118) specimens at stages 9–11. All strains (except [B]) are homozygous for Df(eve) P(EVEΔS2E) second chromosomes, with the third chromosome differing only by rescue transgenes: (A) no rescue transgenes; (C) P(mel 36)/P(mel 36) is a S2Emel-EVE stock; (D) P(yak 74)/TM3 Sb and (E) P(yak 74)/P(yak 74) are S2E
yak
-EVE stocks; (F) P(S2Eo-EVE)/P(S2Eo-EVE) has no S2E; both (G) P(ere 41)/P(ere 41) and (H) P(ere 21)/P(ere 21) are S2Eere-EVE transgenic stocks; and (I) P(pse 91)/P(pse 91) is a S2Epse-EVE stock. Note the variation in distance between third and forth en stripes (arrows) and relative level of en expression in the fourth stripe. Only the first seven parasegments of the en pattern are show (except in [A]). The en protein was visualized by an immunoperoxidase DAB reaction enhanced by nickel. mel: D. melanogaster; yak: D. yakuba; ere: D. erecta; pse: D. pseudoobscura. S2Eo-EVE lacks a S2E.
The D. yakuba S2E also exhibits an en phenotype that correlates with its ability to rescue (Figure 5D and 5E). With two copies of the enhancer present, embryos exhibit a robust en stripe 4, indistinguishable from wild-type. But with only one copy present, en stripe 4 expression is shifted anteriorly relative to its neighbors, an indication that parasegment 3 is not forming properly. Some of these embryos survive to adulthood since we do observe one-copy adults in our viability experiment, albeit at a lower than expected percentage. Although adult flies are superficially “normal,” we can observe subtle morphological defects (mouthparts and thoracic structures) in the segments corresponding to parasegment 3.
Differences in eve S2E Expression Levels
To test whether differential gene expression might be the critical functional difference between the S2Es, we quantified eve stripe 2 protein in early embryos. The experimental design allowed us to normalize eve stripe 2 expression in individually stained embryos relative to stripe 3, thus facilitating comparison across embryos and genotypes. We also developed a PCR method to ascertain the genotype of individually stained embryos.
We validated the quantification procedure by comparing eve stripe 2 expression levels in embryos carrying zero, one, or two copies of the S2E in its native position in a wild-type eve locus—that is, EVEΔS2E/EVEΔS2E, EVEΔS2E/Cy, and Cy/Cy embryos, respectively, and a homozygous w1118 line (see Figure 3G). The expected dose dependence is observed in response to EVEΔS2E copy number prior to cellularization, followed by a shift to dose independence as control of eve stripe expression is transferred to the late (autoregulatory) element. Unexpectedly, a weak early stripe 2 (estimated to be approximately 20% of the wild-type level) can be detected in EVEΔS2E homozygotes; we do not know what drives this stripe.
Normalized stripe 2 expression in early embryos carrying S2Es from D. erecta and D. pseudoobscura is consistent with adult viability (Figure 6). The D. erecta S2E-driven eve expression is too weak to observe statistically significant expression comparing embryos containing zero, one, or two copies of the rescue transgene. Note, however, that this transgene does drive weak eve stripe 2 expression in a fully eve null background (see Figure 1G). Formally, we observe statistically significant effects of gene “dose,” S2E “species” of origin, and most notably a “dose × species” interaction on stripe 2 expression by a mixed-model analysis of variance (ANOVA) (see Tables 1 and S2). Therefore, the major functional evolutionary difference between these enhancers is likely to reside in their activation strengths.
Figure 6 Diverged S2Es Contribute Differentially to EVE Abundance
Fluorescence-labeled antibody staining of EVE in embryos with zero (A, C, and E) or two (B, D, and F) copies of rescue transgene. A dose effect is seen in D. pseudoobscura line 91, (A and B), while none is observed in D. erecta line 41 (C and D) or 21 (E and F). (G) These effects are significant when comparing EVE protein quantity (least square means ± SE) in stripe 2 (Dose × Species, F = 4.69(2, 100.44), p = 0.01; see Tables 1 and S2) D. pseudoobscura (black circles, n = 59) and D. erecta embryos (open circles, n = 71). For D. pseudoobscura the estimated additive component (a) = 0.37 and dominance deviation(d/a) = 0.17.
Table 1 ANOVA on EVE Abundance in Stripe 2 from D. erecta and D. pseudoobscura S2E Rescue Constructs
Mixed-model term “Dose” indicates copy number of rescue transgene; “Time” is a continuous variable of the developmental series; “Species” indicates the origin of the S2E and “DV index” reflects the dorsal–ventral orientation of each embryo. VC indicates variance components, with estimated standard errors and significance according to the z-distribution
ns, not significant; p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001
Discussion
Evolution of Enhancer Structure–Function
The D. melanogaster S2E rescue transgene, and its considerably diverged D. pseudoobscura ortholog, each restore complete eve stripe 2 biological activity when placed in a genetic background lacking a native S2E. The DNA fragment we investigated, therefore, entails both the biological and evolutionary units of enhancer function. We chose this fragment based on its extensive prior characterization, including genetic, reverse genetic, and footprinting analyses [6,16,17,18]. In particular, Stanojevic et al.'s [18] TF footprinting data appear to have nicely delineated the functional enhancer.
Our previous experiments with S2Es of these two species demonstrated that both intact enhancers, but not the chimeras between them, drive the correct spatiotemporal pattern of reporter gene expression [11]. The rescue experiments reported here extend this finding by showing that the two orthologs are in fact biologically indistinguishable. These new results reinforce our contention that the phenotypic character—early stripe 2 expression—must be under stabilizing selection. The character itself remains unchanged over evolutionary time despite substitutions in nearly all the TF-binding sites, the gain and loss of some of them, and considerable change in the spacing between sites. This suggests to us that unlike proteins, where functional conservation usually means selective constraint on important amino acids (such as the active site of an enzyme), enhancers have a more flexible architecture that allows modification, and perhaps even turnover, of their “active” sites. Dissimilarities in the structure–function of enhancers and proteins result in different emergent “rules” of molecular evolution.
But the fact that the D. melanogaster and D. pseudoobscura S2Es are biologically indistinguishable does not necessarily imply that enhancer function has been evolutionary static. Rather, the similar biological activities appear to be the result of convergence. In particular, phylogenetic analysis of S2E sequences indicates that the bcd-3 binding site in D. melanogaster was acquired only recently in the lineage leading to D. melanogaster (see Figure 2B). (There are also lineage-specific deletions in the spacers flanking both sides of the bcd-3 site in the D. melanogaster lineage, which shift the proximal and distal repressors giant and Kruppel binding sites, respectively, closer to this bicoid site. These length changes may have coevolved to enable or increase local repression of this novel activator site.) The bcd-3 site was shown by Small et al. [6] to be required for MSE stripe expression. It seems likely, therefore, that the ancestral S2E lacking this binding site would not properly activate stripe 2 expression in D. melanogaster. Perhaps the sensitivity of the enhancer (or more precisely, the fragment investigated) to activator signals has oscillated over evolutionary time, in which case the similarity between the two distantly related species' S2Es would be an example of functional convergence.
The fact that the S2E fragment from D. erecta is essentially unresponsive to the D. melanogaster morphogen-gradient environment, but the precisely orthologous segment from D. melanogaster (and D. pseudoobscura) responds properly, proves that this fragment must contain evolved differences of functional significance between the species. The lack of biological activity of the D. erecta transgene in D. melanogaster should perhaps come as no surprise, however: Its lower sensitivity to activation may represent the ancestral state of the enhancer. What is surprising is the rapidity with which these functional differences evolve.
Phylogenetic footprinting of distantly related species can readily identify strongly conserved motifs [19] but runs the risk of not detecting enhancers that have retained their function but have evolved structurally. To overcome this, a technique called phylogenetic shadowing—the comparison of noncoding sequences among closely related species—has recently emerged [9]. Our results show that there is no necessary relationship between enhancer phylogenetic (or sequence) relatedness and functional similarity. Closely related species cannot be assumed to be more functionally conserved than distantly related species in enhancer structure–function.
Why Is the D. erecta S2E Transgene Not Functional in D. melanogaster?
D. erecta produces a native early eve stripe 2. Why then does the S2E fragment from this species not produce a robust early stripe when placed in D. melanogaster? The first possibility is that the fragment we investigated no longer contains a functional enhancer and has been replaced by an equivalent enhancer somewhere else in the eve locus. This possibility can easily be ruled out: The overall architecture of the eve locus, including all of its 5′ and 3′ enhancers, is well conserved, and there is no new cluster of the appropriate TF-binding sites that could act as a S2E. Another unlikely possibility is that the locus has been duplicated, and the fragment we investigated has become functionally inert (i.e., equivalent to a pseudogene). There is no indication of a duplicated eve locus in the D. erecta genome, and all features of the eve locus (including its S2E) are intact and do not indicate any degeneration.
This leads us to conclude that the D. erecta fragment used in our experiments contains the S2E. We can consider three additional possibilities. The first is that this fragment is no longer the complete biological unit, that is, novel binding sites have evolved in this species distal or proximal to this fragment, which have become assimilated into the active enhancer by a process we call accretion. As Figure 7 shows, patser, a binding-site prediction program [20] identifies a single potential bicoid-binding site 135 bp upstream of Block-A (Figure S1), the distal end of the D. erecta S2E transgene. This potential site contains an unconventional bicoid-binding motif,
TCAATCCC. The next closest potential binding site is another 350 bp further upstream and also has an unconventional binding-site sequence (
ACAATCGG). So, although we cannot rule out the possibility that these are biologically active sites that contribute to S2E activity, they are relatively distant from the recognized S2E (and other bicoid sites), and their sequences do not have the consensus core motif (
TAATC). Future experiments will allow us to formally test whether this enhancer has physically expanded. If so, this would be the first documented case for accretion, the adaptive expansion of an enhancer.
Figure 7 Predicted Binding-Site Composition and Sequence Conservation in the eve 5′ Noncoding Region
(A) D. melanogaster predicted binding-site composition.
(B) D. yakuba predicted binding-site composition and sequence conservation with D. melanogaster.
(C) D. erecta predicted binding-site composition and sequence conservation with D. melanogaster.
(D) D. pseudoobscura predicted binding-site composition and sequence conservation with D. melanogaster. Coordinates of functionally characterized enhancer sequences are shown in light blue, and unannotated conserved noncoding sequences are shown in pink. The coordinates of the homologous stripe 2 sequences correspond to the constructs in Figure S1, while the coordinates of the AR and stripe 3 enhancers have been estimated based on sequence conservation. Note that the scale of the genomic intervals plotted differs between panels (black bar = 500 bp). Binding sites are indicated by color; bicoid (blue), hunchback (red), and Kruppel (green).
The second possibility is coevolution between the D. erecta S2E and its promoter region, such that it is not capable of driving transcription properly from a D. melanogaster promoter. We also view this as unlikely for several reasons. First, prior to designing these experiments, we investigated this issue with the core promoters and S2Es of D. melanogaster and D. virilis (which is an outgroup to the species studied). We could detect no difference in spatial or temporal expression of each S2E with either promoter (Ludwig, unpublished data). Second, the core promoter regions of D. melanogaster and D. erecta are highly conserved, including complete preservation of both the
TATAA and the
GAGA site. Indeed there are only four nucleotide differences (and no indels) between the species in a 150-bp stretch containing these sites. Finally, one might expect most functional changes in the core promoter to be pleiotropic, given the presence of more than a dozen other separable enhancers in the eve locus, and therefore to be selected against.
The final possibility is that the D. erecta S2E fragment does contain the entire biological enhancer, but that the trans-acting environment—the morphogen gradients to which the enhancer responds—differ between the species, causing the enhancers to have evolved to accommodate the differences. In other words, the sensitivity, or set point, of the binary (on–off) switch function has coevolved with the trans-acting environment in order for the S2E to maintain the appropriate response to evolved activation inputs. This hypothesis implicates, in particular, evolutionary shifts in the bicoid and/or hunchback activator gradients. As noted above, there has been a lineage-specific addition of the functionally required bcd-3 binding site [6] in D. melanogaster that is not present in any of the other species. Second, there is also a lineage-specific loss of the hunchback-1 (hb-1) site in D. erecta (which may be present in its sister taxon, D. yakuba). We propose that the lack of sites for these activators, and the presence of a species-specific six-base-pair insertion in the overlapping hb-2/kr-2 binding sites (Figure S1) reduces the ability of the D. erecta enhancer to respond to the activator gradients of D. melanogaster.
This hypothesis predicts stronger activator gradients in D. erecta than in D. melanogaster. Although we have not yet investigated this possibility directly, we note that native eve blastoderm stripes do not reside in the same physical locations in embryos of the two species, but rather are displaced posteriorly in D. erecta compared to D. melanogaster (compare Figure 1A and 1C). A similar effect can be mimicked in D. melanogaster with the addition of extra copies of bicoid gene [21], which shifts the morphogen gradient posteriorly.
The possible independence of spatiotemporal and rheostat activities [22] relates to a long-standing issue in evolutionary genetics—whether developmental constraints are “tunable” [23]. One school holds that features of development are strongly canalized and that deviation from this path will be strongly selected against. An opposing school holds that these developmental constraints can always be overcome by selection. The eve S2E may exhibit elements of both an immutable developmental constraint and a smoothly evolving trait. Primary pair-rule stripes, such as those laid down by eve in developing blastoderm embryos, establish the positional landmarks that will eventually demarcate segmental identities in the fly. This complex functional network established by gap and pair-rule gene expression imposes strong constraints on spatiotemporal patterns of regulatory gene expression. The S2E must, for example, produce a stripe equidistant from stripes 1 and 3, and at a specific location with respect to other pair-rule genes.
The potential for evolutionary change in the S2E set point or output, on the other hand, may be much less constrained. Genetic variability in the gain and loss of binding sites, polymorphism in binding sites leading to variation in TF binding, and modulation of TF interactions through changes in spacing between binding sites should allow for nearly continuous fine-tuning of these functions. Enhancers should be able to adapt to change in their trans-acting environment. Why might the trans-acting environment for the S2E be evolving? Residing at the head of the hierarchal cascade driving the formation of the A–P axis, perhaps the bicoid morphogen gradient is less constrained than the expression of downstream genes that are more deeply embedded in the interaction network. Or, perhaps there has been natural selection acting on traits such as egg shape, size, or ovariole morphology, leading to changes in the bicoid-diffusion gradient. Egg number and size are, after all, fundamental evolutionary trade-offs. With a properly designed genetic experiment it should be possible to test these hypotheses.
Enhancer Evolution in Relation to Speciation
Postmating isolation is the final step in the speciation process because it involves genetic changes between incipient species that cause hybrid inviability or infertility. Hybrid breakdown is likely to involve evolutionary changes in at least two interacting genes [24,25]. According to this model, the coevolution of a “speciation” gene with its partner(s) allows it to remain functional in its native background but lethal in the hybrid background. One of the mysteries of this process is the regularity and quickness with which molecular incompatibility arises. Thus, for example, exceedingly closely related species, such as D. simulans, D. sechellia and D. mauritiana, whose common ancestors occurred less than 1 MYA, nevertheless, have evolved postmating isolation involving perhaps hundreds of genes [26]. The question is what components of the genome are involved in this coevolutionary conspiracy?
Cis-regulatory modules and the trans-acting TFs with which they interact provide abundant genetic substrates for coevolution leading to hybrid sterility and inviability [2,5]. If our experiments have captured the entire biological S2Es of D. erecta and D. yakuba, then the results suggest that this coevolution could involve changes in expression patterns or levels, rather than changes in the protein sequences of the trans-acting factors. The attractiveness of this hypothesis lies in the fact that there must be many more cis-regulatory modules in a eukaryotic genome than there are encoded proteins. Thus there is ample opportunity for the coevolution of enhancers and trans-factors to produce lethal interactions in hybrids, which may explain the abundance of lethal interactions between closely related species.
The regulation of development is often modeled as a logic circuit, with cis-regulatory sequences functioning as switches controlling information flow. The long-term functional preservation of both the spatiotemporal and the activation strengths of the D. melanogaster and D. pseudoobscura S2Es speaks to the general conservation of this genetic network in fruit fly development. Our results also provide an indication that the stoichiometry of the regulatory components could matter critically for normal development, at odds with theoretical predictions [27]. Epistatic changes accompanying interspecific inviability and sterility may therefore arise more readily as a consequence of quantitative shifts in gene expression than as a result of alterations in the topology of the developmental circuits.
Materials and Methods
Drosophila strains
Df(2R)eve: (Df[eve]) and eveR13 (R13): Df[eve] is a deficiency that includes at least three lethal complementation groups [28]. The R13 is null mutation that truncates the protein within the homeodomain [12].
These lethal mutations were balanced over marked balancer chromosome CyO P(hb-lacZ) to allow identification of mutant embryos by immunostaining for β-galactosidase or by PCR analysis for β-galactosidase gene.
Analysis of embryos
Histochemical staining with guinea pig polyclonal a-Eve [29] at 1:1,000 dilution, or with a-En monoclonal 4D9 [30] at 1:10 dilution was visualized using HRP-DAB enhanced by nickel [31].
Fluorescent staining of Drosophila embryos with polyclonal a-Eve [29] at 1:1,000 dilution was visualized using Alexa Fluor-594 goat antiguinea pig IgG antibodies (Molecular Probes, Eugene, Oregon, United States) at 1:400 dilution.
Optical Z-sectioning (0.8-μm/step) of fluorescent embryos was carried out using motorized microscope Axioplan2 (Carl Zeiss, Thornwood, New York, United States), Hamamatsu C4742–95 camera (Hamamatsu City, Japan), and “Openlab” software (Improvision, Emeryville, California, United States).
Genotyping
Individual embryos were genotyped after imaging, using a PCR method (three pairs of fluor-labeled PCR primers) and Beckman Coulter CEQ™ 8000 genetic analysis system for PCR fragment analysis (Allendale, New Jersey, United States; for detailed protocol see Supporting Information). Three sets of fluor-labeled primers (Proligo, Boulder, Colorado, United States) were used for PCR: P(hb-lacZ): (1) marker for SM1(CyO) balancer second chromosome (156 bp), +106 adh: 5′
TCTGGGAGGCATTGGTCTGGA 3′ and −241 lac: 5′
CGGGCCTCTTCGCTATTACG3′, (2) EVEΔS2E and native eve locus: 592 bp and 113 bp markers for native S2E and S2E with 480 bp MSE deleted, respectively, +23 Df: 5′
TAACTGGCAGGAGCGAGGTATC3′ and −115 Df: 5′
CTCGCGGATCAGGGCTAAGT3′, (3) DMPROSPER, 3rd chromosome microsatellite marker for TM3 Sb balancer and rescue transgene-containing chromosome III, DMPROSRER F: 5′
CGGTACAAAGTGTGTGTTC3′ and DMPROSRER R: 5′
GACTTTTAAACATTTAAGATTAATTCC3′.
S2E mutant construct (EVEΔS2E)
A pCaSpeR-based vector containing wild-type eve genomic DNA from −6.4 to +8.4 kb (EVEG84) was provided by Miki Fujioka [12]. The deficiency eve S2E (EVEΔS2E) mutation was created by deleting the region from −1554 (BstEII) to −1073 kb (BssHII) relative to eve transcription start site (see Figure 2).
S2E rescue constructs (S2E-EVE)
The S2Es used in this study were cloned previously [10], and their accession numbers are given as supplemental information. The sequences employed in this study are presented in Figure S1; they all begin and end at conserved sequences flanking the S2E (blocks A and B).
The S2E-eve rescue transgenes based on our modification of the E-eve pCaSpeR vector provided by M. Fujioka [32] were constructed as follows: 2.76-kb eve CaSpeR (negative control construct, S2E0-EVE) carries the D. melanogaster wild-type eve genomic DNA fragment from −913 (FspI) to +1.85(MluI) relative to transcription start site, which includes 913 bp intact eve 5′ upstream region, protein-coding sequence, and the polyadenylation site. A unique restriction site PmeI was created instead of the FspI site, so that S2Es from different species could be cloned into the 2.76-kb eve CaSpeR vector by using unique PmeI and NotI sites in the proper orientations. The entire eve region in rescue constructs from D. melanogaster and D. erecta, including both the S2E and eve genomic DNA fragment from −913 to +1.85, were confirmed by sequencing.
P-transformation
P-element-mediated germline transformation was carried out as described by Rubin and Spradling [33]. A homozygous viable stock with the S2E deficiency transgene P(EVEΔS2E) on chromosome II was recombined onto eve mutant chromosomes—either Df(eve) or eveR13—to create lines that can only drive eve expression from the EVEΔS2E transgene. These chromosomes are maintained as balanced stocks.
Rescue-transgene lines (S2E-EVE) were chosen for use in the study if they were homozygous viable and were on chromosome III. Between 2 and 4 independent stable transformed lines were generated for each rescue construct and were examined for rescue ability to adulthood and for local eve and en pattern rescue.
Rescue of eve function to adulthood
Each transgenic rescue line was crossed into the eveR13 (R13) P(EVEΔS2E) mutant background, generating flies of the genotype w; b R13 P(EVEΔS2E)/CyO; P(S2EA1-EVE)/TM3 Sb. These flies were reciprocally crossed with flies of the genotype w; b R13 P(EVEΔS2E)/CyO; P(S2EA2-EVE)/TM3 Sb. A1 and A2 indicate independent transformed lines with the identical rescue construct (see Figure 2). Adults were scored for both a wild-type wing phenotype (non-CyO) and the black (b) phenotype (indicating R13 homozygotes).
For each reciprocal cross 50 healthy virgin females from one strain were mated in a standard culture bottle with 100 healthy males from the other. Parents were transferred to fresh culture bottles every 3 d for 24 d. The emerging adult offspring were collected every day from the culture bottles for a period of 10 d for scoring. This approach ensured that mutants with slow development rates were counted. The cultures were kept at 25 °C.
Viability of flies carrying one or two copies of the rescue transgene was measured relative to the number expected based on the count of flies carrying one copy of the dominantly marked second and/or third chromosome. This genetic design allowed us to estimate viability effects of transgene insertion independent of transgene rescue ability by comparing genotypes carrying one (hemizygous) or two (doubly hemizygous) copies of the rescue transgene in genotypes carrying a wild-type eve locus (i.e., EVEΔS2E, R13/CyO; S2E-EVE/TM3 versus EVEΔS2E, R13/CyO; S2E-EVE/S2E-EVE; see Table S1). Viabilities of rescue genotypes were calculated by comparing the number of adult survivors in EVEΔS2E homozygotes (EVEΔS2E, R13/EVEΔS2E,R13) relative to the number of survivors in the corresponding genotype with one copy of a functional eve locus (EVEΔS2E, R13/CyO; S2E-EVE/S2E-EVE).
Localized rescue of eve and en expression patterns
Each transgenic rescue line was crossed into the Df(eve) P(EVEΔS2E) mutant background, generating stock w; Df(eve) P(EVEΔS2E)/CyO P(hb-lacZ); P(S2E-EVE)/TM3 Sb.
The embryos of these stocks were stained with anti-Eve and anti-Engrailed antibodies to determine the pattern and level of genes expression. The embryos inspected for en were dissected flat, and the proctodeum and posterior midgut removed, anterior up, and viewed from the ventral side (see Figure 3F for undissected specimen). Genotypes of the embryos were determined after images were taken, as described above.
Image processing and EVE quantification
eve stripes 1, 3, 4, 5, 6, and 7 are always produced from the EVEΔS2E locus in our experimental flies, whereas eve stripe 2 comes primarily from the independent S2E-EVE rescue transgene. This allowed us to compare stripe 2 expression levels in different embryos and genotypes by measuring stripe 2 expression relative to an adjacent stripe. We chose the adjacent stripe 3 as a reference, as it has similar temporal and quantitative expression, and we report the stripe 2 expression relative to stripe 3. Image stacks (0.8 μm) were deconvoluted in Huygens Essential (version 2.5.0 from Scientific Volume Imaging, Hilversum, the Netherlands) and 3–5 images in the focal plane collapsed to a single image in Image J (version 1.30, free at http://rsb.info.nih.gov/ij/). All were subjected to the same background subtraction and a section corresponding to stripes 1 to 4 in the middle section of the embryo cropped out and saved as a raw pixel file for analysis in Mathematica 5 (www.wolfram.com). The location of each stripe was estimated by fitting second order curves to local maxima/minima of smoothed data, identifying stripes 1 through 4 and the interleaving troughs. The approach worked reasonably, as of 205 embryos surveyed in the rescue experiment only 30 were rejected because the algorithm could not detect a stripe 2. The reason for rejection was in the majority of cases not the lack of stripe 2 expression in individuals homozygous for the EVEΔS2E, but rather the absence of a detectable trough between stripes 1 and 2 during early stages of stripe formation.
The fitted curves were superimposed on the raw data and used as guides for the summing of the signal intensity, from the middle 25%, 50%, or 75% of the stripes and the 25% of the troughs. These percentages were derived from the total length between stripes 1 and 4, and therefore represent comparable geometric areas. This is important as we proceed to compare abundances in stripes 2 and 3. Percentages of stripes were used to account for size variation as variables extracted on the basis of absolute pixels were noisier. The measurements were summed from 5 to 11 sections per embryo, each 30-pixels high. The dependent variables analyzed were ratios of the measured fluorescence in stripe 2 (P2) over stripe 3 (P3) after adjusting the measured background in the separating trough (T2), in general form: Y = (P2 − T2)/(P3 − T2). These variables were derived for the middle 25%, 50%, and 75% of the stripes, but all yielded similar results, and we report on the 75% case. For every embryo we documented a DV index, indicating its degree of rotation along the dorsal–ventral axis (divided into five categories, dorsal, dorsal–middle, middle, middle–ventral, and ventral). In addition we placed the embryos into a series (five classes) around cellularization corresponding to approximately 45 min of development, when the EVE stripes originate and take form (see Figure 3A–3E). The series is based on classes 4–8 for the 14th cleavage cycle available on the FlyEx Web site at http://flyex.ams.sunysb.edu/flyex/ [34].
ANOVA on EVE expression
Mixed-model ANOVA was fitted in SAS v8.2 (2002; SAS Institute, Cary, North Carolina, United States) with the main effects of “dose,” that is, how many copies of rescue construct; “species,” designating the origin of the S2E; and a “DV index,” which accounts for orientation, along the dorsal–ventral axis, of measured embryos. Also, “time,” indicating the inferred stage of development, was used as a covariate. Finally, as each embryo was measured several times, we avoided pseudoreplication by nesting the random variable “embryo” within the fixed effects.
Y = μ + Dose + Species + Time + DV index + D × S + D × T + D × DVI + D × S × DVI + Embryo (D × S × DVI) + ɛ
The results were identical if the multiple recordings on each embryo were designated as repeated measures within the Proc Mixed statement. We also applied a generalized linear model to the least square mean of the phenotypic values for each embryo. Again results were in accord (see Tables 1 and S2). A related model, excluding “species” terms, was used to evaluate the “dose” effects in the EVEΔS2E stock. We also investigated the general capacity of the D. pseudoobscura construct to reconstitute the activity of the endogenous gene. The stock with EVEΔS2E carried over a Cy balancer was used, and embryos of all three genotypes were collected. The mixed model was constructed in the same way, adding “experiment” as a term. All stocks were constructed to have the same genetic background, and we predicted that individuals homozygous for the EVEΔS2E in both datasets would have similar expression. This was corroborated by ANOVA with a reduced mixed model, F(1, 52.54) = 1.70, p = 0.20, n = 71. Similarly, the EVE expression of the homozygous balancer could not be distinguished from a wild-type strain w1118: F(1, 63.71) = 1.00, p = 0.32, n = 76 (see Figure 3G). Using standard quantitative genetic theory [35], we assessed the genotypic effects of the S2E on eve expression in the D. pseudoobscura transgenes and the endogenous gene. The small sample size prohibited formal tests of deviations from additivity.
Sequence analysis
Comparative sequence analysis was conducted with the Avid-Vista suite (http://www-gsd.lbl.gov/vista/, using default parameters [36,37]. Binding-site prediction was performed using patser [20] with identical command-line arguments and position-weight matrices used by Berman et al. [38]. Cutoffs for display of predicted binding sites were set to recover all known binding sites in the D. melanogaster S2E–ln(P) = −6.1 for bicoid, ln(P) = −8.06 for hunchback, and ln(P) = −6.65 for Kruppel.
Supporting Information
Figure S1 Alignment of eve S2E Regions from Four Species of Drosophila
Gaps in aligned sequences are indicated by dashes. The binding sites in D. melanogaster for the trans-acting factors, bicoid (bcd), hunchback (hb), Kruppel (kr), sloppy-paired (slp), and giant (gt), are shown above the sequence. The sequences from the four species begin and end at completely conserved sequences, indicated by blocks A and B, flanking the enhancer. mel: D. melanogaster; yak: D. yakuba; ere: D. erecta; pse: D. pseudoobscura.
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Figure S2 Trans-Factor-Binding Sites in D. melanogaster S2E and Homologous Sequences from Three Other Drosophila Species
Binding sites are for five proteins are designated; bicoid (bcd), hunchback (hb), Kruppel (kr), sloppy-paired (slp), and giant (gt). N/A: no homologous sequence identified. mel: D. melanogaster; yak: D. yakuba; ere: D. erecta; pse: D. pseudoobscura. The figure is expanded from Ludwig et al. [10] and Ludwig [2].
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Protocol S1 Additional Materials and Methods
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Table S1 Viability to Adulthood
Adult viability of individuals from reciprocal crosses between two independent transgenic lines with eve S2E from the four species and from a negative control (S2E0-EVE). See Figure 2 for constructs and crossing scheme. With Mendelian segregation of the 2nd and 3rd chromosomes, ratios of 4:2:2:1 for the genotypic classes are anticipated. This was used to calculate expected counts and rescue percentages (in brackets) for the classes with one and two copies of rescue constructs. Adjusted rescue percentages, which account for possible detrimental effects of two hemizygous transgenic inserts, are also reported. These data summarized over sexes are represented in Figure 4.
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Table S2 ANOVAs on EVE Abundance in Stripe 2
ANOVAs on EVE quantity. (A) Analysis on amounts of EVE in stripe 2 D. erecta and D. pseudoobscura rescue data (B) on the rescue data for all four species and (C) the EVEΔS2E/Cy stock. Mixed-model ANOVA (left) fitted the multiple measures per embryo as random. Generalized linear model (right) was implemented on the least square means calculated for each embryo. “Dose” indicates the copy number of rescue transgene per embryo; “Time” indicates a continuous variable of the developmental series; “Species” indicates the origin of the S2E; “DV index” indicates orientation of each embryo along the dorsal–ventral axis. VC indicates variance components for “embryos” or residual error, with estimated standard errors and significance according to the z-distribution (VCs can only be estimated with a mixed model). ns, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.
(93 KB DOC).
Click here for additional data file.
Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers for the S2Es sequences used in genetic constructs are AF042712 (D. pseudoobscura), AF042711 (D. erecta), AF042710 (D. yakuba), and AF042709 (D. melanogaster). The sequences for genomic alignments of the eve 5′ region were extracted from GenBank (D. melanogaster genomic scaffold, AE003831, and accessions AY190939 and AY190942 for D. erecta and D. pseudoobscura, respectively [39]). The exception was the D. yakuba, sequence, which came from the 7 April 7 2004 version available on the Washington University genome page (http://genome.wustl.edu/).
We thank Miki Fujioka for providing eve plasmids; Nipam Patel, Steven Small, and John Reinitz for providing antibodies; Miki Fujioka, Nipam Patel, John Reinitz, Carlos Alonso, Hilda Janssens, and Erica Bakker for experimental advice; and three anonymous reviewers for constructive comments on the manuscript. This work was supported by grants from the National Science Foundation (9982715) and the National Institutes of Health (R01 GM61001) to MZL and MK.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. MZL and MK conceived and designed the experiments. MZL performed the experiments with EA and JN helping with embryo staining and genotyping. CMB conducted genomic sequence analysis. AP analyzed images. MZL, AP, and MK analyzed the data. MZL and EA contributed reagents/materials/analysis tools. MZL, AP, and MK wrote the paper.
Citation: Ludwig MZ, Palsson A, Alekseeva E, Bergman CM, Nathan J, et al. (2005) Functional evolution of a cis-regulatory module. PLoS Biol 3(4): e93.
Abbreviations
bcd-3bicoid-3
en
engrailed
eve
even-skipped
hb-1hunchback-1
MSEminimal stripe 2 element
MYAmillion years ago
S2Estripe 2 enhancer
TFtranscription factor
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| 15757364 | PMC1064851 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e93 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030093 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1576027810.1371/journal.pbio.0030095Research ArticleGenetics/Genomics/Gene TherapyMicrobiologyYeast and FungiLight Controls Growth and Development via a Conserved Pathway in the Fungal Kingdom Light Regulates C. neoformans DevelopmentIdnurm Alexander
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Heitman Joseph [email protected]
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1Department of Molecular Genetics and Microbiology, Howard Hughes Medical InstituteDuke University Medical Center, Durham, North CarolinaUnited States of AmericaChory Joanne Academic EditorThe Salk Institute for Biological StudiesUnited States of America4 2005 15 3 2005 15 3 2005 3 4 e9520 6 2004 18 1 2005 Copyright: © 2005 Idnurm and Heitman.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
White Collar Proteins Help Fungi Do It in the Dark
Light inhibits mating and haploid fruiting of the human fungal pathogen Cryptococcus neoformans, but the mechanisms involved were unknown. Two genes controlling light responses were discovered through candidate gene and insertional mutagenesis approaches. Deletion of candidate genes encoding a predicted opsin or phytochrome had no effect on mating, while strains mutated in the white collar 1 homolog gene BWC1 mated equally well in the light or the dark. The predicted Bwc1 protein shares identity with Neurospora crassa WC-1, but lacks the zinc finger DNA binding domain. BWC1 regulates cell fusion and repression of hyphal development after fusion in response to blue light. In addition, bwc1 mutant strains are hypersensitive to ultraviolet light. To identify other components required for responses to light, a novel self-fertile haploid strain was created and subjected to Agrobacterium-mediated insertional mutagenesis. One UV-sensitive mutant that filaments equally well in the light and the dark was identified and found to have an insertion in the BWC2 gene, whose product is structurally similar to N. crassa WC-2. The C. neoformans Bwc1 and Bwc2 proteins interact in the yeast two-hybrid assay. Deletion of BWC1 or BWC2 reduces the virulence of C. neoformans in a murine model of infection; the Bwc1-Bwc2 system thus represents a novel protein complex that influences both development and virulence in a pathogenic fungus. These results demonstrate that a role for blue/UV light in controlling development is an ancient process that predates the divergence of the fungi into the ascomycete and basidiomycete phyla.
Two genes controlling light responses - BWC1 and BWC2 - were identified and shown to regulate development and virulence of the fungal pathogen Cryptococcus neoformans
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Introduction
Light is the fundamental energy source for life on earth and as such is a major environmental signal for organisms from all kingdoms of life. In the fungal kingdom, light can regulate growth, the direction of growth, asexual and sexual reproduction, and pigment formation, all of which are important aspects for the survival and dissemination of fungal species. These processes have negative implications to many aspects of human life, as the uncontrolled proliferation of fungi can lead to devastating plant disease, mold, and human disease. On the other hand, fungi are essential for recycling nutrients in the environment, in mycorrhizal interactions with plants, and as a source of food and pharmaceutical metabolites for humans. Understanding the role of environmental signals in fungal development is vital to increase the benefits and decrease the costs that fungi present. Despite the importance of light to fungal development, much has yet to be determined to illuminate the mechanisms fungi use to perceive and respond to light.
The effects of light have been investigated in model fungal species. While spectral analyses and the morphological effects of light have been well characterized in genera such as Coprinus (a basidiomycete) or Phycomyces (a zygomycete), at the molecular level Neurospora crassa (an ascomycete) is best understood based on the functions of the white collar (wc-1 and wc-2) genes in light sensing [1,2,3]. In N. crassa, blue light regulates induction of carotenoid pigment production, protoperithecia (sexual fruiting body) formation and phototropism of perithecial beaks, and circadian rhythm, all of which are abolished by mutations in wc-1 or wc-2 [4]. These two genes encode proteins with several conserved domains, including a zinc finger DNA binding domain found in both proteins [5,6,7]. The two proteins physically interact through PAS (conserved in Per, Arnt, Sim proteins) domains [8,9,10]. The WC-1 protein functions as the blue light receptor through a specialized PAS domain responsible for sensing light, oxygen, and voltage in other proteins (LOV domain), and, together with WC-2, acts as a transcription factor. The WC-1 protein interacts with a flavin chromophore [flavin adenine dinucleotide (FAD)] to function as the blue light receptor [11,12]. A small protein, VIVID, also perceives blue light through a LOV domain and modulates N. crassa sensitivity to light [13]. N. crassa has an additional four candidate photoactive protein homologs whose functions in photoperception remain elusive [14,15].
We set out to identify genes involved in the process by which light inhibits mating of the basidiomycete Cryptococcus neoformans. In nature, cryptococcal varieties are associated with bird excreta, soil, tree hollows, and even caves [16,17]. Thus, the light stimuli studied under laboratory conditions are highly relevant to the varying light signals the fungus experiences in the wild. C. neoformans exists as a haploid yeast with two bipolar mating types (a and α). MATa and MATα cells fuse to form a dikaryotic hypha that terminates in a basidium in which nuclear fusion and meiosis occur, producing four long chains of haploid basidiospores by mitosis and budding. A similar process, known as haploid or monokaryotic fruiting, can occur with only one mating partner that also gives rise to filaments that terminate in basidium-like structures and produce short spore chains. Spores have been implicated as an infectious propagule, further underscoring the importance of understanding the regulatory processes governing basidiospore production [18,19]. Mating and fruiting are controlled in the laboratory by stimuli such as the presence of potential mating partners (via pheromone signaling), nutrient limitation, desiccation, temperature, and light [19]. Many aspects of the transduction pathways for these signals have been elucidated, particularly in response to pheromones and nutrient limitation [20], but no components of light signaling had been reported to date for this important human pathogen. We identify here two genes required for C. neoformans responses to light, and demonstrate their role in blue light regulation of development and sensitivity to UV light, and their requirement for full virulence of this pathogen in a mammalian host.
Results
C. neoformans Expresses Three Candidate Photoreceptors That Could Regulate Development
Mating and fruiting of C. neoformans can be variable during culturing. Previous work in our laboratory and others has endeavored to find environmental factors that lead to this variation. One factor identified was light; cultures wrapped in aluminum foil exhibited enhanced mating and haploid fruiting compared to cultures in the light [19,21]. Our assays used cardboard containers in which 9-cm2 holes were excised from the lid and overlaid with aluminum foil or clear plastic wrap. Under these conditions, light inhibited both mating and haploid fruiting of C. neoformans, thereby ruling out any effects of plate-sealing on CO2 levels or desiccation (Figure 1A and 1B).
Figure 1 Bwc1 Inhibits Filament Formation during C. neoformans Mating or Haploid Fruiting
Filamentation assays were on V8 medium (pH 7) and conducted in the dark or under white fluorescent light.
(A) Filamentation in crosses between wild type (WT), bwc1 mutant, and bwc1 + BWC1 reconstituted strains (48 h). Filament formation develops and is overgrown by yeast cells in wild-type or reconstituted strains crossed in the light.
(B) Haploid fruiting filaments and blastospore production from yeast colonies incubated on filament agar (7 d).
(C) Filament formation in wild-type (WT) or bwc1/bwc1 mutant diploid strains (24 h).
(D) Filamentation in crosses of serotype A strains wild type (WT), bwc1 mutant, and bwc1 + BWC1 reconstituted strains mated with a serotype D bwc1 mutant (48 h).
In a candidate gene approach, the C. neoformans genome was searched for homologs of fungal genes implicated in light signaling or transduction: wc-1 and wc-2, opsin, phytochrome, cryptochrome, vivid, frequency, and photolyase. Unambiguous matches were obtained to opsin (OPS1; GenBank AY882440), phytochrome (PHY1; GenBank AY882439), and white collar 1 (BWC1; GenBank AY882437), and transcription of these genes was confirmed by RT-PCR. Opsins are a class of seven-transmembrane proteins that bind retinal via a conserved lysine residue to form ion pumps or light receptors in animals and Archaea. Opsins have been identified in the genomes of a number of fungi, but as yet have no known function [15,22,23,24]. Phytochromes are histidine or serine/threonine kinase red/far-red light receptors identified in plants, and more recently in bacteria (reviewed in [25]). Two phytochrome homologs have been noted in the genome of N. crassa, but also have as yet no known function [14]. The predicted wc-1 homolog contains a LOV domain, two additional PAS domains, and a nuclear localization signal. In contrast to the N. crassa wc-1 gene, the C. neoformans homolog has no DNA binding domain; our designation of the gene as Basidiomycete White Collar 1 (BWC1) is meant to reflect this unique structure.
BWC1 Mediates Inhibition of Mating by Light
The three putative photoreceptor genes were mutated in a C. neoformans var. neoformans (serotype D) strain by replacing the coding region with the URA5 gene. Single-, double-, and triple-mutant strains were isolated following genetic crosses to test for possible redundant functions in light sensing. The abilities to mate and to haploid-fruit in the light and dark were examined by monitoring the production of filaments. There was no effect on mating or fruiting in strains with the opsin ops1 or phytochrome phy1 mutations, either alone or in combination (unpublished data). In contrast, bwc1 mutants were insensitive to light. All crosses in which both mating partners (bilateral crosses) carried the bwc1 deletion showed equivalent mating in the light and the dark, as assessed by the production of filaments after 24 h and 48 h, while in strains with a wild-type copy of BWC1, light reduced mating (Figure 1A). In unilateral crosses, i.e., in which a bwc1 mutant strain was crossed to wild type, only a very modest increase in filamentation in the light was observed relative to crosses between two wild-type parents. The bwc1 mutant strains exhibited more haploid fruiting in the presence of light compared to BWC1 wild-type strains, and a somewhat higher level of fruiting in the dark (Figure 1B). Reintroduction of a wild-type copy of BWC1 into a bwc1 mutant strain restored the inhibition of mating and fruiting by light (Figure 1). Thus, of the three candidate photoreceptor genes identified, BWC1 functions in the control of mating and fruiting by light, whereas OPS1 and PHY1 do not.
BWC1 Controls Cell Fusion and Filament Development
Because filament formation is a qualitative rather than a quantitive phenotype, auxotrophic derivatives of BWC1 wild-type and bwc1 mutant strains were created, and cell fusion was assayed quantitatively; fusion of two auxotrophic parents (ade2 or lys1) yields prototrophic dikaryotic or diploid strains that can grow on medium lacking adenine and lysine. By this assay, fusion of bwc1 mutant or wild-type strains was equivalent in the dark. However, light reduced fusion of wild-type strains 10- to 15-fold but had no impact on fusion of the bwc1 × bwc1 or bwc1 × wild-type crosses (Figure 2, and unpublished data).
Figure 2 Bwc1 Regulates Fusion of C. neoformans Cells in Response to Blue Light
(A) Auxotrophic strains that were wild type (WT) or bwc1 mutant were mated on V8 medium for 24 h and plated onto minimal medium to select for dikaryotic strains that result from cell fusion events. Light inhibits fusion in wild-type strains, and this inhibition is absent in bwc1 mutant strains.
(B) Fusion efficiency of strains under different wavelengths of light. Fusion is reduced by white or blue light. Matings were between wild-type (+) parents, bwc1 mutant (Δ) parents, or one wild-type and one mutant parent (bwc1 α × WT a, or WT α × bwc1
a). Mutation of bwc1 in either or both mating partners relieves inhibition of fusion by white or blue light. Bars indicate the standard error of the mean of three replicates.
Light also inhibited self-filamentous growth of a MATa/MATα diploid strain. Stable diploid strains were selected after cell fusion by incubation of strains at 37 °C. Filamentation in the light and dark was examined using diploid wild-type or bwc1 mutant heterozygous or homozygous strains. The bwc1/bwc1 mutant strain filamented equally well in the light and the dark, whereas filament development was reduced in the wild-type diploid strain in the light (see Figure 1C). The filamentation of strains heterozygous at the BWC1 locus was inhibited by light, indicating that the bwc1 mutation is recessive (unpublished data). These results demonstrate that BWC1 functions in light responses at both the initial cell fusion step and during subsequent filament formation.
Blue Light Inhibition of Cell Fusion Requires BWC1
To determine the approximate wavelengths of light that inhibit mating of C. neoformans, cell fusion was assayed during growth under colored filters at 24 and 48 h. In four independent experiments, blue light was sufficient to inhibit cell fusion, whereas green or red wavelengths had little or no effect (see Figure 2B for a representative experiment). In all crosses, only one mating parent required a bwc1 mutant allele to escape the inhibition of cell fusion by light. Thus, C. neoformans Bwc1 functions similarly to the N. crassa WC-1 protein in response to blue light.
BWC1 Is Required for UV Resistance
To search further for a function of OPS1, PHY1, and BWC1, growth of the single- and multiple-mutant strains was examined under a variety of in vitro conditions. The mutations had no effect on previously identified attributes required for virulence in mammalian hosts, such as the production of the pigment melanin or the polysaccharide capsule, or growth at 37 °C (unpublished data). The ops1 and phy1 mutant strains were as sensitive to UV light as wild type, but the bwc1 mutants were markedly hypersensitive to UV light (Figure 3). Reintroduction of a wild-type copy of the BWC1 gene into the bwc1 mutant strain restored a wild-type level of sensitivity to UV light. Based on studies in other organisms, one target gene could be that encoding photolyase. However, there is no evidence for a photolyase in C. neoformans, based on the lack of photoreactivation and the absence of a homolog in genome databases (unpublished data). We conclude that Bwc1 functions in response to blue light to inhibit mating, and to UV light to regulate resistance to UV irradiation.
Figure 3
bwc1 Mutants Are Hypersensitive to UV Light
Ten-fold serial dilutions of log-phase yeast cells of bwc1 mutant or wild type (WT) were plated in duplicate on YPD medium, and one plate was UV irradiated (~48 mJ/cm2). Reintroduction of a wild-type copy of the BWC1 gene into the bwc1 + BWC1 mutant strain restores UV sensitivity to the wild-type level.
BWC1 Function Is Conserved between Two Varieties of C. neoformans
C. neoformans is a species complex of three divergent varieties or sibling species. C. neoformans var. neoformans (serotype D), utilized in the experiments described thus far, is commonly studied because of the ready availability of established congenic mating partners. However, this variety is uncommon in clinical settings (representing only 5% of cases), and we therefore re-isolated the bwc1, ops1, and phy1 mutations in the most common pathogenic type, C. neoformans var. grubii (serotype A), in which congenic strains have only recently been developed [26]. Mating of serotype A laboratory strains is less efficient than that of the congenic serotype D strains, and their growth in the light is limited on the V8 (pH 5) medium used for genetic crosses. Serotype A bwc1 bilateral crosses mated better in the dark than wild-type strains. Crosses performed in the light rarely resulted in filaments and were observed to do so only in the bwc1 × bwc1 mutant bilateral crosses (unpublished data). Using V8 (pH 7) medium and the serotype D bwc1 strains as mating type tester strains, the effects of light on mating efficiency of serotype A could be more readily established. When crossed to serotype D bwc1 strains, wild-type serotype A strains yielded fewer filaments than the bwc1 mutant strains in both the light and dark, demonstrating a role for suppression of filament formation by BWC1 under both conditions (see Figure 1D). The serotype A bwc1 strains were also found to be hypersensitive to UV light (unpublished data). Reintroduction of the serotype A BWC1 gene complemented the mutant phenotypes of both the serotype A and D bwc1 mutants (Figures 1 and 3). In summary, BWC1 function is conserved in two divergent cryptococcal varieties, and data derived from experiments on laboratory strains are also of significance to clinical isolates.
Insertional Mutagenesis of a Novel Self-Filamentous Haploid Strain Identifies Other Components Required for Light Responses
In contrast to the N. crassa WC-1 protein, the predicted C. neoformans Bwc1 protein has no zinc finger DNA binding domain or any other known DNA binding motif. Matches to wc-1 were obtained from other fungi and the predicted proteins examined for these domains (Figure 4). The proteins share a similar structure with regard to the PAS domains, and all of the ascomycete wc-1 genes examined contained a zinc finger DNA binding domain, whereas none of the basidiomycete wc-1 homologs encode products with this domain. This suggests that the structural differences between the homologs are conserved in each phylum and are not unique to C. neoformans.
Figure 4 Ascomycete White Collar 1 Homologs Contain a Zinc Finger Domain; The Basidiomycete Homologs Do Not
Comparison of the structure of the predicted WC-1 proteins from the ascomycetes N. crassa (Nc), Aspergillus nidulans (An), Magnaporthe grisea (Mg), Fusarium graminarium (Fg), and Tubor borchii (Tb), and the basidiomycetes C. neoformans (Cn), Coprinus cinereus (Cc), Ustilago maydis (Um), and Phanerochaete chrysosporium (Pc). Other domains are PAS (PER, ARNT, SIM) and NLS (nuclear localization signal), and the specialized PAS domain that interacts with the chromophore FAD is marked. Sequences are from GenBank (Nc, X94300; An, AF515628; Tb, AJ575416), the Broad Institute (Mg, Cn, Fg, Cc, Um), or the Department of Energy (Pc).
We hypothesized that Bwc1 binds to an interacting DNA binding protein, because Bwc1 would be unable to act as a transcription factor on its own. Systematic deletion of all of the C. neoformans transcription factors, assuming these were annotated, is not technically feasible at this stage. Similarly, standard insertional mutagenesis poses a problem because the filamentation phenotype requires that both the MATa and MATα mating partners bear the same mutation. However, overexpression of a mating type-specific homeodomain protein in a haploid strain of the opposite mating type confers a self-filamentous morphology [27]. We reasoned that such a self-filametous strain could be employed to perform random insertional mutagenesis, and devised a screen to identify the hypothetical protein interacting with Bwc1. The SXI1α gene, which encodes the MATα-specific homeodomain protein [27], was introduced into the genome of a MATa haploid strain. The resulting MATa +SXI1α strain (AI49) exhibited self-filamentous growth that was regulated by temperature, light, and nutrients. The strain grew as a budding yeast at 37 °C and filamented at 25 °C, and, like MATa/MATα diploids, filamentation was inhibited by light, and was most robust on V8 mating medium (Figure 5A).
Figure 5 The BWC2 Gene also Mediates UV/Blue Light Responses in C. neoformans
(A) A self-filamentous haploid strain (MATa +SXI1α) exhibits light-repressed filamentation. This strain was mutated by Agrobacterium-mediated T-DNA insertion, and insertional mutant strain 25F8 filaments equally well in the light and the dark.
(B) Comparison of the structures of Bwc1 and Bwc2. The Bwc1 predicted protein (1,097 amino acids) has a LOV domain, two additional PAS domains and a nuclear localization signal. The Bwc2 predicted protein (392 amino acids) has a PAS domain and zinc finger DNA binding domain.
(C) Bilateral mating between wild-type (WT), bwcl, bwc2, or bwc1 bwc2 double (bwc1,2) mutant strains on V8 medium in the dark and the light (48 h). Filamentation is repressed in wild-type crosses in the light, but not in crosses between mutants or in the dark.
(D) Fusion efficiency of strains under different wavelengths of light. Matings were between wild-type (+) partners, bwc2 mutant (Δ) partners, or one wild-type and one mutant partner (bwc2 α × WT a, or WT α × bwc2
a). Mutation of bwc2 in either or both mating partners relieves inhibition of fusion by white or blue light. Bars indicate the standard error of the mean of three replicates.
(E) Filament formation in wild-type or bwc2/bwc2 mutant diploid strains (24 h). Light does not repress filament formation in bwc2/bwc2 diploids.
(F) The bwc2 and double bwc1 bwc2 (bwc1,2) mutants are as hypersensitive to UV light as bwc1 mutants. Ten-fold serial dilutions of yeast cells were plated in duplicate onto YPD medium, and one set was UV irradiated (~48 mJ/cm2).
The self-filamentous MATa +SXI1α haploid strain was mutated with transfer DNA (T-DNA) containing a nourseothricin resistance cassette using the trans-kingdom DNA delivery vehicle Agrobacterium tumefaciens. Then 2,715 individual mutant strains were isolated into 96-well microtiter plates and were analyzed with a stereomicroscope to examine filament formation after 24 and 48 h of growth in the dark and light on V8 agar medium. Three strains were isolated from this mutant library with a phenotype analogous to the bwc1 mutant in that they filamented equally well in the light and dark and were UV-hypersensitive. The DNA regions flanking the T-DNA insertion were obtained by inverse PCR and compared to the C. neoformans genome database. One insertion (strain 1B4) is in a predicted gene with no database similarities (GenBank EAL21986). This gene was also identified in an independent insertion mutant with a different phenotype, and was therefore not analyzed further. The second isolate (28H3) bears an insertion in the promoter of the RUM1a gene, which is located in the mating type locus of C. neoformans [28]. Intriguingly, the Ustilago maydis RUM1 homolog regulates transcription of the bE and bW homeodomain proteins, as well as of genes regulated by the bE/bW homeodomain complex [29]. The third isolate (25F8) contains an insertion in the promoter of a gene, designated BWC2 (for a consistent nomenclature with respect to BWC1; GenBank AY882438), which encodes a predicted protein with a PAS and a zinc finger DNA binding domain (Figure 5B). Importantly, the predicted structure of the Bwc2 protein is strikingly similar to that of the N. crassa WC-2 protein, which does not perceive photons directly but instead interacts physically with the light sensor WC-1 and acts as a transcription factor. We chose to examine this gene further because its structure suggested that it might physically and functionally interact with the C. neoformans Bwc1 protein.
Disruption of BWC2 Results in the Same Phenotype as bwc1 Mutation
A bwc2 mutant allele was isolated in a wild-type background by replacing the coding region with the nourseothricin resistance gene in a serotype D strain. MATa
bwc2 single- and bwc1 bwc2 double-mutant strains were isolated following genetic crosses. In bilateral crosses, the bwc2 mutants exhibit enhanced mating in the light, whereas wild-type mating is repressed (Figure 5C). As in the case of bwc1 mutants, the inhibition of cell fusion, and also of filament formation after fusion, were no longer repressed by light in bwc2 mutants (Figure 5D and 5E). In addition, the bwc2 mutants were also hypersensitive to UV light (Figure 5F). The blind and UV-hypersensitive phenotypes of the bwc1 bwc2 double mutants are comparable to those of the bwc1 and bwc2 single-mutant strains. When a wild-type copy of the BWC2 gene was reintroduced into the bwc2 mutant strain, UV sensitivity and inhibition of mating by light were restored to the wild-type level (unpublished data). A serotype A mutant of bwc2 was also isolated, and exhibited phenotypes similar to the serotype A bwc1 mutant: enhanced mating with the serotype D bwc2 tester strain in the light and UV sensitivity (unpublished data). Thus, the bwc1, bwc2, and bwc1 bwc2 mutant strains all exhibit similar phenotypes, and the double-mutant phenotype is no more severe than that of the single mutants, supporting the hypothesis that the products of the two genes function in a common pathway.
Bwc1 and Bwc2 Interact in the Yeast Two-Hybrid System
A yeast two-hybrid analysis was conducted to test whether Bwc1 and Bwc2 physically interact. cDNA clones were fused to the S. cerevisiae Gal4 transcription factor activation (AD) or DNA binding (BD) domains and expressed in a S. cerevisiae strain in which the GAL promoter regulates ADE2, HIS3, and lacZ reporter genes. In S. cerevisiae strains expressing AD-Bwc1 and BD-Bwc2, or AD-Bwc2 and BD-Bwc1, the ADE2, HIS3, and lacZ reporter genes were all induced and the cells grew in the absence of adenine or histidine and expressed increased levels of β-galactosidase activity (Figure 6). In contrast, S. cerevisiae strains containing single Gal4-Bwc1/2 fusions and the corresponding Gal4 domain did not. These observations indicate that Bwc1 and Bwc2 can interact with one another in vivo. There was no evidence for homodimer formation for either Bwc1 or Bwc2, and no effects of light on the reporter gene-dependent growth of the strains were observed. Attempts to demonstrate Bwc1-Bwc2 interaction in C. neoformans itself via coimmunoprecipitation of epitope-tagged forms of Bwc1 and Bwc2 have been unsuccessful so far, due to the low abundance of the proteins, cross-reactivity of the antibodies with endogenous fungal proteins, and loss-of-function of tagged proteins in strains overexpressing these proteins (unpublished data). These findings demonstrate that Bwc1 and Bwc2 can physically interact when expressed in the nucleus of another fungal species, and that they can do so in a light-independent manner.
Figure 6 Bwc1 and Bwc2 Physically Interact
The coding regions of the BWC1 and BWC2 genes were fused adjacent to the AD or BD of S. cerevisiae GAL4. Plasmids were cotransformed into a S. cerevisiae strain in which Gal4 regulates the ADE2, HIS3, and lacZ genes. Growth of strains in the absence of adenine (−ade) or histidine (−his) and increased β-galactosidase activity (β-gal, ± one standard error, Miller units) indicate protein-protein interactions.
Transcript Levels of BWC2 Are Regulated by BWC1 and Light
In N. crassa, light regulates transcript levels of wc-1 but not wc-2. Transcription of BWC1 and BWC2 was assayed in the light and dark on V8 solid medium (Figure 7A). The levels of transcript, particularly of BWC1, were very low, and therefore samples were enriched approximately 20-fold by purifying mRNA from total RNA for Northern blot analysis. BWC1 transcript levels were constant under these conditions. In contrast, BWC2 is up-regulated in the presence of light, except in the bwc1 mutant, demonstrating that BWC2 is a light-regulated gene and dependent on the presence of BWC1. Thus, interestingly, the pattern of light induction of transcript levels is reversed between the two genes in C. neoformans compared to N. crassa.
Figure 7 Transcript Analysis of BWC1 and BWC2, and Their Effects on Genes Required for Mating
(A) Transcript levels of BWC2 are regulated by light, dependent on BWC1. Wild-type, bwc1 (1Δ) or bwc2 (2Δ) strains were inoculated onto V8 agar medium and wrapped in foil. A set of plates was removed from darkness 1 h, 4 h, and 8 h before the end of a 24-h period. Messenger RNA purified from 200 μg of total RNA isolated from these cultures was separated on an agarose gel, transferred to nitrocellulose, and probed with the BWC1, BWC2, and actin (ACT1) genes. No transcripts of BWC1 or BWC2 are observed in the bwc1 or bwc2 strains, respectively, consistent with the deletion strategy to create these strains. The transcript levels of BWC1 are constant under these conditions. In contrast, BWC2 transcript levels increase in the light, but not in strains bearing the bwc1 mutation.
(B) Bwc1 and Bwc2 regulate transcript levels of pheromone MFα1 and homeodomain SXI1α genes. Crosses between wild-type (WT), bwc1, or bwc2 mutant strains were conducted on V8 pH 7 medium, incubated in the light (L) or dark (D), and cells were harvested 24 h later. RNA was size-fractionated in agarose gels and blotted to nitrocellulose, and probed to detect pheromone (MFα1) or homeodomain (SXI1α) transcription, as well as actin (ACT1) as a control for RNA loading and transfer.
Transcript Levels of Genes Required for Mating Are Regulated by BWC1 and BWC2
The mating phenotype of bwc1 mutants suggested that Bwc1-Bwc2 regulates gene expression during mating. Transcript abundance of the pheromone gene MFα1 and the homeodomain gene SXI1α, both of which are required for efficient mating and are known to be induced during mating and following cell fusion [21,27], was examined by Northern blot analysis of mating cultures grown in the light and dark for 24 h (Figure 7B). In crosses with bwc1 and bwc2 mutants, transcript levels were consistently high in both the light and the dark. In contrast, in crosses with wild-type parents the transcript levels of MFα1 and SXI1α were reduced in the light compared to the dark (Figure 7B). These data suggest that Bwc1-Bwc2 function, directly or indirectly, to repress transcription of these two key genes that regulate mating and completion of the sexual cycle.
BWC1 and BWC2 Regulate Virulence in Mammals
C. neoformans is a pathogenic fungus that causes disease in humans and other animals. The wild-type, bwc1, and bwc2 mutants, as well as the bwc1 + BWC1 and bwc2 + BWC2 complemented strains, were inoculated by inhalation into ten mice each, and host fitness and survival were examined daily (Figure 8). Animals infected with the wild-type or the bwc1 + BWC1 or bwc2 + BWC2 strains all died within 30 d of inoculation (average survival = 20.5 d, 24.4 d, and 21.5 d, respectively). In contrast, the mice infected with the bwc1 or bwc2 mutant strains were all healthy at 30 d after inoculation, and the first animal in these two groups that became moribund did so at day 37 (average survival = 43.2 and 45.1 d for bwc1 and bwc2 mutants, respectively). Bwc1 and Bwc2 are therefore not essential for virulence, but do contribute to the rate with which the fungus causes disease in the mammalian host. Thus, in addition to regulating development, Bwc1-Bwc2 also promote virulence.
Figure 8
BWC1 and BWC2 Are Required for Full Virulence of C. neoformans in a Mammalian Host
Ten mice each were infected intranasally with 1 × 105 cells of wild-type, bwc1 mutant, and bwc2 mutant, and reconstituted (bwc1 + BWC1; bwc2 + BWC2) serotype A strains, and survival monitored daily. Mice infected with the wild-type and complementing strains progress to severe morbidity at the same rate, whereas mice infected with the bwc1 or bwc2 mutant strains survived twice as long.
Discussion
Light inhibits both mating and a related differentiation process known as haploid fruiting in C. neoformans. Two approaches were employed to identify genes regulating these responses to light. First, we examined the genome of C. neoformans to identify homologs of genes involved in light perception in other organisms. Second, we designed a novel strategy to identify genes regulating sexual differentiation, and used a self-filamentous haploid strain in an insertional mutagenesis screen to define novel genes with roles in light responses.
Opsin, phytochrome, and white collar 1 homologs were found in the C. neoformans genome, and the function of these candidate photoreceptors was examined by gene disruption. No phenotypes were conferred by the ops1 or phy1 mutations, but deletion of the wc-1 homolog BWC1 abolished the inhibition of mating and haploid fruiting by light. The bwc1 mutant phenotypes in the clinical background were generally equivalent to those observed in serotype D; however, in the serotype A crosses with the bwc1 mutants, it was clear that mating inhibition occurred with a wild-type copy of BWC1 regardless of the light status. In serotype D, inhibition of mating by light was shown to occur at both the cell fusion and the hyphal developmental stages. Cell fusion assays revealed that only one parent requires a bwc1 mutation to circumvent repression by light, and the release from light repression in fusion is equivalent between unilateral (bwc1 × wild type) and bilateral (bwc1 × bwc1) crosses. This observation suggests that during mating only one cell, independent of mating type, needs to commit to fusion. Because a wild-type level of fusion was observed in unilateral crosses, rather than an expected 50% reduction, there must also be cross-talk between the two cells prior to fusion, which is probably mediated via pheromone sensing. Analysis of diploid strains showed that once cell fusion has occurred, the wild-type Bwc1 allele of the protein has sufficient activity, even in the heterozygous state, to repress filament formation in the presence of light to a level equivalent to that observed in wild-type diploid strains.
In an assay for the wavelength responsible for inhibition of cell fusion, blue light (rather than green or red wavelengths) was found to reduce fusion efficiency between strains with an intact copy of BWC1. No inhibition by white or blue light was observed during fusion of bwc1 mutant strains. These data lead us to hypothesize that Bwc1 functions as a blue light photoreceptor, as is the case for N. crassa WC-1 [11,12]. To test this hypothesis, we initiated efforts to analyze the photochemistry of Bwc1. However, recombinant Bwc1 or fragments of Bwc1 expressed in E. coli cells were either produced in low quantities or were largely insoluble (unpublished data), and thus formal demonstration of photoreceptor function for Bwc1 remains to be established.
The bwc1 mutants were also hypersensitive to UV light, showing that the Bwc1 protein functions in response to both blue (approximately 400–500 nm) and UV light (approximately 200–400 nm) wavelengths. The ability of blue or UV light to induce carotenoid formation in N. crassa was first noted a century ago [30]. Subsequent work has focused on light in the blue wavelengths, which is sensible given that any study on UV light and its regulation of fungal development is likely to be complicated by the effects this radiation has on cell viability and media stability. Nevertheless, there is evidence that N. crassa also perceives UV light through WC-1. Prior to cloning the wc-1 gene, the spectra for inhibition of circadian rhythm and for photoinduction of carotenoid production were found to lie in both the UV and blue wavelengths [31,32]. Light treatment of N. crassa changes the light absorbance properties of mycelia, and the action spectrum of this response is within both the UV and blue wavelengths and closely overlaps that of flavins, with respective peaks at 360 and 470 nm [33]. In particular, the action spectra from physiological data overlap with the properties of the WC-1 protein purified from N. crassa cells, as WC-1-FAD or the chromophore FAD alone show two equal excitation peaks, one at 370 nm (UV) and one at 450 nm (blue) [11,12]. These data suggest that N. crassa WC-1 may also be a UV-responsive protein and function like C. neoformans in protecting the fungus from UV damage. The induction of UV-protecting carotenoids in N. crassa by light in a WC-1-dependent manner supports this hypothesis. Nevertheless, a UV-sensing function for the White collar proteins remains to be demonstrated through analysis of protein photochemistry and spectral and phenotypic analysis.
The predicted Bwc1 protein lacks a DNA binding domain found in the ascomycete WC-1 homologs. We hypothesized that there must be a second protein that interacts with Bwc1, and set out to identify this component via random insertional mutagenesis. To create a haploid self-filamentous strain of C. neoformans, we expressed the MAT-specific Sxi1α homeodomain protein in a MATa haploid cell, resulting in robust induction of filament development. The self-filamentous strain was mutated with T-DNA insertions from Agrobacterium, and three mutant C. neoformans strains with equivalent filament formation in the light and the dark, and UV hypersensitivity, were isolated. In one strain, the T-DNA insertion lies in the promoter of a gene we designated BWC2, which has an analogous structure to the N. crassa wc-2 gene (a PAS domain and zinc finger DNA binding domain) but shares much less sequence similarity relative to that between C. neoformans BWC1 and N. crassa wc-1. The BWC2 gene was not found in the initial candidate gene search because of this low sequence similarity and because the intron structure of C. neoformans confounded its identity. The BWC2 gene was mutated to analyze its function. The bwc2 and bwc1 bwc2 double mutants exhibit phenotypes comparable to bwc1 single mutants, and were nonresponsive to light during mating and haploid fruiting and hypersensitive to UV irradiation, suggesting they function in the same pathway. Furthermore, Bwc1 and Bwc2 interact in the yeast two-hybrid system, supporting a model in which the two proteins represent the integral components of a regulatory complex controlling light-regulated development.
The mating type loci of basidiomycetes have been well studied, and comprise two distinct gene sets: those that encode pheromones and those that encode homeodomain proteins, both of which control different steps in mating [34,35]. We hypothesized that transcription of the C. neoformans pheromone or homeodomain genes would be controlled via Bwc1-Bwc2. We focused on the pheromone genes, because they are important cell-cell signaling molecules, and because mfα1,2,3 triple-mutant strains exhibit a reduction in fusion efficiency similar to that seen in bwc1 mutant strains [21]. In N. crassa, transcription of the pheromone genes is under control of the circadian clock and presumably wc-1 [36]. The mating type specific homeodomain protein Sxi1α of C. neoformans is important for events after cell fusion [27]. Examination of the transcription of MFα1 and SXI1α in the light and dark in wild type compared to bwc1 or bwc2 crosses at 24 h showed that the MFα1 and SXI1α genes are repressed by Bwc1-Bwc2 in the light. It is therefore likely that Bwc1-Bwc2 control mating by influencing the temporal regulation of these genes.
The roles of the C. neoformans BWC1 and BWC2 genes in virulence were examined. We hypothesized that the fungus may be able to sense darkness within the mammalian host and use this as a signal (possibly via Bwc1-Bwc2) to induce virulence. We also tested virulence, because several signal transduction pathways affecting C. neoformans mating also have an impact on virulence. Disruption of both BWC1 and BWC2 reduced the ability of C. neoformans to cause disease, as mice infected with the bwc1 or bwc2 mutant strains survived twice as long as those infected with wild-type or control strains. The Bwc1-Bwc2 system represents a novel class of protein complex that is required for cellular responses to an environmental stimulus and affects both development (mating) and virulence in pathogenic fungi [20]. In contrast to the cAMP signaling and calcineurin pathways, where mutants have reduced mating efficiency and virulence, here the bwc1 and bwc2 mutants have enhanced mating yet reduced virulence. In bwc1 or bwc2 strains there was no reduction in those traits normally associated with C. neoformans virulence, such as production of melanin or capsule, or growth at 37 °C or on minimal media. Identification of the downstream targets for this complex should further elucidate the molecular basis for its role in both mating and virulence.
We propose a model for Bwc1-Bwc2 function that is similar to that of WC-1-WC-2 of N. crassa but differs in several key features (Figure 9). In this model, C. neoformans Bwc1-Bwc2 bind to DNA in the dark and act as weak repressors to reduce filament development. We hypothesize that photons perceived through a flavin cofactor cause a conformational change that enhances repression of filament formation and cell fusion, and activates transcription of genes required for UV resistance/DNA repair. It is also formally possible that light causes Bwc1-Bwc2 to increase transcription of a gene that functions to repress mating, and/or represses transcription of a repressor of UV sensitivity. The N. crassa model is similar but differs in several key features. Recent evidence suggests that a complex of two subunits of WC-1 and one subunit of WC-2 form in response to light [3,10]. The complex positively regulates transcription of genes required for conidiation, mating, and carotenoid production, in marked contrast to the negative regulation of mating observed in C. neoformans. Another difference is that wc-1 is light-regulated in N. crassa, while BWC2 is light-regulated in C. neoformans and BWC1 is not. The N. crassa complex also regulates transcription of frq, and the FRQ protein feedback inhibits the complex, thereby contributing to the wiring of the circadian clock. The roles for WC-1 and WC-2 in N. crassa photoperception also change during the day, adding to the challenge of elucidating their functions. Future studies in C. neoformans will define downstream targets of Bwc1-Bwc2, regulation of Bwc1 and Bwc2 and their complex, and the creation of alleles of Bwc1 bearing mutations in the predicted flavin interacting domain to elucidate the proposed roles of these proteins in light perception.
Figure 9 A Model of How Two Fungi May Respond to Light
The Bwc1-Bwc2 interaction of C. neoformans shares conserved features with the WC-1-WC-2 interaction of N. crassa but also exhibits unique functional characteristics. In this model, C. neoformans Bwc1-Bwc2 bind to DNA in the dark and act as weak repressors to reduce filament development. We hypothesize that photons perceived through a proposed flavin moiety on Bwc1 cause a conformational change that increases repression of filament formation and cell fusion, and activates transcription of genes required for UV resistance. Alternatively, UV sensitivity may be mediated through repression by Bwc1-Bwc2 of a repressor protein. The N. crassa model is simplified from [3]: a complex of two units of WC-1 forms in response to light to cause an initial up-regulation of frq transcription above the levels occurring in the dark (FRQ feedback inhibits the White collar complex). The complex also increases transcription of genes required for other processes.
Components of the White collar sensing system have been identified in other fungi (see Figure 5), and are likely to function in light responses in these and other fungal species. Recently the Trichoderma atroviride homologs of N. crassa WC-1 and WC-2 were isolated and mutated, demonstrating a role for these genes in light-induced conidiation and the induction of photolyase [37]. A gene homologous to BWC1 was identified in the model basidiomycete C. cinereus as mutated in a strain defective in light-regulated development of the mushroom cap [38]. Developmental regulation in C. cinereus is blue-light dependent [39,40], and UV/blue light also regulate development of numerous other fungi (for review see [41]). A homolog of wc-1 was identified in the truffle-forming ascomycete Tubor bruchii, in which blue light inhibits hyphal growth [42]. Thus, this gene and its homologs may have applications even to cultivated, edible fungi. It will be of interest to establish whether White collar-like proteins are present in the genomes of the two other fungal phyla proposed for genome sequencing, the zygomycetes and the chytrids. The responses of the zygomycete Phycomyces to light, particularly blue and UV wavelengths, have been well characterized, and numerous mutants (e.g., ten different mad mutants) affecting responses or sensitivity to particular wavelengths have been isolated, but no photoreceptor genes have as yet been identified [2]. We predict that some of these known mutations will be found to affect White collar homologs.
Finally, we speculate that White collar genes could be of major significance for terrestrial life. The discovery of the BWC1 and BWC2 genes as potential UV-blue light responsive proteins in a basidiomycete indicates that this type of protein complex is ancient in the fungal kingdom. The fossil record shows a clear divergence of the fungal kingdom into the four phyla by the Devonian [416–359 million years ago (mya)], and a Precambrian origin (prior to 542 mya) for the fungi has been suggested [43,44,45,46]. Margulis et al. proposed that sexual recombination and DNA repair were coselected in the Precambrian for protection against UV light [47], and genes are known that control both recombination and sensitivity to UV light. Bwc1-Bwc2 in C. neoformans regulates both UV sensitivity and sexual development, ultimately leading to recombination. During the Silurian division (416–444 mya), the UV-protection role of the WC-1 proteins could have conferred a major selective advantage to the fungi when they and plants cocolonized the continents at a time when there was no shade from solar radiation. The proteins could have been especially important at other times of global ecological change associated with elevated UV irradiation due to atmospheric and vegetation changes, such as at the end of the Permian (250 mya) or Cretaceous (65 mya), when there are spikes of fungi in the fossil record [48,49]. The UV-protecting ability of the WC-1 proteins is a likely selective force that has served to maintain their presence in fungi to this day.
Materials and Methods
Gene identification and fungal strains
Candidate photoreceptors were identified in the C. neoformans genome projects. Gene transcription was tested using RT-PCR and rapid amplification of cDNA ends (RACE) with the GeneRacer Kit (Invitrogen, Carlsbad, California, United States). Three genes were mutated in serotype D strain JEC43 (MATα ura5) and serotype A strain JF99 (MATa
ura5). Mutations were made by biolistic introduction of disruption alleles generated by overlap PCR with 1.5-kb DNA on either side of the URA5 gene [50,51]. BWC2 was disrupted using a nourseothricin resistance cassette [52,53] to replace the gene in the serotype D strain JEC21 or the serotype A strain KN99α (both MATα). Gene disruption was confirmed by PCR and Southern blot analysis using standard methods. The mutant serotype D strains were crossed to the congenic strain JEC20 (MATa) to obtain strains with the opposite mating type. Through a series of crosses, strains with double or triple mutations in both mating types were isolated. A set of strains with auxotrophic markers (either lys1 or ade2) with the mutation or wild type at the BWC1 locus was also generated by crossing. The serotype A bwc1::URA5 strain was crossed to strain KN99α to obtain a MATα bwc1 strain. For reconstitution, the BWC1 or BWC2 genes were amplified with primers JOHE8744 and JOHE8745 or JOHE12641 and JOHE12642, respectively, from genomic DNA of strain H99, and ligated adjacent to a cassette conferring resistance to neomycin (G418). A linearized version of this vector was introduced into bwc1 or bwc2 mutant strains. The self-filamentous serotype D strain was obtained by introducing the SXI1α gene adjacent to URA5 into strain JEC34 (MATa
ura5). This strain was mutated with Agrobacterium-mediated integration of T-DNA containing the NAT gene [52,54]. Strains created and primers used in strain construction are listed in Tables S1 and S2.
Phenotypic analysis of mutant strains
Strains were compared to each other and reference laboratory strains for the ability to mate in the presence of white fluorescent light (1,500–3,500 lux) or darkness on V8 medium at pH 5 (serotype A) or pH 7 (serotype A and D). The growth of strains was also examined at 37 °C on YPD medium, for melanin production on bird seed agar (70 g/L ground bird seed, 0.1% glucose, 0.05% Tween-20) or on low-glucose (0.1%) medium supplemented with the diphenolic molecule L-DOPA (100 mg/ml). Capsule production was assayed by growing strains in liquid medium with low levels of glucose (0.5%) and iron (20 mg/L of the chelator EDDHA), and examining exclusion of India ink particles from fungal cells. Strains were grown to logarithmic phase in liquid YPD medium and serial dilutions spotted onto YPD agar plates, which were then irradiated with UV light (0.2 min setting, approximately 48 mJ/cm2; UV Stratalinker 2400, Stratagene, La Jolla California, United States) to test for UV sensitivity.
Wavelength of inhibition and analysis of light inhibition during stages of mating
Crosses between lys1 or ade2 auxotrophic strains with or without the bwc1 or bwc2 mutations were conducted under illumination modified with filters to provide blue, green, or red light (LE 4747 blue, LE 4758 green and LE 4725 red; Calumet, Bensenville Illinois, United States). Yeast cells (1 × 107/ml) were inoculated in 5-μl drops onto V8 (pH 7) medium, and 24 or 48 h later the mating mix scraped from the surface, and cells were resuspended in sterile water and plated onto minimal medium (yeast nitrogen base; YNB) to select for prototrophs that result from fusion events. Stable diploid yeast strains were created by incubating the cells at 37 °C on YNB medium.
Transcription analysis
Strains (1.25 × 108 cells) were inoculated onto 15-cm diameter petri dishes containing V8 pH 7 medium, which induces mating. Cells were scraped from the surface 24 h later, frozen, and lyophilized. Total RNA was isolated from cells using TRIzol reagent (Invitrogen) according to manufacturer's instructions. Messenger RNA was isolated from 200 μg of total RNA using the PolyATtract isolation system (Promega, Madison, Wisconsin, United States). RNA was separated on denaturing agarose gels, blotted to nitrocellulose (Zeta-Probe, Bio-Rad, Hercules California, United States), and probed with [32P]-dCTP-radiolabeled DNA fragments. Probes comprising ACT1 (encoding actin), BWC1, BWC2, SXI1α, and MFα1 genes were amplified from genomic DNA (primers in Table S2). Crosses comprised wild-type strains JEC20 x JEC21, bwc1 mutant strains AI5 (MATα) × AI6 (MATa), or bwc2 mutant strains AI76 (MATα) × AI78 (MATa), and were maintained in constant light or dark. For analysis of BWC1 and BWC2 transcription, cultures of wild-type, bwc1, or bwc2 (strains JEC21, AI5, and AI76, respectively) were wrapped in aluminum foil and exposed to light 0 h, 1 h, 4 h, or 8 h prior to the end of a 24-h incubation.
Yeast two-hybrid system
cDNAs of BWC1 and BWC2 were amplified either by overlap PCR from genomic DNA or from RT-PCR from RNA, and sequenced to identify clones without errors. Products were cloned into plasmids pGBD.c1 and pGAD.c1, and the S. cerevisiae reporter strain PJ69–4A was cotransformed with plasmids using the lithium acetate/heat shock method [55]. Double transformants were selected on media lacking leucine and tryptophan. Interactions were assessed by growth in the absence of adenine or histidine (+ 5 mM 3-aminotriazole) and β-galactosidase assays [56].
C. neoformans
virulence assay
For murine killing assays, serotype A C. neoformans cells were used to infect 25-g female A/Jcr mice (NCI/Charles River Laboratories, Frederick, Maryland, United States) by nasal inhalation [57]. Ten mice were inoculated each with a 50-μl drop containing 1 × 105 yeast cells of KN99α, bwc1, bwc1 + BWC1 reconstituted, bwc2 and bwc2 + BWC2 reconstituted strains. Survival data were analyzed with a logrank test to determine statistical significance. The murine experiment protocol was approved by the Duke University Animal Use Committee.
Supporting Information
Table S1 Cryptococcus neoformans strains
Mutant alleles were created by replacing the coding region of genes with markers to complement uracil auxotrophy (URA5) or confer resistance to nourseothricin (NAT). Mutations were complemented by reintroduction of wild-type copies of the gene fused to a gene conferring resistance to neomycin (NEO).
(58 KB DOC).
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Table S2 Oligonucleotides
Primers used to create gene disruption alleles, probes, and clones for yeast two-hybrid assays.
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Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers of the genes discussed in this paper are Aspergillus nidulans wc-1 (AF515628), BWC1 (AY882437), BWC1 (AY882438), N. crassa wc-1 (X94300), OPS1 (AY882440), PHY1 (AY882439), and T. borchii Tbwc-1 (encodes wc-1 protein) (AJ575416). The Broad Institute (http://www.broad.mit.edu/annotation/fungi/fgi/) has sequence for White collar 1 homologs from Coprinus cinereus, Fusarium graminearum, Magnaporthe grisea, and Ustilago maydis. The Department of Energy (http://genome.jgi-psf.org/whiterot1/whiterot1.home.html) has the sequence for the White collar 1 homolog of Phanerochaete chrysosporium.
We thank Cristl Arndt, Marie-Josée Boily, and Felicia Walton for technical assistance; Yong-Sun Bahn, Gary Cox, Steven Giles, Kirsten Nielsen, and John Perfect for assistance with animal experiments and protocols; Wei-Chiang Shen for discussion; Stephanie Diezmann for translation; and Jay Dunlap, James Fraser, Peter Kraus, and Robin Wharton for critical reading of the manuscript. Analysis of the C. neoformans genome and searches for homologs in other fungi used genome databases at the Institute for Genomic Research, Stanford and Duke Universities, the Whitehead Institute, the National Center for Biotechnology Information, and the Department of Energy. This research was supported by National Institute for Allergy and Infectious Diseases R01 grants AI39115 and AI50113. JH is a Burroughs Wellcome Scholar in Molecular Pathogenic Mycology and an investigator of the Howard Hughes Medical Institute.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. AI and JH conceived and designed the experiments. AI performed the experiments. AI and JH analyzed the data. AI and JH wrote the paper.
Citation: Idnurm A, Heitman J (2005) Light controls growth and development via a conserved pathway in the fungal kingdom. PLoS Biol 3(4): e95.
Abbreviations
ADactivation domain
BDbinding domain
FADflavin adenine dinucleotide
LOVlight
myamillion years ago
PASPer-Arnt-Sim
T-DNAtransfer DNA
UVultraviolet
WCWhite collar
==== Refs
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| 15760278 | PMC1064852 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e95 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030095 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1576026910.1371/journal.pbio.0030096Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaTwo Distinct E3 Ubiquitin Ligases Have Complementary Functions in the Regulation of Delta and Serrate Signaling in Drosophila
D-mib Regulates Dl and Ser SignalingLe Borgne Roland
1
Remaud Sylvie
1
Hamel Sophie
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Schweisguth François [email protected]
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1Ecole Normale Supérieure, CNRS UMR 8542ParisFranceMartinez Arias Alfonso Academic EditorCambridge UniversityUnited Kingdom4 2005 15 3 2005 15 3 2005 3 4 e968 11 2004 15 1 2005 Copyright: © 2005 Le Borgne et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Crucial Roles in Drosophila Development for Little-Known Protein
Signaling by the Notch ligands Delta (Dl) and Serrate (Ser) regulates a wide variety of essential cell-fate decisions during animal development. Two distinct E3 ubiquitin ligases, Neuralized (Neur) and Mind bomb (Mib), have been shown to regulate Dl signaling in Drosophila melanogaster and Danio rerio, respectively. While the neur and mib genes are evolutionarily conserved, their respective roles in the context of a single organism have not yet been examined. We show here that the Drosophila mind bomb (D-mib) gene regulates a subset of Notch signaling events, including wing margin specification, leg segmentation, and vein determination, that are distinct from those events requiring neur activity. D-mib also modulates lateral inhibition, a neur- and Dl-dependent signaling event, suggesting that D-mib regulates Dl signaling. During wing development, expression of D-mib in dorsal cells appears to be necessary and sufficient for wing margin specification, indicating that D-mib also regulates Ser signaling. Moreover, the activity of the D-mib gene is required for the endocytosis of Ser in wing imaginal disc cells. Finally, ectopic expression of neur in D-mib mutant larvae rescues the wing D-mib phenotype, indicating that Neur can compensate for the lack of D-mib activity. We conclude that D-mib and Neur are two structurally distinct proteins that have similar molecular activities but distinct developmental functions in Drosophila.
The Notch pathway is an important mechanism for communication between cells. In this paper, the roles of two related proteins in the Notch pathway are unravelled
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Introduction
Cell-to-cell signaling mediated by receptors of the Notch (N) family has been implicated in various developmental decisions in organisms ranging from nematodes to mammals [1]. N is well-known for its role in lateral inhibition, a key patterning process that organizes the regular spacing of distinct cell types within groups of equipotent cells. Additionally, N mediates inductive signaling between cells with distinct identities. In both signaling events, N signals via a conserved mechanism that involves the cleavage and release from the membrane of the N intracellular domain that acts as a transcriptional co-activator for DNA-binding proteins of the CBF1/Suppressor of Hairless/Lag-2 (CSL) family [2].
Two transmembrane ligands of N are known in Drosophila, Delta (Dl) and Serrate (Ser) [3]. Dl and Ser have distinct functions. For instance, Dl (but not Ser) is essential for lateral inhibition during early neurogenesis in the embryo [4]. Conversely, Ser (but not Dl) is specifically required for segmental patterning [5]. Some developmental decisions, however, require the activity of both genes: Dl and Ser are both required for the specification of wing margin cells during imaginal development [6,7,8,9,10]. These different requirements for Dl and Ser appear to primarily result from their non-overlapping expression patterns rather than from distinct signaling properties. Consistent with this interpretation, Dl and Ser have been proposed to act redundantly in the sensory bristle lineage where they are co-expressed ([11]; note, however, that results from another study have indicated a non-redundant function for Dl in the bristle lineage [12]). Furthermore, Dl and Ser appear to be partially interchangeable because the forced expression of Ser can partially rescue the Dl neurogenic phenotype [13]. Additionally, the ectopic expression of Dl can partially rescue the Ser wing phenotype [14]. The notion that Dl and Ser have similar signaling properties has, however, recently been challenged by the observation that human homologs of Dl and Ser have distinct instructive signaling activity [15].
Endocytosis has recently emerged as a key mechanism regulating the signaling activity of Dl. First, clonal analysis in Drosophila has suggested that dynamin-dependent endocytosis is required not only in signal-receiving cells but also in signal-sending cells to promote N activation [16]. Second, mutant Dl proteins that are endocytosis defective exhibit reduced signaling activity [17]. Third, two distinct E3 ubiquitin ligases, Neuralized (Neur) and Mind bomb (Mib), have recently been shown to regulate Dl endocytosis and N activation in Drosophila and Danio rerio, respectively [18,19,20,21,22,23,24,25]. Ubiquitin is a 76-amino-acid polypeptide that is covalently linked to substrates in a multi-step process that involves a ubiquitin-activating enzyme (E1), a ubiquitin-conjugating enzyme (E2), and a ubiquitin–protein ligase (E3). E3s recognize specific substrates and catalyze the transfer of ubiquitin to the protein substrate. Ubiquitin was first identified as a tag for proteins destined for degradation. More recently, ubiquitin has also been shown to serve as a signal for endocytosis [26,27]. Mib in D. rerio and Neur in Drosophila and Xenopus have been shown to associate with Dl, regulate Dl ubiquitination, and promote its endocytosis [18,19,20,22,25,28]. Moreover, genetic and transplantation studies have indicated that both Neur and Mib act in a non-autonomous manner [18,21,22,23,25,29], indicating that endocytosis of Dl is associated with increased Dl signaling activity. Finally, epsin, a regulator of endocytosis that contains a ubiquitin-interacting motif and that is known in Drosophila as Liquid facet, is essential for Dl signaling [30,31]. In one study, Liquid facet was proposed to target Dl to an endocytic recycling compartment, suggesting that recycling of Dl may be required for signaling. Accordingly, signaling would not be linked directly to endocytosis, but endocytosis would be prerequisite for signaling [30]. How endocytosis of Dl leads to the activation of N remains to be elucidated. Also, whether the signaling activity of Ser is similarly regulated by endocytosis is not known.
Neur and Mib proteins completely differ in primary structure. Drosophila Neur is a 754-amino-acid protein that contains two conserved Neur homology repeats of unknown function and one C-terminal catalytic really interesting new gene (RING) domain. D. rerio Mib (also known as DIP-1 in the mouse [32]) is a 1,030-amino-acid protein with one ZZ zinc finger domain surrounded by two Mib/HERC2 domains, two Mib repeats, eight ankyrin repeats, two atypical RING domains, and one C-terminal catalytic RING domain. Both genes have been conserved from flies to mammals [18,19,33,34]. While genetic analysis has revealed that neur in Drosophila and mib in D. rerio are strictly required for N signaling, knockout studies of mouse Neur1 has indicated that NEUR1 is not strictly required for N signaling [33,34]. One possible explanation is functional redundancy with the mouse Neur2 gene. Conversely, the function of Drosophila mib (D-mib), the homolog of D. rerio mib gene, is not known.
To establish the respective roles of these two distinct E3 ligases in the context of a single model organism, we have studied the function of the Drosophila D-mib gene. We report here that D-mib, like D. rerio Mib, appears to regulate Dl signaling during leg segmentation, wing vein formation, and lateral inhibition in the adult notum. We further show that D-mib is specifically required for Ser endocytosis and signaling during wing development, indicating for the first time, to our knowledge, that endocytosis regulates Ser signaling. Interestingly, the D-mib activity was found necessary for a subset of N signaling events that are distinct from those requiring the activity of the neur gene. Nevertheless, the ectopic expression of Neur compensates for the loss of D-mib activity in the wing, indicating that Neur and D-mib have overlapping functions. We conclude that D-mib and Neur are two structurally distinct proteins with similar molecular activities but distinct and complementary functions in Drosophila.
Results
Isolation of D-mib Mutations
The closest Drosophila homolog of the vertebrate mib gene is the predicted gene CG5841, D-mib [18]. The D-mib mutations identified are shown in Figure 1. A P-element inserted into the 5′ untranslated region of the D-mib gene was recently isolated (http://flypush.imgen.bcm.tmc.edu/pscreen/) (Figure 1A). Insertion of this P-element confers late pupal lethality. Lethality was reverted by precise excision of the P-element, suggesting that insertion of this P-element is a D-mib mutation, referred to as D-mib1. A 13.6-kb deletion that removes the entire D-mib coding region was selected by imprecise excision of this P-element. This deletion represents a null allele of D-mib and was named D-mib2. This deletion also deletes the 3′ flanking RpS31 gene (Figure 1A). The D-mib1 and D-mib2mutant alleles did not complement the l(3)72CdaJ12 and l(3)72CdaI5 lethal mutations that have been mapped to the same cytological interval as the D-mib gene [35]. This indicates that these two lethal mutations are D-mib mutant alleles, and they were therefore renamed D-mib3 and D-mib4, respectively. The D-mib1 and D-mib3 mutations behave as genetic null alleles (see Materials and Methods). In contrast, D-mib4 is a partial loss-of-function allele because flies trans-heterozygous for D-mib4 and any other D-mib null alleles are viable.
Figure 1 Molecular and Genetic Characterization of D-mib Mutations
(A) Molecular map of the D-mib locus showing the position of the P-element inserted into the 5′ untranslated region (allele D-mib1) and the 13.6 kb deletion that removes the D-mib and the RpS31 genes (allele D-mib2). Transcribed regions are indicated with arrows, and exons are indicated with boxes. Open reading frames are shown in black.
(B) Domain composition of D-mib and D. rerio Mib. Both proteins show identical domain organization. D-mib has an N-terminal ZZ zinc finger flanked on either side by a Mib/HERC2 (M-H) domain, followed by two Mib repeats, six ankyrin repeats, two atypical RING domains, and a C-terminal protypical RING that has been associated with catalytic E3 ubiquitin ligase activity. The D-mib3 mutant allele is predicted to produce a truncated protein devoid of E3 ubiquitin ligase activity whereas the D-mib4 protein carries a mutation at a conserved position in the second Mib repeat.
(C and C′) Western blot analysis of D-mib (C). The endogenous D-mib protein (predicted size: 130 kDa) was detected in S2 cells (lane 2) and in imaginal discs from wild-type larvae (lane 3) but was not detectable in homozygous D-mib1 (lane 4) and D-mib1/D-mib3 (lane 5) third instar larvae. The D-mib protein produced in transfected S2 cells from the cDNA used in this study (lane 1) runs exactly as endogenous D-mib (lane 2). Panel C′ shows a Red Ponceau staining of the gel with the same protein samples as in panel C.
(D–H) Wings from wild-type (D), D-mib1 (E), SerRX82/Serrev6.1 (F), D-mib2/D-mib4 (G), and UAS-D-mib2/+; D-mib1/D-mib2 flies (H). D-mib (E) and Ser (F) mutant flies showed similar wing loss phenotypes. The D-mib mutant phenotype could be almost fully rescued by a leaky UAS-D-mib transgene (H). (D′) and (G′) show high magnification views of (D) and (G), respectively, to show that D-mib2/D-mib4 mutant flies (G′) exhibited ectopic sensilla (arrowheads) along vein L3.
(I–N) Nota (I–K) and legs (L–N) from wild-type (I and L), D-mib1 (J and M), and SerRX82/Serrev6.1 (K and N) flies. D-mib mutant flies showed a weak neurogenic phenotype (J) that was not observed in Ser mutant flies (K). Ectopic sensory organs in D-mib mutant flies developed from ectopic sensory organ precursor cells (not shown). D-mib (M) and Ser (N) mutant legs also showed distinct growth and/or elongation defects. Arrows in (J) show ectopic macrochaetes. Arrows in (L–N) indicate the joints. Ti, tibia; t1 to t5, tarsal segments 1 to 5.
These four mutations identify the CG5841 gene as D-mib by the following evidence. First, lethality of homozygous D-mib1 pupae is associated with the insertion of a P-element into the 5′ UTR of the D-mib gene. Second, genomic sequencing of the D-mib3 allele revealed the presence of a stop codon at position 258 (Figure 1B). This allele is therefore predicted to produce a truncated protein devoid of the catalytic RING domain, consistent with D-mib3 being a null allele. Genomic sequencing of the D-mib4 allele showed that this mutation is associated with a valine-to-methionine substitution at a conserved position in the second Mib repeat (Figure 1B). Third, Western blot analysis showed that the D-mib protein was not detectable in imaginal disc and brain complex extracts prepared from homozygous D-mib1 and D-mib1/D-mib2 larvae (Figure 1C and C′). Fourth, the leaky, GAL4-independent expression of a UAS-D-mib transgene fully rescued the lethality of D-mib1/D-mib2 flies (data not shown; see also Figure 1H). Thus, our analysis identified both complete and partial D-mib loss-of-function alleles.
D-mib Regulates Dl Signaling
Complete loss of zygotic D-mib activity in homozygous D-mib1 and trans-heterozygous D-mib2/D-mib3, D-mib1/D-mib3 and D-mib1/D-mib2 individuals led to late pupal lethality. Mutant pupae died as pharate adults showing ectopic macrochaetes, increased microchaete density on the dorsal thorax (Figure 1I and 1J), short legs lacking tarsal segmentation (Figure 1L and 1M), and nearly complete loss of eye and wing tissues (Figure 1D and 1E). Tissue losses were associated with a dramatic reduction in size of the eye field and of the wing pouch in mutant discs of third instar larvae (Figure 2A–2E). Hypomorphic D-mib2/D-mib4 mutant flies only showed ectopic sensory organs, rough eyes, small wings, and thickened veins (Figure 1D, 1D′, 1G, and 1G′; data not shown).
Figure 2 The D-mib and neur Genes Have Distinct Functions during Wing Development
(A–E) Wing imaginal discs (B–E) from wild-type (B and D), D-mib1 (C), and D-mib1/D-mib2 (E) third instar larvae stained for Cut (B and C) and wg-lacZ (D and E). D-mib mutant discs showed a dramatically reduced size of the wing pouch (see diagram in [A] showing the different regions of the wing imaginal disc; V, ventral; D, dorsal), as well as a complete loss of Cut and wg-lacZ (red arrows in [B–E]) expression at the wing margin. Expression of wg-lacZ in the hinge region (arrowheads in [D] and [E]) and the accumulation of Cut in sensory cells (small arrows in [B] and [C]) and muscle precursor cells (large arrowheads in [B] and [C]) appeared to be largely unaffected).
(F and F′) Expression of Cut (red) at the wing margin was not affected by the complete loss of neur activity in neur1F65 mutant clones (indicated by the loss of the nuclear green fluorescent protein [GFP] marker, in green).
Bar is 50 μm in (B–E) and 20 μm in (F and F′).
All these phenotypes may result from reduced N signaling. More specifically, the bristle and leg phenotypes are likely to result from reduced signaling by Dl (and not by Ser). Indeed, a reduction in Dl-mediated lateral inhibition can result in ectopic sensory organs and increased bristle density on the body surface. In contrast, a complete loss of Ser signaling had no effect on bristle density (Figure 1K). Likewise, loss of Dl signaling has been shown to result in short unsegmented legs, similar to the ones seen in the absence of D-mib activity (Figure 1M), whereas a complete loss of Ser activity led to the formation of elongated unsegmented legs (Figure 1N) [36,37,38]. Finally, the vein phenotype seen in D-mib hypomorphic flies is similar to the one seen in Dlts mutant flies [39]. Together, these observations suggest that D-mib regulates Dl signaling in several developmental contexts. Consistent with this conclusion, we have shown that D-mib binds Dl and promotes Dl signaling and that overexpression of D-mib down-regulates the accumulation of Dl at the cell surface (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data).
D-mib and neur Have Distinct Functions
We then studied in more detail the function of D-mib during wing development. Growth of the wing pouch depends on the activity of an organizing center located at the dorsal-ventral (D-V) boundary [40,41]. This boundary is established in first instar larvae and is defined by the apterous expression boundary. Apterous activates the expression of the Ser and fringe genes in dorsal cells. High levels of Ser in dorsal cells activate N in trans in ventral cells and suppress N activation in cis in dorsal cells, whereas Fringe modifies N in dorsal cells such that dorsal cells located at the D-V boundary respond to Dl. Thus, composite signaling by Ser and Dl leads to symmetric N activation in margin cells located along the D-V boundary [8,9,42,43]. N then regulates the expression of the vestigial and wingless (wg) genes that cooperate to promote growth of the wing pouch. N also regulates expression of the cut gene in margin cells [44]. Thus, loss of N signaling results in a reduction in size of the wing pouch accompanied by the loss of cut and wg expression along the D-V boundary.
A complete loss of Cut and Wg accumulation and wg-lacZ expression was observed in the central region of third instar D-mib mutant wing discs (data not shown). Thus, the D-mib wing phenotype may result from defective N inductive signaling at the D-V boundary. We conclude that the activity of the D-mib gene is required for the specification of the wing margin and, hence, growth of the wing pouch. Interestingly, wing margin formation and expression of Cut are not affected by the complete loss of neur activity (Figure 2F and 2F′) [45]. Similarly, loss of neur activity had no detectable effect on leg segmentation (data not shown) and vein determination [45], two processes shown here to depend on D-mib gene activity. We therefore conclude that D-mib and neur have distinct and complementary functions in Drosophila.
D-mib Co-Localizes with Dl and Ser at the Apical Cortex
We next studied the subcellular localization of D-mib (Figure 3). Anti-D-mib antibodies were generated that specifically detected D-mib on Western blots (see Figure 1C) and on fixed tissues (Figure 3F–F″). Using these antibodies, we found that D-mib was detected in all imaginal disc cells (Figure 3A and 3B). We then examined D-mib subcellular distribution in epithelial cells located along the edge of the wing discs because cross-sectional imaging affords better resolution along the apical-basal axis. D-mib co-localized with Ser, Dl, and N at the apical cortex (Figure 3B–3D′′′). Dl and Ser were also detected in large intracellular vesicles that probably correspond to multivesicular bodies in that they also stained for hepatocyte growth factor-regulated tyrosine kinase substrate [46] (Figure 3B–3C′′′; data not shown). The intracellular dots seen with the anti-D-mib antibodies were distinct from the Dl- and Ser-positive dots and appeared to result from background staining (data not shown). The reduced cytoplasmic staining seen in D-mib mutant cells (Figure 3F–3F′′) suggests that D-mib is also present in the cytoplasm. A similar localization at the apical cortex and in the cytoplasm was seen for a functional yellow fluorescent protein (YFP)::D-mib fusion protein (see Figure 6 below). These localization data suggest that D-mib may act at the apical cortex to regulate the activity of Dl and/or Ser.
Figure 3 D-mib Co-Localizes with Dl and Ser at the Apical Cell Cortex
(A and A′) D-mib (green) is detected in all cells of the wing imaginal disc. In (A), Ser is in red and Discs-large (Dlg) is in blue.
(B–D′′′) D-mib (green in B, B′, C, C′, D, and D′) co-localized with Ser (red in [B and B′′]), Dl (red in [C and C′′]), N (red in [D and D′′]), and E-Cadherin (E-Cad; blue in [D and D′′′]) and was found apical to Discs-large (Dlg; blue in [B, B′′′, C, and C′′′]) in notum cells located at the edges of the wing discs.
(E–E′′) D-mib (green in [E and E′]) co-localized with Dl (red in [E and E′′]) at the apical cortex of wing pouch cells.
(F–F′′) D-mib staining at the apical cortex (blue in [F and F′]) was not detected in D-mib2 mutant clone (marked by loss of nuclear GFP staining; green in [F]). Loss of D-mib activity has no detectable effect on the apical accumulation of Dl (red in [F and F′′]).
Bar is 50 μm for (A and A′) and 10 μm for (B–F′′).
Figure 6
D-mib Is Required in Dorsal Cells for Margin Expression of Cut
Large dorsal clones of D-mib2 mutant cells (marked by the loss of nuclear GFP, in green) resulted in a complete loss of Cut (red) expression (A and B). This indicates that D-mib is required for Ser signaling by dorsal cells. In contrast, ventral clones did not prevent the expression of Cut (C and D), implying that D-mib is not strictly required for Dl signaling. Note that mutant ventral cells abutting wild-type dorsal cells expressed Cut (arrow in [D]), indicating that D-mib is not required for N signal transduction. Low-magnification views of the wing portion of the discs are shown in (A) and (C). (B) and (D) show high-magnification views of the areas boxed in (A) and (C), respectively.
D-mib Regulates the Cell-Surface Level of Ser
We next examined the potential role of D-mib in regulating Dl and Ser distribution in wing imaginal discs. We focused our analysis on the notum region since D-mib mutant discs have no wing pouch (Figure 4). Dl and Ser co-localized both at the apical cortex and in large intracellular vesicles in wild-type cells (Figure 4A–4C′). The complete loss of D-mib activity in D-mib1 mutant discs did not detectably change the subcellular localization of Dl (Figure 4C, 4C′, 4F, and 4F′). In contrast, the accumulation of Ser at the apical cortex was strongly increased (Figure 4E) and Ser accumulation in Dl-positive vesicles was dramatically reduced (Figure 4E′) in D-mib1 mutant discs. Similar results were also obtained in D-mib2 mutant clones, which showed strongly elevated levels of cortical Ser (Figure 4H) whereas the amount of Dl at the apical cortex was not detectably modified (see Figures 3F–3F′′ and 4J). Of note, loss of D-mib2 activity in clones did not block the accumulation of Ser into intracellular dots (Figure 4H′). Thus, trafficking of Ser towards this intracellular compartment is, at least in part, D-mib-independent. We therefore conclude that the D-mib gene is required to regulate the level of Ser at the apical cortex of wing disc cells.
Figure 4 D-mib Is Required to Down-Regulate Ser at the Apical Cortex
(A–F′) Distribution of Dl (green) and Ser (red) in the notum region of wild-type (A–C′) and D-mib1 mutant (D–F′) wing imaginal discs. The boxed areas in (A) and (D) are shown at higher magnification in (B–F′). The specific loss of Ser accumulation into intracellular vesicles (compare [E′] with [B′]) correlated with the elevated levels of Ser seen at the apical cortex of D-mib mutant cells (compare [E] with [B]).
(G–J′) Ser (red in [H and H′]) accumulated at the apical cortex (H) as well as in intracellular dots (H′) in D-mib2 mutant cells (marked by the loss of nuclear GFP; green in [G]). Cut is shown in blue (G). The distribution of Dl (red in [J and J′]) was not affected by the loss of D-mib activity. Low-magnification views of the wing portion of the discs are shown in (G) and (I). (H and H′) and (J and J′) show high magnification views of the areas boxed in (G) and (I), respectively. Clone boundaries are outlined in (H and H′) and (J and J′).
Bar is 40 μm for (A, D, G), 5 μm for (B–C′ and E–F′), and 10 μm for (H–J′).
D-mib Is Required for Ser Endocytosis
To test whether this specific increase in the level of Ser at the apical cortex resulted from reduced Ser endocytosis in D-mib mutant cells, we followed the endocytosis of Ser in living imaginal discs using an antibody uptake assay. Briefly, dissected wing discs were cultured for 15 min in the presence of antibodies that recognize the extracellular part of Ser or Dl, then washed, cultured for another 45 min in medium without antibodies, and then fixed. The uptake of anti-Ser and anti-Dl antibodies was then assessed using secondary antibodies. The results are shown in Figure 5. Using this assay, we found that anti-Ser-and anti-Dl antibodies were internalized in wild-type epithelial cells (Figure 5A–5C′′). The complete loss of D-mib activity in D-mib1 wing discs did not significantly change the internalization of anti-Dl antibodies (Figure 5D′′, 5E′′, and 5F′′), indicating that D-mib is not required for Dl endocytosis in this tissue. However, the loss of D-mib activity strongly inhibited the endocytosis of anti-Ser antibodies (Figure 5E′). Moreover, high levels of anti-Ser antibodies were seen at the apical surface (Figure 5D′ and 5F′), confirming that D-mib mutant cells accumulate high levels of Ser at their surface. We therefore conclude that D-mib is specifically required for the endocytosis of Ser in wing discs.
Figure 5 D-mib Is Required for Ser Endocytosis
Localization of the anti-Ser (red) and anti-Dl (green) antibodies that have been internalized by wild-type (A–C′′) and D-mib1 mutant (D–F′′) cells in the notum region of wing discs. (A–A′′) and (D–D′′) show apical sections and (B–B′′) and (E–E′′) show basal sections. (C–C′′) and (F–F′′) show confocal z-sections. The z-section axes are shown with a double-headed arrow in (A) and (D). Internalized anti-Ser and anti-Dl antibodies co-localized in wild-type cells. In contrast, high levels of anti-Ser antibodies were detected at the cell surface of D-mib mutant epithelial cells whereas anti-Dl antibodies were efficiently internalized.
Bar is 10 μm for all panels.
Ubiquitin-mediated endocytosis is thought to depend on monoubiquitination. Thus, by analogy with the function of Mib in D. rerio [18,28], we suggest that D-mib may directly monoubiquitinate Ser. Consistent with this hypothesis, we show in a companion paper that D-mib binds Ser (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data). Moreover, a mutation in the C-terminal catalytic RING domain of D-mib abolished its ability to internalize Ser in transfected S2 cells (R. L. B. and F. S., unpublished data) implying that the E3 ubiquitin ligase activity of D-mib is required for Ser internalization. Biochemical analysis of the ubiquitination events regulated by D-mib will be needed to further define the mechanism by which D-mib regulates the endocytosis of Ser in vivo.
D-mib Regulates Ser Signaling
The regulation of Ser endocytosis by D-mib suggests that D-mib may regulate Ser signaling. Ser expression is restricted to dorsal cells in second instar wing imaginal discs [7,10,44,47,48]. Ser in dorsal cells signals across the D-V boundary to activate N in ventral cells [8,9]. If D-mib is required for Ser signaling during wing development, then loss of D-mib activity in dorsal cells should affect the specification of the wing margin in a non-autonomous manner. Loss of D-mib activity in large dorsal clones of D-mib2 mutant cells resulted in a loss of Cut expression at the D-V interface (Figure 6A and 6B). The lack of Cut expression in wild-type ventral cells abutting the D-V boundary indicates that D-mib is required for Ser signaling by dorsal cells and acts in a non-autonomous manner to activate N in ventral cells. Conversely, loss of D-mib activity in large ventral clones (Figure 6C and 6D) did not disrupt margin specification, indicating that D-mib is not strictly required for Dl signaling by ventral cells. However, a narrowing of the Cut-positive margin was observed (Figure 6D), suggesting that D-mib contributes to regulating the level of Dl signaling. Of note, ventral D-mib mutant cells expressed Cut, implying that D-mib is not required for N signal transduction.
We next tested whether expression of D-mib in dorsal cells is sufficient to rescue the D-mib wing phenotype. D-mib was expressed in dorsal cells of D-mib2/D-mib3 mutant discs using Ser-GAL4. Similarly to the expression of the Ser gene, Ser-GAL4 expression is restricted to dorsal cells in second/early third instar larvae and is weakly expressed in ventral cells in mid/late third instar larvae, i.e., after margin cell specification [49,50]. Expression of D-mib in dorsal cells was sufficient to rescue growth of the wing pouch and of the expression of Cut in margin cells in D-mib mutant discs (Figure 7A). This result confirmed that D-mib regulates Ser signaling by dorsal cells.
Figure 7 Expression of D-mib in Dorsal Cells Is Sufficient to Rescue the D-mib Mutant Phenotype
(A) Expression of D-mib (green) in dorsal cells, using Ser-GAL4, rescued the growth of the wing pouch and margin Cut (red) expression in D-mib2/D-mib3 mutant discs.
(B–D′′′) Ser-GAL4-driven expression of YFP::D-mib (green) rescued the D-mib2/D-mib3 phenotype and strongly reduced the level of Dl (blue in [B, B′, C, C′′, D, and D′′]) and Ser (red in [B, B′′, C, C′′′, D, and D′′′]) in dorsal cells. (C–D′′′) are high-magnification views (apical [C–C′′′] and basal [D–D′′′]) of the disc shown in (B–B′′). YFP::D-mib co-localized with Dl and Ser at the apical cortex in cells expressing only low levels of YFP::D-mib.
Bar is 50 μm for (A–B′′) and 10 μm for (C–D′′′).
A similar rescue was observed with a YFP::D-mib protein (Figure 7B–7B″), indicating that YFP::D-mib is functional. YFP::D-mib localized at the apical cortex and in the cytoplasm (Figure 7C–7 D′′′), as seen for endogenous D-mib (see Figure 3). YFP::D-mib co-localized with Dl and Ser at the apical cortex of cells expressing low levels of YFP::D-mib. However, cells expressing high levels of YFP::D-mib showed a strong reduction in the level of both Dl and Ser at the cortex (Figure 7C–7C′′′), further indicating that D-mib down-regulates the levels of both Ser and Dl at the apical cortex (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data).
D-mib Acts Downstream of Ser and Upstream of Activated N
The functional assay was then used to genetically position the requirement for the D-mib gene activity relative to Ser and N (Figure 8). Expression of an activated version of N, Ncdc10 [51], led to the activation of Cut and promoted growth in dorsal cells of D-mib2/D-mib3 mutant discs (Figure 8C). This indicates that D-mib acts at a step upstream of N activation. By contrast, elevated levels of Ser expression failed to restore Cut expression and growth of the wing pouch in D-mib2/D-mib3 mutant larvae (Figure 8B). This confirms that Ser signaling requires the activity of the D-mib gene, i.e., that D-mib acts downstream of Ser.
Figure 8 Expression of Neur in Dorsal Cells Is Sufficient to Rescue the D-mib Mutant Phenotype
D-mib2/D-mib3 mutant discs expressing GFP (A) (GFP staining not shown), Ser (B), Ncdc10 (C), or Neur (D) under the control of Ser-GAL4 were stained for Cut (red). Expression of Ser in dorsal cells did not rescue the D-mib2/D-mib3 wing pouch mutant phenotype (compare [B] with [A]), consistent with D-mib being required for Ser signaling. By contrast, expression of Ncdc10, an activated version of N, led to the deregulated growth of the dorsal compartment and the expression of Cut in most dorsal cells (C), indicating that activated N acts downstream of D-mib. Expression of Neur in dorsal cells was sufficient to compensate for the loss of D-mib activity (D).
Bar is 40 μm for all panels.
neur and D-mib Functions Partially Overlap
The different requirements for neur and D-mib gene activity may suggest that Neur and D-mib have distinct molecular activities. Alternatively, this difference may reflect a difference in gene expression. Consistent with the latter hypothesis, the neur gene is not expressed in wing pouch and wing margin cells, where it is not required, and appears to be expressed only in sensory cells [52], where it is required. By contrast, D-mib appears to be uniformly expressed in imaginal discs. To test this hypothesis, we examined whether the forced ubiquitous expression of the neur gene can suppress the D-mib loss-of-function phenotype. Expression of Neur, using actin-GAL4, restored growth of the wing pouch and formation of the wing margin (data not shown). Moreover, expression of Neur in dorsal cells, using Ser-GAL4, was sufficient to rescue growth of the wing pouch as well as the expression of Cut in margin cells in D-mib mutant discs (Figure 8D). We conclude that ectopic expression of Neur compensates for the loss of D-mib activity.
In a converse experiment, we found that the neur-driven expression of D-mib, using neurPGAL4, did not rescue the cuticular neurogenic phenotype of neurPGAL4/neur1F65 embryos. Three UAS-D-mib transgenic lines were tested, and none showed detectable rescue whereas the two UAS-neur lines used as positive controls either fully or partially rescued the cuticular neurogenic phenotype of neurPGAL4/neur1F65 embryos (data not shown; UAS-D-mib/+, neurPGAL4/+ embryos developed normally). This indicates that a key function of Neur in the embryo cannot be provided by D-mib. We therefore suggest that Neur and D-mib functions overlap but are not strictly identical.
Discussion
Many recent studies have revealed that endocytosis plays multiple roles in the regulation of N signaling (reviewed in [2]; see also [53,54]). Here, we show that the conserved E3 ubiquitin ligases Neur and D-mib have similar molecular activities in the regulation of Dl and Ser endocytosis but distinct developmental functions in Drosophila.
Our analysis first establishes that D-mib regulates Ser signaling during wing development. First, clonal analysis revealed that the activity of the D-mib gene is specifically required in dorsal cells for the expression of Cut at the wing margin. Second, expression of D-mib in the dorsal Ser-signaling cells was sufficient to rescue the D-mib mutant wing phenotype. Third, results from an in vivo antibody uptake assay indicated that the endocytosis of Ser (but not of Dl) was strongly inhibited in D-mib mutant cells. This inhibition correlated with the strong accumulation of Ser (but not Dl) at the apical cortex of D-mib mutant cells. Thus, an essential function of D-mib in the wing is to regulate the endocytosis of Ser in dorsal cells to non-autonomously promote the activation of N along the D-V boundary. By analogy, the defective growth of the eye tissue may similarly result from the lack of Ser signaling and of N activation along the D-V boundary [55]. Because D-mib co-localizes with Ser at the apical cortex of wing disc cells, acts in a RING-finger-dependent manner to regulate Ser endocytosis in S2 cells (R. L. B. and F. S., unpublished results), and physically associates with Ser in co-immunoprecipitation experiments (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data), D-mib may ubiquitinate Ser and directly regulate its endocytosis.
Our analysis further suggests that endocytosis of Ser is required for Ser signaling. This conclusion is consistent with observations made earlier showing that secreted versions of Ser cannot activate N but instead antagonize Ser signaling [56,57]. Thus, endocytosis of both N ligands appears to be strictly required for N activation in Drosophila. Different models have been proposed to explain how endocytosis of the ligand, which removes the ligand from the cell surface, results in N receptor activation (discussed in [17,20,21,30]). Interestingly, the strong requirement for Dl and Ser endocytosis seen in Drosophila is not conserved in Caenorhabditis elegans, in which secreted ligands have been shown to be functional [58,59]. Noticeably, there is no C. elegans Mib homolog, and the function of C. elegans neur (F10D7.5) is not known. We speculate that endocytosis of the ligands may have evolved as a means to ensure tight spatial regulation of the activation of N.
Our analysis also establishes that the activity of the D-mib gene is required for a subset of N signaling events that are distinct from those that require the activity of the neur gene. We have shown that the D-mib gene regulates wing margin formation, leg segmentation, and vein formation, whereas none of these three processes depend on neur gene activity ([45,60]; this study). Conversely, the activity of the neur gene is essential for binary cell-fate decisions in the bristle lineage [22] that do not require the activity of the D-mib gene (no bristle defects were seen in D-mib mutant flies). The activity of the neur gene is also required for lateral inhibition during neurogenesis in embryos and pupae [4,45,61]. This process is largely independent of D-mib gene activity since the complete loss of D-mib function only resulted in a mild neurogenic phenotype in the notum. These data thus indicate that the neur and D-mib genes have largely distinct and complementary functions in Drosophila. Whether a similar functional relationship between Neur and D-mib exists in vertebrates awaits the study of the D. rerio neur genes and/or of the murine Mib and Neur genes.
The functional differences observed between D-mib and neur cannot be simply explained by obvious differences in molecular activity and/or substrate specificity. First, both Neur and D-mib physically interact with Dl ([20]; E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data) and promote the down-regulation of Dl from the apical membrane when overexpressed (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data). Furthermore, Dl signaling appears to require the activity of either Neur or D-mib, depending on the developmental contexts. We have shown here that specific aspects of the D-mib phenotype in legs and in the notum cannot simply result from loss of Ser signaling and are consistent with reduced Dl signaling, suggesting that D-mib regulates Dl signaling. Consistent with this interpretation, overexpression studies indicate that D-mib up-regulates the signaling activity of Dl, whereas a dominant-negative form of D-mib inhibits it (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data). We note, however, that no clear defects in Dl subcellular localization and/or trafficking were observed in D-mib mutant cells. It is conceivable that the contribution of D-mib to the endocytosis of Dl is masked by the activity of D-mib-independent processes that may, or may not, be linked to Dl signaling. We have also shown that, reciprocally, Neur and D-mib may similarly regulate Ser. Neur and D-mib were shown to similarly promote down-regulation of Ser from the cell surface when overexpressed (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data). Moreover, D-mib binds Ser (E. C. Lai, F. Roegiers, X. Qin, R. Le Borgne, F. Schweisguth, et al., unpublished data) and regulates Ser signaling (this study). Whether endogenous Neur binds and activates Ser remains to be tested. However, the ability of Neur to rescue the D-mib mutant wing phenotype when expressed in dorsal cells strongly indicates that Neur can promote Ser signaling. Together, these data indicate that Neur and D-mib have similar molecular activities.
D-mib and Neur may have identical molecular activities but distinct expression patterns, hence distinct functions at the level of the organism. Consistent with this possibility, D-mib is uniformly distributed in imaginal discs, whereas Neur is specifically detected in sensory cells [52]. Importantly, the rescue of the D-mib mutant phenotype by ectopic expression of Neur strongly supports this interpretation. This result further suggests that Neur can regulate Ser signaling. Consistent with this idea, overexpression of Neur in imaginal discs resulted in a strong reduction of Ser accumulation at the apical cortex (data not shown). Thus, despite their obvious structural differences, Neur and D-mib appear to act similarly to promote the endocytosis of Dl and Ser. Nevertheless, our observation that D-mib could not compensate for the loss of neur activity in the embryo indicates that D-mib and Neur have overlapping rather than identical molecular activities.
In conclusion, Neur and D-mib appear to have similar molecular activities in the regulation of Dl and Ser endocytosis but distinct developmental functions in Drosophila. The conservation from Drosophila to mammals of these two structurally distinct but functionally similar E3 ubiquitin ligases is likely to reflect a combination of evolutionary advantages associated with: (i) specialized expression pattern, as evidenced by the cell-specific expression of the neur gene in sensory organ precursor cells [52]; (ii) specialized function, as suggested by the role of murine MIB in TNFα signaling [32]; (iii) regulation of protein stability, localization, and/or activity. For instance, Neur, but not D-mib, localizes asymmetrically during asymmetric sensory organ precursor cell divisions [22].
Materials and Methods
Flies
The D-mib1 mutation corresponds to the EY97600 P-element insertion generated by the Gene Disruption Project (http://flypush.imgen.bcm.tmc.edu/pscreen/). The D-mib2 allele was selected as w− D-mib mutant derivative by imprecise excision of the EY97600 P-element. The precise breakpoints of the D-mib2 deletion were determined by sequencing a PCR fragment amplified from genomic DNA prepared from D-mib2 homozygous larvae. The l(3)72CdaJ12 and l(3)72CdaI5 alleles originally isolated by [35] failed to complement the D-mib1 and D-mib2 mutations and were renamed D-mib3 and D-mib4. The D-mib1, D-mib2, and D-mib3 alleles appear to be genetically null alleles since the phenotypes of D-mib1/D-mib1 and D-mib1/D-mib3 mutant pupae are indistinguishable from the ones seen in D-mib1/D-mib2 and D-mib2/D-mib3 pupae. Sequence analysis of the D-mib3 and D-mib4 alleles was carried on PCR products prepared from genomic DNA prepared from D-mib3/D-mib2 and D-mib4/D-mib2 mutant pupae. Genomic DNA from l(3)72Cda/D-mib2 mutant pupae was used as control for polymorphism.
D-mib2 mutant clones were generated in y w hs-flp;FRT2A D-mib2/FRT2A M(3)i55 ubi-nlsGFP larvae. neur1F65 mutant clones were generated as previously described [22].
UAS-D-mib and UAS-YFP::D-mib lines were generated via standard P-element transformation. These constructs were derived from the SD05267 cDNA obtained from ResGen (Invitrogen, Carlsbad, California, United States). Cloning details for these constructs are available upon request. UAS-Dl (gift from M. Muskavith), UAS-Ser (gift from R. Fleming), UAS-Neur (gift from C. Delidakis), UAS-Ncdc10 (gift of T. Klein), Ser-GAL4 lines, and Ser mutant alleles are described in FlyBase (http://flybase.bio.indiana.edu/).
Antibodies
Dissected imaginal discs were fixed in 4% paraformaldehyde (15 min) and incubated with antibodies at room temperature in PBS 1X with 0.1% TritonX-100. Rabbit polyclonal anti-D-mib antibodies were raised against the CYNERKTDDSELPGN peptide (CovalAb, Lyon, France). Immunopurified anti-D-mib antibodies (rabbit 541) were used (immunofluorescence, 1:100; Western blot, 1:1,000). Other primary antibodies were mouse anti-Cut (2B10; Developmental Studies Hybridoma Bank [DSHB, Iowa City, Iowa, United States]; 1:500); rat anti-DE-Cadherin (gift from T. Uemura; 1:50); guinea pig anti-Discs-large (gift from P. Bryant; 1:3,000); anti-β-galactosidase (Cappel [MP Biomedicals, Irvine, California, United States]; 1:1,000); mouse anti-DeltaECD (C594.9B; DSHB; 1:1,000); mouse anti-NotchECD (C548.2H; DSHB; 1:1,000); rat anti-Ser (gift from K. Irvine; 1:2,000); rat anti-Ser (gift from S. Cohen; 1:200); rabbit anti-Ser (gift from E. Knust; 1:10); and guinea pig anti-Senseless (gift from H. Bellen; 1:3,000). Cy2-, Cy3-, and Cy5-coupled secondary antibodies were from Jackson Laboratory (Bar Harbor, Maine, United States). Alexa488-coupled secondary antibodies and phalloidin were from Molecular Probes (Eugene, Oregon, United States). Images were acquired on a Leica (Wetzlar, Germany) SP2 microscope and assembled using Adobe Photoshop (Adobe Systems, San Jose, California, United States).
Endocytosis assay
Third instar larvae wing discs were dissected in Schneider's Drosophila medium (Gibco BRL, San Diego, California, United States) containing 10% fetal calf serum (Gibco BRL). Wing discs were cut between the wing pouch and the thorax to facilitate antibody diffusion. Wing discs were cultured for 15 min with mouse anti-Dl (C594–9B at 1:100) and rat anti-Ser antibody (1:500; from K. Irvine). Following three medium changes and a 45-min chase period, wing discs were fixed and incubated with secondary antibodies.
Supporting Information
Accession Numbers
The FlyBase accession numbers for the gene products discussed in this paper are Dl (FBgn0000463), N (FBgn0004647), Neur (FBgn0002932), P-element inserted into the 5′ untranslated region of the D-mib gene (FBgn0036558), and Ser (FBgn0004197). The NCBI Entrez Protein (http://www.ncbi.nlm.nih.gov/entrez/) accession number for D. rerio Mib is NP_779353.
We thank H. Bellen, G. Boulianne, P. J. Bryant, S. Cohen, C. Delidakis, R. Fleming, M. Haenlin, K. Irvine, D. Kalderon, T. Klein, E. Knust, M. Muskavitch, T. Uemura, DSHB, and the Bloomington stock center for flies, plasmids, and antibodies. We also thank O. Beaudoin-Massiani and P. Dubar for excellent technical assistance. We thank A. Bardin, C. Brou, J. E. Gomes, C. Goridis, and T. Klein for critical reading. This work was supported by grants from the Ministry of Research (ACI program grant) and from the Association pour la Recherche sur le Cancer (ARC 3415).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. RL, SR, SH, and FS conceived and designed the experiments. RL, SR, SH, and FS performed the experiments. RL, SR, SH, and FS analyzed the data. RL and FS wrote the paper.
Citation: Le Borgne R, Remaud S, Hamel S, Schweisguth F (2005) Two distinct E3 ubiquitin ligases have complementary functions in the regulation of Delta and Serrate signaling in Drosophila. PLoS Biol 3(4): e96.
Abbreviations
DlDelta
D-mib
Drosophila mind bomb
DSHBDevelopmental Studies Hybridoma Bank
D-Vdorsal-ventral
GFPgreen fluorescent protein
MibMind bomb
NNotch
NeurNeuralized
RINGreally interesting new gene
SerSerrate
wgwingless
YFPyellow fluorescent protein
==== Refs
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| 15760269 | PMC1064853 | CC BY | 2021-01-05 08:28:13 | no | PLoS Biol. 2005 Apr 15; 3(4):e96 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030096 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1576027010.1371/journal.pbio.0030101Research ArticleGenetics/Genomics/Gene TherapyPhysiologyMus (Mouse)PGC-1α Deficiency Causes Multi-System Energy Metabolic Derangements: Muscle Dysfunction, Abnormal Weight Control and Hepatic Steatosis Metabolic Derangements in PGC-1α DeficiencyLeone Teresa C
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Lehman John J
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Finck Brian N
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Schaeffer Paul J
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Wende Adam R
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Boudina Sihem
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Courtois Michael
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Wozniak David F
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Sambandam Nandakumar
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Bernal-Mizrachi Carlos
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Chen Zhouji
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O. Holloszy John
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Medeiros Denis M
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Schmidt Robert E
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Saffitz Jeffrey E
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Abel E. Dale
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Semenkovich Clay F
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Kelly Daniel P [email protected]
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1Center for Cardiovascular Research, Washington University School of MedicineSt Louis, MissouriUnited States of America2Department of Medicine, Washington University School of MedicineSt Louis, MissouriUnited States of America3Program in Human Molecular Biology and Genetics, Division of EndocrinologyMetabolism and Diabetes, University of Utah, Salt Lake City, UtahUnited States of America4Department of Psychiatry, Washington University School of MedicineSt Louis, MissouriUnited States of America5Department of Human Nutrition, Kansas State UniversityManhattan, KansasUnited States of America6Department of Pathology, Washington University School of MedicineSt Louis, MissouriUnited States of America7Department of Molecular Biology and Pharmacology, Washington University School of MedicineSt Louis, MissouriUnited States of America8Department of Pediatrics, Washington University School of MedicineSt Louis, MissouriUnited States of AmericaVidal-Puig Antonio Academic EditorUniversity of CambridgeUnited Kingdom4 2005 15 3 2005 15 3 2005 3 4 e1019 10 2004 21 1 2005 Copyright: © 2005 Leone et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
A New Role for a Protein Involved in Energy Metabolism
The gene encoding the transcriptional coactivator peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) was targeted in mice. PGC-1α null (PGC-1α−/−) mice were viable. However, extensive phenotyping revealed multi-system abnormalities indicative of an abnormal energy metabolic phenotype. The postnatal growth of heart and slow-twitch skeletal muscle, organs with high mitochondrial energy demands, is blunted in PGC-1α−/− mice. With age, the PGC-1α−/− mice develop abnormally increased body fat, a phenotype that is more severe in females. Mitochondrial number and respiratory capacity is diminished in slow-twitch skeletal muscle of PGC-1α−/− mice, leading to reduced muscle performance and exercise capacity. PGC-1α−/− mice exhibit a modest diminution in cardiac function related largely to abnormal control of heart rate. The PGC-1α−/− mice were unable to maintain core body temperature following exposure to cold, consistent with an altered thermogenic response. Following short-term starvation, PGC-1α−/− mice develop hepatic steatosis due to a combination of reduced mitochondrial respiratory capacity and an increased expression of lipogenic genes. Surprisingly, PGC-1α−/− mice were less susceptible to diet-induced insulin resistance than wild-type controls. Lastly, vacuolar lesions were detected in the central nervous system of PGC-1α−/− mice. These results demonstrate that PGC-1α is necessary for appropriate adaptation to the metabolic and physiologic stressors of postnatal life.
Eliminating the activity of the gene PGC-1 α in mice reveals its role in post-natal metabolism and provides a link to obesity and some intriguing differences with another report of this knockout
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Introduction
Mitochondrial functional capacity is dynamically regulated to meet the diverse energy demands imposed on the mammalian organism following birth. Postnatal mitochondrial biogenesis involves multiple signaling and transcriptional regulatory pathways that control the coordinate expression of nuclear and mitochondrial genes involved in mitochondrial structure, metabolism, and proliferation [1]. Recent evidence points toward a transcriptional coactivator, peroxisome proliferator-activated receptor-γ (PPARγ) coactivator-1α (PGC-1α), as an integrator of the molecular regulatory circuitry involved in the transcriptional control of cellular energy metabolism, including mitochondrial function and biogenesis [1,2]. PGC-1α was discovered in a yeast two-hybrid screen for brown adipose-specific factors that interact with the adipogenic nuclear receptor PPARγ [2]. Subsequently, two additional PGC-1 family members were identified, PGC-1 related coactivator (PRC) [3] and PGC-1β [4,5]. PGC-1α serves as a direct transcriptional coactivator of nuclear and nonnuclear receptor transcription factors involved in cellular energy metabolism [6]. PGC-1α is distinct among most coactivators in that it exhibits a tissue-enriched expression pattern and is highly inducible by physiologic conditions known to increase the demand for mitochondrial ATP or heat production [2,6,7]. PGC-1α is enriched in brown adipose tissue (BAT), heart, slow-twitch skeletal muscle, and kidney—all tissues with high-capacity mitochondrial systems. The expression of the gene encoding PGC-1α is rapidly induced by cold exposure, short-term exercise, and fasting [2,8,9,10,11,12,13,14,15]. These latter observations suggest that PGC-1α is involved in the physiologic control of energy metabolism.
Several lines of evidence, based on the results of overexpression studies, indicate that PGC-1α is sufficient to promote mitochondrial biogenesis and regulate mitochondrial respiratory capacity. First, PGC-1α activates the transcription of mitochondrial uncoupling protein-1 (UCP-1) in BAT through interactions with the nuclear hormone receptors PPARγ and thyroid receptor [2]. Second, forced expression studies in adipogenic and myogenic mammalian cell lines demonstrated that PGC-1α activates mitochondrial biogenesis through a group of transcription factor targets including nuclear respiratory factors 1 and 2 (NRF-1 and -2) and mitochondrial transcription factor A (Tfam), key transcriptional regulators of mitochondrial DNA transcription and replication [8]. Third, studies in primary cardiac myocytes in culture and in the hearts of transgenic mice have demonstrated that overexpression of PGC-1α promotes mitochondrial biogenesis [10,16]. Lastly, forced expression of PGC-1α in skeletal muscle of transgenic mice triggers mitochondrial proliferation and the formation of mitochondrial-rich type I, oxidative (“slow-twitch”) muscle fibers [17]. Collectively, these results indicate that PGC-1α is sufficient to drive mitochondrial biogenesis.
Recent evidence also implicates PGC-1α in the homeostatic control of systemic energy metabolism. PGC-1α has been shown to regulate several key hepatic gluconeogenic genes [18,19,20,21]. Recent studies have also shown altered expression of PGC-1α and downstream mitochondrial target pathways in skeletal muscle of humans with insulin resistance and diabetes [22,23,24]. In addition, single nucleotide polymorphisms within the human PGC-1α gene have been shown to be associated with obesity, hypertension, and diabetes [25,26,27,28,29,30].
The gain-of-function studies described to date provide compelling evidence that PGC-1α is capable of regulating postnatal energy metabolism. However, the necessity of PGC-1α for energy metabolic homeostasis, mitochondrial biogenesis, development, and growth can only be addressed using loss-of-function strategies. To this end, we have established and characterized mice with targeted deletion of the PGC-1α gene. Our studies of PGC-1α−/− mice demonstrate that PGC-1α is not absolutely required for prenatal viability including mitochondrial biogenesis. However, our findings indicate that the coactivator PGC-1α serves a critical role in the normal metabolic function of multiple organs and for appropriate adaptation to physiologic stress during postnatal life.
Results
Disruption of the PGC-1α Gene in Mice
A neomycin-based gene targeting vector was generated to delete exons 4 and 5 of the murine PGC-1α gene. The targeting event resulted in a 3′ homologous recombination with insertion of the remainder of the construct (Figure 1A). The insertion/recombination event was confirmed by Southern blotting and DNA sequencing. The insertion caused an exon 3 duplication between exons 5 and 6 that creates a coding region frameshift resulting in a premature termination at amino acid 255. Germline transmission of the mutant allele was confirmed using Southern blotting (Figure 1B) and PCR (unpublished data). The PGC-1α gene disruption resulted in an unstable transcript that could not be detected by RNA blot analysis in heart and other tissues in PGC-1α−/− mice (Figure 1C and unpublished data). Quantitative RT-PCR was utilized to further evaluate the efficacy of the gene targeting. For these studies, PCR primers were designed to amplify a region of the PGC-1α gene transcript containing the exon 5–6 border (predicted to be absent in PGC-1α−/− mice) or the exon 5–3 border (predicted to be present only in the PGC-1α−/− mice). The exon 5–6 amplicon was detected in heart and BAT of wild-type (WT) but not PGC-1α−/− mice (Figure 1D). Conversely, the exon 5–3 product was present only in PGC-1α−/− mice (Figure 1D). An exon 10–11 border amplicon (predicted to be present in both genotypes) was detected in WT and PGC-1α−/− mice, but was greatly diminished in the PGC-1α−/− mice, indicating that the mutant transcript is unstable. PGC-1α protein was not detected in whole cell (Figure 1E) or nuclear protein extracts (unpublished data) isolated from BAT of PGC-1α−/− mice under basal conditions or in response to cold exposure, a condition known to markedly induce the expression of PGC-1α in BAT. Smaller mutant PGC-1α proteins were also not detected by Western blot analysis (unpublished data). Lastly, expression of the genes encoding the other known PGC-1 family members, PGC-1β and PRC, was not significantly altered in heart of PGC-1α−/− mice (Figure 1C). Taken together, these results support the conclusion that the gene targeting event resulted in a PGC-1α null allele.
Figure 1 Deletion of the PGC-1α Gene
(A) Schematic of the gene targeting strategy. A region of the murine PGC-1α gene containing exons 3–6 is shown schematically at the top. Relevant restriction endonuclease sites are also shown. The targeting construct containing a neomycin (Neo) cassette is shown below the PGC-1α gene with dashed lines indicating the regions targeted for homologous recombination. Homologous recombination between the 3′ end of the targeting vector and the PGC-1α gene is indicated by the solid lines. The targeting construct inserted into the PGC-1α gene resulting in a duplication of exon 3 and incorporation of the targeting construct DNA into the final recombinant as shown. Probes used for the Southern blot studies and relevant restriction fragments predicted by digestion of the recombinant are also shown.
(B) Southern blot analysis of embryonic stem cells (ESC) (digested with Xba1) and tail DNA (digested with Pst1) is shown. The blots were hybridized with the probes shown in Figure 1A. Results for PGC-1α+/+ (+/+), PGC-1α+/− (+/−), and PGC-1α−/− (−/−) genotypes are shown as denoted at the bottom.
(C) Northern blot analysis using RNA isolated from the hearts of the three relevant PGC-1α genotypes (as in Figure 1B) is shown using PGC-1α cDNA as a probe. In addition, PGC-1β and PRC cDNA probes were used as shown. Ethidium bromide staining of 18S ribosomal RNA is shown at the bottom.
(D) Quantitative real time RT-PCR (Sybr green) was used to detect PGC-1α transcripts using primer sets crossing different exon borders as denoted. The exon 5–3 primer set detects only the mutant transcript (−/−), whereas the exon 5–6 primer set detects only the WT transcript. The values represent arbitrary units for RNA isolated from the tissues shown for the three amplicons comparing PGC-1α+/+ and PGC-1α−/−. N.D. = not detectable. The exon 10–11 amplicon was evaluated to assess levels of mutant versus WT transcripts. The values represent arbitrary units normalized to actin control.
(E) Western blot analysis using whole-cell protein extracts prepared from BAT under basal conditions and following exposure to 4 °C for 8 h. The signal shown was obtained with polyclonal anti-PGC-1α antibody [10]. An epitope-tagged PGC-1α, overexpressed in neonatal cardiac myocytes using an adenoviral vector (Ad-PGC-1α), is shown as a positive control. The Ponceau S stain of the protein gel is shown at the bottom.
General Characteristics of the PGC-1α−/− Mice: Age- and Sex-Dependent Obesity
Heterozygous (PGC-1α+/−) mice were bred to generate PGC-1α−/− offspring. The observed genotype ratios of the offspring were consistent with the expected Mendelian ratios (unpublished data). Unexpected deaths of the offspring were not observed, and PGC-1α+/− and PGC-1α−/− offspring appeared normal. Total body weights obtained 1 wk after birth revealed a 15%–20% reduction in total body mass for male and female PGC-1α−/− mice relative to sex-matched PGC-1α+/+ littermates (Figure 2). The weight decrement between PGC-1α−/− and PGC-1α+/+ littermates disappeared by 3 wk of age (Figure 2A). At 18 wk of age, body weight was modestly but significantly greater in male and female PGC-1α−/− mice compared to sex-matched PGC-1α+/+ controls (Figure 2A). This weight difference was also significant for female PGC-1α−/− mice at 24 wk of age (Figure 2A). The abnormal weight gain in PGC-1α−/− mice was not associated with differences in food intake (unpublished data) or alterations in general activity as monitored for 48 h (Figure S1). Percent body fat, as determined by dual-energy X-ray absorption (DEXA), was greater in 18- and 24-wk-old female PGC-1α−/− mice compared to age-matched female PGC-1α+/+ counterparts, indicating that the body weight difference was due, at least in part, to increased body fat (Figure 2A). Lean mass was not significantly different between the genotypes (unpublished data). Although DEXA did not detect excess body fat in male PGC-1α−/− mice at 18 or 24 wk of age, older male mutant mice (over 7 mo of age) accumulated more body fat than male WT controls (Figure 2A and unpublished data).
Figure 2 Evidence for Tissue-Specific Growth Abnormalities and Mild Sex-Limited, Age-Dependent Obesity in PGC-1α−/− Mice
(A) The bars represent total body weight for the ages indicated for male (left graph) and female (center graph) PGC-1α+/+ and PGC-1α−/− mice. The body weight (BW) of the 1-wk-old PGC-1α−/− mice was normalized to that of PGC-1α+/+ littermates, which was assigned a value of 100 (left axis). For the 3-, 18-, and 24-wk time points, absolute weights of PGC-1α−/− mice were compared to age-matched controls (right axis). Percent fat as determined by DEXA scanning for PGC-1α+/+ and PGC-1α−/− mice (right graph). The results represent n = 4 (males) and n ≥ 11 (females) for each genotype at 24 wk. * p < 0.05 compared to corresponding PGC-1α+/+ mice.
(B) The bars represent organ weights corrected to body weight for 3-wk-old male and female PGC-1α+/+ and PGC-1α−/− mice. The error bars represent ± SEM. Results represent n ≥ 14 for each group. * p < 0.05 compared to corresponding PGC-1α+/+ mice.
Individual organ weights were assessed, given the importance of mitochondrial energy metabolism for postnatal growth in certain organs. The weights of heart and slow-twitch fiber-enriched skeletal muscles, including gastrocnemius and soleus, but not the less oxidative tibialis anterior, were significantly lower in male and female PGC-1α−/− mice compared with age and sex-matched PGC-1α+/+ controls at 3 and 8 wk of age (Figure 2B and unpublished data). In contrast, the weights of brain, liver, kidney, and BAT were not significantly different between the genotypes at the 3-wk time point (Figure 2B). Thus, certain tissues with high mitochondrial energy requirements, such as heart and slow-twitch skeletal muscle, exhibit modest growth defects in PGC-1α−/− mice.
Abnormal Muscle Mitochondrial Phenotype in PGC-1α−/− Mice
General histologic analyses were performed to begin to evaluate the mild growth defect found in postnatal heart and skeletal muscle of the PGC-1α−/− mice. There were no obvious abnormalities in cellularity, cell size, or extracellular matrix in the tissues of 1–2-mo-old PGC-1α−/− mice (unpublished data). Given the important role of PGC-1α in mitochondrial function and biogenesis, we examined mitochondrial ultrastructure in the relevant tissues. Electron microscopic analysis revealed fewer and smaller mitochondria in soleus muscle of PGC-1α−/− mice compared to sex- and age- matched PGC-1α+/+ controls (Figure 3A). Quantitative morphometry of the electron micrographs confirmed that the cellular volume density of soleus mitochondria was significantly lower in PGC-1α−/− mice compared to PGC-1α+/+ controls independent of changes in the myofibrillar component (Figure 3B). Consistent with a defect in mitochondrial biogenesis, we found a reduction in the expression of nuclear genes encoding proteins involved in mitochondrial electron transport (cytochrome c and cytochrome oxidase IV) and oxidative phosphorylation (beta subunit of ATP synthase) in soleus muscle of PGC-1α−/− mice compared with PGC-1α+/+ controls. In addition, the expression of Tfam, a known PGC-1α target involved in mitochondrial DNA replication/transcription, was diminished in PGC-1α−/− soleus, providing one potential mechanism for defective mitochondrial biogenesis (Figure 3C). In contrast to the results with soleus, no significant differences in mitochondrial ultrastructure or volume density were noted in heart or BAT of PGC-1α−/− mice (unpublished data).
Figure 3 Abnormal Mitochondrial Phenotype in Slow-Twitch Skeletal Muscle of PGC-1α−/− Mice
(A) Representative electron micrograph of soleus muscle from 1-mo-old female PGC-1α+/+ and PGC-1α−/− mice.
(B) Quantitative morphometric measurements of the cellular volume density for the mitochondrial (Mito) and myofibrillar (Myo) fractions based on analysis of electron micrographs (three sections from three animals per group). The bars represent mean ± SEM. * p < 0.05 compared to corresponding PGC-1α+/+ values.
(C) Gene expression data. The results of real-time PCR analysis of nuclear and mitochondrial genes involved in various components of mitochondrial metabolism and mitochondrial biogenesis: cytochrome C (Cyto c), ATP synthase β, Tfam, and cytochrome oxidase IV (COX IV). Eight littermate pairs were used for analysis at 1–2 mo of age and normalized to the WT value, which was assigned a value of 100, in each case. * p < 0.05 compared to corresponding PGC-1α+/+ values.
(D) Mitochondrial respiration rates as determined by oxygen consumption (VO2) performed on saponin-permeabilized muscle strips prepared from soleus of PGC-1α+/+ and PGC-1α−/− mice (as described in Materials and Methods). The results are based on six female animals in each group, using succinate as a substrate in the presence of rotenone. Mean values (± SEM) are shown for state 2 (basal), state 3 (ADP-stimulated), and state 4 respiration (presence of oligomycin).
To determine whether mitochondrial function was altered in the skeletal muscle of PGC-1α−/− mice, mitochondrial respiration rates were measured using tissue strips prepared from soleus muscle. In soleus of PGC-1α−/− mice, a significant defect in state 3 (ADP-stimulated) respiration, but not state 2 (basal), was detected using succinate as the substrate (Figure 3D). State 4 respiration rates (in the presence of oligomycin) were also similar between the genotypes, indicating that the coupling of respiration to ATP production was not significantly altered in PGC-1α−/− mice. These results are consistent with the modest but significant reduction in mitochondrial volume density.
Altered Skeletal Muscle Function in PGC-1α−/− Mice
The abnormality in mitochondrial number and respiratory function in skeletal muscle led us to further evaluate the skeletal muscle phenotype. As an initial step, we measured locomotor activity levels over a 1-h period using a high-resolution photobeam system. PGC-1α−/− male mice exhibited a significantly lower mean number of ambulations and rearings during the hour compared to the PGC-1α+/+ age-matched controls (Figure 4). However, an analysis of exploratory behavior showed that the PGC-1α−/− mice were reluctant to go into the center of the “field” compared to controls. Specifically, PGC-1α−/− mice made significantly fewer entries into, spent significantly less time in, and traveled a significantly shorter distance in the central area of the “field,” although differences in distance traveled in the peripheral zone of the “field” was not significantly different between groups (Figure S2). These data suggest that the general activity level may have been affected by the reluctance of the PGC-1α−/− mice to go into the central area of the field and thus remain in the periphery (thigmotaxis), possibly reflecting altered emotionality such as increased fear.
Figure 4 PGC-1α−/− Mice Exhibit an Abnormal Skeletal Muscle Functional Phenotype
(A) Measures of general activity and muscle strength. General activity levels were measured in 3.5-mo-old male PGC-1α+/+ (n = 8) and PGC-1α−/− (n = 11) mice using a photobeam system as described in Materials and Methods. Total ambulations (left graph), and rearings (center graph) provide a general measure of locomotor activity. Time spent on an inverted screen (right graph) represents a general measure of extremity muscle strength. The results from two trials are shown. * p < 0.05 compared to corresponding PGC-1α+/+.
(B) Exercise studies. Male 6–8-mo-old PGC-1α−/− and PGC-1α+/+ mice were subjected to a run-to-exhaustion protocol on a motorized treadmill (left graph) as described in Materials and Methods. * p < 0.001 compared to corresponding PGC-1α+/+ values. VO2max measurements were determined for 2-mo-old male mice for each genotype using a motorized treadmill at an elevation of 150 m and indirect calorimetry set-up (right graph) as described in Materials and Methods. * p < 0.05 compared to corresponding PGC-1α+/+ values.
(C) Time course of fatigue following repeated stimulation of soleus muscle is shown for 4-mo-old male PGC-1α−/− (n = 5) and PGC-1α+/+ (n = 5) mice (left graph). The mean percent force remaining at 2 min (Fatigue Resistance Index) is shown (right graph). * p < 0.05 compared to corresponding PGC-1α+/+ values.
A battery of tests was performed to further evaluate the general sensorimotor phenotype of the PGC-1α−/− mice. No differences were found between PGC-1α−/− mice and PGC-1α+/+ controls on the ledge, platform, walking initiation, and 60° and 90° inclined screen tests (unpublished data), suggesting that several sensorimotor functions were intact in the PGC-1α−/− mice. However, the PGC-1α−/− mice were unable to remain on an inverted screen for as long as the PGC-1α+/+ controls (Figure 4A). Since the groups did not differ on the times it took to turn around and climb to the top of 60° and 90° inclined screens, the differences on the inverted screen test suggest that impaired strength rather than deficits in coordination were responsible for these differences.
To further evaluate the skeletal muscle phenotype, exercise capacity was assessed in the PGC-1α−/− mice. To this end, the PGC-1α−/− mice were exercised on a motorized treadmill apparatus using a run-to-exhaustion format. PGC-1α−/− mice (6–8 mo of age) exhibited a markedly reduced capacity to sustain running exercise (PGC-1α−/− mice, 64 ± 6 s; age-matched PGC-1α+/+ mice, 586 ± 104 s; Figure 4B). The same result was obtained with younger PGC-1α−/− mice, i.e., at 3.5 mo of age (unpublished data). To quantify aerobic exercise capacity, VO2max (maximum oxygen consumption, measured in milliliters of oxygen per kilogram of body weight per minute) was measured with the treadmill-running protocol using indirect calorimetry. VO2max was significantly lower for the PGC-1α−/− mice (120.9 ± 2.0 ml O2 · kg−1 · min−1) compared to PGC-1α+/+ controls (141.6 ± 2.1 ml O2 · kg−1 · min−1) (Figure 4B). To directly evaluate muscle fatigability, the force response to repetitive stimulation of isolated soleus muscle was determined. The capacity to generate force following a series of tetani is dependent upon mitochondrial ATP production. During the initial phase of the stimulation period, there was no difference in force generation in muscles isolated from PGC-1α−/− mice and PGC-1α+/+ controls. However, fatigue resistance index, defined as the percent of initial force generated following a 2-min series of fatiguing contractions, was significantly lower in the PGC-1α−/− mice (14.6 ± 1.5%) compared to PGC-1α+/+ controls (24.8 ± 2.9%) (Figure 4C). These results, together with the observed abnormalities in skeletal muscle mitochondrial structure and function, indicate that PGC-1α is necessary for functional adaptation of skeletal muscle to physiologic demands.
Functional Abnormalities in Hearts of PGC-1α−/− Mice
PGC-1α expression is enriched in heart, a tissue that relies heavily on mitochondrial energy metabolism to maintain pump function throughout the postnatal life of the mammalian organism. Echocardiographic screening studies of PGC-1α−/− mice at ages 4–6 mo did not reveal any significant differences in chamber sizes or ventricular function compared to WT controls (unpublished data). Cardiac functional and metabolic reserve was evaluated using exercise echocardiographic stress testing (EST). Given that the exercise capacity of PGC-1α−/− mice is diminished, a series of preliminary treadmill exercise studies were performed to define a reasonable exercise duration for run-to-exhaustion to be used as a target duration for the EST. Based on the results of these studies, an EST regimen was performed in which PGC-1α+/+ control animals were exercised for a duration of 60 s to match the predicted average for the PGC-1α−/− mice (ages 6–8 mo). Echocardiographic images were obtained immediately following 60 s of exercise for the PGC-1α+/+ controls or at the point of exhaustion for PGC-1α−/− mice (mean 60 ± 6.1 s, range 45–90 s). Echocardiographic-determined left ventricular fractional shortening and heart rate were monitored for the 10-min period immediately post exercise. The mean heart rate of the PGC-1α−/− mice exhibited an inappropriate decline during the post exercise period (Figure 5A). In addition, echocardiographically determined left ventricular fractional shortening was decreased in the PGC-1α−/− mice, but not the PGC-1α+/+ mice during the first 4 min of the post exercise period (Figure 5A).
Figure 5 Abnormal Cardiac Response to Physiologic Stress in PGC-1α−/− Mice
(A) Exercise echocardiographic studies. PGC-1α+/+ (n = 4) and PGC-1α−/− (n = 8) female mice aged 6–8 mo were subjected to an exercise protocol on a motorized treadmill. This protocol was designed such that the PGC-1α−/− mice ran to exhaustion based on the results of the exercise studies shown in Figure 4. Accordingly, an exercise regimen of 60 s was used for both groups. The graphs depict the heart rate (left graph) and echocardiographically-determined ventricular fractional shortening (FS) as a percent (right graph). Responses were monitored for 10 min immediately post exercise.
(B) In vivo hemodynamic response to the β1,α1-adrenergic agonist dobutamine. Male and female PGC-1α+/+ (n = 6) and PGC-1α−/− (n = 6) mice at 10–12 wk of age were anesthetized and a 1.4-French Millar catheter was placed through the carotid artery into the left ventricle as described in Materials and Methods. Heart rate (left graph) and a measurement of ventricular systolic performance, dP/dt (right graph), were measured following infusion of dobutamine. * p < 0.05.
The results of the EST did not distinguish between a primary cardiac abnormality versus effects secondary to the exhaustion caused by reduced exercise tolerance related to skeletal muscle dysfunction. To directly assess cardiac function, the hearts of PGC-1α−/− and PGC-1α+/+ mice were isolated and perfused in the working mode. Hearts isolated from PGC-1α−/− mice generated lower cardiac work (cardiac output multiplied by peak systolic pressure) compared to PGC-1α+/+ mice at identical loading conditions (Table 1). This reduction in cardiac work was due to a reduced cardiac output (Table 1). The relative contribution of heart rate and stroke volume to diminished cardiac output in the PGC-1α−/− mice could not be delineated, because both were decreased but neither to a significant degree (Table 1). To further distinguish between abnormalities in heart rate and ventricular function, in vivo hemodynamic response to the β1,α1-adrenergic-selective agonist dobutamine was evaluated using a miniaturized Millar catheter. The ventricular functional response to dobutamine was similar in PGC-1α+/+ and PGC-1α−/− mice (Figure 5B, right graph). However, PGC-1α−/− mice exhibited a significantly blunted heart rate response to β-adrenergic stimulation (Figure 5B, left graph). Taken together with the EST, these results strongly suggest that the PGC-1α−/− hearts are unable to mount an appropriate chronotropic response to exercise and other physiologic stimuli that activate β-adrenergic input to the heart. However, our results did not reveal evidence for contractile dysfunction.
Table 1 Cardiac Hemodynamics Measured in Isolated Working Hearts of PGC-1α+/+ and PGC-1α−/− Mice
Values represent mean ± SEM
a
p < 0.05
PGC-1α−/− Mice Exhibit an Abnormal Thermogenic Response
PGC-1α has been implicated as an inducible regulator of mitochondrial respiratory uncoupling, an important source of heat production in BAT [2]. To determine whether PGC-1α is necessary for an appropriate thermogenic response, PGC-1α+/+ and PGC-1α−/− mice were subjected to cold exposure (4 °C) for a 5-h period while core body temperature was monitored. PGC-1α−/− mice exhibited a markedly abnormal drop in core temperature compared to the WT controls (Figure 6A). Specifically, the mean decline in core temperature was greater than 12 °C at the 5-h time point in PGC-1α−/− mice, compared to an approximately 3 °C decrement in PGC-1α+/+ controls. Although this thermogenic phenotype was consistently present in mice aged 28–37 d, it was absent in older mice (unpublished data).
Figure 6 PGC-1α−/− Mice Exhibit an Abnormal Thermogenic Response
(A) PGC-1α+/+ (n = 15) and PGC-1α−/− (n = 21) mice aged 28–37 d were subjected to cold (4 °C). Core rectal temperature was monitored over a 5-h period. The change in core temperature ± SEM is shown in the graph (left) as a function of time. * p < 0.05.
(B) Representative Northern blot analysis (blot and gel at top) performed with RNA isolated from BAT to detect UCP-1 transcript at baseline (RT) and after 5 h of exposure to cold (4 °C) (UCP1). Ethidium bromide (Eth Br) staining of ribosomal RNA is shown as a control. Quantitative real-time RT-PCR for UCP-1 transcript is shown on the graph at the bottom. The values represent mean arbitrary units normalized to a 36B4 transcript (control).
(C) Altered response to β3-adrenergic agonist. To evaluate the oxygen consumption (VO2) in response to the stimulation of BAT uncoupled respiration, the β3-adrenergic agonist BRL 37344 was administered to littermate PGC-1α+/+ (n = 5) and PGC-1α−/− (n = 5) female mice followed by measurement of VO2 by indirect calorimetry. Mean ± SEM VO2 is shown. * p < 0.05.
The histologic appearance and neutral lipid stores of BAT were assessed as an initial step to characterize the thermogenic phenotype exhibited by PGC-1α−/− mice. Histologic and lipid quantification studies were performed. Electron microscopic analyses indicated that the mitochondrial ultrastructure was similar in BAT isolated from PGC-1α−/− and PGC-1α+/+ mice before and after cold exposure (unpublished data). In addition, levels of BAT triglyceride were similar between the two genotypes (unpublished data). UCP-1 is a cold-inducible protein involved in mitochondrial respiratory uncoupling to generate heat in BAT. UCP-1 gene transcription is known to be activated by PGC-1α [2]. Surprisingly, basal and cold-induced BAT UCP-1 mRNA levels were similar in PGC-1α+/+ and PGC-1α−/− mice (Figure 6B). These results suggest that PGC-1α is not necessary for the induction of the expression of UCP-1 with cold exposure, and that other factors, such as reduced capacity for mitochondrial respiration, likely contribute to the abnormal thermogenic response in the PGC-1α−/− mice.
Thermogenesis in rodents related to mitochondrial uncoupling is under the control of β3-adrenergic receptor coupled signaling. Accordingly, the in vivo oxygen consumption response to β3-adrenergic stimulation was examined in PGC-1α−/− mice. For these experiments, VO2 (oxygen consumption) was measured following administration of the β3-agonist BRL 37344 using indirect calorimetry. VO2 was significantly increased in response to BRL 3744 in PGC-1α+/+ but not PGC-1α−/− mice (Figure 6C). These results indicate that the metabolic response of BAT to an acute stimulus such as cold and/or β3-adrenergic stimulation is altered in the PGC-1α null mice, likely related to reduced capacity for mitochondrial respiratory uncoupling.
Fasting-Induced Hepatic Steatosis in PGC-1α−/− Mice
Previous studies have implicated PGC-1α in several hepatic metabolic functions including fatty acid oxidation and gluconeogenesis [18,19,20,21]. Accordingly, the hepatic phenotype was evaluated under basal conditions and following a 24-h fast, a stimulus known to induce fatty acid oxidation and gluconeogenic rates in liver. Under basal fed conditions, the livers of the PGC-1α−/− mice appeared grossly normal and did not exhibit histologic abnormalities (unpublished data). However, following a 24 h-fast, the PGC-1α−/− mice exhibited marked hepatic steatosis as determined by gross inspection, oil red O staining, electron microscopy, and measurements of liver triglyceride (TAG) levels (Figure 7). There were no differences in plasma triglycerides or free fatty acids between the genotypes in fed or fasted states (unpublished data). To further investigate the mechanisms involved in the fasting-induced hepatic steatotic response, hepatocytes were isolated from PGC-1α−/− mice and WT controls. Oleate loading experiments revealed that the PGC-1α−/− hepatocytes accumulated neutral lipid to a significantly greater extent than the WT cells (Figure 8A). 3H-palmitate oxidation rates were significantly lower in PGC-1α−/− hepatocytes compared to PGC-1α+/+ hepatocytes under basal conditions and following exposure to oleate (Figure 8B). Taken together, these latter results indicate a cell-autonomous defect in PGC-1α−/− hepatocytes that results in an inability to maintain cellular lipid balance in the context of increased delivery of lipid such as occurs with fasting.
Figure 7 Fasting-Induced Hepatic Steatosis Develops in PGC-1α−/− Mice
(A) The photograph depicts the development of a pale liver in PGC-1α−/− mice subjected to a 24-h fast.
(B) Oil red O staining of histologic sections of liver taken from PGC-1α−/− mice under fed and 24 h fasted conditions. The red staining indicates neutral lipid.
(C) Representative electron micrographs of the liver from PGC-1α+/+ and PGC-1α−/− mice following a 24-h fast. The droplets are indicative of neutral lipid accumulation.
(D) Mean liver TAG levels in PGC-1α+/+ (n = 5) and PGC-1α−/− (n = 5) mice under fed and 24-h fasted conditions. * p < 0.05.
Figure 8 Hepatocytes Isolated from PGC-1α−/− Mice Exhibit Reduced Oxidative Capacity
(A) Oil red O staining of isolated hepatocytes exposed to BSA alone (BSA) or 50 μM oleate complexed to BSA (oleate).
(B) 3H-palmitate oxidation rates. 3H-palmitate oxidation rates determined in hepatocytes isolated from PGC-1α+/+ and PGC-1α−/− mice under cell culture conditions containing BSA or BSA + 50 μM oleate (2:1 oleate/BSA ratio). Values were derived from ten sets of triplicates for each group using hepatocytes from 5 mice of each genotype. The bars represent mean oxidation rates (n = 100) normalized to the condition of PGC-1α+/+ in BSA alone. * p < 0.05 compared to the corresponding PGC-1α+/+ mice. † p < 0.05 compared to PGC-1α+/+ with BSA treatment.
(C) State 2 and 3 respiration rates determined for hepatocytes isolated from PGC-1α+/+ (n = 3) and PGC-1α−/− (n = 3) mice using succinate/rotenone as a substrate. * p < 0.05 compared to corresponding PGC-1α+/+.
(D) TAG synthesis rates in isolated hepatocytes. The bars represent mean TAG synthesis rates (glycerol incorporation, see Materials and Methods) for hepatocytes isolated from PGC-1α+/+ (n = 6) and PGC-1α−/− (n = 6) mice. * p < 0.05 compared to the corresponding PGC-1α+/+ condition.
PPAR, a known regulator of hepatic mitochondrial fatty acid oxidation enzyme gene expression, is a target for coactivation by PGC-1α [31]. Therefore, we sought to determine whether the steatotic phenotype of the PGC-1α−/− mice related to reduced expression of PPAR target genes. To this end, a survey of candidate genes and gene expression profiling experiments were performed. Surprisingly, the hepatic expression of PPAR target genes involved in cellular fatty acid and oxidation (MCPT and MCAD) were not significantly different between the genotypes under fed or fasted conditions (Table 2). Next, we performed experiments to determine whether the reduced capacity for fat oxidation in the hepatocytes of the PGC-1α−/− mice was related to altered mitochondrial respiratory function. Compared to the WT controls, PGC-1α−/− hepatocytes exhibited a modest but significant reduction in both state 2 and state 3 respiration rates (Figure 8C). These results identify one potential mechanism responsible for the fasting-induced hepatic steatosis: reduced capacity for fat oxidation due to mitochondrial respiratory dysfunction.
Table 2 Metabolic Gene Expression in Liver of Fed and Fasted PGC-1α+/+ and PGC-1α−/− Mice
Values represent mean (± SEM) (n ≥ 6 for each group) mRNA levels as determined by real-time RT-PCR corrected for GAPDH signal intensity and normalized (to 1.0) to the value of fed PGC-1α+/+ mice
a
p < 0.05 versus fed mice of the same genotype
b
p < 0.05 versus PGC-1α+/+ mice of the same dietary treatment
L-CPT I, liver-type carnitine palmitoyltransferase; MCAD, medium-chain acyl-CoA dehydrogenase; SREBP, sterol regulatory element binding protein; SCD, steroyl-CoA desaturase; FAS, fatty acid synthase; GPAT, glycerol-3-phosphate acyltransferase; DGAT, diacylglycerol acyltransferase
Although the liver gene expression profiling studies did not reveal abnormalities in the fatty acid oxidation pathway in the PGC-1α−/− mice, several interesting differences in the activity of the sterol regulatory element binding protein-1c (SREBP-1c) pathway were noted. Specifically, the fasting-mediated down-regulation of SREBP-1c and its target gene stearoyl-CoA desaturase (SCD1), was abolished in PGC-1α−/− mice (Table 2). Furthermore, expression of the gene encoding diglyceride acyltransferase (DGAT), which catalyzes the last step in TAG synthesis, was activated at baseline and induced by fasting to a greater level in PGC-1α−/− mice (Table 2). These results suggest that, in addition to a defect in oxidation, components of the TAG synthesis pathway are activated in the PGC-1α−/− mice. To evaluate this possibility directly, rates of 3H-glycerol incorporation into TAG were determined in isolated hepatocytes. 3H-TAG incorporation was increased nearly 50% in hepatocytes isolated from PGC-1α−/− mice compared to PGC-1α+/+ controls (Figure 8D), confirming that TAG synthesis rates are increased in PGC-1α null hepatocytes, identifying a second potential mechanism contributing to the fasting-induced hepatic steatosis.
Despite a Mild Obese Phenotype, Female PGC-1α−/− Mice Do Not Exhibit Insulin Resistance
Recent studies have suggested that specific PGC-1α single nucleotide polymorphisms and haplotypes may influence the development of insulin resistance and diabetes [27,30] and that PGC-1 activity is diminished in insulin-resistant and diabetic muscle [22,23]. Accordingly, peripheral glucose disposal and insulin responsiveness were examined in PGC-1α−/− mice. Glucose tolerance testing of 2-mo-old male and female mice revealed no significant difference in blood glucose excursion between PGC-1α+/+ and PGC-1α−/− groups (unpublished data). Given that older female PGC-1α−/− mice develop an increase in body fat stores, glucose tolerance and insulin responsiveness were further evaluated in this group. Glucose tolerance testing in 4.5-mo-old female PGC-1α−/− mice revealed that, despite increased body weight [mean ± standard error of the mean (SEM) weight of PGC-1α+/+ mice = 22.4 ± 0.79 g; PGC-1α−/− mice = 25.2 ± 1.04 g), PGC-1α−/− mice exhibited similar levels of glucose tolerance compared to WT mice on standard rodent chow (Figure 9). To examine glucose homeostasis in response to high-fat diet, female PGC-1α+/+ and PGC-1α−/− mice were placed on high-fat chow (43% calories from fat) for 6 wk starting at 8 wk of age. The weight gained on the high-fat diet was similar for the PGC-1α+/+ and PGC-1α−/− groups (Figure S3). Surprisingly, the PGC-1α−/− mice on a high-fat diet were significantly more glucose-tolerant and insulin-sensitive compared to the PGC-1α+/+ mice (Figure 9B). Taken together, these results indicate that, despite excess body fat under standard conditions, the female PGC-1α−/− mice do not exhibit insulin resistance. Moreover, the PGC-1α−/− mice are more glucose-tolerant and insulin-sensitive than WT mice on a high-fat diet.
Figure 9 Female PGC-1α−/− Mice Are More Glucose Tolerant and Insulin Sensitive Compared to PGC-1α+/+ on High-Fat Diet
(A) At 4.5 mo of age, glucose tolerance testing (GTT) was performed on female PGC-1α+/+ (n = 6) and PGC-1α−/− (n = 6) mice maintained on standard chow.
(B) At 8 wk of age, PGC-1α+/+ (n = 8) and PGC-1α−/− (n = 11) mice were provided a diet containing 43% of its calories from fat (HF chow). The graphs depict blood glucose levels ± SEM in PGC-1α−/− mice during GTT (left graph) and ITT (right graph) studies. Studies were performed 5 wk (GTT) and 6 wk (ITT) after the initiation of the high-fat diet. * p < 0.05 compared to PGC-1α+/+ mice at the same time point.
Structural Abnormalities of the Central Nervous System in PGC-1α−/− Mice
In surveying the tissues of the PGC-1α−/− mice, structural abnormalities of the brain were observed. Light microscopic examination of PGC-1α−/− brain tissue samples demonstrated a well-preserved cerebral cortical neuronal complement, a result confirmed by measurement of neuron density in sections of the parietal lobe (PGC-1α+/+, 1,261 ± 91 neurons/mm2 versus PGC-1α−/−, 1,299 ± 82 neurons/mm2; not significant). Patchy areas of microvacuolation involving the neuropil and individual pyramidal neurons of the deep layers of the cerebral cortex were noted in the PGC-1α−/− mice but not the PGC-1α+/+ mice (Figure 10A). Immunolocalization of an astrocytic marker, glial fibrillary acidic protein, failed to show an increase in numbers of astrocytic processes in PGC-1α−/− mouse cerebral cortex (unpublished data). The hippocampus also showed neuronal microvacuolation, albeit to a lesser degree than the parietal cortex. Microvacuolation of the neuropil and neurons of the PGC-1α−/− basal ganglia (caudate and putamen) was also noted in association with a patchy increase in the number and intensity of glial fibrillary acidic protein-immunoreactive astrocytic processes (unpublished data). Areas of microvacuolation also involved multiple brainstem regions. Only rare vacuolated Purkinje and granule cell neurons were identified in the PGC-1α−/− cerebellar cortex. Neither microglial proliferation nor perivascular lymphocytic inflammatory infiltrates were noted in the PGC-1α−/− CNS.
Figure 10 Neuropathology of the Central Nervous System of PGC-1α−/− Mice
(A) Light microscopic appearance of representative cerebral cortex of 2-mo-old PGC-1α−/− mice demonstrates marked vacuolation of the neuropil (arrows) and scattered neuronal perikarya, which are absent in PGC-1α+/+ mice (hematoxylin and eosin). The scale bar shown is applicable to all sections.
(B) Ultrastructural appearance of typical vacuoles containing membranous debris, denoted by the arrow, in the cerebral cortex of a representative PGC-1α−/− mouse in comparison to PGC-1α+/+ (magnification 4000×).
Ultrastructural examination of the PGC-1α−/− parietal cerebral cortex confirmed the presence of microvacuolated neurons and neuropil (Figure 10B). Vacuoles containing aggregates of membranous material were present in a subset of cortical neurons. Subcellular localization of the vacuoles was difficult to establish; some may represent vacuolated elements of the neuropil, material in phagocytic cells, presynaptic nerve terminals compressing the soma, or genuine intraperikaryal deposits.
Discussion
Previous studies using gain-of-function strategies have shown that the coactivator PGC-1α is capable of coactivating an array of transcription factors involved in energy metabolic processes including fatty acid oxidation, electron transport, and oxidative phosphorylation [6]. Forced expression of PGC-1α triggers mitochondrial biogenesis by activating a complex circuitry of factors including NRF-1, NRF-2, and the orphan nuclear receptor estrogen-related receptor α [23,32]. However, gain-of-function strategies cannot determine whether PGC-1α is essential for critical energy metabolic processes including mitochondrial biogenesis and function. Using targeted gene deletion in mice, we show here that PGC-1α is not essential for normal embryologic development or the fundamental events of mitochondrial biogenesis. However, several lines of evidence support the conclusion that PGC-1α is necessary for the programs that regulate postnatal mitochondrial function and cellular energy metabolism, processes that equip the organism for the energy metabolic rigors of the postnatal environment. First, mitochondrial volume density is diminished in slow-twitch skeletal muscle of PGC-1α−/− mice. Second, mitochondrial respiratory capacity is modestly but significantly altered in skeletal muscle and liver of PGC-1α−/− mice. Third, the growth of heart and soleus muscle, tissues with high reliance on mitochondrial energy production, is blunted. Fourth, control of body fat mass is abnormal in the PGC-1α−/− mice. Finally, PGC-1α−/− mice do not respond normally to a variety of physiologic and dietary stresses known to increase oxidative energy demands. Taken together, these results strongly suggest that PGC-1α is necessary for the terminal stages of mitochondrial maturation necessary to meet the energy demands of the postnatal environment.
Extensive phenotypic analyses demonstrated that mice lacking PGC-1α are unable to cope with physiologic stressors relevant to postnatal survival. For example, a skeletal muscle phenotype was unveiled in PGC-1α−/− mice under conditions in which energy supply becomes limiting. This was most clearly demonstrated by the profound abnormalities exhibited by PGC-1α−/− mice with exercise-to-exhaustion and repetitive muscle stimulation studies. Similarly, cardiac performance of PGC-1α−/− mice was compromised following severe exertion. This effect was largely due to an abnormal heart rate response. The basis for the observed abnormalities of cardiac heart rate, including a blunted response to β-adrenergic stimulation, is unknown, but could be related to the effects of late-stage growth arrest and corresponding derangements in energy metabolism on sinus node function. PGC-1α was first identified as a coactivator in BAT [2]. Indeed, we found that exposure of the PGC-1α−/− mice to cold, another relevant physiologic stress, resulted in an untoward drop in core body temperature consistent with an abnormality in thermogenesis despite normal cold induction of UCP-1 mRNA in BAT. Studies with a β3-adrenergic agonist confirmed that the peak oxygen consumption rate in thermogenic tissue is diminished in PGC-1α−/− mice. We propose that the thermogenic phenotype is related to reduced capacity for mitochondrial respiration in BAT. Interestingly, this phenotype was only evident during a rather narrow window of postnatal life. Animals at an older age did not exhibit cold intolerance, possibly due to the insulating properties of increased body mass. Collectively, these results demonstrate the importance of PGC-1α as a key transducer of physiologic stimuli to the control of energy metabolism.
The observation of fasting-induced hepatic steatosis is another example of the inability of PGC-1α−/− mice to respond to postnatal environmental metabolic demands. Following short-term starvation, we found that the PGC-1α−/− mice developed marked hepatocyte triglyceride accumulation. Further analysis revealed that palmitate oxidation rates were reduced in hepatocytes isolated from the PGC-1α−/− mice, which would predispose to lipid accumulation. Surprisingly, the reduction in fatty acid oxidation rates in PGC-1α null hepatocytes was not due to altered expression of PGC-1α/PPAR target genes involved in mitochondrial fatty acid oxidation. However, mitochondrial respiratory rates were diminished. In addition, we found that triglyceride synthesis was abnormally activated, and the expression of genes encoding SREBP-1c and SCD-1, key proteins in the hepatic lipogenic pathway, failed to be appropriately down-regulated in fasted PGC-1α−/− mice. The mechanism involved in this latter finding is unknown. Indeed, the relative contribution of increased triglyceride synthesis rates to the steatotic phenotype cannot be fully discerned from our data, given that this response could reflect the direct effects of PGC-1α deficiency on target genes or a secondary compensatory response to hepatocyte fatty acid accumulation. Consistent with the former possibility, recent evidence indicates that PGC-1α coactivates the nuclear receptor FXR, a negative regulator of SREBP-1c expression and triglyceride synthesis [33]. We conclude that reduced hepatocyte mitochondrial respiratory capacity, and possibly activation of lipogenic programs, result in hepatocyte triglyceride accumulation in the context of increased hepatic delivery of fatty acids such as occurs with fasting.
We found that after 18 wk of age, female PGC-1α−/− mice exhibit a mild but significantly abnormal weight increase associated with increased fat stores. Lean mass was unchanged at the time points examined. With further aging, a modest but significant increase in body fat was also noted in male PGC-1α−/− mice (unpublished data). The basis for the observed abnormalities in weight control is unknown. We did not find differences in food intake or activity levels in female PGC-1α−/− mice. It is possible that a reduction in systemic energy utilization, related to the mitochondrial dysfunction, leads to increased fat mass and weight gain in the PGC-1α−/− mice. Interestingly, an association between PGC-1α gene polymorphisms and obesity in humans has been recently reported [26]. Clearly, future studies of male and female PGC-1α−/− mice in pure-strain backgrounds over a range of ages will be necessary to fully investigate the observed abnormalities in weight control and fat distribution.
We did not find evidence for glucose intolerance or insulin resistance in the PGC-1α−/− animals on standard chow. Moreover, female PGC-1α−/− mice were more glucose-tolerant and insulin-sensitive than PGC-1α+/+ controls when consuming a high-fat diet. These findings are surprising, given the results of several recent studies demonstrating reduced expression of PGC-1α in human diabetic skeletal muscle [24,34]. It is certainly possible that compensatory metabolic regulatory mechanisms have been activated in the PGC-1α-deficient mice, accounting for this observation. Alternatively, PGC-1α could serve as a coactivator of factors that mediate diet-induced insulin resistance. Consistent with this notion, we and others have shown that mice lacking the PGC-1α target PPAR exhibit resistance to diet-induced glucose intolerance [21,35,36].
Histologic surveys of the PGC-1α−/− mice revealed ultrastructural abnormalities in the central nervous system. Inspection of sections prepared from the brains of PGC-1α−/− mice revealed patchy areas of microvacuolation in the pyramidal neurons of the cerebral cortex, accompanied by a mild increase in the number of astrocytes in the basal ganglia. The basis for this interesting but relatively nonspecific finding is unknown. It is possible that PGC-1α plays an important role in lipid metabolism related to membrane synthesis. Alternatively, the normal process of cellular debris turnover could be altered due to a defect in the energetics of the microglial component of the central nervous system. Although overt neurologic dysfunction was not apparent in PGC-1α−/− mice during the first 6 mo of life (no group differences were found on five of six sensorimotor tests), the PGC-1α−/− mice showed clear deficits on the inverted screen test. These deficits are likely due to impaired muscle strength in the PGC-1α−/− mice, but contributions by peripheral or central nervous system determinants (or both) could be contributory. Moreover, evidence of altered emotionality from the 1-h locomotor activity test also suggests the possibility of altered brain function in PGC-1α−/− mice. It will be of interest to determine whether the neurologic abnormalities contribute to the systemic metabolic abnormalities of the PGC-1α null mice.
During the preparation of this manuscript, Lin et al. reported an independent mouse line in which the PGC-1α gene was targeted [37]. Phenotypic comparison of the our PGC-1α-deficient line with that of Lin et al. reveals a number of similarities and several interesting differences. Both PGC-1α-deficient lines exhibit cold intolerance, reduced hepatocyte respiration rates, and neurologic lesions. However, a number of interesting differences are notable. First, in contrast to Lin et al., the PGC-1α−/− mice described here do not exhibit any postnatal mortality. Second, we did not find evidence for a defect in gluconeogenesis based on fasting blood glucose levels (unpublished data). In addition, whereas Lin et al. found an abnormal expression profile for
CCAAT-enhancer-binding protein β and δ and the gluconeogenic genes encoding phosphoenolpyruvate carboxykinase and glucose-6-phosphatase at baseline and with fasting in the PGC-1α−/− mice, we did not (unpublished data). Third, we found evidence for an age-related increase in body fat in PGC-1α−/− mice (females earlier than males), whereas Lin et al. identified a male-specific resistance to diet-induced obesity and insulin resistance. We have also found that male PGC-1α-deficient mice are somewhat protected against diet-induced obesity (Figure S4). However, we observed that the insulin-sensitive phenotype of the female PGC-1α−/− mice occurred in the context of normal weight gain with high-fat diet. These latter results indicate that the insulin-sensitive phenotype of PGC-1α−/− mice cannot be fully explained by a lean phenotype. Of interest, mice lacking the nuclear receptor estrogen-related receptor α, a known target of PGC-1α, exhibit resistance to diet-induced obesity similar to that of male PGC-1α null mice [38]. Fourth, the PGC-1α−/− mice described here exhibit a dramatic fasting-induced hepatic steatotic phenotype, whereas the Lin et al. mouse does not. Fifth, Lin et al. found a neurologic phenotype in males characterized by hyperactivity, whereas the PGC-1α−/− mice described here show reduced locomotor activity. However, it should be noted that we did not study activity levels over an extended period of time in males as did Lin et al., so it is possible that our findings reflect an emotional disturbance that manifests only when the animals are placed in a new environment. Finally, we report significant skeletal muscle and cardiac functional abnormalities (although the report by Lin et al. did not address these phenotypes, so this may not represent a true difference).
The reasons for the interesting differences between the two PGC-1α-deficient mouse lines are not clear. It is possible that distinct genetic backgrounds related to hybrid strains confer different degrees of secondary compensatory responses. In addition, the incompletely penetrant postnatal mortality noted in the PGC-1α−/− mice reported by Lin et al. could have resulted in a selection bias toward greater levels of compensatory responses in liver and other tissues in the surviving group. It is also possible that the method of gene targeting led to different phenotypes. Lin et al. generated PGC-1α−/− mice by Cre recombinase-mediated excision of exons 3–5 in oocytes. The PGC-1α−/− mice described here were generated by a targeting event that involved a 3′ homologous recombination leading to an insertion of the targeting vector including an extra exon 3 between exons 5 and 6. The exon 3 insertion, which was confirmed by RT-PCR, results in a mutant transcript that encodes a truncated protein. We were unable to detect normal transcript containing an exon 5–6 border, indicating that the targeting was accurate and complete. In addition, we could not detect full-length or smaller PGC-1α proteins by Western blotting. However, we cannot exclude the possibility that the sensitivity of the immunoblotting was not high enough to pick up a small amount of mutant (truncated) PGC-1α protein that could have some activity, given that it would contain nuclear receptor-interacting domains and the amino-terminal activation domain. If small amounts of PGC-1α activity are present in the mice reported here, it could explain some of the observed differences between the models. However, the bulk of data presented here support the conclusion that the PGC-1α−/− mice described are completely deficient in PGC-1α. Future direct comparison of the two mouse lines in pure background strains will be of interest.
In summary, this body of work provides evidence that PGC-lα is critical for the adaptive responses necessary to meet postnatal energy demands. Our results also suggest a broader role for inducible transcriptional coactivators such as PGC-1α in transducing cellular signals triggered by physiologic and developmental cues to the transcriptional control of energy metabolism and other dynamic cellular processes. In this regard, the inducible coactivator PGC-1α serves as a transcriptional “booster” to augment the capacity of downstream metabolic pathways critical for metabolic maturation and postnatal growth. Indeed, although PGC-1α null mice survive in the protected environment of the laboratory, our results indicate that in the rigors of a typical external environment, PGC-1α would be necessary for survival. Lastly, we propose that the PGC-1α−/− mice should serve as a useful murine model to investigate the role of altered energy metabolism in obesity, diabetes, hepatic steatosis, and diseases of the heart, skeletal muscle, and central nervous system.
Materials and Methods
Targeting the PGC-1α gene in mice
A BAC genomic clone containing the murine PGC-1α gene, isolated from an Sv129 genomic library, was obtained from Incyte Genomics (Palo Alto, California, United States). A 3-kb region spanning exon 3 was amplified from the genomic clone. A 5′ primer was designed to amplify a fragment with the 5′ end beginning 732 bp upstream of exon 3 just upstream of an endogenous Kpn1 restriction site (5′-
AGTTTCCTTAGCAACTTCATA-3′). The 3′ primer contained a BamH1 site engineered by mutating the bases shown in lowercase (5′-
AAGGATTTTAgGATcc
CAGTAC-3′). A second fragment downstream of exon 5 was amplified. In this latter amplicon, Not1 and Xho1 sites were engineered into the 5′ and 3′ primers, respectively (5′-
TGGAGTgc
GGCCGCTGGGA-3′ and 5′-
AAAGAGTCTCgAg
AATAGTTTCT-3′). The fragments were cloned into p1339-PGK-Neomycin targeting vector. The construct was linearized with Xho1 and electroporated into RW4 ES cells (Sv129 derived) using G418 selection. The electroporation was performed by the Siteman Cancer Center ES Cell Core at Washington University (St. Louis, Missouri, United States). The clones were screened by Southern blot using an Xba1 digest (see Figure 1A and 1B). One clone out of approximately 400 screened was positive for the homologous recombination on the 3′ end and an insertion on the 5′ end. This clone was injected into a C57BL6/J blastocyst. Chimeras were mated to C57BL6/J mice and germline transmission was confirmed by Southern blotting of tail DNA (see Figure 1B). All experiments were performed using sex- and age-matched or littermate controls as noted.
General animal studies
All animal experiments and euthanasia protocols were conducted in strict accordance with the National Institutes of Health guidelines for humane treatment of animals and were reviewed and approved by the Animal Care Committee of Washington University.
Animals were weighed at different time points. Male and female 3- to 8-wk-old PGC-1α+/+ and PGC-1α−/− mice were euthanized, and tissues were dissected and weighed on an analytical balance. Tissue weights were corrected for total body weight before comparison. DEXA studies were performed as previously described [39] using a Lunar PIXIMUS DEXA system at 10, 18, and 24 wk in male and female PGC-1α+/+ and PGC-1α−/− mice. For cold exposure experiments, male and female PGC-1α+/+ and PGC-1α−/− mice were singly housed and placed at 4 °C for 5 h without food. Core body temperatures were monitored by rectal probe at baseline and every hour thereafter. Mice were monitored at least every 30 min to check for lethargy. At the end of 5 h, mice were sacrificed and tissues harvested for RNA and protein extraction. For fasting studies, animals were singly housed and given water ad libitum. Food was removed from cages in the morning and tissues harvested at 24 h for RNA and histology. Photography of the mice was performed by MedPic at Washington University School of Medicine. 48-h activity monitoring was performed by JAX Services (The Jackson Laboratory, West Sacramento, California, United States) using a Comprehensive Laboratory Animal Monitoring System (CLAMS, Columbus Instruments, Columbus, Ohio, United States). Briefly, 3-mo-old female mice were acclimated for 17 h before data collection. Data were collected every 30 min. Total beam breaks in the XY direction were tabulated for the 12-h light and dark cycles and compared across genotypes.
RNA, DNA, and protein analyses
Total RNA was isolated by the RNAzol method (Tel-Test, Friendswood, Texas) and Northern blotting was performed as previously described [40]. The PGC-1β and PRC cDNAs were generous gifts from Bruce Spiegelman and Richard Scarpulla, respectively. The UCP-1 cDNA was a gift from Daniel Ricquier. RT-PCR was performed as described [41]. In brief, total RNA isolated from soleus muscle, BAT, and heart of 1–2-mo-old PGC-1α+/+ or PGCα−/− mice was reverse transcribed with Taqman reverse transcription reagents (Applied Biosystems, Foster City, California, United States). Reactions were performed in triplicate in 96-well format using Taqman core reagents and a Prism 7500 Sequence Detector (Applied Biosystems). The mouse-specific primer-probe sets used to detect specific gene expression can be found in Table S1. The primers for UCP-1 have been previously described [42]. Actin primer-probe set (Applied Biosystems) was included in a separate well and used to normalize the soleus, BAT, and heart gene expression data. GAPDH Rodent primers (Applied Biosystems) were used in the same well to normalize the liver gene expression data.
For Southern blot studies, 5 μg of genomic DNA was digested with Pst1 or Xba1, electrophoresed on a 0.8% TAE gel and transferred to a Gene Screen (Perkin Elmer, Wellesley, California, United States) membrane for hybridization. Western blotting was performed as described [43] using the Enhanced Chemiluminescence detection system (Amersham Pharmacia Biotech, Piscataway, New Jersey, United States). Ponceau S (Sigma, St. Louis, Missouri, United States) staining of the membrane was used as a control.
Mitochondrial respiration studies
Mitochondrial respiration was assessed in saponin-skinned soleus fibers with succinate as substrate and in the presence of rotenone as previously described [44]. In brief, 3-mo-old female mice were anesthetized with chloral hydrate (400 mg/kg of body weight). Soleus fibers were separated and then transferred to a buffer (2.77 mM K2Ca-EGTA, 7.23 mM K2EGTA, 6.56 mM MgCl2, 20 mM imidazole, 53.3 mM K-MES, 20 mM taurine, 5.3 mM ATP, 15 mM PCr, 3 mM KH2PO4, 0.5 mM DTT [pH 7.1]) supplemented with 50 μg/ml saponin and permeabilized for 30 min at 4 °C with gentle stirring. Fibers were then washed twice for 10 min each (2.77 mM K2Ca-EGTA, 7.23 mM K2EGTA, 1.38 mM MgCl2, 20 mM imidazole, 100 mM K-MES, 20 mM taurine, 3 mM KH2PO4, 0.5 mM DTT, 2 mg/ml BSA [pH 7.1]). Respiration was measured at 25 °C using an optical probe (Oxygen FOXY Probe, Ocean Optics, Dunedin, Florida, United States). Following measurement of basal state 2 respiration, maximal (ADP-stimulated) state 3 respiration was determined by exposing fibers to 1 mM ADP. The integrity of the outer mitochondrial membrane was assessed by adding 8 μM exogenous cytochrome c to ADP-stimulated fibers. State 4 respiration (uncoupled) was evaluated following addition of oligomycin (1 μg/ml). The solubility of oxygen in the respiration buffer at 25 °C was taken as 246.87 nmol O2 · ml−1. Respiration rates were expressed as nmol O2 · min−1 · mgdw−1.
Insulin and glucose tolerance tests
Glucose and Insulin tolerance tests were performed as described [35]. Prior to studies, mice were fasted overnight (GTT) or 6 h (ITT). In GTT studies, mice were injected with a 10% solution of D-glucose (1 g/kg). For ITT, mice received an IP injection of human regular insulin (Eli Lilly, Indianapolis, Indiana, United States) at a dose of 0.75 units/kg of body weight. Tail blood glucose was determined at 0, 30, 60, and 120 min after challenge using a B-GLUCOSE Analyzer (Hemacue AB, Angelholm, Sweden).
Indirect calorimetry
Oxygen consumption rates (VO2) of 5-wk-old female mice were measured using a Columbus Instruments Oxymax System. Resting baseline oxygen consumption rates were assessed for at least 1.0 h. For β3-adrenergic stimulation studies, BRL 37344 (Sigma) was dissolved in sterile saline and injected IP (2 μg/g of body weight) [45]. Postagonist assessment of oxygen consumption was recorded for an additional 1.0 h, with data collected at the 40-min time point.
Histology and electron microscopy
Soleus muscle and liver were dissected and fixed overnight in 2% glutaraldehyde, 1% paraformaldehyde, and 0.08% sodium cacodylate buffer. The tissues were postfixed in 1% osmium tetroxide, dehydrated in graded ethanol, embedded in Poly Bed plastic resin, and sectioned for electron microscopy. Cardiac and skeletal muscle mitochondrial and myofibrillar volume densities were determined from electron micrographs as described previously [10]. For each animal, three different fields at the magnification of 7500× were quantified in blinded fashion. Data were expressed as mean volume density of mitochondria or myofibrils in each field.
For electron microscopic analysis of the brain, tissue was prepared as previously described [46]. Ultrathin sections of cortex were cut onto formvar-coated slot grids stained with uranyl acetate and lead citrate and examined with a JEOL 1200 electron microscope. For H&E staining, sections of brain, including cerebral cortex, brainstem, and cerebellum, were dehydrated in graded concentrations of alcohol and embedded in paraffin from which 5-μm sections were prepared.
Primary mouse hepatocyte studies
Primary cultures of mouse hepatocytes were prepared from male PGC-1α+/+ and PGC-1α−/− mice essentially as described [47]. Fatty acid oxidation and triglyceride synthesis experiments were commenced 2–3 h after the cells were plated. Triglyceride synthesis studies, were performed as previously described [47]. Palmitate oxidation rates were quantified using [9,10-3H]-palmitic acid as described [48] and corrected for total cellular protein content. For respiration studies, cells were spun down prior to plating and resuspended in a permeabilization buffer (described above) containing 50 μg/ml saponin. Respiration studies were performed in the presence of 5 mM succinate in the presence of 10 μM rotenone. Respiration rates were expressed as nmol O2 · min−1 · mg of protein−1.
Evaluation of locomotor activity, sensorimotor capabilities, and muscle function
To evaluate general activity levels and muscle use, mice were evaluated over a 1-h period in transparent (47.6 cm × 25.4 cm × 20.6 cm) polystyrene enclosures as previously described [49] using a high-resolution photobeam system (Motor Monitor, Hamilton-Kinder, Poway, California, United States). Each enclosure was surrounded by a frame containing a 4 × 8 matrix of photocell pairs, the output of which was fed to an on-line computer. The system software (Hamilton-Kinder) was used to define a 33 cm × 11 cm central zone and a peripheral or surrounding zone that was 5.5 cm wide with the sides of the cage being the outermost boundary. This peripheral area extended along the entire perimeter of the cage. Variables that were analyzed included the total number of ambulations, as well as the number of entries, the time spent, and the distance traveled in the center area as well as the distance traveled in the periphery surrounding the center. The total number of ambulations and rearings were recorded. For the inverted screen test, mice were placed on a wire mesh grid (16 squares per 10 cm) and the screen was inverted to 180°. A maximum score of 60 s was given if an animal did not fall.
The tests included in the sensorimotor battery [50] and accompanying protocols were designed as follows. (1) Inclined screen and inverted screen tests: For the inclined screen tests, each mouse was placed on top of an elevated (47 cm above the floor) wire mesh grid (16 squares per 10 cm) that was inclined to 60° or 90°. Each animal was placed in the middle of the screen with its head oriented down and was timed for how long it remained on the screen and how long it took to climb to the top of the screen. For the inverted screen test, mice were placed as above and then the screen was inverted to 180°. A maximum score of 60 s was given if an animal did not fall; (2) Platform test: Each mouse was timed for how long it remained on an elevated (47 cm above the floor) circular platform (1.0 cm thick and 3.0 cm in diameter). A maximum score of 60 s was assigned if the mouse remained on the platform for the maximum amount of time or if it could climb down on a very thin pole that supported the platform, without falling; (3) Ledge test: Each mouse was timed for how long it could maintain its balance on a 0.75-cm wide Plexiglas ledge without falling (60 s maximum). A score of 60 s was also assigned if the mouse traversed the entire length (51 cm) of the Plexiglas ledge and returned to the starting place in less than 60 s without falling; (4) Walking initiation test: Each mouse was placed in the middle of a square outlined by white cloth tape (21 cm × 21 cm) on a smooth black surface of a large tabletop. The time it took each mouse to leave the square (place all four paws outside of the tape) was recorded. The maximum time allowed was 60 s.
6–8-mo-old PGC-1α+/+ (n = 4) and PGC-1α−/− (n = 8) mice were run to exhaustion employing a motorized, speed controlled, modular treadmill system (Columbus Instruments). The treadmill was equipped with an electric shock stimulus and an adjustable inclination angle. Running velocity was set at 35 m/min, with a level inclination angle.
VO2max studies
VO2max was determined while the mice were running on a treadmill using an open flow system (Columbus Instruments Oxymax System). All measurements of oxygen consumption took place at an elevation of 150 m (ambient PBAR = 745 torr). Animals were placed into the metabolic chamber for 3–5 min to allow the system to equilibrate. Mice were then induced to run up an 18° incline at a speed of 40 m/min using a shock grid in the rear of the chamber. The speed was increased by 5 m/min every 2 min until the animals were unable to continue. Maximal effort was determined when oxygen uptake did not increase with power output and subsequently the mouse failed to maintain effort. VO2max was calculated using the averaged values over 1 min during which the animal's O2 consumption reached a plateau.
Isolated muscle stimulation studies
Animals were anesthetized with ketamine/xylazine and the soleus muscle was removed from one leg. Upon removal, the muscle was suspended in a Krebs solution aerated with 95% O2 and 5% CO2. The muscle and Krebs solution were suspended within a water bath maintained at 37 °C, and the muscle was anchored to a Grass (West Warwick, Rhode Island, United States) isometric force transducer (model FTO3C). Muscles were stimulated to contract with a Grass stimulator (model S88) generating a field stimulus through electrodes located at both ends of the muscle. Force-voltage (maximal force at about 100 V) and length-tension relationships were determined using single twitch stimuli. The stimulator then delivered repeating trains of stimuli at one per second at 40 Hz for 2 min. Each train lasted 330 ms, and were digitally recorded using MacLab (AD Instruments, Colorado Springs, Colorado, United States). Fatigue resistance was calculated as the ratio of the force generated by the last tetanus divided by the highest force generated multiplied by 100 to give the percent of force generation that remained after the fatigue protocol.
Exercise echocardiography
Adult female mice (6–8 mo old) were exercised on the motorized treadmill using the run-to-exhaustion settings described above for 60 s or until exhaustion, whichever came first. Immediately following its treadmill run, the mouse was subjected to serial echocardiography using an Acuson Sequoia Echocardiography System performed as previously described [51].
In vivo cardiac hemodynamic studies
Hemodynamic studies were performed as previously described with some modifications [52]. In brief, adult mice (10–12 wk) were anesthetized intraperitoneally (IP) with thiopental sodium (60 mg/kg). The mice were intubated and ventilated with a Harvard ventilator. The right carotid artery was isolated in the region of the trachea and cannulated with a 1.4-French high-fidelity micromanometer catheter (Millar Instruments, Houston, Texas, United States), which was inserted into the left ventricle retrograde across the aortic valve. Hemodynamic measurements were recorded at baseline and 3 min following continuous infusion of incremental doses of dobutamine (β1, β2, and α1-adrenergic agonist) up to 32 ng · gBW−1· min−1 [53]. Continuous pressure-volume data were acquired and digitized with the BioBench computer software data acquisition system (National Instruments, Austin, Texas, United States).
Isolated working mouse heart perfusion
Isolated working mouse heart perfusion was based on a previously described procedure [54]. Adult mice (4–7 mo old) were heparinized (100 units IP) 10 min prior to anesthesia. Animals were then deeply anesthetized with 5–10 mg of sodium pentobarbital IP. Hearts were excised and placed in an ice-cold Krebs-Henseleit bicarbonate (KHB) solution [118 mM NaCl, 25 mM NaHCO3, 4.7 mM KCl, 1.2 mM KH2PO4, 2.5 mM CaCl2, 5.0 mM glucose, and 100 units/L insulin (pH 7.4)]. Hearts were cannulated first via the aorta and perfused retrogradely by the Langendorff method. Following left atrial cannulation, perfusion was switched to the working mode with KHB solution containing 1.2 mM palmitate bound to 3% fatty acid-free BSA with a preload pressure of 11.5 mm Hg and an afterload pressure of 50 mm Hg for 60 min with oxygenated buffer solution. Functional measurements, namely cardiac output, aortic flows, peak systolic pressure, and heart rate were acquired every 10 min using inline flow probes (Transonic Systems, Ithaca, New York, United States), a pressure transducer (TSD 104A, BIOPAC Systems, Santa Barbara, California, United States) and data acquired with the MP100 system from AcqKnowledge (BIOPAC Systems). Cardiac work was calculated as the product of peak systolic pressure and cardiac output.
Statistics
Data were analyzed using T-tests or ANOVAs (measures of general activity and sensorimotor battery). The level of significance was set at p < 0.05 in all cases. Data are reported as mean values ± SEM, unless otherwise noted. The ANOVA model used to analyze each sensorimotor test included one between-subjects variable (genotype), and one within-subjects variable (trials). When ANOVAs with repeated measures were conducted, the Huynh-Feldt (H-F) adjustment of alpha levels was used for all within-subjects effects containing more than two levels, in order to protect against violations of the sphericity/compound symmetry assumptions underlying this ANOVA model. In addition, Bonferroni correction was used when appropriate to help maintain prescribed alpha levels (e.g., p < 0.05) when multiple comparisons were conducted.
Supporting Information
Figure S1 Activity Levels in Female PGC-1α−/− Mice Is Unchanged
Using a CLAMS system, PGC-1α+/+ (n = 4) and PGC-1α−/− (n = 3) female mice were monitored for 48 h after a 17-h period of acclimation. XY beam breaks were tabulated over the 12-h light and dark cycles as denoted on the bottom. The bars represent mean (± SEM) beam breaks per each 12-h cycle.
(342 KB EPS).
Click here for additional data file.
Figure S2 Altered Emotionality in PGC-1α−/− Mice
An analysis of exploratory behavior included the number of entries into the center of the cage (upper left), the time spent in the center of the cage in seconds (sec) (upper right), the distance traveled in the center of the cage in meters (m) (lower left) as well as the distance traveled in the periphery (lower right). * p < 0.05 compared to the PGC-1α+/+ mice.
(620 KB EPS).
Click here for additional data file.
Figure S3 No Difference in Weight Gain on a High-Fat Diet in Female PGC-1α−/− Mice Compared to WT Controls
8-wk-old female mice were fed a diet high in fat (43% calories from fat) for 6 wk. The change in body weight (grams) after 6 wk on a high-fat diet is shown for PGC-1α+/+ (n = 8) and PGC-1α−/− (n = 11) mice. NS, not significant.
(264 KB EPS).
Click here for additional data file.
Figure S4 Male PGC-1α−/− Mice Are Somewhat Resistant to Diet-Induced Obesity
Male and female PGC-1α+/+ (n ≥ 6) and PGC-1α−/− (n ≥ 6) mice were fed a high-fat diet (43% calories from fat) beginning at 4 wk of age. Body weight was monitored weekly as shown on the graph on the left. The mean (± SEM) change in body weight is shown in the bar graph on the right. *, significant difference compared to the PGC-1α+/+ controls, p < 0.05.
(794 KB EPS).
Click here for additional data file.
Table S1 Probes and Primers
Sequences of mouse-specific probes and primers used for real-time RT-PCR.
(25 KB DOC).
Click here for additional data file.
Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers of the vector discussed in this paper is p1339-PGK-Neomycin targeting vector (AF335420).
We would like to thank Deanna Young and Karen Hemker for help with mouse husbandry, Carolyn Mansfield for ES cell and mouse genotyping, Anthony Nardi for assistance with the studies to assess general activity and muscle strength, Bill Kraft for expert technical assistance with electron microscopy, Carla Weinheimer for performing cardiac catheterizations, Trey Coleman for performing the TAG assays, Krista Olson for expert technical assistance with the Langendorff method, and Mary Wingate for assistance with manuscript preparation. This work was supported by National Institutes of Health grants RO1 DK45416, RO1 HL58427, PO1 HL57278, Digestive Diseases Research Core Center (P30 DK52574), Clinical Nutrition Research Unit Core Center (P30 DK56341), and K08 AG24844. SB is supported by a postdoctoral fellowship from the Juvenile Diabetes Foundation. EDA is supported by the American Heart Association (Established Investigator) and RO1 HL70070.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. TCL, JJL, BNF, PJS, and DPK conceived and designed the experiments. TCL, JJL, BNF, PJS, ARW, SB, MC, DFW, NS, CBM, ZC, DMM, RES, and EDA performed the experiments. TCL, JJL, BNF, PJS, ARW, SB, MC, DFW, NS, CBM, ZC, JOH, DMM, RES, JES, EDA, CFS, and DPK analyzed the data. TCL, BNF, and DPK wrote the paper.
Citation: Leone TC, Lehman JJ, Finck BN, Schaeffer PJ, Wende AR, et al. (2005) PGC-1α-deficiency causes multi-system energy metabolic derangements: Muscle dysfunction, abnormal weight control and hepatic steatosis. PLoS Biol 3(4): e101.
Abbreviations
BATbrown adipose tissue
DEXAdual-energy X-ray absorption
ESTechocardiographic stress testing
IPintraperitoneal(ly)
NRFnuclear respiratory factor
PGCPPARγ coactivator
PPARperoxisome proliferator-activated receptor
PRCPGC-1 related coactivator
SCDsteroyl CoA desaturase
SREBPsterol regulatory element binding protein
SEMstandard error of the mean
TAGtriglyceride
Tfammitochondrial transcription factor A
UCPuncoupling protein
WTwild-type
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030125SynopsisCell BiologyDevelopmentCardiology/Cardiac SurgeryMus (Mouse)Heart Repair Gets New Muscle Synopsis4 2005 15 3 2005 15 3 2005 3 4 e125Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
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Alchemy and the New Age of Cardiac Muscle Cell Biology
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When you think of the cell as the fundamental unit of life, it's not surprising that some organs deal with injury better than others. A flesh wound or muscle tear might hurt, but, assuming you are otherwise healthy, both will heal. The prognosis for a heart attack, on the other hand, is not so clear-cut. What accounts for the difference?
Skin cells reproduce regularly to replace dead cells, and simply increase production in the event of injury. Skeletal muscles recruit new muscle cells from a type of precursor cell within the muscle, called satellite cells, to repair a tear. Cardiac cells (cardiomyocytes), it has long been thought, appear to lack this capacity for self-renewal and repair, impeding the chances of a full recovery. That's why therapies derived from stem cells—which retain a unique ability to morph into any of the body's 200-plus cell types—hold such promise. But stem cells are a hot-button issue in the United States, complicating efforts to explore this promise.
Recent evidence suggests that the heart might harbor stem cells after all and that such cells can be transformed into cardiomyocytes. In a new study, Neal Epstein and colleagues report that cells isolated from the skeletal muscle of adult mice can turn into beating cardiomyocytes in a test tube within days of isolation—and without the addition of gene-altering drugs or special cardiac factors. When freshly isolated cells (called skeletal precursors of cardiomyocytes, or Spoc cells) are injected into the tail veins of mice with heart damage, they migrate to the damaged tissue and differentiate into cardiac muscle cells.
What distinguishes a cardiomyocyte from a skeletal muscle cell? Specialized cells produce unique proteins, allowing scientists to use those proteins as identifying markers. The so-called Spoc cells do not express any of the usual markers associated with either skeletal muscle satellite cells or partially differentiated skeletal muscle cells. By day 7 in culture, Spoc cells have undergone several rounds of cell division and have begun to express a (mostly) cardiac-specific protein, and have formed clusters of cardiac precursor cells, some of which beat. These precursors in turn express other cardiac-specific proteins.
Epstein and colleagues further divided Spoc-derived precursor cells into two groups based on whether or not they expressed another protein marker (Sca-1, a common marker found on blood stem cells). About 80% of cells without this protein differentiated into immature beating cells after proliferating for seven to ten days. They remained in an immature state (round and loosely attached) for over two months in culture, but differentiated into mature beating heart cells (elongated and adherent) when mixed with Sca-1 cells. The authors use video microscopy to track the cells' progression to beating cells, complete with contraction-generating thick myosin filaments that are “nearly identical” to those seen in developing cardiomyocytes. Epstein and colleagues also demonstrate that the Spoc cells are distinct from stem cells cultured out of bone marrow, heart, or fat tissue—sources of beating cells in other studies. The authors also injected these Spoc cells into mice with acute heart lesions to test the cells' ability to integrate into the damaged tissue. Many cells successfully migrated to and engrafted into the site of injury; some of these cells developed into cardiomyocytes. The cells showed a similar, though less robust, response to an older heart injury.
Spoc cells can help repair a damaged heart
Epstein and colleagues argue that Spoc cells are more likely to be precursors to cardiomyocytes than to be some other type of skeletal muscle stem cell. This is based on an absence of protein markers for skeletal muscle or skeletal satellite cells in Spoc cells, as well as the fact that Spoc-derived cells display spontaneous rhythmic beating and express cardiac markers, whether they are grown in a test tube or have migrated to injured hearts in study mice. The authors can't say why skeletal muscle would harbor cardiac stem cells or why so few of these cells pitch in to repair a cardiac injury. But for now, the Spoc cells provide a valuable tool for studying heart cell differentiation. And with time, they might prove an important resource for developing cell-based therapies for heart disease. See also the Primer “Alchemy and the New Age of Cardiac Muscle Cell Biology” (DOI: 10.1371/journal.pbio.0030131).
| 0 | PMC1064855 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e125 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030125 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030132SynopsisEcologyZoologyBirdsMammalsGray Wolves Help Scavengers Ride Out Climate Change Synopsis4 2005 15 3 2005 15 3 2005 3 4 e132Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Gray Wolves as Climate Change Buffers in Yellowstone
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Average earth temperatures rose 0.6 °C over the last century, according to the latest Intergovernmental Panel on Climate Change. But that increase pales in comparison to the 1.4–5.8 °C expected increase over this century. As temperatures climb, climate models predict that high-latitude, high-altitude regions like Yellowstone National Park will experience shorter winters and earlier snow melts. How these environmental shifts will impact species and ecosystems remains to be seen.
The effects of climate change are already evident at the species level, with disruptions in range, reproductive success, and seasonal phenomena like migration, and the decoupling of evolutionarily paired events like new births and food availability. Both experimental and data-driven modeling studies predict that climate change may well precipitate shifts in the structure of ecosystems as well.
In a new study, Christopher Wilmers and Wayne Getz investigated the effects of climate change on ecosystem dynamics by studying a keystone species in Yellowstone, the gray wolf (Canis lupus). Gray wolves inhabited most of North America until US extirpation campaigns nearly eradicated them by the 1930s. In 1995, the US Fish and Wildlife Service reintroduced the persecuted predator into Yellowstone.
Wilmers and Getz used data from the past 50 years at two weather stations in the park's northern range (where elk over winter and four to six wolf packs now live) to establish winter trends and model wolves' impact on the fate of resident scavengers faced with a changing climate. Not surprisingly, their models show that this top predator exerts significant influence over animals at lower levels in the food chain: wolf kills temper the potentially devastating effects of climate-related carrion shortages on scavengers. Unlike mountain lions and grizzly bears, wolves abandon their prey (usually elk or moose) once sated, leaving much-coveted leftovers for ravens, eagles, coyotes, bears, and other scavengers. These findings indicate that individual species stand a better chance of adapting to climate change in an ecosystem with an intact food chain.
Wilmers and Getz's weather data analysis found that both late-winter snow depth and snow-cover duration have decreased significantly since 1948—winters in Yellowstone are getting shorter. That's good news for elk—navigating deep snow taxes stamina and reduces access to forage—but bad news for scavengers that rely on elk carcasses to carry them through the winter.
The authors generated two sets of models to estimate the effects of shorter winters on the wolf–elk–scavenger dynamics. In the first, late-winter carrion availability drops by 66% without wolves but by only 11% when the predators are present. The second model examines the impact of elk and wolf population dynamics on carrion availability. This analysis predicts that more elk will die in early winter than in late winter, a scenario that favors eagles and ravens—which can cover a lot of ground quickly—over bears and coyotes. Altogether, these modeling studies show that shorter winters without wolves will create intermittent food supplies that no longer track the needs of local scavengers. With or without wolves, late-winter carrion abundance will decline with shorter winters. But wolf kills buffer these shortages, providing meals that could determine whether scavengers will be able to survive and reproduce.
Reintroduced wolves do their part: an intact food chain buffers the impact of deteriorating environmental conditions (Photo: Dan Hartman)
It seems clear that wolves have the potential to provide a safety net for scavengers, extending the time they need to adapt to a changing environment. Thanks to a rebounding wolf population, field researchers can measure the magnitude of this predicted buffer effect. The models described here can guide their efforts and help species adjust to major environmental shifts like climate change.
As a young US ranger “full of trigger-itch,” Aldo Leopold killed his share of wolves under the federal eradication policy—until he “watched a fierce green fire dying” in the eyes of a slain mother flush with pups and realized he had not understood the wolf's ecological role. Wilmers and Getz's study shows that a robust food chain—including this still embattled top predator—may be even more important as ecological conditions deteriorate.
| 0 | PMC1064856 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e132 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030132 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030133SynopsisGenetics/Genomics/Gene TherapyPhysiologyMus (Mouse)A New Role for a Protein Involved in Energy Metabolism Synopsis4 2005 15 3 2005 15 3 2005 3 4 e133Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
PGC-1α Deficiency Causes Multi-System Energy Metabolic Derangements: Muscle Dysfunction, Abnormal Weight Control and Hepatic Steatosis
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Adjusting to life after birth takes a lot of energy. One way cells meet increased demand is by ramping up synthesis of mitochondria, the cells' power generators This ability to increase mitochondria becomes limited in a variety of diseases including diabetes and heart failure. Therefore, it is important to identify the factors that control mitochondrial function. One way researchers have searched for candidate proteins that play a role in this process is by overexpressing proteins in targeted cells to see what happens. That's how several previous studies concluded that a protein called PGC-1α triggers pathways that promote mitochondrial synthesis and regulate both mitochondrial activity and energy metabolism.
In a new study, Daniel Kelly and colleagues took a different approach. Rather than increasing the protein's activity, they blocked it. To do that, Kelly and colleagues engineered “knockout” mice that lack functional copies of the PGC-1α gene. PGC-1α, they found, isn't absolutely required for mitochondrial biogenesis but plays a vital role later in life by “boosting” the ability of cells to increase mitochondrial function in response to the shifting energy demands and physiological stresses encountered after birth.
Though leaner than the control mice soon after birth, by 18 weeks the female knockouts were slightly heavier and had more body fat, even though their food intake and activity levels matched the controls. Knockout mice had observable growth defects in skeletal and heart muscle—tissues with high mitochondrial energy requirements—were less active and more easily fatigued than the controls, and had abnormal heart rates after physical exertion. And their livers showed a propensity to accumulate fat because of abnormal mitochondria.
PGC-1α deficient mice can't keep pace (Photo: MedPic, Washington University)
Altogether, these results demonstrate PGC-1α's critical role in regulating the adaptive metabolic responses required by the increasing energy demands and changing physiological stimuli associated with a growing organism. The increased fat stores and weight gain in the knockout mice, the authors propose, could result from a systemic reduction in energy use, related to defective mitochondria. Given the recently reported link between PGC-1α mutations and human obesity and diabetes, this connection will likely trigger further investigations. And given the pivotal role mitochondria play in a wide range of organs, this mouse model could help shed light on metabolic defects associated with a wide range of diseases.
Interestingly, another group, led by Bruce Spiegelman, reported on a PGC-1α knockout model last year. Their mice share traits with the mice described here, but also exhibit a number of contrasting traits, including hyperactive, lean males, which the Spiegelman group attributed to a neurological defect. Kelly and colleagues speculate on possible causes for the differences in the results of the two studies, but only direct comparison of both mouse models will explain the inconsistencies.
| 0 | PMC1064857 | CC BY | 2021-01-05 08:21:20 | no | PLoS Biol. 2005 Apr 15; 3(4):e133 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030133 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030138SynopsisEvolutionGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyDrosophilaPlus Ça Change: Gene Enhancers Upset Evolutionary Assumption Synopsis4 2005 15 3 2005 15 3 2005 3 4 e138Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Functional Evolution of a cis-Regulatory Module
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A standard evolutionary assumption is that the DNA of closely related species should be more similar in both structure and function than that of more distantly related ones. This parsimonious rule of thumb holds true across wide expanses of time and among widely divergent species, but it has exceptions. In this issue, Michael Ludwig and colleagues show that one exception is in an enhancer region of a key developmental gene in fruitflies of the genus Drosophila. Here, the enhancer is functionally similar enough in two species that diverged 60 million years ago that switching them produces normal development, but different enough in two species separated by only 10 million years that exchanging them between the two flies aborts development.
The gene in question is even-skipped, a patterning gene that creates seven transverse stripes along the anterior–posterior axis in the fruitfly embryo. Its expression is regulated by five enhancing elements located upstream from the promoter. The best characterized of these, the stripe 2 enhancer (S2E), binds five different transcription factors at multiple locations.
Ludwig et al. deleted S2E in D. melanogaster, and then added back S2E from one of four Drosophila species: D. melanogaster itself; D. yakuba or D. erecta, both of which have been separated from D. melanogaster for 10–12 million years; or D. pseudoobscura, which split from the D. melanogaster line 40–60 million years ago. Despite serving identical functional roles in each species, the structures of these enhancers differ, with large deletions and insertions between transcription factor binding sites, as well as other changes. Nonetheless, within each species, the spatiotemporal pattern of expression induced by S2E is essentially identical, suggesting that, despite their structural differences, they might be functionally interchangeable.
Since loss of S2E is lethal, the viability of the embryos that resulted from these experiments gives a measure of each enhancer's ability to function in its new environment. The authors found that while viability of D. melanogaster with the D. pseudoobscura S2E was identical to that with D. melanogaster's own, viability with S2E from the more closely related D. erecta was almost zero, essentially the same as not having the enhancer at all. S2E from D. yakuba impaired the viability of D. melanogaster as well, although not as much as that from D. erecta. Viability was closely correlated with the level of stripe 2 expression induced by each S2E, with very low levels induced by the D. erecta enhancer and normal levels by that of D. melanogaster and D. pseudoobscura.
Why did the D. erecta enhancer fail to respond in the D. melanogaster environment? Ludwig et al. suggest it may be due to a change in the sensitivity of the “set point” in D. erecta's enhancer, which acts like an on–off switch governing gene expression, making the enhancer unresponsive to the gradients of transcription factors found in D. melanogaster. This change may be due to relatively small differences in the two species' enhancers that have accumulated since their evolutionary split.
The results of this study indicate an important caveat about interpreting the evolution of gene regulatory regions. As a complex functional unit that integrates a host of signals, the S2E is likely to be under strong stabilizing selection, maintaining its output within narrow limits. Thus, the phenotypic result of the enhancer—the location and timing of stripe formation it induces in its native environment—remains conserved among the four species. However, unlike an enzyme or structural protein, in which structural changes are tightly constrained by their effects on function, the structure of any particular enhancer need not be so rigidly preserved. As long as the consequences of change in one region, such as loss of a transcription factor binding site, are matched by compensatory changes in another, such as gain of one, or, as Ludwig et al. speculate, by complementary changes in genetic background, the final output of the enhancer can remain the same. Thus, the utility of structural similarities in understanding evolutionary relationships is likely to be less for gene regulatory regions than for structural genes or the proteins they encode.
| 0 | PMC1064858 | CC BY | 2021-01-05 08:28:18 | no | PLoS Biol. 2005 Apr 15; 3(4):e138 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030138 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030138SynopsisEvolutionGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyDrosophilaPlus Ça Change: Gene Enhancers Upset Evolutionary Assumption Synopsis4 2005 15 3 2005 15 3 2005 3 4 e138Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Functional Evolution of a cis-Regulatory Module
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A standard evolutionary assumption is that the DNA of closely related species should be more similar in both structure and function than that of more distantly related ones. This parsimonious rule of thumb holds true across wide expanses of time and among widely divergent species, but it has exceptions. In this issue, Michael Ludwig and colleagues show that one exception is in an enhancer region of a key developmental gene in fruitflies of the genus Drosophila. Here, the enhancer is functionally similar enough in two species that diverged 60 million years ago that switching them produces normal development, but different enough in two species separated by only 10 million years that exchanging them between the two flies aborts development.
The gene in question is even-skipped, a patterning gene that creates seven transverse stripes along the anterior–posterior axis in the fruitfly embryo. Its expression is regulated by five enhancing elements located upstream from the promoter. The best characterized of these, the stripe 2 enhancer (S2E), binds five different transcription factors at multiple locations.
Ludwig et al. deleted S2E in D. melanogaster, and then added back S2E from one of four Drosophila species: D. melanogaster itself; D. yakuba or D. erecta, both of which have been separated from D. melanogaster for 10–12 million years; or D. pseudoobscura, which split from the D. melanogaster line 40–60 million years ago. Despite serving identical functional roles in each species, the structures of these enhancers differ, with large deletions and insertions between transcription factor binding sites, as well as other changes. Nonetheless, within each species, the spatiotemporal pattern of expression induced by S2E is essentially identical, suggesting that, despite their structural differences, they might be functionally interchangeable.
Since loss of S2E is lethal, the viability of the embryos that resulted from these experiments gives a measure of each enhancer's ability to function in its new environment. The authors found that while viability of D. melanogaster with the D. pseudoobscura S2E was identical to that with D. melanogaster's own, viability with S2E from the more closely related D. erecta was almost zero, essentially the same as not having the enhancer at all. S2E from D. yakuba impaired the viability of D. melanogaster as well, although not as much as that from D. erecta. Viability was closely correlated with the level of stripe 2 expression induced by each S2E, with very low levels induced by the D. erecta enhancer and normal levels by that of D. melanogaster and D. pseudoobscura.
Why did the D. erecta enhancer fail to respond in the D. melanogaster environment? Ludwig et al. suggest it may be due to a change in the sensitivity of the “set point” in D. erecta's enhancer, which acts like an on–off switch governing gene expression, making the enhancer unresponsive to the gradients of transcription factors found in D. melanogaster. This change may be due to relatively small differences in the two species' enhancers that have accumulated since their evolutionary split.
The results of this study indicate an important caveat about interpreting the evolution of gene regulatory regions. As a complex functional unit that integrates a host of signals, the S2E is likely to be under strong stabilizing selection, maintaining its output within narrow limits. Thus, the phenotypic result of the enhancer—the location and timing of stripe formation it induces in its native environment—remains conserved among the four species. However, unlike an enzyme or structural protein, in which structural changes are tightly constrained by their effects on function, the structure of any particular enhancer need not be so rigidly preserved. As long as the consequences of change in one region, such as loss of a transcription factor binding site, are matched by compensatory changes in another, such as gain of one, or, as Ludwig et al. speculate, by complementary changes in genetic background, the final output of the enhancer can remain the same. Thus, the utility of structural similarities in understanding evolutionary relationships is likely to be less for gene regulatory regions than for structural genes or the proteins they encode.
| 0 | PMC1064859 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e139 | latin-1 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030139 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030142SynopsisGenetics/Genomics/Gene TherapyMicrobiologyYeast and FungiWhite Collar Proteins Help Fungi Do It in the Dark Synopsis4 2005 15 3 2005 15 3 2005 3 4 e142Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Light Controls Growth and Development via a Conserved Pathway in the Fungal Kingdom
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Fungi live mainly in the dark, and they like it that way—out of the sunlight, they can avoid desiccation and damage from ultraviolet rays. The ability to sense light, therefore, is adaptive for fungi of all kinds. A pair of light-sensing proteins had been identified in the model fungus Neurospora crassa, an ascomycete (one of the three fungal subgroups, defined by the production of sexual spores within sac-like structures), but little was known about mechanisms in other fungal phyla. In a new study, Alexander Idnurm and Joseph Heitman show that the basidiomycete (a subgroup defined by the production of sexual spores on the ends of club-like structures) Cryptococcus neoformans employs a similar protein pair, which regulate mating, growth, and virulence of this human fungal pathogen.
In N. crassa, blue light is sensed by the protein White collar 1, which interacts with a flavin (light-absorbing pigment) tuned to photons in the blue region of the spectrum. White collar 1 then binds to White collar 2, and the complex serves as a transcription factor. In this study, the authors searched the C. neoformans genome for genes with similar evolutionary origins to these two genes (called homologs), as well as others implicated in light sensing, and identified Basidiomycete white collar 1, or BWC1, along with other light-sensor candidates, including an opsin and a phytochrome homolog. Mutations of BWC1, but not the other candidate photoreceptors, rendered C. neoformans insensitive to light. While mating and fruiting in the wild-type fungus is suppressed by exposure to blue light, bwc1 mutants were unaffected by light. Interestingly, the mating process was released from light inhibition when either one of the two mating strains were mutated, suggesting that the cell fusion process at the heart of fungal mating requires only one cell to commit to fusion. In addition, bwc1 mutants were extremely sensitive to ultraviolet radiation. No homolog of photolyase, a protein that uses light to repair DNA damage, was identified in the genome. Future studies will be necessary to understand how Bwc1 functions in ultraviolet resistance, but these findings suggest the protein could sense photons in both the ultraviolet and blue wavelengths.
To identify other proteins with which the Bwc1 protein functionally interacts, the authors examined nearly 3,000 mutant strains, yielding three with a phenotype similar to the bwc1 mutant. They found that the gene for one of these, dubbed BWC2, is a homolog of N. crassa White collar 2, and that its protein binds to Bwc1. Together, the two influence transcript levels of two key genes required for C. neoformans mating, further strengthening the case that the pair function as a transcription factor as do their homologs in N. crassa. Interestingly, mutants of either BWC1 or BWC2 were less virulent than the wild-type strain of the fungus, revealing a novel environmental signaling pathway involved in C. neoformans virulence.
The functional and structural similarities of the ascomycote and basidiomycote White collar proteins indicate that they arose prior to the split of these two lineages more than 500 million years ago. The fungal kingdom contains an estimated one million species. The authors suggest that the ultraviolet protection afforded by the White collar system may have been crucial to the evolutionary diversification of this kingdom, in particular when ultraviolet radiation on the earth's surface was higher than it is today, such as when life emerged from the sea and colonized the barren continents. They also note that the same proteins are found in clinical isolates of C. neoformans, and that the mitigation of virulence by bwc1 and bwc2 mutations will be useful in the identification of new genes required for disease development in this important pathogen.
| 0 | PMC1064860 | CC BY | 2021-01-05 08:21:21 | no | PLoS Biol. 2005 Apr 15; 3(4):e142 | utf-8 | PLoS Biol | 2,005 | 10.1371/journal.pbio.0030142 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar12171553583210.1186/ar1217Research ArticleCollagen type II (CII)-specific antibodies induce arthritis in the absence of T or B cells but the arthritis progression is enhanced by CII-reactive T cells Nandakumar Kutty Selva [email protected]äcklund Johan [email protected] Mikael [email protected] Rikard [email protected] Section for Medical Inflammation Research, Lund University, Sweden2004 23 9 2004 6 6 R544 R550 11 5 2004 26 5 2004 16 6 2004 30 6 2004 Copyright © 2004 Nandakumar et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Antibodies against type II collagen (anti-CII) are arthritogenic and have a crucial role in the initiation of collagen-induced arthritis. Here, we have determined the dependence of T and B cells in collagen-antibody-induced arthritis (CAIA) during different phases of arthritis. Mice deficient for B and/or T cells were susceptible to the CAIA, showing that the antibodies induce arthritis even in the absence of an adaptive immune system. To determine whether CII-reactive T cells could have a role in enhancing arthritis development at the effector level of arthritis pathogenesis, we established a T cell line reactive with CII. This T cell line was oligoclonal and responded to different post-translational forms of the major CII epitope at position 260–270 bound to the Aq class II molecule. Importantly, it cross-reacted with the mouse peptide although it is bound with lower affinity to the Aq molecule than the corresponding rat peptide. The T cell line could not induce clinical arthritis per se in Aq-expressing mice even if these mice expressed the major heterologous CII epitope in cartilage, as in the transgenic MMC (mutated mouse collagen) mouse. However, a combined treatment with anti-CII monoclonal antibodies and CII-reactive T cells enhanced the progression of severe arthritis.
arthritisB cellscollagen type IImonoclonal antibodiesT cells
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Introduction
Collagen-induced arthritis (CIA) is a widely used animal model for rheumatoid arthritis (RA). Immunization with native collagen type II (CII) in adjuvant induces autoimmune polyarthritis in susceptible rodents and primates [1]. The separate roles of T cells and B cells in both the initial and the progression phases of arthritis in this model are still undefined. Clearly, immunization with heterologous CII activates both CII-reactive T cells and B cells. The T cell response is dominated by reactivity to CII used for immunization, and T cells do not readily cross-react with mouse CII [2]. In contrast, B cells produce high levels of autoreactive and arthritogenic IgG antibodies reactive with both heterologous and homologous CII. The most likely scenario is that the heteroreactive T cells give help to autoreactive B cells that cross-react with mouse CII. Molecular identification of the relevant epitopes supports this interpretation because there is a critical difference in the T cell epitope but not in the major B cell epitopes between mouse CII and heterologous CII. Furthermore, depletion of T cells with anti-CD4 or anti-T-cell receptor (anti-TCR) antibodies is more effective if given before immunization than if given afterwards [3,4]. Finally, severe arthritis is readily induced with anti-CII antibodies [5], whereas transfer with T cells induces only synovitis and not clinical arthritis [6].
However, it is unlikely that CIA pathogenesis can be reduced to mediation by anti-CII antibodies alone. The question is whether autoreactive T cells might have an additional role in CIA, in particular whether they have a role in the further progression of arthritis and during the chronic relapsing disease course that follows the initial arthritis in some mouse strains. This possibility has also been highlighted by the finding that many heteroreactive T cells are most probably potentially autoreactive to CII in vivo, because a major difference is the binding of the peptide to the MHC rather than interaction with TCR [2,7]. The difference between the mouse and the heterologous immunodominant peptide is dependent on differences in binding to the MHC class II molecule Aq. Thus, they recognize the same peptide but different densities of the peptide are presented depending on whether the CII is of mouse or of heterologous origin. Interestingly, immunization with mouse CII induces arthritis in a smaller number of mice but gives a more chronic disease course than immunization with heterologous CII [8,9]. Furthermore, in the mutated mouse collagen (MMC) mouse, which expresses a mutated CII with the heterologous CII – namely mutated at position 266, changing Asp to Glu – the heterologous CII is expressed in the joints. In this mouse T cells are partly tolerized and the development of arthritis is differently genetically controlled [10,11].
The development of arthritis after injection of collagen antibodies (collagen-antibody-induced arthritis; CAIA) is thus likely to be different from the development of arthritis in CIA, although the resulting clinical arthritis shares many common characteristics [5]. CAIA is known to develop independently of MHC alleles, whereas CIA is crucially dependent on MHC alleles, with the Aq molecule as one of the most susceptible alleles. This suggests that CAIA develops independently of MHC-restricted T cells, and thereby also independently of T cell-dependent B cells. To confirm this assumption directly we investigated mice deficient in B cells and T cells on backgrounds susceptible to CIA. Interestingly, such mice not only developed CAIA but had a more severe disease, suggesting that these cells have a modifying role in this model. We also readdressed the role of transferred T cells by using a T cell line reactive with the major CII epitope 260–270 but with oligoclonal reactivity to the various post-translational modifications. As expected, these T cells could not induce clinical arthritis in either wild-type or MMC mice. However, the transferred T cells enhanced the CII-antibody-induced arthritis into a more prolonged disease course.
Materials and methods
Animals
Male B10.Q and QD ([B10.Q × DBA/1]F1) mice at 4–6 months of age were used in the present study. The B10.Q strain was obtained from Professor Jan Klein (Tübingen, Germany), and DBA/1 mice were from Jackson Laboratories (Ban Harbor, ME, USA). B cell-deficient mice (μMT mice kindly provided by Dr Werner Muller [Cologne, Germany]) on the (C57Bl/6 × 129)F1 background were backcrossed to B10.Q background (12n) and T cell-deficient mice (lacking αβ T cells as a result of targeted germline mutation in their TCRβ gene, obtained from Jackson Laboratories) on the (C57Bl/6 × 129)F1 background were backcrossed to B10.Q background (6n). To obtain mice deficient in both B and T cells, heterozygous female mice deficient in B cells and T cells were crossed with doubly deficient males, and offspring were investigated for the absence of B cells and T cells by cytofluorimetric analysis. Blood cells were stained with anti-CD45Ra (B220 coupled to fluorescein isothiocyanate) and anti-TcR (145-2C11 coupled to phycoerythrin) before analysis. MMC transgenic mice (previously named MMC-1), which originated on the C3H.Q background as described previously [10], were backcrossed for eight generations onto the B10.Q background. The transgene MMC is a mutated mouse CII gene in which position 266 has a been changed from aspartic acid (D) to glutamic acid (E), thereby expressing the rat CII260–270 epitope in a CII-restricted fashion. All mice were kept in a conventional but barrier animal facility (as defined in ) with a climate-controlled environment having 12 hours light/12 hours dark cycles in polystyrene cages containing wood shavings; the mice were fed with standard rodent chow and water ad libitum. All animal experiments had been approved by the local animal welfare authorities.
CII-specific monoclonal antibodies
The CII-specific hybridomas were generated and characterized as described in detail elsewhere [12-14]. From the panel of monoclonal antibodies generated, a combination of an IgG2b antibody of the clone M2139 binding to the J1 epitope (amino acids 551–564) and an IgG2a antibody of the clone CIIC1 binding to the C1I epitope (amino acids 358–363) was found to be more arthritogenic [5], whereas CIIF4 monoclonal antibody binding to F4 epitope (amino acids 926–936) was found to be inhibitory [15]. Recent studies in vitro also emphasize that these arthritogenic monoclonal antibodies M2139 and CIIC1 suppressed the self-assembly of CII into fibrils, whereas CIIF4 was found to be inert [16]. Figure 1 illustrates the B cell and T cell epitopes present in the CII α-chain recognized by the monoclonal antibodies and the T cell line used in this study. Monoclonal antibodies were generated as culture supernatants and purified by affinity chromatography with γ-bind plus affinity gel matrix (Pharmacia, Uppsala, Sweden). The IgG content was determined by freeze-drying. The antibody solutions were filter-sterilized using syringe filters with a pore size of 0.2 μm (Dynagard; Spectrum Laboratories, CA, USA), aliquoted and stored at – 70°C until use. The amount of endotoxin in the antibody solutions prepared was found to be in the range 0.02–0.08 EU/mg of protein as analysed with the Limulus amebocyte lysate (Pyrochrome) method (Cape Cod Inc., Falmouth, MA, USA).
Passive transfer of antibodies
The cocktail of M2139 and C1 monoclonal antibodies was prepared by mixing equal concentrations of each of the sterile filtered antibody solutions to get a final amount of 9 mg. Mice were injected intravenously twice with 0.25–0.4 ml of antibody solution, with a minimum interval of 3 hours. As a control, groups of mice received equal volumes of PBS. On day 5, lipopolysaccharide (25 μg per mouse) was injected intraperitoneally in all mice. A pair of irrelevant antibodies of the same subclass (mouse anti-human HLA-DRα, IgG2a [L243] and mouse anti-human parathyroid epithelial cells, IgG2b [G11]) did not induce arthritis in the most susceptible strain, BALB/c mice [5].
Characteristics of CII-specific T cell line
A T cell line specific for rat CII was established as described previously [17]. In brief, draining lymph nodes from rat CII-immunized QD mice (on day 8) were stimulated in vitro with rat CII for 4 days. These cells were allowed to rest for a week in the presence of interleukin-2 (IL-2) without antigen-presenting cells. T cells were subsequently re-stimulated with irradiated (3000 rad) syngenic splenocytes and rat CII for 3 days (5 × 105 T cells/ml, 5 × 106 antigen-presenting cells/ml, 10 μg/ml rat CII) followed by 2 weeks of resting in medium containing IL-2. At the time of re-stimulation, an aliquot of the cell line was tested for antigen specificity. Lathyritic CII was used for the first in vitro re-stimulation, to avoid contamination of pepsin-reactive T cells. The cell line responded towards denatured CII, the non-modified CII 256–270 peptide and the glycosylated CII 256–270 peptide with proliferation and interferon-γ (IFN-γ) production (Fig. 2), but no response towards pepsin was observed. IFN-γ was measured by enzyme-linked immunosorbent assay as described previously [18].
Arthritis development
Development of clinical arthritis was followed by means of visual scoring of the mice. Mice were examined daily or on alternate days for arthritis development until the end of the experiment. Arthritis was scored with an extended scoring protocol ranging from 1 to 15 for each paw, with a maximum score of 60 per mouse, based on the number of inflamed joints in each paw, inflammation being defined by swelling and redness. Each arthritic toe and knuckle was scored as 1, with a maximum of 10 per paw, and an arthritic ankle or mid-paw was given a score of 5.
Statistics
Arthritis incidence and severity were analysed by χ2 analysis and the Mann–Whitney U-test respectively. P ≤ 0.05 was considered as significant.
Results
Antibody-mediated arthritis in mice deficient in B and/or T cells
To understand the role of B and T cells in antibody-mediated inflammation, mice deficient in either B cells or T cells or both were injected with a combination of two different monoclonal antibodies against CII. The antibodies have been shown to bind to cartilage surfaces shortly after intravenous injection [19] and the epitopes recognized are depicted in Fig. 1. As shown in Fig. 3a, most of the B cell-deficient (71%) and T cell-deficient (87%) mice developed severe arthritis; 50% of the mice deficient in both B and T cells also developed arthritis, whereas only 25% of littermate controls developed the disease (B cell-deficient versus controls, cumulative incidence P ≤ 0.0354; T cell-deficient versus controls, cumulative incidence P ≤ 0.0117). Mice deficient in either B or T cells developed more severe arthritis than mice deficient in both populations or than the control littermates (Fig. 3b); however, the difference in arthritis severity between the groups on different days was not significant. These data show that neither T cells nor B cells are necessary for CAIA development. Furthermore, the observed enhancement in the frequency of arthritis in the T cell-deficient mice and the B cell-deficient mice suggest that these cells might play regulatory roles in the initiation of disease.
Effect of T cells transfer on CAIA
To ascertain whether a transfer of CII-specific T cells after antibody injection induced more susceptibility or prolonged the disease period, we established a rat CII-specific T cell line. The line was established from rat CII-immunized QD mice and re-stimulated in vitro four or five times with rat CII before transfer. The established T cell line was Aq-restricted and oligoclonal because it responded to both the non-modified and hydroxylated, as well as the glycosylated, versions of the major CII peptide 260–270 containing various post-translational modifications at the major T cell recognition site on lysine 264 (Figs 1 and 2). The strongest reactivity was seen to the galactosylated peptide, but the hydroxylated peptide also mounted a strong response. Interestingly, the T cell line cross-reacted to the glycosylated mouse peptide, and the lower reactivity to the mouse glycopeptide than to the rat glycopeptide is most probably dependent on both the lower affinity of the mouse peptide for Aq and also a different reactivity of the clonally distinct glycopeptide-reactive T cells [7].
To investigate the role of T cells in the acute effector stage of clinical arthritis, newly activated T cells were injected into QD mice intravenously 1 day after the antibody transfer. As expected, injection of the antibodies alone was sufficient to induce arthritis, but co-transfer of T cells did not enhance the initiation of arthritis (Fig. 4). However, transfer of both antibodies and T cells did result in persistent disease activity. As the T cell line was mainly considered as heteroreactive when transferred into wild-type mice (Fig. 2), co-transfer of T cells and antibodies was also performed in MMC mice (which express the rat CII epitope in cartilage) to see whether the presence of truly autoreactive T cells could have a different effect on the acute phase of arthritis. However, T cells again did not affect the initiation phase of the disease; instead, the effect was noted in the more chronic phase of the disease. Still, co-transfer of T cells into MMC mice resulted in an even more pronounced and significant progression of arthritis than in mice that had antibodies transferred. In contrast, wild-type (MMC-negative) mice that had both T cells and antibodies transferred did not show a significant difference from antibody-transferred mice (Fig. 4). Furthermore, an ovalbumin-specific T cell line failed to enhance and perpetuate the arthritis induced by anti-CII antibodies (data not shown).
Discussion
As we show in the present study, anti-CII monoclonal antibodies are capable of initiating disease independently of B and T cells during the effector phase of arthritis. This is not an unexpected finding because CAIA is induced in naive mice by preformed anti-CII antibodies. Interestingly, however, immune cells might have a regulatory role in CAIA because mice deficient in both T cells and B cells are more susceptible to arthritis than their control littermates. There are several possible explanations for this observation. Clearly, B cells could be regulatory owing to the secretion of a cytokine such as IL-10 [20], the expression of inhibitory receptors such as FcgRIIb [21] or the secretion of regulatory antibodies such as anti-CII antibodies [15,22]. Similarly, there are several ways in which T cells might be regulatory in an effector state like this: for example, they might control bone destruction through interaction with the osteoprotegrin system [23] or through the regulation of cytokines such as IFN-β, tumour necrosis factor-α or IL-4 [24-26]. However, a surprising finding was that mice deficient in both cell types were not as susceptible as the respective single-cell deficient mice. In the doubly deficient mice a complete absence of the adaptive immune response could have led to a more predominant role for the innate immune system in the regulation of the antibody-mediated inflammatory response. In addition, we have shown here that already activated CII-reactive T cells reactive to glycosylated CII could prolong the disease initiated by antibodies, a finding that is highly relevant for comparison with the CIA model.
As in RA, susceptibility to CIA is linked to the expression of certain class II MHC alleles, explaining the crucial role depicted to T cells. The predominant role of T cells in CIA development was demonstrated by using anti-CD4 or anti-TCRαβ monoclonal antibodies and T cell-deficient mice [3,4,27]. Mice deficient in the co-stimulatory molecule CD28 were found to be resistant to CIA [28]. Similarly, administration of CTLA4Ig at the time of immunization prevented the development of CIA [29]. These studies demonstrate the importance of T cell activation in CIA pathogenesis. Depletion of CD4+ T cells has a major influence during the priming phase of arthritis [3] and suppressed the adoptive transfer of disease to severe combined immunodeficient mice using spleen cells from CII-immunized mice [30]. Partial protection of CD4-deficient B10.Q mice and significantly reduced incidence in CD8-deficient mice from CIA suggested an initiating role for the T cells during the priming phase of CIA [31]. However, T cell reactivity alone could not explain the disease pathology in CIA. Transfer of synovitis but not clinical arthritis using CII-specific T cells has previously been shown. In contrast, the high incidence of arthritis induced by native but not denatured collagen indicated the importance of B cells in CIA. It has been shown that mice pre-sensitized with heat-denatured collagen developed progressive arthritis after the transfer of anti-CII antibodies. In addition, adoptive transfer of lymphoid cells together with antibody in the T cell-depleted mice was shown to induce arthritis, and the effector cells were identified as Thy-1+ and L3T4+Lyt-2- [32].
The recognition of CII by T cells is critical to the establishment of autoimmune arthritis in CIA. However, it is debatable whether T cells are capable of recognizing tissue antigens such as insoluble CII in the cartilage tissue. It therefore becomes all the more important to understand the role of antigen-specific T cells in arthritis pathogenesis. Antigen-specific T cells might have important roles either during the initiation phase of arthritis or in the perpetuation and exacerbation of the disease after the onset, or they might simply maintain immunity to CII and perpetuate antibody production. Results from the present study demonstrate that CII-specific T cells could have a role in the perpetuation and exacerbation of already established disease rather than having any direct influence on the initiation phase of arthritis.
It is possible that the pro-inflammatory cytokines induced and/or secreted by the co-transferred CII-specific cells could provide a constant cytokine milieu in or near the joints for exacerbating the events induced by the formation of collagen–IgG immune complexes. It should also be noted that the ovalbumin-specific T cell line failed to enhance and perpetuate the arthritis induced by anti-CII antibodies. With the use of CII-specific monoclonal antibodies, it has been shown that IL-1 and tumour necrosis factor-α are the important cytokines for disease development [33], similar to anti-glucose-6-phosphate isomerase antibody-induced disease [34]. The observed enhancement of arthritis in the T cell and B cell singly deficient mice also suggests that these cells might have regulatory roles in the initiation of disease by modulating the cytokine environment. Despite a prolongation of arthritis, co-transfer of the CII-specific T cell line with the monoclonal antibodies did not alter the acute phase of antibody-mediated disease into a chronic disease course, suggesting the importance of other cellular mediators in the pathogenesis of arthritis. However, experiments to understand the factor(s) and cells involved during the progression of arthritis from the initiation stage will provide tools for effective intervention in arthritis progression in patients with RA.
Conclusions
We demonstrated that anti-CII monoclonal antibodies are capable of initiating arthritis independently of B and T cells during the effector phase of arthritis. Already activated CII-reactive T cells, especially reactive to glycosylated CII, could prolong the disease initiated by antibodies, a finding that is highly relevant for comparison with the CIA model. Experiments to understand the factor(s) and cells involved during the progression of arthritis from the initiation stage could therefore provide tools for effective intervention in arthritis progression in patients with RA.
Competing interests
None declared.
Abbreviations
CAIA = collagen-antibody-induced arthritis; CIA = collagen-induced arthritis; CII = type II collagen; IFN-γ = interferon-γ ; IL = interleukin; MMC = mutated mouse collagen; RA = rheumatoid arthritis; TCR = T cell receptor.
Acknowledgements
We are grateful to Carlos Palestro for taking excellent care of the mice. We also thank the Crafoord, the Gustav V, the Kock and Österlund Foundations, the Swedish Association against Rheumatism, the Science Research Council and the Swedish Foundation for Strategic Research.
Figures and Tables
Figure 1 Type II collagen (CII)-specific B and T cell epitopes. Illustration of T (residues 260–267) and B (C1I, residues 358–363; J1, residues 551–564) epitopes present in the triple-helical form of the collagen type II recognized by the monoclonal antibodies and the T cells used in this study. As indicated, mouse CII differs from rat CII in position 266. Aspartic acid (D) in mCII is replaced by glutamic acid (E) in rCII. Major post-translational modifications in the CII peptide 260–267 occur in lysine at position 264.
Figure 2 Characteristics of the type II collagen (CII)-specific T cell line. CII-specific T cell line QDHT during passage 5 was used in this study. An aliquot of the cell line used for transfer was tested for proliferation (a) and interferon-γ secretion (b) after stimulation with different peptides. Mouse and rat CII (mK and rK, respectively), hydroxylated rat CII (rHyK), denatured rat CII (dCII) and mouse and rat galactosylated CII (mGalHyK and rGalHyK, respectively) peptides were used. Rat CII was heat denatured at 50°C for 20 min. Results are representative of several experiments performed with this cell line.
Figure 3 Collagen-antibody-induced arthritis in mice deficient in B cells and T cells. Frequency (a) and severity (b) of arthritis in groups (n = 6–14) of B10.Q mice deficient in B cells (B KO), T cells (T KO), in both B and T cells (B KO+T KO) and wild-type littermate controls (WT). Mice were injected intravenously with 9 mg of monoclonal antibody cocktail on day 0. Another set of animals (n = 4–8) from each group were injected with PBS on day 0 as control. All the mice received lipopolysaccharide (25 μg per mouse) intraperitoneally on day 5. All the animals were scored for arthritis up to 1 month and were included in the calculations. None of the control mice developed arthritis. Mice deficient in B cells or T cells were compared with WT mice for statistical calculations. There was no significant difference in arthritis frequency and severity between B cell-deficient and T cell-deficient mice. The error bars in (b) indicate SEM.
Figure 4 Role of co-transferred type II collagen (CII)-specific T cells in collagen-antibody-induced arthritis. Incidence (a) and severity (b) of arthritis in different groups of age-matched male mutated mouse collagen (MMC)-positive and MMC-negative littermates (n = 7–11) that were injected with either monoclonal antibodies (day 0), T cells (107, day 1) or antibodies and T cells. All mice were monitored for the development of arthritis for 38 days and were included in the calculations. MMC-positive mice injected with antibodies were compared with mice co-transferred with antibodies and T cells. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.005. The error bars in (b) indicate SEM.
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| 15535832 | PMC1064861 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 23; 6(6):R544-R550 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1217 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar12211553583010.1186/ar1221Research ArticleUse of HLA-B27 tetramers to identify low-frequency antigen-specific T cells in Chlamydia-triggered reactive arthritis Appel Heiner [email protected] Wolfgang [email protected] Maren [email protected] Peihua [email protected] Stefanie 1Kollnberger Simon [email protected] Andreas [email protected] Paul [email protected] Joachim [email protected] Charite Berlin, Campus Benjamin Franklin, Department for Gastroenterology, Infectiology and Rheumatology, Berlin, Germany2 Deutsches Rheumaforschungszentrum Berlin, Germany3 MRC HIU, Institute of Molecular Medicine, John Radcliffe Hospital, Oxford, UK2004 23 9 2004 6 6 R521 R534 7 4 2004 13 5 2004 8 6 2004 2 7 2004 Copyright © 2004 Appel et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Reports of the use of HLA-B27/peptide tetrameric complexes to study peptide-specific CD8+ T cells in HLA-B27+-related diseases are rare. To establish HLA-B27 tetramers we first compared the function of HLA-B27 tetramers with HLA-A2 tetramers by using viral epitopes. HLA-B27 and HLA-A2 tetramers loaded with immunodominant peptides from Epstein–Barr virus were generated with comparable yields and both molecules detected antigen-specific CD8+ T cells. The application of HLA-B27 tetramers in HLA-B27-related diseases was performed with nine recently described Chlamydia-derived peptides in synovial fluid and peripheral blood, to examine the CD8+ T cell response against Chlamydia trachomatis antigens in nine patients with Chlamydia-triggered reactive arthritis (Ct-ReA). Four of six HLA-B27+ Ct-ReA patients had specific synovial T cell binding to at least one HLA-B27/Chlamydia peptide tetramer. The HLA-B27/Chlamydia peptide 195 tetramer bound to synovial T cells from three of six patients and HLA-B27/Chlamydia peptide 133 tetramer to synovial T cells from two patients. However, the frequency of these cells was low (0.02–0.09%). Moreover, we demonstrate two methods to generate HLA-B27-restricted T cell lines. First, HLA-B27 tetramers and magnetic beads were used to sort antigen-specific CD8+ T cells. Second, Chlamydia-infected dendritic cells were used to stimulate CD8+ T cells ex vivo. Highly pure CD8 T cell lines could be generated ex vivo by magnetic sorting by using HLA-B27 tetramers loaded with an EBV peptide. The frequency of Chlamydia-specific, HLA-B27 tetramer-binding CD8+ T cells could be increased by stimulating CD8+ T cells ex vivo with Chlamydia-infected dendritic cells. We conclude that HLA-B27 tetramers are a useful tool for the detection and expansion of HLA-B27-restricted CD8+ T cells. T cells specific for one or more of three Chlamydia-derived peptides were found at low frequency in synovial fluid from HLA-B27+ patients with Ct-ReA. These cells can be expanded ex vivo, suggesting that they are immunologically functional.
HLA-B27T cellstetramersreactive arthritis
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Introduction
Chlamydia-triggered reactive arthritis (Ct-ReA) is strongly associated with HLA-B27 like other spondylarthropathies, and especially ankylosing spondylitis [1,2]. ReA occurs 1 to 4 weeks after urogenital infection with Chlamydia trachomatis or gastroenteral infection with enterobacteria such as Yersinia enterocolitica [3]. After acute onset, most patients have a self-limiting course, but up to 20% suffer from a disease duration of more than 1 year [4]. Of HLA-B27+-reactive arthritis patients, 20–40% move on to ankylosing spondylitis after 10–20 years, suggesting that the ReA-associated bacteria can cause ankylosing spondylitis [5] and that immune mechanisms triggering the disease are induced by T cell responses to microbial antigens. The main hypothesis advanced for the association between HLA-B27 and spondylarthropathies is the arthritogenic peptide theory. It states that some HLA-B27 subtype alleles, owing to their unique amino acid residues, bind a specific arthritogenic peptide that is recognized by CD8+ T cells [6-9]. Recently we and several other groups have reported on Chlamydia-specific CD8+ T cells capable of lysing target cells primed with Chlamydia antigens [10-12]. CD8+ T cell responses in spondylarthropathies other than Ct-ReA have also been described [13-15].
Recently a new method for antigen-specific T cell recognition has been established by using multimerized MHC/peptide molecules [16]. These molecules are called tetramers because they contain four soluble and biotinylated MHC molecules linked to labelled streptavidin that specifically bind with high avidity to T cell receptors. In comparison with intracellular cytokine staining, the major advantage of tetramer technology is the identification of antigen-specific T cells independently of their cytokine secretion profile, the possibility of sorting unstimulated T cells and of having a tool for the antigen-specific detection of T cells in experiments in situ [17].
In humans, MHC class I tetramers are widely used, and HLA-A2 tetramers in particular are an important tool in tumour immunology [18]. However, the use of HLA-B27 tetramers in HLA-B27-related diseases is rare [10,19]. The rarity of their use might be related to heavy protein aggregation during the refolding procedure of the recombinant HLA-B27 monomer [19,20]. To determine optimised conditions for the refolding procedure of soluble HLA-B27 monomers with bacteria-derived epitopes we first used HLA-B27 tetramers with a well-described HLA-B27-restricted viral epitope from Epstein–Barr virus (EBV). We analysed the refolding rate of HLA-B27 monomers and compared our results with refolding gained with an HLA-A2 molecule loaded with a viral epitope from EBV [21]. On the basis of these results we applied the HLA-B27 tetramer technology to specify the HLA-B27-restricted CD8+ T cell response to Chlamydia-derived peptides in patients with Ct-ReA.
This is the first report of a systematic use of HLA-B27 tetramers in humans in an HLA-B27-related disease.
Methods
Patients
We analysed six HLA-B27+ and three HLA-B27- patients with ReA after infection with Chlamydia trachomatis (Table 1). We diagnosed ReA if patients had a prior urogenital infection, which was confirmed by the detection of Chlamydia trachomatis in the morning urine by polymerase chain reaction. An additional criterion was the detection of Chlamydia-specific antibodies [6] at the beginning of the disease or highest synovial T cell proliferation against Chlamydia trachomatis [22] in proliferation assays with whole Chlamydia antigen. The results were compared with tetramer staining in six HLA-B27+ healthy blood donors. We also examined synovial T cells from three HLA-B27+ patients with ReA after gastroenteritis and having highest synovial proliferation against enterobacteria. We also tested the synovial fluid of three patients with rheumatoid arthritis. In addition we used HLA-B27+ and HLA-A2+ blood donors with previous EBV infection for experiments comparing HLA-B27 and HLA-A2 tetramers.
The ethical committee of the Benjamin Franklin Medical Centre gave ethical approval for this study.
Search for peptide binding affinity
The quantification of HLA-B27 binding affinity was conducted with two different programs that analyse HLA-peptide binding motifs, one called SYFPEITHI described by Rammensee and colleagues [23] and the other called BioInformatics and Molecular Analysis Section (BIMAS; ).
Peptide synthesis
Nonamer peptides were synthesized by standard 9-fluorenyl-methyloxy-carbonyl solid-phase synthesis methods on a Syro-Synthesizer (MultiSyn Tech, Witten, Germany), purified by high-performance liquid chromatography (Shimadzu LC-10; Shimadzu Scientific Instruments, Duisburg, Germany) and identified by mass spectroscopy (LCQ, ion trap; Thermoquest, Eberbach, Germany). The purity of the peptides was more than 95%. Peptides were dissolved in dimethyl sulphoxide. For T cell stimulation and fluorescence-activated cell sorting (FACS) analysis of intracellular cytokine staining, the peptides were further diluted with serum-free medium at a concentration of 5 mg/ml and frozen at -80°C.
FACS analysis of antigen-specific T cells with HLA-B27 tetramers
HLA-B27 tetramers were generated as described previously [19], with some modifications. The expression vector pLM1-HLA-B27 was modified by tagging with the BirA recognition sequence as described previously and by mutating the cysteine residue at position 67 to serine. After being refolded, the recombinant protein was concentrated and centrifuged at 13,000 rpm (16,060g; Haereus Biofuge Pico; Kendro Laboratories, Langenselbold, Germany) followed by biotinylation and gel filtration with a Superose 12 column (Pharmacia) on an Äkta Basic system (Pharmacia). Correct folding and biotinylation were analysed by gel filtration (Äkta Basic, Pharmacia) and gel electrophoresis (Bio-Rad). Tetramers were generated by adding phycoerythrin (PE)-labelled streptavidin (Molecular Probes) at a ratio of 1.5:1. We generated HLA-B27 tetramers with the EBV EBNA peptide (residues 258–266) [24]. For the detection of Chlamydia-peptide-specific CD8+ T cells we used the previously described immunodominant peptides 8, 68, 80, 131, 133, 138, 144, 145, 146, 194, 195 and 196 [10] (Table 2). Peptides 144 and 194 caused heavy aggregation during refolding procedure and were excluded from tetramer staining; peptide 146 was excluded because of high background staining in more than 50% of the patients.
HLA-A2 monomers with the EBV peptide [21] were generated with an HLA-A2 heavy chain (gift from Dr KH Lee, Berlin, Germany) with the same protocol.
For FACS analysis, frozen mononuclear cells (MNCs) from synovial fluid or peripheral blood were incubated with tetramer and PerCP-labelled anti-human CD8 antibody (BD Pharmingen, San Diego, USA) in parallel for 30 min at room temperature (20°C) followed by washing twice with phosphate-buffered saline (PBS)/2% bovine serum albumin (BSA) and incubation with Cy5-labelled anti-human CD3 antibody for 30 min at room temperature. Cells were washed twice in PBS/2% BSA and resuspended in Annexin V buffer (Molecular Probes) and 2.5 μl of Alexa 488-labelled Annexin V (Molecular Probes) was added. CD8+ and tetramer-positive T cells were analysed after gates were set on CD3+ and Annexin V-negative cells. Depending on the availability of additional synovial lymphocytes we repeated the staining experiments, which was true for the synovial fluid of patient no. 6.
T cell lines from magnetic activated cell sorting (MACS)-sorted HLA-B27 tetramer-positive CD8+ T cells
Peripheral MNCs were incubated for 30 min with Cy5-labelled anti-CD8 antibody (BD) and 5 μg/ml PE-labelled HLA-B27/EBV EBNA (258–266) tetramer at room temperature. Cells were washed twice and incubated for 15 min at 4°C with anti-PE-labelled MACS beads (Miltenyi) at a ratio of 20 μl of beads to 80 μl of cell suspension. Labelled cells were loaded on an LS MACS column (Miltenyi) and eluted after the column had been washed three times with washing buffer including PBS, EDTA and BSA. MACS-sorted tetramer-positive and CD8+ T cells were further separated by FACS sorting. Sorted cells (1000) were incubated with 500,000 autologous antigen-presenting cells in the presence of 20 U/ml interleukin (IL)-2, 10 ng/ml IL-7 and 10 ng/ml IL-15 added every 3–4 days.
Determination of the refolding rate of recombinant HLA-B27 monomers
The refolding rate of recombinant HLA-B27 monomer was analysed by gel filtration and by determining the relative amount of soluble HLA-B27 monomer eluted at 13.7 ml in comparison with precipitated protein eluted earlier in a Superose 12 column (Pharmacia). An Akta basic system (Pharmacia) was used. The elution profile was analysed by using Unicorn (version 4) software (Pharmacia). Refolding was defined as ++ when more than 75% of proteins loaded on the gel filtration column after refolding, biotinylation and sharp centrifugation was soluble HLA-B27 monomer molecule; + for more than 50% soluble HLA-B27 monomer, (+) for more than 10% soluble HLA-B27 monomer, and - for less than 10% soluble HLA-B27 monomer (Table 2).
FACS analysis of intracellular cytokine staining
Intracellular cytokine staining was used after antigen-specific T cell stimulation. Synovial MNCs and peripheral MNCs were stimulated for 6 hours in 1 ml of culture medium with anti-CD28 antibody (1 μg/ml) plus single peptides (10 μg/ml) or without antigenic peptide as a negative control. Brefeldin A was added after 2 hours to stop the stimulation, and cells were harvested after a further 4 hours and then stained with 5 μg/ml anti-CD69-PE antibody (BD Pharmingen) and 1 μg/ml anti-CD8-PerCP (BD Pharmingen). Cells were then fixed in 2% formalin and resuspended in saponin buffer, followed by incubation with 1 μg/ml Cy5-conjugated anti-human interferon-γ antibody (IFN-γ; BD). Gated CD8+ T cells that were positive for early activation marker CD69 and for intracellular IFN-γ were counted as antigen-specific. Analysis was performed with a BD Biosciences FACScan flow cytometer with CellQuest software.
Infection of peripheral-blood-derived dendritic cells in vitro with viable Chlamydia trachomatis
CD14+ cells from peripheral blood were incubated for 1 hour with anti-CD14-conjugated magnetic beads (Miltenyi) and sorted by MACS. The purity of separated cells was confirmed by FACS analysis. Cells (500,000) were cultured for 7 days in 24-well plates at 37°C at 5% CO2 in 1 ml of RPMI culture medium supplemented with 10% fetal calf serum, 2 mM L-glutamine and 50 ng/ml granulocyte/macrophage colony-stimulating factor and 10 ng/ml IL-4 to induce transformation to dendritic cells (DCs). Cells were washed and harvested and incubated for 24 hours with infectious elementary bodies of Chlamydia trachomatis at a ratio of 1:50. DCs were analysed by FACS with the use of anti-CD80, anti-CD86, anti-HLA-DR, anti-CD14 (BD Pharmingen) and anti-Chlamydia trachomatis lipopolysaccharide antibodies (Dako) before and after infection with viable Chlamydia trachomatis.
Expansion of Chlamydia-specific CD8+ T cells in vitro with Chlamydia-infected peripheral-blood-derived dendritic cells
We stimulated CD8+ T cells from peripheral blood with Chlamydia trachomatis-infected peripheral-blood-derived DCs at a ratio of 50:1 in RPMI culture medium supplemented with 10% fetal calf serum, 2 mM L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. Recombinant IL-7 (10 ng/ml) and IL-15 (10 ng/ml) were added on both days 2 and 7. T cells were analysed by FACS on day 14.
Results
MHC class I tetramer staining with HLA-A2/EBV peptide-specific and HLA-B27/EBV peptide-specific tetramers
To determine optimal conditions for the refolding procedure of soluble HLA-B27 monomers we used HLA-A2 tetramers and HLA-B27 tetramers with well-described HLA-B27 and HLA-A2 restricted viral epitopes from EBV. The binding scores of the two immunodominant EBV peptides to the HLA-A2 [21] (score 29) and HLA-B27 [24] (score 28) receptor were almost identical in the SYFPEITHI program. By generating the HLA-B27 tetramer (Fig. 1a,1c; lanes 1 and 2) and the HLA-A2 tetramer (Fig. 1b,1c; lanes 3 and 4) the percentages of protein aggregates eluted between 7 and 13 ml in gel filtration, and the refolded monomer eluted at 13.7 ml in gel filtration, were also similar. The large peak at 16 ml most probably contained reagents from the biotinylation reaction because we did not detect any proteins with a molecular mass of more than 5 kDa by SDS-PAGE. This peak was excluded when the relative amount of soluble HLA-B27 monomers was estimated. In FACS analysis, tetramer-positive antigen-specific T cells could be detected with both tetramers (Fig. 1d), although HLA-A2 tetramers stained with greater intensity (log 0.8 more) than the HLA-B27. On the basis of these results we generated HLA-B27/Chlamydia peptide tetramers.
Generation of antigen-specific CD8+ T cell lines after MACS sorting of HLA-B27 tetramer-positive T cells
To determine whether tetramer-binding CD8+ T cells could be sorted and further cultured we stained peripheral MNCs with HLA-B27/EBV EBNA (258–266) tetramer. Before MACS, 0.22% of peripheral MNCs were CD8+ and tetramer-positive. After MACS, EBV EBNA (258–266)-specific T cells were enriched to 41.4%. MACS-sorted cells were further separated by FACS sorting and cultured for 4 weeks in the presence of IL-2. The purity of antigen-specific CD8+ was increased to 95.0%, as shown by tetramer staining (Fig. 2a). In parallel we performed intracellular cytokine staining after peptide-specific stimulation of peripheral MNCs and of the tetramer-sorted T cell line after 4 weeks of culture. In comparison with HLA-B27 tetramer staining, only 68.3% of these antigen-specific T cells were detected by intracellular cytokine staining of IFN-γ (Fig. 2b).
Generating HLA-B27/Chlamydia peptide tetramers
The generation of HLA-B27 tetramers with Chlamydia-derived peptides strongly indicated that the yield of refolded and soluble HLA-B27/Chlamydia peptide monomers depended on the binding affinity of the peptide for HLA-B27. Gel-filtration analysis showed that Chlamydia peptide 133 (Table 2; binding score 25 in [23]) (Fig. 3a) induced significantly more protein aggregation, seen by protein elution between 7 and 13 ml, than Chlamydia peptide 8 (Table 2; binding score 26 in [23] but 10,000 in BIMAS) (Fig. 3b). In SDS-PAGE analysis the large quantity of aggregated proteins is also shown by numerous bands of higher molecular mass (Fig. 3a). After the addition of streptavidin, the major band with biotinylated HLA-B27 molecule could be captured to become a tetramer (Fig. 3a,3b; SDS-PAGE). This phenomenon of protein aggregation depending on the affinity between peptide and HLA-B27 could also be observed with the other Chlamydia-derived peptides. The refolding rate of all HLA-B27 tetramers used in this manuscript are summarized in Table 2.
HLA-B27/Chlamydia peptide tetramer staining of synovial T cells
On the basis of our recently identified Chlamydia-derived immunodominant peptides in Ct-ReA [10] we successfully synthesized nine HLA-B27 Chlamydia peptide tetramers and used them to stain MNCs from the synovial fluid of nine patients (six HLA-B27+, three HLA-B27-) with Ct-ReA. Four of the six HLA-B27+ patients had a specific T cell binding to at least one HLA-B27/Chlamydia peptide tetramer.
The results of tetramer staining in all patients are summarized in Table 3; HLA-B27/Chlamydia peptide 195 tetramer bound to the synovial T cells of three (patient nos 2, 3 and 5) of these four patients. Two patients (nos 5 and 6) showed a T cell response to Chlamydia peptide 133 as detected by tetramer staining, and one (patient no. 3) had a T cell response to Chlamydia peptide 68.
The results of three patients are illustrated in Figs 4 and 5. Figure 4a shows that T cells specific for Chlamydia peptides 195 and 68 were detected with HLA-B27/Chlamydia peptide tetramers in patient no. 3: 0.09% of CD8+ T cells were positive for peptide 195 and 0.06% were positive for peptide 68. All other HLA-B27 tetramers with Chlamydia-derived peptides such as peptide 138 were negative (data not shown). In patient no. 2 we detected 0.06% HLA-B27/Chlamydia peptide 195 tetramer-positive T cells (Fig. 4b). All other HLA-B27 tetramers such as HLA-B27/Chlamydia peptide 138 were negative (data not shown). We did not analyse the cytokine secretion profile of CD8+ T cells in response to chlamydial peptides in these two patients. The example of patient no. 6 is shown in Fig. 5a, with 0.02% of HLA-B27/Chlamydia peptide 133 tetramer binding to CD8+ T cells but no binding to any of the other HLA-B27 Chlamydia peptide tetramers. In patient no. 6 we were able to repeat this experiment and obtained a similar result, with 0.02% of tetramer binding to CD8+ T cells.
To confirm the specificity of the T cell response to peptide 133, two further experiments were performed in this patient. First, synovial T cells were expanded by Chlamydia peptide 133-specific T cell stimulation for 1 week ex vivo, which revealed 0.22% tetramer-positive CD8+ T cells (Fig. 5a). Second, when FACS analysis of IFN-γ secretion after peptide-specific stimulation was done in the same patient, only peptide 133 induced this cytokine secretion (Fig. 5b), again confirming the specificity of this response.
We also analysed CD8+ T cells from peripheral blood of patient nos 2, 3, 5 and 6, who were responders when synovial fluid was tested for HLA-B27/Chlamydia peptide binding, but we could not detect any specific binding (data not shown). The HLA-B27- patients with Ct-ReA and all six HLA-B27+ healthy controls had no HLA-B27-restricted, Chlamydia-peptide-specific T cell response (data not shown). Tetramer staining of synovial T cells from three HLA-B27+ patients with enterobacteria-triggered ReA and from three patients with rheumatoid arthritis revealed no specific staining of CD8+ T cells with 0–0.01% tetramer binding to CD8+ T cells.
Expansion of Chlamydia-specific CD8+ T cells after stimulation with Chlamydia-infected dendritic cells
Because the frequency of Chlamydia-specific CD8+ T cells in these patients is low in synovial fluid and absent in peripheral blood with both methods (tetramer staining and intracellular cytokine staining), we investigated whether enrichment of these cells could be achieved by short-term stimulation with autologous Chlamydia-infected DCs. By doing this we intended to obtain a higher frequency of tetramer-positive CD8+ T cells, to underline the specificity of tetramer staining.
We generated DCs from CD14+ monocytes from peripheral blood of patient no. 5; they were separated by MACS first. After 7 days of cultivation in vitro, the cells turned into DCs, as indicated by the loss of CD14 receptors and the upregulation of HLA-DR, CD80 and CD86 receptors (data not shown). We infected these DCs with viable Chlamydia trachomatis and confirmed infection by using an anti-Chlamydia trachomatis lipopolysaccharide antibody and by quantification of Chlamydia-positive cells by FACS analysis (data not shown). We revealed at least 41.3% Chlamydia-infected DCs.
Peripheral MNCs from the same patient were stimulated with these Chlamydia-infected DCs for 2 weeks in the presence of IL-7 and IL-15. Subsequently, FACS analysis for intracellular cytokine staining for IFN-γ performed after restimulation of this cell line with Chlamydia-infected DCs revealed 0.11% IFN-γ-secreting CD8+ T cells, and stimulation with different peptide pools including the nine relevant peptides revealed between 0.07% and 0.21% antigen-specific IFN-γ-secreting CD8+ T cells (data not shown). When the cell line was analysed with HLA-B27/Chlamydia peptide tetramers we found a similar quantity of expanded CD8+ T cells with significant tetramer staining of CD8+ T cells specific for Chlamydia peptides 8 (0.09%), 68 (0.10%), 133 (0.17%), 138 (0.08%), 195 (0.23%) and 196 (0.06%) (Fig. 6) and a weaker response to the other Chlamydia-derived peptides. HLA-B27 tetramer staining with peptides 133 and 195 showed some unusual bright staining, which was also frequently observed with the HLA-B27/EBV EBNA (258–266) tetramer and might have been caused by aggregated tetramers. Staining of untreated peripheral MNCs from the same patient did not reveal any tetramer binding (data not shown); staining with an HLA-B27/EBV peptide tetramer was performed as a positive control. We repeated this procedure in an HLA-B27- patient with Ct-ReA (Fig. 7) and in an HLA-B27+ healthy blood donor (data not shown). In neither case could we observe staining with any of the HLA-B27/Chlamydia peptide tetramers even after stimulation with Chlamydia-infected DCs (patient no. 9; Fig. 7).
Discussion
The arthritogenic peptide theory states that some HLA-B27 subtype alleles, owing to their unique amino acid residues, bind one or more specific arthritogenic peptides that are recognized by CD8+ T cells [6-9]. To test this theory it is of great importance to establish methods to identify the peptide specificity of such CD8+ T cells in human beings with HLA-B27-associated arthritis. The use of MHC class I tetramers to detect antigen-specific CD8+ T cells is well established [16]. However, surprisingly few publications present data with HLA-B27 tetramers. We have reported preliminary experiments with HLA-B27 tetramers in single patients with Ct-ReA [10]. HLA-B27 tetramers were also used to determine critical T cell receptor binding regions in HLA-B27-restricted T cells specific for an immunodominant peptide from influenza virus [19]. The biochemical features of the protein might be the limiting factor for using this molecule as frequently as other MHC class I molecules such as HLA-A2 tetramers.
During the refolding process of recombinant HLA-B27, which is expressed in inclusion bodies, significant amounts of aggregated proteins occur [19,20]. The free cysteine residue at position 67 in the HLA-B27 α-chain is chemically highly reactive, causing homodimerization and protein aggregation [19,20,25-27]. It was therefore a reasonable strategy to generate HLA-B27 tetramers by substituting serine for cysteine at position 67 [10,19]. The mutated HLA-B27 heavy chain was also used in these experiments. However, even with the mutated HLA-B27 molecule we experienced significant protein aggregation when HLA-B27 molecules were generated with Chlamydia-derived peptides, especially with those with a low binding affinity for HLA-B27. We addressed the question of whether this finding was related to the protocols we used or whether it was specifically related to HLA-B27. We generated an HLA-B27 tetramer with a well-described immunodominant peptide from EBV and compared the results with those for an HLA-A2 molecule also loaded with an immunodominant peptide from EBV having almost the same binding affinity. The refolding rate of both molecules was almost the same, and we obtained comparable results when these molecules were used in FACS analysis. From this we concluded that the use of HLA-B27 tetramers is limited if the binding affinity of a peptide is too low for the molecule to remain stable. We therefore excluded peptides causing heavy protein aggregation and high background staining from further experiments.
Here we have also demonstrated another useful property of HLA-B27 tetramers as a 'proof of principle'. We sorted antigen-specific tetramer-positive CD8+ T cells and generated highly specific T cell lines. After 4 weeks of non-specific stimulation, 95% of T cells were antigen-specific, which could be detected by tetramers but not by intracellular cytokine staining. The latter experiments detected only 68.3% IFN-γ secreting CD8+ T cells after antigen-specific stimulation. These results show clearly that HLA-B27 tetramers have the advantage of detecting antigen-specific T cells independently of their cytokine-secreting profile. Tetramers are also capable of detecting resting antigen-specific T cells, which probably constitute most non-IFN-γ-secreting CD8+ T cells of the T cell line in Fig. 4B (CD69- and IFN-γ-).
Using HLA-B27-restricted Chlamydia peptides with higher binding scores, previously defined as immunodominant in Ct-ReA [10], we have generated tetramers and identified Chlamydia-peptide-specific T cell responses in four of six patients with Ct-ReA. These experiments suggest that Chlamydia peptides 195 and 133 are immunologically important epitopes, because we could detect CD8+ T cells with such specificity in three and two, respectively, out of six HLA-B27+ patients. We identified these antigen-specific T cells at a frequency of 0.02–0.09% in the synovial fluid of these patients, which is concordant with previous results [10]. The low frequency of Chlamydia-peptide-specific CD8+ T cells detected with HLA-B27 tetramers sometimes makes discrimination from non-specific staining difficult. We confirmed our tetramer staining result in one patient by expansion of peptide-specific CD8+ T cells followed by tetramer staining with increased amounts of tetramer-binding CD8+ T cells; even more importantly, peptide-specific CD8+ T cells were also detected by intracellular IFN-γ staining after peptide-specific stimulation. However, for future experiments it would be useful to confirm such findings with antigen-specific T cell expansion as shown here (Figs 2, 5a and 6a) and also in collaboration with other authors [28].
To underline further the specificity of HLA-B27 tetramer staining we performed peptide-specific expansion of CD8+ T cells specific for Chlamydia. For this, we generated Chlamydia-infected DCs, which are assumed to be excellent antigen-presenting cells for both CD4+ and CD8+ T cells [29], for Chlamydia-antigen-specific CD8+ T cell stimulation; we obtained antigen-specific T cell expansion. This Chlamydia-specific CD8+ T cell line showed an increased response to HLA-B27/Chlamydia peptides 8, 68, 133, 138, 195 and 196 tetramers and a weaker response to the other HLA-B27/Chlamydia peptide tetramers. The generation of CD8+ T cell lines with Chlamydia-infected DCs has recently been described, but without defining the MHC restriction and peptide specificity of such T cells [30].
Because we could not detect any Chlamydia-peptide-specific CD8+ T cells from peripheral blood with either method (tetramer staining and intracellular cytokine staining) without prior stimulation, we assume that the frequency of CD8+ T cells with such specificity in the peripheral blood is below the sensitivity of both methods. In contrast, low frequencies of Chlamydia-derived peptide-specific CD8+ T cells in the peripheral blood were observed by another group by using HLA-A2 tetramers [31]. These researchers detected Chlamydia trachomatis major outer membrane protein (MOMP) 258 peptide-specific and MOMP 249 peptide-specific CD8+ T cells in patients with acute urogenital tract infection. They found 0.01–0.2% MOMP-specific CD8+ T cells in the peripheral blood of these individuals with acute infection, who had no clinical symptoms of Ct-ReA.
Conclusion
We conclude that HLA-B27 tetramers are useful tools for the study of HLA-B27/peptide-specific T cells in HLA-B27-associated diseases. Although Chlamydia-specific HLA-B27-restricted CD8 T cells were detected in the synovial fluid of four of six HLA-B27+ patients with Chlamydia-induced reactive arthritis, their frequency was low, arguing against a major role in fighting Chlamydia and in the pathogenesis of arthritis. However, these antigens might be able to induce a cross-reactive T cell response to self-antigens, implying that the 'arthritogenic peptide' is not necessarily identical with the immunodominant peptide that is capable of inducing the T cell response to eliminate the microbe. Nevertheless, their frequency could be significantly expanded after stimulation in vitro with Chlamydia-infected autologous DCs, suggesting that these cells have full replicative capacity.
Competing interests
None declared.
Abbreviations
BIMAS = BioInformatics and Molecular Analysis Section; BSA = bovine serum albumin; Ct-ReA = Chlamydia-triggered reactive arthritis; DC = dendritic cell; EBV = Epstein–Barr virus; FACS = fluorescence-activated cell sorting; IFN-γ = interferon-γ ; IL = interleukin; MACS = magnetic activated cell sorting; MNC = mononuclear cell; MOMP = major outer membrane protein; PBS = phosphate-buffered saline; PE = phycoerythrin.
Acknowledgement
This work was supported by a grant from the Deutsche Forschungsgemeinschaft (Ap 82/2-1 and Ap 82/2-2), by the ARC UK (SK) and by the MRC (PB).
Figures and Tables
Figure 1 Gel filtration and gel electrophoresis of refolded HLA-A2/EBV peptide monomers (a; c, lanes 3 and 4) and HLA-B27/EBV peptide monomers (b; c, lanes 1 and 2). In both experiments the amount of protein aggregation is low, indicated by small amounts of eluted proteins between 7 and 13 ml. The peaks eluted at 13.7 ml contained the soluble MHC monomer. The gel in (c) shows that both eluted monomers are highly purified (lanes 1 and 3) and that most soluble monomers bind to streptavidin if added (lanes 2 and 4). (d) Antigen-specific T cells could be detected with both tetramers, although HLA-A2 tetramers stained with greater intensity (log 0.8 more) than HLA-B27. A280, absorption at 280 nm; asterisks, protein aggregates; SA, streptavidin.
Figure 2 HLA-B27/EBV tetramers and use of magnetic beads to enrich for HLA-B27-restricted tetramer-positive CD8+ T cells. In peripheral blood from an HLA-B27+ blood donor, 0.22% EBV EBNA (258–266) peptide-specific T cells were detected with phycoerythrin (PE)-labelled HLA-B27 tetramer (a) and 0.66% of CD8+ T cells by performing intracellular cytokine staining of IFN-γ-secreting cells (b). After magnetic activated cell sorting with anti-PE magnetic beads, antigen-specific T cells were enriched to 41.4% (a). These cells were further purified by fluorescence-activated cell sorting (not shown) and cultured for 4 weeks. After 4 weeks of culturing in the presence of interleukin-2, 95% of the T cell line were antigen-specific as shown by HLA-B27 tetramer staining (a), whereas only 68.3% of antigen-specific T cells could be identified by intracellular cytokine staining (b).
Figure 3 Gel filtration and gel electrophoresis of refolded HLA-B27/Chlamydia peptide monomers loaded with Chlamydia peptide 133 (a) and with Chlamydia peptide 8 (b). The peaks eluted at 13.7 ml contained the soluble MHC monomer. The amount of refolded HLA-B27 monomer with Chlamydia peptide 8 (b) was higher than that of peptide 133 (a) with less protein aggregation (proteins eluted between 7 and 13 ml), indicating that peptide 8 has a higher binding affinity for HLA-B27. The SDS-PAGE in (a) and (b) shows that both eluted monomers are the major protein in the eluted fraction and that most soluble monomers bind to streptavidin if added. In both experiments the amount of protein aggregation was higher than refolding of HLA-B27 with a viral epitope, indicated by a greater amount of eluted proteins between 7 and 13 ml (Fig. 1). A280, absorption at 280 nm; asterisks, protein aggregates; SA, streptavidin.
Figure 4 HLA-B27/Chlamydia peptide tetramer-binding synovial T cells in patient nos 2 and 3. In patient no. 3, 0.09% Chlamydia peptide 195-specific and 0.06% peptide 68-specific CD8+ T cells were identified. All other HLA-B27 tetramers with Chlamydia-derived peptides such as peptide 138 did not bind synovial T cells in this patient (data not shown). In patient no. 2, 0.06% Chlamydia peptide 195-specific CD8+ T cells were detected by HLA-B27 tetramers. All other HLA-B27 tetramers with Chlamydia-derived peptides such as peptide 138 did not bind synovial T cells in this patient (data not shown). Peripheral blood from a blood donor with HLA-B27/EBV EBNA (258–266)-positive CD8+ T cells was used as a control.
Figure 5 HLA-B27/Chlamydia peptide tetramer staining and intracellular cytokine staining in patient no. 6. When synovial T cells where stained with HLA-B27 Chlamydia peptide tetramers, only HLA-B27/Chlamydia peptide 133 was positive for CD8+ T cells (0.02%). All other HLA-B27 tetramers with Chlamydia peptides did not bind synovial T cells in this patient (not shown). The specificity of this staining could be confirmed by an increased quantity of tetramer-positive T cells after peptide-specific T cell stimulation (0.22%) after 1 week of peptide-specific stimulation loaded on synovial mononuclear cells (a) and Chlamydia peptide 133-specific induction of IFN-γ production in intracellular cytokine staining (b).
Figure 6 Staining of peripheral blood-derived CD8+ T cell lines generated with Chlamydia-infected dendritic cells with nine different HLA-B27/Chlamydia peptide tetramers in patient no. 5. A polyclonal T cell response was observed with a significant amount of T cell expansion specific for Chlamydia peptides 8, 68, 133, 138, 195 and 196, and there was a weaker response to the other Chlamydia-derived peptides.
Figure 7 No tetramer staining of peripheral blood-derived CD8+ T cell lines generated with Chlamydia-infected dendritic cells with nine different HLA-B27/Chlamydia peptide tetramers in the HLA-B27- Chlamydia-triggered reactive arthritis (Ct-ReA) patient no. 9. The same experiment was performed in an HLA-B27- patient with Ct-ReA: even after stimulation with autologous Chlamydia-infected dendritic cells, Chlamydia-peptide-specific CD8+ T cells could not be detected in peripheral blood by HLA-B27/Chlamydia peptide tetramers. Chl, Chlamydia trachomatis.
Table 1 Characteristics of patients
Patient no. B27 Disease Sex Age (years) Disease duration Chlamydia in urine (PCR) Synovial T cell proliferation Antibodies
1 + ReA Male 18 5 months + Chlamydia trachomatis n.d.
2 + ReA Male 20 2 months + Chlamydia trachomatis n.d.
3 + ReA Male 32 5 months + Chlamydia trachomatis +
4 + ReA Male 34 3 months + Chlamydia trachomatis n.d.
5 + ReA Male 20 1 month + n.d. +
6 + ReA Male 26 1 month + Chlamydia trachomatis +
7 - ReA Male 56 1 month + Chlamydia trachomatis n.d.
8 - ReA Male 43 3 months + Chlamydia trachomatis n.d.
9 - ReA Female 32 6 months + Chlamydia trachomatis n.d.
10 + Chronic ReA Male 20 7 years - Enterobacteria n.d.
11 + ReA Male 47 3 weeks - Enterobacteria n.d.
12 + ReA Female 49 6 months - Enterobacteria n.d.
13 n.d. RA Female 67 10 years n.d. n.d. n.d.
14 n.d. RA Female 49 >1 year n.d. n.d. n.d.
15 n.d. RA Female 62 14 years n.d. n.d. n.d.
n.d., not done; PCR, polymerase chain reaction; RA, rheumatoid arthritis; ReA, reactive arthritis.
Table 2 Sequence and binding scores of peptides to HLA-B27 and HLA-A2
Name of peptide Sequence of peptide Binding scorea Binding scoreb Refolding Reference
HLA-B27/EBNA (258–266) (EBV) RRIYDLIEL 28 2000 ++ [24]
HLA-A2/BMLF1 lytic antigen peptide 280–288 (EBV) GLCTLVAML 29 6000 ++ [21]
HLA-B27/Influenza NP 383–391 SRYWAIRTR 26 1500 + [32]
HLA-B27/Chlamydia peptide 8 NRFSVAYML 26 10,000 ++ [10]
HLA-B27/Chlamydia peptide 68 NRAKQVIKL 26 2000 (+) [10]
HLA-B27/Chlamydia peptide 80 IRMFKILPL 26 2000 + [10]
HLA-B27/Chlamydia peptide 131 KRLAETLAL 26 6000 (+) [10]
HLA-B27/Chlamydia peptide 133 IRSSVQNKL 27 2000 (+) [10]
HLA-B27/Chlamydia peptide 138 ARKLLLDNL 26 2000 ++ [10]
HLA-B27/Chlamydia peptide 144 MRDHTITLL 25 2000 - [10]
HLA-B27/Chlamydia peptide 145 DRLALLANL 27 200 + [10]
HLA-B27/Chlamydia peptide 146 YRLLLTRVL 25 600 (+) [10]
HLA-B27/Chlamydia peptide 194 EREQTLNQL 25 200 - [10]
HLA-B27/Chlamydia peptide 195 NRELIQQEL 25 2000 (+) [10]
HLA-B27/Chlamydia peptide 196 ERFLAQEQL 27 1000 (+) [10]
Refolding rates are designated as follows: ++, more than 75% soluble HLA-B27 monomer in gel-filtration analysis after refolding and biotinylation; +, more than 50% refolding; (+), more than 10% refolding; -, less than 10% refolding.
aSYFPEITHI, HG Rammensee, University of Tübingen [23].
bHLA-peptide binding motifs (the BioInformatics and Molecular Analysis Section; ). EBV, Epstein–Barr virus.
Table 3 HLA-B27/Chlamydia peptide tetramer staining of CD8+ T cells from synovial fluid of six HLA-B27+ patients (nos 1–6) and three HLA-B27- patients (nos 7–9) with Chlamydia-triggered reactive arthritis
Patient no. B27 No. 68 No. 133 No. 195
1 + - - -
2 + - - +
3 + + - +
4 + - - -
5 + - + +
6 + - + -
7 - - - -
8 - - - -
9 - - - -
10 + - - -
11 + - - -
12 + - - -
13 n.d. - - -
14 n.d. - - -
15 n.d. - - -
CD8+ T cells from the synovial fluid of three HLA-B27+ patients with enterobacteria-triggered reactive arthritis (patient nos 10–12), three patients with rheumatoid arthritis (patient nos 13–15) were analysed; the peripheral blood of six healthy HLA-B27+ blood donors was also analysed (not shown). Positive results are indicated with +, no staining with -. All other peptides tested negative. n.d., not done.
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| 15535830 | PMC1064864 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 23; 6(6):R521-R534 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1221 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar12231553582910.1186/ar1223Research ArticleExpression analysis of three isoforms of hyaluronan synthase and hyaluronidase in the synovium of knees in osteoarthritis and rheumatoid arthritis by quantitative real-time reverse transcriptase polymerase chain reaction Yoshida Mamoru [email protected] Shigaku [email protected] Keishi [email protected] Takaaki [email protected] Naoki [email protected] Koji [email protected] Katsuyuki [email protected] Department of Orthopaedic Surgery, The Jikei University School of Medicine, Tokyo, Japan2 Institute for Molecular Science of Medicine, Aichi Medical University, Aichi, Japan2004 22 9 2004 6 6 R514 R520 2 10 2003 30 10 2003 28 6 2004 16 7 2004 Copyright © 2004 Yoshida et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Hyaluronan is a major molecule in joint fluid and plays a crucial role in joint motion and the maintenance of joint homeostasis. The concentration and average molecular weight of hyaluronan in the joint fluids are reduced in osteoarthritis and rheumatoid arthritis. To elucidate the underlying mechanism, we analyzed the message expression of three isoforms of hyaluronan synthase and hyaluronidase from knee synovium, using real-time reverse transcriptase polymerase chain reaction. Synovia were obtained from 17 patients with osteoarthritis, 14 patients with rheumatoid arthritis, and 20 healthy control donors. The message expression of hyaluronan synthase-1 and -2 in the synovium of both types of arthritis was significantly less than in the control synovium, whereas that of hyaluronidase-2 in the synovium of both arthritides was significantly greater than in the control synovium. The decreased expression of the messages for hyaluronan synthase-1 and -2 and/or the increased expression of the message for hyaluronidase-2 may be reflected in the reduced concentration and decreased average molecular weight of hyaluronan in the joint fluids of patients with osteoarthritis and rheumatoid arthritis.
arthritishyaluronanhyaluronan synthasehyaluronidasesynovium
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Introduction
High-molecular-weight (HMW) hyaluronan (average molecular weight 6–7 × 106 Da) is a major component of synovial joint fluids [1-5]. It physically acts as a viscous lubricant for slow joint movements, such as walking, and as an elastic shock absorber during rapid movements, such as running [6]. HMW hyaluronan has a variety of biologic effects on cells in vitro, such as: the inhibition of prostaglandin E2 synthesis and the release of arachidonic acid induced by interleukin-1 from cultured fibroblasts [7,8]; protection against proteoglycan depletion and cytotoxicity induced by oxygen-derived free radicals, interleukin-1, and mononuclear-cell-conditioned medium [9,10]; and the suppression of phagocytosis, of locomotion, and of enzyme release by leukocytes and macrophages [11-14]. HMW hyaluronan has been shown to suppress the degradation of cartilage matrix induced by fibronectin fragments [15,16] and cytokines [17]. Moreover, it has been shown to relieve joint pain by masking free nerve ending organelles in animal experiments [18,19]. Hence, it is suggested that HMW hyaluronan is an indispensable component in the maintenance of articular joint homeostasis. Reductions in the concentration and average molecular weight of hyaluronan in knee synovial fluids from patients with osteoarthritis (OA) or rheumatoid arthritis (RA) have been reported [2,3,20-25]. These reductions indicate hyaluronan's involvement in the pathogenesis of these joint disorders and are reflected in the pathological changes of hyaluronan metabolism.
Hyaluronan is synthesized by hyaluronan synthases (HASs) located at the plasma membrane of cells [26]. Three HAS isoforms, encoded by three distinct genes, are expressed in human knee synovium [27]. It is believed that joint fluid hyaluronan is mainly supplied from type B cells – proper synoviocytes – of the synovial lining [2-5,28]. Little is known about hyaluronan catabolism in synovial fluid. It is thought that hyaluronan is eliminated by the lymphatic or vascular system after fragmentation by an unknown process [29] or that macrophagic type A cells of the synovial lining absorb and digest hyaluronan, because type A cells have many vesicles and vacuoles containing lysosomal enzyme – such as nonspecific esterase, acid phosphatase, and cathepsins B, D, and L – and type A cells are active in the uptake of substances in synovial fluids [28]. Hyaluronidase, which specifically degrades hyaluronan, is a lysosomal enzyme. Among five homologous isozymes in humans, hyaluronidase-1, -2, and -3 are thought to be expressed in synovium and involved in hyaluronan degradation, since hyaluronidase-4 is a chondroitinase and hyaluronidase-5, the sperm-specific enzyme PH-20, is specifically expressed in sperm [30]. The messages of hyaluronidase-1, -2, and -3 are expressed in chondrocyte monolayer cultures and in extracts of fresh human cartilage [31].
In the present study, we investigated message expression levels for three isoforms of HAS and hyaluronidase in knee synovium obtained from control donors and patients with OA or RA, by quantitative reverse transcriptase polymerase chain reaction (RT-PCR), in order to confirm whether message levels differed.
Materials and methods
Materials
An RNeasy kit was purchased from QIAGEN KK (Tokyo, Japan). Primer Express computer software, gene-specific primer pairs and probes, TaqMan Gold RT-PCR reagents without controls, Pre-Developed TaqMan assay reagents of endogenous control human beta-actin, and a 7700 sequence detector were purchased from Perkin-Elmer Corp (Norwalk, CT, USA). The Hyaluronate-Chugai test kit was from Chugai Pharmaceutical Corp (Tokyo, Japan).
Patients and controls
Baseline data for patients with OA or RA and for control donors from whom synovial samples were obtained are summarized in Table 1. Two of us (MY and SS), both physicians, clinically diagnosed OA and diagnosed RA according to the criteria of the American Rheumatism Association. Pharmacological treatment before sampling was limited to analgesics or nonsteroidal anti-inflammatory drugs in all study subjects. Rheumatoid arthritis patients were classified in stage II or stage III, and class II grade according to the Steinbrocker classification. The radiographic grades of all knee joints were determined on frontal views of the tibiofemoral joints according to the radiographic atlas recommended by the Osteoarthritis Research Society [32]. Grade B radiographic appearance, corresponding to grade 1 of the Larsen grading system, is defined by the presence of grade 1 joint space narrowing combined with osteophytes, or of grade 2 or 3 joint space narrowing. Control synovial samples were obtained from donors who had no intra-articular pathologic findings under arthroscopy at second-look observations after partial meniscectomy or from donors who complained of knee pain of unknown etiology but who had no intra-articular pathologic findings under arthroscopy on routine examination. The control synovium donors were significantly younger than the patients with OA or RA (P < 0.01).
Sampling of synovial tissues and isolation of total ribonucleic acid
We obtained informed consent from all the study subjects and approval by the university ethical committee and the institutional review board. Synovial tissue samples were obtained from the central area of the suprapatellar pouches of the knees during arthroscopic examination, arthroscopic surgery, or open surgery performed in a hospital of the Jikei University School of Medicine. After subsynovial or fatty tissues were macroscopically resected from the obtained samples, all synovial samples were immediately frozen with liquid nitrogen and stored at -80°C. The total RNA of each sample was isolated using an RNeasy kit.
Analysis of hyaluronan in joint fluid
Joint fluid was aspirated from the knee immediately before an examination or surgery and was stored at -80°C. Joint fluid was obtained from 10 healthy control donors, 10 patients with OA, and 10 with RA. Hyaluronan concentration in joint fluid was measured by a sandwich binding protein assay using a Hyaluronate-Chugai test kit [33]. The molecular weight of hyaluronan was calculated from the intrinsic viscosity of hyaluronan in fluid, which was measured with a capillary viscometer [34] after pronase treatment. This method was chosen because it is more precise than HPLC analysis for the measurement of the average molecular weight of HMW hyaluronan.
Analysis of message expression by quantitative real-time RT-PCR
Message expression in the synovium of knees and the relative differences in message levels between the control group and patients with OA or RA were determined by real-time RT-PCR in accordance with the manufacturer's instructions and reported methods [35-39]. The gene-specific PCR oligonucleotide primer pairs and gene-specific oligonucleotide probes labelled with a reporter fluorescent dye (FAM) at the 5'-end and a quencher fluorescent dye (TAMURA) at the 3'-end were designed using the Primer Express computer software for HAS-1, -2, and -3 and hyaluronidase-1, -2, and -3 genes. For the HAS-2 probe, a minor groove binder probe was used to achieve an optimal melting temperature, because a suitable site for the regular probe was not found in the DNA sequences of HAS-2 (Table 2). A minor groove binder is an enhancer of the probe's melting temperature. Total RNA (200 ng for each) was added to a 50 μL RT-PCR reaction buffer containing 0.2 mmol/L deoxynucleotide triphosphates, 1.5 mmol/L MgSO4, 2.5 μmol/L random hexamers, 0.1 U/mL MultiScribe reverse transcriptase, 0.1 U/μL AmpliTaq Gold DNA polymerase, 900 nmol/L concentration of PCR primer pairs, 200 nmol/L concentration of the corresponding probe, and 2.5 μL Pre-Developed TaqMan assay reagents of endogenous control human β-actin, which contained β-actin-specific primers and probes labeled with a different reporter fluorescent dye (VIC). RT-PCR was carried out in a 96-well plate under the following conditions: one cycle at 50°C for 2 minutes to activate the uracil N-glycosylase, one cycle at 60°C for 30 minutes, one cycle at 95°C for 5 minutes, and 50 cycles at 95°C for 20 seconds and 60°C for 1 minute. The fluorescence energy emitted from the reporter dye without a quencher was monitored directly by a 7700 sequence detector in real time when the annealed probes were broken by DNA polymerases during the polymerization period. The threshold cycle numbers (CT), from which the logarithmic amplification phase of the PCR reaction started, were determined simultaneously for the messages of both target gene and β-actin gene in the same sample tube when the intensity of the reporter fluorescent signal reached 10 times the standard deviation of the baseline of fluorescent signal intensity. The CT value of the β-actin message was used as an internal standard.
When the target messages were detected in both the control samples and the OA or RA samples by RT-PCR, the ratio for the amount of the message expressed in control samples to the amount of the message expressed in OA or RA samples was determined as a relative expression level. Relative expression levels of the target messages were calculated as follows: the ΔCT of each target message was obtained by subtracting the CT of β-actin message from the CT of each target message in the same RNA sample. The ΔCT values of the same target message between the control and OA or RA groups were analyzed statistically. When these ΔCT values were significantly different (P < 0.01), the averageΔCT value of each target message was calculated from all the ΔCT values. The ΔaverageΔCT value of each message was obtained by subtracting the averageΔCT of control samples from the averageΔCT of OA or RA samples. Finally, the relative expression level of each target message was determined using the formula: relative expression level = 2-ΔaverageΔCT
Statistical analysis
Statistical analysis was with Wilcoxon's matched-pairs signed rank test. A probability value of <0.01 was considered statistically significant.
Results
Concentration and average molecular weight of hyaluronan in synovial fluid
The concentration and average molecular weight of hyaluronan in the synovial fluid of OA or RA patients were significantly lower than those of control donors (Table 3).
Expression profile of hyaluronan synthase isoform messages
Expressed messages for all three HAS isoforms were detected in all synovial samples. The expression of the messages for HAS-1 and HAS-2 was significantly less in the synovium of OA than in the control synovium (83% and 48% of the respective control values), whereas no significant difference was observed for HAS-3 message expression. HAS-1 and HAS-2 message expression in RA synovium was significantly less than in control synovium (30% and 77% of the respective control values), while the expression of HAS-3 message was significantly greater than that in the control synovium (250% of the control value) (Fig. 1).
Expression profile of hyaluronidase isozyme messages
Message expression of all three hyaluronidase isozymes was detected in all synovial samples. Message expression for hyaluronidase-2 in OA synovium was significantly increased (to 430% of that in control synovium), while no significant differences were observed for hyaluronidase-1 and hyaluronidase-3 message expression. The expression of the message for hyaluronidase-2 was significantly greater (400% of the control value), while the expression of messages for hyaluronidase-1 and hyaluronidase-3 was significantly decreased in RA synovium (to 40% and 3% of the respective control values) (Fig. 1).
Discussion
The present study showed that HAS-1 and HAS-2 message expression was decreased in OA and RA synovium. This finding suggests that the protein expression of HAS-1 and -2 is decreased, as it has been reported that message levels are correlated with HAS protein levels and with the production of hyaluronan in cultured cells [40]. It has been suggested that the expression level of HAS proteins and their synthetic activities regulate the total volume of hyaluronan produced by cells, because detergent-purified HAS proteins alone can synthesize hyaluronan and no associated proteins or components are necessary for hyaluronan synthesis in vitro [41]. HAS activity of stable transfectants of HAS-2 is approximately 1.2 times that of HAS-1 or HAS-3 [42]. Stable transfectants of HAS-1 and HAS-3 produce hyaluronan with a broad size distribution (molecular weights of 2 × 105 Da to approximately 2 × 106 Da), whereas stable transfectants of HAS-2 produce hyaluronan with a broad size distribution that ranges higher (average molecular weight of >2 × 106 Da) [41]. Among HAS isoforms, the predominant message expressed in human knee synovium is HAS-1 [27]. Therefore, synovial production of hyaluronan, including HMW hyaluronan, may be decreased in OA or RA. A reduced production of HMW hyaluronan may be involved in the pathogenesis of these joint disorders, since HMW hyaluronan has important physical and biologic functions, as described in the Introduction.
An age-associated change in synoviocyte population revealed that the number of type B cells was significantly decreased in older animals, although this was not confirmed in humans [28]. The message levels of all three HAS isoforms were not uniformly decreased in the knee synovium of OA or RA patients, even though the patients were significantly older than the control donors. Hence, it is unclear whether the different expression profiles of HAS messages in the controls, OA and RA patients are attributable to age-associated change, to physical senility, or to a pathologic factor specific for arthritic joint disorders.
Hyaluronidase activity was detected in human knee synovial fluid of OA or RA patients when the assay was performed at the acidic pH of 4.5, but not at pH 5.0–7.0 [43]. Hyaluronidase-1 may be present in the fluids, because it is a major isozyme in plasma and urine and is unable to bind hyaluronan at neutral pH [30]. We suggest that soluble forms of hyaluronidases in synovial fluids are not involved in the direct digestion of hyaluronan in joint fluids, because a neutral pH is maintained in synovial fluids, and so hyaluronidase-1 may function only within lysosomes.
Hyaluronidase-2 is linked to the outer cell membrane by a glycosylphosphatidyl-inositol (GPI) anchor and it digests hyaluronan to intermediate-sized fragments of approximately 20 kDa, while hyaluronidase-1 digests hyaluronan to tetrasaccharides [30]. A process of hyaluronan catabolism in somatic cells proposed in the review literature [30] is that hyaluronan is taken up into unique endocytic vesicles by an unknown mechanism and is digested into 20-kDa fragments by hyaluronidase-2 located in vesicles at an acidic pH; subsequently, the fragments are transported into lysosomes, where hyaluronidase-1 and two exoglycosidases digest hyaluronan into monosaccharides. The present study showed that the message expression of hyaluronidase-2 in the synovium of OA and RA was approximately four times that in the control synovium. This finding suggests that in OA and RA, the protein expression of hyaluronidase-2 in the synovium is increased and the hyaluronan digestion by hyaluronidase-2 is accelerated.
Little is known about hyaluronidase-3. Strong hybridization expression patterns are found in mammalian testis and bone marrow [30]. Hyaluronidase-3 message expression was detected in synovium in the present study. This isozyme may work only in the lysosomes, as does hyaluronidase-1 [30]. The expression level in RA synovium was significantly lower than in OA or control synovium. The reduction in message expression may be due to the different cellular populations found in OA versus RA, since many inflammatory cells such as granulocytes or lymphocytes appeared in RA synovium.
Joint fluid hyaluronan concentration is determined by the production volume of hyaluronan, the elimination volume of hyaluronan from the joint, and the total volume of joint fluid. The production of hyaluronan in OA or RA may be decreased because of the reduced expression of HAS-1 and -2 messages. The elimination volume of hyaluronan may be increased by the elevated expression of hyaluronidase-2, because hyaluronidase-2 digests hyaluronan in the endosome after uptake of hyaluronan into cells [30]. Hence, it is thought that the decreased expression of HAS-1 and -2 and/or the increased expression of hyaluronidase-2 are among the causes leading to the reduced hyaluronan concentration in OA or RA synovial fluid.
The average molecular weight of hyaluronan in synovial fluid is determined by the molecular weights of hyaluronan produced and hyaluronan digested in the fluid. The average molecular weight of newly produced hyaluronan may be reduced by the decreased expression of HAS-2, because, of the three HAS isoenzymes, HAS-2 synthesizes the highest-molecular-weight hyaluronan [42]. The decreased expression of HAS-2 may be one of the causes for the reduced average molecular weight of hyaluronan in joint fluid. Moreover, there may be a mechanism whereby HMW hyaluronan is digested into low-molecular-weight hyaluronans in synovial fluid, since the average molecular weight of hyaluronan in OA or RA fluid is lower than that of hyaluronan synthesized by HAS-1 or -3.
HAS-3 message expression was increased in RA synovium, although hyaluronan concentration was reduced. The increased expression of HAS-3 message may be due to the increased number of inflammatory cells invading the pannus tissue (which is inflammatory and proliferative granular synovial tissue specific for RA), since a high expression level of HAS-3 message in inflammatory cells was observed in another study by two of us (NI and KK). It is supposed that the hyaluronan produced by inflammatory cells does not diffuse into the joint cavity and that it surrounds cells, protecting them or aiding their migration, because it has been reported that pannus tissue with inflammatory cells contains a greater amount of hyaluronan than is found in OA or traumatic injury [44].
Conclusion
Message expression for three isoforms of hyaluronan synthase and hyaluronidase in knee synovium differs in OA or RA from that in healthy controls. Differential expression of hyaluronan synthases and/or hyaluronidases may be reflected in the pathological metabolism of hyaluronan in the knee synovial fluid of patients with OA or RA.
Competing interests
None declared.
Abbreviations
HAS-1/-2/-3 = hyaluronan synthase-1/-2/-3; HMW = high-molecular-weight; HPLC = high-performance liquid chromatography; OA = osteoarthritis; PCR = polymerase chain reaction; RA = rheumatoid arthritis; RT-PCR = reverse transcriptase polymerase chain reaction; SD = standard deviation.
Acknowledgements
We are grateful to all the members of the Department of Orthopaedic Surgery, The Jikei University School of Medicine, for the collection of samples.
Figures and Tables
Figure 1 Relative expression levels of the messages for hyaluronan synthase-1, -2, and -3 and hyaluronidase-1, -2, and -3 in the synovium of knees in osteoarthritis (OA) and rheumatoid arthritis (RA). Total RNA was isolated from knee synovium and expression levels of the messages were relatively quantified by real-time reverse transcriptase polymerase chain reaction. The expression levels of the messages in OA and RA are expressed by longitudinal bars relative to those of the control value expressed as 1.0. The lines outside the bars represent the standard deviation of the change in threshold cycle numbers (ΔCT) corrected with CT values of β-actin message used as an internal standard. * P < 0.01.
Table 1 Baseline data for subjects with osteoarthritis (OA) or rheumatoid arthritis (RA) or without arthritis
Age (years)
Subjects Number (male/female) Mean ± SD Range Radiographic gradea
With OA 17 (9/8) 64.77 ± 9.02 45 – 83 B
With RA 14 (2/12) 60.55 ± 12.05 35 – 74 B
Controls 20 (11/9) 37.25 ± 7.59 29 – 59 Normal
aDetermined on x-ray frontal views of the tibiofemoral joints in accordance with the radiographic atlas recommended by the Osteoarthritis Research Society [32]. SD, standard deviation.
Table 2 Sequences of the gene-specific oligonucleotide primers and probes for real-time reverse transcriptase polymerase chain reaction
Primer and probe Sequence Position
HAS-1 855F GACTCCTGGGTCAGCTTCCTAAG 855 – 877
995R AAACTGCTGCAAGAGGTTATTCCT 995 – 972
probe TATCCTGCATCAGCGGTCCTCTAGGC 940 – 965
HAS-2 20F CTATGCTTGACCCAGCCTCATC 20 – 41
149R ACACTGCTGAGGAATGAGATCCA 149 – 127
MGB probea AGATGTCCAGATTTTA 96 – 111
HAS-3 888F TGTCCAGATCCTCAACAAGTACGA 888 – 911
1005R AATACACTGCACACAGCCAAAGTAG 1005 – 981
probe TCATGGATTTCCTTCCTGAGCAGCGT 913 – 938
HYAL-1 1561F AGTGGTGCTCTGGGTGAGCT 1561 – 1580
1667R TGGTCACGTTCAGGATGAAGG 1667 – 1647
probe CCAAGGAATCATGTCAGGCCATCAAGG 1596 – 1622
HYAL-2 1069F CGCAGCTGGTGTCATCCTCT 1069 – 1088
1159R CAGCAGCCGTGTCAGGTAATC 1159 – 1139
probe TACACCACAAGCACGGAGACCTGCC 1103 – 1127
HYAL-3 1130F GCCTCACACACCGGAGATCT 1130 – 1149
1202R GCTGCACTCACACCAATGGA 1202 – 1183
probe TCCTGTCCCAGGATGACCTTGTGCA 1157 – 1181
aMinor groove binder (MGB) enhances the melting temperature of the probe (see Materials and methods). HAS, hyaluronan synthase; HYAL, hyaluronidase.
Table 3 Concentration and molecular weight (mean ± standard deviation) of hyaluronan in synovial fluid of patients with osteoarthritis (OA) or rheumatoid arthritis (RA) and in controls
Subjects Concentration (mg/mL) Molecular weight (×104 Da)
Controls 3.1 ± 0.4 365 ± 52
OA 1.2 ± 0.2* 212 ± 37*
RA 0.8 ± 0.3* 163 ± 33*
* P < 0.01 in comparison with controls.
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| 15535829 | PMC1064865 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 22; 6(6):R514-R520 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1223 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar12241553583410.1186/ar1224Research ArticleAnalysis of HLA DR, HLA DQ, C4A, FcγRIIa, FcγRIIIa, MBL, and IL-1Ra allelic variants in Caucasian systemic lupus erythematosus patients suggests an effect of the combined FcγRIIa R/R and IL-1Ra 2/2 genotypes on disease susceptibility Jönsen Andreas [email protected] Anders A [email protected] Gunnar [email protected] Lennart [email protected] Department of Rheumatology, Lund University Hospital, Lund, Sweden2 Department of Laboratory Medicine, Section of Microbiology, Immunology and Glycobiology, Lund University, Lund, Sweden2004 23 9 2004 6 6 R557 R562 22 12 2003 12 1 2004 16 6 2004 16 7 2004 Copyright © 2004 Jönsen et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Dysfunction in various parts of immune defence, such as immune response, immune complex clearance, and inflammation, has an impact on pathogenesis in systemic lupus erythematosus (SLE). We hypothesised that combinations of common variants of genes involved in these immune functions are associated with susceptibility to SLE. The following variants were analysed: HLA DR3, HLA DQ2, C4AQ0, Fcγ receptor IIa (FcγRIIa) genotype R/R, Fcγ receptor IIIa (FcRγIIIa) genotype F/F, mannan-binding lectin (MBL) genotype conferring a low serum concentration of MBL (MBL-low), and interleukin-1 receptor antagonist (IL-1Ra) genotype 2/2. Polymorphisms were analysed in 143 Caucasian patients with SLE and 200 healthy controls. HLA DR3 in SLE patients was in 90% part of the haplotype HLA DR3-DQ2-C4AQ0, which was strongly associated with SLE (odds ratio [OR] 2.8, 95% CI 1.7–4.5). Analysis of combinations of gene variants revealed that the strong association with SLE for HLA DR3-DQ2-C4AQ0 remained after combination with FcγRIIa R/R, FcγRIIIa F/F, and MBL-low (OR>2). Furthermore, the combination of the FcγRIIa R/R and IL-1Ra 2/2 genotypes yielded a strong correlation with SLE (OR 11.8, 95% CI 1.5–95.4). This study demonstrates that certain combinations of gene variants may increase susceptibility to SLE, suggesting this approach for future studies. It also confirms earlier findings regarding the HLA DR3-DQ2-C4AQ0 haplotype.
Fcγ receptorHLAinterleukin-1 receptor antagonistmannan-binding lectinsystemic lupus erythematosus
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Introduction
The genetic contribution to the aetiology of systemic lupus erythematosus (SLE) is high, as is indicated by familial aggregation and a higher concordance rate in monozygotic than dizygotic twins [1]. The major histocompatibility complex (MHC) haplotype HLA DR3-DQ2-C4AQ0 is strongly associated with SLE in Caucasians [2,3]. The IgG Fc receptors appear to be important in the pathogenesis of SLE, as recently reviewed by Salmon and Pricop [4]. With the allelic variant of R (arginine) instead of H (histidine) on amino acid position 131, the ability of Fcγ receptor IIa (FcγRIIa) to bind IgG2 is diminished [5]. Similarly, an amino acid substitution in position 158 (phenylalanine [F] instead of valine [V]) in the Fcγ receptor IIIa (FcγRIIIa) reduces the IgG1-, IgG3-, and IgG4-binding capacity of the receptor [6]. These variants can result in suboptimal clearance of immune complexes from the circulation, which might contribute to the pathogenesis of immune-complex-mediated manifestations [7].
Mannan-binding lectin (MBL) is structurally similar to C1q and has the ability to activate the complement cascade through the lectin pathway. Point mutations are found in the structural gene that affect the MBL serum concentration and the stability of MBL complex formation required for efficient complement activation [8]. In the promoter regions, there are two polymorphisms that influence serum concentration, with LX conferring the lowest MBL level, LY a medium level, and HY the highest [8-11]. MBL variant alleles have been suggested as a minor risk factor in susceptibility to SLE in several populations [8,10,12]. Interleukin-1 receptor antagonist (IL-1Ra) is a naturally occurring competitive inhibitor of IL-1. The IL-1Ra gene contains a polymorphism in intron 2 consisting of a variable number of copies of an 86-base-pair repeat sequence (two, three, four, five, or six copies) [13]. An association has been found between the IL-1Ra 2 allele and SLE [13,14]. Multiple genes are involved in the development of SLE, and the relative importance of these genes may vary between populations and with environmental exposure. We investigated common variant alleles involved in the immune response, immune complex clearance, and regulation of inflammation, with the hypothesis that combinations of polymorphic candidate genes could have synergistic effects on disease susceptibility. Therefore, we have analysed polymorphisms in the genes HLA DR, HLA DQ, C4A, FcγRIIa, FcγRIIIa, MBL, and IL-1Ra and their association with the development of SLE.
Materials and methods
Patients
The study population comprised 124 female and 14 male Caucasian SLE patients, and 200 blood donors (100 men, 100 women) were used as controls. One hundred thirty-eight patients fulfilled four or more criteria of the American College of Rheumatology (ACR) classification for SLE [15]. Five patients with a clinical SLE diagnosis were included in the study even though they fulfilled only three ACR classification criteria; these five patients had multisystemic disease with an immunologic disorder, i.e. presence of anitnuclear antibodies and symptoms characteristic of SLE such as arthritis, photosensitivity, serositis, nephritis, thrombocytopenia, and leucopenia [16]. A breakdown of the ACR criteria is shown in Table 1. There were 129 families with a single case of SLE and 14 families in which multiple cases were recorded. However, from each multicase family, only the first family member with SLE diagnosis, the index case, was included in the statistical analysis. The mean age at diagnosis of the patients was 40 years (range 10–83) and the mean disease duration was 16 years (range 1–42). The mean Systemic Lupus International Collaborating Clinics/ACR-Damage Index score was 1.9 (range 0–9) [17]. The study was approved by the local ethics committee at Lund University.
Genetic analyses
DNA was extracted by the salting-out method described by Miller and colleagues [18]. Analysis of genetic polymorphism was predominantly performed by polymerase chain reaction (PCR).
HLA
HLA DR and DQ alleles were determined with PCR (Olerup SSP™ DQ-DR SSP Combi Tray, Olerup SSP AB, Stockholm, Sweden). However, a minority of the patients had previously been typed with a lymphocytotoxicity test or by restriction fragment length polymorphism as described before [2]. C4A gene deletion was determined by PCR as described by Grant and colleagues [19], or in a few cases by analysis of restriction fragment length polymorphism and determination of MHC haplotypes [2]. With the presence of a DR3 allele together with a DQ2 and a C4AQ0 allele, due to C4A gene deletion, the subject was considered to have the haplotype HLA DR3-DQ2-C4AQ0, although family studies were not uniformly performed to confirm this assumption.
FcγRIIa gene polymorphism
The genetic polymorphism resulting in amino acid R or H in amino acid position 131 was determined as previously described [20].
Analysis of FcγRIIIa gene polymorphism
The analysis of the F/V polymorphism was performed essentially as previously described [21].
MBL gene polymorphism
Variants of MBL due to mutations at codon 52 (D), 54 (B), and 57 (C) in exon 1 of the MBL gene and promotor variants at position -550 (H/L) and -221 (X/Y) were determined by allele-specific PCR amplification, essentially as described before [9]. The wild-type structural allele is designated A, while 0 is a description of the mutant alleles B, C, and D. Based on previously described associations between MBL genotype and serum concentrations, which were confirmed in our 200 healthy controls, the MBL genotypes were divided into three groups. Group 1 (MBL-low) consisted of patients with two structural mutant alleles (0/0) or on one haplotype a structural mutant allele together with another haplotype containing an LX promoter and the wild-type structural allele (ALX/0). Group 2 (MBL-intermediate) consisted of patients with the promoters LX conferring low serum MBL on both haplotypes but with normal structural alleles (ALX/ALX), or, alternatively, haplotypes with one mutant and one wild-type structural allele with a non-LX promoter together with the wild-type allele. Group 3 (MBL-high) included patients with the A/A genotype and at least one non-LX promoter.
IL-1Ra gene polymorphism
Genetic polymorphism in the IL-1Ra gene was determined with a PCR essentially as previously described [13,22], although one primer was modified.
Primers: 5'-CTC AGC AAC ACT CCT AT-3'
5'-TTC CAC CAC ATG GAA C-3'
The amplified fragment size depends on the number of repeats (two repeats, designated allele 2; three, allele 4; four, allele 1; five, allele 3; six, allele 5).
Statistics
Two group comparison tests were performed using the Fisher exact test. Comparisons between multiple groups were made using the χ2 multiple comparison test. Significance was considered when P <0.05. Correction for multiple comparisons was not applied to the results, because the study design consisted in hypothesis testing. The presence of synergistic interaction between genetic variants was investigated by calculating relative excess risk due to interaction (RERI) [23].
Results
A strong association between the HLA DR3-DQ2-C4AQ0 haplotype and SLE was found, although this haplotype also was common among the controls. HLA DR2 was present in 50 of the 143 SLE patients and 72 of the 200 controls, while DR4 frequencies were 45/143 and 72/200, respectively. In the SLE group, HLA DQ2 was present in 80 of 143 cases, while DQ3 and DQ6 was recorded in 60 of 143 and 85 of 143 cases, respectively. The corresponding numbers in the control group were for DQ2, 73/200; for DQ3, 100/200; and for DQ6, 112/200. Other DR and DQ variants were less common. Ninety percent of the SLE patients with HLA DR3 displayed the haplotype DR3-DQ2-C4AQ0, compared with 86% of the controls. The frequencies of the FcγRIIa, FcγRIIIa, MBL, and IL-1Ra genotypes are displayed in Fig. 1. The FcγRIIa R/R, FcγRIIIa F/F, IL-1Ra 2/2, and MBL-low genotypes were not individually associated with SLE.
Additionally, the combination of genetic variants and susceptibility to SLE was tested (Table 2). HLA DR3-DQ2-C4AQ0 in combination with FcγRIIa R/R, FcγRIIIa F/F, or MBL-low was still associated with SLE but did not significantly increase the odds ratio (OR) in comparison with HLA DR3-DQ2-C4AQ0 alone. A combination of FcγRIIa R/R and IL-1Ra 2/2 yielded a strong association with SLE (OR 11.8), although the confidence interval was wide (1.5–95.4). Testing of RERI did not confirm the hypothesis that this interaction was synergistic (RERI 11.1, 95% CI -13.8 – 36.1, P = 0.38). A combined analysis of carriage rates for the R allele and the 2 allele (i.e. the patient should have at least one R allele and one 2 allele) was also performed, but no significant difference was detected between the SLE and the control group. No other combination displayed any association with SLE.
Discussion
The increasing number of reports on polymorphic genes involved in susceptibility to SLE prompted us to investigate whether a combination of polymorphic candidate genes, tentatively thought to be involved in the pathogenesis of SLE, could further elucidate the genetic basis of the disease. In the present study we found that the combination of the FcγRIIa R/R genotype with the IL-1Ra 2/2 genotype was strongly associated with SLE. Although only a few of the patients had this particular genetic background, the results indicate that certain combinations of susceptibility genes can be of crucial importance. Furthermore, a strong association between the haplotype HLA DR3-DQ2-C4AQ0 and susceptibility to SLE was seen in this study, which is in concordance with the findings of previous studies [2,22,24,25]. The patients and controls studied were all from a homogeneous Caucasian population, although a possible bias exists in the fact that the controls used were blood donors, which principally include only healthy individuals, instead of age-matched controls from the normal population. The distributions of the polymorphic variants in the controls were in agreement with data published by others [13,26,27].
There have been ample studies on the association between FcγRIIa and SLE [24,28-30]. However, the results are somewhat conflicting regarding whether or not the R allele is associated with increased susceptibility to SLE in general or for SLE glomerulonephritis or other clinical manifestations of SLE. In our study, there was no association between either the R allele or the R/R genotype and susceptibility to SLE, with a glomerulonephritis frequency of 27%.
The MBL genotype did not seem to be involved in susceptibility to SLE in our Caucasian cohort. This differs from a finding of a recent meta-analysis in which MBL variant alleles were found to be associated with SLE [27]. Furthermore, in that study the conclusion was drawn that several studies are too small to detect an increased SLE susceptibility dependent on MBL risk alleles, which could also explain the lack of association in our study.
An increased carriage rate of the 2 allele of the IL-1Ra gene has been shown for SLE patients [13,14]. In our study, the 2/2 genotype in conjunction with the FcγRIIa R/R genotype was associated with SLE. This IL-1Ra genotype is associated with higher IL-1 beta concentrations as well as higher serum IL-1Ra levels [31,32]. Furthermore, immune complex binding to Fc receptors can influence the production of IL-1Ra [33], which provides a possibility for a pathogenetic mechanism concordant with the genetic interaction seen in our study. Analyses of disease phenotypes were beyond the scope of this study and will be addressed in future studies. However, there were no apparent associations between the various genotypes and clinical subsets of SLE. Because of the low number of patients included in the study, the results must be interpreted cautiously, and independent confirmation is needed.
Conclusion
Our findings suggest that the combination of the FcγRIIa R/R and IL-1Ra 2/2 genotypes is associated with SLE in Caucasian patients, whereas individually these genotypes do not increase susceptibility to the disease. This finding illustrates that combinations of polymorphic genes may act in concert in the pathogenesis of SLE, a concept that may be instrumental in the analysis of the genetics of SLE as well as providing hypotheses for pathways in the pathogenesis of lupus.
Competing interests
None declared.
Author contributions
AJ was responsible for data analysis and interpretation and wrote the report.
AAB contributed to the data analysis and interpretation.
GS and LT were both responsible for the planning of the work and contributed to data analysis, interpretation, and write-up.
Abbreviations
ACR = American College of Rheumatology; F = phenylalanine; FcγRIIa = Fcγ receptor IIa; FcγRIIIa = Fcγ receptor IIIa; H = histidine; IL-1Ra = interleukin-1 receptor antagonist; MBL = mannan-binding lectin; MBL-low/-intermediate/-high = MBL genotype conferring a low/intermediate/high serum concentration of MBL; MHC = major histocompatibility complex; OR = odds ratio; PCR = polymerase chain reaction; R = arginine; RERI = relative excess risk due to interaction; SLE = systemic lupus erythematosus; V = valine.
Acknowledgements
We thank Mrs Birgitta Gullstrand and Mrs Gertrud Hellmer for their skilful work with the genetic typing and Jonas Björk, PhD, for valuable statistical aid. The study was supported by grants from the Swedish Rheumatism Association, the Swedish Research Council (grant nos. 13489 and 15092), the Medical Faculty of the University of Lund, Alfred Österlund's Foundation, The Crafoord Foundation, Greta and Johan Kock's Foundation, The King Gustaf V's 80th Birthday Fund, Lund University Hospital and Prof Nanna Svartz' Foundation
Figures and Tables
Figure 1 Distribution of genetic variants studied in 143 patients with SLE and 200 healthy blood donors. DR3 represents the haplotype DR3-DQ2-C4AQ0. F, phenylalanine; H, histidine; Int, intermediate; MBL, mannan-binding lectin; R, arginine; V, valine.
Table 1 Distribution of American College of Rheumatology (ACR) classification criteria in 143 patients with SLE
ACR criterion Patients
No. %
Malar rash 79 55
Discoid rash 55 38
Photosensitivity 102 71
Oral ulcers 38 27
Arthritis 118 83
Serositis 76 53
Renal disorder 38 27
Neurologic disorder 12 8
Hematologic disorder 73 51
Immunologic disorder 103 76
Antinuclear antibody 143 100
Table 2 Comparisons of genetic variants in 143 patients with SLE and 200 healthy blood donors
Genetic variant Patients Controls
No. (%) No. (%) Pa ORa 95% CIa
HLA DR3-DQ2-C4AQ0 61 (43) 42 (21) <0.0001 2.8 1.7–4.5
FcγRIIa R/R 46 (32) 51 (26) 0.18 1.4 0.86–2.2
FcγRIIIa F/F 68 (48) 90 (45) 0.66 1.1 0.72–1.7
MBL-low 19 (13) 28 (14) 0.88 0.94 0.50–1.8
IL-1 Ra 2/2 14 (9.8) 14 (7.0) 0.42 1.4 0.66–3.1
HLA DR3-DQ2-C4AQ0 / FcγRIIa R/R 20 (14) 7 (3.5) 0.0005 4.5 1.8–10.9
HLA DR3-DQ2-C4AQ0 / FcγRIIIa F/F 29 (20) 19 (9.5) 0.007 2.4 1.3–4.5
HLA DR3-DQ2-C4AQ0 / MBL-low 11 (7.7) 5 (2.5) 0.04 3.3 1.1–9.6
HLA DR3-DQ2-C4AQ0 / IL-1 Ra 2/2 4 (2.8) 3 (1.5) 0.46 1.9 0.42–8.6
FcγRIIa R/R / FcγRIIIa F/F 31 (22) 32 (16) 0.20 1.5 0.84–2.5
FcγRIIa R/R / MBL-low 3 (2.1) 6 (3.0) 0.74 0.69 0.17–2.8
FcγRIIa R/R / IL-1 Ra 2/2 8 (5.6) 1 (0.5) 0.005 11.8 1.5–95.4
FcγRIIIa F/F / MBL-low 8 (5.6) 12 (6.0) 1.0 0.92 0.37–2.3
FcγRIIIa F/F / IL-1 Ra 2/2 8 (5.6) 4 (2.0) 0.13 2.9 0.86–9.8
MBL-low / IL-1 Ra 2/2 1 (0.7) 0 (0.0) 0.42 4.2 0.17–104
aBold type indicates statistical significance (P <0.05); CI, confidence interval; F, phenylalanine; MBL, mannan-binding lectin; MBL-low, MBL genotype conferring a low serum concentration of MBL; OR, odds ratio; R, arginine.
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| 15535834 | PMC1064866 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 23; 6(6):R557-R562 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1224 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar12281553583310.1186/ar1228Research ArticleIntrathecal levels of matrix metalloproteinases in systemic lupus erythematosus with central nervous system engagement Trysberg Estelle [email protected] Kaj [email protected] Olof [email protected] Andrej 1Andrej.Tarkowski.rheuma.gu.se1 Department of Rheumatology and Inflammation Research, Göteborg University, Sahlgrenska University Hospital, Göteborg, Sweden2 Institute of Clinical Neuroscience, Göteborg University, Sahlgrenska University Hospital, Göteborg, Sweden2004 23 9 2004 6 6 R551 R556 11 4 2004 19 5 2004 15 6 2004 23 7 2004 Copyright © 2004 Trysberg et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Symptoms originating from the central nervous system (CNS) occur frequently in patients with systemic lupus erythematosus (SLE), and CNS involvement in lupus is associated with increased morbidity and mortality. We recently showed that neurones and astrocytes are continuously damaged during the course of CNS lupus. The matrix metalloproteinases (MMPs) are a group of tissue degrading enzymes that may be involved in this ongoing brain destruction. The aim of this study was to examine endogenous levels of free, enzymatically active MMP-2 and MMP-9 in cerebrospinal fluid from patients with SLE. A total of 123 patients with SLE were evaluated clinically, with magnetic resonance imaging of brain and cerebrospinal fluid (CSF) analyses. Levels of free MMP-2 and MMP-9 were determined in CSF using an enzymatic activity assay. CSF samples from another 22 cerebrally healthy individuals were used as a control. Intrathecal MMP-9 levels were significantly increased in patients with neuropsychiatric SLE as compared with SLE patients without CNS involvement (P < 0.05) and healthy control individuals (P = 0.0012). Interestingly, significant correlations between MMP-9 and intrathecal levels of neuronal and glial degradation products were noted, indicating ongoing intrathecal degeneration in the brains of lupus patients expressing MMP-9. In addition, intrathecal levels of IL-6 and IL-8 – two cytokines that are known to upregulate MMP-9 – both exhibited significant correlation with MMP-9 levels in CSF (P < 0.0001), suggesting a potential MMP-9 activation pathway. Our findings suggest that proinflammatory cytokine induced MMP-9 production leads to brain damage in patients with CNS lupus.
cerebrospinal fluidmagnetic resonance imaging of the brainmatrix metalloproteinase-2matrix metalloproteinase-9neuropsychiatric involvement in systemic lupus erythematosus
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Introduction
Central nervous system (CNS) involvement has been reported to occur in 14–75% of all systemic lupus erythematosus (SLE) patients [1-3]. However, frequency rates vary considerably, depending on the diagnostic criteria applied. CNS lupus can occur at any time during the course of SLE, and its symptoms are diverse. The features of this condition can include seizures, stroke, depression, psychoses and disordered mentation. Neuropsychiatric involvement in SLE (NPSLE) has been shown to predict a high frequency of flares, and it is considered a major cause of long-standing functional impairment as well as a cause of mortality [4]. Over the past decade CNS lupus has been treated with cytotoxic drugs, which improve disease outcome [5,6].
Because of the multiple pathogenic mechanisms that cause manifestations of CNS lupus, no single confirmatory diagnostic test is available. Several clinical, laboratory and radiographic test findings are reported to be abnormal in some but not all patients with CNS lupus. Magnetic resonance imaging (MRI) of brain has been shown to be valuable in detecting even minor lesions caused by CNS lupus, and these correlate with CNS manifestations in SLE [7]. Pleocytosis and elevated protein levels are found in some but not all patients with CNS lupus [8,9]. Elevated concentrations of IgG in cerebrospinal fluid (CSF), IgG:albumin ratio and IgG index, and the presence of oligoclonal bands have all been described with varying frequencies in patients with NPSLE [10-12]. Few studies have demonstrated elevated IL-6 levels in CSF from patients with CNS lupus [13-17]. Some other reports have described increased levels of IL-1 [13], IL-8 [15] and interferon-γ [18] in CSF from patients with CNS lupus. We recently reported neuronal damage, astrogliosis and formation of toxic metabolic products such as Aβ42 in patients with NPSLE [19].
One of the prototypical destructive events in the human brain, initiated by the release of inflammatory cytokines and ending with tissue destruction, is production of matrix metalloproteinases (MMPs). The MMPs are a family of endopeptidases produced by a variety of inflammatory cells [20]. All of the cell types that exist in the CNS are potential sources of MMPs. In vitro, neurones, astrocytes, microglia [21,22] and oligodendrocytes [23] express various MMP family members, and production of MMPs by neuronal cells can be upregulated by several inflammatory cytokines. MMPs have a multitude of regulatory functions, including control of influx of inflammatory mononuclear cells into the CNS, participation in myelin destruction and disruption of the integrity of the blood–brain barrier [24]. With respect to MMP-9, it was recently shown that CSF levels of this enzyme increase during bacterial meningitis and that it is associated with brain damage [25,26]. The aim of the present study was to measure levels of free active MMP-2 and MMP-9 in CSF of SLE patients with CNS lupus, and to relate these data to clinical and laboratory measures of brain parenchymal degradation. Our results suggest that MMP-9 but not MMP-2 actively participates in brain destruction in CNS lupus.
Methods
Participants
A total of 122 patients fulfilled four or more of the American Rheumatism Association 1987 revised criteria for the classification of SLE [27]. The patients (106 females and 16 males, aged 17–91 years [mean age ± standard deviation 42 ± 14 years]) were being treated at the Department of Rheumatology at Sahlgrenska University Hospital. Disease duration varied between 0 and 49 years (mean duration 8 ± 9 years). The patients were consecutively included in the study. They underwent a thorough clinical examination by experienced staff rheumatologist, neurologist and neuropsychologist. Examination of CNS signs and symptoms included lumbar puncture, neuropsychological tests and MRI of the brain.
The proposed definition of CNS lupus in the American Rheumatism Association criteria for SLE [27] appears inadequate, given that only two elements – psychosis and seizures – are included. As previously described [28], we defined CNS lupus as the presence of at least two of the following seven items in association with clinical evidence of disease progression: recent onset psychosis, transverse myelitis, aseptic meningitis, seizures, pathological brain MRI, severely abnormal neuropsychiatric test findings [29] and oligoclonal IgG bands in CSF. The pathogenesis of antiphospholipid antibody mediated brain damage is a thrombotic rather than inflammatory complication of SLE, and so we decided to exclude this condition from the definition of CNS lupus. Non-SLE causes of neurological events (e.g. cerebral infections) were also ruled out. Based on the criteria above, patients were divided into three distinct groups: patients with CNS lupus (n = 43); patients with SLE but without any signs of CNS involvement (n = 71); and patients with SLE complicated by antiphospholipid syndrome or patients who met the CNS lupus criteria but were ruled out because of non-SLE origin of neurological events (n = 9). The frequencies of various CNS manifestations and patient treatments in the SLE patients are summarized in Tables 1 and 2, respectively. The study was approved by the ethical committee of the University of Göteborg.
Control individuals
CSF from 22 healthy individuals (mean age ± standard deviation: 38 ± 11 years; 12 females and 10 males), without previous history of neurological disorder and with normal neurological status, served as control individuals. There were no significant differences between males and females with respect to intrathecal levels of MMP-9 (0.17 ± 0.17 pg/ml versus 0.30 ± 0.30 pg/ml; not significant) or intrathecal levels of MMP-2 (388 ± 48 pg/ml versus 379 ± 48 pg/ml; not significant).
Cerebrospinal fluid analyses
Levels of MMP-2 and MMP-9 were determined using an activity assay system (Amersham Pharmacia Biotech, Buckinghamshire, UK), which was constructed to measure enzymatically active forms of MMP-2 and MMP-9. The detection level was 190 pg/ml for MMP-2 and 125 pg/ml for MMP-9. All values below the detection levels were considered negative. Paired serum and CSF samples were analyzed for albumin and IgG levels using nephelometry. As an indicator of blood–brain barrier function, the quotient of CSF albumin × 103/serum albumin was analyzed (normal values <6.8 [<45 years of age] and <10.2 [>45 years of age]) [30]. The CSF/serum IgG index was used as a measure of intrathecal IgG production and calculated by using the following formula (normal value <0.7): (CSF IgG × 103)/([CSF albumin × 103]/serum albumin). All CSF samples were also analyzed by isoelectric focusing to permit detection of oligoclonal IgG bands.
Magnetic resonance imaging analyses
Neuroimaging was performed to evaluate the extent and localization of brain lesions. The neuroimaging technique used was multiplanar MRI. The MRI examinations (Philips Gyroscan T5-II, Einhoven, The Netherlands) were performed with axial proton density and T2-weighted images of the brain. MRI abnormalities were seen in 72% of patients with CNS lupus and in 33% of SLE cases classified as cerebrally healthy.
Statistical analysis
Statistical comparisons were made using the nonparametric Mann–Whitney U-test or, in case of follow-up data, the Wilcoxon's test for paired data. Results are presented as means ± standard error of the mean. P < 0.05 was considered statistically significant. Spearman-rank correlation was used for calculation of correlation. The statistical analyses were conducted using the Statview® (SAS, Cary, NC, USA) program.
Results
A total of 123 patients met criteria for SLE. Forty-three patients were found to have NPSLE (in accordance with the criteria presented in the Methods section above), and nine patients either were found to have phospholipid antibody syndrome or met the CNS lupus criteria but were excluded because of non-SLE origin of neurological events. The remaining 71 SLE patients were considered to be cerebrally healthy.
Mild pleocytosis was seen in patients with CNS lupus (9 × 106 ± 6 × 106 cells/l) as compared with cerebrally healthy SLE patients (2 × 106 ± 0.5 × 106 cells/l; not significant). As previously validated [31], we found an increased number of oligoclonal bands in CSF from the CNS lupus group (1.7 ± 0.4) as compared with SLE patients without CNS involvement (0.5 ± 0.1; P < 0.05). The mean level of CSF:serum albumin ratio was not increased in patients with NPSLE (6.1 ± 0.7 mg/dl) as compared with cerebrally healthy SLE patients (5.2 ± 0.3 mg/dl; not significant). There were no significant differences in levels of serum antibodies specific for native DNA or in complement levels (C3 and C4) between SLE patients with and those without CNS involvement.
Intrathecal MMP-9 levels were significantly increased in all SLE patients as compared with cerebrally healthy control individuals (153 ± 27 versus 0.28 ± 0.16 pg/ml; P = 0.016). In CNS lupus patients MMP-9 levels were significantly increased, both compared with cerebrally healthy SLE patients (240 ± 60 versus 100 ± 20 pg/ml; P < 0.05; Fig. 1) and compared with cerebrally healthy control individuals (240 ± 60 versus 0.28 ± 0.16 pg/ml; P = 0.0012). On stratification of all SLE patients into two groups with respect to the presence or absence of brain MRI pathology, we found increased CSF MMP-9 levels in SLE patients with MRI pathology as compared with those without (160 ± 50 versus 140 ± 30 pg/ml; not significant).
There were no statistically significant differences in CSF MMP-2 levels between NPSLE and patients without CNS lupus (499 ± 70 versus 555 ± 70 pg/ml; not significant) or compared with cerebrally healthy control individuals (499 ± 70 versus 384 ± 33 pg/ml; not significant; Fig. 2).
CSF levels of IL-6 (47 ± 25 pg/ml versus 15 ± 3 pg/ml; P < 0.008) and IL-8 (91 ± 23 pg/ml versus 45 ± 6 pg/ml; P < 0.05) were both significantly increased in CSF from patients with CNS lupus as compared with cerebrally healthy SLE patients, supporting our previous findings [15,19]. Importantly, intrathecal levels of IL-6 and IL-8 significantly correlated with those of MMP-9 (r = 0.30 [P < 0.002] and r = 0.47 [P < 0.0001], respectively; Table 3 and Fig. 3). A neuronal degeneration marker (protein tau) and an astrocytic degeneration marker (glial fibrillary acidic protein) were both significantly increased in CSF from NPSLE patients as compared with CSF from SLE patients who were clinically free from CNS involvement (311 ± 78 pg/ml versus 178 ± 16 pg/ml [P < 0.05] and 1288 ± 708 pg/ml versus 396 ± 30 pg/ml [P < 0.009]), in concordance with previous findings [28,32]. Importantly, a significant correlation was noted between intrathecal MMP-9 and levels of tau and glial fibrillary acidic protein (P < 0.05 in both cases; Table 3).
Discussion
We previously reported that patients with CNS lupus express increased intrathecal levels of proinflammatory cytokines IL-6, IL-8 and interferon-γ. Furthermore, we recently demonstrated that inflammation in CNS during SLE leads to neurodegeneration, which manifests as increased levels of neuronal and astrocytic degradation products [28,32]. However, the mechanisms responsible for the brain damage occurring during CNS lupus have never been clarified. It was previously demonstrated [33,34] that proinflammatory cytokines are able to trigger production and release of tissue-derived MMPs. It is also established that most of the resident cells in human brain have the capacity to produce MMP-2 and MMP-9 – enzymes that are known to participate in brain damage (e.g. in case of infectious meningitis) [35-37].
In the present study we found significantly higher levels of MMP-9 in CSF from NPSLE patients than in CSF from SLE patients without CNS lupus and healthy control individuals. This finding may be of clinical significance because of correlation between MMP-9 and levels of neuronal/astrocytic degradation products in CSF, reflecting the potential of MMP-9 to damage brain parenchyma. Findings of a relationship between MMP-9 and brain damage in SLE are new and should be scrutinized critically. We believe that effort should be invested in analyzing free (i.e. non-tissue inhibitor of metalloproteinase bound) and metabolically active enzyme to ascertain the validity of our conclusions. The method used in the present study fulfils both of these requirements.
MMP-9 plays an essential role in the breakdown of extracellular matrix molecules at the blood–brain barrier. This, together with locally elevated levels of the chemokine IL-8, greatly facilitates the transmigration of activated inflammatory cells across the endothelium. Indeed, a significant correlation was observed between the intrathecal levels of MMP-9 and IL-8.
A broad range of cytokines that are expressed in viral meningitis are able to regulate the production of MMPs and tissue inhibitors of metalloproteinase [38,39]. IL-1α and IL-1β, as well as IL-6, induce or upregulate the transcription of MMP genes [40]. This, along with increased IL-6 levels in CSF of patients with CNS lupus, may provide an explanation for the local synthesis of MMP-9.
CSF levels of enzymatically active MMP-2 were not increased in NPSLE patients as compared with SLE patients without CNS involvement or compared with cerebrally healthy control individuals. This finding is consistent with a previous study of viral meningitis, in which increased expression of MMP-9 but constitutive expression of MMP-2 was found [37]. The reason for this discrepancy, seen in the present study as well as in the previous one, might be differential regulation of gene promotors for MMP-9 and MMP-2. Indeed, whereas the promotor regions of MMP-9, which is regulated by cytokines, harbor a nuclear factor-κB responsive element, those of MMP-2 lack a TATA box and contain two SP-1 sites, both characteristic of house keeping genes [40,41].
Conclusion
Our present and previous findings indicate that the following chain of events takes place during NPSLE: intrathecal release of inflammatory cytokines, leading to synthesis and release of MMP-9, potentially culminating in insult to brain parenchyma, resulting in release of neuronal and astrocytic degradation products and culminating in MRI verifiable lesions and clinical states of brain deficiency. Interestingly, a recent study [42] indicated that IgG dose-dependently downregulates secretion of MMP-9 by macrophages, suggesting a possible early therapeutic manipulation in NPSLE.
Competing interests
None declared.
Abbreviations
CNS = central nervous system; CSF = cerebrospinal fluid; IL = interleukin; MMP = matrix metalloproteinase; MRI = magnetic resonance imaging; NPSLE = neuropsychiatric involvement in systemic lupus erythematosus; SLE = systemic lupus erythematosus.
Acknowledgement
This work was supported by the Göteborg Medical Society, Swedish Association against Rheumatism, King Gustaf V: s Foundation, Swedish Medical Research Council, Nanna Svartz' Foundation, Börje Dahlin's Foundation, Swedish National Inflammation Network, Swedish National Infection and Vaccination Network, AME Wolff Foundation, and the University of Göteborg.
Figures and Tables
Figure 1 Cerebrospinal fluid expression of active matrix metalloproteinase (MMP)-9 in patients with systemic lupus erythematosus with (NPSLE) and without (No NPSLE) central nervous system involvement, and in cerebrally healthy control individuals.
Figure 2 Cerebrospinal fluid expression of active matrix metalloproteinase (MMP)-2 in patients with systemic lupus erythematosus with (NPSLE) and without (No NPSLE) central nervous system involvement, and in cerebrally healthy control individuals.
Figure 3 Scattergram showing relationship between IL-8 and matrix metalloproteinase (MMP)-9 in cerebrospinal fluid of all patients with systemic lupus erythematosus (r = 0.47; P < 0.0001).
Table 1 Clinical central nervous system manifestations in systemic lupus erythematosus patients included in the study
CNS manifestations NPSLE
(n = 43) No NPSLE
(n = 71) aPL syndrome
(n = 9)
Acute confusional state 1 1 0
Anxiety disorder 0 5 0
Aseptic meningitis 1 0 0
Cerebrovascular disease 6 8 4
Cognitive dysfunction 9 19 1
Demyelinating syndrome 4 1 0
Headache 8 21 4
Mood disorders 5 11 1
Movement disorder 2 0 3
Myastenia gravis 0 2 0
Myelopathy 1 1 0
Polyneuropathy 0 1 0
Mononeuropathy 0 1 0
Neuropathy 0 1 0
Psychosis 7 1 0
Seizure disorders 6 2 0
Tiredness 0 8 0
Each patient may have had multiple clinical manifestations of central nervous system (CNS) involvement. aPL, antiphospholipid; NPSLE, neuropsychiatric involvement in systemic lupus erythematosus.
Table 2 Pharmacological treatment of patients with systemic lupus erythematosus included in the study at the time the lumbar puncture was performed
Treatment NPSLE
(n = 43) No NPSLE
(n = 71) aPL syndrome
(n = 9)
Prednisolone (≤10 mg) 18 37 3
Prednisolone (>10 mg) 7 8 0
No prednisolone 18 23 5
Antimalarials 1 8 0
Azathioprine 5 10 3
Azathioprine + cyclosporin A 2 3 0
Azathioprine + antimalarials 0 0 0
Methotrexate 5 1 0
Cyclosporin A 0 6 1
Cyclophosphamide 14 10 0
Cyclophosphamide + cyclosporin A 3 0 0
Cyclophosphamide + antimalarials 0 1 0
No cytotoxic drug 11 30 4
Antihypertensive treatment 14 15 2
Low-dose aspirin 8 19 3
Warfarin 3 2 2
aPL, antiphospholipid; NPSLE, neuropsychiatric involvement in systemic lupus erythematosus.
Table 3 Relationship between intrathecal expression of matrix metalloproteinase-9 and inflammatory cytokines, and astrocytic and neuronal degradation products in 119 patients with systemic lupus erythematosus
MMP-9
r P
MMP-2 0.40 <0.0001
IL-6 0.30 0.002
IL-8 0.47 <0.0001
Tau 0.26 0.007
GFAP 0.33 <0.04
GFAP, glial fibrillary acidic protein; MMP, matrix metalloproteinase.
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| 15535833 | PMC1064867 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 23; 6(6):R551-R556 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1228 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14401553583110.1186/ar1440Research ArticleInfliximab therapy in rheumatoid arthritis and ankylosing spondylitis-induced specific antinuclear and antiphospholipid autoantibodies without autoimmune clinical manifestations: a two-year prospective study Ferraro-Peyret Carole [email protected] Fabienne [email protected] Jacques G [email protected] Jacques [email protected] Nicole [email protected] UF Autoimmunité, Laboratoire d'Immunologie, Centre Hospitalier Lyon-Sud, Pierre Bénite, France2 Service de Rhumatologie, Centre Hospitalier Lyon-Sud, Pierre Bénite, France2004 23 9 2004 6 6 R535 R543 22 4 2004 2 6 2004 30 6 2004 29 7 2004 Copyright © 2004 Ferraro-Peyret et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Treatment of rheumatoid arthritis (RA) with infliximab (Remicade®) has been associated with the induction of antinuclear autoantibodies (ANA) and anti-double-stranded DNA (anti-dsDNA) autoantibodies. In the present study we investigated the humoral immune response induced by infliximab against organ-specific or non-organ-specific antigens not only in RA patients but also in patients with ankylosing spondylitis (AS) during a two-year followup. The association between the presence of autoantibodies and clinical manifestations was then examined. The occurrence of the various autoantibodies was analyzed in 24 RA and 15 AS patients all treated with infliximab and in 30 RA patients receiving methotrexate but not infliximab, using the appropriate methods of detection. Infliximab led to a significant induction of ANA and anti-dsDNA autoantibodies in 86.7% and 57% of RA patients and in 85% and 31% of AS patients, respectively. The incidence of antiphospholipid (aPL) autoantibodies was significantly higher in both RA patients (21%) and AS patients (27%) than in the control group. Most anti-dsDNA and aPL autoantibodies were of IgM isotype and were not associated with infusion side effects, lupus-like manifestations or infectious disease. No other autoantibodies were shown to be induced by the treatment. Our results confirmed the occurrence of ANA and anti-dsDNA autoantibodies and demonstrated that the induction of ANA, anti-dsDNA and aPL autoantibodies is related to infliximab treatment in both RA and AS, with no significant relationship to clinical manifestations.
ankylosing spondylitisanti-β2-glycoprotein I autoantibodiesantiphospholipid autoantibodiesinfliximabrheumatoid arthritis.
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Introduction
Clinical trials in rheumatoid arthritis (RA) have demonstrated that antibodies directed against tumor necrosis factor α(TNF-α) (adalimumab, infliximab [Remicade®]) are highly beneficial for most patients who are refractory to classic treatment with disease-modifying anti-rheumatic drugs, methotrexate or steroid therapy [1-4]. These anti-inflammatory effects of infliximab have led to their use in other inflammatory diseases such as Crohn's disease [5] and ankylosing spondylitis (AS), with a similar efficacy to that in RA [6-8].
The side effects of these treatments are acknowledged to be very infrequent, with the exception of opportunistic intracellular infection, due particularly to the reactivation of latent Mycobacterium tuberculosis. The other major side effects are an exacerbation of demyelinating disorders and the induction of severe neutropenia and thrombocytopenia [1,2,4,9-11]. Infusion reactions have also been observed and have been correlated with the induction of anti-chimeric antibodies against infliximab [12]. The development of autoantibodies that are usually associated with systemic lupus erythematosus (SLE), namely antinuclear (ANA) and anti-double-stranded DNA (anti-dsDNA) autoantibodies, has also been observed after infliximab treatment in 63.8% and 13% of RA patients and in 49.1% and 21.5% of Crohn's disease patients, respectively [13-15]. Among the sera that were positive for anti-dsDNA autoantibodies, 9% were also positive for anti-Sm autoantibodies, which are specific for SLE [13]. However, only a few cases of SLE-like syndrome have been reported in infliximab-treated patients [9,13,16-18].
As yet, the occurrence of other autoantibodies has not been clearly demonstrated, such as antiphospholipid (aPL) autoantibodies and anti-β2-glycoprotein I (anti-β2GPI) autoantibodies, which are often associated with SLE [19,20], or autoantibodies associated with vasculitis, autoimmune hepatitis or autoimmune endocrine diseases, which have been reported in therapy that interferes with cytokine balance [21].
In the present study we investigate the prevalence of such autoantibodies during 2 years of follow-up in patients with RA or AS successfully treated with infliximab. The aim of the study was to discover whether the humoral response induced by infliximab is restricted to non-organ specific autoantibodies and to identify any associated clinical presentations, with the aim of monitoring their occurrence by detecting these autoantibodies. Concurrently, 30 patients whose RA was controlled only by methotrexate were analyzed at 1-year intervals as controls for autoantibody production.
Materials and methods
Patient sera
Twenty-four patients with RA and 15 patients with AS, fulfilling the ACR criteria [22] and the modified New York criteria [23], respectively, were monitored for autoantibody production over a 2-year period during which they were good responders, as defined by the modified disease activity scores [24], to a combination of methotrexate and infliximab. Concurrently, 30 RA patients well controlled by methotrexate for 6–15 years (mean 12 years) gave blood samples at 1-year intervals as controls for autoantibody production. Demographic and clinical statuses are presented in Table 1. Patients were followed clinically by the same physician during this period at regular intervals and in particular when they were receiving infliximab infusions. Clinical assessment (painful and swollen joint count, spine stiffness, careful examination of side effects, significant concomitant clinical features suggestive of infections or autoimmune disorders) were recorded accurately (Table 1). Nine patients discontinued infliximab treatment before the end of the study, between 3 and 18 months, because of adverse events, treatment inefficacy or severe infectious disease. Further details are given in Table 1.
Treatment protocol
Twenty-four RA and 15 AS patients were treated with infliximab (Centocor, Malvern, PA, USA). In RA patients, infliximab was administered in accordance with the schedule of the ATTRACT phase III clinical trials [4]. Patients were given infliximab at a dose of 3 mg/kg at 0, 2, 4 and 6 weeks and thereafter every 8 weeks. In AS patients, after the initial 6-week protocol with 5 mg/kg, infliximab was delivered every 6 or 8 weeks, depending on the clinical response. When AS patients presented a remission, the timing of infusions was dictated by disease relapse [25].
Follow-up of autoantibodies
Tests for autoantibodies were performed at baseline before the start of infliximab treatment and during the 24-month duration of infliximab treatment as indicated below. The sera of the 30 control RA patients were analyzed twice with a 1-year interval.
Detection of ANA
Tests for ANA were performed at the start of infliximab treatment and at 6, 12, 18 and 24 months, by an indirect immunofluorescence technique (IIF) using HEp2 cells (Bio-Rad, Marnes-la-Coquette, France). Sera were diluted 1:80 and the conjugate was a goat anti-human F(ab')2 IgG, A, M (H+L) antibody conjugated to fluorescein isothiocyanate (diluted 1:100) (Bio-Rad). Classic titration of each ANA positive at a titer of 1:80 was performed by serial dilutions to 1:5120. A titer equal to or greater than 1:160 was interpreted as a positive result. For positive sera that had nuclear granular or cytoplasmic staining, the identification of autoantibodies against (ENA) was further investigated by enzyme-linked immunosorbent assay (ELISA) with an anti-human IgG (H+L) conjugate (Biomedical Diagnostics, Marne-la-Vallée, France).
Detection of anti-dsDNA autoantibodies
Tests for anti-dsDNA autoantibodies were performed at the start of infliximab treatment and, depending on the formation of ANA, at 6, 12, 18 and 24 months of treatment with the use of a radioimmunological test (Dade Behring, Paris, France) in accordance with the manufacturer's instructions. A titer equal or greater than 5 IU/ml was interpreted as a positive result. For positive sera, the anti-dsDNA autoantibody isotype was determined by ELISA (Pharmacia, Freiburg, Germany) with an anti-human IgG (H+L) (Bio-Rad) or an anti-human IgM (H+L) (Dako) conjugate.
Detection of anti-smooth muscle (SMA), anti-mitochondrial (AMA), anti-liver kidney microsomes (LKM), anti-thyroid peroxidase (TPO), anti-thyroglobulin (TG) and anti-adrenal (ADA) autoantibodies
Tests for SMA, AMA, LKM, TPO, TG and ADA autoantibodies were performed at the start of infliximab treatment and then at 3, 6, 12, 18 and 24 months. The sera of the 30 control RA patients were analyzed twice with a 1-year interval. For SMA, AMA, LKM and ADA, sera diluted 1:30 were tested on mouse stomach, kidney, liver or adrenal sections (Biomedical Diagnostics), with the same technique as described for ANA. For TPO and TG autoantibodies, an ELISA technique was performed in accordance with the manufacturer's instructions with an anti-human IgG (H+L) conjugate (Pharmacia, Saint-Quentin-en-Yvelines, France).
Detection of anti-neutrophil cytoplasmic autoantibodies (ANCA)
Tests for ANCA were performed at the start of infliximab treatment and then after 6 and 12 months. The sera of the 30 control RA patients were analyzed twice with a 1-year interval. The sera diluted 1:20 were tested by IIF on human neutrophils fixed in ethanol (Menarini Diagnostics, Antony, France) with the same technique as for ANA. Positive sera were further tested for reactivity against myeloperoxidase and proteinase 3 by using an ELISA with an anti-human IgG (H+L) conjugate (Bioadvance, Emerainville, France). Titers were considered positive when they were 20 arbitrary units (AU)/ml or more.
Detection of aPL and anti-β2GPI autoantibodies
Investigation of aPL autoantibodies was performed by the detection of anticardiolipin autoantibodies (ACL). ACL and anti-β2GPI autoantibodies were evaluated at baseline and at 6, 12 and 24 months after the start of infliximab treatment. The sera of the 30 control RA patients were analyzed twice with a 1-year interval. ACL were detected with an ELISA in accordance with the manufacturer's instructions by using anti-human IgG (H+L) or IgM (H+L) conjugates. Values were expressed as arbitrary G phospholipid (GPL) or M phospholipid (MPL) units. Positive results were graded as low positivity (IgG 11–23 GPL, IgM 6–10 MPL), moderate positivity (IgG 24–39 GPL, IgM 11–29 MPL) or high positivity (IgG ≥ 40 GPL, IgM ≥ 30 MPL). The anti-β2GPI autoantibodies were detected by using a home-made assay previously described [26] with serum samples diluted 1:50 and peroxidase-conjugated anti-human IgG (H+L) or IgM (H+L) (Cappell, ICN Biomedicals, OH, USA) diluted 1:400. Positive results were graded as low positivity for a value from a ratio of 1.2–1.9 AU/ml (attenuance ['optical density'] of the sample divided by attenuance of the cut-off), moderate positive for a value of ratio between 2 and 3 AU/ml and high positive for a value superior to a ratio of 3 AU/ml. The cut-off was determined by using the mean plus 5 standard deviations of attenuance of 100 sera from blood donors (data not shown).
Statistics
Statistical analysis (95% and 99% confidence interval) was performed with the χ2 test when applicable and with Fisher's exact test in other conditions.
Ethics
Written informed consent was obtained from all patients and the study was approved by the Research and Ethics Committee of the Hospices Civils de Lyon.
Results
Occurrence of ANA and anti-dsDNA autoantibodies in RA and AS patients
At baseline, 9 of 24 (37.5%) infliximab-treated RA patients, 2 of 15 (13.3%) AS patients and 5 of 30 (16.7%) control RA patients were tested positive for ANA (Table 2). After 12 months of therapy, the induction of ANA was observed in 12 infliximab-treated RA patients, 8 AS patients and 4 control RA patients. At that time, the total number of positive ANA patients was 21 of 24 (87.5%) for infliximab-treated RA patients, 10 of 15 (66.7%) AS patients and 9 of 30 (30%) control RA patients. The difference between the number of induced ANA compared with the number of positive ANA at baseline was statistically significant (P < 0.0001) for infliximab-treated RA and AS patients, whereas the difference was not significant for the RA control group. The difference in induction was also significant for the infliximab-treated RA patients (P < 0.0001) comparing the two RA groups.
After 2 years of infliximab therapy, ANA became positive in one other infliximab-treated RA patient and three more AS patients, giving a total induction of 87% in RA and 85% in AS. The induction of ANA appeared between 3 and 18 months (mean 6.35 months) for RA and between 3 and 24 months (mean 10.6 months) for AS. Except in two RA patients, all the induced ANA were still positive at the end of the study, including in eight of nine patients who discontinued the treatment. One RA became negative 3 months after the end of the treatment. Furthermore, in six of the nine sera of infliximab-treated RA patients positive at baseline, the ANA titer increased up to twofold (data not shown). The titer of ANA showed a higher level between the positive ANA at the baseline compared with the titer of induced ANA, but the difference was not significant. In most ANA-positive sera during infliximab treatment, the pattern of staining was homogeneous. Two of the 22 infliximab-treated RA ANA-positive sera had granular nuclear staining characteristic of ENA. The specific target could not be identified with the use of the classic ELISA kit for ENA detection.
For anti-dsDNA autoantibodies, 1 of 24 infliximab-treated RA patients (4.2%), 2 of 15 AS patients (13.3%) and none of the control RA patients were positive at baseline (Table 2). After 12 months of treatment, induction of anti-dsDNA autoantibodies was observed in 10 of 23 (46.5%) infliximab-treated RA patients, 3 of 13 (23%) AS patients and 2 of 30 (6.7%) control patients. The induction was observed between 3 and 12 months. Three further infliximab-treated RA patients and one further AS patient became positive at 18 and 24 months, respectively, giving a total induction of 57% in RA and 31% in AS.
After the 2-year follow-up, the total number of positive patients was 14 of 24 (58.33%) for infliximab-treated RA patients, 6 of 15 (40%) for AS patients and 2 of 30 (6.7%) for control RA patients. All patients who became positive for anti-dsDNA autoantibodies were also positive for ANA. All the induced anti-dsDNA autoantibodies remained positive until the end of the study, including in three of four positive patients who discontinued the treatment. One RA patient became negative 3 months after the end of the treatment. The difference between the number of induced anti-dsDNA autoantibodies and the number of positive anti-dsDNA autoantibodies at baseline was statistically significant for the infliximab-treated RA patients (P < 0.0001) and for the infliximab-treated AS patients (P < 0.02) compared with the RA control group. Comparing the two RA groups, the difference in induction was also significant for the infliximab-treated RA patients (P < 0.0001). The titer of anti-dsDNA autoantibodies showed a higher level between the positive autoantibodies at baseline compared with the induced autoantibodies. The formation of ANA and anti-dsDNA autoantibodies was not linked to clinical events, namely infectious side effects, allergy or lack of efficacy.
Occurrence of aPL/ACL and anti-β2GPI autoantibodies in RA and AS patients
At baseline, no RA or AS patients were positive for ACL or for anti-β2GPI autoantibodies. At the end of the study, significant levels of ACL were found in infliximab-treated RA patients (5 of 24, P < 0.01) and in infliximab-treated AS patients (4 of 15, P < 0.01) compared with the RA control group (Table 3).
Induction of anti-β2GPI autoantibodies was observed in 2 of 24 infliximab-treated RA patients and in none of the control RA patients (Table 3). The difference was not significant between the two RA populations nor within the RA and AS group, comparing the number of induced autoantibodies at baseline and after treatment. In infliximab-treated RA patients, sera were positive for ACL or anti-β2GPI autoantibodies (Table 3). In the group of AS patients, the two induced sera were positive for both ACL and anti-β2GPI autoantibodies.
All ACL and anti-β2GPI autoantibodies were from patients positive for ANA. Five of 11 ACL or anti-β2GPI autoantibody-positive sera were positive for anti-dsDNA autoantibodies. Induction did not occur simultaneously and did not seem to be determined by clinical events. Two AS sera positive for anti-dsDNA autoantibodies at baseline became positive for ACL autoantibodies 6 and 10 months after the beginning of treatment. For the other sera, anti-dsDNA, ACL and anti-β2GPI autoantibodies developed between 6 and 12 months after the start of treatment. No correlation was found between the occurrence of side effects (including infections), clinical status (including lupus-like symptoms, thrombopenia or thrombosis) and anti-β2GPI or ACL autoantibodies.
Isotypes of induced anti-dsDNA, aPL/ACL and anti-β2GPI autoantibodies in RA and AS patients
Most of the anti-dsDNA autoantibodies detected during infliximab treatment of RA patients, of AS patients and in the control RA population were of IgM isotype (11 of 13 [85%], 4 of 4 [100%] and 2 of 2 [100%] respectively). One of the sera from infliximab-treated RA patients and one from AS patients were positive for both IgG and IgM. Two sera in the infliximab-treated RA group were positive only for IgG. The presence of the IgG isotype was not associated with any particular clinical pattern such as infections, lupus-like syndrome or side effects of infusion.
Among the ACL, five of five infliximab-treated RA patients and one of four AS patients were of IgM isotype. Three AS patients were of IgG isotype. The isotypes of the positive anti-β2GPI autoantibodies were IgM and IgG (one case), IgG (one case) for RA and IgM or IgG for AS. As for the IgG isotype in induced anti-dsDNA autoantibodies, no significant clinical association was observed in patients presenting the IgG ACL and/or anti-β2GPI autoantibody profile.
Occurrence of TPO, TG, AMA, LKM, SMA, ADA and ANCA autoantibodies
Three of the 24 (12.5%) infliximab-treated RA patients, 6 of 30 (20%) control RA patients and no AS patients had TPO or TG autoantibodies at baseline. Patients with RA remained positive during infliximab treatment and at the 1-year intervals of methotrexate treatment. Only one patient (1 of 21, 4.8%) in the infliximab-treated RA group developed both TPO and TG autoantibody positivity after 12 months of treatment.
One of 24 infliximab-treated RA patients (4.2%) and 2 of 15 AS patients (13.3%) who were negative at baseline became positive for ANCA as determined by IIF. The target of these ANCA was identified by ELISA as proteinase 3 for two sera (25 and 40 AU/ml) and both myeloperoxidase and proteinase 3 for one serum (45 and 30 AU/ml). For the RA control group, ANCA were observed in two patients at baseline and remained positive at 1 year. The target of these ANCA was neither proteinase 3 nor myeloperoxidase. No other patient developed such autoantibodies after the 1-year interval analysis.
Three of the 24 infliximab-treated RA patients (12.5%) and 2 of 30 RA controls (6.7%) were SMA positive at baseline. Three of 21 infliximab-treated RA sera (14.3%) and 2 of 15 AS sera (13.3%) that were negative at baseline became positive for SMA autoantibodies at 1.5, 3, 6, 3 and 6 months respectively. These SMA autoantibodies were not antiactin autoantibodies, the only autoantibodies that are specific for autoimmune hepatitis.
Neither RA nor AS patients developed AMA, LKM or ADA autoantibodies.
The occurrence of TPO, TG, ANCA, AMA, LKM, SMA or ADA autoantibodies during infliximab therapy was not statistically significant.
Discussion
The occurrence of a large panel of autoantibodies that are considered as biological markers of various autoimmune diseases has been investigated in a population of RA and AS patients treated with infliximab for 2 years, the longest period described so far for this kind of management. To avoid the bias of spontaneous autoantibody production under methotrexate, a control population of RA patients treated only with methotrexate was analyzed in parallel at 1-year intervals.
ANA, anti-dsDNA and aPL were the only autoantibodies to be significantly induced by infliximab treatment in RA and AS patients. This induction has already been described for ANA and anti-dsDNA autoantibodies [13,27] but our study demonstrates for the first time that infliximab treatment can also induce aPL autoantibodies in both RA and AS patients.
Our observation of ANA in up to 91.7% and 86.7% of RA and AS patients, respectively, after infliximab therapy is consistent with recent data published during the course of the present study [27]. However, the occurrence of anti-dsDNA autoantibodies was higher in our study for both RA and AS [27,28]. These discrepant results may be due to the longer period of our analysis. Indeed, the previous study analyzed this occurrence for 8.5 months after the initiation of infliximab treatment [27]; we found that anti-dsDNA autoantibodies can be induced after this period, the latest induction being found 24 months after the onset of infliximab treatment.
Clinical monitoring of the patients did not show any symptoms characteristic of SLE in the subgroup that was positive for ANA/anti-dsDNA autoantibodies.
Antiphospholipid autoantibodies were induced in 21% (5 of 24) and 27% (4 of 15) of our RA and AS patients, respectively. Anti-β2GPI autoantibodies were induced in 8% and 13% of our RA and AS patients, respectively. Until now, the induction of such autoantibodies has not been described in patients treated with infliximab therapy. However, it has been demonstrated in a single study in 5 of 8 (63%) RA patients treated with etanercept [29]. In that study, the presence of aPL autoantibodies along with anti-dsDNA autoantibodies was concomitant with several infections [29]. In our study, we also found an association between anti-dsDNA and aPL autoantibodies but clinical monitoring of the patients did not show any relationship between a particular serological profile and the occurrence of infection, thrombosis or thrombocytopenia for the aPL autoantibody-positive subgroup.
In contrast with other studies showing aPL autoantibodies in RA and AS populations, we found no aPL autoantibodies at baseline [30-32]. Most of the studies reporting a high frequency of aPL autoantibodies were conducted with non-standard tests; furthermore, the titers of most of the positive sera were very low. The difference in sensitivity might also be due to the choice of a different cut-off of positivity for the aPL autoantibody test and to the different clinical characteristics of the patients analyzed. Thus, the ACL test is not specific with low-positive results, so we chose a high cut-off for this test [33].
The induction of ANA and aPL autoantibodies was clearly due to infliximab, especially in the RA group, because no such induction was observed in the control RA group treated with methotrexate alone. The mechanisms that underlie autoantibody development during infliximab treatment are intriguing. These autoantibodies do in fact occur in a variety of disorders, such as RA, AS and Crohn's disease, which are characterized by different physiopathological mechanisms and different doses of infliximab. One can then postulate that this particular induction is due to the partial blockage of TNF-α induced by infliximab therapies. The role of the disturbance of the cytokine network in such induction has already been demonstrated for another cytokine, interferon γ, inducing the development of autoantibodies in patients with hepatitis C viral infection or RA [21,34,35].
Induction of autoantibodies could be a predictable consequence of anti-TNF-α blockade because this blockade could promote humoral autoimmunity by inhibiting the induction of cytotoxic T lymphocyte response, which normally suppresses autoreactive B-cells [36]. Infliximab might also act by neutralizing the biological activity of TNF-α by binding the soluble forms of TNF-α, thereby preventing the interaction of TNF-α with its cellular receptors, p55 and p75. Infliximab also binds the transmembrane form of TNF-α and could induce antibody-dependent or complement-dependent cellular cytotoxicity of the cells expressing the cytokine [37]. Furthermore, infliximab has been shown to increase the number of apoptotic T lymphocytes in the lamina propria [38] and apoptotic monocytes in peripheral blood in Crohn's disease [39]. In this case, one hypothesis concerning the development of autoimmune diseases such as SLE is that an increased apoptotic process could promote the release of numerous autoantigens, leading to the development of autoantibodies against cytoplasmic and nuclear compounds such as ANA and dsDNA [40], especially if production of these autoantibodies is no longer suppressed by the action of infliximab on the suppressor T cell population. This apoptotic process might not occur in organ-specific cells because these cells, namely thyrocytes, do not harbor TNF-α receptor, thus shedding some light on the findings concerning the absence of organ-specific autoantibodies associated with autoimmune vasculitis, hepatitis or endocrine diseases.
Like Charles and colleagues [13], we demonstrated that most of the anti-dsDNA autoantibodies detected during the treatment of RA with infliximab were of IgM isotype. Furthermore, we showed that most of the detected aPL autoantibodies were also of IgM isotype. The role of these IgM in the development of autoimmune diseases remains to be elucidated. Natural autoreactive IgM autoantibodies might suppress autoimmunity by inducing B cell tolerance and thus by participating in the negative selection of autoreactive B cells. The larger pool of autoantibodies of IgM isotype observed during infliximab treatment might be the consequence of a higher production of natural autoreactive IgM, but might also be an induced population that can further switch to IgG with a well-known pathogenic effect. A high frequency of IgM might also result from TNF blockade, as it was demonstrated in a murine model of collagen-induced arthritis that anti-TNF-α monoclonal antibodies reduce isotype switching to IgG in the local draining lymph node [41].
Conclusion
Our results show that infliximab induces ANA, anti-dsDNA and aPL autoantibodies at various times after the start of treatment. However, it seems that the development of such autoantibodies is not predictive of the development of SLE-like syndrome, because during the 2-year follow-up of infliximab therapy no APL syndrome or SLE syndrome appeared. Nevertheless, these findings do not exclude the possibility that such pathology might develop after a longer period of infliximab treatment. They underline the need to monitor the humoral response, namely autoantibodies and clinical manifestations, in patients treated with infliximab over a longer period.
Competing interests
None declared.
Abbreviations
ACL = anticardiolipin; ADA = anti-adrenal autoantibodies; AMA = anti-mitochondrial autoantibodies; ANA = antinuclear autoantibodies; ANCA = anti-neutrophil cytoplasmic autoantibodies; aPL = antiphospholipid; AS = ankylosing spondylitis; dsDNA = double-stranded DNA; ELISA = enzyme-linked immunosorbent assay; ENA = anti-extractible nuclear antigen; β2GPI = β2-glycoprotein I autoantibodies; GPL = G phospholipid; IIF = indirect immunofluorescence; LKM = anti-liver kidney microsomes; MPL = M phospholipid; RA = rheumatoid arthritis; SLE = systemic lupus erythematosus; SMA = anti-smooth muscle; TG = anti-thyroglobulin; TNF-α = tumor necrosis factor α; TPO = thyroid peroxidase.
Acknowledgements
We thank MC Letroublon and all the biological technicians of the laboratory for their technical assistance.
Figures and Tables
Table 1 Clinical characteristics of patients
Control Rheumatoid arthritis Ankylosing spondylitis
Number of patients 30 24 15
Mean age, years (range) 63 (30–83) 56 (26–77) 41 (26–57)
Number of women (%) 23 (76.7) 16 (66.7) 4 (26.7)
Disease duration, years (range) 7.4 (1–22) 12 (3–32) 17 (6–30)
Infliximab treatment (mg/kg) 0 3 5
Concomitant medication
Number of patients with
NSAID 22 10 5
Corticosteroids 19 19 8
Methotrexate 30 24 6
Side effects
Number of patients with
Allergy 3 Ma, 1 Sa 1 Sa
Infections 2 Ma, 2 Sa 2 Ma, 1 Sa
Other (amyloidosis) 0 3 Ma
Inefficacy of treatment
Number of patients 2 2
Months 8 and 10 Months 5 and 7
AS, ankylosing spondylitis; NSAID, non-steroidal anti-inflammatory drugs; RA, rheumatoid arthritis.
aSignificant side effects and inefficacy that could lead to infliximab discontinuation. If side effects were severe (S), infliximab was stopped; thus nine patients discontinued infliximab treatment before the end of the study, between 3 and 18 months in 5 RA and 2 AS patients. If moderate (M), infliximab was continued. The severe infections were pulmonary tuberculosis (RA), septic pericarditis (AS) and Streptococcus bovis endocarditis (RA). Two severe anaphylactic reactions during infliximab infusion (1 RA, 1 AS) led to discontinuation of treatment and in one case (RA) required resuscitation. Three patients with severe long-standing AS were suspected of amyloidosis at the start of infliximab treatment because of nephritic proteinuria. The diagnosis was confirmed by renal biopsy and the infusion carried out.
Table 2 Detection of ANA and anti-dsDNA autoantibodies during infliximab treatment
Number of positive sera ANA Anti-dsDNA autoantibodies
Infliximab RA (n = 24)
n
0
9 1
ni12 12 10
ni24 13 13
n
t
22 14
Infliximab AS (n = 15)
n
0
2 2
ni12 8 3
ni24 11 4
n
t
13 6
Control RA (n = 30)
n
0
5 0
ni12 4 2
n
t
9 2
ANA, antinuclear autoantibodies; anti-dsDNA, anti-double-stranded DNA autoantibodies; n0, number of positive sera before treatment; ni12, number of positive sera-induced autoantibodies after 12 months of treatment; ni24, number of positive sera-induced autoantibodies after 24 months of treatment; nt, number of positive sera before treatment plus number of induced autoantibodies during infliximab treatment.
Table 3 Detection of ACL and anti-β2GPI autoantibody-positive sera during infliximab treatment
Number of positive sera Anti-β2GPI autoantibodiesa aCL autoantibodiesb
IgG IgM IgG IgM
Infliximab, RA (n = 24) n0 0 0 0 0
nt 2 1c 0 5
Infliximab, AS (n = 15) n0 0 0 0 0
nt 0 2 3 1
Control, RA (n = 30) n0 0 0 0 0
nt 0 0 0 0
Anti-β2GPI, anti-β2-glycoprotein I autoantibodies; aCL, anticardiolipin autoantibodies; n0, number of positive sera before treatment; nt, number of positive sera before treatment plus number of induced autoantibodies during infliximab treatment.
aAnti-β2GPI with one high titer (5.4 AU/ml [IgG], 5 AU/ml [IgM]) and one moderate titer (2.1 AU/ml [IgG]), in 2 of 15 AS with two high titers (8.7 AU/ml [IgG], 3.4 AU/ml [IgM]).
bLow, moderate and high titers were observed in two patients (15.1, 23 G phospholipid), in six patients (22.4, 22.8, 15.4, 21, 22.1 M phospholipid, 27 G phospholipid), and in one patient (41.5 M phospholipid) respectively.
cBoth IgG and IgM.
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| 15535831 | PMC1064868 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Sep 23; 6(6):R535-R543 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1440 | oa_comm |
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Arthritis Res TherArthritis Res. TherArthritis Research & Therapy1478-63541478-6362BioMed Central ar144310.1186/ar1443Research ArticleApolipoprotein A-I infiltration in rheumatoid arthritis synovial tissue: a control mechanism of cytokine production? Bresnihan Barry [email protected] Martina [email protected] Oliver [email protected] Jean-Michel [email protected] Danielle [email protected] Department of Rheumatology, St Vincents University Hospital, Dublin, Ireland2 Service of Immunology and Allergy, Faculty of Medicine, Geneva, Switzerland2004 6 10 2004 6 6 R563 R566 22 6 2004 19 8 2004 Copyright © 2004 Bresnihan et al.; licensee BioMed Central Ltd.2004Bresnihan et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.The production of tumor necrosis factor α (TNF-α) and interleukin-1β (IL-1β) by monocytes is strongly induced by direct contact with stimulated T lymphocytes, and this mechanism may be critical in the pathogenesis of rheumatoid arthritis (RA). Apolipoprotein A-I (apoA-I) blocks contact-mediated activation of monocytes, causing inhibition of TNF-α and IL-1β production. This study examined the hypothesis that apoA-I may have a regulatory role at sites of macrophage activation by T lymphocytes in inflamed RA synovial tissue. Synovial tissue samples were obtained after arthroscopy from patients with early untreated RA or treated RA and from normal subjects. As determined by immunohistochemistry, apoA-I was consistently present in inflamed synovial tissue that contained infiltrating T cells and macrophages, but it was absent from noninflamed tissue samples obtained from treated patients and from normal subjects. ApoA-I staining was abundant in the perivascular areas and extended in a halo-like pattern to the surrounding cellular infiltrate. C-reactive protein and serum amyloid A were not detected in the same perivascular areas of inflamed tissues. The abundant presence of apoA-I in the perivascular cellular infiltrates of inflamed RA synovial tissue extends the observations in vitro that showed that apoA-I can modify contact-mediated macrophage production of TNF-α and IL-1β. ApoA-I was not present in synovium from patients in apparent remission, suggesting that it has a specific role during phases of disease activity. These findings support the suggestion that the biologic properties of apoA-I, about which knowledge is newly emerging, include anti-inflammatory activities and therefore have important implications for the treatment of chronic inflammatory diseases.
apolipoprotein A-Icytokinesinflammationrheumatoid arthritissynovium
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Introduction
Inflammation is a critical host-defense mechanism. One of its functions is to direct plasma factors and immunoinflammatory cells to lesions in order to eradicate infection and facilitate tissue repair. In many chronic inflammatory diseases, infiltration of the target tissue by blood-derived cells precedes tissue damage. For example, it is believed that in rheumatoid arthritis (RA), the initial cellular event in synovial tissue is proliferation of fibroblast-like synoviocytes, which release chemokines that contribute to the recruitment of inflammatory cells, including monocytes and lymphocytes [1]. It has been proposed that the first cells to infiltrate synovial tissue are T lymphocytes, suggesting that they have an important role in pathogenesis. We previously showed that stimulated T cells induced pathological effects through direct cellular contact with monocyte–macrophages, causing the abundant production of interleukin-1β (IL-1β) and tumor necrosis factor α (TNF-α). This observation has been confirmed by others (for review see [2]). The unregulated production of IL-1β and TNF-α in RA has been recognized for several years, and their role in the pathophysiology has been confirmed by the demonstration that targeted blockade improves patients' clinical status [3,4].
We therefore postulate that contact-mediated cytokine production is highly relevant to the pathogenesis and the maintenance of chronic inflammation in diseases such as RA. Regulating a potent mechanism that induces both IL-1β and TNF-α may be important in maintaining a low level of monocyte activation within the bloodstream. We recently identified apolipoprotein A-I (apoA-I) as a specific inhibitor of contact-mediated activation of monocytes [5]. ApoA-I is a 'negative acute-phase protein' and the principal protein of high-density lipoproteins (HDLs). Variations of apoA-I concentration have been observed in several inflammatory diseases. In RA, the levels of circulating apoA-I and HDL cholesterol in untreated patients are lower than in normal controls [6-8]. In contrast, apoA-I levels were increased in the synovial fluid of patients with RA [9], although these were still only one-tenth those in plasma. The elevation of apoA-I levels in the synovial fluid of untreated patients with RA was accompanied by increased cholesterol levels, suggesting infiltration of HDL particles in the inflamed joint. In this study, we examined synovial tissue from patients with active RA in order to determine if apoA-I infiltration had occurred at sites of contact between T lymphocytes and macrophages.
Materials and methods
Synovial tissue samples
Synovial biopsies were obtained from the knee joints after arthroscopy in patients diagnosed with RA, who had all given their informed consent. Normal synovium was obtained from a patient without arthritis who was having a leg amputated. Arthroscopy and biopsy were performed under local anesthesia using a 2.7-mm Storz arthroscope and a 1.5-mm grasping forceps. The sampled tissue was immediately embedded in Tissue-Tek® OCT compound (Sakura, Zoeterwoude, the Netherlands) and snap frozen in liquid nitrogen.
Monoclonal antibodies
All antibodies used were murine antihuman monoclonal antibodies (antibodies were diluted in PBS; anti-apoA-I contained 0.6 M sodium chloride); anti-apoA-I, type 2 (Calbiochem-Novabiochem Corporation, Darmstadt, Germany), was used at 1/3000 dilution; anti-C-reactive protein (CRP), clone CRP-8 (Sigma Chemicals, St Louis, MO, USA), at 1/200 dilution; anti-Von Willebrand factor/factor VIII-related antigen (FVIII), clone F8/86 (DAKO, Glostrup, Denmark), at 1/50 dilution; and anti-acute-phase serum amyloid A (A-SAA) (gift from Dr AS Whitehead, Philadelphia, PA, USA), at 1/1200. Isotype-matched murine IgG1 (DAKO) was used at the same concentration as each of the primary antibodies.
Immunohistochemistry
Synovial tissue sections were cut at 7 μm and mounted on slides coated with 3-aminopropyltriethoxy-silane (Sigma). Slides were air-dried overnight, wrapped in foil, and stored at -80°C. A standard three-stage immunoperoxidase technique was used, with a Peroxidase VECTASTAIN® Elite ABC kit (Vector Laboratories, Burlingame, CA, USA). Slides were removed from the -80°C freezer and allowed to thaw at room temperature for 20 minutes. Sections were fixed in acetone for 10 minutes and with normal horse serum (VECTASTAIN® Elite ABC kit) for 15 minutes. The relevant primary antibody was added to sections for 1 hour at room temperature. Sections were washed and incubated with PBS for 5 minutes. Anti-mouse IgG secondary antibody (VECTASTAIN® Elite ABC kit) was added for 30 minutes and the ABC solution (VECTASTAIN® Elite ABC kit) was added to sections for 30 minutes. Sections were treated with 3% hydrogen peroxide for 7 minutes, washed in distilled water for 1 minute, and incubated in PBS for 5 minutes, followed by the addition of 3,3'-diaminobenzidine (Sigma) for 12 minutes. The chromogenic reaction was stopped by immersion in water. Sections were counterstained in Mayer's hemalum, dehydrated in alcohol, cleared in xylene, and mounted in DPX (BDH, Poole, UK).
Results
The demographic and clinical details of the patients studied are outlined in Table 1. Synovial tissue samples from eight patients with active RA were selected. The mean duration of disease was 19 (range 1–48) months, he mean swollen joint count was 20 (range 10–36), and the mean CRP level was 12.3 (range <3 to 22)mg/L. Six patients were receiving nonsteroidal anti-inflammatory drugs at the time of synovial biopsy. Two were receiving a disease-modifying anti-rheumatic drug, methotrexate, 15 mg/week in both cases. Two were receiving prednisolone, 10 mg/day. None had received an intra-articular corticosteroid injection to the biopsied knee joint. Synovial tissue was also obtained from two patients with quiescent RA (no swollen joints, CRP <3 mg/L) and from one patient who was unaffected by arthritis. Both patients with quiescent RA were receiving methotrexate, 7.5 mg/week.
Table 1 Demographic and clinical details of patients with active rheumatoid arthritis
Total no. of patients 8
Mean duration of disease (range) 19 (1–48) months
Mean no. of swollen joints (range) 20 (10–36)
Mean C-reactive protein (range) 12.3 (0–22)mg/dL
No. of patients receiving:
NSAIDs 6
DMARDs (MTX 15 mg/wk) 2
Prednisolone 2
DMARD, disease-modifying antirheumatic drug; MTX, methotrexate; NSAID, nonsteroidal anti-inflammatory drug.
All synovial tissue sections from the eight patients with active RA showed prominent blood vessels and perivascular cellular infiltration. Specific apoA-I staining was present in all samples. The immunohistologic appearances were consistent, and included prominent endothelial apoA-I staining of most blood vessels (Fig. 1a). The vessels were surrounded by a confined area of intense staining that was consistent with extravasation of apoA-I within the perivascular cell infiltrate. No staining was observed in the negative control tissue sections (Fig. 1b). In tissue samples obtained from patients with RA that were in apparent remission, only faint vascular and perivascular apoA-I staining was present (Fig. 1e), even though the sections contained blood vessels that were easily identified (Fig. 1f). As expected, the cellular infiltrate in these sections was less intense. There was no perivascular apoA-I staining in the synovial tissue sample obtained from the knee joint unaffected by arthritis (Fig. 1c). Contrary to the abundant presence of perivascular apoA-I staining in tissue sections obtained from patients with active RA, there was no evidence of perivascular CRP or A-SAA. Tissue samples from three patients were studied for the presence of perivascular CRP. The serum CRP levels were elevated in all three at the time of biopsy (11–20 mg/L). Faint CRP staining of endothelial cells was observed (Fig. 1g). Tissue samples from five patients were studied for the presence of perivascular A-SAA. As expected, A-SAA staining was demonstrated in lining layer cells but not within the perivascular infiltrate (Fig. 1h).
Figure 1 Apolipoprotein A-I (apoA-I) is localized in the perivascular region of the inflamed synovium. (a) Active rheumatoid arthritis (RA) synovium stained with anti-apoA-I; (b) active RA synovium stained with isotype-matched negative control; (c) normal synovium stained with anti-apoA-I; (d) normal synovium stained with anti-factor VIII; (e) remission RA synovium stained with anti-apoA-I; (f) remission RA synovium stained with anti-factor VIII; (g) active RA synovium stained with anti-C-reactive protein; (h) active RA synovium stained with antibody against serum amyloid A.
Discussion
The most salient observation from this study was apoA-I infiltration in inflamed synovial tissue and its retention in perivascular regions, where T lymphocytes and macrophages accumulate. The localization of positive acute-phase proteins, such as CRP and A-SAA, was different from that of apoA-I: only faint staining, limited to vascular endothelium, was observed for CRP, and A-SAA was observed in lining layer cells, which are a known source of local synthesis [10].
We have previously shown apoA-I to inhibit the production of both IL-1β and TNF-α in monocytes activated by direct contact with stimulated T cells. This mechanism may have a role in regulating monocyte activation in the bloodstream [5]. This study demonstrated that apoA-I infiltrated perivascular regions of the synovium where A-SAA, which can dissociate apoA-I from HDLs [11], and CRP were absent. The perivascular localization of apoA-I suggests that it could have an inhibitory role in zones where T lymphocytes are in close contact with monocyte–macrophages, with a tendency to form 'lymphoid microstructures' [12]. The absence of A-SAA suggests that it is unlikely to restrict the inhibitory activity of apoA-I in the contact-mediated induction of IL-1β and TNF-α production in tissue [13]. To overcome apoA-I inhibition, A-SAA would be expected to localize in the same area. Since apoA-I is virtually absent from the synovial tissue of patients with inactive RA (Fig. 1c), its presence in actively inflamed tissue suggests that its infiltration during a flare-up may represent a physiologic mechanism that inhibits proinflammatory cytokine production and limits disease recurrence. The transient infiltration of apoA-I may also explain why RA, like many other chronic inflammatory diseases, characteristically presents as a relapsing–remitting disease in many patients. During phases of RA associated with joint damage, the inhibitory effects of apoA-I on the destructive mechanisms may not be sufficiently potent.
In RA, variations of apoA-I concentrations were observed in plasma, where it was decreased, and in synovial fluid, where it was increased [6-9]. The elevation of apoA-I levels in synovial fluid of RA patients correlated with a rise in cholesterol, suggesting infiltration of HDL particles into the inflamed joint. Similarly, active juvenile RA was associated with reduced HDL blood levels and a significant decrease in apoA-I concentration in plasma [14]. These studies suggest that variations of apoA-I levels may inversely correlate with disease activity. The observation that apoA-I can infiltrate and be retained at the inflammatory site suggests that apoA-I may inhibit the local triggering of IL-1β and TNF-α release by monocyte–macrophages that are in direct contact with stimulated T cells in these areas [15].
Conclusion
In conclusion, the localization of apoA-I in inflamed synovium suggests that it can locally inhibit the production of proinflammatory cytokines by monocyte–macrophages upon direct contact with stimulated T cells. Thus, it is possible that after immune cell infiltration, formation of lymphoid-like microstructures, and the proliferation of blood vessels that resemble high-endothelial venules, inhibitory plasma components may infiltrate the developing inflammatory lesion. ApoA-I that binds surface factors on stimulated T cells is retained in the perivascular regions, where it may limit contact-mediated cytokine induction in monocyte–macrophages [5] and inhibit critical pathways associated with disease exacerbation. The alterations in apoA-I infiltration may also explain fluctuations of disease activity. The finding that apoA-I can infiltrate inflamed tissue, together with its newly emerging anti-inflammatory properties, may have important implications for treatment in chronic inflammatory diseases.
Competing interests
The authors declare that they have no competing interests.
Author contributions
BB and OF cared for the patients included in this study and supervised arthroscopy and biopsy procedures.
MG carried out the histochemical study.
BB, JMD, and DB conceived of the study and participated in its design and coordination.
All authors read and approved the final manuscript.
Abbreviations
apo A-I = apolipoprotein A-I; A-SAA = acute-phase serum amyloid A; CRP = C-reactive protein; HDL = high-density lipoprotein; IL-1β = interleukin-1β; PBS = phosphate-buffered saline; RA = rheumatoid arthritis; TNF-α = tumor necrosis factor α.
Acknowledgements
This work was supported by grant #3200-068286.02 from the Swiss National Science Foundation.
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| 15540281 | PMC1064871 | CC BY | 2021-01-05 02:11:20 | no | Arthritis Res Ther. 2004 Oct 6; 6(6):R563-R566 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1443 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14451553583510.1186/ar1445Research ArticleResistance to IL-10 inhibition of interferon gamma production and expression of suppressor of cytokine signaling 1 in CD4+ T cells from patients with rheumatoid arthritis Yamana Jiro [email protected] Masahiro [email protected] Akira [email protected] Tetsushi [email protected] Mitsuhiro [email protected] Katsue [email protected] Hirofumi [email protected] Department of Medicine and Clinical Science, Graduate School of Medicine and Dentistry, Okayama University, Okayama, Japan2004 13 10 2004 6 6 R567 R577 26 5 2004 1 7 2004 20 7 2004 25 8 2004 Copyright © 2004 Yamana et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
IL-10 has been shown to block the antigen-specific T-cell cytokine response by inhibiting the CD28 signaling pathway. We found that peripheral blood CD4+ T cells from patients with active rheumatoid arthritis (RA) were able to produce greater amounts of interferon gamma after CD3 and CD28 costimulation in the presence of 1 ng/ml IL-10 than were normal control CD4+ T cells, although their surface expression of the type 1 IL-10 receptor was increased. The phosphorylation of signal transducer and activator of transcription 3 was sustained in both blood and synovial tissue CD4+ T cells of RA, but it was not augmented by the presence of 1 ng/ml IL-10. Sera from RA patients induced signal transducer and activator of transcription 3 phosphorylation in normal CD4+ T cells, which was mostly abolished by neutralizing anti-IL-6 antibody. Preincubation of normal CD4+ T cells with IL-6 reduced IL-10-mediated inhibition of interferon gamma production. Blood CD4+ T cells from RA patients contained higher levels of suppressor of cytokine signaling 1 but lower levels of suppressor of cytokine signaling 3 mRNA compared with control CD4+ T cells, as determined by real-time PCR. These results indicate that RA CD4+ T cells become resistant to the immunosuppressive effect of IL-10 before migration into synovial tissue, and this impaired IL-10 signaling may be associated with sustained signal transducer and activator of transcription 3 activation and suppressor of cytokine signaling 1 induction.
CD4+ T cellsIL-10rheumatoid arthritissignal transducer and activator of transcription 3suppressor of cytokine signaling 1
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Introduction
IL-10 is a key cytokine in regulating inflammatory responses, mainly by inhibiting the production and function of proinflammatory cytokines. IL-10 binds to the IL-10 receptor (IL-10R) complex that is composed of two subunits, the primary ligand-binding component type 1 IL-10R (IL-10R1) and the accessory component type 2 IL-10R [1]. The interaction of IL-10 and IL-10R engages the Janus kinase (JAK) family tyrosine kinases Jak1 and Tyk2, which are constitutively associated with IL-10R1 and type 2 IL-10R, respectively [2]. IL-10 induces tyrosine phosphorylation and activation of the latent transcriptional factors signal transducer and activator of transcription (STAT) 3 and STAT1 [3]. Upon phosphorylation, STAT1 and STAT3 proteins form homodimers or heterodimers, rapidly translocate into the nucleus, and modulate gene transcription. Intriguingly, STAT3 is indispensable for both IL-10-derived anti-inflammatory and IL-6-derived proinflammatory responses [4]. Studies of cell-type-specific STAT3-deficient mice have shown that STAT3 activation is essential for IL-10-mediated anti-inflammatory reactions in macrophages and neutrophils [5], but is responsible for IL-6-mediated prevention of apoptosis in T cells [6]. The suppressor of cytokine signaling (SOCS) proteins have been identified as a family of endogenous JAK kinase inhibitors that can act in classic feedback inhibition loops, but their roles as the mediators of crosstalk inhibition by opposing cytokine signaling pathways have been clarified [7]. Recent studies indicate that SOCS3 plays a key role in regulating the divergent action of IL-10 and IL-6, by specifically blocking STAT3 activation induced by IL-6 but not that induced by IL-10 [8,9].
The synovial membrane of rheumatoid arthritis (RA) is characterized by an infiltrate of a variety of inflammatory cells, such as lymphocytes, macrophages, and dendritic cells, together with proliferation of synovial fibroblast-like cells. Numerous cytokines are overproduced in the inflamed joint, and macrophages and synovial fibroblasts are an important source of proinflammatory cytokines. Tumor necrosis factor alpha (TNF-α) and IL-1, two major macrophage products, are crucial in the process of chronic inflammation and joint destruction, and they give rise to effector components, including other inflammatory cytokines, chemokines, growth factors, matrix proteases, nitric oxide, and reactive oxygen species [10]. IL-6 is a pleiotropic cytokine produced substantially by activated fibroblasts, and its proinflammatory actions include simulating the acute-phase response, B-cell maturation into plasma cells, T-cell functions, and hematopoietic precursor cell differentiation [11].
However, anti-inflammatory cytokines and cytokine inhibitors are also present in large quantities in RA joints. IL-10, produced by macrophages and partly by T cells in the synovial tissue (ST), is best known as a negative regulator for macrophage and Th1 cells, but the expression level is insufficient to counterbalance the cascade of proinflammatory events [12]. In addition, the anti-inflammatory action of IL-10 appears to be modulated at the level of signal transduction during chronic inflammation. IL-10 signaling is impaired in macrophages upon chronic exposure to proinflammatory cytokines such as TNF-α and IL-1 and immune complexes [13,14]. Cell surface expression of IL-10R1 is decreased in synovial fluid dendritic cells due to the presence of TNF-α, IL-1, and granulocyte–macrophage colony-stimulating factor [15].
CD4+ T cells may be activated by arthritogenic antigens, in conjunction with CD28-mediated costimulatory signaling, in RA. The significance of this autoimmune process has been supported by the linkage of the MHC class II antigens HLA-DRB1*0404 and HLA-DRB1*0401 with disease susceptibility and severity [16,17], and by the high-level expression of MHC class II molecules and both CD28 ligands, CD80 and CD86, in the inflamed ST [18-20]. The continuing emergence of activated CD4+ T cells, even though few in number, may be crucial in sustaining the activation of macrophages and synovial fibroblasts through cell surface signaling by means of cell surface CD69 and CD11, as well as the release of proinflammatory Th1 cytokines such as interferon gamma (IFN)-γ and IL-17 [21,22]. In addition, CD4+ T cells could stimulate B-cell production of autoantibodies such as rheumatoid factor and osteoclast-mediated bone destruction. Their obligatory role in RA synovitis was recently proved by successful treatment of active disease by selective inhibition of T-cell activation with fusion protein of cytotoxic T-cell-associated antigen 4 (CD152)-IgG, which can block the engagement of CD28 on T cells by binding to CD80 and CD86 with high avidity [23].
IL-10 efficiently blocks the antigen-specific T-cell cytokine response by inhibiting the CD28 signaling pathway [24], as well as indirectly by downregulating the function of antigen-presenting cells. To elucidate the resistance of CD4+ T cells to this direct inhibition in RA, we investigated the production of IFN-γ after CD3 and CD28 costimulation in the presence of IL-10, the induction of STAT1 and STAT3 phosphorylation by IL-10, and the expression of SOCS1 and SOCS3 mRNA in peripheral blood (PB) CD4+ T cells from RA patients.
Materials and methods
Patients and samples
The total patient population consisted of 32 patients with RA (25 women and seven men; mean ± standard deviation age, 52.8 ± 12.4 years) diagnosed according to the revised 1987 criteria of the American College of Rheumatology (formally, the American Rheumatism Association) [25]. All patients were receiving prednisolone (≤ 7.5 mg/day) and disease-modifying antirheumatic drugs. Clinical parameters in the study patients were as follows (mean ± standard deviation): erythrocyte sedimentation rate, 55.9 ± 35.4 mm/hour; serum C-reactive protein (CRP) level, 32.0 ± 32.0 mg/l; and IgM class rheumatoid factor titer, 142 ± 158 U/ml. Patients were divided into two groups: 24 patients with active disease, who had multiple tender and/or swollen joints and elevated serum CRP level (≥ 10 mg/l); and eight patients with inactive disease, who satisfied the American College of Rheumatology preliminary criteria for clinical remission [26]. Sixteen healthy volunteers (11 women and five men; age, 45.8 ± 11.2 years) served as controls. ST samples were obtained from three RA patients undergoing total knee replacement. All patients gave informed consent.
Isolation of CD4+ T cells
Peripheral blood mononuclear cells (PBMC) were prepared from heparinized blood samples by centrifugation over Ficoll-Hypaque density gradients (Pharmacia, Uppsala, Sweden). CD4+ T cells were purified from PBMC by positive selection using anti-CD4 mAb-coated magnetic beads (Miltenyi Biotec, Gladbach, Germany), according to the manufacturer's instructions. CD4+ T cells were isolated from ST samples, as previously described [27]. Briefly, fresh ST samples were fragmented and digested with collagenase and DNase for 1 hour at 37°C. After removing tissue debris, ST cell suspensions in culture medium (RPMI 1640 medium; Life Technologies, Gaithersburg, MD, USA) supplemented with 25 mM HEPES (2 mM L-glutamine, 2% nonessential amino acids, 100 IU/ml penicillin, and 100 mg/ml streptomycin; Life Technologies) with 10% heat-inactivated FCS (Life Technologies) were incubated at 37°C in six-well plates (Coster, Cambridge, MA, USA) for 45 min. Non-adherent cells were harvested and CD4+ T cells were purified by positive selection as already described.
Culture of CD4+ T cells
PB CD4+ T-cell populations were resuspended at a density of 1 × 106 cells/ml in culture medium with 10% FCS, and 0.5 ml cell suspensions were dispensed into the wells of 24-well microtiter plates (Coster) coated with 1 μg/ml anti-CD3 mAb (Immunotech, Marseille, France). The cells were incubated with 1 μg/ml anti-CD28 mAb (Immunotech) in the presence or absence of the indicated concentrations of IL-10 (Becton Dickinson, San Jose, CA, USA) at 37°C in a humidified atmosphere containing 5% CO2 [28]. Culture supernatants were collected 36 hours later and cell-free samples were stored at -30°C until cytokine assay.
To examine the effect of IL-6 on T-cell responsiveness to IL-10, CD4+ T cells from healthy controls were incubated in culture medium with 10% FCS in the presence or absence of 10 ng/ml IL-6 (Becton Dickinson) for 36 hours. Cells were then stimulated for 36 hours with anti-CD3 mAb and anti-CD28 mAb in the presence or absence of 1 ng/ml IL-10. Culture supernatants were measured for IFN-γ concentrations.
Flow cytometric analysis for IL-10R1 expression
A sample of 5 × 105 cells of PBMC was resuspended in PBS with 1% FCS. PBMC were incubated with saturating concentrations of anti-IL-10R1 mAb (IgG1; R&D systems, Minneapolis, MN, USA) or with isotype-matched control mAb (Immunotech), followed by incubation with FITC-conjugated goat anti-mouse IgG1 polyclonal antibody (Santa Cruz Biotechnologies, Santa Cruz, CA, USA). Cells were then incubated with phycoerythrin-conjugated anti-CD4 mAb (Becton Dickinson). Cells were washed well with 1% FCS/PBS between incubations. Analysis was performed on a FACScan flow cytometer (Becton Dickinson).
Immunoassay for IFN-γ and IL-2
Concentrations of IFN-γ and IL-2 in culture supernatants of CD4+ T cells were measured in duplicate by the quantitative sandwich ELISA using cytokine-specific capture with biotinylated detection mAb and recombinant cytokine proteins (all from Becton Dickinson), according to the manufacturer's protocol. The detection limits for IFN-γ and IL-2 were 15 pg/ml.
Isolation of mRNA and real-time PCR
Total cellular RNA was extracted from PB CD4+ T cells using an RNA isolation kit (RNeasy Mini kit; Qiagen, Valencia, CA, USA), according to the manufacturer's instructions. cDNA was synthesized from total RNA with Molony murine leukemia virus reverse transcriptase (US Biochemical, Cleveland, OH, USA) and oligo-(dT)15 primers (Promega, Madison, WI, USA). Real-time PCR was performed with the LightCycler Instrument (Roche Diagnostics, Penzberg, Germany) in glass capillaries. The reaction mix containing Taq DNA polymerase and DNA double-strand-specific SYBR Green I dye (Lightcycler FastStart DNA Master SYBR Green I; Roche Diagnostics) and specific primers were added to cDNA dilutions.
The cDNA samples were denatured at 95° C for 10 min, and were then amplified for 40–50 cycles: at 95° C (10 s), at 65° C (15 s), and 72° C (22 s) for β-actin; at 95° C (10 s), at 62° C (15 s), and at 72° C (10 s) for SOCS1; and at 96° C (10 s), at 68° C (15 s), and at 72° C (15 s) for SOCS3. Amplification curves of the fluorescence values versus cycle number were obtained, and a melting curve analysis was then performed. The levels of SOCS1 and SOCS3 expression were determined by normalizing relative to β-actin expression. The forward and reverse primers were as follows: for β-actin, 5'-GTGGGGCGCCCCAGGCACCA-3' and 5'-CTCCTTAATGTCACGCACGATTTC-3' ; for SOCS1, 5'-AGACCCCTTCTCACCTCTTG-3' and 5'-GCACAGCAGAAAAATAAAGC-3' ; and for SOCS3, 5'-CCCGCCGGCACCTTTCTG-3' and 5'-AGGGGCCGGCTCAACACC-3'.
Western blot analysis
CD4+ T cells were stimulated for 20 min by the indicated concentrations of IL-10 and IL-6 at a density of 5 × 105 cells in 0.5 ml culture medium with 10% FCS. To examine the effect of serum IL-6 on STAT phosphorylation, normal CD4+ T cells were stimulated for 20 min with 30% active RA serum in culture medium with 40 μg/ml neutralizing goat anti-IL-6 polyclonal antibody (IgG; Techne, Princeton, NJ, USA) or control goat IgG (Techne). Whole cell lysates were prepared by placing cells in 100 μl SDS lysing buffer (62.5 mM Tris–HCl [pH 6.8], 2% SDS, 10% glycerol, 50 mM dithiothreitol, 0.1% bromphenol blue). Then 20 μl protein samples were fractionated on 10% SDS-polyacrylamide gels and were transferred to nitrocellulose membranes (Amersham, Buckinghamshire, UK), and the membrane was blocked with 5% skim milk in Tris-buffered saline with 0.1% Tween 20.
Tyrosine phosphorylation of STAT1 and STAT3 was detected using commercial available kits (Cell Signaling Technology, Beverly, MA, USA) according to the manufacturer's instructions. Briefly, the membrane was incubated with the antibodies (rabbit IgG) anti-STAT1 antibody, anti-phosphorylated tyrosine 701 of STAT1 antibody, anti-STAT3 antibody, and anti-phosphorylated tyrosine 705 of STAT3 antibody, diluted as recommended at 1/2000 with Tris-buffered saline with 0.1% Tween 20 with 5% BSA. Antibody binding was detected by horseradish peroxidase-conjugated anti-rabbit IgG antibody diluted at 1/4000 with Tris-buffered saline with 0.1% Tween 20 with 5% BSA, and was revealed using the chemiluminescence system. Protein bands were quantified by densitometry using NIH-Image analysis, and STAT phosphorylation was compared with the total amount of STAT protein. IFN-γ-stimulated Hela cells were used as a positive control for STAT1 phosophorylation.
Statistical analysis
Data are expressed as the mean value ± standard error of the mean or box plots. The statistical significance of differences between two groups was determined by the Mann–Whitney U test or the Wilcoxon signed rank test. P < 0.05 was considered significant.
Results
Resistance to IL-10 inhibition of IFN-γ production in RA CD4+ T cells
The CD28 costimulatory pathway is crucial for effective antigen-specific T-cell cytokine production, and IL-10 can directly suppress this response by inhibiting CD28 tyrosine phosphorylation and binding of phosphatidylinositol 3-kinase [24]. To evaluate the responsiveness of RA CD4+ T cells to IL-10, purified PB CD4+ T cells from three patients with active RA and from three healthy controls were stimulated by immobilized anti-CD3 antibody and anti-CD28 antibody with or without diluted concentrations of IL-10 for 36 hours, and IFN-γ production was measured by ELISA. As shown in Fig. 1, IFN-γ production by activated normal CD4+ T cells was mostly inhibited at concentrations as low as 1 ng/ml IL-10. However, RA CD4+ T cells were able to produce significant amounts of IFN-γ in the presence of 1 ng/ml IL-10, and the maximal but not complete inhibition by IL-10 was obtained at 10–100 ng/ml.
We thus compared the levels of IFN-γ production by CD4+ T cells after CD3 and CD28 costimulation in the presence of 1 ng/ml IL-10 in RA patients with active disease (multiple inflammatory joints, CRP level ≥ 10 mg/l) and inactive disease (in remission, CRP level < 10 mg/l) [26] and in healthy controls. There were no statistically significant differences in IFN-γ production without IL-10 among these three groups (Fig. 2a), but the inhibitory effect of IL-10 on IFN-γ production was significantly limited in the active RA group as compared with the inactive RA group and healthy controls (percentage decrease: active RA, 2.9 ± 14.4%; inactive RA, 45.6 ± 14.4%; controls, 65.8 ± 7.9%) (Fig. 2b). As a consequence, CD4+ T cells from active RA patients produced higher levels of IFN-γ in the presence of 1 ng/ml IL-10 than did normal CD4+ T cells (Fig. 2a).
In addition, we compared IL-2 production by CD4+ T cells after CD3 and CD28 costimulation in the presence of IL-10 in active RA patients and in healthy controls. Similarly, IL-2 production was not affected by 1 ng/ml IL-10 in RA patients (percentage decrease, -2.1 ± 13.8%), while it was significantly reduced in healthy controls (61.1 ± 13.7%; P < 0.05). Taken together, these results indicate that RA CD4+ T cells become less susceptible to the immunoregulatory effect of IL-10 during the active phase.
Increased expression of cell surface IL-10R1 on RA CD4+ T cells
The functional receptor complex of IL-10 consists of two subunits, the primary ligand-binding component IL-10R1 and the accessory component type 2 IL-10R [1]. IL-10R1 expression plays a critical role in cellular responses to IL-10 [29]. To examine whether the resistance to IL-10 inhibition in RA CD4+ T cells was due to limited receptor expression, the cell surface expression of IL-10R1 on PB CD4+ T cells from active RA patients and from healthy controls was determined by flow cytometric analysis. As shown in Fig. 3a,3b, the intensity of IL-10R1 expression on CD4+ T cells was significantly increased in RA patients compared with in healthy controls. These results suggest that the intracellular signal transduction pathway of IL-10 may be impaired in CD4+ T cells of active RA.
Defective IL-10-mediated STAT3 phosphorylation in RA CD4+ T cells
The interaction of IL-10R with IL-10 induces tyrosine phosphorylation and activation of the latent transcription factors STAT1 and STAT3 [3]. Macrophage-specific STAT3-deficient mice demonstrated that STAT3 plays a dominant role in IL-10-mediated anti-inflammatory responses [5], which has recently been confirmed in human macrophages by studies of dominant-negative STAT3 overexpression [30]. The induction of STAT1 and STAT3 phosphorylation by IL-10 in PB CD4+ T cells from active RA patients and from healthy controls was examined using western blotting. STAT3 phosphorylation was dose-dependently induced after IL-10 activation for 20 min in normal CD4+ T cells (Fig. 4a,4b). In contrast, STAT3 was phosphorylated in freshly isolated PB CD4+ cells from RA patients and this STAT3 phosphorylation was detectable for up to 6 hours. STAT3 phosphorylation was augmented only when activated by as much as 10 ng/ml IL-10. Both sustained STAT3 phosphorylation and defective IL-10-induced STAT3 phosphorylation were found in RA ST CD4+ T cells (Fig. 4c). On the other hand, IL-10-induced STAT1 phosphorylation was not detected in either RA CD4+ T cells or normal CD4+ T cells (Fig. 4a). These results indicate that STAT3 is the major IL-10-activated STAT in CD4+ T cells, and IL-10-induced STAT3 activation may be diminished in active RA, in association with sustained STAT3 phosphorylation.
IL-6-mediated STAT3 phosphorylation and inhibition of IL-10 effect in normal CD4+ T cells
STAT3 is activated by many cytokines and growth factors such as the IL-6 family of cytokines (IL-6, IL-11, leukemia inhibitory factor, and oncostatin M), platelet-derived growth factor, and epidermal growth factor, in addition to IL-10 [4], but previous studies have demonstrated that IL-6 is the major factor in RA synovial fluid that induces constitutive activation of STAT3 in mononuclear cells [31]. Since IL-6 is also abundant in sera of active RA patients, frequently detected at > 1 ng/ml [27], we examined whether persistent exposure of CD4+ T cells to high concentrations of IL-6 in the blood circulation was responsible for their sustained STAT3 activation and resistance to IL-10 inhibition in active RA. Both STAT1 and STAT3 phosphorylation was activated by IL-6 in normal CD4+ T cells (data not shown), in agreement with previous observations [4]. Normal CD4+ T cells were thus incubated for 20 min with culture medium containing 30% serum from active RA patients and neutralizing anti-IL-6 antibody or control antibody, and STAT phosphorylation was examined by western blot analysis. RA serum was able to induce tyrosine phosphorylation of STAT3 but not STAT1, and this STAT3 activation was mostly abolished by neutralization of IL-6 activity (Fig. 5a). These results indicate that IL-6 is the dominant STAT3-activating factor contained in sera of active RA patients. The lack of STAT1 activation by RA serum suggests that much higher concentrations of IL-6 may be required for STAT1 activation as compared with STAT3 activation, or that inhibitors of STAT1 signaling may be present in RA serum.
We next examined whether IL-6 could suppress the effect of IL-10 to inhibit IFN-γ production by CD4+ T cells. After preincubation with or without 10 ng/ml IL-6 for 36 hours, normal CD4+ T cells were stimulated by CD3 and CD28 costimulation in the presence or absence of 1 ng/ml IL-10 for 36 hours, and the IFN-γ production was measured by ELISA. IL-6 pretreatment of normal cells reduced IL-10-mediated inhibition of IFN-γ production (Fig. 5b), indicating that high concentrations of IL-6 could modulate T-cell responsiveness to IL-10. Taken together, these findings suggest that persistent exposure to serum IL-6 may have a role in both the induction of STAT3 activation and the resistance to the inhibitory effect of IL-10 in RA CD4+ T cells.
High expression of SOCS1 mRNA in RA CD4+ T cells
IL-6 induces two potent inhibitors of JAKs (SOCS1 and SOCS3 proteins) that not only act as mediators of negative feedback inhibition, but also play a major role in crosstalk inhibition by opposing other cytokine-signaling pathways [7]. SOCS3 has recently been shown to specifically inhibit STAT3 activation induced by IL-6 but not by IL-10, thereby regulating the divergent action of IL-6 and IL-10 [8,9]. On the contrary, SOCS1 is able to partially inhibit IL-10-mediated STAT3 activation and cellular responses, as well as IFN-γ-mediated STAT1 activation [32]. To determine whether SOCSs were involved in the defective IL-10-induced STAT3 activation of RA CD4+ T cells, the levels of SOCS1 and SOCS3 mRNA expression in PB CD4+ T cells from active RA patients and from healthy controls were compared by semiquantitative real-time PCR. The RA CD4+ T cells contained higher levels of SOCS1 but lower levels of SOCS3 transcripts than did control CD4+ T cells (Fig. 6a). Constitutive expression of SOCS1 mRNA in RA CD4+ T cells was comparable with the expression in normal CD4+ T cells stimulated by 10 ng/ml IL-6 (Fig. 6b), supporting its functional significance. Defective IL-10-induced STAT3 activation therefore appears to be due at least in part to an abundance of SOCS1 in RA CD4+ T cells.
Discussion
CD4+ T cells orchestrate the Th1-type cell-mediated immune response in RA [22]. Activated CD4+ T cells stimulate macrophages, synovial fibroblasts, B cells, and osteoclasts through the expression of cell surface molecules and Th1 cytokines, thereby contributing to both the chronic inflammation and the joint destruction. CD4+ T cells require two signals to be activated; antigen receptor occupancy and CD28-mediated costimulation. In the ST lesion, the CD28 ligands, both CD80 and CD86, together with MHC class II antigens, are substantially expressed by antigen-presenting cells such as macrophages and dendritic cells [18-20]. The significance of CD28 engagement in the T-cell-mediated disease process has recently been proven by the clinical efficacy of its blocker cytotoxic T-cell-associated antigen 4 (CD152)-IgG in RA patients [23].
IL-10 plays a predominant role in limiting immune and inflammatory responses by regulating the function of both macrophages and Th1 cells [1]. IL-10 inhibits the tyrosine phosphorylation of the CD28 molecule and the subsequent phosphatidylinositol 3-kinase binding in T cells, and thereby directly acts on T cells [24]. In the present study, we found that PB CD4+ T cells from patients with active RA, in the presence of IL-10, are able to produce higher levels of IFN-γ after CD3 and CD28 costimulation than normal CD4+ T cells. Despite high-level IL-10R1 expression and constitutive STAT3 activation, IL-10-induced tyrosine phosphorylation of STAT3 is suppressed in RA CD4+ T cells, in contrast to normal CD4+ T cells, where STAT3 phosphorylation is dose-dependently inducible by IL-10. Serum IL-6 from RA patients induces STAT3 but not STAT1 phosphorylation in normal CD4+ T cells, and exogenous IL-6 induces the resistance to IL-10 inhibition of IFN-γ production. RA CD4+ T cells contain higher levels of SOCS1 but contain lower levels of SOCS3 transcripts in comparison with normal CD4+ T cells. These findings indicate that CD4+ T cells become resistant to the inhibitory effect of IL-10 before migration into the inflamed ST, and suggest that this resistance may be attributable to impaired IL-10-dependent STAT3 activation, in association with sustained STAT3 phosphorylation and SOCS1 induction.
IL-10-mediated inhibition of CD4+ T-cell cytokine production is principally dependent on its inhibition of macrophage antigen-presenting cell function [1]. However, this indirect inhibitory effect is thought to be restricted at the site of T-cell activation in RA, because macrophages in the ST express high levels of cytokines, CD80 and CD86 molecules, and MHC class II antigens [10,18-20]. More recently, IL-10 has been shown to induce the antigen-specific T-cell unresponsiveness by inhibiting CD28 tyrosine phosphorylation [33]. This direct effect also may be limited in active RA patients, because their PB CD4+ T cells showed a defective IL-10 inhibition of CD28-costimulated production of both IFN-γ and IL-2.
Numerous cytokines, both proinflammatory and anti-inflammatory, have been detected in the ST of RA, and the balance between these opposing cytokine activities regulates disease severity [10]. Endogenous IL-10, produced mainly by macrophages and T cells, inhibits proinflammatory cytokine production by ST cells [12]. However, this regulatory activity seems to be restricted during chronic inflammation. The activation of both the extracellular stimulus-regulated kinase and p38 kinase pathways, induced by TNF-α and IL-1, inhibits the Jak1–STAT3 signaling pathway shared by IL-10 and IL-6 in adhered macrophages [13]. More importantly, IL-10-mediated STAT3 activation is mostly undetectable in RA synovial macrophages. This impaired IL-10 signaling is probably induced by chronic exposure to immune complexes in vivo, because both cell surface IL-10R1 expression and IL-10-induced Jak1 activation are suppressed in IFN-γ-primed macrophages by a protein kinase C-dependent pathway following ligation of the IgG Fc gamma receptor [14]. Furthermore, dendritic cells from RA synovial fluids are resistant to the immunoregulatory effect of IL-10 due to decreased transport of intracellular IL-10R1 in the presence of proinflammatory cytokine stimuli such as TNF-α, IL-1, and granulocyte–macrophage colony-stimulating factor [15]. We have demonstrated that the resistance of RA CD4+ T cells to IL-10 may be associated with defective IL-10-dependent STAT3 activation, but not with IL-10R1 expression. Inhibitory effects of IL-10 on these inflammatory cell types are therefore differentially modulated at the signal transduction level under the inflammatory environment in RA.
In association with impaired IL-10-mediated STAT3 activation, STAT3 was found to be tyrosine phosphorylated persistently (up to 6 hours) in freshly isolated PB and ST CD4+ T cells from RA patients. STAT3 is activated by a variety of cytokines, notably the IL-6 family of cytokines (e.g. IL-6, IL-11, leukemia inhibitory factor, and oncostatin M) and growth factors, in addition to IL-10 [4]. Of these cytokines, IL-6 plays a predominant role in eliciting a systemic reaction such as the acute phase response in active RA, due mainly to its abundance in the blood circulation [27]. Consistent with this notion, IL-6 was the major STAT3-activating factor contained in the serum of active RA patients, and the responsiveness to IL-10 was suppressed in normal CD4+ T cells after 36 hours of incubation with IL-6. These results suggest that both the sustained STAT3 activation and the resistance to IL-10 inhibition found in RA CD4+ T cells may be induced after chronic exposure in vivo to high concentrations of serum IL-6. However, it is also possible that STAT3 activity could be constitutively induced in CD4+ T cells by their own IL-10 secretion, leading to the loss of sensitivity to exogenous IL-10, because RA CD4+ T cells in the ST are capable of producing significant levels of IL-10 [34].
CD4+ T cells isolated from the ST of RA also showed a defect in the IL-10-induced STAT3 signaling pathway. It is most probable that the resistance of CD4+ T cells to IL-10 can be even augmented after migration into the inflamed ST, because IL-6 is highly concentrated compared with the blood level [27]. In addition, the involvement of other essential proinflammatory cytokines in this process was suggested by our preliminary experiments demonstrating that IL-10-mediated IFN-γ inhibition in CD4+ T cells was reduced by pretreatment with IL-1β and TNF-α, although less effectively than by IL-6 (data not shown). Furthermore, IFN-γ and IL-10 produced by CD4+ T cells themselves could be responsible for impaired IL-10 signaling in the ST, because T-cell infiltrates produce both cytokines [34,35]. In an autocrine fashion, IL-10 may persistently stimulate STAT3 activation and IFN-γ can induce SOCS1 protein as a crosstalk inhibitor of IL-10 signaling [32]. The T-cell-inhibitory effect of IL-10 may therefore be modulated complicatedly upon exposure to an inflammatory environment in RA joints, where many cytokines are present substantially [10].
STAT3 activation has been implicated in the pathogenesis of RA. Active STAT3 is constitutively expressed in synovial fluid mononuclear cells from RA patients [36]. IL-6 is the major STAT3-activating factor present in synovial fluid, which has a crucial role in the activation of monocyte functions such as gene expression of the Fc gamma receptor type I and type III and of HLA-DR [31]. More recently, high levels of activated STAT3, thought to be induced mainly by IL-6, have been detected in the ST, and STAT3 activation has been shown to be involved in synovial fibroblast proliferation and IL-6 production [37]. In this regard, STAT3 is critical in the survival and expansion of growth factor-dependent synovial fibroblasts [38]. Furthermore, the significance of persistent STAT3 signaling in Th1-cell-dominated autoimmune arthritis has been suggested by studies of the gp130F759/F759 mice, in which the Src homology phosphatase-2 binding site of gp130 (the transmembrane glycoprotein β subunit of the IL-6 family cytokine receptor), tyrosine 759, was mutated to phenylalanine [39]. In the gp130F759/F759 mice, T cells, particularly the CD4+ T-cell subset, are chronically activated and resistant to activation-induced cell death through gp130-mediated STAT3 activation.
The longevity of cytokine signals transduced by the JAK–STAT pathway is regulated by the SOCS family proteins [7]. We found that CD4+ T cells from patients with active RA expressed higher levels of SOCS1, but lower levels of SOCS3, compared with normal CD4+ T cells. SOCS1 prevents activation of JAK by directly binding to JAK, and SOCS3 inhibits the action of JAK by binding to the Src homology phosphatase-2-binding domain of receptors such as gp130 [40]. SOCS1 and SOCS3 are induced by various cytokines, including IL-6 and IL-10, as mediators of negative feedback and crosstalk inhibition [7]. Recent studies with mice lacking SOCS3 or SOCS1 revealed that SOCS3 is a negative regulator of IL-6 signaling but not of IL-10 signaling. Studies of conditional SOCS3-deficient mice have shown that SOCS3 deficiency, but not SOCS1 deficiency, results in sustained activation of STAT3 in response to IL-6 [8,41]. The analysis of SOCS3-deficient macrophages has indicated that SOCS3 is a crucial inhibitor of the IL-6-induced transcriptional response [42]. However, SOCS3 is dispensable for both the negative feedback inhibition and the immunoregulatory action of IL-10 in macrophages [41]. On the contrary, SOCS1 was found to directly inhibit IL-10-mediated signaling [43]. Increased SOCS1 expression in RA CD4+ T cells may therefore be associated with both the impaired responsiveness to IL-10 and to IL-10-mediated STAT3 activation, and defective SOCS3 expression may be responsible for persistent STAT3 activation in response to serum IL-6.
There is a possibility that SOCS1 induction may be associated with the ability of CD4+ T cells to produce IFN-γ, because CD4+ T cells from active RA could produce high levels of IFN-γ in the presence of IL-10, and because IFN-γ has been known as a potent inducer of SOCS1 [32]. It is of interest in this regard to indicate that polarized Th1 and Th2 cells express high levels of SOCS1 and SOCS3 mRNA, respectively [44]. IL-12-induced STAT4 activation is inhibited by SOCS3 induction in Th2 cells, whereas IL-4-induced STAT6 signaling is diminished by SOCS1 induction in Th1 cells. SOCS1 and SOCS3 may thus have important roles as Th1-specific and Th2-specific, mutually exclusive, cross-talk repressors of the IL-4–STAT6 and the IL-12–STAT4 signaling pathways, respectively. Consistent with this notion, PB T cells from patients with allergic diseases significantly express high levels of SOCS3 transcripts, and the SOCS3 expression correlates well with serum IgE levels and disease pathology [45]. Higher SOCS1 expression with lower SOCS3 expression in PB CD4+ T cells from RA patients, compared with healthy controls, is therefore probably consistent with their systemic bias towards a Th1 phenotype, as has previously been demonstrated [46-49].
Conclusion
CD4+ T cells from active RA patients are characterized by their resistance to IL-10 inhibition of IFN-γ production, due to constitutive STAT3 phosphorylation and impaired IL-10-mediated STAT3 activation. The defective STAT3 signaling is possibly associated with SOCS1 predominance over SOCS3. These abnormalities in active RA are thought to be induced mainly after chronic exposure to high concentrations of IL-6. The limited efficacy of IL-10 treatment of RA patients [50] may be explained in part by the unresponsiveness to IL-10 of inflammatory cells, including T cells. On the contrary, the therapeutic efficacy of anti-IL-6 receptor antibody has been reported in RA patients [51], and one of the effects of this therapy may be to normalize T cells through the inhibition of IL-6-dependent STAT3 activation. More specific therapy targeting STAT3 activation will be awaited; for example, the induction of the SOCS3 gene, the efficacy of which has been demonstrated in animal models [37].
Abbreviations
BSA = bovine serum albumin; CRP = C-reactive protein; ELISA = enzyme-linked immunosorbent assay; Fc = crystallazibe fragment; FCS = fetal calf serum; FITC = fluorescein isothiocyanate; IFN-γ = interferon gamma; IL = interleukin; IL-10R = interleukin-10 receptor; IL-10R1 = type 1 interleukin-10 receptor; JAK = Janus kinase; mAb = monoclonal antibody; MHC = major histocompatibility complex; PB = peripheral blood; PBMC = peripheral blood mononuclear cells; PBS = phosphate-buffered saline; PCR = polymerase chain reaction; RA = rheumatoid arthritis; SOXS = suppressor of cytokine signaling; ST = synovial tissue; STAT = signal transducer and activator of transcription; Th = T helper cells; TNF-α = tumor necrosis factor alpha.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Jiro Yamana was responsible for the experiments and data analysis and wrote the report. Masahiro Yamamura was responsible for the planning of the research and wrote up the manuscript. Akira Okamoto, Tetsushi Aita, Mitsuhiro Iwahashi, and Katsue Sunahori assisted the experiments. Hirofumi Makino critically read the manuscript.
Acknowledgements
The authors thank Dr S. Yamana (Higashihiroshima Memorial Hospital, Hiroshima, Japan) for providing clinical samples. This work was supported in part by grants-in-aid (14570413/16590982) from the Ministry of Education, Science, Culture, and Technology of Japan.
Figures and Tables
Figure 1 Dose response of IL-10 inhibition of interferon gamma (IFN-γ) production by CD4+ T cells after CD3 and CD28 costimulation in patients with rheumatoid arthritis (RA) and in healthy controls (HC). CD4+ T cells were purified from peripheral blood mononuclear cells of three RA patients and three HC by positive selection with anti-CD4 antibody. CD4+ T cells (5 × 105 cells in 0.5 ml culture medium with 10% FCS) were stimulated by immobilized anti-CD3 antibody and anti-CD28 antibody in the presence or absence of diluted IL-10 concentrations for 36 hours. Culture supernatants were measured for concentrations of IFN-γ by ELISA. IFN-γ production with IL-10 expressed as % IFN-γ production without IL-10. Values are the mean ± standard error of the mean.
Figure 2 (a) Interferon gamma (IFN-γ) production by CD3 and CD28 costimulated CD4+ T cells in the presence of IL-10 in patients with rheumatoid arthritis (RA) and in healthy controls (HC). CD4+ T cells (5 × 105 cells in 0.5 ml culture medium with 10% FCS) were stimulated by anti-CD3 antibody and anti-CD28 antibody with or without 1 ng/ml IL-10. Concentrations of IFN-γ in culture supernatants were measured in duplicate by ELISA. RA patients were divided into those with active disease (multiple inflammatory joints and CRP level ≥ 10 mg/l) and inactive disease (in remission and CRP level ≤ 4 mg/l). The results are represented as a box plot; upper and lower bars, 90th and 10th percentiles, respectively; upper, center and lower lines of box, 75th, 50th, and 25th percentiles, respectively. (b) Percentage of IFN-γ production. IFN-γ production with IL-10 expressed as % IFN-γ production without IL-10. Values are the mean ± standard error of the mean. n, number of samples tested.
Figure 3 (a) Cell surface expression of type 1 interleukin-10 receptor (IL-10R1) on CD4+ T cells from patients with rheumatoid arthritis (RA) and from healthy controls (HC). Peripheral blood mononuclear cells were stained with anti-IL-10R1 antibody or with isotype-matched control antibody, followed by incubation with FITC-conjugated goat anti-mouse IgG1 polyclonal antibody, and were then stained with phycoerythrin-conjugated anti-CD4 mAb. The expression of CD4 and IL-10R1 was determined by flow cytometric analysis. Representative histographic patterns of IL-10R1 expression on CD4+ T cells from RA patients and HC are shown. (b) The intensity of IL-10R1 on CD4+ T cells was expressed as the ratio of the mean fluorescence intensity (MFI) of staining with anti-IL-10R1 to control antibody. Values are the mean ± standard error of the mean. n, number of samples tested.
Figure 4 (a) IL-10-mediated phosphorylation of signal transducer and activator of transcription (STAT) 1 and STAT3 in CD4+ T cells from patients with rheumatoid arthritis (RA) and from healthy controls (HC). CD4+ T cells (5 × 105 cells in 0.5 ml culture medium with 10% FCS) were incubated with or without IL-10 (1 and 10 ng/ml) and cells were harvested 20 min later. Whole cell extracts were prepared by placing cells in SDS buffer, and tyrosine phosophorylation (p-Tyr) of STAT1 and STAT3 was detected by western blot analysis. IFN-γ-stimulated Hela cells were used as a positive control for STAT1 phosphorylation. (b) Percentage of IL-10-activated STAT3 phosphorylation in CD4+ T cells from RA patients and from HC. Protein bands were quantified by densitometry using NIH-Image analysis, and STAT3 phosphorylation was expressed as % total STAT3 protein. (c) STAT3 phosphorylation in ST CD4+ T cells from RA patients. Representative results of STAT3 phosphorylation in CD4+ T cells from three synovial tissue samples of RA patients and three peripheral blood samples of HC are shown. Values are the mean ± standard error of the mean. n, number of samples tested.
Figure 5 (a) Activation of signal transducer and activator of transcription (STAT) 3 in normal CD4+ T cells by serum IL-6 from patients with rheumatoid arthritis (RA). CD4+ T cells from healthy controls (5 × 105 cells in 0.5 ml culture medium) were stimulated by 30% RA serum in the presence of neutralizing anti-IL-6 antibody (Ab) (40 μg/ml) or of control antibody (40 μg/ml) for 20 min. Phosophorylation of STAT1 and STAT3 was detected by western blot analysis.(b) Effect of IL-6 pretreatment on IL-10 inhibition of IFN-γ production by CD4+ T cells. CD4+ T cells (5 × 105 cells in 0.5 ml culture medium with 10% FCS) were incubated with or without IL-6 (10 ng/ml) for 36 hours, and were then stimulated by anti-CD3 antibody and anti-CD28 antibody in the presence or absence of IL-10 (1 ng/ml) for 36 hours. Concentrations of IFN-γ in culture supernatants were measured in duplicate by ELISA. IFN-γ production with IL-10 was expressed as % IFN-γ production without IL-10. Values are the mean ± standard error of the mean. n, number of samples tested. P-Tyr, tyrosine phosophorylation.
Figure 6 (a) The mRNA expression of SOCS1 and SOCS3 in CD4+ T cells from patients with RA and healthy controls (HC). Total cellular RNA was extracted from freshly isolated CD4+ T cells and mRNA expression of SOCS1 and SOCS3 was analyzed by real time-PCR as described in Patients and Methods. Levels of SOCS1 and SOCS3 mRNAs were normalized relative to β-actin expression. Values are the mean ± SEM. n = number of samples tested. (b) Kinetics of IL-6-induced SOCS1 mRNA expression in normal CD4+ T cells. CD4+ T cells from HC (5 × 105 cells in 0.5 ml of culture medium with 10% FCS) were stimulated with IL-6 (10 ng/ml) and SOCS1 mRNA expression was determined at the indicated time after stimulation.
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| 15535835 | PMC1064873 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2004 Oct 13; 6(6):R567-R577 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1445 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14481564213210.1186/ar1448Research Articlep53 tumor suppressor gene mutations in fibroblast-like synoviocytes from erosion synovium and non-erosion synovium in rheumatoid arthritis Yamanishi Yuji 12Boyle David L 2Green Douglas R 3Keystone Edward C 4Connor Alison 4Zollman Susan 4Firestein Gary S [email protected] Department of Rheumatology, Hiroshima City Hospital, Hiroshima, Japan2 Division of Rheumatology, Allergy, and Immunology, School of Medicine, University of California at San Diego, La Jolla, California, USA3 La Jolla Institute of Allergy and Immunology, La Jolla, California, USA4 Department of Medicine, University of Toronto, Canada2005 29 10 2004 7 1 R12 R18 9 5 2004 8 6 2004 9 8 2004 8 9 2004 Copyright © 2004 Yamanishi et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Abnormalities in the p53 tumor suppressor gene have been detected in rheumatoid arthritis (RA) and could contribute to the pathogenesis of chronic disease. To determine whether synoviocytes from invasive synovium in RA have an increased number of mutations compared with non-erosion synoviocytes, p53 cDNA subclones from fibroblast-like synoviocytes (FLS) derived from erosion and non-erosion sites of the same synovium were examined in patients requiring total joint replacement. Ten erosion FLS lines and nine non-erosion FLS lines were established from nine patients with RA. Exons 5–10 from 209 p53 subclones were sequenced (114 from erosion FLS, 95 from non-erosion FLS). Sixty percent of RA FLS cell lines and 8.6% of the p53 subclones isolated from FLS contained p53 mutations. No significant differences were observed between the erosion and non-erosion FLS with regard to the frequency or type of p53 mutation. The majority of the mutations were missense transition mutations, which are characteristic of oxidative damage. In addition, paired intact RA synovium and cultured FLS from the same joints were evaluated for p53 mutations. Matched synovium and cultured synoviocytes contained p53 mutations, although there was no overlap in the specific mutations identified in the paired samples. Clusters of p53 mutations in subclones were detected in some FLS, including one in codon 249, which is a well-recognized 'hot spot' associated with cancer. Our data are consistent with the hypothesis that p53 mutations are randomly induced by genotoxic exposure in small numbers of RA synoviocytes localized to erosion and non-erosion regions of RA synovium. The determining factor for invasiveness might be proximity to bone or cartilage rather than the presence of a p53 mutation.
erosionfibroblast-like synoviocytesinvasivenessp53 mutationrheumatoid arthritis
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Introduction
Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovial tissue proliferation with progressive joint destruction. The etiology of RA remains unknown, but many factors, including autoimmunity, cytokines and genetic factors, participate in its pathogenesis [1,2]. Although inflammation and joint destruction can be intimately related, the two processes might also be independent in some circumstances [3,4]. This observation might be explained, at least in part, by autonomous behavior by fibroblast-like synoviocytes (FLS) [5]. These cells exhibit some features of transformation in RA, including loss of contact inhibition, anchorage-independent growth, oncogene activation, autonomous invasion into cartilage and somatic gene mutations [4,6-9]. One potential gene that might contribute to this phenotype is the p53 tumor suppressor gene, which plays a critical role in cell-cycle regulation, DNA repair, senescence, genomic stability and apoptosis [10]. p53 is expressed in many inflammatory and autoimmune diseases [11-13], and it may serve a protective function by suppressing cytokine production and matrix destruction [14,15]. For instance, mice lacking the p53 gene have significantly greater joint destruction compared with wild-type controls in the collagen-induced arthritis model [16].
The function of p53 can be altered through somatic mutations in both neoplasia and non-malignant conditions such as ulcerative colitis, sun-exposed skin and chronic RA [8,17,18]. The mutations in RA synovium reside primarily in the intimal lining and have also been identified in cultured FLS, which are thought to originate from this region [8,9]. p53 sequencing studies in RA have until now focused on synoviocytes derived from non-erosion regions of the synovium that are readily obtained at the time of joint replacement surgery. Because some reports suggest that monoclonal expansion and oligoclonal expansion of synoviocytes occur at sites of invasion [19], we evaluated the relative frequency of p53 mutations in FLS in paired erosion and non-erosion synoviocytes from the same patients. Our data suggest that mutations are present in both regions and that the tendency to invade may be related to proximity to the extracellar matrix.
Materials and methods
Synovial tissues and preparation of FLS
Synovial tissue samples were collected with informed consent at the time of joint replacement from patients with RA. The diagnosis of RA conformed to the 1987 revised American College of Rheumatology criteria [20]. Separate samples were obtained, at the University of Toronto, from within erosions at the periphery of the articular surface and from non-erosion sites collected from the intracapsular non-articular surface. Erosion FLS lines and non-erosion FLS lines were then prepared from each patient.
FLS cell lines were established as previously described [21]. Briefly, tissues were minced by sterilized scissors, and were incubated with 1 mg/ml collagenase in serum-free DMEM for 2 hours at 37°C, were filtered through a nylon mesh, were extensively washed, and were cultured in DMEM containing 10% fetal calf serum, 2 mmol/l glutamine, 50 μg/ml gentamicin, 100 U/ml penicillin, and 100 μg/ml streptomycin, in a humidified 5% CO2 atmosphere. After overnight culture, cells were trypsinized, split in a 1:3 ratio, and were recultured in medium. FLS from passages 5–8 were used in these experiments, during which time they represented a homogeneous population of FLS (< 1% CD 11b, < 1% phagocytic, and < 1% Fc-gamma RII receptor-positive). A second set of FLS samples obtained from the University of California at San Diego were obtained with a matched sample of synovium, which was embedded in TissueTek OCT compound (Miles Diagnostics, Elkhart, IN, USA), snap frozen, and stored at -80°C until use. Frozen tissues with approximate area of 10 mm2 were cut into 10 μl sections at the time of PCR analysis for p53 mutations.
Production of immunoreactive MMP-1
FLS were cultured to near confluence in six-well tissue culture plates (Falcon, Bedford, MA, USA). IL-1β (3 ng/ml) or medium was added to the wells and was incubated for 72 hours at 37°C in a humidified 5% CO2 atmosphere. Supernatants were collected and MMP-1 concentrations were determined by ELISA according to the manufacturer's instructions (Total MMP1Biotrak; Amersham Biosciences, Piscataway, NJ, USA) [22].
Statistical analysis
Comparisons between two groups were analyzed by the Wilcoxon signed rank test. P < 0.05 was considered significant.
Results
p53 mutations in erosion FLS and non-erosion FLS
To determine whether invasive synovium in RA has an increased number of mutations, p53 cDNA subclones from FLS derived from erosion sites and non-erosion sites were examined. Ten erosion FLS lines and nine non-erosion FLS lines were established from nine patients with RA (two erosion lines and one non-erosion line were examined in one patient). A total of 209 p53 subclones were subjected to sequence analysis (114 from erosion FLS, 95 from non-erosion FLS), and p53 exons 5–10 were examined. As shown in Table 1, p53 mutations were identified in 11 out of 19 FLS lines (four of 10 erosion FLS lines, and seven of nine non-erosion FLS lines). Eighteen subclones out of the total 209 (8.6%) contained mutations. There were no significant differences in the frequency of p53 mutations between erosion FLS and non-erosion FLS (7.9% and 9.5%, respectively). Nested PCR was required for one of the FLS lines (RA4 non-erosion FLS line). The rate of mutation with this line was similar to the other erosion and non-erosion FLS.
As in previous reports [8,9,23], most p53 mutations (eight of nine in erosion FLS and eight of nine in non-erosion FLS) were transition mutations (i.e. G>A or C>T), which are characteristic of mutations caused by oxidative damage [24,25], and no transversion mutations (i.e. G>T, A>T, C>A, C>G) were seen (see Fig. 1 for the pooled data). One single base deletion and one multinucleotide insertion were detected. The majority of p53 mutations (78%) were missense (see Fig. 1 for pooled data). Most of the mutations were identified in a single subclone, although multiple copies of one mutation in codon 321 AAA to GAA were observed in an erosion FLS line. These data suggest that the frequency and types of mutations are similar in FLS isolated from either sites of erosion or from regions that are not invading into bone or cartilage.
MMP-1 expression in erosion and non-erosion FLS
Two matched erosion and non-erosion FLS lines were also evaluated for medium-stimulated and IL-1β-stimulated collagenase gene expression (MMP-1). There were no differences between the erosion or non-erosion samples with regard to either basal or cytokine-stimulated MMP-1 protein concentrations in the culture supernatants (basal, 5.5 ± 1.2 ng/ml; IL-1 stimulated, 24.4 ± 3.2 ng/ml).
p53 mutations in matched RA FLS and synovial tissue samples
After evaluating the matched FLS lines, we then examined the mutations in whole RA synovium and FLS isolated from the same joint. The matched erosion and non-erosion synovia from the preceding analysis were not available, so subclones from four additional paired RA FLS and synovial tissues from non-erosion regions were sequenced for p53 mutations (see Table 2). Mutations were detected in 12 of the 43 p53 subclones from RA FLS (28%), and in five of 46 subclones from the paired RA synovial tissues (11%). The relatively higher percentage of mutations in this limited number of lines compared with those presented in Table 1 is within the range observed for RA FLS in other studies. A few of the subclones contained two mutations (two of the RA FLS subclones, and one of the RA tissue subclones). Distinct patterns were found in the RA FLS compared with the paired tissue p53 subclones (see Table 2), although the frequency of mutations was somewhat higher in these samples compared with those presented in Table 1 and previous reports [8,9,23].
Mutation analysis of FLS revealed eight transitions, three transversions, and three deletions among 14 mutations in RA FLS. Of the base changes, 11 were missense and three were silent (Table 2). The RA tissue samples had five transition mutations (four A>G, one C>T) and one transversion mutation (G>T), with four mutations identified as missense and two as silent. Nested PCR was required for two FLS lines, RA11 and RA12 FLS, and these results were similar to the cell lines that did not require nested PCR. RA13 synovial tissue had no mutations identified despite the use of nested PCR, indicating the fidelity of this process.
Interestingly, FLS from one patient had multiple subclones containing a mutation at codon 249 (AGG>GGG [Arg>Gly]) (see Table 2). Codon 249 missense mutations have been detected frequently in malignant tissues [26,27]. In another patient, a silent codon 213 base change (CGA>CGG [Arg>Arg]) was detected in 50% of p53 subclones from both FLS and synovial tissues. This same base change was also present in peripheral blood mononuclear cells of the patient (data not shown) and probably represents a known p53 germline polymorphism [28].
Discussion
The aggressive nature of RA synovium and the ability of cultured synoviocytes to invade autonomously in cartilage suggest that these cells might be permanently altered or imprinted by their sojourn through the rheumatoid joint [4,5]. Additional data evaluating expression of X-linked genes indicate that oligoclonal or monoclonal expansion of synoviocytes can occur in chronically inflamed rheumatoid synovial tissue, especially at sites of erosions [19]. More recently, we showed that islands of cells expressing mutant p53 genes are present in the rheumatoid synovial intimal lining and that these regions produce significantly higher amounts of IL-6 [9]. However, the relationship between mutations and the synovial invasion has not been systematically examined.
In the present study, we first examined paired samples of synoviocytes isolated from the erosive front of synovium and from regions not directly adherent to bone or cartilage for p53 mutations. Sixty percent of the cell lines examined had mutations, and 8.6% of the subclones isolated from either site contained mutations. No significant differences were observed between erosion and non-erosion FLS with regard to the frequency or type of mutation. Previous reports describe p53 base changes in 15–50% of RA FLS lines, with a frequency of mutations within the p53 cDNA pool varying from 0% to 30% [8,23,29]. The broad range might relate to the methods used to identify mutations, some of which are less sensitive (e.g. single-strand conformation polymorphism), or may be due to the evaluation of different numbers of exons. Other investigators failed to find mutations, perhaps because the experiments focused on sequencing the unfractionated p53 cDNA pool rather than subclones, on evaluation of less severe disease, or on sequencing a limited number of subclones [30,31].
The mutations observed in this study are mainly transition missense base changes, as previously described [8,9,23]. These are characteristic of oxidative deamination by nitric oxide or oxygen radicals [24,25] and are consistent with the hypothesis that the p53 mutations are caused by oxidative stress in the inflammatory environment, although this still has not been proven [32]. Relaxation of the DNA mismatch repair mechanisms in synoviocytes also probably contributes to susceptibility to DNA damage. For instance, suppression of hMSH6 expression in RA synovium has been associated with synovial microsatellite instability [33]. The majority of mutations in the present study were only identified in individual subclones. However, multiple copies were detected in the sequenced subclones from three patients. One of these at codon 249 is a well-recognized 'hot spot' associated with lung cancer and hepatocellular carcinoma [26,27]. Inazuka and colleagues previously identified another 'hot spot' codon 245 transition mutation in RA FLS lines from two patients [23]. Table 3 summarizes p53 mutation clusters (i.e. detected in more than one subclone) in the present study and in the literature in cultured RA FLS or synovial tissue. More than 90% of the repeat p53 mutations are missense and have been frequently detected in malignant tissues.
Nishioka and colleagues demonstrated oligoclonal expansion or monoclonal expansion of synoviocytes at the sites of erosion in RA [19]. Furthermore, p53 expression tends to be greatest at sites of invasion in the severe combined immunodeficiency mouse model where cultured FLS erode into cartilage explants [34]. We expected to see an increased number of mutations at these sites as well. However, there were no differences between matched erosion and non-erosion FLS with regard to the number or types of p53 mutations. The mutations in very late stage of disease are therefore equally abundant in all regions of the rheumatoid synovium. Although they are clearly present at the invasive front, which might contribute to local tissue destruction, they are not over-represented compared with other sites that have been exposed to the genotoxic environment for the same period of time. The lack of association between invasion into bone and p53 mutations is consistent with recent data suggesting that osteoclasts, rather than synoviocytes, are the primary mediators of bone erosions [35]. FLS might play a more important role in cartilage damage through the elaboration of proteolytic enzymes and cytokines, which are increased in cells lacking functional p53 protein. Mutations in erosion had unique sequences when compared with the paired non-erosion mutations from the same patient, which may not be surprising given the results of microdissection studies demonstrating multiple independent islands of mutant cells rather than diffuse monoclonal expansion [9].
In addition to studying paired erosion and non-erosion FLS, we analyzed a second set of samples where we had the opportunity to examine paired whole non-erosion synovium with FLS isolated from the same joint. Mutations were identified in the matched samples, with similar ratios of transitions and missense changes between cell lines and tissues. There was no overlap in the specific base changes. Because cells in the lining form islands with oligoclonal expansion of individual mutations, cell lines derived from a different fragment of synovium would be expected to have different mutations compared with another region. The percentage of cDNA subclones with mutations was higher in the FLS than in matched synovium. This is most probably because the cells bearing mutations in the intact tissue (synoviocytes in the intimal lining) represent only about 20% of the total compared with 100% of the cells in the homogeneous cell cultures.
Our data are consistent with the proposed hypothesis that p53 mutations are randomly induced by genotoxic exposure in small numbers of RA synovial lining cells in both erosion and non-erosion regions. Based on the association of these same mutations with neoplasia and our previous studies showing that these mutations can be dominant negative [36], it is reasonable to suggest that some of the p53 mutant cells in RA have selective growth advantage and thus form clusters in RA synovial tissue. Subsequently, the islands can influence cells in the environment through the elaboration of cytokines and factors that are normally suppressed by wild-type p53 (such as IL-6). A careful evaluation of erosion and non-erosion sites suggests that cells in both regions are equally likely to contain mutant cells, and that the expression of proteins related to matrix invasion, like MMP-1, was similar in the two cell populations. The determining factor for invasiveness might be proximity to bone or cartilage rather than the presence of a p53 mutation. Hence, cells directly adjacent to the matrix can potentially adhere and invade, whereas those cells in non-erosion regions would only have an indirect influence on destruction.
Conclusions
Mutations of the p53 tumor suppressor gene were present in synoviocytes isolated from both erosion and non-erosion sites in longstanding RA. Clusters of mutations can occur in RA synovium, but the abnormal clones are not unique to sites of joint destruction. The determining factor for invasiveness might be proximity to bone or cartilage rather than the presence of a p53 mutation. Hence, cells directly adjacent to the matrix can potentially adhere and invade whereas those cells in non-erosion regions would only have an indirect influence on destruction.
Abbreviations
DMEM = Dulbecco's modified Eagle's medium; ELISA = enzyme-linked immunosorbent assay; FLS = fibroblast-like synoviocytes; IL = interleukin; MMP-1 = matrix metalloproteinase-1; PCR = polymerase chain reaction; RA = rheumatoid arthritis
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Yuji Yamanishi designed and executed the study and prepared the manuscript. Edward Keystone, Alison Connor, and Susan Zollman developed erosion and non-erosion FLS. David Boyle assisted with the design of experiments and obtained UCSD clinical samples. Douglas Green evaluated and interpreted data and assisted with preparation of the manuscript. Gary Firestein supervised the project, evaluated and interpreted data, and prepared the manuscript.
Figures and Tables
Figure 1 Types of p53 mutations in rheumatoid arthritis fibroblast-like synoviocytes (FLS). Because no differences were observed between erosion FLS and non-erosion FLS (see Table 1), results were pooled.
Table 1 p53 mutations in matched erosion fibroblast-like synoviocytes (FLS) and non-erosion FLS from rheumatoid arthritis(RA) patients
Patient Erosion FLS Non-erosion FLS
Mutation Frequency Mutation Frequency
RA1 (two lines examined)
Line 1 Codon 318 CCA>TCA (Pro>Ser) 1/12 None 0/11
Line 2 None 0/11
RA2 Codon 194 CTT>TTT (Leu>Phe) 1/13 Codon 274 GTT>ATT (Val>Ile) 1/9
Codon 226 GGC>GAC (Gly>Asp) 1/13
RA3 None 0/13 Codon 186 GAT>AAT (Asp>Asn) 1/12
RA4 Codon 155 ACC>GCC (Thr>Ala) 1/12 Codon 266 GGA>AGA (Gly>Arg) 1/12
Codon 321 AAA>GAA (Lys>Glu) 2/12
RA5 None 0/10 None 0/11
RA6 None 0/9 Codon 276 GCC>GTC (Ala>Val) 1/7
RA7 None 0/11 Codon 167 CAG>CAA (Gln>Gln) 1/10
Codon 312 ATC>ATT (Ile>Ile) 1/10
RA8 Codon 212 TTT>TTC (Phe>Phe) 1/12 Codon 176 TGC>TAC (Cys>Tyr) 1/12
Codon 309 CCC>CCT (Pro>Pro) 1/12 Codon 239 AAC>AGC (Asn>Ser) 1/12
Codon 317 CAG>AG (deletion) 1/12
RA9 None 0/11 Codon 261-C262 AGT (Ser) insertion 1/11
Total 9/114 9/95
Data presented as number of mutant subclones/total number of subclones analyzed. Nested PCR used for the RA4 non-erosion FLS.
Table 2 p53 mutations in paired rheumatoid arthritis (RA) fibroblast-like synoviocytes and synovial tissue samples
Patient Fibroblast-like synoviocytes Synovial tissue
Mutation Frequency Mutation Frequency
RA10 Codon 119 GCC>ACC (Ala>Thr) 1/10 Codon 196 CGA>TGA (Arg>stop) 1/12
Codon 178 CAC>AC (deletion) 1/10
Codon 223 CCT>CAT (Pro>His) 1/10
Codon 360 GGG>AGG (Gly>Arg) 1/10
Long deletion codon 143 – codon 220 1/10
Codon 213 CGA>CGG (Arg>Arg) 5/10a Codon 213 CGA>CGG (Arg>Arg) 6/12
RA11 Codon 307 GCA>GCT (Ala>Ala) 2/9 Codon 147 GTT>ATT (Val>IIe) 1/12
Deletion codon 304 – codon 337 1/9 Codon 225 GTT>GTG (Val>Val) 1/12
RA12 Codon 355 GCT>GCC (Ala>Ala) 1/12 Codon 213 CGA>CAA (Arg>Gln) 1/11
Codon 213 CGA>CGG (Arg>Arg) 1/11
Codon 269 AGC>AAC (Ser>Asn) 1/11
RA13 Codon 134 TTT>CTT (Phe>Leu) 1/12 None 0/11
Codon 216 GTG>ATG (Val>Met) 1/12
Codon 249 AGG>GGG (Arg>Gly) 3/12
Data presented as number of mutant subclones/total number of subclones analyzed. Nested PCR required for RA12 and RA13 FLS and RA10 – RA13 synovial tissues. a Recognized p53 polymorphism also detected in normal peripheral blood mononuclear cells (most probably germline).
Table 3 p53 mutation clusters identified in greater than one subclone isolated from rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLS) or from synovial tissue
p53 mutation Origin Reference
Codon 138 GCC>GTC (Ala>Val) Microdissected synovial tissue [9]
Deletion codon 143 – codon 220 Microdissected synovial tissue [9]
Codon 144 CAG>TAG (Gly>stop) Microdissected synovial tissue [9]
Codon 176 TGC>CGC (Cys>Arg) Microdissected synovial tissue [9]
Codon 213 CGA>TGA (Arg>stop) Microdissected synovial tissue [9]
Codon 245 GGC>GAC (Gly>Asp) Cultured FLS [23]
Codon 249 AGG>GGG (Arg>Gly) Cultured FLS Present study
Codon 300 CCC>TCC(Pro>Ser) Microdissected synovial tissue [9]
Codon 307 GCA>GCT (Ala>Ala) Cultured FLS Present study
Codon 321 AAA>GAA (Lys>Glu) Cultured FLS Present study
Codon 337 CGC>AGC (Arg>Ser) Microdissected synovial tissue [9]
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| 15642132 | PMC1064878 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Oct 29; 7(1):R12-R18 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1448 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14491564214210.1186/ar1449Research ArticleThe critical role of arginine residues in the binding of human monoclonal antibodies to cardiolipin Giles Ian [email protected] Nancy [email protected] David [email protected] Pojen [email protected] Reginald [email protected] David [email protected] Anisur [email protected] Centre for Rheumatology, Department of Medicine, University College London, UK2 Medical Molecular Biology Unit, Institute of Child Health, University College London, UK3 Department of Medicine, Division of Rheumatology, University of California, Los Angeles, USA2005 16 11 2004 7 1 R47 R56 28 5 2004 10 8 2004 31 8 2004 23 9 2004 Copyright © 2004 Giles et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Previously we reported that the variable heavy chain region (VH) of a human beta2 glycoprotein I-dependent monoclonal antiphospholipid antibody (IS4) was dominant in conferring the ability to bind cardiolipin (CL). In contrast, the identity of the paired variable light chain region (VL) determined the strength of CL binding. In the present study, we examine the importance of specific arginine residues in IS4VH and paired VL in CL binding. The distribution of arginine residues in complementarity determining regions (CDRs) of VH and VL sequences was altered by site-directed mutagenesis or by CDR exchange. Ten different 2a2 germline gene-derived VL sequences were expressed with IS4VH and the VH of an anti-dsDNA antibody, B3. Six variants of IS4VH, containing different patterns of arginine residues in CDR3, were paired with B3VL and IS4VL. The ability of the 32 expressed heavy chain/light chain combinations to bind CL was determined by ELISA. Of four arginine residues in IS4VH CDR3 substituted to serines, two residues at positions 100 and 100 g had a major influence on the strength of CL binding while the two residues at positions 96 and 97 had no effect. In CDR exchange studies, VL containing B3VL CDR1 were associated with elevated CL binding, which was reduced significantly by substitution of a CDR1 arginine residue at position 27a with serine. In contrast, arginine residues in VL CDR2 or VL CDR3 did not enhance CL binding, and in one case may have contributed to inhibition of this binding. Subsets of arginine residues at specific locations in the CDRs of heavy chains and light chains of pathogenic antiphospholipid antibodies are important in determining their ability to bind CL.
antiphospholipid antibodiesargininebindingcardiolipin
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Introduction
The identification of antiphospholipid antibodies (aPL) is a key laboratory feature in the diagnosis of patients with antiphospholipid antibody syndrome (APS). The cardinal manifestations of this syndrome are vascular thrombosis, recurrent pregnancy loss, livedo reticularis and thrombocytopenia [1,2]. APS may affect any organ of the body, leading to a broad spectrum of manifestations [3]. It is the commonest cause of acquired hypercoagulability in the general population [4] and a major cause of pregnancy morbidity.
APS may occur as a 'freestanding' syndrome (primary APS) [5] or in association with other autoimmune rheumatic diseases (secondary APS) [6]. In both primary APS and secondary APS, recurrence rates of up to 29% for thrombosis and a mortality of up to 10% over a 10-year follow-up period have been reported [7]. The only treatment that reduces the risk of thrombosis in APS is long-term anticoagulation [8]. This treatment may have severe side effects, notably bleeding. It is therefore important to develop a greater understanding of how aPL interact with their target antigens so that new treatments for APS, which are both more effective and more accurately targeted to the causes of the disease process, may be developed.
aPL occur in 1.5–5% of healthy people and may also occur in various medical conditions without causing clinical features of APS [9]. The aPL that are found in patients with APS differ from those found in healthy people in that they target predominantly negatively charged phospholipid antibodies and are in fact directed against a variety of phospholipid binding serum proteins. These proteins include protein C, protein S, prothrombin and beta2 glycoprotein I (β2GPI) [10-13]. β2GPI is the most extensively studied of these proteins and appears to be the most relevant clinically [14-16]. Furthermore, high levels of IgG aPL, rather than IgM aPL, are closely related to the occurrence of thrombosis in APS [17,18].
Sequence analysis of human monoclonal aPL has shown that IgG aPL, but not IgM aPL, often contain large numbers of somatic mutations in their variable heavy chain region (VH) and variable light chain region (VL) sequences [19]. The distribution of these somatic mutations suggests that they have accumulated under an antigen-driven influence [20]. These monoclonal aPL tend to have accumulations of arginine residues, asparagine residues and lysine residues in their complementarity determining region (CDRs). Arginine residues have also been noted to play an important role in the CDRs of some murine monoclonal aPL [21,22].
Arginine residues, lysine residues and asparagine residues also occur very commonly in the CDRs of human and murine antibodies to dsDNA (anti-dsDNA) [23-25], particularly arginine residues in VH CDR3 [25-27]. It has been suggested that the structure of these amino acids allows them to form charge interactions and hydrogen bonds with the negatively charged DNA phosphodiester backbone [25,28]. We hypothesise that the same types of interaction may occur between negatively charged epitopes upon phospholipid antibodies/β2GPI and arginine residues, asparagine residues and lysine residues at the binding sites of high-affinity pathogenic IgG aPL.
We have previously described a system for the in vitro expression of whole IgG molecules from cloned VH and VL sequences of human monoclonal aPL antibodies [29]. This system was used to test the binding properties of combinations of heavy chains and light chains derived from a range of human antibodies. One of these antibodies, IS4, is an IgG antibody derived from a primary APS patient. IS4 binds to anionic phospholipid antibodies only in the presence of β2GPI, can bind to β2GPI alone and is pathogenic in a murine model [30]. It is therefore likely to be relevant in the pathogenesis of APS.
We found that the sequence of IS4VH was dominant in conferring the ability to bind cardiolipin (CL) while the identity of the VL paired with this heavy chain was important in determining the strength of CL binding [29].
Modelling studies have shown that multiple surface-exposed arginine residues were prominent features of the heavy chains and light chains that conferred the highest ability to bind CL. The CDR3 region of IS4VH contains five arginine residues, of which four are predicted by the model to be surface-exposed, and therefore is potentially important in binding to CL [29].
The purpose of the study reported in this paper was to define the contribution of different CDRs, and of individual arginine residues within those CDRs, in binding to CL. Patterns of CDR arginine residues in the cloned VH and VL sequences were altered by site-directed mutagenesis or by CDR exchange. The altered heavy chains and light chains were expressed transiently in COS-7 cells. Binding of the different heavy chain/light chain combinations to CL was tested by direct ELISA.
Materials and methods
Human monoclonal antibodies
IS4, B3 and UK4 are all human IgG monoclonal antibodies produced from lymphocytes of three different patients. IS4 was derived from a primary APS patient by the Epstein–Barr virus transformation of peripheral blood mononuclear cells and fusion with the human-mouse heterohybridoma K6H6/B5 cell line [31]. IS4 binds to CL in the presence of bovine and human β2GPI, and to human β2GPI alone [31]. B3 [32] and UK4 [33] were isolated by fusion of peripheral B lymphocytes from systemic lupus erythematosus patients with cells of the mouse human heteromyeloma line CB-F7. B3 binds single-stranded DNA, dsDNA, CL and histones [32,34]. UK4 binds negatively charged (but not neutral) phospholipid antibodies in the absence of β2GPI and does not bind DNA [33].
Assembly of constructs for expression
Wild-type heavy chain and light chain constructs
Constructs containing the wild-type heavy chain and light chain were prepared as detailed fully in previous articles [29,35]. UK4VH could not be cloned into the appropriate plasmid, hence only UK4VL was available for analysis. The expression vectors (pLN10, pLN100 and pG1D210) were all kind gifts from Dr Katy Kettleborough and Dr Tarran Jones (Aeres Biomedical, London, UK).
Hybrid VL chain constructs
Each hybrid VL chain construct contained the CDR1 of one of the human monoclonal IgG antibodies IS4, B3 or UK4 and the CDR2 and CDR3 of a different one of these antibodies. Two hybrid VL chains (BU and UB) had previously been made by Dr Haley and colleagues [36], and a further four chains (IB, IU, BI and UI) were made by a similar method, as follows.
Two different wild-type VL expression vectors were digested with Acc65 I and Pvu I (Promega, Southampton, UK). Acc65 I cuts IS4, B3 or UK4 VL sequences at a position in FR2 that is 106 base pairs from the beginning of VL, but does not cut the expression vector outside the insert. Pvu I cuts the vectors at a single site approximately 1 kb downstream of the insert. Each vector was therefore digested into two linear bands; one of approximately 1.5 kb and the other of approximately 6 kb. The 1.5 kb fragment contained CDR2 and CDR3 of the IgG VL region and also part of the downstream expression vector containing the lambda constant region cDNA, while the 6 kb fragment contained CDR1 and the rest of the vector. The 6 kb fragment derived from one VL expression vector was ligated with the 1.5 kb fragment derived from the other. The resulting plasmid would contain CDR1 of one VL sequence and CDR2 and CRD3 of another VL sequence.
Since IS4, B3 and UK4 VL sequences differ in their content of the restriction sites Aat II and Ava I, we checked that the desired parts of each sequence were present in the new hybrid sequences by carrying out Aat II, Hind III/Ava I and Aat II/Bam HI digests.
Site-directed mutagenesis of IS4VH
We generated six mutant forms of IS4VH in which particular arginine residues were mutated to serine, using the QuikChange site-directed mutagenesis kit (Stratagene, La Jolla, CA, USA) according to the manufacturer's protocol. Serine was chosen because it is nonpolar. Germline reversion could not be performed because the exact germline DH gene of IS4VH CDR3 is unknown. Four mutants, named IS4VHi, IS4VHii, IS4VHiii and IS4VHiv, contained single mutations of arginine residues at positions 96, 97, 100 and 100 g, respectively. The remaining two forms contained two arginine to serine mutations, at positions 96 and 97 in the IS4VHi&ii mutant and at all four sites in mutant IS4VHx.
Expression of whole IgG molecules
The whole IgG molecules were expressed in COS-7 cells as described previously [29,37].
Detection and quantitation of whole IgG molecules in COS-7 supernatant by ELISA
Whole IgG molecules were detected and quantitated in the COS-7 cell supernatants using a direct ELISA, as described in previous papers [29,35,37].
Detection of binding to CL by ELISA
The binding of IgG molecules to CL was measured by direct ELISA as described previously [29].
Results
Sequences of light chains expressed
Amino acid sequences of IS4VL, UK4VL, B3VL and germline gene 2a2 are shown in Fig. 1a. All of these light chains contain numerous somatic mutations. Previous statistical analysis has shown that the observed pattern of replacement mutations in the CDRs of these sequences is consistent with antigen-driven selection [32,33,35,38-40]. The light chain B3aVL, shown in Fig. 1a, was derived from B3VL by site-directed mutagenesis of Arg27a to serine [37].
The VL sequences of IS4, B3 and UK4 are all encoded by the germline Vλ gene 2a2, but differ in their patterns of somatic mutation. B3Vλ contains two adjacent arginine residues in CDR1, both produced by somatic mutations. UK4Vλ has a single somatic mutation to arginine in CDR3 at position 94. A serine residue in CDR3 of IS4VL is replaced by asparagine.
Figure 1a also shows the amino acid sequences of the Vλ CDR hybrids in which each newly formed chain construct contains CDR1 of one antibody with CDR2 and CDR3 of a different antibody. These hybrid sequences were named by combining the names of the two parent antibodies such that the first letter represented the antibody from which CDR1 was derived and the last letter represented the antibody from which both CDR2 and CDR3 were derived. Hybrid IB thus contains CDR1 from IS4, and CDR2 and CDR3 from B3, whereas hybrid BI contains the reverse combination (CDR1 from B3, and CDR2 and CDR3 from IS4).
Sequences of heavy chains expressed
The amino acid sequences of IS4VH and B3VH chain and the corresponding germline genes are displayed in Fig. 1b. B3VH has a single somatic mutation to arginine in CDR2. The CDR2 of IS4VH contains an asparagine residue created by somatic mutation and in CDR3 there are multiple arginine residues, which are highly likely to have arisen as a result of antigen-driven influence. The four surface-exposed arginine residues that were mutated to serine to create the six mutant forms of IS4VH are underlined in Fig. 1b.
Expression of whole IgG
Each of the 10 light chains shown in Fig. 1a was paired with B3VH and IS4VH. Each of the six mutant forms of IS4VH was paired with IS4VL and B3VL. A total of 32 heavy chain/light chain combinations were expressed in COS-7 cells. At least two expression experiments were carried out for each combination. IgG was obtained in the supernatant for all of the combinations.
The range of concentrations of IgG obtained in COS-7 cell supernatants, determined by ELISA, from each of the 32 heavy chain/light chain combinations are presented in Table 1. Identical concentrations were obtained for the combination IS4VHii/B3VL from two different expression experiments. In each case the negative control sample, in which COS-7 cells were electroporated without any plasmid DNA, contained no detectable IgG. Consistently high yields were obtained with the B3VH/BIVL, B3VH/UIVL and IS4VH/UIVL combinations compared with the other antibody combinations. The phenomenon of variable expression with different VH and VL constructs is well documented both in this antibody expression system and in other systems [35,37], although the reason for the occurrence of variable expression is not clear.
Results of anti-CL ELISA
For each heavy chain/light chain combination that bound CL, the linear portion of the binding curve for absorbance against antibody concentration was determined empirically, by dilution of antibody over a wide range of concentrations. Similar patterns of binding were obtained for each combination from repeated expression experiments, hence representative results from a single experiment only are shown in Figs 2,3,4.
The importance of arginine residues in IS4VH
As reported previously, the presence of the heavy chain of IS4 plays a dominant role in binding to CL [29]. IS4VH binds CL in combination with six of the 10 light chains tested (see Figs 2a and 3): B3VL, B3aVL, BIVL, IS4VL, IBVL and UIVL. Only one of these light chains (B3VL) binds CL in combination with B3VH (Fig. 2b).
To identify the features of IS4VH that enhance binding to CL, we focused on the combination IS4VH/B3VL. This combination shows high binding to CL. This binding could be altered by the replacement of some or all of the four surface-exposed arginine residues in IS4VH CDR3 to serine, as shown in Fig. 4. Substitution of all four arginine residues with serine residues (IS4VHx) abolished CL binding completely. This effect seems probably due entirely to the changes at positions 100 and 100 g. This is supported by the fact that heavy chain combinations containing arginine to serine mutations at these positions (IS4VHiii and IS4VHiv) displayed approximately 50% weaker binding to CL in combination with B3VL than did the wild-type IS4VH/B3VL combination. In contrast, there were no reductions in CL binding for the heavy chains containing arginine to serine mutations at position 96 (IS4VHi), at position 97 (IS4VHii) or at both positions (IS4VHi&ii).
The importance of arginine residues in the light chain CDRs
Six light chains bound CL in conjunction with IS4VH (Figs 2a and 3). The strongest binding was seen with light chains containing B3VL CDR1, namely B3VL, B3aVL and BI VL, in combination with IS4VH. In contrast, light chains IB and UB, containing CDR2 and CDR3 from B3, showed weak binding and no binding to CL, respectively, in combination with IS4VH.
To test the hypothesis that the arginine at position 27a in B3VL CDR1 is responsible for the favourable effect of this CDR on binding to CL, we expressed combinations of IS4VH and B3VH with B3aVL, in which Arg27a has been mutated to serine. As shown in Fig. 3, there was a significant decrease in CL binding of B3VH/B3aVL compared with B3VH/B3VL. Although the combination IS4VH/B3aVL binds CL less strongly than does IS4VH/B3VL, reduction in binding is not as great as that seen when these light chains are combined with B3VH. This observation is consistent with the idea that IS4VH plays a dominant role in binding to CL.
Despite being tested at a range of concentrations up to 75 times higher than those that gave maximal CL binding for the other combinations containing IS4VH, none of the light chains containing CDR2 and CDR3 derived from UK4VL, including UK4 wild-type, IU and BU, showed any binding to CL.
Discussion
Previously we have shown the important roles played in antigen binding by IS4VH and B3VL, which both contain multiple nongermline-encoded arginine residues in their CDRs, supporting the idea that this amino acid is important in creating a CL binding site [29]. The results described in the present study demonstrate that it is not just the presence of, but the precise location of arginine residues in the CDRs that is important in determining the ability to bind CL.
The importance of arginine residues at specific positions in the VH and VL sequences of anti-DNA antibodies has been examined by many groups, by expressing the antibodies in vitro and then altering the sequence of the expressed immunoglobulins by chain swapping or mutagenesis [27,37,41-43]. In general, these studies have shown that altering the numbers of arginine residues in the CDRs of these antibodies can lead to significant alterations in binding to DNA. Arginines in VH CDR3 often play a particularly important role in binding to this antigen [27,37,41-43]. Behrendt and colleagues recently demonstrated that the affinity of human phage-derived anti-dsDNA Fabs from a lupus patient correlated with the presence of somatically mutated arginine residues in CDR1 and CDR2 of the heavy chain [44].
Previous studies of the contribution of aPL heavy chains or light chains to CL binding have yielded conflicting results. Different groups have reported important contributions from the heavy chain [21,45], from the light chain [46], or from both chains [43,47]. In one of these studies the role of arginine residues was examined in a murine antibody (3H9) with dual specificity for phospholipid antibodies and DNA [21]. The introduction of arginine residues into the VH at positions known to mediate DNA binding enhanced binding to phosphatidylserine–β2GPI complexes and to apoptotic cell debris, which may be an important physiological source of both these antigens [48].
Our data show that combinations of IS4VH with light chains containing CDR1 of B3 (B3VL, B3aVL and BIVL) produced the strongest binding to CL. The CDR1 of B3VL and BIVL contains two surface-exposed arginine residues at positions 27 and 27a, while B3aVL contains only one arginine at position 27. Previous modelling studies have suggested that the binding of B3VH/B3VL to dsDNA is stabilised by the interaction of dsDNA with Arg27a in CDR1 and Arg54 in CDR2 of the light chain [34]. Expression and mutagenesis studies from our group confirmed that mutation of Arg27a to serine led to a reduction in binding to DNA [37]. In the present study the same change has been shown to reduce binding to CL, supporting the conclusion of Cocca and colleagues that arginines at particular positions can enhance binding to both DNA and CL [21].
It is important, however, not to overlook the possible contribution of other amino acids in B3VL to CL binding. For example, substitution of histidine at position 53 with lysine and substitution of serine at position 29 with glycine could significantly influence the stability of the antigen binding site. In fact, we have previously shown that introduction of the Ser29 to glycine mutation in addition to the Arg27a to serine mutation in the light chain of B3VL/B3VH leads to a further reduction in binding to dsDNA [37].
The presence of UK4VL CDR2 and CDR3 in any light chain blocked binding to CL, even when combined with B3VL CDR1 (light chain BU). UK4VLCDR1, however, does not block binding. We have previously shown that the presence of UK4VL CDR2 and CDR3 blocks binding to DNA and histones but not to the Ro antigen [36,37]. Modelling studies have shown that an arginine at position 94 in CDR3 of UK4VL hinders DNA binding sterically. A similar effect may be occurring with regards to the binding of UK4VL to CL.
The effect of point mutations of specific arginine residues in CDR3 of IS4VH upon CL binding is shown in Fig. 4. The low binding of IS4VH/IS4VL was abolished by inclusion of any one of these mutations. This is not the case, however, when these mutants are expressed with B3VL. In this case the arginine residues at 100 and 100 g confer a greater effect on CL binding compared with the arginine residues at positions 96 and 97. Substitutions of all four of these IS4VH CDR3 arginine residues were sufficient to completely abolish all binding to CL.
An accumulation of arginine residues in VH CDR3 has been noted in most, but not in all, sequences of pathogenic monoclonal aPL. From our detailed analysis of all published sequences of monoclonal aPL we found that of 13 monoclonal aPL that had been examined in various biological assays, eight monoclonal aPL had been shown to be pathogenic [49]. Three aPL derived from patients with primary APS and a healthy subject induced a significantly higher rate of foetal resorptions and a significant reduction in foetal and placental weight following intravenous injection into mated BALB/c mice [50,51]. Five other aPL derived from patients with primary APS and systemic lupus erythematosus/APS were found to be thrombogenic in an in vivo model of thrombosis [30]. We compared the sequences of these eight pathogenic antibodies with those of the other five antibodies, observing no evidence of pathogenicity in these bioassays. There was no evidence of preferential gene usage in either antibody group and somatic mutations were common in both groups. The presence of arginine residues in VH CDR3, however, did differ between pathogenic aPL and nonpathogenic aPL. Six of the eight pathogenic aPL, but only one of five nonpathogenic aPL, contain at least two arginine residues in VH CDR3 [49].
Our data confirm that the effect of arginine residues on binding to CL is highly dependent on the positions that they occupy in the sequence. The precise location of arginine residues has been shown to be important in the binding of both murine and human anti-dsDNA to DNA in numerous studies [25,26,37]. Interestingly, Krishnan and colleagues have demonstrated a strong correlation between specificity for dsDNA and the relative position of arginine residues in VH CDR3 [52,53]. They reported that the frequency of arginine expression among murine anti-dsDNA antibodies was highest at position 100, and they postulate that the importance of this residue in binding to dsDNA lies in its position at the centre of the VH CDR3 loop in the structure of the antigen combining site [52]. Assuming that this loop would be projected outward from the antigen combining site, an arginine residue at position 100 would be located at the apex of the VH CDR3 loop.
Conclusion
We have demonstrated the relative importance of certain surface-exposed arginine residues at critical positions within the light chain CDR1 and heavy chain CDR3 of different human monoclonal antibodies in conferring the ability to bind CL in a direct ELISA. It is now important to test the effects of sequence changes involving these amino acids on pathogenic functions of these aPL, by expressing the altered antibodies in larger quantities from stably transfected cells, and then testing them in bioassays.
Abbreviations
aPL = antiphospholipid antibodies; APS = antiphospholipid syndrome; β2GPI = beta2 glycoprotein I; CDR = complementarity determining region; CL = cardiolipin; dsDNA = double-stranded DNA; ELISA = enzyme-linked immunosorbent assay; Fab = antigen-binding fragment; VH = variable heavy chainregion; VL = variable light chainregion.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
IG produced four hybrid light chains, participated in the production of the mutant heavy chains, antibody expression and study design, and drafted the manuscript. NL participated in the production of the mutant heavy chains and antibody expression. PC and RC produced the human monoclonal aPL IS4. DL and DI participated in study design and coordination. AR conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
The authors are indebted to Dr David Faulkes, Dr Siobhan O'Brien and Dr Alison Levy for their help and advice on the assembly of constructs for expression. They are also grateful to Dr Sylvia Nagl for producing models of IS4 [29]. Ian Giles is supported by the Arthritis Research Campaign.
Figures and Tables
Figure 1 Sequence alignment of the expressed variable light chainregion (VL) and variable heavy chainregion (VH), using DNAplot software in VBASE. (a) Sequences of expressed Vλ regions compared with gene 2a2. (b) Sequences of expressed VH regions compared with genes 1-03 (IS4) and 3–23 (B3). The DH regions could not be matched to germline genes. Arginine residues altered by site-directed mutagenesis to serine residues in IS4VH complementarity determining region (CDR) 3 are underlined. Amino acids are numbered according to Kabat. Dots inserted to facilitate the alignment. Dashes indicate homology with the corresponding germline sequence. FR, framework region.
Figure 2 Effect of complementarity determining region exchange in the light chains. Cardiolipin binding of IgG in COS-7 cell supernatants containing wild-type heavy chains expressed with wild-type or hybrid light chain constructs. (a) Light chains expressed with IS4 variable heavy chainregion (VH). (b) Light chains expressed with B3VH. Presented as concentration of IgG in the supernatant versus optical density (OD) at 405 nm in the anti-cardiolipin ELISA.
Figure 3 Effect of point mutation Arg27a to serine in B3 variable light chainregion (VL) complementarity determining region 1. Comparison of cardiolipin binding of IgG in COS-7 cell supernatants containing wild-type heavy chains expressed with B3VL or B3VLa. Presented as concentration of IgG in the supernatant versus optical density (OD) at 405 nm in the anti-cardiolipin ELISA.
Figure 4 Effect of arginine to serine point mutations in IS4 variable heavy chainregion (VH) complementarity determining region 3. Cardiolipin binding of IgG in COS-7 cell supernatants containing wild-type or mutant forms of IS4 heavy chain expressed with wild-type B3 or IS4 light chains. The IS4VH mutants VHi, VHii, VHiii and VHiv contain single arginine to serine point mutations at positions 96, 97, 100 and 100 g, respectively; VHi&ii contains arginine to serine point mutations at positions 96 and 97; and VHx has an arginine to serine point mutation at all four positions. Presented as concentration of IgG in the supernatant versus optical density (OD) at 405 nm in the anti-cardiolipin ELISA.
Table 1 The range of IgG concentrations (ng/ml) produced by expression of the 32 heavy chain/light chain combinations
Heavy chain Light chain contributing CDR1 Light chain contributing CDR2 and CDR3 Light chain name IgG concentration (ng/ml)
IS4 IS4 IS4 IS4 24–368
IS4 IS4 B3 IB 22–140
IS4 IS4 UK4 IU 70–194
IS4 B3 B3 B3 5–14
IS4 B3 IS4 BI 50–60
IS4 B3 UK4 BU 5–60
IS4 UK4 UK4 UK4 11–22
IS4 UK4 IS4 UI 50–480
IS4 UK4 B3 UB 9–50
B3 IS4 IS4 IS4 71–192
B3 IS4 B3 IB 41–96
B3 IS4 UK4 IU 89–376
B3 B3 B3 B3 3.5–6
B3 B3 IS4 BI 120–608
B3 B3 UK4 BU 40–68
B3 UK4 UK4 UK4 8–28
B3 UK4 IS4 UI 60–480
B3 UK4 B3 UB 2–20
IS4 B3(Arg27aSer) B3 B3a 48–60
B3 B3(Arg27aSer) B3 B3a 2.5–4
IS4VHi IS4 IS4 IS4 50–56
IS4VHii IS4 IS4 IS4 65–70
IS4VHiii IS4 IS4 IS4 48–90
IS4VHiv IS4 IS4 IS4 48–90
IS4VHx IS4 IS4 IS4 78–94
IS4VHi&ii IS4 IS4 IS4 74–80
IS4VHi B3 B3 B3 24–54
IS4VHii B3 B3 B3 30
IS4VHiii B3 B3 B3 30–34
IS4VHiv B3 B3 B3 28–30
IS4VHx B3 B3 B3 32–34
IS4VHi&ii B3 B3 B3 32–47
IgG concentrations in COS-7 cell supernatants were determined by ELISA. The hybrid light chains were named by combining the names of the two parent antibodies such that the first letter represented the antibody from which the complementarity determining region (CDR) 1 was derived and the last letter represented the antibody from which both the CDR2 and CDR3 were derived. At least two expression experiments were carried out for each combination; identical concentrations were obtained for IS4VHii/B3VL from two different expression experiments.
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14511564212910.1186/ar1451Research ArticleRelaxin's induction of metalloproteinases is associated with the loss of collagen and glycosaminoglycans in synovial joint fibrocartilaginous explants Naqvi Tabassum [email protected] Trang T [email protected] Gihan [email protected] Momotoshi [email protected] Qin [email protected] Sunil [email protected] Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, Michigan, USA2005 29 10 2004 7 1 R1 R11 20 7 2004 17 9 2004 19 9 2004 27 9 2004 Copyright © 2004 Naqvi et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Diseases of specific fibrocartilaginous joints are especially common in women of reproductive age, suggesting that female hormones contribute to their etiopathogenesis. Previously, we showed that relaxin dose-dependently induces matrix metalloproteinase (MMP) expression in isolated joint fibrocartilaginous cells. Here we determined the effects of relaxin with or without β-estradiol on the modulation of MMPs in joint fibrocartilaginous explants, and assessed the contribution of these proteinases to the loss of collagen and glycosaminoglycan (GAG) in this tissue. Fibrocartilaginous discs from temporomandibular joints of female rabbits were cultured in medium alone or in medium containing relaxin (0.1 ng/ml) or β-estradiol (20 ng/ml) or relaxin plus β-estradiol. Additional experiments were done in the presence of the MMP inhibitor GM6001 or its control analog. After 48 hours of culture, the medium was assayed for MMPs and the discs were analyzed for collagen and GAG concentrations. Relaxin and β-estradiol plus relaxin induced the MMPs collagenase-1 and stromelysin-1 in fibrocartilaginous explants – a finding similar to that which we observed in pubic symphysis fibrocartilage, but not in articular cartilage explants. The induction of these proteinases by relaxin or β-estradiol plus relaxin was accompanied by a loss of GAGs and collagen in joint fibrocartilage. None of the hormone treatments altered the synthesis of GAGs, suggesting that the loss of this matrix molecule probably resulted from increased matrix degradation. Indeed, fibrocartilaginous explants cultured in the presence of GM6001 showed an inhibition of relaxin-induced and β-estradiol plus relaxin-induced collagenase and stromelysin activities to control baseline levels that were accompanied by the maintenance of collagen or GAG content at control levels. These findings show for the first time that relaxin has degradative effects on non-reproductive synovial joint fibrocartilaginous tissue and provide evidence for a link between relaxin, MMPs, and matrix degradation.
β-estradiolcollagencollagenase-1fibrocartilageglycosaminoglycansrelaxin
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Introduction
In certain sites in and around joints, ligaments and tendons subjected to complex tensile and compressive loading specialize into fibrocartilaginous tissues [1-3] containing types I and II collagens and cartilage-specific proteoglycans. These tissues include specific regions of the metacarpophalangeal ligament and the deep flexor tendon, the temporomandibular joint (TMJ) disc, and the pubic symphysis. Within the pubic symphysis of several species, the reproductive hormone relaxin induces matrix remodeling activity during pregnancy and parturition, causing a marked decrease in collagen content through partly characterized mechanisms that transform this tissue into a ligamentous structure [4-9]. The relaxin-mediated loss of matrix macromolecules in the pubic symphysis and other tissues is exacerbated by estrogen [4,7,8,10]. The relative contribution of matrix synthesis and degradation to these relaxin-mediated changes is not clear, although collagen loss through increased proteolysis has been suggested [4], and studies in relaxin-knockout mice have implicated increased collagenase activity [11].
To understand the potential basis for relaxin and estrogen's modulation of the composition of fibrocartilaginous tissues, we previously studied cells isolated from rabbit TMJ discs. Relaxin induced the expression of the matrix metalloproteinases (MMPs) collagenase-1 (MMP-1) and stromelysin-1 (MMP-3) in a dose-dependent fashion but had little effect on the expression of tissue inhibitor of metalloproteinase-1 (TIMP-1) or TIMP-2 [12]. In cells primed with β-estradiol, however, the relaxin concentration required for maximal induction of collagenase-1 and stromelysin-1 was 90–99% lower than in unprimed cells. Notably, the MMP response to relaxin was specific to fibrocartilaginous cells and was not observed in TMJ synoviocytes. These findings suggest that relaxin, by targeting fibrocartilage, might predispose women to musculoskeletal diseases of fibrocartilaginous joints.
One such disease is TMJ disorders, which affect some 11 million adults in the USA [13,14], predominantly women, with a female : male ratio of 2:1 to 6:1 [14]. Unlike similar diseases of other joints, TMJ disorders occur primarily in women of reproductive age [14]. Given the gender and age distribution of these disorders and the relaxin-induced loss of matrix macromolecules in the pubic symphysis fibrocartilage [4,6,7,9] and isolated TMJ fibrocartilaginous cells [12], we have proposed that relaxin compromises the integrity of fibrocartilaginous tissues by enhancing the degradation of their matrices directly through the induction of specific MMPs. However, although relaxin causes a loss of collagens and proteoglycans in reproductive organs [6,7] and also increases MMP expression in specific tissues [6,12,15-21], the induction of MMPs by relaxin has not been demonstrated in joint fibrocartilaginous tissues or its induction of MMPs has not been linked to the loss of matrix macromolecules in any tissue.
In this study we determined the effects of relaxin with or without β-estradiol on the modulation of MMPs, and assessed the contribution of these proteinases to the changes in collagen and glycosaminoglycan (GAG) content in fibrocartilaginous disc explants. Our findings are consistent with the hypothesis that relaxin-mediated induction of MMPs is associated with the loss of matrix macromolecules that could compromise tissue function and biomechanics and might lead to joint disease.
Materials and methods
Materials
Twenty-week-old female New Zealand white rabbits were obtained from Nita Bell Laboratories (Hayward, California, USA). Ketamine hydrochloride was from Parke Davis (Morris Plains, New Jersey, USA), and xylazine was from Rugby Lab (Rockville Center, New York, USA). Lactalbumin hydrolysate, α-casein, β-estradiol-17-valerate, pepsin, papain, chondroitin sulfate A sodium from bovine trachea, Safranin-O, Fast Green, cetylpyridinium chloride, and other reagents were from Sigma (St Louis, Missouri, USA). 1,9-Dimethylmethylene blue (DMMB) was from Molecular Probes (Eugene, Oregon, USA), and 35S was from Amersham (Arlington Heights, IL, USA). Protein assay kits, gelatin (EIA grade), and nitrocellulose membrane were from Bio-Rad (Hercules, California). α-Minimal essential medium, trypsin, penicillin–streptomycin, and Fungizone® were from Gibco (Grand Island, New York, USA). All other standard chemicals were from Sigma or Fisher Scientific (Pittsburg, Pennsylvania, USA).
Rabbit anti-human collagenase-1 polyclonal antibody and rabbit anti-mouse stromelysin-1 monoclonal antibody, horseradish peroxidase-conjugated secondary antibodies, and the MMP inhibitor GM6001 and its control analog were from Chemicon International (Temecula, California, USA). Rabbit anti-human-TIMP-1 antibody that cross-reacts with the rabbit inhibitor [12] was from Triple Point Biologics (Forest Grove, Oregon, USA). Enhanced chemiluminescence reagent for western blotting was from Amersham International (Little Chalfont, Bucks., UK). Sircol collagen assay kit was from Accurate Chemical and Scientific Corporation (Westbury, New York, USA), and fluorescein isothiocyanate (FITC)-labelled collagen was from Chondrex (Seattle, Washington, USA). Recombinant human relaxin was kindly provided by Connetics Corporation (Palo Alto, California, USA).
Retrieval and culturing of TMJ discs, pubic symphysis, and articular cartilage
All procedures on rabbits were approved by the Committee on Animal Research of the University of California, San Francisco, and conducted in accord with accepted standards of humane animal care. Rabbits were anesthetized with ketamine hydrochloride (40 mg/kg) and xylazine (3–5 mg/kg), and the TMJ discs were harvested bilaterally under sterile conditions and immediately placed in calcium-free and magnesium-free phosphate-buffered saline (PBS) containing antibiotics (100 U/ml penicillin, 100 mg/ml streptomycin, and 100 U/ml Fungizone). After removal of the synovium under a dissecting microscope, each disc was washed three times in PBS and bisected longitudinally such that four samples from each rabbit were available (three for hormone treatments and one for control). The hemisections were weighed, placed in wells of a 24-well culture plate, covered with 1 ml of serum-free medium (phenol-free α-minimal essential medium with 0.2% lactalbumin hydrolysate, glutamine, nonessential amino acids, 100 U/ml penicillin, and 100 mg/ml streptomycin) with or without hormones, and cultured at 37°C in air containing 5% CO2.
For determination of MMPs and GAG staining, 32 hemisections from eight rabbits were exposed to medium alone, β-estradiol (20 ng/ml), relaxin (0.1 ng/ml), or both hormones at the same doses for 48 hours. The conditioned medium was collected and stored for MMP assays, and the discs were processed for GAG staining. To assess the contribution of relaxin-induced MMPs to the loss of collagen and GAGs, 24 hemisections from six rabbits were cultured with the MMP inhibitor GM6001 or its control analog 2 hours before and during the hormone treatments. The inhibitor was used at 10 μM, because this concentration was shown to inhibit collagenase activity induced by 0.1 ng/ml relaxin in dose–response experiments to baseline levels. The conditioned medium was collected and stored at -70°C for total protein and MMP assays. The discs were dried in a SpeedVac®, weighed, digested, and used for the determination of GAG and collagen content.
To determine whether the observed induction of collagenase by relaxin is specific to fibrocartilage, experiments were performed with pubic symphysis fibrocartilage, which is a known target site for β-estradiol and relaxin as a positive control, and with articular cartilage from the knee. For retrieval of articular cartilage, the joint was shaved, the articular surfaces were exposed, and the cartilage was scraped from the articular surfaces of the femur and tibia and incubated in PBS with antibiotic as described above. Similarly, the pubic bones and symphyseal areas were exposed under sterile conditions and the pubic symphysis (fibrocartilaginous tissues between the pubic bones) was dissected, removed, and incubated in PBS with antibiotics. The tissues were weighed, placed in wells of a 24-well culture plate, and studied as described above.
Western blotting
Hormone-induced changes in collagenase-1, stromelysin-1, and TIMP-1 were determined by western blotting. Disc-conditioned medium was mixed with 4 × sample buffer and subjected to SDS–polyacrylamide-gel electrophoresis with 10% or 18% gels. Equal amounts of protein (determined with a bicinchoninic acid protein assay kit) were loaded in each lane. The proteins were transferred to nitrocellulose membranes, which were blocked, washed, and incubated for 1 hour with antibodies against TIMP-1 (1:250 dilution), collagenase-1 (1:250 dilution in Tris-buffered saline), or stromelysin-1 (1:500 dilution). The membranes were then washed, incubated with horseradish peroxidase-conjugated goat anti-rabbit antibody (1:1000 dilution), and washed again. Bands were revealed by incubation with enhanced chemiluminescence reagent and exposure to radiographic film. The bands for TIMP-1 western blots were quantified by videodensitometry as described [22]. Conditioned medium from pubic symphysis and articular cartilage explants was similarly subjected to western blot analysis for collagenase-1 and stromelysin-1.
Substrate zymography
Enzyme activities were quantified by substrate zymography of conditioned media from 32 hemisections (mean wet weight 13 ± 9 mg). The samples were standardized by total protein and subjected to SDS–polyacrylamide-gel electrophoresis with 10% gels containing 2 mg/ml gelatin or casein at 15°C as described [22]. The gels were washed in 2.5% Triton X-100 for 30 min with one change of wash buffer, incubated at 37°C for 60–72 hours in incubation buffer (50 mM Tris-HCl buffer pH 8, 5 mM CaCl2, 0.02% NaN3), stained with 5% Coomassie blue, and destained in 10% acetic acid and 40% methanol until proteinase bands were clearly visible. Images of the gels were captured with a charge-coupled device camera and NIH image software. The levels of 53/58 kDa gelatinolytic and 51/54 kDa caseinolytic enzymes and their low-molecular-mass activated forms were quantified by videodensitometry [22]. The substrate zymograms rather than western blots were used to quantify hormone-mediated increases in proteinase levels because zymograms are more sensitive, often display both pro-forms and active forms of proteinases, show a greater linear range of densitometric values and have good reproducibility that together enable a reliable quantification of the enzymes from these gels [23-25]. In addition, gelatin zymograms selectively detect proteinase activity at 53/58 kDa and at 43 kDa attributable primarily to collagenase rather than stromelysin because gelatin is a poor substrate for stromelysin [25,26].
Histochemical staining and quantification of GAGs
To assess changes in GAG levels, the discs were washed three times in PBS, frozen in OCT compound, and sectioned with a cryostat. The section were defrosted for 30 min, fixed for 10 min in methanol, air-dried for 15 min, stained with 1% Fast Green solution for 3 min, placed in 1% acetic acid for 1 min, stained with 2% Safranin-O for 2 min, dehydrated through successive ethanol and xylene washes, and mounted with coverslips. Ten sections of each hemisection were analyzed by an examiner blinded to the hormone treatment. The stained discs were videodigitized and analyzed with a software program that automatically outlined the total and Safranin-O-stained areas with threshold settings (Photoshop 4.0; Adobe, San Jose, California, USA). These areas were then quantified with NIH Image 1.62, and the percentage of disc staining positive for GAGs was calculated from the ratio of the stained area to the total area in each section. The average of the 10 values for each half disc was used for analysis.
Determination of GAG synthesis by 35S radiolabeling
To quantify GAG biosynthesis, 32 disc hemisections (mean weight 14 ± 4 mg) were incubated at 37°C for 6 hours in 1 ml of phenol-free and serum-free medium with or without hormones and 165 kBq (0.0044 mCi) of 35S as described [27]. The discs were washed three times with medium containing 1 mg/ml sodium sulfate and digested for 24 hours with 20 U/ml papain. The digest (500 μl) was incubated for 30 min with 100 μl of 5% cetyl pyridiuium chloride in 0.3 M potassium chloride at room temperature (20–22°C) to precipitate GAGs. After centrifugation (3000 g for 20 min), the supernatant was removed and the precipitate was dissolved in 600 μl of concentrated formic acid by heating to 70°C for 10 min. Aliquots (20 μl) of this solution were added to 3 ml of scintillation fluid and subjected to liquid scintillation counting. The radioactivity (counts/min) was standardized to the total dry disc weight.
Quantification of GAGs and collagen
Each disc hemisection was digested in 600 μl of 3 mg/ml pepsin in 0.05 M acetic acid and incubated at 37°C for 18–20 hours in a dry bath. DMMB binding assays for GAGs, and Sircol assays for collagen content, were performed in triplicate on 24 disc hemisections. The DMMB reagent was prepared as described [28]. Pepsin digests (200 μl) from each treatment group (GM6001 or analog control) were mixed with 1 ml of DMMB reagent, and absorbance at 525 nm was determined with a spectrophotometer. The GAG concentration (μg/ml) was determined by comparing the absorbance of the sample against a standard curve prepared from bovine chondroitin sulfate A, and the disc GAG content was standardized to the total dry tissue weight.
For the collagen assay, 200 μl of pepsin digest was mixed with 1 ml of Sircol dye reagent, incubated for 30 min at room temperature, and centrifuged at 10,000 g to separate the unbound dye from the collagen-bound dye. After removal of the unbound dye, 1 ml of the alkali reagent was added to the collagen–dye complex and vortex-mixed to dissolve the collagen-bound dye completely. Aliquots (200 μl) were transferred to the 96-well plates, and absorbance at 550 nm was determined with a microtiter plate reader (Molecular Devices, Sunnyvale, California, USA). The collagen concentration (μg/ml) was determined against a collagen standard curve, and the disc collagen content was standardized to the total disc dry weight.
Quantification of collagenase activity
Collagenase activity in conditioned medium from discs cultured with GM6001 or control analog was assessed by FITC–collagen assay. A 96-well plate was coated with FITC–collagen (10 μg per well) overnight at 4°C and washed twice with PBS. Disc-conditioned medium (100 μl) was added to the wells, and the plate was incubated at 35°C for 1 hour. As a reference, 100 μl of blank medium containing 3000 ng of bacterial collagenase was added to one set of wells for complete digestion of FITC–collagen. After incubation, 90 μl from each well was transferred to another 96-well plate, and the fluorescence intensity of degraded FITC–collagen products was determined with a microplate spectrofluorometer (Spectramax Gemini XS; Molecular Devices) with excitation at 494 nm and emission at 518 nm. The data were converted to relative fluorescence units of collagenase activity as described by the manufacturer and standardized to the dry weight of each half disc. The fold differences in collagenase activity in medium from control and hormone-treated discs were determined for each experiment. All assays were performed in duplicate.
Statistical analysis
Because of inherent variability in matrix content and proteinase activity in discs from different rabbits, three disc hemisections from each rabbit were treated with hormones and one served as control. MMP levels and the GAG and collagen content in each hormone-treated disc hemisection were standardized to the values of the control hemisection within each animal and the fold changes were plotted as histograms. The statistical significance of differences was determined by single-factorial analysis of variance (ANOVA). Intergroup differences were analyzed by Fisher's multiple comparisons test; P < 0.05 was considered statistically significant. Values are expressed as means ± SD.
Results
Relaxin and β-estradiol induce collagenase-1 and stromelysin-1 in TMJ disc explants
Explanted discs constitutively expressed collagenase-1 (MMP-1) (Fig. 1a, lane 1), and the expression of this proteinase was increased by exposure to relaxin alone or to β-estradiol plus relaxin (Fig. 1a, lanes 3 and 4). Gelatin substrate zymograms confirmed the induction of 53/58 kDa proteinase by these hormones and, because this assay is more sensitive than western blots, showed an additional 43 kDa gelatinolytic enzyme (Fig. 1b). Because the gelatinolytic enzymes were inhibited by 1,10-phenanthroline (Fig. 1b, lane 5), these proteinases were characterized as MMPs, most probably procollagenase-1 and active collagenase-1. Western blots with conditioned medium from a disc explant exposed to relaxin showed that the 53/58 kDa and 43 kDa activities corresponded to procollagenase-1 and collagenase-1, respectively (Fig. 1b, lane 6). Proteinase expression was about 1.7-fold higher in relaxin-treated and β-estradiol plus relaxin-treated discs than in control cultures (P < 0.05) and was not potentiated by β-estradiol (Fig. 1c).
Because the expression of stromelysin and collagenase is often coordinately regulated [29], we assessed stromelysin expression. Western blots showed that all three hormone treatments induced stromelysin-1 (MMP-3) (Fig. 1d). Casein substrate zymograms demonstrated a 51/54 kDa caseinolytic proteinase (Fig. 1e, lanes 1–4) that was inhibited by 1,10-phenanthroline (Fig. 1e, lane 5), indicating a metalloprotease. This characterization was confirmed by western blotting (Fig. 1e, lane 6). Proteinase expression in relaxin-treated cultures was double that in control cultures (P < 0.05) and was not potentiated by β-estradiol.
Relaxin induces collagenase-1 and stromelysin-1 in fibrocartilage but not in articular cartilage
In pubic symphysis fibrocartilage, which is a known target site for β-estradiol and relaxin, β-estradiol caused slight increases in collagenase-1, while relaxin alone or in combination with β-estradiol induced a substantially greater expression of collagenase-1 relative to untreated discs (Fig. 2a). Relaxin also increased stromelysin-1 levels in pubic symphysis fibrocartilage. However, β-estradiol alone or in conjunction with relaxin produced substantially greater increases in stromelysin-1 levels than relaxin alone. In knee articular cartilage, although β-estradiol induced collagenase-1 and stromelysin-1, neither relaxin nor β-estradiol plus relaxin increased the expression of these proteinases over control levels (Fig. 2b,2d). Indeed, relaxin alone seemed to inhibit stromelysin-1 expression in articular cartilage.
Loss of GAGs parallels the induction of MMPs by relaxin but not by β-estradiol
Because all hormone treatments induced stromelysin-1 expression in explanted discs, we assessed the level of a known substrate, proteoglycans, in Safranin-O-stained sections. The GAG-positive area was larger in control discs (30.1 ± 2.8% of total disc area) and discs treated with β-estradiol (29.7 ± 4.7%) than in those treated with relaxin (19.2 ± 3.3%) or β-estradiol plus relaxin (16.9 ± 2.7%) (Fig. 3a). These findings reflect statistically significant differences (P < 0.01, ANOVA) in GAG staining between control discs and those treated with relaxin (P < 0.05, Fisher's test) or β-estradiol plus relaxin (P < 0.01). Similarly, the GAG-positive area was significantly smaller (P < 0.04, ANOVA) in discs treated with relaxin (P < 0.05, Fisher's test) or β-estradiol plus relaxin (P < 0.05) than in those treated with β-estradiol alone.
β-Estradiol induces TIMP-1
To determine why GAG loss did not increase in parallel with stromelysin expression in explants treated with β-estradiol alone, we assessed GAG synthesis and TIMP-1 expression. Except for a significantly lower GAG synthesis in discs exposed to β-estradiol plus relaxin than in those exposed to β-estradiol alone (P < 0.05), differences between the other groups were not significant (Fig. 3b). β-Estradiol caused a significant (P < 0.01) twofold induction in TIMP-1 expression over controls (Fig. 3c,3d). However, neither relaxin alone nor β-estradiol plus relaxin modulated any changes in TIMP-1 expression in the disc explants.
Inhibition of MMP activity prevents relaxin-mediated loss of GAGs
To establish an association between the increased MMP activity and the loss of GAGs in explants treated with relaxin or β-estradiol plus relaxin, we cultured the explants with the MMP inhibitor GM6001 or its control analog. Western blot analysis showed a higher expression of stromelysin-1 in hormone-treated than untreated disc explants in the presence of GM6001 or its control analog (data not shown). However, zymography showed increased 51/54 kDa caseinolytic activity (stromelysin-1) only in hormone-treated explants incubated with the control analog (Fig. 4a), and not in those incubated with GM6001 (Fig. 4b).
DMMB assays showed that hormone treatments in the presence of control analog decreased the GAG content (P < 0.0001, ANOVA), which was 30% lower in relaxin-treated explants (P < 0.001, Fisher's test) and 40% lower in those treated with β-estradiol plus relaxin (P < 0.001) than in untreated explants (Fig. 4c). Similarly, the GAG content was lower (P < 0.0001, ANOVA) in discs treated with relaxin (P < 0.05, Fisher's test) or β-estradiol plus relaxin (P < 0.001) than in those treated with β-estradiol alone. In the presence of GM6001, however, hormone treatments did not affect the GAG content (Fig. 4d).
Relaxin-induced collagenase activity contributes to loss of disc collagen
All three hormone treatments increased the expression of procollagenase-1 in the presence of GM6001 or its control analog similarly to that shown in Fig. 1a,1b,1c. However, as shown by FITC-collagen degradation assays, collagenase activity was significantly increased only by relaxin or β-estradiol plus relaxin in the presence of the control analog (Fig. 5a). In discs incubated with GM6001, hormone-induced collagenase activity was inhibited to control levels (Fig. 5b). Conversely, Sircol assays showed the collagen content was significantly decreased (P < 0.0001, ANOVA) only in the presence of the control analog and only by relaxin (40% of control and β-estradiol alone; P < 0.0001, Fisher's test) or β-estradiol plus relaxin (60% versus control and β-estradiol alone; P < 0.0001) (Fig. 5c). In the presence of GM6001, hormone treatments did not affect collagen content (Fig. 5d).
Discussion
This study shows that relaxin induced the expression of collagenase-1 and stromelysin-1 in rabbit TMJ disc explants, accompanied by a loss of GAGs and collagen, but did not affect GAG synthesis. In explants cultured with the MMP inhibitor GM6001, collagenase-1 and stromelysin-1 activities in hormone-treated discs were inhibited to baseline levels, and collagen and GAG content were maintained at control levels. These findings show that relaxin has degradative effects on nonreproductive synovial joint fibrocartilaginous tissue and provide evidence that increases in MMP activity mediated by relaxin and β-estradiol plus relaxin contribute directly to the loss of disc collagen and GAGs. The lack of effect on GAG synthesis further validates the importance of the degradative component of the remodeling cycle in relaxin's modulation of matrix loss in fibrocartilage.
Because the MMP inhibitor used in our studies is not specific for collagenase-1 and stromelysin-1, the hormone-induced loss of collagen and GAGs cannot be specifically linked to those two proteinases. Rather, our findings implicate MMPs in general in this response. However, because GM6001 has a low dissociation constant for both collagenase-1 and stromelysin-1 [30], and their induction by relaxin was accompanied by a loss of their matrix substrates, collagenase-1 and stromelysin-1 are probably involved in the relaxin-mediated loss of collagen and GAGs, respectively.
In contrast to the results obtained with relaxin and β-estradiol plus relaxin, the induction of collagenase-1 and stromelysin-1 by β-estradiol alone was not accompanied by changes in GAG or collagen content within the disc. How can we explain this apparent discrepancy? β-Estradiol had little effect on GAG synthesis, as measured by 35S incorporation, but it produced a statistically significant increase in TIMP-1 expression that could have counteracted any increases in degradative activity due to increased expression of collagenase-1 and stromelysin-1. Indeed, the results of the collagen degradation assay lend credence to this hypothesis. These findings imply that relaxin and β-estradiol selectively contribute to the degeneration of fibrocartilaginous tissue by differentially modulating MMP expression, matrix synthesis, and net matrix content.
The potential similarities in the responsiveness of TMJ fibrocartilaginous explants and the pubic symphysis fibrocartilage to relaxin are reflected not only by the relaxin's induction of collagenase but also by the comparable loss of collagen on the exposure of these tissues to the hormone [4,9]. Thus, the extent of collagen loss in fibrocartilaginous disc explants exposed to relaxin (40%) or β-estradiol plus relaxin (60%) was similar to that in the pubic symphysis of unprimed and β-estradiol-primed ovariectomized nonpregnant rats (64 ± 4% and 68 ± 6%, respectively) [4]. Similarly, in pregnant ovariectomized rats, relaxin decreased collagen to 39% of the levels in nonpregnant animals [9]. Additionally, β-estradiol alone had minimal effects on the collagen content of the fibrocartilaginous TMJ disc, which is also similar to observations on the pubic symphysis [4,9]. Thus, relaxin with or without β-estradiol, but not β-estradiol alone, has a potent effect on the amount of collagen in fibrocartilaginous tissues from different sites, including the pubic symphysis and synovial joints. These findings also suggest that in fibrocartilaginous tissues, including the TMJ disc and possibly the pubic symphysis, relaxin decreases collagen and GAG content primarily by inducing MMP expression.
The response of articular cartilage to relaxin or β-estradiol plus relaxin was substantially different from that of the TMJ disc and pubic symphysis fibrocartilages. Although the reasons for these differences remain to be determined, it is well accepted that articular cartilage is a cartilaginous tissue containing chondrocytic cells, whereas fibrocartilage is a heterogenous tissue composed of cartilage and fibrous tissue that contains cells of fibroblastic, chondrocytic, and fibrochondocytic phenotypes. It is plausible that of these cells, the fibroblastic and/or fibrochondrocytic cells found in fibrocartilage, rather than the chondrocytic cells, are those that produce the observed responses to relaxin and β-estradiol plus relaxin. Indeed, previous findings on both dermal fibroblasts showing a potent induction of MMP-1 [18] and on articular chondrocytes that show minimal modulation of total collagen synthesis by relaxin [31] lend credence to this hypothesis. Additional studies are indicated to address the mechanistic basis for the differences in responsiveness of fibrocartilaginous versus cartilaginous cells to relaxin.
Our findings are consistent with emerging data suggesting that the mechanisms for the loss of matrix macromolecules caused by relaxin are tissue-specific [32]. Thus, for example whereas relaxin increases collagenase-1 expression in TMJ disc and pubic symphyseal [6] fibrocartilages, it had minimal effects on its expression in articular cartilage explants. In monolayer articular or multilayer growth plate rabbit chondrocytes, relaxin produces no net change in collagen synthesis and no alterations in type II collagen mRNA levels, but increases the expression of types I and III collagen mRNA, thereby amplifying the dedifferentiation process [31]. In contrast, relaxin downregulates collagen expression by up to 40% and induces collagenase expression in cultured dermal fibroblasts [18]. As in our study, relaxin increases collagenase activity in human cervical stromal cells; however, in contrast to our findings, it also increases GAG synthesis [15,16].
MMPs contribute substantially to tissue degeneration in inflammatory joint diseases, including rheumatoid arthritis and osteoarthritis [33-35]. Our findings show that relaxin directly modulates MMP expression and probably causes matrix loss in fibrocartilaginous tissues from a synovial joint. Although the effects of relaxin on loss of matrix macromolecules, particularly collagen, have been demonstrated in the fibrocartilaginous pubic symphysis [4-7], this is the first study to demonstrate a similar targeting of fibrocartilaginous tissues from the synovial TMJ, and may implicate this hormone in the pathogenesis of TMJ disease in a subset of women with these disorders. Because even subtle alterations in collagen and GAG composition can affect the structural properties and the ability of joint tissues to function normally, this modulation of MMPs and resulting matrix loss in the fibrocartilaginous TMJ disc by relaxin might explain the distinct age and gender distribution of TMJ diseases. Furthermore, these findings have potential physiologic relevance because the induction of collagenase-1 and stromelysin-1 and the loss of collagen and GAGs occurred at concentrations of relaxin found systemically in cycling women [36-38]. Although the ability of systemic relaxin to access the TMJ and reach the avascular disc remains to be determined, our recent findings in vivo showing relaxin-mediated decreases in GAG concentration in the TMJ discs of ovariectomized rabbits suggest that this systemic hormone can indeed access the TMJ disc and contribute to its degradation [39].
Conclusions
Relaxin causes the targeted induction of collagenase-1 and stromelysin-1 in synovial joint and pubic symphysis fibrocartilages but not in articular cartilage. This induction of MMPs in joint fibrocartilage is accompanied by a loss of collagen and GAGs that is prevented by an MMP inhibitor, suggesting a link between relaxin, MMPs, and matrix degradation. These studies provide the first evidence that relaxin contributes to the degradative remodeling of joint fibrocartilage and that there is an association between relaxin-induced MMPs and matrix loss; they also suggest a potential mechanism of action of relaxin in contributing to TMJ diseases in a subset of women with these disorders.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
TN performed all experiments, assays and analysis in which MMP inhibitors were used. TTD performed all experiments to characterize the changes in MMPs and GAGs in joint fibrocartilage in response to relaxin and β-estradiol. GH and QZ characterized the responses of the pubic symphysis fibrocartilage and articular cartilage to the hormones. MS retrieved tissues from animals and assisted in several MMP assays. SK conceived the study, participated in its design and coordination, supervised the statistical analysis, and wrote the manuscript. All authors read and approved the final manuscript.
Abbreviations
ANOVA = analysis of variance; DMMB = 1,9-dimethylmethylene blue; GAG = glycosaminoglycan; FITC = fluorescin isothycyanate; MMP = matrix metalloproteinase; PBS = phosphate-buffered saline; TIMP = tissue inhibitor of metalloproteinase; TMJ = temporomandibular joint.
Acknowledgements
We are grateful to the late Ms Nilda Ubana for processing and staining tissue sections. We also thank Connetics Corporation for providing the recombinant human relaxin. This research was performed at the University of California, San Francisco. This study was supported by grants R29 DE11993 and KO2 DE00458 from the National Institutes of Health and by a University of California San Francisco Academic Senate Shared Equipment Grant to SK and grant T32 DE 07236 from NIDCR to GH. Part of this work was awarded the Harry Sicher First Assay Research Award (to Dr Duong) by the American Association of Orthodontists.
Figures and Tables
Figure 1 Relaxin induces collagenase-1 and stromelysin-1 in fibrocartilaginous explants from temporomandibular joint. Disc hemisections were exposed for 48 hours to basal control medium (Ct), β-estradiol (Es, 20 ng/ml), or relaxin (R, 0.1 ng/ml) or to β-estradiol plus relaxin (Es+R). Conditioned medium, standardized by tissue weight, was subjected to SDS–polyacrylamide-gel electrophoresis and transferred to membranes for western immunoblots for collagenase-1 (a) or stromelysin-1 (d) or assayed in gels containing gelatin (b) or α-casein (e). Images of the substrate gels were digitized, and the 53/58 kDa and 43 kDa gelatinase activities (collagenase and active collagenase, respectively) (c) and the 51/54 kDa caseinolytic activity (stromelysin) (f) were quantified by videodensitometry. The samples used in lane 6 of panels (b) and (e) are positive controls for collagenase-1 and stromelysin-1. P, gels incubated in buffer containing the metalloproteinase inhibitor 1,10-phenanthroline; Cl-1, collagenase-1; ACl-1, active collagenase-1; Sl-1, stromelysin-1; α-Cl, anti-collagenase-1 antibody; α-Sl, anti-stromelysin-1 antibody. * P < 0.05.
Figure 2 Relaxin induces collagenase-1 and stromelysin-1 in pubic symphysis fibrocartilage but not in articular cartilage. Pubic symphysis fibrocartilage or knee articular cartilage explants were exposed for 48 hours to basal control medium (Ct), β-estradiol (Es, 20 ng/ml), or relaxin (R, 0.1 ng/ml) or to β-estradiol plus relaxin (Es+R). Conditioned medium, standardized by tissue weight, was subjected to western blotting for collagenase-1 (a, b) or stromelysin-1 (c, d). Cl-1, collagenase-1; Sl-1, stromelysin-1.
Figure 3 Induction of matrix metalloproteinases by relaxin but not by estrogen is accompanied by loss of glycosaminoglycans (GAGs). (a) Disc explants were cultured for 48 hours in basal control medium (Ct), β-estradiol (Es, 20 ng/ml), or relaxin (R, 0.1 ng/ml) or in β-estradiol plus relaxin (Es+R), then sectioned and stained with Safranin-O for GAGs. The percentage area staining positive for GAGs was determined histomorphometrically and plotted. Values are means ± SD. (b) Hormone-mediated changes in GAG synthesis were assessed by 35S-labeling of fibrocartilaginous disc explants. The explants were washed and digested with papain, and the radioactivity was measured. Fold changes (means ± SD) in 35S incorporated into the explants incubated with hormones relative to that in control discs were determined and plotted. (c) To evaluate the modulation of tissue inhibitor of metalloproteinases-1 (TIMP-1) by hormones, the conditioned medium, standardized dry tissue weight (mg), was resolved electrophoretically and transferred to nitrocellulose membranes, and the membranes were probed with anti-TIMP-1 antibody. (d) The bands were quantified by videodensitometry, and the fold induction (mean ± SD) of TIMP-1 by various hormone treatments relative to untreated control explants was plotted. T-1, TIMP-1. * P < 0.05, ** P < 0.01 by Fisher's test.
Figure 4 Inhibition of matrix metalloproteinase (MMP) activity prevents relaxin-mediated loss of glycosaminoglycans (GAGs). Conditioned medium from disc hemisections incubated with β-estradiol (Es), relaxin (R), or β-estradiol plus relaxin (Es+R) in the presence of the MMP inhibitor GM6001 or its control analog was assayed by casein substrate zymograms (a, b). Disc digests from these experiments were assayed for GAGs with the 1,9-dimethylmethylene blue assay, and the results were standardized to tissue dry weight (mg). Fold changes in GAG concentration (mean ± SD) were calculated and plotted (c, d). The untreated control (Ct) discs used in all experiments were exposed to control analog only. * P < 0.05, ** P < 0.01, *** P < 0.001 by Fisher's test.
Figure 5 Relaxin-induced collagenase activity contributes to loss of disc collagen. Conditioned medium from disc incubated with control medium (Ct), β-estradiol (Es), relaxin (R), or β-estradiol plus relaxin (Es+R) in the presence of the matrix metalloproteinase inhibitor GM6001 or its control analog was subjected to fluorescein isothiocyanate-labelled collagen degradation assay. The collagenase activity (relative fluorescence units [RFU]/ml) was standardized by the dry weight of the tissue (mg), and fold changes (means ± SD) were plotted (a, b). Disc digests from these experiments were assayed for collagen with the Sircol assay, and the results were standardized to tissue dry weight (mg). Fold changes in collagen concentration (means ± SD) were calculated and plotted (c, d). The untreated control (Ct) discs used in all experiments were exposed to control analog only. ** P < 0.01, *** P < 0.0001 by Fisher's test.
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| 15642129 | PMC1064880 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Oct 29; 7(1):R1-R11 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1451 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14521564214610.1186/ar1452Research ArticleInterleukin-7 deficiency in rheumatoid arthritis: consequences for therapy-induced lymphopenia Ponchel Frederique [email protected] Robert J 3Bingham Sarah J 2Brown Andrew K 2Moore John 4Protheroe Andrew 5Short Kath 5Lawson Catherine A 12Morgan Ann W 12Quinn Mark 2Buch Maya 2Field Sarah L 1Maltby Sarah L 1Masurel Aurelie 1Douglas Susan H 1Straszynski Liz 1Fearon Ursula 2Veale Douglas J 2Patel Poulam 5McGonagle Dennis 2Snowden John 6Markham Alexander F 1Ma David 4van Laar Jacob M 3Papadaki Helen A 7Emery Paul 2Isaacs John D 1281 Molecular Medicine Unit, University of Leeds, Leeds, UK2 Academic Unit of Musculoskeletal Disease, Leeds General Infirmary, Leeds, UK3 Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands4 Hematology Department, St Vincent Hospital, Sydney, Australia5 Cancer Research UK, University of Leeds, Leeds, UK6 Department of Haematology, Royal Hallamshire Hospital, Sheffield, UK7 Department of Hematology, University of Crete School of Medicine, Heraklion, Crete, Greece8 School of Clinical Medical Sciences (Musculoskeletal Research Group), The University of Newcastle, Newcastle upon Tyne, UK2005 16 11 2004 7 1 R80 R92 3 8 2004 9 9 2004 15 9 2004 27 9 2004 Copyright © 2004 Ponchel et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
We previously demonstrated prolonged, profound CD4+ T-lymphopenia in rheumatoid arthritis (RA) patients following lymphocyte-depleting therapy. Poor reconstitution could result either from reduced de novo T-cell production through the thymus or from poor peripheral expansion of residual T-cells. Interleukin-7 (IL-7) is known to stimulate the thymus to produce new T-cells and to allow circulating mature T-cells to expand, thereby playing a critical role in T-cell homeostasis. In the present study we demonstrated reduced levels of circulating IL-7 in a cross-section of RA patients. IL-7 production by bone marrow stromal cell cultures was also compromised in RA. To investigate whether such an IL-7 deficiency could account for the prolonged lymphopenia observed in RA following therapeutic lymphodepletion, we compared RA patients and patients with solid cancers treated with high-dose chemotherapy and autologous progenitor cell rescue. Chemotherapy rendered all patients similarly lymphopenic, but this was sustained in RA patients at 12 months, as compared with the reconstitution that occurred in cancer patients by 3–4 months. Both cohorts produced naïve T-cells containing T-cell receptor excision circles. The main distinguishing feature between the groups was a failure to expand peripheral T-cells in RA, particularly memory cells during the first 3 months after treatment. Most importantly, there was no increase in serum IL-7 levels in RA, as compared with a fourfold rise in non-RA control individuals at the time of lymphopenia. Our data therefore suggest that RA patients are relatively IL-7 deficient and that this deficiency is likely to be an important contributing factor to poor early T-cell reconstitution in RA following therapeutic lymphodepletion. Furthermore, in RA patients with stable, well controlled disease, IL-7 levels were positively correlated with the T-cell receptor excision circle content of CD4+ T-cells, demonstrating a direct effect of IL-7 on thymic activity in this cohort.
immune reconstitutioninterleukin-7T-cell differentiationtherapeutic lymphodepletion
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Introduction
Peripheral blood T-cell lymphopenia is long-lasting in patients with rheumatoid arthritis (RA) receiving lymphodepleting therapies, such as monoclonal antibodies [1-3] or high-dose cyclophosphamide with autologous stem cell rescue (autologous stem cell transplantation) [4,5]. It has now been extensively documented in a number of systems that IL-7 drives the survival and proliferation of human T-cells after lymphodepletion (for review [6]). In particular, high circulating levels of this cytokine have been documented in patients rendered lymphopenic either by lymphocytotoxic treatment [7] or by HIV infection [8-10]. IL-7 produced in response to lymphopenia stimulates proliferation of both naïve and memory human T-cells [7], but also has a direct stimulating effect on thymic activity [11]. IL-7 plays many other roles such as the induction/enhancement of a T-helper-1 immune response [12,13], maturation of monocytes into dendritic cells, recruitment and expansion of T-cell clones [14-16], and induction of natural killer cell lytic activity [17-19]. These make IL-7 a master modulator of T-cell-mediated immune responses, particularly in tumour surveillance and eradication, in addition to its role as master regulator of peripheral T-cell homeostasis [8]
Specific abnormalities within the naïve T-cell compartment in RA, such as repertoire contraction and shortened telomeres, have suggested a possible defect in generating and/or maintaining naive T-cells [20-23]. Furthermore, we recently showed [24] that RA patients possessed fewer naïve CD4+ T-cells than did healthy control individuals and that a smaller proportion of these cells contained a T-cell receptor excision circle (TREC). Circulating C-reactive protein (CRP) levels correlated inversely with the TREC content of naïve CD4+ T-cells, suggesting that inflammation was driving naïve CD4+ T-cell proliferation and differentiation, leading to dilution of TREC-containing cells. We could not, however, exclude an additional intrinsic defect in thymic T-cell production in RA patients [24].
In recent studies we reported persistent and profound CD4+ T-cell lymphopenia in RA patients as long as 7 years after a single course of CAMPATH-1H monoclonal antibody treatment [25] and up to 36 months after autologous stem cell transplantation [26]. RA patients usually reconstitute their B and natural killer cells rapidly, whereas CD8+ T-cell reconstitution takes longer and full recovery of CD4+ T cells may never occur. This is in contrast to patients undergoing bone marrow or stem cell transplantation for haematological malignancy or solid tumours, in whom both T-cell compartments reconstitute within 1 year of follow up [27-29]. Poor reconstitution after lymphodepleting therapy is likely to result either from reduced de novo T-cell production from the thymus or from poor peripheral expansion of naïve and memory cells, both of which processes are driven by IL-7.
Here we report on a deficit in circulating levels of IL-7 in a cross-section of RA patients. This is associated with a reduced production of IL-7 in bone marrow derived stromal cell cultures, and may contribute to the defective CD4+ T-cell reconstitution that occurs following therapeutic lymphodepletion, primarily at the level of mature T-cell expansion in the periphery. Furthermore, we show that TREC levels correlate with circulating levels of IL-7 in patients in whom inflammation is controlled.
Methods
Patient cohorts
Ethical approval for the project was obtained from the Leeds Teaching Hospitals National Health Service Trust Ethics Committee, and informed consent was obtained from each participant. Healthy control individuals were recruited from among local blood donors (n = 34). RA (n = 28) and osteoarthritis (OA; n = 12) patients were recruited through routine clinics at the Leeds General Infirmary (Table 1). They included patients with early, drug naïve (n = 7) and long-lasting, refractory (n = 21) RA (CRP range 5–155 mg/l) and patients with established, long-lasting OA (n = 12; CRP below detection range).
For the reconstitution studies we analyzed three RA patient cohorts (n = 31) and a cohort of non-RA patients with solid tumours (n = 7; Table 2). Each RA patient received high-dose cytotoxic therapy followed by autologous haematological transplants [26,30,31]. Each had disease that had proved resistant to multiple conventional antirheumatic drugs. Cohort 1 received an unmanipulated graft; cohort 2 received a graft that had undergone selection for CD34+ cells; and cohort 3 received a graft that had been CD34+ cell selected and T-cell depleted. The clinical progress of these patients was previously described elsewhere [26,30,31]. Control patients (Table 2) included five individuals with lung carcinoma, one with breast carcinoma and one with melanoma. They received unmanipulated autologous grafts following high-dose chemotherapy, as previously documented [32]. For the IL-7 longitudinal studies, we analyzed four lymphoma and three sarcoma patients. All received intensive chemotherapy followed by reinfusion of unmanipulated autologous stem cells (Table 2). In addition, we studied three patients with systemic vasculitis who received the lymphocytotoxic monoclonal antibody CAMPATH 1H [33].
For our work on RA patients in clinical remission (Table 1), we recruited consecutive patients (n = 36) attending the rheumatology outpatient clinics with stable RA. They possessed no clinically significant synovitis and were deemed to be in 'remission' by the assessing consultant rheumatologist. Patients satisfied all of the following inclusion criteria: previous certified diagnosis of RA; over 18 years of age; disease duration of at least 12 months before remission; no disease flare within preceding 6 months; stable treatment within preceding 6 months; nil or minimal clinical evidence of active inflammatory disease and CRP below 15 mg/l within preceding 6 months; and no clinical indication to change treatment. We further refined this cohort by separating patients into those who satisfied the American College of Rheumatology (ACR) remission criteria and those who did not (Table 3).
Cytokine measurements
IL-7, transforming growth factor-β1, IL-6, tumour necrosis factor (TNF)-α and oncostatin M levels in sera and in tissue culture supernatants were measured using enzyme-linked immunosorbent assay (ELISA; R&D, Abingdon, UK), in accordance with the manufacturer's instructions. The sensitivities of the assay were <0.1 pg/ml for IL-7, 0.2 pg/ml for IL-6, 0.5 pg/ml for TNF-α, and 20 pg/ml for oncostatin M.
T-cell subset separation
Peripheral blood mononuclear cells (PBMCs) were recovered as described previously [24], and CD4+ and CD8+ T cells were separated by negative selection (Metachem, Meylan, France). Purified CD4+ and CD8+ T cells (>92% pure for CD4+ and 89% pure for CD8+ T cells) were stained for CD45RB (FITC; Dako, Ely, UK), CD45RA (PE; Serotec, Oxford, UK), CD45RO (PE-CY5; Serotec) and CD62L (ECD Coulter, High Wycombe, UK) using conventional methods. Naïve T-cells were further sorted according to their CD45RBbright, CD45RA+ and CD62L+ phenotype, using a FACS-Vantage cell sorter (Becton Dickinson, Oxford, UK). Memory cells and other subsets were identified based on their expression of CD45RBbright/dull, CD45RA±, CD45RObright/dull, and CD62L± [24].
Real-time polymerase chain reaction quantification of T-cell receptor excision circles
DNA was extracted from the different lymphocyte populations using standard proteinase K digestion followed by a phenol/chloroform extraction, either from total CD4+ and CD8+ populations after magnetic separation or from naïve cells after further cell sorting. TRECs were quantified using a real-time polymerase chain reaction based assay, as described previously [24]. Briefly, TREC primers were F (d-CAC CTC TGG GCT ACG TGC TAG) and R (d-GAA CAC ATG CTG AGG TTT AAA GAG AAT); and glyceraldehyde-3-phosphate dehydrogenase primers were F (D-AAC AGC GAC ACC CAT CCT C) and R (d-CAT ACC AGG AAA TGA GCT TGA CAA). This analysis provided a final value that represented TREC DNA as a proportion of glyceraldehyde-3-phosphate dehydrogenase DNA, which is equivalent to the percentage of cells containing a TREC. Following the release of the entire T-cell receptor locus sequence late in 2002, we validated our assay utilizing an alternative set of TREC primers designed to minimize background signal when using PBMC DNA.
Proliferation assays
PBMCs were separated as above from 5 ml blood from RA patients and healthy control individuals. An aliquot of PBMCs was stained with a combination of CD127 (FITC; Serotec), CD19 (PE; Serotec) and CD4 or CD8 (PE-CY5; Serotec) to quantify IL-7 receptor expression on different cell types by flow cytometry. Cells were resuspended in RPMI 1640 supplemented with penicillin and streptomycin, glutamine and 10% human AB+ serum (Sigma, Aldwich, UK) and proliferation was assessed in response to PHA (10 μg/ml, Sigma), IL-2 (20 units/ml; Sigma), IL-7 (1–100 ng/ml; Sigma) or anti-CD3 antibody (OKT3; 1 μg/ml) with or without anti-CD28 antibody (YTH913.12; 5 μg/ml) co-coated on plastic Proliferation was quantified by incorporation of 3H-thymidine (1 μCi/well) after 5 days of culture.
Long-term bone marrow cultures
Bone marrow mononuclear cells were obtained from posterior iliac crest aspirates from RA patients and healthy control individuals after informed consent had been obtained (with local research ethics committee approval), following centrifugation on Lymphoprep (Nycomed Pharma AS, Oslo, Norway), as previously described [34,35]. Aspirates from RA patients were repeated after 6–8 months of therapy with infliximab (Remicade; Schering Plough, Kenilworth, NJ, USA). Long-term bone marrow cultures from 107 bone marrow mononuclear cells were grown, in accordance with standard techniques [34,35]. By allowing the formation of an adherent layer consisting mainly of macrophages and cells of mesenchymal origin, this culture system has been considered appropriate for evaluating the regulatory role played by the bone marrow microenvironment in haematopoiesis [36]. At weekly intervals, cultures were fed by demi-depopulation. The adherent layer was usually confluent after 3–4 weeks, and at that time point cell-free supernatants were harvested and stored at -70°C for cytokine quantification.
Statistical methods
Nonparametric tests were used throughout. The Mann–Whitney U-test for two independent samples was used to compare healthy control individuals with RA patients. The Spearman rank correlation coefficient was used to determine correlations between two variables. A Wilcoxon sign rank test was used to compare pretherapy and post-therapy outcomes.
Results
Basal interleukin-7 production is reduced in rheumatoid arthritis
We measured serum levels of IL-7 in a cross-section of active RA patients (n = 28), healthy control individuals (n = 34) and OA patients (n = 12). There was no correlation between serum levels of IL-7 and age in healthy control individuals [37,38], and sex did not make any difference. Circulating IL-7 levels (Fig. 1a) were significantly lower in RA patients than in healthy control individuals (P < 0.00001). In RA there was no association between levels of circulating IL-7 and disease duration, inflammation as measured by CRP (Fig. 1b; nonsignificant correlation [R = 0.201, P = 0.161]), presence of a shared epitope (n = 17), or antirheumatic therapy (nonsteroidal anti-inflammatory drugs, methotrexate, or steroids). OA patients exhibited slightly lower IL-7 levels than did control individuals (P = 0.035) but they had significantly higher IL-7 levels than did RA patients (P < 0.00001). After Bonferroni correction there was no longer a significant difference between control individuals and OA patients, but other results remained unaffected. Regression analysis did not reveal any further trends.
There are several sources of IL-7 production, including stromal cells in the bone marrow, dendritic cells and epithelial cells in the thymus, skin and gut [6]. We compared the ability of bone marrow stromal cells, derived from RA patients (n = 9) and healthy control individuals (n = 15), to produce IL-7 spontaneously in long-term cultures (Fig. 1c). The production of IL-7 was significantly lower in RA patients than in control individuals (P = 0.001). Furthermore, production did not consistently change after clinical remission induced by therapeutic TNF-α blockade (n = 8; P = 0.725). We also examined the PBMC response to IL-7 in RA patients and healthy control individuals. Whereas RA PBMCs responded suboptimally to IL-2, mitogen (PHA) or antigen (anti-CD3/CD28), as previously documented [39], their response to IL-7 was similar to that in control individuals (Fig. 1d). Importantly, in a cross-sectional comparison of 10 RA patients and 10 healthy control individuals, we could not find a significant difference in the number of cells expressing the IL-7 receptor (CD127) or in its level of expression (data not shown). Altogether, these findings suggest a deficit in circulating levels of IL-7 in RA, possibly due to an inability to produce IL-7, at least in stromal cells of bone marrow origin.
Defective T-cell expansion in rheumatoid arthritis
Patients receiving lymphocytotoxic therapy for conditions other than RA reconstitute more rapidly and completely than do RA patients. We previously studied three cohorts of RA patients who had received high-dose chemotherapy followed by stem cell reinfusion (Table 2). As previously reported [26,30,31], CD4+ counts fell after treatment and subsequently remained low in all cohorts, with no significant differences due to graft manipulation (data not shown). In contrast, CD8+ T-cell counts initially rose before rapidly returning to basal levels.
In the present study we compared T-cell reconstitution in 12 RA patients (six from each of cohorts 2 and 3) and seven patients with solid tumours (Fig. 2 and Table 2). To avoid potential confounding effects of immunosuppressive drugs, RA patients were removed from the analysis if it subsequently became necessary to reinstitute antirheumatic therapies at times when disease activity resumed. The figure therefore represents 12 RA patients pretreatment and seven at 9 months.
The chemotherapy regimens differed between RA and non-RA patients, but the nadir lymphocyte counts were similar. Figure 2 illustrates the composition of the peripheral T-cell pool at baseline and at various times after treatment. The individual subsets were defined according to the lymphocyte differentiation pathway suggested by our previous work [24]. The most naïve cells are represented in grey at the top of each bar chart. These cells progress to conventional memory cells and their precursors (striped bars) via post-naïve cells (white bars). Presumed 'central' memory cells are presented in black. Notably, at baseline RA patients possessed no CD4+ and CD8+ central memory cells in peripheral blood, as reported previously [24]. After chemotherapy there was simultaneous accumulation of all subsets in cancer patients, resulting in rapid restitution of CD4+ T-cell counts within 3 months. The same was true of the CD8+ subsets except that there was an 'overshoot' of memory CD8+ T-cells. In contrast, there was no early expansion of any T-cell subset in RA, although some long-term restoration of naïve CD4+ subsets was observed. Naïve CD8+ T-cells also accumulated slowly, and there was a brief expansion of CD8+ memory cells. These marked differences between RA and cancer patients demonstrate that a limited early peripheral expansion after treatment may account, in part, for the lack of T-cell reconstitution in RA.
Although graft manipulation differed between cancer patients (un-manipulated) and RA (CD34 selected ± T-cell depletion) as mentioned above, we found that graft manipulation did not affect the rate of reconstitution in RA patients (data not shown). Other factors that differ between the RA and control group reflect the underlying disease. For example, RA patients may have been exposed to low-dose corticosteroid therapy as part of their prior treatment. It is not possible to exclude an effect of such a factor on our data.
Delayed thymic activity in rheumatoid arthritis
In order to compare thymic activity after lymphodepletion, we measured TRECs longitudinally in CD4+ T-cells in the same RA and cancer patient cohorts. As a molecular marker of T-cell receptor rearrangement, TRECs provide a surrogate measure of recent thymic activity [40,41]. We measured TREC content in total CD4+-T cells as well as in naïve CD4+-T cells in isolation. Patterns of TREC variation were consistent between the seven cancer patients. TRECs rapidly accumulated after treatment but returned to baseline by 3 months (Fig. 3; open diamonds). Our data in cancer are therefore consistent with an early surge in thymic activity, followed by a slow return to baseline at a time when the T-cell counts have returned to baseline levels. Variation in the TREC content of naïve cells also followed that pattern. The reduction in TREC content of an individual subset such as naïve cells is better explained by proliferation within that subset [24,42,43], therefore suggesting that naïve T-cells underwent peripheral expansion, resulting in TREC dilution in CD4+ cells (open diamonds). Similar results were observed for CD8+ T cells (data not shown).
The early thymic response to lymphopenia did not occur in the 12 RA patients. In contrast, the TREC content of total CD4+ T-cells climbed gradually for several months after treatment (Fig. 3; closed diamonds). The TREC measurements in naïve cells also did not return to baseline, however, suggesting a lack of proliferation of CD4+ naïve cells. Therefore, a delay in achieving good release of newly developed T-cells also appeared to contribute to slow T-cell reconstitution in RA after high-dose chemotherapy. Similar results were observed for CD8+ T-cells (data not shown).
Lymphopenia-induced interleukin-7 production is defective in rheumatoid arthritis
Figures 2 and 3 suggest that the development and expansion of CD4+ T-cells were compromised in lymphopenic RA patients. Both the development and expansion of T cells have been extensively documented in relation to IL-7 (for review [6]). The relative deficiency in circulating IL-7 levels in RA patients identified in Fig. 1 therefore suggests a significant role for IL-7 in impaired T-cell reconstitution following high-dose chemotherapy. We measured serum levels of IL-7 longitudinally in four RA patients after lymphodepleting therapy (cohort 3, without relapse within 12 months) and seven non-RA patients (Table 2). Figure 4 clearly demonstrates a four- to fivefold rise and subsequent decrease in IL-7 levels, coincident with short-term lymphopenia in non-RA patients (triangles). In marked contrast, IL-7 levels did not change significantly in four RA patients over 12 months of follow up (squares).
Interleukin-7 levels correlated with thymic activity in patients with well controlled rheumatoid arthritis
In RA patients whose disease was controlled by in vivo TNF blockade, spontaneous release of IL-7 from bone marrow derived stromal cell cultures was variable, remaining reduced in some patients but returning to normal in others (Fig. 1). We therefore decided to investigate IL-7 levels in patients with well controlled disease and minimal levels of disease activity for at least 6 months before recruitment (n = 36; Table 1). Levels of IL-7 were heterogeneous and ranged from 2.47 to 16.25 pg/ml. No clinical parameter was significantly correlated with IL-7 levels (disease duration, remission duration, previous or current therapy, rheumatoid factor).
We measured TREC in total CD4+ T-cells in these patients in clinical remission in relation to age. The results were also heterogeneous (Fig. 5a; all triangles). Comparing these values with our previous results in healthy control individuals (small circles [24]), there appeared to be two distinct patient groups. One of these groups had a CD4+ T-cell TREC content similar to or higher than that in age-matched healthy control individuals, and the other group exhibited lower TREC content. We used the median TREC content to distinguish two groups. Open and closed triangles relate to group 1 (above the median TREC value) and group 2 (below the median TREC value), respectively. The relationship between TREC content and age was present in group 1 (thick line; R = -0.738, P = 0.001) but not in group 2. No clinical parameter was able to predict TREC content (disease duration, remission duration, previous or current therapy, rheumatoid factor).
We reanalyzed the IL-7 data with respect to this dichotomy in TREC levels, and there was a significant difference in circulating levels of IL-7 between these two groups (group 1, n = 17: IL-7 12.71 ± 2.76 pg/ml, range 9.57–16.25 pg/ml; group 2, n = 19: IL-7 6.50 ± 1.88 pg/ml, range 2.47–9.30 pg/ml; P < 0.00001). Furthermore, there was a positive correlation between the levels of circulating IL-7 and the TREC content of total CD4+ T cells (Fig. 5b; n = 36, all diamonds; R = 0.777, P < 0.00001). No similar relationship was observed in healthy control individuals (n = 12; R = 0.219, P = 0.595).
We subsequently reanalyzed the data according to the ACR criteria for clinical remission [44,45]. Patients fulfilling or not fulfilling the ACR criteria (Table 3) are shown as open and black diamonds, respectively, in Fig. 5b. The two populations were undistinguishable in terms of TREC content (P = 0.807). There was no difference in their circulating levels of IL-7 (ACR positive: 9.07 ± 3.33 pg/ml, range 4.9–15.23 pg/ml; ACR negative: 9.31 ± 4.01 pg/ml, range 2.47–16.25 pg/ml; P = 0.838). Furthermore, the correlation between IL-7 and TREC content was maintained in both groups (ACR positive, n = 17: R = 0.680, P = 0.005; ACR negative, n = 19: R = 0.779, P = 0.001). These data suggest that, removing any influence of systemic inflammation, RA patients form two groups that are characterized by normal or low levels of thymic activity and IL-7. It is not possible to predict from these data whether these abnormalities are primary or, indeed, whether they have pathogenic significance. However, they may be important in the context of reconstitution capacity after lymphodepleting therapies.
Discussion
We previously demonstrated that RA patients failed to reconstitute their peripheral T-cell pool even several years after lymphodepleting therapy [25,26,30,31]. The aim of the present work was to identify possible factors underlying this observation. IL-7 drives the expansion of human T-cells [8,46,47], and moreover it is an important thymic stimulant [11]. We identified a deficit in circulating levels of IL-7 in a cross-section of patients with active RA (Fig. 1). It was therefore possible that a similar deficit in IL-7 was a critical factor in the suboptimal response to lymphopenia in RA patients. Our data suggest that the RA thymus has a similar reserve to the thymus of disease control individuals (Fig. 3; similar peak levels at 9 months in RA as at 1 month in cancer), although it exhibits a more sluggish response to lymphopenia. However, both naïve and memory RA T-cells expand poorly in response to lymphopenia (Fig. 2), and this appears to be the major factor limiting reconstitution. We have also demonstrated low levels of lymphopenia-induced circulating IL-7 in RA patients (Fig. 4), and low basal IL-7 production from stromal cells originating from the bone marrow (Fig. 1). Finally, we showed a direct correlation between circulating levels of IL-7 and thymic capacity to produce new T-cells in RA patients with clinically undetectable disease activity (Fig. 5).
To date, IL-7 is not a cytokine that has been associated with RA. However, there are conflicting results regarding its expression in RA patients. In one study [48] IL-7 was present at high levels in the serum of adult RA patients, and it correlated with CRP. In contrast, in children with systemic juvenile RA, plasma levels of IL-7 were unrelated to disease activity (joint counts and circulating IL-6) and undetectable in synovial fluids [49]. In another study, IL-7 was elevated in RA synovial fluid but not in OA [50] and its production was associated with stromal cells in the synovium [51]. Circulating levels of IL-7 in healthy control individuals are also very heterogeneous between publications (ranging from 0.1 to 30 pg/ml), possibly because of the use of different ELISA systems (commercial IL-7 ELISA kits using monoclonal or polyclonal antibodies, in-house sandwich ELISA using polyclonal rabbit antisera). In our study we found that IL-7 levels were highly dependent on the condition of serum collection (in particular, the type of Vacutainer [Greiner Bio-one, Knemsmuster, Austria; standard NHS supply]) and we standardized our collection protocol (blood taken into plain glass tubes, clotting time of 2 hours at room temperature, centrifugation at 1000 g for 10 min, storage at -20°C). In addition, in a recent report from Fry and Mackall [8], circulating levels of IL-7 in CD4+ T-cell depleted and repleted HIV patients were in keeping with our findings (<30 pg/ml and 10–20 pg/ml, respectively).
Peripheral T-cell expansion differed greatly between our patient groups, as shown in Fig. 2. This was particularly obvious for memory cells and their precursors, and appeared sufficient to account for the reconstitution defect in RA. However, lack of TREC dilution in naïve T-cells (Fig. 3) also suggested an absence of expansion within that subset in RA. IL-7 deficiency may again be relevant. IL-7 is produced in response to lymphopenia [7] and stimulates proliferation of both naïve and memory human T-cells. Although serum was not available from our cohort of solid tumour patients, we found high circulating levels of IL-7 in lymphodepleted patients with other tumours and with systemic vasculitis (Fig. 4), which is in keeping with the literature. In contrast, we found that basal serum IL-7 levels were reduced in a range of RA patients, irrespective of inflammation or medication (Fig. 1b). Furthermore, there was no IL-7 rise following lymphodepletion (Fig. 4). RA and control PBMCs responded equivalently to IL-7 stimulation in vitro, suggesting no defect in IL-7 receptor expression or signalling (Fig. 1d).
Circulating IL-7 levels may also reflect the availability of specific binding sites on T-cells [6], but our two patient groups were similarly lymphopenic, making this explanation unlikely. Lymph node-resident dendritic-like cells may also produce IL-7 [52]. Although we were unable to examine these cells directly, our data do not suggest compensatory production from that source. Therefore, although we cannot definitively exclude alternative explanations for reduced IL-7 levels, low levels in lymphopenic RA patients (Fig. 4) and the variable ability to recover IL-7 in remission (Fig. 5) strongly implicate an underlying defect in IL-7 regulation, also highlighted by the bone marrow derived stromal culture (Fig. 1). IL-7 expression is upregulated or downregulated by different cytokines in different tissues (transforming growth factor-β, interferon-γ, TNF-α, IL-1 and IL-2, among others) and further work is necessary to uncover the mechanisms that control circulating levels of IL-7.
CD8+ lymphopenia is also associated with raised circulating IL-7 levels [53] but this correlation is less strong. This suggests that factors other than IL-7 can effectively drive CD8+ T-cell expansion, and it is notable that transient expansion of CD8+ memory T-cells did occur in RA patients. Our experience and that of others suggests that such expansions may be driven by intercurrent infections (Isaacs JD, unpublished observations) [54]. This may also underlie the CD8+ T-cell over-compensation observed in cancer patients (Fig. 2).
The RA thymus was clearly capable of producing new T-cells. This was evident not only when comparing naïve T-cell reconstitution in RA and cancer cohorts (Fig. 2) but also when TREC-containing cells were examined (Fig. 3). There is a complex relationship between thymic activity, T-cell proliferation and death, and TREC measurements [24,42,43,55]. Just after lymphocytotoxic therapy, however, TREC levels and T-cell counts are low and their subsequent accumulation must therefore reflect thymic output. TRECs achieved similar peak levels in both RA and cancer patients, suggesting an equivalent thymic capacity for T-cell production in these two groups. In cancer patients, however, TREC levels peaked early, as compared with a slow rise in RA patients. An association between higher levels of IL-7 and thymic capacity to produce new T-cells was predictable, based on the direct stimulatory effect of IL-7 on thymic activity at many stages in T-cell progenitor development [6,11,56-60] High levels of IL-7, as detected in lymphopenic control patients, could therefore result in a burst of thymic activity. In contrast, it is not immediately obvious what other factor(s) could determine the delayed rise in thymic activity in RA patients. Other growth factors are also able to stimulate the thymus [61], but another plausible mechanism is the removal of inhibition. Several of the cytokines that are abundant in RA, such as IL-6, oncostatin M and leukaemia inhibitory factor, suppress thymic function [37]. Levels of TNF-α, IL-6 and oncostatin M fell after high-dose chemotherapy in RA patients (data not shown) as the disease entered remission, and this may have resulted in a corresponding slow increase in thymic activity.
Our data have pathogenic and therapeutic implications. First, they provide further support for a stromal cell function defect in RA. Previous studies of bone marrow progenitor cell reserve and stromal function in RA patients were more consistent with a defect secondary to TNF-α associated toxicity [34]. In those studies, progenitor cell reserve was reduced, and RA stroma was unable to support haematopoiesis from healthy CD34+ progenitors. Both abnormalities correlated with TNF-α levels in bone marrow culture supernatants and significantly improved after in vivo TNF-α blockade. Those data therefore support a scenario in which the RA marrow was suppressed by chronic exposure to TNF-α and potentially other proinflammatory cytokines. However, our data relating both to circulating IL-7 and to bone marrow production demonstrate independence from the inflammatory process (Fig. 1) at least in some patients, and are consistent with a primary abnormality.
Therefore, supplementation with recombinant IL-7 may be necessary to improve lymphocyte reconstitution in lymphopenic RA patients, with the caveat that this cytokine is also a co-stimulatory factor for T-cells. It may therefore encourage the expansion of autoreactive T-cells with a worsening of disease. For example, IL-7 has been associated with preferential expansion [62] and activation [63] of autoreactive T-cells in multiple sclerosis. Additionally, IL-7 has been associated with lymphoproliferative disorders [64-66] to which RA patients are already predisposed. Furthermore, our data do not exclude additional contributions to limited T-cell expansion, and proliferative exhaustion is a factor that may not be amenable to therapeutic intervention. It is therefore possible that terminally differentiated memory T-cells, resulting from chronic immune activation in RA, cannot proliferate in response to lymphopenia. This does not explain the lack of proliferation of naïve T-cells from RA patients, however (Figs 2 and 3).
Conclusion
In conclusion, although our data are necessarily an averaged view of events that occur after lymphodepletion, we have made a number of observations relevant to poor T-cell reconstitution in lymphopenic RA patients. Importantly, the RA thymus is capable of producing naïve T-cells but its function is compromised by an IL-7 deficiency. The latter also severely limits the peripheral expansion of both naïve and memory T-cells. Our data suggest potential approaches to correct lymphocyte reconstitution defects in RA patients receiving lymphocytotoxic therapies and provide further insights into the disease process itself.
Abbreviations
ACR = American College of Rheumatology; CRP = C-reactive protein; ELISA = enzyme-linked immunosorbent assay; IL = interleukin; OA = osteoarthritis; PBMC = peripheral blood mononuclear cell; RA = rheumatoid arthritis; TNF = tumour necrosis factor; TREC = T-cell receptor excision circle.
Competing interests
The author(s) delcare that they have no competing interests.
Authors' contributions
Frederique Ponchel designed, optimized and performed the work on TREC quantification, T-cell differentiation, ELISA and statistics. Robert J. Verburg provided clinical sample (RA, Leiden). Sarah J Bingham provided clinical sample (RA, Leeds). Andrew K Brown provided clinical sample (RA in remission, Leeds). John Moore provided clinical sample (RA, Sydney). Andrew Protheroe provided clinical sample (cancer, Leeds). Kath Short collected clinical samples (cancer, Leeds). Catherine A Lawson processed clinical samples. Ann W Morgan provided clinical sample (RA, Leeds). Mark Quinn provided clinical sample (RA, Leeds). Maya Buch provided clinical sample (RA, Leeds). Sarah L Field provided technical support. Sarah L Maltby provided technical support. Aurelie Masurel provided technical support. Susan H Douglas provided technical support. Liz Straszynski provided technical support. Ursula Fearon provided technical support. Douglas J Veale provided provided support. Poulam Patel is Head of Department (cancer, Leeds). Dennis McGonagle provided support (Leeds). John Snowden provided support (Sheffield). Alexander F Markham is Head of Department (Leeds). David Ma is Head of Department (Sydney). Jacob M van Laar provided support and clinical material (Leiden). Helen A Papadaki is Head of Department and provided clinical samples (Heraklion). Paul Emery is Head of Department (Leeds). John D Isaacs is Head of Department (Leeds).
Acknowledgements
We thank Professor Herman Waldmann for the supply of YTH 913.12 antibody. Baxter supplied Isolex 300i Columns and Chugai supplied GCSF free of charge for the RA patients treated in Leeds, UK. We thank the Wellcome Trust for supporting SL Maltby on a summer vacation grant.
This work was supported by grants from the Arthritis Research Campaign (P0566) and the Dutch Arthritis Association (NR99-1-301).
Figures and Tables
Figure 1 IL-7 deficiency in rheumatoid arthritis (RA). (a) IL-7 levels were measured in serum from 34 healthy control individuals (median age 46 years), 28 patients with RA (seven with recent onset RA before institution of therapy and 21 with established, refractory RA; median age 55 years) and 12 patients with established osteoarthritis (OA; median age 56 years). Control individuals had significantly higher levels of circulating IL-7 than did RA patients (P < 0.00001). OA patients tended to have lower IL-7 levels than healthy control individuals (P = 0.035) but higher than RA patients (P < 0.00001). (b) IL-7 levels were plotted against C-reactive protein (CRP) values for 28 patients with active RA, but no relationship could be identified (R = 0.201, P = 0.161). (c) Bone marrow was obtained by aspiration from the iliac crest from healthy control individuals (n = 15) and from RA patients (n = 8) before and after therapeutic tumour necrosis factor (TNF)-α blockade. Long-term bone marrow stromal cell cultures were established, and spontaneous IL-7 release was measured. Control bone marrow stromal cells released significantly more IL-7 than did RA marrow (P = 0.001). There was no consistent effect of anti-TNF-α therapy on IL-7 expression (paired pre-post treatment test). (d) Peripheral blood mononuclear cells from healthy control individuals (n = 3) and RA patients (n = 3) were cultured in the presence of PHA (10 μg/ml), IL2 (20 U/ml), anti-CD3 (1 μg/ml) plus anti-CD28 (5 μg/ml), or titrated doses of IL-7 (1–100 ng/ml), for 5 days. Proliferation was assayed by 3H-thymidine incorporation. RA and healthy cells responded similarly to IL-7, but RA cells were hyporesponsive to other stimuli.
Figure 2 Poor T-cell expansion in rheumatoid arthritis (RA) patients. Phenotyping of isolated CD4+ and CD8+ T-cell populations was performed using the cell surface markers CD45RB, CD45RA, CD45RO and CD62L. Differentiation subsets were defined as naïve cells (grey bars: CD45RBbright, CD45RA+, CD45RO-, CD62L+), conventional memory cells and their precursors (striped bars: CD45RBbright/dull, CD45RA-, CD45RO+, CD62L-) and post-naïve intermediates (white bars: CD45RBbright/dull, CD45RA-, CD45RO-/dull, CD62L+), as described previously [24]. Presumed 'central' memory cells (black bars) are CD45RBdull, CD45RA+, CD45RO+ and CD62L+. Total T-cell numbers are indicated by the height of the bars. Lines across the graphs indicate the lower limits of the normal range for CD4+ and CD8+ T-cell counts. Cancer patients (n = 7 solid tumours [Table 2]) reconstitute CD4+ T cells largely by expansion of intermediate and memory subsets. This is not seen in RA patients (n = 12 at baseline and 1 month, n = 7 at 9 months; six patients from each of cohorts 2 and 3 [Table 2]). A similar expansion accounts for the 'overshoot' above baseline in CD8+ T-cells in cancer patients, whereas only a minimal transient expansion of memory CD8+ T-cells is observed in RA.
Figure 3 Thymic reserve in lymphopenic cancer and rheumatoid arthritis (RA) patients. The proportion of T cells containing a T-cell receptor excision circle (TREC) was measured longitudinally in cancer (n = 7 solid tumours [Table 2]) and RA (n = 12 at baseline and 1 month, six patients from both of cohorts 2 and 3 [Table 2]; and n = 7 at 9 months, three from cohort 2 and four from cohort 3) patients' pure CD4+ T-cells and following cell sorting of naïve cells. In cancer patients TRECs rapidly rose within 1 month and then slowly returned to pretreatment levels by 8 months. In RA patients there was a slow but sustained rise in TRECs over that time, achieving similar peak levels to cancer patients by 9 months.
Figure 4 Lower circulating levels of IL-7 in rheumatoid arthritis (RA). IL-7 levels were measured in serum samples taken longitudinally from RA patients (n = 6 at baseline and 1 month, n = 4 subsequently; patients without relapse all from cohort 3) or patients with lymphoma, cancer or systemic vasculitis (n = 4 up to 3 month, n = 2 afterward), all of whom were lymphopenic for up to 3 months after lymphodepleting therapies. IL-7 circulating levels remained low in RA patients, compared with a substantial rise in the mixed cohort of non-RA patients.
Figure 5 Circulating IL-7 levels are directly correlated with the TREC content of CD4+ T-cells in rheumatoid arthritis (RA) patients in clinical remission. (a) The T-cell receptor excision circle (TREC) content of total CD4+ T-cells, measured in patients in clinical remission (n = 36, all triangles [Table 1]), is heterogeneous, ranging from values observed in healthy control individuals to values in active RA patients. Using the age relationship to TREC content in healthy control individuals (black circles and thin line, correlation coefficient R = -0.816, P < 0.00001; previously reported [24]), two groups of patients can be differentiated: group 1 exhibits TREC content similar to or greater than that in age-matched healthy control individuals; and group 2 exhibits lower TREC content. We used the median value for TREC content to separate patients into two groups. We refer to these two groups as group 1 (G1; above median value, indicated by black triangles) and group 2 (G2; below median value, indicated by open triangles). The age relationship to TREC content is recovered only in group 1 (thick line; correlation coefficient R = -0.738, P = 0.001; for group 2 R = 0.341, P = 0.174). (b) Circulating IL-7 levels are directly correlated with TREC content of CD4+ T-cells in 36 patients in clinical remission (R = 0.777, P < 0.00001). In addition, patients satisfying the American College of Rheumatology (ACR) criteria for remission are indicated by open diamonds and patients not satisfying the ACR criteria by closed diamonds (Table 3). These two groups are undistinguishable.
Table 1 Rheumatoid arthritis patients with active or stable, well controlled disease and control individuals
Parameter Controls Active RA OA RA in remission
n 34 28 12 36
Age (mean ± standard deviation [range]; years) 48 ± 16 (24–62) 51 ± 17 (20–83) 60 ± 9 (49–73) 48 ± 11 (25–67)
Sex (male/female) 6/17 9/28 3/9 7/29
Disease duration (mean ± standard deviation [range]; years) NA 5.1 ± 7.5 (0.1–37) NA 9.3 ± 6.8 (2–28)
Remission duration (mean ± standard error [range]; months) NA NA NA 29 ± 29 (6–144)
CRP (mean ± standard deviation [range]; mg/l), below /above detectiona NA 55 ± 52 (5–164), 0/28 NA 3.5 ± 5.2 (0–12), 23/13
aC-reactive protein (CRP) values <5 mg/l are considered below the detection range. CRP values <10 mg/l are considered normal among the local population. NA, not applicable; OA, osteoarthritis; RA, rheumatoid arthritis.
Table 2 Patients receiving depleting therapies
Patients n Agea range (median; years) Sex (male/female) Graft manipulation
RA cohort 1 9 32–61 (42) 2/7 Unmanipulated
RA cohort 2 16 43–55 (47) 4/12 CD34 selection
RA cohort 3 6 24–61 (39) 3/3 CD34 selection, T-cell depletion
Solid tumours 7 39–64 (44) 2/5 Unmanipulated
Lymphoma/sarcoma 7 22–64 (46) 5/2 Unmanipulated
Systemic vasculitis (depleting antibody therapy) 3 43–61 (52) 2/1 Not applicable
aAge at time of transplantation. RA, rheumatoid arthritis.
Table 3 Patients in clinical remission satisfying or not satisfying the American College of Rheumatology criteria for remission
Parameter Remission by ACR criteriaa Nonremission by ACR criteriaa
N 17 19
Age (mean ± standard deviation [range]; years) 48 ± 11 (28–67) 54 ± 11 (39–67)
Sex (male/female) 3/14 4/15
Disease duration (mean ± standard deviation [range]; years) 9.8 ± 6.6 (3–25) 9.7 ± 6.3 (2–28)
Remission duration (mean ± standard deviation [range]; months) 26 ± 16 (6–60) 30 ± 36 (6–144)
CRP (mean ± standard deviation [range]; mg/l), below/above detectionb 3.0 ± 3.8 (0–10), 10/7 4.0 ± 5.2 (0–12), 13/6
aAmerican College of Rheumatology (ACR) remission criteria : less than 15 min morning stiffness; no fatigue; no joint pain; no joint tenderness or pain on motion; no swelling of soft tissue in joint or tendon sheaths; and <30 mm/h erythrocyte sedimentation rate. bC-reactive protein (CRP) values <5 mg/l are considered below the detection range. CRP values <10 mg/l are considered normal among the local population.
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| 15642146 | PMC1064881 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 16; 7(1):R80-R92 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1452 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14531564214010.1186/ar1453Research ArticleNatural killer cell dysfunction is a distinguishing feature of systemic onset juvenile rheumatoid arthritis and macrophage activation syndrome Villanueva Joyce 1Lee Susan 1Giannini Edward H 2Graham Thomas B 2Passo Murray H 2Filipovich Alexandra 1Grom Alexei A [email protected] Division of Hematology/Oncology, Children's Hospital Medical Center, Cincinnati, Ohio, USA2 William S Rowe Division of Rheumatology, Children's Hospital Medical Center, Cincinnati, Ohio, USA2005 10 11 2004 7 1 R30 R37 13 7 2004 10 9 2004 21 9 2004 27 9 2004 Copyright © 2004 Villanueva et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Macrophage activation syndrome (MAS) has been reported in association with many rheumatic diseases, most commonly in systemic juvenile rheumatoid arthritis (sJRA). Clinically, MAS is similar to hemophagocytic lymphohistiocytosis (HLH), a genetic disorder with absent or depressed natural killer (NK) function. We have previously reported that, as in HLH, patients with MAS have profoundly decreased NK activity, suggesting that this abnormality might be relevant to the pathogenesis of the syndrome. Here we examined the extent of NK dysfunction across the spectrum of diseases that comprise juvenile rheumatoid arthritis (JRA). Peripheral blood mononuclear cells (PBMC) were collected from patients with pauciarticular (n = 4), polyarticular (n = 16), and systemic (n = 20) forms of JRA. NK cytolytic activity was measured after co-incubation of PBMC with the NK-sensitive K562 cell line. NK cells (CD56+/T cell receptor [TCR]-αβ-), NK T cells (CD56+/TCR-αβ+), and CD8+ T cells were also assessed for perforin and granzyme B expression by flow cytometry. Overall, NK cytolytic activity was significantly lower in patients with sJRA than in other JRA patients and controls. In a subgroup of patients with predominantly sJRA, NK cell activity was profoundly decreased: in 10 of 20 patients with sJRA and in only 1 of 20 patients with other JRA, levels of NK activity were below two standard deviations of pediatric controls (P = 0.002). Some decrease in perforin expression in NK cells and cytotoxic T lymphocytes was seen in patients within each of the JRA groups with no statistically significant differences. There was a profound decrease in the proportion of circulating CD56bright NK cells in three sJRA patients, a pattern similar to that previously observed in MAS and HLH. In conclusion, a subgroup of patients with JRA who have not yet had an episode of MAS showed decreased NK function and an absence of circulating CD56bright population, similar to the abnormalities observed in patients with MAS and HLH. This phenomenon was particularly common in the systemic form of JRA, a clinical entity strongly associated with MAS.
juvenile rheumatoid arthritismacrophage activation syndromenatural killer cellsperforinreactive hemophagocytic lymphohistiocytosis
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Introduction
The term 'macrophage activation syndrome' (MAS) in pediatric rheumatology refers to a set of symptoms caused by the excessive activation and proliferation of T cells and well-differentiated macrophages [1-4]. This activation leads to an overwhelming inflammatory reaction that can be fatal. The pathognomonic features of this syndrome are found in bone marrow aspirates: numerous, well-differentiated macrophages (or histiocytes) actively phagocytosing hematopoietic elements. Although MAS has been increasingly recognized in association with almost any rheumatic disease, it is by far most common in the systemic form of juvenile rheumatoid arthritis (JRA) [1,5-11].
Clinically, MAS has strong similarities to familial hemophagocytic lymphohistiocytosis (FHLH) and virus-associated or reactive hemophagocytic lymphohistiocytosis (HLH) [2-4]. The immune abnormalities in the familial form of HLH have been studied extensively, and the most consistent finding has been global impairment of cytotoxic lymphocyte and natural killer (NK) cell function [12-14]. In about 50% of patients with FHLH in North America, these immunologic abnormalities are secondary to mutations in the gene encoding perforin, a protein that mediates the cytotoxic activity of NK and T cells [15]. Although it has been proposed that abnormal cytotoxic cells might fail to provide appropriate apoptotic signals for the removal of activated macrophages and T-cells after infection is cleared [16], the exact pathways that would link the decreased NK and cytotoxic T cell function with macrophage expansion have not been confirmed.
We have previously reported that, as in HLH, NK function is profoundly depressed in the vast majority of patients with MAS [17] suggesting that this immunologic abnormality might be relevant to the pathogenesis of the syndrome. In the present study we sought to assess the extent of NK dysfunction in the most common rheumatic disease of childhood, JRA.
JRA is a chronic, idiopathic, inflammatory disorder with diverse clinical symptoms both at onset and during the course of the disease. Classification of this heterogeneous disease has been based primarily on the type of onset, namely the clinical manifestations during the first six months [18,19]. There are at least three major onset types: pauciarticular (four or fewer joints involved), polyarticular (five or more joints), and systemic. The systemic onset form, with its markedly febrile presentation, is certainly the most distinct clinical subtype of the disease. In contrast to patients with pauciarticular and polyarticular JRA, in whom the joint disease usually overshadows the more general symptomatology, in systemic onset JRA extra-articular features such as spiking fevers, evanescent macular rash, hepatosplenomegaly, lymphadenopathy, and, occasionally, polyserositis are most prominent [20]. The reason for the increased incidence of MAS in patients with systemic forms of JRA in comparison with other clinical forms of this disease is not clear, but NK cell abnormalities might have a role [3]. The main purpose of this cross-sectional study was to characterize numbers of circulating NK cells, their cytolytic activity, CD56bright : CD56dim subset ratio, and perforin/granzyme B expression in the major cytotoxic cell populations in patients with different clinical forms of JRA.
Materials and methods
Patients
In all patients included in the study, the diagnosis of JRA was established on the basis of the American College of Rheumatology (ACR) diagnostic criteria [18]. The main clinical characteristics of the patients are summarized in Table 1. Peripheral blood samples were collected from the patients after obtaining informed consent under an institutional review board-approved study of JRA.
Flow cytometric analysis
Relative and absolute numbers of NK, CD8+, and NK T cells, as well as perforin and granzyme B expression in these cell populations, were determined as described previously [14,21]. In brief, whole blood samples were first surface stained with the following antibodies: fluorescein isothiocyanate-labelled T cell receptor (TCR)-αβ, CD8-peridinin chlorophyll protein (CD8-PerCP) (BD Immunocytometry Systems, San Jose, CA), and CD56-allophycocyanin (CD56-APC) (Immunotech, Brea, CA). Red cells were then lysed with FACSlyse (BD Immunocytometry Systems) and washed. The resultant white cell pellets were then fixed and permeabilized with Cytofix/Cytoperm (BD Pharmingen, San Diego, CA) and stained with either phycoerythrin-conjugated mouse IgG2b anti-perforin or phycoerythrin-conjugated mouse IgG1 anti-granzyme B antibodies (BD Pharmingen). After being washed, cells were resuspended in 1% paraformaldehyde and stored at 4°C until analyzed with a FACSCalibur flow cytometer (Becton Dickinson, San Jose, CA). The following gates were used to distinguish the three populations of interest: CD8+ T cells were defined as being TCR-αβ+, CD8+, and CD56-; NK cells as TCR-αβ- and CD56+; and NK T cells as TCR-αβ+ and CD56+. All populations were also restricted to a live cell gate based on forward versus side scatter. The perforin-positive or granzyme B-positive regions were set by using isotype-matched negative control samples, and the percentage positive for each gate was reported.
NK cell cytotoxicity analysis
NK activity was assessed after co-incubation of peripheral blood mononuclear cell preparations (effector cells) with 51Cr-labeled target cells at various effector : target cell ratios as described previously [21]. The NK-sensitive K562 line was used as a source of target cells. The levels of radioactivity released from target cells into supernatants were assessed by gamma scintillation after 4 hours of incubation. All experiments were performed in triplicate in a 96-well microtiter plate. Spontaneous and maximum release wells were included on each plate as controls. Spontaneous release was assessed in the wells containing 51Cr-labeled target cells in medium without effector cells. Maximum release was determined in the wells containing labeled target cells in the presence of detergent to promote total lysis. The percentage lysis was calculated as described previously [21]: percentage lysis = 100 × (mean radioactivity of sample minus mean radioactivity of background)/(mean maximum radioactivity minus mean radioactivity of background).
Lytic units were calculated from the curve of the percentage lysis. One lytic unit was defined as the number of effector cells needed to produce 10% lysis of 103 target cells during the 4 hours of incubation.
Controls
The results were compared with the normal ranges for age-matched controls that have been developed in our clinical laboratory by studying 41 pediatric samples obtained from the out-patient clinic during routine 'well-child' visits from children considered 'healthy' [14].
Statistical analysis
The unpaired t-test and Wilcoxon two-sample test were used to compare NK cytolytic activity and perforin/granzyme B expression between the patient and control groups. The rank correlation test was used to characterize the relationship between NK cell activity and perforin expression. The unpaired t-test and logistic regression analysis were used to assess the possible contribution of treatment regimens to the development of NK cell dysfunction.
Results
NK cell cytolytic activity and NK cell numbers
As shown in Fig. 1, some decrease in NK cell cytolytic activity was noted in both clinical groups of JRA patients. This trend was particularly strong in patients with systemic JRA (sJRA). The mean cytolytic activity in the sJRA group was 4.0 (SEM = 1.2) compared with 8.2 (SEM = 1.6) in patients with pauciarticular/polyarticular JRA (unpaired t-test, P = 0.042; Wilcoxon two-sample test, P = 0.0062). Furthermore, in a subgroup of patients with sJRA, NK cell function was profoundly depressed. Thus, in 10 of 20 patients with sJRA and in only 1 of 20 patients with pauciarticular/polyarticular JRA, the NK cell cytolytic activity was below two standard deviations (SD) of the control group (χ2, P = 0.002). The same degree of NK cell dysfunction was observed in our previous studies of patients with MAS and HLH [17].
As shown in Table 1, a significant proportion of the patients studied were on immunosuppressive medications, including prednisone, methotrexate, and tumor necrosis factor (TNF)-blocking agents. Because NK function can be affected by such medications [22], we sought to assess whether the differences in treatment regimens between patients with sJRA and pauciarticular/polyarticular JRA might have contributed to the observed differences in NK function. Overall, patients receiving immunosuppressive drugs had somewhat lower levels of NK cytolytic activity. However, logistic regression analysis showed no statistically significant differences in NK function between patients with or without immunosuppressive treatment (independent variables: prednisone, methotrexate, and TNF-blocking agents; dependent variable NK cytolytic activity below 2SD of control samples, χ2 = 0.877, P = 0.831).
Because low NK cell numbers in patients with sJRA have been previously noted in one study [23], we assessed whether the decrease in NK cell cytolytic activity might have been related to low NK cell counts. A moderate correlation between NK function and the proportion of peripheral blood mononuclear cells that were NK cells was found for both groups of patients with JRA (r = 0.52, 95% confidence interval 0.08–0.8 in the sJRA group; r = 0.47, 95% confidence interval 0.7–0.75 in the other JRA group). Correlation coefficients between function and number of NK cells were not significantly different between the two JRA groups (Fisher's Z transformation; P = 0.7). The mean number of NK cells (expressed as a proportion of TCR-αβ-/CD56+ cells in a population of peripheral blood mononuclear cells) among the sJRA group was 0.077 (SD 0.04) in comparison with 0.081 (SD 0.034) among the other JRA group was not significantly different (P = 0.72 on the basis of the two-tailed independent t-test). Thus, it seems that suppressed NK function is not simply a result of reduced numbers of NK cells among patients with sJRA.
CD56dim and CD56bright NK cells
On the basis of on the intensity of CD56 staining, human NK cells have been recently subdivided into two distinct subsets with distinct functional characteristics. CD56bright NK cells have the ability to produce high levels of immunoregulatory cytokines, in particular interferon (IFN)-γ, but are in general poorly cytotoxic (reviewed in [24]). By contrast, CD56dim NK cells produce relatively low levels of cytokines and are potent cytotoxic effector cells expressing high levels of peforin. We have previously described a lack of the circulating CD56bright NK cells in patients with MAS and HLH [25]. The analysis of the fluorescence-activated cell sorting data in the current study revealed that three patients with sJRA had a similar abnormality, namely an almost complete absence of circulating CD56bright cells. Figure 2 shows examples of CD56 staining in such patients.
Perforin expression
Because reduced perforin expression in cytotoxic effector cells has previously been reported in sJRA [26,27], we assessed perforin content and relative proportions of perforin-positive NK cells, CD8+ lymphocytes, and NK T cells. High variability was noted in both groups of patients with JRA. The comparison of the mean values between patients with sJRA versus other JRA groups did not reveal statistically significant differences. However, the examination of individual patterns of perforin staining in NK cells revealed mean channel fluorescence (MCF) values below 2SD of the control group in seven patients, five of whom had sJRA. In addition to low MCF, one of these patients with sJRA had profoundly decreased proportions of perforin-positive cells in all three cytotoxic cell populations, a pattern that we have previously described in patients with sJRA who have had multiple episodes of MAS [17]. Examples of such abnormal patterns of perforin expression are shown in Fig. 3. In contrast, the patterns of staining for granzyme B were similar between patients and controls in all three cytotoxic cell populations (data not shown).
Because perforin is a protein that mediates the cytotoxic activity of NK cells, we assessed whether decreased perforin expression might have contributed to the development of NK dysfunction in JRA. In the group of seven patients with low perforin levels in NK cells, only three had NK cytolytic activity below 2SD of the control group. Furthermore, there was no significant correlation between MCF in NK cells and their cytolytic activity (r = 0.1596 for patients with sJRA, and r = 0.1991 for patients with other JRA subtypes).
Discussion
In this study, profoundly depressed NK cell activity was observed in a large subgroup of patients with sJRA and in only 1 of 20 patients with the polyarticular form of the disease. The extent of NK dysfunction in this group of patients was similar to that seen in patients with MAS [17] or HLH [12,13]. The two study groups (sJRA versus other JRA subtypes) were well matched in terms of age, duration of the disease, and treatment regimens with the exception of a slightly higher proportion of patients with sJRA receiving steroids. Steroids have been reported to suppress the cytolytic activity of NK cells [22], and this might potentially have contributed to the observed differences in NK function. However, the logistic regression analysis did not show significant differences between groups defined on the basis of treatment regimens. In addition, several patients with sJRA who demonstrated profoundly depressed NK cell cytolytic activity were receiving only non-steroidal anti-inflammatory drugs. Owing to the limitations of the statistical power with the numbers of study subjects enrolled, it is possible that some effects of the immunosuppressive medications might have been underestimated. Nevertheless, NK dysfunction seems to be a distinguishing feature of sJRA that is intrinsic to the disease itself.
Further analysis of the flow cytometry data revealed that some of the patients with sJRA had a rather selective disappearance of the circulating immunoregulatory CD56bright subset of NK cells, a pattern previously seen in patients with MAS or HLH [25]. These cells express low levels of perforin and are, in general, poorly cytotoxic [24]. The disappearance of CD56bright NK cells from peripheral circulation is therefore unlikely to account for the defects in cytolytic activity of NK cells. In contrast, CD56bright NK cells might have a function in regulating the CD56dim perforinbright cells, and in this case their disappearance might have an effect on cytolytic activity in some sJRA patients. Alternatively, the apparent absence of immunoregulatory NK cells in peripheral circulation might reflect their active recruitment to sites of inflammation.
Although the observed NK dysfunction in a subgroup of patients with sJRA might not be of primary etiological significance for JRA itself, the similarities to the immunologic abnormalities seen in MAS and HLH suggest that depressed NK cell activity is likely to be relevant to the pathogenesis of MAS in sJRA. It is important to mention that low NK cell activity has been noted in many rheumatic diseases [28], most notably in systemic lupus erythematosus [29]. In our study, however, in a subgroup of patients with sJRA, the extent of NK dysfunction was profound, with an almost complete absence of cytolytic activity. This parallels the fact that, although MAS has been described in association with almost any rheumatic disease and it is not uncommon in systemic lupus erythematosus [30], it is by far most common in sJRA [4-11].
Other groups have noted low levels of perforin expression in cytotoxic cells from patients with sJRA in comparison with other clinical forms of the disease, suggesting that this feature might be responsible for the increased incidence of MAS [26,27]. In our study, when patients with sJRA were analyzed as a group, perforin levels were not significantly different from those in patients with other JRA types. However, the examination of the individual patterns of perforin staining revealed a small subgroup of JRA patients with a very low perforin content in NK cells. Most of these patients had sJRA. Furthermore, one of them had profoundly decreased proportions of perforin-positive cells in all three major cytotoxic cell populations, a pattern that has been previously reported in patients with MAS [17] and in the carriers of perforin-deficient FHLH [14]. Although no overall correlation between perforin expression and NK cell cytolytic activty was noted in our study, we still cannot exclude the possibility that, at least in some patients, reduced perforin expression might have functional significance. In other words, there might be some heterogeneity in the mechanisms underlying NK cell dysfunction in sJRA. The existence of such heterogeneity was also noted in our previous study of MAS patients [17] that included ethnically diverse Caucasian, African American, and Latin American patients. The ethnic heterogeneity of the patients with JRA included in this study might also underlie the discrepancy between our results and the study by Wulffraat and colleagues [26], which showed that patients with sJRA as a group had lower perforin expression in cytotoxic effector lymphocytes. That study included a much more ethnically homogeneous population of Dutch children.
Granzyme B is another important component of the perforin-mediated cytotoxicity pathway. In our study both patient groups had granzyme B expression patterns indistinguishable from those seen in healthy controls, suggesting that the observed NK dysfunction is not likely to be related to abnormal granzyme B expression.
The cytolytic activity of NK cells in our study was measured by using NK-sensitive K562 cells, which are lymphoblasts derived from a patient with chronic myelogenous leukemia. The exact receptors involved in the NK-mediated lysis of K562 cells have not yet been identified. However, the lysis of some similar cell lines has been recently shown to be mediated through the natural cytotoxicity receptors (NKp46, NKp30, and NKp44) [31]. These receptors have important biologic functions in the innate immune system, and their abnormal expression might have a function in the development of NK dysfunction in sJRA.
On the basis of our data, the feature that distinguishes systemic onset JRA from other forms of JRA, and is common to the major hemophagocytic syndromes, is NK cell dysfunction. The exact mechanisms that would link deficient NK cell function and, in some cases, depressed perforin expression with the expansion of activated macrophages are not clear. One possible explanation is that decreased NK function might be responsible for a diminished ability to clear the infecting pathogen and remove the source of antigenic stimulation at early stages of infection [32]. This would lead to persistent antigen-driven T cell activation associated with an increased production of cytokines, such as IFN-γ and granulocyte/macrophage colony-stimulating factor, that stimulate macrophages. Subsequently, the sustained macrophage activation would result in tissue infiltration and in the production of high levels of TNF-α, interleukin-1, and interleukin-6, which have a major role in the various clinical symptoms and tissue damage.
Several recent studies using perforin-deficient and NKcell-depleted mice indicate that NK cells and perforin-based systems are also involved in the downregulation of immune responses through a direct effect of NK cells and/or perforin-based systems on the survival of activated lymphocytes [33-36]. NK dysfunction might therefore lead to a failure to provide homeostatic signals for the removal of activated T cells. For instance, Su and colleagues [33] demonstrated that the infection of NK-depleted mice with murine CMV results in an exaggerated immune response associated with more persistent expansion of cytotoxic CD8+ T cells that secrete IFN-γ, an important macrophage activator. Another possible explanation is related to the recently discovered ability of NK cells to lyse autologous antigen-presenting cells such as dendritic cells, thus limiting the magnitude of an immune response [37]. Interestingly, this interaction might involve the above-mentioned natural cytotoxicity receptors [38].
Conclusions
NK cell dysfunction is the feature that distinguishes systemic onset JRA from other forms of JRA, and is common to the major hemophagocytic syndromes. This suggests that impaired cytotoxic functions and/or deficiency of immunoregulatory NK cells are relevant to the development of MAS. Patients with sJRA who have these immunologic abnormalities may therefore be a high-risk group that might benefit from closer observation.
Abbreviations
IFN = interferon; HLH = hemophagocytic lymphohistiocytosis; JRA = juvenile rheumatoid arthritis; MAS = macrophage activation syndrome; NK = natural killer; MCF = mean channel fluorescence; sJRA = systemic juvenile rheumatoid arthritis; TCR = T cell receptor; TNF = tumor necrosis factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JV carried out sample collection, flow cytometry, data analysis and manuscript preparation. SL carried out NK cytotoxicity assays and manuscript preparation. EHG carried out statistical analysis and manuscript preparation. TBG carried out patient referral, clinical data analysis and manuscript preparation. MHP carried out patient referral, clinical data analysis and manuscript preparation. AF carried out study design, NK studies oversight and manuscript preparation. AAG carried out study design, project oversight, patient referral, data analysis and manuscript preparation. All authors read and approved the final manuscript.
Acknowledgements
This work was supported, in part, by NIH grant PO1 AR048929 (to AAG), by a grant from the Histiocyte Association of America (to AF), and by a Translational Research Initiative Grant from the Children's Hospital Research Foundation of Cincinnati (to AAG).
Figures and Tables
Figure 1 NK cell cytolytic activity in patients with juvenile rheumatoid arthritis (JRA). Activity of NK cells was determined after co-incubation of peripheral blood mononuclear cells with the NK-sensitive K562 cell line and expressed in cytolytic units (LU, y-axis). NK activity values in 20 patients with systemic JRA, 20 patients with other subtypes of JRA and 41 healthy children are shown as dots. The difference between results for patients with systemic JRA and other JRA is statistically significant (Wilcoxon two-sample test, P = 0.0062). For comparison, mean ± 2SD values determined in healthy individuals are shown as horizontal lines.
Figure 2 Flow cytometric analysis of CD56bright and CD56dim natural killer cells. (a) In healthy individuals, most circulating NK cells are CD56dim (about 90%) and express high levels of perforin. In contrast, CD56bright NK cells comprise about 10% of all human NK cells and express low levels of perforin. (b, c) Two distinct patterns of CD56 staining observed in patients with systemic juvenile rheumatoid arthritis (sJRA): (b) normal pattern (observed in 17 of 20 patients with sJRA as well as in all controls, and all other JRA patients); (c) almost complete disappearance of CD56bright subset of NK cells (observed in 3 of 20 patients with sJRA only).
Figure 3 Flow cytometric analysis of perforin expression in NK cells and cytotoxic CD8+ cells. (a) Normal control: 95% of NK cells and 14% of CD8+ cells are perforin-positive. (b) A patient with systemic juvenile rheumatoid arthritis (sJRA) with a normal pattern of perforin expression: normal mean channel fluorescence (MCF) in NK cells with 92% of NK cells and 10% of CD8+ T cells being perforin-positive (this pattern was observed in all controls, in 18 of 20 other JRA patients, and in 15 of 20 sJRA patients). (c) A patient with sJRA with low MCF in NK cells and mildly decreased proportions of perforin-positive NK cells (74%) and CD8+ T lymphocytes (5%) (this pattern was observed in 2 of 20 other JRA patients and in 5 of 20 sJRA patients). (d) A patient with sJRA with low MCF in NK cells and very low proportions of perforin-positive NK cells (20%) and no perforin-positive CD8+ T lymphocytes (observed in 1 of 20 sJRA patients only).
Table 1 Clinical characteristics of patients with juvenile rheumatoid arthritis
Parameter Systemic onset JRA (n = 20) Polyarticular JRA (n = 16) or pauciarticular JRA (n = 4)
Male/female 9/11 14/6
Mean age, years (range) 11.5 (2–23) 12.8 (3–22)
Mean disease duration, years (range) 4.1 (0.2–16) 5.8 (0.9–15)
No. with active arthritis (%) 17 (85) 17 (85)
No. with active systemic disease (%) 8 (40) -
Mean ESR, mm/h (range) 30 (5–85) 21 (5–57)
No. (%) receiving
Prednisone 6 (30) 3 (15)
Methotrexate 12 (60) 10 (50)
TNF-blocking agent 3 (15) 5 (25)
Intra-articular steroids 1 (5) 4 (20)
NSAID 17 (85) 18 (90)
ESR, erythrocyte sedimentation rate; JRA, juvenile rheumatoid arthritis; NSAID, non-steroidal anti-inflammatory drugs; TNF, tumor necrosis factor.
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| 15642140 | PMC1064882 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Nov 10; 7(1):R30-R37 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1453 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14541564214510.1186/ar1454Research ArticleElevated matrix metalloproteinase-9 in patients with systemic sclerosis Kim Wan-Uk [email protected] So-Youn [email protected] Mi-La [email protected] Kyung-Hee [email protected] Yong-Joo [email protected] Sung-Hwan [email protected] Chul-Soo [email protected] Division of Rheumatology, Department of Internal Medicine, Catholic Research Institutes of Medical Science, School of Medicine, The Catholic University of Korea, Seoul, Republic of Korea2005 10 11 2004 7 1 R71 R79 19 3 2004 19 4 2004 23 9 2004 1 10 2004 Copyright © 2004 Kim et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Matrix metalloproteinase-9 (MMP-9) has been implicated in the pathogenesis of cancer, autoimmune disease, and various pathologic conditions characterized by excessive fibrosis. In this study, we investigated the expression of MMP-9 and its clinical significance in systemic sclerosis (SSc). The patients (n = 42) with SSc had higher concentrations of MMP-9 and of tissue inhibitor of metalloproteinase-1 (TIMP-1) and a higher ratio of MMP-9 to TIMP-1 in sera than healthy controls (n = 32). Serum MMP-9 concentrations were significantly higher in the diffuse type (n = 23) than the limited type of SSc (n = 19). Serum concentrations of MMP-9 correlated well with the degree of skin involvement, as determined by the Rodnan score and with serum concentrations of transforming growth factor β. Moreover, dermal fibroblasts from patients with SSc produced more MMP-9 than those from healthy controls when they were stimulated with IL-1β, tumor necrosis factor α, or transforming growth factor β. Such an increase in MMP-9 production was partially blocked by treatment with cyclosporin A. In summary, the serum MMP-9 concentrations were elevated in SSc patients and correlated well with skin scores. The increased MMP-9 concentrations may be attributable to overproduction by dermal fibroblasts in SSc. These findings suggest that the enhanced production of MMP-9 may contribute to fibrogenic remodeling during the progression of skin sclerosis in SSc.
dermal fibroblastsmetalloproteinase-9skin scoresystemic sclerosis
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Introduction
Systemic sclerosis (SSc) is a generalized disorder of connective tissue characterized by microvacular damage and excessive fibrosis in the skin and internal organs, including the heart, lungs, and gastrointestinal tract. One of the major hallmarks of the disease is an increased amount of collagen deposits in the affected tissue. The relative proportion of two major types of skin procollagen, types I and III, is higher in SSc lesions than in healthy controls [1,2]. This increase in collagen deposits may be associated with changes in the dermal microvasculature in SSc. In particular, alterations in the structure of the basement membrane, a critical component of the vessel, may lead to changes in the surrounding tissue and to subsequent development of fibrosis in SSc [3]. The finding that the synthesis of type IV collagen, a major collagen type in basement membrane, is disproportionately increased in the dermal fibroblasts and sera of patients with SSc supports this notion [4,5].
The enhanced expression of matrix collagen is presumably associated with abnormal immune responses to collagen in SSc [6-10]. For example, autoantibodies to type IV collagen have been observed in some SSc patients and may be involved in endothelial injury [7,8]. Immunization of mice with autologous type IV collagen leads to the activation of fibroblasts and to fibrosis [9]. Furthermore, type IV collagen activates T cells from patients with SSc [10], suggesting that the selective immunity to type IV collagen may influence the clinical expression of SSc. The excessive production of type IV collagen and subsequent autoimmune T-cell responses to type IV collagen may set off a self-perpetuating cycle in SSc through the interaction between lymphocytes and fibroblasts.
The matrix metalloproteinases (MMPs) are a family of extracellular endopeptidases that selectively degrade the components of various extracellular matrixes. Of these, MMP-9 (92–96 kD gelatinase B), whose substrates include type IV collagen in basement membrane [11], has been thought to be involved in the cellular invasion of the basement membrane by cells involved in arthritis and cancer (e.g. T cells, mononuclear phagocytes, synovial fibroblasts, and metastatic tumor cells) [12-15]. MMP-9 has been associated with chronic inflammatory autoimmune diseases, including rheumatoid arthritis, Sjögren's syndrome, idiopathic uveitis, and systemic lupus erythematosus [16-19]. Moreover, the overexpression of MMP-9 has been reported in various pathologic conditions characterized by excessive fibrosis, including idiopathic pulmonary fibrosis, bronchial asthma, experimental biliary cirrhosis, and chronic pancreatitis [20-23], suggesting that elevated MMP-9 is closely linked to fibrogenic remodeling in target organs. In the present study, we measured the expression of MMP-9 and tissue inhibitor of metalloproteinase-1 (TIMP-1), an inhibitor of MMP-9, in the sera and culture supernatants of dermal fibroblasts from SSc patients and compared them with serum concentrations of transforming growth factor β (TGFβ) and with clinical and laboratory parameters of SSc.
Materials and methods
Patients
This study was conducted in accordance with the principles embodied in the Declaration of Helsinki and was approved by the Ethical Committees in the Catholic Research Institutes of Medical Sciences. Before the study, informed consent was obtained from all patients and healthy controls. Forty-two patients (1 man and 41 women), all of whom fulfilled the criteria of the American Rheumatism Association for SSc [24], were studied; their mean age was 43.7 years (range 24–69 years). The mean duration of disease was 80.8 months (range 5–276 months). The comparisons were made with 32 healthy controls (all women) who had no rheumatic disease; their mean age was 44.2 years (range 21–62 years). The ages and sexes of the patient and control groups did not differ significantly.
Clinical and laboratory evaluation
Clinical and laboratory assessments were done at the time of sampling. The clinical variables were age, sex, disease duration, type of SSc [25], modified Rodan score [26], presence or absence of esophageal involvement on endoscopy and esophageal manometry, interstitial lung disease on chest radioagrapy and/or high-resolution computerized tomography, diffusion capacity (DLCO; diffusion of carbon monoxide in the lung) on the pulmonary function test, arthritis, sicca syndrome, and antibodies to Scl-70 or centromere using ELISA kits (MBL, Nagoya, Japan). Interstitial lung disease was defined as bibasilar interstitial fibrosis on chest radiographs, or, in patients with no abnormalities on chest radiographs, as the presence of alveolitis on high-resolution computerized tomography.
ELISA for serum MMP-9, TIMP-1, and TGFβ
The total MMP-9 and TIMP-1 concentrations were determined in the serum and the culture supernatant using a commercial ELISA kit (R&D Systems Inc, Minneapolis, MN, USA). In accordance with the manufacturer's recommendations, the aliquots of serum were diluted to a ratio of 1:100 in the assay buffer. The detection limits of the MMP-9 and TIMP-1 kits were 0.15 ng/ml and 0.08 ng/ml, respectively. The MMP-9 assay kit detected pro-MMP-9 and complexes of pro-MMP-9 with TIMP-1 and had no significant cross-reactivity with MMP-1, MMP-2, MMP-3, TIMP-1, or TIMP-2. Again, the TIMP-1 detection kit detected TIMP-1 either free or in complex with some MMPs and showed no cross-reactivity or interference with TIMP-2.
Circulating TGFβ was measured in the same samples using ELISA, as described previously [27]. Briefly, 2 μg/ml of monoclonal antibodies to TGFβ1, β2, and β3 (R&D Systems) were added to 96-well plates (Nunc Inc, Roskilde, Denmark). They were incubated overnight at 4°C and blocked with PBS containing 1% bovine serum albumin and 0.05% Tween 20 for 2 hours at room temperature. A sample (50 μl) of each patient's serum was diluted 1:2 with PBS, acidified with 50 μl of 2.5 M acetic acid and 10 M urea for 10 minutes at room temperature and then was neutralized with 50 μl of 2.7 M NaOH and 1 M HEPES. The patient's sera and the standard recombinant TGFβ (R&D Systems) were then put into 96-well plates and incubated at room temperature for 2 hours. Biotinylated polyclonal antibodies (50 ng/ml) to human TGFβ (R&D Systems) were added and the reaction was allowed to proceed for 2 hours at room temperature. Streptavidin–alkaline phosphatase (Sigma Bioscience, St Louis, MO, USA) diluted 1:2000 with PBS was added, and the reaction was again allowed to proceed for 2 hours. p-Nitrophenylphosphate (1 mg/ml) (Sigma Bioscience) dissolved in diethanolamine (Sigma Bioscience) was added to induce a color reaction, and 1 N NaOH (Fisher Scientific, Pittsburgh, PA, USA) was used to stop the reaction. An automated microplate reader (Vmax, Molecular Devices, Palo Alto, CA, USA) was used to measure the optical density at 405 nm. Between each of these steps, the plates were washed four times with PBS containing 0.05% Tween 20. A standard curve was drawn by plotting the optical density versus the log of the recombinant TGFβ concentration. The detection limit for TGFβ was 30 pg/ml.
Detection of MMP-9 activities by gel zymography
MMP-9 and MMP-2 activities were also tested by gelatin zymography. A 0.5-μl sample of serum diluted in 30 μl of SDS buffer was separated in 10% SDS–PAGE gel polymerized with 1 mg/ml gelatin (Invitrogen Life Technologies, Carlsbad, CA, USA). Culture supernatants of HT1080 cell lines (malignant human fibroblasts) stimulated with 10 μg/ml of concanavalin A were used as a positive control. Gels were washed once for 3 hours in 2.5% Triton X-100 to remove the SDS and once for 30 minutes in the reaction buffer containing 50 mM Tris/HCl, 200 mM NaCl, 10 mM CaCl2, and 0.02% (w/v) Brij 35 (pH 7.5). The reaction buffer was changed to a fresh one, and the gels were incubated at 37°C for 24 hours. Gelatinolytic activity was visualized by staining the gels with 0.5% Coomassie brilliant blue and was quantified by densitometry.
Isolation and culture of dermal fibroblasts
Dermal fibroblasts were obtained from affected skin of two SSc patients and from two healthy controls, as described previously [28]. Fibroblasts were grown from explants in Dulbecco's modified Eagle's medium (DMEM) at 37°C in 5% CO2. The cells were then centrifuged at 500 g, resuspended in DMEM supplemented with 10% fetal calf serum (Gibco-BRL, Grand Island, NY, USA), 2 mM glutamine, penicillin (100 U/ml), and streptomycin (100 μg/ml), and plated in 25-cm2 flasks. The cultures were kept at 37°C in 5% CO2 and the culture medium was replaced every 3 days. When cells approached confluence, they were detached with trypsin, passed after dilution 1:3 with fresh medium, and recultured until use. Cells were housed in a 37°C humidified incubator with 5% CO2. Second- or third-passage cells were used for all experiments. Fibroblasts were seeded in 24-well plates at 5 × 104 cells per well in serum-free DMEM supplemented with insulin–transferrin–selenium A (ITSA; Gibco BRL). After the cells had been grown in selected medium alone for 12 hours, we added cytokines – IL-1β (10 ng/ml), tumor necrosis factor α (TNF-α) (10 ng/ml), and TGFβ (10 ng/ml) – to stimulate the fibroblasts. After 24 hours of incubation, cell-free media were collected and stored at -20°C until assay. All cultures were set up in triplicate or quadruplicate.
Statistics
Data are expressed as means ± standard error of the mean (SEM). Numerical data for groups were compared using the Mann–Whitney rank sum test, and data for categories were compared using a chi-square test. Correlation between two variables was tested using Spearman's rank correlation coefficient. P values less than 0.05 were considered statistically significant.
Results
Elevated serum MMP-9 and TIMP-1 concentrations in SSc patients
The serum concentrations of MMP-9 were significantly higher in patients with SSc (n = 42) than in healthy controls (n = 32) (317.6 ± 33.5 ng/ml versus 81.2 ± 6.8 ng/ml, P < 0.001) (Fig. 1). The serum concentration of TIMP-1, an inhibitor of MMP-9, was also higher in SSc patients than in healthy controls (157.1 ± 13.2 ng/ml versus 77.7 ± 12.5 ng/ml, P < 0.001), but SSc patients had higher MMP-9/TIMP-1 ratios than healthy controls (233.0 ± 27.1 versus 69.5 ± 24.3, P < 0.001). There was no correlation between MMP-9 and TIMP-1 concentrations in SSc patients or in healthy controls. SSc patients with the diffuse type (n = 23) and had higher concentrations of MMP-9 than those with the limited type (n = 19) (364.6 ± 32.4 ng/ml versus 260.0 ± 34.6 ng/ml, P = 0.034) (Fig. 2). No significant differences were found between the two groups of patients with regard to age, sex, disease duration, and prednisolone usage or the kinds of immunosuppressive agents being used (e.g. D-penicillamine and cyclosphosphamide) (Table 1).
MMP-9 activities measured by gel zymography
We used gel zymography to study sera of 20 SSc patients and 10 healthy controls, all selected unsystematically, to ascertain the serum gelatinase activity of MMP-9. As can be seen in Fig. 3, the 92 kDa band, consistent with the latent form of MMP-9, was detected in the sera of all subjects. The bands in Fig. 3 represent the latent form of MMP-9 (92 kDa, upper band) and the latent form of MMP-2 (72 kDa, lower band). The serum MMP-9 activities of SSc patients were higher than those of healthy controls. Densitometric analysis in sera of 20 SSc patients and 10 healthy controls indicated that the mean MMP-9 activity for SSc patients was 137.2 ± 21.7 densitometry units and for healthy controls, 38.5 ± 4.2 densitometry units (P < 0.001). Furthermore, a good linear correlation was found between the densitometry units measured by zymogram and the respective concentrations of MMP-9 measured by immunoassay in the sera of SSc patients (r = 0.875 and P < 0.001; data not shown). However, the intensity of the 86 kDa band (active MMP-9) was generally weak and was often not measurable.
Correlation of serum MMP-9 concentrations with skin scores
To determine the association of MMP-9 concentrations with a definite clinical manifestation of SSc, we compared the serum MMP-9 concentrations with clinical and laboratory characteristics in patients (n = 35) with SSc. The patients with severe skin involvement (n = 18), defined by a modified Rodnan score ≥20, had significantly higher concentrations of circulating MMP-9 than those with mild to moderate skin involvement (n = 17) (modified Rodnan score <20) (Table 2). Moreover, the serum MMP-9 concentrations correlated well with the Rodnan scores (n = 35, r = 0.425, P = 0.011) and with the serum TGFβ concentrations (n = 41, r = 0.736, P < 0.001) (Fig. 4a,4b). However, a correlation between MMP-9 and TGFβ was not found in the sera from healthy controls (data not shown). There were no differences in the MMP-9 concentrations with respect to age, the presence of esophageal involvement, interstitial lung disease, decrease of diffusion capacity (DLCO < 70%), digital ulcer, arthritis, sicca syndrome, and antibodies to Scl-70 or centromere-B (Table 2).
MMP-9 production by dermal fibroblasts
The finding that MMP-9 concentrations correlated with skin scores prompted us to investigate the in vitro MMP-9 production by dermal fibroblasts from SSc patients. The spontaneous MMP-9 concentrations in the culture supernatants of dermal fibroblasts were not greatly different between SSc patients and healthy controls (Fig. 5). However, stimulation of SSc fibroblasts with IL-1β, TNF-α, or TGFβ strongly increased MMP-9 production relative to the unstimulated concentration, by factors of 3.5, 3.2, and 2.3, respectively, whereas fibroblasts of healthy controls responded weakly to these cytokines (by factors of 1.6, 1.5, and 1.2, respectively). The increase in MMP-9 production by IL-1β and TNF-α appears to be triggered at least in part by a cyclosporin A (CsA)-sensitive pathway, since 500 ng/ml CsA limited MMP-9 production in SSc fibroblasts stimulated with IL-1β or TNF-α to 63% and 57% of original responses, respectively.
Discussion
We have shown that circulating MMP-9 is higher in patients with SSc than in healthy controls, particularly in the diffuse type of SSc, and correlates well with the extent of skin fibrosis. This finding supports earlier reports that overexpression of MMP-9 is closely linked with various diseases characterized by excessive fibrosis [20-23]. Recent studies support the evidence for a crucial role of MMP-9 in fibrotic diseases. For example, MMP-9-deficient mice exhibit significantly less pulmonary fibrosis in response to bleomycin than their with MMP-9+/+ littermates [29]. In the hepatic fibrosis model infected by Schistosoma mansoni, the severity of fibrosis was most closely associated with the increased MMP-9 activity [30]. Similarly, in response to bleomycin, mice deficient in γ-glutamyl transpeptidase showed a reduction in pulmonary fibrosis, in part associated with lower MMP-9 activity in lung tissues [31].
What, then, are the plausible mechanisms by which MMP-9 participates in fibrotic response? One possible explanation comes from the role of MMP-9 in chronic inflammation, resulting in fibrosis. MMP-9 can trigger inflammation directly, by tissue destruction, or indirectly, by generation of an inflammatory signal or recruitment of inflammatory cells [32]. Infiltration of inflammatory cells is closely associated with an abnormal fibrotic response [33]. Moreover, in mice, targeted deletion of MMP-9 attenuated collagen accumulation, which was correlated with decreased infiltration of neutrophils and macrophages in resolving experimental myocardial infarction [34]. In SSc, several proinflammatory cytokines activate fibroblasts to increase MMP-9 production, as depicted in Fig. 5. The overproduced MMP-9 may induce microvascular damage and leakage of substances that further augment endothelial cell damage or fibroblast activation in SSc. This damage may facilitate the movement of inflammatory cells across the basement membrane [11,35], ultimately leading to excessive fibrosis. In this context, type IV collagen autoimmunity, as mentioned in the Introduction, would play an additional role in fibroblast activation through the interaction between T lymphocytes and fibroblasts [9,10]. Such a hypothesis is supported by the findings in SSc patients that microvascular injury precedes fibrosis and that the degree of hypoxia is correlated with skin fibrosis [36,37].
Although the role of TGFβ in SSc remains elusive, several reports have suggested that it may be an ideal candidate as a mediator of skin fibrosis in SSc [38,39]. In the present study, the circulating TGFβ strongly correlated with the MMP-9 concentrations, a finding consistent with the observation that MMP-9 concentrations correlated best with skin scores of SSc. It is known that TGFβ increases the production of MMP-9 in several cell types, possibly through a process requiring protein synthesis that leads to increased statility of MMP-9 mRNA [40,41]. On the other hand, the increased MMP, in turn, is able to cleave latent TGFβ, leading to activation of TGFβ [42], in a process that may constitute a self-perpetuating cycle. If this is the case in SSc patients, MMP-9 may indirectly participate in the fibrotic reaction through the activation of TGFβ, a potent fibrogenic growth factor.
The expression of MMP-9 has been reported to be elevated in the culture medium of alveolar macrophages from patients with idiopathic pulmonary fibrosis or bronchial asthma [20,21,43]. Serum MMP-9 and the MMP-9/TIMP-1 ratio also correlate with the severity of the airway inflammation [44]. In the present study, we did not find any association between serum MMP-9 and the presence or severity of interstitial lung disease, even in a subgroup of SSc patients with diffuse or limited disease (data not shown). The contribution of interstitial lung disease to MMP-9 elevation may be obscured by the stronger effect of skin fibrosis.
The sources of MMP-9 are keratinocytes, monocytes, leukocytes, macrophages, and fibroblasts [12-15]. Fibroblasts from patients with early SSc exhibited higher concentrations of other MMPs (MMP-1 and MMP-3) than fibroblasts from normal individuals [45]. In addition, the finding that MMP-9 correlated best with skin scores prompted us to explore the production of MMP-9 by dermal fibroblasts in SSc patients. This study has shown that SSc fibroblasts produced more MMP-9 after stimulation with IL-1β, TNF-α, and TGFβ than fibroblasts of healthy controls. These findings show that one of the sources for MMP-9 production in SSc is dermal fibroblasts. Moreover, CsA, a calcineurin inhibitor, partially blocked IL-1β-induced or TNF-α-induced MMP-9 production by SSc fibroblasts. This finding suggests that activation of calcineurin and further downstream dephosphorylation of nuclear factor of activated T cells plays a role in the induction of MMP-9 [46] and that CsA may exert its therapeutic effect against SSc [47] by modulating MMP-9 activity.
The findings we report here are in sharp contrast to those in a recently published paper by Kikuchi and colleagues [48], who found decreased concentrations of the active form of MMP-9 in the sera of patients with diffuse SSc. It seems unlikely that this discrepancy is attributable to a difference in the ELISA method (e.g. assay for total MMP-9 in this study versus active MMP-9 in the earlier report), because our patients showed a strong correlation between total MMP-9 and active MMP-9 in the additional test using the ELISA kit (R&D Systems; r = 0.745, P < 0.001; data not shown). In our study, 33 patients (79%) required corticosteroid plus penicillamine or cyclosphosphamide to control the disease, whereas in the study by Kikuchi and colleagues, only 13 (21%) of 62 patients had been treated with these drugs, suggesting that our patients were in a more active and inflammatory stage of the disease. Given that MMP-9 is abundant in highly inflammatory lesions [32], differences in the stage of disease and clinical features of the patients assessed could account for the opposite results.
Accumulating evidence indicates the importance of TIMP activities in the progression of fibrosis in various pathologic conditions, including asthmatic bronchitis, cirrhosis of the liver, and SSc [49-51]. Moreover, both TIMP1- and TIMP-2 can promote the proliferation of fibroblasts in vitro [52,53]. Therefore, it remains to be defined whether the elevated expression of MMP-9 relative to that of TIMP-1 in SSc is directly involved in skin fibrosis or merely reflects biological compensation for excessive fibrosis. Studies of the effect of active MMP-9 or its inhibitor on fibrogenic remodeling in animal models of SSc are needed to clarify this issue.
Conclusion
Circulating MMP-9 concentrations were elevated in the patients with SSc and correlated best with the skin scores and serum TGFβ concentrations. The production of MMP-9 by dermal fibroblasts of SSc patients was strongly upregulated by stimulation with IL-1β, TNF-α, and TGFβ and such an increase was suppressed by a CsA-sensitive mechanism. Our findings suggest that MMP-9 may play a role in the progression of skin fibrosis in SSc.
Abbreviations
CsA = cyclosporin A; DMEM = Dulbecco's modified Eagle's medium; ELISA = enzyme-linked immunosorbent assay; IL-1β = interleukin-1β; MMP = matrix metalloproteinase; PBS = phosphate-buffered saline; SSc = systemic sclerosis; TGFβ = transforming growth factor β; TIMP = tissue inhibitor of metalloproteinase; TNF-α = tumor necrosis factor α.
Competing interests
This work was supported by grants from the Korea Research Foundation Grant (KRF-2002-041-E00107) and the Catholic Research Institutes of Medical Science, Republic of Korea.
Authors' contributions
W-UK collected the clinical data and analyzed it. S-YM and Y-JS cultured dermal fibroblasts and measured the MMP-9 concentration in the culture supernatant. M-LC performed the gel zymography. K-HH determined the concentrations of MMP-9 and TIMP-1 in the sera. M-LC drafted the manuscript. C-SC designed the study. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by grants from Korea Research Foundation (KRF-2002-041-E00107) and the Catholic Research Institutes of Medical Science.
Figures and Tables
Figure 1 Comparison of serum concentrations of matrix metalloproteinase-9 (MMP-9) and tissue inhibitor of metalloproteinase-1 (TIMP-1) in patients with systemic sclerosis versus healthy controls. Data are presented as means ± standard error of the mean (Mann–Whitney rank sum test).
Figure 2 Concentrations of circulating matrix metalloproteinase-9 (MMP-9) in patients with diffuse (n = 23) or limited (n = 19) systemic sclerosis and in healthy controls (n = 32). Data are presented as means ± standard error of the mean (Mann–Whitney rank sum test).
Figure 3 Gelatinase activity of matrix metalloproteinase (MMP)-2 (72 kDa) and MMP-9 (92 kDa) in sera of patients with systemic sclerosis (SSc) and healthy controls. Sera (0.5 μl) from 20 patients with SSc and 10 healthy controls were analyzed for their MMP-2 and MMP-9 activities by gel zymography. As a positive control, supernatants from cultured HT1080 cell lines (HT) stimulated with 10 μg/ml of concanavalin A were used. Numbers in parentheses are MMP-9 concentrations (ng/ml) determined by ELISA. The figure shows representative results for serum samples from the two groups.
Figure 4 Correlation of circulating matrix metalloproteinase-9 (MMP-9) concentrations with skin fibrosis and with concentrations of transforming growth factor β (TGFβ). (a) Correlation of MMP-9 concentrations with skin scores. The extent of skin involvement of systemic sclerosis was determined by modified Rodnan scores. Broken line indicates cutoff value for patients with severe skin involvement (Rodnan score ≥20). Bars represent the mean ± standard error of the mean of MMP-9 in patients with severe versus mild-to-moderate skin involvement (Rodnan score <20). (b) Correlation of circulatory MMP-9 concentrations with TGFβ concentrations.
Figure 5 The production of matrix metalloproteinase-9 (MMP-9) and tissue inhibitor of metalloproteinase-1 (TIMP-1) from cultured dermal fibroblasts. Dermal fibroblasts were obtained from affected skin of two patients with systemic sclerosis (SSc) and two healthy controls. Second- or third-passage fibroblasts (5 × 104 cells) were cultured for 24 hours in Dulbecco's modified Eagle's medium alone and in the presence of IL-1β (10 ng/ml), tumor necrosis factor (TNF)-α (10 ng/ml), transforming growth factor β (TGFβ) (10 ng/ml), IL-1β (10 ng/ml) plus cyclosporin A (CsA) (500 ng/ml), or TNF-α (10 ng/ml) plus CsA (500 ng/ml). The concentrations of MMP-9 in the supernatants were determined by ELISA. Data are expressed as means ± standard error of the mean (SEM) of two independent experiments performed in triplicate using different cell lines. *P < 0.05, **P < 0.01, ***P < 0.001 versus medium alone (Mann–Whitney rank sum test).
Table 1 Demographics of patients with systemic sclerosis (SSc), and medications being taken
With diffuse SSca (n = 23) With limited SSca (n = 19)
Age (y) [mean ± SEM] 42.5 ± 2.8 46.5 ± 2.9
% female 95.7 100
Disease duration (mo.) [mean ± SEM] 72.7 ± 15.7 95.4 ± 17.1
Medications (% of sample)
Prednisone 82.6 68.4
D-penicillamine 69.6 57.9
Cyclophosphamide 17.4 5.3
aP ≥ 0.05 in a comparison of the two groups (Mann–Whitney rank sum test). SEM, standard error of the mean.
Table 2 Association of circulating matrix metalloproteinase (MMP)-9 concentrations with laboratory and clinical variables in patients (n = 35) with systemic sclerosis
MMP-9 (ng/ml)a
Variable Variable present (%)b Variable absent
Modified Rodnan score >20 388 ± 29 (51)* 248 ± 38*
Esophageal involvementc 342 ± 33 (63) 304 ± 41
Interstitial lung diseased 321 ± 32 (60) 335 ± 41
Decrease of DLCO (<70%)e 350 ± 42 (43) 314 ± 34
Digital ulcer 364 ± 32 (43) 260 ± 35
Arthritis 333 ± 58 (14) 333 ± 28
Sicca syndrome 358 ± 40 (31) 320 ± 31
Antibodies to Scl-70f 348 ± 36 (37) 323 ± 45
Antibodies to centromere-Bf 288 ± 34 (37) 370 ± 39
aConcentrations are presented as mean ± standard error of the mean. bPercentage of patients in whom the variable was clearly present; all other patients are included in the 'Variable absent' group. cDetermined by endoscopy and esophageal manometry. dEvaluated by chest x-ray and/or high-resolution computerized tomography, if necessary. eAbnormal diffusion capacity (DLCO, diffusion of carbon monoxide in the lung) on pulmonary function test was defined as below 70% of that in healthy controls. fMeasured by ELISA. *P = 0.006 in a comparison of the two groups (Mann–Whitney rank sum test). All other differences were not significant.
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| 15642145 | PMC1064883 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 10; 7(1):R71-R79 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1454 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14551564213910.1186/ar1455Research ArticleIdentification of a SmD3 epitope with a single symmetrical dimethylation of an arginine residue as a specific target of a subpopulation of anti-Sm antibodies Mahler Michael [email protected] Marvin J [email protected]üthner Martin [email protected] Director Development and Production, Dr. Fooke Laboratorien GmbH, Neuss, Germany2 Professor of Medicine, Faculty of Medicine, University of Calgary, Calgary, Canada3 Vice Director of Autoimmune Diagnostics, Laboratory of Prof. Seelig and colleagues, Karlsruhe, Germany2005 10 11 2004 7 1 R19 R29 9 8 2004 26 8 2004 31 8 2004 1 10 2004 Copyright © 2004 Mahler et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Anti-Sm antibodies, identified in 1966 by Tan and Kunkel, are highly specific serological markers for systemic lupus erythrematosus (SLE). Anti-Sm reactivity is found in 5–30% of SLE patients, depending on the autoantibody detection system and the racial background of the SLE population. The Sm autoantigen complex comprises at least nine different polypeptides. All of these core proteins can serve as targets of the anti-Sm B-cell response, but most frequently the B and D polypeptides are involved. Because the BB'Sm proteins share cross-reactive epitopes (PPPGMRPP) with U1 specific ribonucleoproteins, which are more frequently targeted by antibodies that are present in patients with mixed connective tissue disease, the SmD polypeptides are regarded as the Sm autoantigens that are most specific to SLE. It was recently shown that the polypeptides D1, D3 and BB' contain symmetrical dimethylarginine, which is a component of a major autoepitope within the carboxyl-terminus of SmD1. In one of those studies, a synthetic dimethylated peptide of SmD1 (amino acids 95–119) exhibited significantly increased immunoreactivity as compared with unmodified SmD1 peptide. Using immobilized peptides, we confirmed that the dimethylated arginine residues play an essential role in the formation of major SmD1 and SmD3 autoepitopes. Moreover, we demonstrated that one particular peptide of SmD3 represents a more sensitive and more reliable substrate for the detection of a subclass of anti-Sm antibodies. Twenty-eight out of 176 (15.9%) SLE patients but only one out of 449 (0.2%) control individuals tested positive for the anti-SmD3 peptide (SMP) antibodies in a new ELISA system. These data indicate that anti-SMP antibodies are exclusively present in sera from SLE patients. Thus, anti-SMP detection using ELISA represents a new serological marker with which to diagnose and discriminate between systemic autoimmune disorders.
anti-SmautoantibodyELISAepitopesystemic lupus erythematosus
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Introduction
Systemic rheumatic diseases are characterized by circulating autoantibodies to defined intracellular targets (for review [1]). Historically, among the earliest of these autoantibodies to be identified was anti-Sm, which was subsequently considered a serological hallmark of systemic lupus erythematosus (SLE) [2]. Thus, anti-Sm antibodies have been included among the American College of Rheumatology (ACR) criteria for classification of this disease [3]. In addition to autoantibodies that target the Sm complex, anti-double-stranded DNA (dsDNA), anti-proliferating cell nuclear antigen, anti-U1-RNP, anti-nucleosome, anti-histone, anti-Ro/SS-A, anti-La/SS-B, anti-ribosomal RNP, and anti-phospholipid antibodies are also frequently found in sera from SLE patients [1]. Of interest, certain SLE-associated autoantibodies have been shown to be present before the clinical onset of the disease and thus have high prognostic value [4].
On average, anti-Sm reactivity is found in 5–30% of patients with SLE, although the specific frequency depends on the detection system used and the racial and genetic makeup of the SLE population [5,6]. The Sm autoantigen is part of the spliceosomal complex that participates in the splicing of nuclear pre-mRNA [7]. The complex itself is comprised of at least nine different core polypeptides with molecular weights that range from 9 to 29.5 kDa [8]: B (B1; 28 kDa), B' (B2; 29 kDa), N (B3; 29.5 kDa), D1 (16 kDa), D2 (16.5 kDa), D3 (18 kDa), E (12 kDa), F (11 kDa) and G (9 kDa). All of these core proteins can be targets of the anti-Sm immune response, but the most prevalent response is to the B and D polypeptides, which are therefore considered the major antigens [8-10].
Because SmBB' share cross-reactive epitopes with U1-specific RNPs, which are more frequently targeted by antibodies that are present in patients with mixed connective tissue disease (MCTD), SmD is regarded as the Sm autoantigen that is most specific to SLE [11]. Within the SmD family, the SmD1/D3 reactivity pattern is at least four times more common than SmD1/D2/D3 recognition, with immunoreactivity to SmD1 being the most dominant [11]. Several linear and conformational epitopes have been mapped on the SmB and SmD proteins [12-14]. On SmD1 and SmBB' the major reactivity was found in the carboxyl-terminal regions [13-15]. The epitope PPPGMRPP, which occurs three times within the carboxyl-terminus of SmBB', was shown to crossreact with other proline-rich structures of spliceosomal autoantigens, including the U1-specific RNPs, and of retroviral proteins such as HIV-1 p24gag [16]. Follow-up studies and immunization experiments revealed this motif to be consistently the earliest detectable SmBB' epitope, indicating that it acts as a potential starting point for epitope-spreading events associated with the SmBB' molecule and SmD polypeptides [17,18].
A recent study [19] identified five linear epitopes on SmD2 and four on SmD3 that were distributed along the full length of the molecules. All of these epitopes share basic properties and are exposed on the surface of the protein, rendering them antigenic [19]. One of the B-cell epitopes on SmD3 (epitope 4; amino acids 104–126) exhibited sequence similarity with an antigenic region from the SmD1 protein, and this may account for some cross-reactivity [19]. For diagnostic purposes, a synthetic peptide corresponding to the carboxyl-terminal domain of SmD1 was used to develop an ELISA system with diagnostic sensitivities and specificities ranging from 36% to 70% and from 91.7% to 97.2%, respectively [6,20]. It was recently shown that the polypeptides D1, D3 and BB' contain symmetrical dimethylarginine (sDMA), which constitutes a major autoepitope within the carboxyl-terminus of SmD1 [21,22].
The aims of the present study were to develop a peptide-based ELISA system for the detection of anti-Sm antibodies and to evaluate the diagnostic properties of the peptide assay. Moreover, epitope-mapping experiments were performed to shed more light on the controversial findings of the importance of sDMA residues within the SmD1 and SmD3 sequences and their relationship to SLE.
Methods
Patients and sera evaluated
Sera (n = 628) were collected from patients suffering from SLE (n = 176), rheumatoid arthritis (n = 86), Sjögren's syndrome (n = 24), MCTD (n = 26), systemic sclerosis (n = 26) and polymyositis/dermatomyositis (n = 13), as well as from patients with overlap syndromes (n = 8). All patients were classified according to the ACR criteria for each disease [3,23-27]. Clinical, serological and demographic data were available from 101 SLE patients (SLE panel 1). This cohort contained 34 samples from white patients, 51 from black patients, five from hispanic patients, one from an east Indian patient, and one from an oriental patient. The racial background of four patients was not known. Of these patients, 13 were male and 86 female (in two the sex was unknown), and the mean age was 40 years (range 16–80 years). The sera were kindly provided by Drs R Mierau and E Genth (Rheumaklinik Aachen, Aachen, Germany), Prof. Dr MJ Fritzler (University of Calgary, Calgary, Canada) and by Labor Limbach (Heidelberg, Germany).
To assess further the assay specificity, we analyzed a group of sera from patients with infectious diseases (n = 77), including hepatitis C virus (n = 30), cytomegalovirus (n = 22) and Epstein–Barr virus (EBV; n = 25) infections, as well as from 192 healthy blood donors. All sera were stored at -80°C until use. For the epitope-mapping study, a panel of five sera (all from SLE patients) containing anti-Sm antibodies that were available in greater quantities preselected by ELISA (Varelisa® Sm; Pharmacia Diagnostics, Freiburg, Germany) was used. Autoimmune sera with antibody specificities other than anti-Sm were selected as negative controls, including anti-RNP, anti-SS-A (Ro), anti-SS-B (La), anti-PM/Scl, anti-centromere protein (CENP) and anti-Scl-70.
Serological characterization of randomly selected systemic lupus erythematosus sera
The sera from SLE patients represented in panel 1 with clinical, serological and demographic data, as well as the autoimmune controls, were tested for autoantibodies to histones (cutoff 30 U/ml), dsDNA (cutoff 55 U/ml) and the Sm complex (cutoff 15 U/ml, or ratio 1) using quantitative ELISA tests (Varelisa®; Pharmacia Diagnostics; catalog nos 14196, 16296 and 16496). SLE sera and samples that exhibited unexpected results were also measured using the semiquantitative Split anti-nuclear antibody (ANA) profile (Pharmacia Diagnostics; cutoff ratio 1). The latter assay contains the autoantigens U1-68 kDa, U1-A, U1-C, SmBB', SmD, Ro-52, Ro-60 and La. All ELISAs were performed in accordance with the manufacturer's instructions.
Reference serum panels
The US Centers for Disease Control and Prevention (CDC) ANA serum panel [28] and the Association of Medical Laboratory Immunologists (AMLI) samples [29] were used to characterize the new Sm peptide-based immunoassay.
Epitope-mapping with immobilized oligopeptides
The published sequences of SmD1 (P13641) and SmD3 (P43331) were used to synthesize overlapping 15 mer peptides with a pipetting robot (ASP222; Abimed, Langenfield, Germany), in accordance with the protocol described by Gausepohl and Behn [30-33]. The carboxyl-terminal extensions of both polypeptides were synthesized with an offset of two amino acids (13 amino acid overlap). Each arginine-containing peptide was synthesized as three variants, one with natural arginine, one with sDMA and one with asymmetrical dimethylarginine at the respective positions. In addition, a highly reactive SmD3 peptide was synthesized with certain combinations of natural arginine and sDMA. Following completion of the peptide synthesis, nonspecific binding sites were blocked by overnight incubation of the membranes in blocking buffer (2% milk in Tris-buffered saline) at room temperature. After one washing step for 5 min, the membranes were incubated with serum samples diluted 1:100 in blocking buffer for 2 hours at room temperature. Unbound antibodies were removed by three washing steps in Tris-buffered saline–0.2% Tween. The membranes were incubated for 75 min at room temperature in a peroxidase-conjugated goat-anti-human IgG antibody (Dianova, Hamburg, Germany) that was diluted 1:5000 in blocking buffer. Unbound secondary antibodies were removed by three changes of Tris-buffered saline. Finally, bound antibodies were visualized using the ECL detection system (Amersham Bioscience, Freiburg, Germany).
Synthesis of the SmD3 peptide
The candidate SmD3 peptide (SMP) identified in the epitope-mapping study (108AARG sDMA GRGMGRGNIF122) was synthesized with an additional cysteine residue at the carboxyl-terminus, in accordance with Fmoc-chemistry at the Peptide Specialty Laboratories GmbH (Heidelberg, Germany). sDMA (Ref. B-3345.0001) was purchased from Bachem AG (Bubendorf, Switzerland) and used for synthesis. Crude fraction was purified using high-performance liquid chromatography. Quality and purity of the peptide was assessed by mass spectrometry and analytical high-performance liquid chromatography. The molecular mass was found to be 1708.1 Da (average; monoisotopic mass 1706.9), and a purity in excess of 95% was identified.
SmD3 peptide ELISA
Microtiterplates (Maxisorb, Nunc, Denmark) were coated with the uncoupled 16 mer peptide at a concentration of 2.5 μg/ml in phosphate-buffered saline (pH = 7.6). After an incubation time of 15 hours at 15°C, the plates were blocked with 1% bovine serum albumin in phosphate-buffered saline for 30 min at room temperature. The assay was performed in accordance with the general protocol for the Varelisa® system (Pharmacia Diagnostics). In brief, following a prewashing (300 μl/well) step, the serum samples were diluted 1:101 in sample buffer (phosphate-buffered saline, containing bovine serum albumin and Tween), added to the wells and then incubated for 30 min (100 μl/well). After three washing steps (300 μl/well), horseradish peroxidase conjugated anti-human IgG was added and incubated for 30 min (100 μl/well). Visualization was done by incubation in 3,3',5,5'-tetra-methyl benzidine substrate for 10 min (100 μl/well), and the reaction was terminated by adding 50 μl stop solution (0.5 mol/l H2SO4) to each well. All steps were carried out at room temperature.
A standard curve was developed using a highly reactive index serum that bound the SmD3 peptide and had a defined reactivity of 40,000 U/ml. The curve was plotted at six standard points (0, 3, 7, 16, 40 and 100 U/ml). Each serum sample was tested in duplicate and serum samples that had reactivity above the assay range were serially diluted to 1:500, 1:2500, 1:12,500 and 1:62,500.
To define further the assay characteristics, 192 normal human sera were assayed in accordance with the instructions for use. Blood donors exhibited a reactivity range of 0.4–11.5 U/ml, a mean value of 2.2 U/ml and a standard deviation of 1.2 U/ml. The cutoff was set at 13 U/ml following receiver operating characteristic (ROC) analysis. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated at different cutoff values using Analyse-it software (Version 1.62; Analyse-it Software Ltd, Leeds, UK).
Precision and reproducibility
Measurements of imprecision (interassay and intra-assay variability) were taken over four and six replicates, respectively. To assess the precision of the anti-SMP ELISA, suitable anti-Sm sera – a low value sample (L), a medium value sample (M) and a high value sample (H) – were assayed in five independent tests on one day (interassay) or in a single run (intra-assay). For within-run precision, the L, M and H samples were measured in six replicates on one solid phase. The precision data were calculated using analysis of variance.
Linearity
Linearity was analyzed by testing dilutions (1:1, 2:3, 1:2, 1:4, 1:8, 1:16, 1:32) of the highest standard point (S6) and of the high value sample from the precision analysis (H). For each dilution point, a ratio of the measured reactivity to the expected value was calculated, and 1 was subtracted from this quotient.
Results
Epitope fine mapping of the carboxyl-terminal extensions of SmD1 and SmD3
To evaluate the effect of arginine dimethylation on the antigenicity of SmD1 and SmD3 and to localize relevant epitopes on both polypeptides, a panel of anti-Sm sera was tested for reactivity with peptide arrays (15 mer, two offset) covering the carboxyl-terminal region of SmD1 (P13641) and SmD3 (P43331). The results show that dimethylation of arginine residues significantly affects the binding of anti-Sm antibodies to carboxyl-terminal SmD1 and SmD3 peptides (Fig. 1). All anti-Sm sera exhibited increased binding to SmD1 peptides containing sDMA as compared with those containing unmethylated arginine (Fig. 1a). In particular, peptides that exclusively consist of glycine and sDMA repeats exhibited strong reactivity with the antibodies (peptide nos 9, 10 and 11). Nevertheless, SmD1 peptides containing sDMA represent a rather unspecific substrate for anti-Sm antibodies because they were also bound by sera that contained anti-centromere antibodies. Interestingly, those anti-centromere antibodies also bound to peptides containing the asymmetrical form of DMA but to a lesser extent.
Binding experiments with peptides derived from SmD3 yielded similar results. Only SmD3 peptides containing sDMA reacted with anti-Sm antibodies, confirming the importance of the symmetrical dimethylation of arginine residues (Fig. 1b). In contrast to SmD1, none of the control sera reacted with SmD3 derived peptides, which reflects high binding specificity. One particular peptide (no. 77; 108AA sDMA G sDMA G sDMA GMG sDMA GNIF122) was strongly recognized by three out of five anti-Sm sera. Using a mutational analysis in which arginine residues of peptide no. 77 (108AARGRGRGMGRGNIF122) were successively replaced by sDMA, we were able to show that a particular peptide with a single dimethylated arginine residue at position 112 exhibited immunoreactivity with all of the five anti-Sm sera but not with the control sera (Fig. 1c). Thus, by introducing only one sDMA and at a defined position (amino acid 112) in SmD3, the sensitivity of binding to this peptide (108AARG sDMA GRGMGRGNIF122; SMP) was remarkably increased without loss in specificity. This candidate peptide was subsequently synthesized as a soluble antigen and used as a substrate in ELISA.
Immunoserological characterization of the systemic lupus erythematosus patient cohort
In order to characterize the SLE cohort with regard to autoantibody profiles, 101 SLE patient sera were tested for U1-68 kD, U1-A, U1-C, SmBB', SmD, Ro-52/SS-A, Ro-60/SS-A, La/SS-B, histone, dsDNA and β2-glycoprotein I reactivity. The prevalences of the different autoantibodies were as follows: 15.8% for U1-68, 24.8% for U1-A, 25.7% for U1-C, 21.8% for SmBB', 15.8% for SmD, 21.8% for Ro-52, 47.5% for Ro-60, 21.8% for La, 37.6% for histone, 51% for dsDNA and 17% for β2-glycoprotein I. The prevalences were therefore consistent with those in previous studies [1,6]. Thus, with regard to their autoantibody profiles, our SLE cohort appears to be representative of SLE patients in general.
Anti-SmD3 peptide ELISA
A 15 amino acid soluble peptide exhibited highest sensitivity and specificity in the SPOT assay (108AARG sDMA GRGMGRGNIF122) was, for coupling purposes, synthesized with an additional cysteine at the carboxyl-terminus. Nevertheless, the uncoupled 16 mer peptide was subsequently used to develop an ELISA system based on the general protocol of the Varelisa® tests (Pharmacia Diagnostics).
Assay performance characteristics
To evaluate the performance of the assay, the precision, reproducibility and linearity were analyzed. The intra-assay and interassay variabilities (coefficient of variation in %) for three samples ranged from 1.82% to 6.52% and from 2.27% to 7.42%, respectively. Even after five serial dilutions, two samples exhibited a linear range of reactivity (<20% deviation). The cutoff was defined by ROC analysis, performed with SLE and control sera. The assay performance characteristics of the new anti-SMP test are summarized in Fig. 2, including intra-assay and interassay variability (Fig. 2a), linearity (Fig. 2b), and ROC analysis, PPV, NPV and efficiency (Fig. 2c).
To determine the cutoff, the focus was set to yield a high specificity and a technical cutoff was defined at 13 U/ml. Sera from 176 SLE patients, 181 other autoimmune patients, 77 patients with infectious diseases, and from 192 normal donors were analyzed in the new ELISA system (Table 1). Twenty-eight SLE patients (15.9%) tested positive for anti-SMP antibodies, exhibiting a significantly increased reactivity of up to 1190 U/ml with a mean value of 43.0 U/ml (standard deviation 160.2 U/ml). Sera from patients with related disorders had significantly reduced reactivity (mean 3.36 U/ml). Only one patient in the rheumatoid arthritis group had a positive test result (24.6 U/ml). None of the remaining control individuals, including patients suffering from systemic sclerosis (n = 26), polymyositis/dermatomyositis (n = 13), MCTD (n = 26), Sjögren's syndrome (n = 24), or infectious diseases (n = 77), exhibited reactivity to the SmD3 peptide. The serum samples from patients with infectious diseases demonstrated reduced reactivity (mean 0.67 U/ml; top value 3.3 U/ml), even when compared with sera from healthy donors (mean 2.21 U/ml; top value 11.5 U/ml). The highest assay value in the infectious disease sera was found in patients with EBV infection (3.3 U/ml).
In summary, 28 samples in the SLE cohort (n = 176) and only one serum sample from the control group (n = 449; 0.2%) tested positive. This resulted in a diagnostic specificity of 99.8% and a sensitivity of 15.9%. PPV, NPV and diagnostic efficiency were calculated at 96.6%, 75.3% and 76.3%, respectively (Fig. 2c). These data indicate that, within the assay parameters used here, anti-SMP antibodies appear to be exclusively present in sera from SLE patients. Apart from anti-SMP reactivity, it is interesting to note that the positive rheumatoid arthritis serum contained high titres of antibodies to U1-RNPs 68 kDa (ratio 4.5), U1-C (ratio 9.4) and histone (133.8 U/ml). Anti-SmBB'and anti-SmD titres, as determined by ELISA, were elevated when compared with controls, but they were still below the cutoff values. No reactivity could be found to U1-A, Ro-52, Ro-60, La, dsDNA, or β2-glycoprotein.
Correlation with other autoantibodies
A statistical evaluation was performed using sera from a cohort of 101 patients with clinically defined SLE to evaluate correlations between anti-SmD3 peptide antibodies and other autoantibodies. Significant correlations were found with dsDNA (P = 0.0058, χ2 = 7.6), U1-68 (P < 0.0001, χ2 = 15.42), U1-A (P < 0.0001, χ2 = 25.49), U1-C (P < 0.0001, χ2 = 18.05), SmBB' (P < 0.0001, χ2 = 24.04) and SmD (P < 0.0001, χ2 = 38.76), but not to histone (P = 0.0259, χ2 = 4.96), La (P = 0.8747, χ2 = 0.02), Ro-52 (P = 0.4034, χ2 = 0.7), Ro-60 (P = 0.0143, χ2 = 6.0) and β2-glycoprotein antibodies (P = 0.3819, χ2 = 0.74; Table 2). When reactivity with components of the Sm complex was evaluated, five samples of the clinically defined SLE patients (n = 101) reacted with the purified SmD antigen, but not with SMP. The remaining 11 SmD positive sera (68.8%) also tested positive in the new anti-SMP ELISA. Interestingly, among the patients studied we found four (nos 89, 92, 20627 and 9811) who fulfilled SLE criteria and were all anti-SmD negative, but exhibited anti-SMP reactivities of 15.4, 21.3, 41.3 and 13.9 units, respectively.
Correlation with racial and clinical parameters
When correlating autoantibody specificities with race, there was a statistically significant association of autoantibodies to U1-68 kDa (P = 0.002), U1-A (P < 0.0001), U1-C (P = 0.0002), SmBB' (P = 0.0004), dsDNA (P = 0.0128) and SmD (P = 0.0002) with black race among SLE patients. There was no statistically significant association of other autoantibody specificities, including SmD3 peptide (P = 0.0253), Ro-52 (P = 0.8023), Ro-60 (P = 0.0399), La (P = 0.7137) and histones (P = 0.9831), with the race of the patients under investigation (data not shown). In addition, there was no significant correlation of SMP reactivity with renal (P = 0.2810) or central nerve system involvement (P = 0.5066).
Reference panels
The CDC and the AMLI reference panels for ANA were evaluated using the new SMP ELISA. Increased titres were found in ANA 1 (10.3 U/ml) and ANA 5 (910 U/ml) from the CDC panel and in samples I (1420 U/ml) and J (13.9 U/ml) from the AMLI panel [28,29] (Table 3).
Discussion
In the present study we analyzed the anti-Sm immune response directed toward the Sm antigens D1 and D3, which are considered SLE-specific autoantigens [11]. Using immobilized peptides prepared by the SPOT technology, it was shown that symmetric dimethylation of arginine residues plays an important role in the B-cell epitope recognition of both autoantigens. This observation is in accordance with the findings of Brahms and coworkers [21] but it is not in keeping with those of Riemekasten and colleagues [20]. In addition, we found the specificity of antibody binding to SmD3 peptides to be higher than that to SmD1 peptides, both of which were prepared using the SPOT method.
McClain and colleagues [19] described four antigenic regions on SmD3, of which antigenic region 4 encompasses amino acids 104–126. In their study, peptides synthesized on pins were subjected to analysis without using the modified form of arginine. In our study, we found reactivity within this region only when natural arginine was replaced by sDMA. These apparently contradictory results might be explained by the use of different sera or methology, and/or by the different peptide length used in the two studies. Three out of five of our sera specifically recognized the peptide 108AA sDMA G sDMA G sDMA GMG sDMA GNIF122. Interestingly, the dimethylation of only one arginine at a defined position (amino acid 112) was able to increase further the sensitivity of this particular peptide without loss of specificity.
Although it has been shown that all arginine residues within the peptide 108AA sDMA G sDMA G sDMA GMG sDMA GNIF122 become dimethylated in vivo, it is unclear whether the identified peptide with single dimethylation occurs in vivo and thus serves as the triggering epitope, or rather whether it represents an artificial structure that is more suitable for in vitro assays [21].
Fewer than 20 proteins have been identified during the past 40 years as containing dimethylated arginines [34]. The two major catalyzing enzymes of this reaction are the type I and type II protein arginine methyltransferases, which preferentially methylate arginines located in RG clusters. Recently, however, using arginine methyl-specific antibodies and HeLa cell extracts, it was shown that more than 200 proteins contained symmetrically dimethylated arginines; among these were a remarkable number of known autoantigens [34]. Further studies are necessary to screen known autoantigens containing dimethylated arginine residues for epitopes.
Based on data from epitope analysis, we used a candidate peptide (108AARG sDMA GRGMGRGNIF122) to develop an ELISA system. The new anti-Sm assay (anti-SMP) had a sensitivity of 15.9% and a specificity of 99.8% for SLE, resulting in a high PPV (96.6%) and NPV (75.3%), and a remarkable diagnostic efficiency of 76.3%. Therefore, this test appears to offer a new approach to serological evaluation and diagnosis of SLE.
Because no international 'gold standard' is available for detection of anti-Sm antibodies, we compared results with the new peptide-based ELISA with the results of an anti-Sm ELISA using purified Sm antigen (Varelisa® Sm; Pharmacia Diagnostics). Using this approach, we found comparable sensitivities but significant differences in the specificity. The specificity of the conventional ELISA (88%) was significantly lower than the specificity of the new peptide-based assay (99.8%; data not shown). Further studies are in progress to compare the assay performance of the anti-SMP assay with that of other commercially available anti-Sm immunoassays. For epitope-mapping, anti-Sm sera from SLE patients were preselected, based on ELISA results (Varelisa® Sm). That this selection method might have affected the results of epitope-mapping cannot be excluded. Nevertheless, the high sensitivity and specificity of the SmD3 peptide with a single dimethylated arginine could be confirmed using the soluble peptide in ELISA.
Evaluation of the biochemical properties of the identified Sm epitopes suggested that the isoelectric point (pI) of the peptide can be regarded as a predictor of antigenicity on the Sm complex. On U1-RNP-A, SmB' and SmD1, the average pI of antigenic regions was 10.4 (nonantigenic 6.0) and on SmD2 and SmD3 the pIs were 9.0 or higher [19]. These findings fit well with the observed pI (>12.88) of the SMP. Further investigation is warranted to determine whether the basic character of the epitope simply increases the probability of surface exposure of these regions and thus accessibility for immune recognition.
Epstein–Barr virus, Epstein–Barr virus nuclear antigen 1 and anti-SmD antibodies
Epitope-mapping studies on SmD1 have identified an epitope motif (amino acids 95–119) that crossreacts with a homologous sequence (amino acids 35-58) of the EBV nuclear antigen 1 [35,36]. A more recent study showed that this epitope also crossreacts with a homologous region of SmD3 containing glycine and arginine repeats (RGRGRGMGR) [19]. It is also evident that GPRR (amino acids 114–119 on SmD1) represents a common crossreactive autoepitope motif, which is present not only on EBV nuclear antigen 1, but also on a variety of autoantigens including CENP-A, CENP-B, CENP-C, SmBB', SmD1 and Ro-52, to name but a few [30]. Thus, patients suffering from infectious mononucleosis or SLE-related disorders may have a positive carboxyl-terminal SmD1 or SmD3 ELISA that might be regarded as a false-positive result. Of interest, several studies have suggested an influence of EBV on the development of SLE-like conditions [37,38]. Among the 25 EBV disease controls we evaluated, we found no false-positive samples, confirming the suggested high specificity of the anti-SMP assay. Therefore, we consider the use of EBV-positive sera with high titres of EBV-associated antibodies to be important reagents for developing highly specific and reliable anti-SmD immunoassays. Unfortunately, other investigators did not include an EBV patient group in the evaluation of anti-Sm antibody assays [20].
Correlations with other autoantibodies
Coincident reactivity with dsDNA and Sm antigens has been reported by several authors [39-41]. Although in those studies full-length SmD was used, in our investigation there was also a correlation of anti-dsDNA and anti-SMP reactivity (P = 0.0058, χ2 = 7.6). Apart from DNA we found also a positive correlation of anti-SMP antibodies with U1-68 (P < 0.0001, χ2 = 15.42), U1-A (P < 0.0001, χ2 = 25.49), U1-C (P < 0.0001, χ2 = 18.05), SmBB' (P < 0.0001, χ2 = 24.04) and SmD (P < 0.0001, χ2 = 38.76), but not to histone (P = 0.0259, χ2 = 4.96), La (P = 0.8747, χ2 = 0.02), Ro-52 (P = 0.4034, χ2 = 0.7), Ro-60 (P = 0.0143, χ2 = 6.0) and β2-glycoprotein (P = 0.3819, χ2 = 0.74) antibodies. Whether the observed associations are caused by cross-reactivity or by different autoantibody species that often occur simultaneously remains unclear. Preliminary results of inhibition experiments have shown no inhibiting effect of the SmD3 peptide on the binding of anti-Sm antibodies to the native Sm antigen in ELISA (Varelisa® Sm; data not shown). The absence of inhibition can be explained by the variety of different anti-Sm antibody subpopulations and by the variety of corresponding epitopes. Recently, two polyclonal antibodies (SYM10, SYM11) were generated that specifically bind to the symmetrical form of dimethylarginine and react with a variety of other known autoantigens [42]. Those antibodies may shed more light on the correlation of anti-SMP antibodies with other known autoantibody specificities.
Reference sera
The reference sera for ANA obtained from the CDC and the AMLI were tested using the anti-SMP assay [28,29]. Although sample J (AMLI) was defined as a RNP-positive serum, we found reactivity to the SMP (13.9 U/ml). None of five investigators found precipitating anti-Sm antibodies, and only two out of 21 reported Sm reactivity in their enzyme immunoassay in this serum, which was derived from a patient with SLE. In the immunoblot of the AMLI study, both serum I and serum J exhibited reactivity to the SmD proteins. Surprisingly, the reactivity to SmD3 was significantly greater in sample J than in serum I. Thus, the anti-SMP antibody test had a higher sensitivity and clinical accuracy than the anti-Sm tests used in most of the participating laboratories [29].
The apparent disparity between the results of the present study and those of Riemekasten [20] and Brahms [21] and their groups might be explained by the existence of different epitopes on the carboxyl-terminal extensions of SmD1 and SmD3. The peptide (amino acids 83–119) [20] may form a conformational epitope, whereas the shorter peptides used in the second study contain primarily linear, sDMA-dependent binding sites [21]. Furthermore, the reduced reactivity against the full-length SmD1 [20], as compared with SmD183–119 peptide, suggests that this epitope represents a cryptic structure. This observation raises the issue of which epitopes are 'seen' in vivo and which ones play a central role in the pathogenesis of SLE. In a recent study [43] it was observed that the injection of SmD183–119 fused to a carrier protein is able to accelerate the pathogenic process in SLE-prone mice.
Summary
In the present study we showed that dimethylation of arginine residues of the major SmD1 and SmD3 autoepitopes results in remarkably increased binding by SLE autoantibodies. Moreover, it could be shown that one particular SmD3 peptide represents a highly specific substrate for detecting a subclass of anti-Sm antibodies by ELISA. At a defined cutoff value of 13 U/ml, the sensitivity was 15.9% and the specificity was 99.8%, yielding a diagnostic efficiency of 76.3%.
Conclusion
Based on the findings of the present study, we conclude that anti-SMP antibodies are exclusively present in sera from SLE patients and that the new anti-SMP ELISA test appears to offer a new serological reagent that will improve our ability to diagnosis SLE and to discriminate SLE from other autoimmune and infectious diseases.
Abbreviations
ACR = American College of Rheumatology; AMLI = Association of Medical Laboratory Immunologists; ANA = anti-nuclear antibody; CDC = Centers for Disease Control and Prevention; CENP = centromere protein; dsDNA = double-stranded DNA; EBV = Epstein–Barr virus; ELISA = enzyme-linked immunosorbent assay; MCTD = mixed connective tissue disease; NPV = negative predictive value; pI = isoelectric point; PPV = positive predictive value; ROC = receiver operating characteristic; sDMA = symmetrical dimethylarginine; SLE = systemic lupus erythematosus; SMP = SmD3 peptide.
Competing interests
MM receives royalties for the commercial ELISA system from Pharmacia Diagnostics (Freiburg, Germany).
Authors' contributions
MM planned and initiated the present study. He carried out the epitope-mapping of SmD1 and SmD3, and developed and evaluated the ELISA system. Based on the results he filed a draft verison of the manuscript. MB advised MM regarding the planning of the epitope -mapping experiments and contributed to the preparation of the manuscript. MF delivered clinically defined sera, advised MM on evaluating the clincal part of the study, and contributed to the preparation of the manuscript.
Acknowledgements
We thank Dr R Mierau and Prof. E Genth (Rheumaklinik Aachen, Aachen, Germany) for providing clinically defined sera.
Figures and Tables
Figure 1 Epitope analysis of SmD1 and SmD3. Carboxyl-terminal regions of (a) SmD1 and (b) SmD3 were synthesized as peptide arrays (15 mers; two amino acids offset) and probed with patient sera. Each arginine containing peptide was synthesized as three variants, one with natural arginine (R), one with symmetrical dimethylarginine (sDMA) and one with asymmetrical dimethylarginine (asDMA) at the respective positions. In addition, a highly reactive SmD3 peptide was synthesized with certain combinations of natural arginine and sDMA. A significant effect of dimethylation of arginine residues on the antigenicity of SmD derived peptides was observed (black squares indicate strong reactivity; white indicate no reactivity). Binding of an anti-Sm negative serum sample (Varelisa® Sm) that contained anti-centromere antibodies (ACA) could be observed with SmD1 but not with SmD3 peptides. Thus, the immunoreactive peptide no. 77 was further tested in (c) a replacement experiment. The SmD3 peptide exhibited exclusive reactivity with the Sm-positive sera.
Figure 2 Assay performance characteristics of the anti-SmD3 peptide (SMP) assay. (a) Intra-assay and interassay variability, (b) linearity, and (c) receiver operating characteristic analysis. The intra-assay and interassay variability, expressed as coefficient of variation in percentage (CV%), of three samples ranged from 1.82 to 6.52% and from 2.27 to 7.42%, respectively. Serial dilution series of two samples with high titres of anti-Sm antibodies (S6 and H) exhibited a linear binding response (<20% deviation). Definition of the cutoff, using receiver operating characteristic (ROC) analysis, was performed with SLE and control sera.
Table 1 Results of ELISA using SmD3 peptide with systemic lupus erythematosus and various control sera
Sera (n) Number (%) of anti-SMP positive sera Mean value (U/ml) Top value (U/ml)
SLE (176) 28 (15.9) 43.0 1190.0
Rheumatic diseases (181) 1 (0.6) 2.2 24.6
RA (86) 1 (1.2) 1.6 24.6
pSS (24) 0 1.9 3.9
MCTD (26) 0 3.1 12.8
SSc (26) 0 2.4 4.3
PM/DM (13) 0 2.8 9.6
Overlap syndromes (8) 0 2.1 3.6
Infectious diseases (77) 0 0.67 3.3
HCV (30) 0 0.42 1.1
CMV (22) 0 0.8 3.2
EBV (25) 0 0.78 3.3
Healthy individuals (192) 0 2.21 11.5
CMV, cytomegalovirus; EBV, Epstein – Barr virus; HCV, hepatitis C virus; MCTD, mixed connective tissue disease; PM/DM, polymyositis/dermatomyositis; pSS, primary Sjögren's syndrome; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; SMP, SmD3 peptide; SSc, systemic sclerosis.
Table 2 Association between anti-SmD3 peptide positivity and other autoantibody species in systemic lupus erythematosus
Autoantibody to
U1-68 kD U1-A U1-C SmBB' SmD Ro-52 Ro-60 La histone dsDNA β2-Glycoprotein
SMP positive 8/16 12/25 11/26 11/22 11/16 5/22 12/48 4/22 10/38 13/51 4/16
Percentage 50% 48% 42.3% 50% 68.8% 22.7% 25% 18.2% 26.3% 25.5% 25%
P <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.4034 0.0143 0.8747 0.0259 0.0058 0.3891
χ2statistic 15.42* 25.49* 18.05* 24.04* 38.76* 0.7 6.0 0.02 4.96 7.6* 0.74
* χ2 test: statistically significant (χ2 > 7).
Table 3 Results of the reference sera from the CDC and AMLI in the new anti-SmD3 peptide ELISA
Sample ID U/ml Sample ID U/ml Diagnosis
ANA 1 10.3 SLR Research A 2.5 CREST
ANA 2 1.4 SLR Research B 2.9 Scl
ANA 3 1.9 SLR Research D 2.8 MCTD
ANA 4 2.5 SLR Research E 3.1 SS, SLE
ANA 5 910.0 SLR Research F 1.5 PM
ANA 6 3.1 SLR Research G 4.5 SS
ANA 7 0.0 SLR Research I 1420.0 SLE
ANA 8 0.1 SLR Research J 13.9 SLE
ANA 9 1.7 SLR Research K 0.0 HD
ANA 10 0.5 SLR Research L 0.2 HD
AMLI, Association of Medical Laboratory Immunologists; CDC, Center for Disease Control and Prevention; CREST, calcinosis cutis, Raynaud's phenomenon, esophageal dysfunction, sclerodactyly and telangiectasia; HD, healthy donor; MCTD, mixed connective tissue disease; PM, polymyositis; Scl, scleroderma; SLE, systemic lupus erythematosus; SS, Sjögren's syndrome.
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| 15642139 | PMC1064884 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 10; 7(1):R19-R29 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1455 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14571564214710.1186/ar1457Research ArticleNifedipine protects against overproduction of superoxide anion by monocytes from patients with systemic sclerosis Allanore Yannick [email protected] Didier 2Périanin Axel 3Lemaréchal Hervé 2Ekindjian Ohvanesse Garabed 2Kahan André 11 Rheumatology A Department, Paris V University, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France2 Biochemistry A Department, Paris V University, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France3 Cell Biology Department, Cochin Institute, CNRS UMR 8104, INSERM 0567, Paris, France2005 16 11 2004 7 1 R93 R100 5 6 2004 4 8 2004 4 10 2004 8 10 2004 Copyright © 2004 Allanore et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We have reported previously that dihydropyridine-type calcium-channel antagonists (DTCCA) such as nifedipine decrease plasma markers of oxidative stress damage in systemic sclerosis (SSc). To clarify the cellular basis of these beneficial effects, we investigated the effects in vivo and in vitro of nifedipine on superoxide anion (O2•-) production by peripheral blood monocytes. We compared 10 healthy controls with 12 patients with SSc, first after interruption of treatment with DTCCA and second after 2 weeks of treatment with nifedipine (60 mg/day). O2•- production by monocytes stimulated with phorbol myristate acetate (PMA) was quantified by the cytochrome c reduction method. We also investigated the effects in vitro of DTCCA on O2•- production and protein phosphorylation in healthy monocytes and on protein kinase C (PKC) activity using recombinant PKC. After DTCCA had been washed out, monocytes from patients with SSc produced more O2•- than those from controls. Nifedipine treatment considerably decreased O2•- production by PMA-stimulated monocytes. Treatment of healthy monocytes with nifedipine in vitro inhibited PMA-induced O2•- production and protein phosphorylation in a dose-dependent manner. Finally, nifedipine strongly inhibited the activity of recombinant PKC in vitro. Thus, the oxidative stress damage observed in SSc is consistent with O2•- overproduction by primed monocytes. This was decreased by nifedipine treatment both in vivo and in vitro. This beneficial property of nifedipine seems to be mediated by its cellular action and by the inhibition of PKC activity. This supports the hypothesis that this drug could be useful for the treatment of diseases associated with oxidative stress.
monocytenifedipineprotein kinase Csuperoxide anionsystemic sclerosis
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Introduction
Systemic sclerosis (SSc) is a connective tissue disease, characterised by vascular involvement with generalised microangiopathy culminating in systemic fibrosis. Several lines of evidence suggest that the generation of oxygen free radicals is of major importance in the pathogenesis of SSc [1]. Frequent episodes of reperfusion injury generate oxygen free radicals locally, but increased lipid peroxidation is not related to Raynaud's phenomenon only [2] and the inflammatory process might also generate oxidative stress [1].
Histological studies of cutaneous SSc lesions have revealed early mononuclear cell infiltration of perivascular spaces around small vessels [3,4], and mononuclear cells might affect vascular and tissue lesions by producing various molecules [5]. Monocytes seem to have a key function in several disorders (for example atherosclerosis) associated with free radical generation [6]. Monocytes from patients with SSc produce greater amounts of superoxide anion (O2•-) than those from healthy subjects and patients with primary Raynaud's phenomenon [7].
Dihydropyridine-type calcium-channel antagonists (DTCCA) are an essential treatment in SSc because they decrease vasospastic propensity. These drugs are suspected to have anti-oxidant properties in other diseases [8,9]. We reported previously that nifedipine and nicardipine decrease circulating markers of oxidative stress damage in patients with SSc [10,11]. The aim of the present study was to investigate O2•- production ex vivo by monocytes from patients with SSc after a wash-out and during nifedipine treatment. We also investigated the effects of nifedipine in vitro on O2•- release from human monocytes, on protein phosphorylation with phorbol myristate acetate (PMA) as stimulator and on protein kinase C (PKC) activity.
Methods
Reagents, except when specified, were provided by Sigma (St Louis, MO, USA).
Patients
Twelve non-smoking patients with SSc were included (three men and nine women); their mean age was 56 (±10 SD) years and the mean disease duration was 8 ± 5 years. Each of the patients had been hospitalised for systematic follow-up of the disease. SSc was classified as limited cutaneous or diffuse cutaneous according to the criteria of LeRoy and colleagues [12]. The clinical features of their disease were assessed as recommended [13]; results are detailed in Table 1.
The initial evaluation included biological tests performed on the morning of admission, after 1 hour of rest at room temperature; patients stopped taking calcium-channel blockers 3 days before admission [10]. This wash-out period was long enough for DTCCAs to have ceased to have an effect because the half-lives of nifedipine and nicardipine lie between 6 and 11 h and these drugs are converted into inactive metabolites. Patients were also evaluated 2 weeks later, when they were receiving stable treatment (including 20 mg of nifedipine three times a day). As before, this was done in the morning, after 1 h of rest at room temperature. All patients gave written informed consent. The control subjects were 10 healthy non-smokers from the laboratory staff (8 women and 2 men, mean age 48 ± 9 years).
Monocyte preparation
Blood samples (20 ml) were collected on EDTA. Whole blood was diluted 1:2 in RPMI 1640 solution (Eurobio, Les Ulis, France), layered on 15 ml of Ficoll-Hypaque (relative density 1.077; Eurobio) and centrifuged at 150 g for 25 min at room temperature to separate mononuclear cells from red blood cells and polymorphonuclear neutrophils. Mononuclear cells collected at the interface were washed twice in RPMI 1640 solution and separated into lymphocytes and monocytes by gradient centrifugation with 47% Percoll (Pharmacia, Uppsala, Sweden) at 150 g for 25 min. The purity of the monocyte population (more than 90%) was assessed by May–Grunwald–Giemsa staining. Cells were diluted in RPMI and used immediately.
O2•- (respiratory burst) assay
O2•- was quantified by the cytochrome c reduction assay at 550 nm [14] with a Uvikon® spectrophotometer equipped with a thermostat-controlled cuvette holder and a magnetic stirrer. In brief, 840 µl of monocytes (2 × 106 cells) were incubated at 37°C with 80 µM cytochome c in 1 ml of PBS with stirring before stimulation with 10 µl of PMA (100 nM). Absorbance was read against a reference blank curve containing PBS and cytochrome c at 5, 10 and 15 min. Results are expressed in nmoles of O2•- per 106 cells using 21.2 mM/cm as the extinction coefficient. For in vitro evaluation of the effects of DTCCAs, control monocytes were preincubated for 30 min with the desired concentration of drugs or vehicle (dimethyl sulphoxide, 100 µM) before stimulation with PMA.
The respiratory burst of control and nifedipine-treated monocytes induced by formyl-Met-Leu-Phe (fMLP) was measured by a sensitive fluorimetric assay for hydrogen peroxide under standard conditions [15] using the Amplex Red fluorescent probe (Molecular Probes, Eugene, OR, USA). The increase in the fluorescent signal (excitation and emission wavelengths of 530 and 590 nm respectively) was monitored continuously after the stimulation of monocytes with 1 µM fMLP. Results are expressed as the percentage of control values, representing the total hydrogen peroxide production.
The scavenging effect of nifedipine was studied in an assay system containing nifedipine at the desired concentration with 0.15 mM hypoxanthine, 1.5 mM EDTA and 0.025 mM cytochrome c. The reaction was started by adding 0.1 U/ml xanthine oxidase. Absorbance was read at 550 nm.
Protein phosphorylation in monocytes and in a cell-free system
Phosphorylated proteins were detected by using the fluorescent Pro-Q-Diamond dye (Molecular Probes), which can directly detect phosphate groups attached to tyrosine, serine or threonine residues in gels. In brief, monocytes from healthy donors were treated with or without nifedipine and stimulated for 10 min with 100 nM PMA (which induces the activation of PKC). Reactions were stopped with ice-cold buffer and cell lysates were prepared by incubating monocytes for 30 min in 0.5 ml of ice-cold extraction buffer (350 nM NaCl, 20 mM Hepes-KOH, pH 7.9, 1 mM EDTA, 0.1 mM EGTA, 20% glycerol, 1% Nonidet P40, 1 mM dithiothreitol, 0.5 mM phenylmethylsulphonyl fluoride, 20 mM ß-glycerophosphate, 0.1 mM Na3VO4 and 1 µg/ml each of aprotinin, pepstatin and leupeptin). Cells were then disrupted by sonication (three times 5 s) and proteins were delipidated and desalted before being subjected to SDS gel electrophoresis. Protein phosphorylation was also studied after treatment of a cytosolic fraction of resting monocytes with PMA for 10 min. Protein samples (150 µg of a 1 mg/ml protein sample) were treated in vitro with nifedipine for 30 min before stimulation of PKC with a mixture of diacylglycerol and calcium. This was done with the PKC Biotrak® enzyme assay (Amersham Biosciences, Little Chalfont, Buckinghamshire, UK) in accordance with the manufacturer's instructions. Proteins (50 µg) were separated by SDS gel electrophoresis. Proteins were fluorescently stained by fixing the gels overnight in 50% methanol and 10% trichloracetic acid. The gels were washed with deionised water for 10–20 min, stained with Pro-Q-Diamond for 180 min and destained by three washes in 4% acetonitrile in 50 mM sodium acetate, pH 4, for 2 hours. Gels were scanned with a fluorimager, a Typhoon 9400 laser scanner (Amersham Biosciences), with excitation at 532 nm and a 580 nm band pass emission filter for Pro-Q-diamond dye detection. Phosphorylated proteins were quantified densitometrically with the Image Quant software. Results represent the net phosphorylation induced by PMA, expressed as percentages of the control PMA lane (taken as 100%).
PKC assay
PKC assays were performed in vitro with a biologically active full-length recombinant isoform (Calbiochem, EMB Biosciences, San Diego, CA, USA) and a non-radioactive PKC kinase assay kit (Stressgen, Victoria, BC, Canada). The principle is based on an enzyme-linked immunosorbent assay method that uses a specific synthetic peptide as a substrate for PKC and a polyclonal antibody that recognised the phosphorylated form of the substrate. In brief, 1 µl of recombinant PKC (5 µg/ml) in 30 µl of kinase assay dilution buffer was incubated for 20 min at 37°C with the drug vehicle (control) or with nifedipine or two other PKC inhibitors (calphostin and GFI09203). PKC was then activated by 10 µl of PMA (100 nM) for 10 min at 37°C and the reaction was blocked by incubating tubes in ice-cold water. PKC activity was then determined as recommended by the manufacturer. Results are expressed as percentages of the net activity induced by PMA (taken as 100%).
Approval and consent
The study was approved by local ethics committee, and all patients gave written informed consent.
Statistical analysis
Data were compared with the nonparametric Mann–Whitney (unpaired data) and Wilcoxon (paired data) tests. P < 0.05 was considered significant. All quantitative data are expressed as means ± SD.
Results
Monocyte O2•- production and respiratory burst
Monocytes from patients with SSc (n = 12) not treated by nifedipine produced a significantly greater amount of O2•- than controls (n = 10) after stimulation ex vivo with the PKC activator PMA for 15 min (9.1 ± 1.7 versus 5.4 ± 0.7 nmol per 106 cells; P < 0.01; Fig. 1a). This difference was also observed when monocytes were stimulated with PMA for 10 min (7.6 ± 2.3 versus 4.3 ± 0.8 nmol per 106 cells; P < 0.01) but not when they were stimulated for 5 min (2.7 ± 0.8 versus 2.1 ± 0.4 nmol per 106 cells; not significant). These observations suggest that the biochemical modifications affecting monocytes from patients with SSc might not affect the kinetics of NADPH oxidase but rather its late regulatory mechanisms.
Monocytes from patients treated with nifedipine (60 mg/day) for 14 days produced a significantly smaller amount of O2•- than monocytes from untreated patients with SSc after stimulation with PMA for 5 min (1.0 ± 0.7 versus 2.7 ± 0.8 nmol per 106 cells; P < 0.05), 10 min (1.6 ± 1.4 versus 7.6 ± 2.3 nmol per 106 cells; P < 0.001) and 15 min (1.8 ± 1.0 versus 9.1 ± 1.7 nmol per 106 cells; P < 0.001; Fig. 1a). Disease subsets, in particular the cutaneous subtype, did not influence baseline O2•- production by monocytes or the decrease in O2•- production with nifedipine treatment.
To determine whether monocytes were directly altered by nifedipine, monocytes from healthy donors were treated in vitro with nifedipine for 30 min before stimulation with PMA. Nifedipine induced a concentration-dependent inhibition of O2•- production at concentrations of 10 and 50 µM (n = 5) (Fig. 1b). PMA induction was inhibited by 50% (IC50) by approximately 3–5 µM nifedipine. This inhibitory effect of nifedipine was not due to any cytotoxic effect, as determined by the trypan blue exclusion test (cell death less than 5%). The effects of nifedipine were next studied on the monocyte respiratory burst induced by an inflammatory agonist such as fMLP. However, fMLP induced very weak monocyte O2•- production in standard conditions, so respiratory burst was measured with a fluorimetric assay. Treatment of monocytes for 30 min with 5 and 10 µM nifedipine significantly inhibited hydrogen peroxide production by 55 ± 7% and 90 ± 9%, respectively, relative to controls (n = 3).
Next we studied whether other calcium-channel blockers alter the O2•- production of healthy monocytes (n = 5) (Fig. 1c). A strong and similar inhibitory effect was observed with nifedipine or nicardipine, but a less marked inhibitory effect was observed with diltiazem. To investigate whether the inhibitory effect of nifedipine was due to the scavenging of free radicals, O2•- was generated by the xanthine/xanthine oxidase system in the presence of various concentrations of nifedipine. In these conditions, nifedipine did not affect O2•- generation, ruling out a scavenger effect (data not shown).
Protein phosphorylation
The production of O2•- by phagocytes is dependent on the activation of PKC, a family of isoenzymes that phosphorylate various proteins including some components of the NADPH oxidase. Because PMA directly activates various PKC isoforms, we studied the effect of nifedipine on PKC-dependent protein phosphorylation in healthy monocytes. Phosphorylated proteins were directly detected in polyacrylamide gels by using Pro-Q-Diamond, a fluorescent dye that binds to phosphate groups on proteins. In the absence of PMA, nifedipine inhibited the basal phosphorylation of various proteins in monocytes (Fig. 2a). In monocytes not treated with nifedipine, stimulation with PMA markedly increased the phosphorylation state of various proteins and nifedipine strongly decreased the PMA-induced phosphorylation of proteins. Densitometric analysis of 10 major phosphorylated protein bands with molecular masses of between 60 and 15 kDa showed that nifedipine inhibited the net protein phosphorylation induced by PMA in a concentration-dependent manner. The inhibition of phosphorylation was also investigated in this cell system under the same conditions with the classical PKC inhibitor calphostin; the inhibition of protein phosphorylation had a quite similar profile (Fig. 2b) with closed results on densitometric analysis.
The inhibitory effects of nifedipine were also demonstrated in a cell-free system in which the activity of PKCs present in the cytosolic fraction of resting monocytes was directly stimulated in the presence of a mixture of calcium and diacylglycerol. In these conditions, treatment of the cytosolic fraction with nifedipine abolished PKC-dependent protein phosphorylation (Fig. 2c).
PKC activity in vitro
To determine whether PKC is a direct target of nifedipine, we investigated kinase activity in vitro with a biologically active PKC recombinant that was pretreated with or without various concentrations of nifedipine or with other classical PKC inhibitors such as calphostin or GF109203X [16]. Nifedipine (1, 10 and 50 µM) dose-dependently inhibited PKC activity with an inhibition reaching about 80%. Similar inhibition was observed with calphostin and GF109203X (n = 3; Fig. 3). These results strongly suggest that nifedipine might directly inhibit PKC.
Discussion
In this report we show that, first, ex vivo monocytes from patients with SSc not treated with calcium-channel blockers produce more O2•- than control monocytes; second, nifedipine (60 mg/day) strongly inhibits the ability of monocytes to produce O2•- ex vivo; and third, nifedipine inhibits O2•- production by healthy monocytes in vitro, a property associated with the inhibition of PKC-dependent protein phosphorylation and PKC activity.
Since the proposal that free radicals have a function in SSc [17], several reports have provided evidence that free radicals are involved in the pathogenesis of this complex disease [1,2,7,18]. Vasospasm and ischaemia–reperfusion are known to generate free radicals, but other factors such as the inflammatory process might also be involved [1]. It has been suggested that resting and PMA-stimulated monocytes produce more O2•- than controls [7]. This is consistent with our observation that circulating monocytes from patients with SSc stimulated ex vivo by PMA release about twice as much O2•- as controls, which is similar to previous findings [7]. This PMA-dependent increase in O2•- suggests that circulating monocytes are in a primed state, which is consistent with their prior exposure to a substimulatory dose of agonists such as cytokines or immune complexes [19]. These data show that free radicals are overproduced in SSc and that this is due at least partly to monocytes. The mechanism of monocyte activation remains unknown. However, ischaemic insult does not seem sufficient to explain this phenomenon, which is associated with NADPH oxidase activation and might involve cytokines [7].
The main finding of this study is that the treatment of patients with SSc with nifedipine (60 mg/day) strongly decreases the production of O2•- by monocytes and leads to the disappearance of the monocyte priming state. PMA was chosen because it mimics the activation of NADPH oxidase by PKC, a mechanism also induced by most of the physiological activators of monocytes. Mononuclear cells are thought to be important in SSc [3-5] and in oxidative stress because they release various mediators leading to tissue injury. Unlike monocytes, polymorphonuclear neutrophils do not seem to be activated in SSc [7]. The results reported here are consistent with our previous data showing that dihydropyridine-type calcium-channel blockers can reduce plasma markers of oxidative stress [10]. Together with data showing a concomitant improvement of endothelial injury [11], these results suggest that calcium-channel blockers, especially the dihydropyridine type, are not only of major importance for the treatment of Raynaud's phenomenon and vasospastic propensity [20,21] but should also be regarded as essential drugs that limit monocyte activation and oxidative stress.
Several reports have demonstrated the anti-oxidant properties of calcium-channel blockers. In hypertensive rats, dihydropyridine calcium-channel blockers decrease lipoprotein oxidation and prolong survival independently of modifications of blood pressure [22]. In hypertensive patients, treatment with nifedipine decreases lipoperoxide and isoprostane concentrations and increases the plasma antioxidant capacity while increasing endothelium-dependent vasodilation by restoring nitric oxide bioavailability [23]. At the cellular level, nifedipine can prevent ischaemia-induced endothelial permeability mediated primarily by the inhibition of PKC-a [24]. A study with activated human and rabbit neutrophils [25] and another study in vascular smooth muscle cells [26] suggested that PKC can be inhibited by nicardipine. Our results are consistent with these findings and further emphasise the ability of nifedipine to inhibit PKC activity in vitro, as shown by the strong decrease in PMA-induced protein phosphorylation in monocytes and in a cell-free system and the inhibition of a recombinant isoform of PKC. Analysis of the protein phosphorylation pattern showed that nifedipine also inhibits basal phosphorylation of proteins, indicating that other protein kinases might be altered. The molecular mechanism by which nifedipine inhibits PKC cannot be derived from our results and will require further experiments. However, nifedipine inhibits both basal and PMA-induced protein phosphorylation as well as PKC activity, which suggests that nifedipine might interact with both the regulatory and catalytic domains of PKC.
The inhibition of PKC-dependent protein phosphorylation might be part of the molecular mechanism responsible for the loss of the primed state of monocytes from patients with SSc treated with nifedipine. This is emphasised by the fact that IC50 values were above 3–5 µM nifedipine for PMA-induced O2•- production in vitro (Fig. 1b) and also for PKC-dependent phosphorylation (Fig. 2a). However, this hypothesis must be tested with monocytes from patients with SSc.
Nifedipine (10 and 50 µM) significantly inhibited O2•- release by activated monocytes in vitro; this is consistent with results previously obtained with human neutrophils [25]. The IC50 for calcium channels antagonist activity of nifedipine is close to 100 pM [27] and the clinical plasma concentration of nifedipine is close to 0.2 µM [28]. We used higher nifedipine concentrations for experiments in vitro; however, nifedipine can accumulate in membrane-bounded structures, resulting in higher localised concentrations [29]. Thus, our data with nifedipine and other calcium-channel blockers in vitro could explain the results obtained with monocytes from patients with SSc treated with nifedipine.
We found that diltiazem had a weaker effect on O2•- production by activated monocytes than other calcium-channel blockers evaluated; this was previously reported using human neutrophils [30] and suggests that dihydropyridine-type calcium-channel blockers are the strongest inhibitors of O2•- production.
Most of the classical properties of nifedipine are exerted through the modulation of L-type voltage-gated calcium channels on smooth muscle cells. Such channels are also present on monocytes; we suspect that the molecular mechanism highlighted is independent of these channels, but this will require further investigations. Calcium channel-independent properties have previously been reported and because these are not represented in endothelial cells they cannot account for these cellular effects [31]. It was suggested that these drugs act as scavengers; however, our data, together with those on human polymorphonuclear neutrophils [24], do not support this hypothesis.
Conclusion
Monocytes from patients with SSc adopt a priming state resulting in increased ex vivo PKC-dependent production of O2•- relative to healthy monocytes. Treatment of patients with SSc or of healthy monocytes in vitro markedly decreased the ability of monocytes to generate O2•- . Biochemical analysis of protein phosphorylation in monocytes and cell-free systems suggested that nifedipine directly inhibits PKC activity. These results demonstrate the potential anti-oxidant effects of this drug, which might have important clinical implications for SSc and other oxidative stress-associated diseases.
Abbreviations
DTCCA = dihydropyridine-type calcium-channel antagonists; fMLP = formyl-Met-Leu-Phe; IC50 = concentration for 50% inhibition; PKC = protein kinase C; PMA = phorbol myristate acetate; SSc = systemic sclerosis.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
YA, DB and AP devised the study. YA, HL and AP performed all the experiments. OGE and AK assisted in the writing of the report. All authors read and approved the final manuscript.
Acknowledgements
The authors are indebted to Mrs Carole Desbas for her expert secretarial assistance. This work was supported by a grant from the Association des Sclérodermiques de France.
Figures and Tables
Figure 1 Superoxide anion production by monocytes after induction with phorbol myristate acetate (PMA; 100 nM) for 15 min. From healthy controls (n = 10), patients with systemic sclerosis (SSc) after disruption of calcium-channel blocker treatment (n = 12) (baseline) and patients with SSc after 14 days of treatment with 60 mg/day nifedipine (NIF) (n = 12) (a), from healthy donors (n = 5) treated with or without various concentrations of nifedipine for 30 min before stimulation with PMA (b) and from healthy donors treated with or without calcium-channel blockers (10 µM) before stimulation with PMA (c). *P < 0.05; **P < 0.01 relative to controls.
Figure 2 Effects of nifedipine (NIF) on phorbol myristate acetate (PMA)-induced protein phosphorylation in monocytes as detected with the fluorescent probe ProQ-Diamond. (a, b) Representative gels of phosphorylated protein from homogenates of monocytes from healthy donors, treated with the indicated concentrations of nifedipine (a, b) or calphostin (CAL) (b) for 30 min and then stimulated with 100 nM PMA for 10 min. Right panels show a representative densitometric analysis of 10 protein bands (molecular masses from 15 to 60 kDa) that were strongly phosphorylated in the presence of PMA. (c) Effects of nifedipine on the cytosolic fraction of control monocytes treated in vitro with the indicated concentrations of nifedipine for 30 min before stimulation of protein kinase C with a mixture of calcium and diacylglycerol. Results are the net phosphorylation induced by PMA, expressed relative to the PMA lane (taken as 100%).
Figure 3 Nifedipine (NIF), like the classical protein kinase C (PKC) inhibitors calphostin (CAL) or GF109203X, inhibited the PKC activity of recombinant PKC as evaluated by an enzyme-linked immunosorbent assay method (n = 3). Results are the net phosphorylation induced by phorbol myristate acetate (PMA), expressed relative to the PMA lane (taken as 100%).
Table 1 Clinical and biological characteristics of patients with systemic sclerosis (SSc)
Characteristic Limited SSc (n = 6) Diffuse SSc (n = 6)
Raynaud's phenomenon, n 6 6
Current digital vascular ulceration, n 0 0
Lung fibrosis (computed tomography scan), n 2 4
Forced vital capacity <75%, n 0 4
DLCOc/haemoglobin <80%, n 3 3
Pulmonary hypertension (sPAP >40 mmHg), n 0 0
Positive for anti-topoisomerase I antibodies, n 0 4
Positive for anti-centromere antibodies, n 3 0
Creatinine, µmol/l (range) 73.4 ± 12.5a (56–81) 76.4 ± 13.5a (60–90)
ESR, mm/h 13.6 ± 11a 18.6 ± 15a
C-reactive protein concentration, mg/l 7.6 ± 6.1a 8.8 ± 7.4a
Low dose of prednisone (ongoing treatment), n (mean mg/day) 2 (7) 4 (7.5)
Angiotensin-converting enzyme inhibitors, n 0 3
D-Penicillamine, n 0 2
Omeprazole, n 6 6
aMean ± SD.
DLCOc, carbon monoxide diffusing capacity of the lung corrected for haemoglobin; ESR, erythrocyte sedimentation rate; sPAP, systolic pulmonary arterial pressure.
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| 15642147 | PMC1064885 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 16; 7(1):R93-R100 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1457 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14581564213010.1186/ar1458Research ArticleGene expression profiles in the rat streptococcal cell wall-induced arthritis model identified using microarray analysis Rioja Inmaculada [email protected] Chris L [email protected] Simon J [email protected] Paul F [email protected] Marion C [email protected] Rheumatoid Arthritis Disease Biology Department, GlaxoSmithKline, Medicines Research Centre, Stevenage, UK2 Transcriptome Analysis Department, GlaxoSmithKline, Medicines Research Centre, Stevenage, UK2005 19 11 2004 7 1 R101 R117 3 7 2004 16 9 2004 4 10 2004 9 10 2004 Copyright © 2004 Rioja et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. Use of microarray-based methods represents a new and challenging approach that allows molecular dissection of complex autoimmune diseases such as arthritis. In order to characterize the temporal gene expression profile in joints from the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats, total RNA was extracted from ankle joints from naïve, SCW injected, or phosphate buffered saline injected animals (time course study) and gene expression was analyzed using Affymetrix oligonucleotide microarray technology (RAE230A). After normalization and statistical analysis of data, 631 differentially expressed genes were sorted into clusters based on their levels and kinetics of expression using Spotfire® profile search and K-mean cluster analysis. Microarray-based data for a subset of genes were validated using real-time PCR TaqMan® analysis. Analysis of the microarray data identified 631 genes (441 upregulated and 190 downregulated) that were differentially expressed (Delta > 1.8, P < 0.01), showing specific levels and patterns of gene expression. The genes exhibiting the highest fold increase in expression on days -13.8, -13, or 3 were involved in chemotaxis, inflammatory response, cell adhesion and extracellular matrix remodelling. Transcriptome analysis identified 10 upregulated genes (Delta > 5), which have not previously been associated with arthritis pathology and are located in genomic regions associated with autoimmune disease. The majority of the downregulated genes were associated with metabolism, transport and regulation of muscle development. In conclusion, the present study describes the temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. These findings improve our understanding of the molecular events that underlie the pathology in this animal model, which is potentially a valuable comparator to human rheumatoid arthritis (RA).
arthritisdifferential gene expressionmicroarrayratSCW induced arthritis
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Introduction
Rheumatoid arthritis (RA) is an autoimmune chronic inflammatory disease of unknown aetiology that is characterized by infiltration of monocytes, T cells and polymorphonuclear cells into the synovial joints. The pathogenesis of this disease is still poorly understood, and fundamental questions regarding the precise molecular nature and biological significance of the inflammatory changes remain to be answered [1,2]. A powerful way to gain insight into the molecular complexity and pathogenesis of arthritis has arisen from oligonucleotide-based microarray technology [3], because this platform provides an opportunity to analyze simultaneously the expression of a large number of genes in disease tissues.
The earliest preclinical stages of human RA are not easily accessible to investigation, but a diverse range of experimental arthritis models are considered valuable tools for delineating mechanisms of inflammation and autoimmune phenomena. An animal model that shares some of the hallmarks of human RA is the reactivation model of streptococcal cell wall (SCW)-induced arthritis in rats. In this model, a synovitis with maximal swelling at 24 hours is induced by local injection of SCW antigen directly into an ankle joint. The initial response is reactivated by systemic (intravenous) challenge with SCW, which produces a more prolonged and severe inflammation confined to the joint previously injected with SCW. In contrast to some other animal models, in which the arthritic response develops gradually and unpredictably, in this model the flare response develops synchronously, allowing precise analysis of pathophysiological mechanisms [4,5].
Some pathological changes observed in SCW-induced arthritis that are of relevance to human RA include infiltration of polymorphonuclear cells, CD4+ T cells and macrophages, hyperplasia of the synovial lining layer, pannus formation and moderate erosion of cartilage and bone [4]. Previous reports have shown the dependency of this model on tumour necrosis factor (TNF)-α, IL-1α, IL-4, P-selectin, vascular cell adhesion molecule-1, macrophage inflammatory protein (MIP)-2, MIP-1α and monocyte chemoattractant protein (MCP)-1 [6,7]. Although the involvement of nitric oxide synthase (NOS) [8] and cyclo-oxygenase [9] in the development of SCW-induced arthritis has also been noted, a global analysis of coordinated gene expression during the time course of disease in this experimental arthritis model has not been investigated.
Arthritis involves many cell types from tissues adjacent to the synovium. Therefore, as shown in previous studies [10,11], analysis of gene expression profiles by processing whole homogenized joints can provide useful information about dysregulated genes, not only in synoviocytes but also in other, neighbouring cells (myocytes, osteocytes and chondrocytes) that may also contribute to disease pathology.
In the present study, whole homogenized rat ankle joints from naïve, SCW-injected and phosphate-buffered saline (PBS; vehicle)-injected animals, included in a time-course study, were analyzed for differential gene expression using the RAE230A Affymetrix GeneChip® microarray (Affymetrix Inc., Santa Clara, CA, USA). In order to identify different patterns of gene expression during the course of SCW-induced arthritis, a selected set of genes whose expression was statistically significantly different between arthritic and control animals on days -13.8, -13 and 3 were analyzed using agglomerative hierarchical clustering, Spotfire® (Spotfire Inc., Cambridge, MA, USA) profile search and K-means cluster analysis. Validation of microarray data for a subset of genes was performed by real-time RT-PCR TaqMan® (Applied Biosystems, Foster City, CA, USA) analysis, which provides a highly accurate method for quantifying mRNA expression levels for any particular differentially expressed gene. To further investigate the possible association of 20 selected upregulated genes with arthritis pathogenesis, their chromosomal locations and the chromosomal locations of their corresponding human orthologue were compared with the locations of previously reported quantitative trait loci (QTLs) for inflammation, arthritis and other autoimmune diseases. Our findings show, for the first time, the gene expression profiles and kinetics of expression of hundreds of genes that are differentially expressed in arthritic joints from the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat, thereby improving our understanding of the biological pathways that contribute to the pathogenesis of arthritis in this animal model and providing a valuable comparator to human RA.
Methods
Reagents
The peptidoglycan–polysaccharide (PG-PS) 100p fraction of SCW was purchased from Lee Laboratories (Grayson, GA, USA). RAE230A Affymetrix GeneChip® were purchased from Affymetrix Inc. All reagents required for RT-PCR were from PE Applied Biosystems (Warrington, UK). Forward and reverse primers were purchased from Invitrogen™ Life Technologies (Invitrogen Ltd, Paisley, UK). TaqMan® probes were synthesized by PE Applied Biosystems. RiboGreen, used to quantify RNA, was obtained from Molecular Probes Inc. (Leiden, The Netherlands) and RNA 6000 Nano LabChip Kit®, used to assess RNA integrity, was from Agilent Technologies Inc. (Stockport, UK).
Animals
All in vivo studies were undertaken in certified, dedicated in vivo experimental laboratories at the GlaxoSmithKline Medicines Research Centre (Stevenage, UK). The studies complied with national legislation and with local policies on the care and use of animals, and with related codes of practice. Male Lewis (LEW/N) rats obtained from Harlan UK Ltd (Oxon, UK), at age 6–7 weeks, were housed under standard conditions and received food and water ad libitum. Animals were habituated to the holding room for a minimum of 1 week before the experimental procedures.
Induction and assessment of SCW-induced arthritis
SCW arthritis was induced in 6- to 8-week-old male Lewis (LEW/N) rats (weight 125–150 g) following a method similar to that previously described by Esser and coworkers [4]. A SCW preparation (PG-PS, 100p fraction) was suspended in PBS and 10 μl of the suspension containing 5 μg PG-PS from Streptococcus pyogenes was injected into the right ankle joint (day -14). Animals from control groups were injected similarly with 10 μl PBS. A group of noninjected rats was also included in our study to assess gene expression profiles in joints from naïve animals. Reactivation of the arthritic inflammation was induced 14 days after intra-articular injection (designated day 0) by intravenous injection of 200 μg PG-PS. This resulted in monoarticular arthritis involving the joint originally injected with PG-PS [7]. Ankle swelling at different time points was measured using a caliper. The inflammatory response is expressed as the change in ankle diameter relative to the starting diameter. Five animals injected with PG-PS or PBS were killed at different time points (4 hours after intra-articular injection [day -13.8], day -13, day -10, day 0, 6 hours after intravenous challenge [day 0.4], day 1, day 3 and day 7) and ankle joints were dissected, snap frozen in liquid nitrogen and stored at -80°C for subsequent analysis.
Total RNA isolation from rat joints
Frozen ankle joints were pulverized in liquid nitrogen using a mortar and pestle, and total RNA was isolated from individual homogenized joints (four or five animals/group) using RNeasy® Mini-kits (Qiagen Ltd, Crawley, UK), following the manufacturer's instructions. In our experimental design, a nonpooling strategy for total RNA samples was used (a total of 75 samples from different animals were analyzed). In order to ensure that no contamination with genomic DNA occurred, samples were treated for 15 min with 10 units of RNase-free DNase (Qiagen Ltd) at room temperature. RiboGreen® RNA Quantitation Kit (Molecular Probes Inc.) with optical densities at 260 nm and 280 nm was used to determine the total RNA concentration of the samples. The quality of the RNA was assessed based on demonstration of distinct intact 28S and 18S ribosomal RNA bands using RNA 6000 Nano LabChip Kit® (Agilent 2100 Bioanalyser; Agilent Technologies UK Ltd, Stockport, UK). Five of the 75 total RNA samples exhibited evidence of RNA degradation and were excluded from subsequent analyses.
Oligonucleotide microarray analysis
The rat RAE230A GeneChip® oligonucleotide microarray (Affymetrix Inc.), containing about 16,000 probe sets, representing 4699 well annotated full-length genes, 10,467 expressed sequence tags (ESTs) and 700 non-ESTs (excluding full lengths), was used to analyze gene expression profiles in joints from SCW-injected or PBS-injected animals during the course of disease. Isolated total RNA (10 μg/chip) was used to generate biotin-labelled cRNA. Aliquots of each sample (n = 70) were then hybridized to RAE230A Affymetrix GeneChip® arrays at 45°C for 16 hours, followed by washing and staining, in accordance with the standard protocol described in the Affymetrix GeneChip® Expression Analysis Technical Manual [12]. The GeneChip®s were scanned using the Affymetrix 3000 Scanner™ and the expression levels were calculated for all 16,000 probe sets (about 12,000 genes) with Affymetrix® MicroArraySuite software (MAS 5.0).
Statistical analysis of microarray data
The Affymetrix GeneChip® data were processed, normalized and statistically analyzed (analysis of variance [ANOVA]) using Rosetta Resolver® v3.2 software (Rosetta Biosoftware, Kirkland, WA, USA). Genes with P < 0.01 (ANOVA) were considered to be differentially expressed. Fold changes in gene expression were calculated by dividing the mean intensity signal from all the individual SCW-injected rats included in each group by the mean intensity signal from the corresponding PBS control group. The level of statistical significance was determined by ANOVA. Subsequent data analysis involved two-dimensional data visualization, principal component analysis (PCA) using SIMCA-P v10.2 Statistical Analysis Software (Umetrics, Windsor, UK) [13] and agglomerative hierarchical clustering analysis [14]. For identification of different temporal patterns and levels of gene expression, Spotfire® profile search analysis and K-means clustering analysis [15] were performed using the Spotfire® DecisionSite for Functional Genomics programme. In this analysis the mean signal intensity of gene expression in each group included in the study (four to five samples/group) was used. Identification of the ontology, accession number and chromosomal location of the genes of interest was performed combining information from GlaxoSmithKline Bioinformatics Databases and other existing public databases . The mapping of the differentially expressed genes to QTLs for arthritis was investigated using Rat and Human Genome browsers from Ensembl , Rat Genome Database and the ARB Rat Genetic Database .
Quantitative real-time PCR (TaqMan®)
Expression levels of selected genes found to be upregulated by gene array analysis were validated by real-time RT-PCR TaqMan® analysis using the ABI Prism 7900 Sequence Detector System® (PE Applied Biosystems, Foster City, CA, USA), as previously described [16]. For cDNA synthesis 600 ng total RNA (from the same samples analysed by RAE230A GeneChip® microarray) were reverse transcribed using TaqMan® RT reagents (PE Applied Biosystems) in a MJ Research PTC-200 PCR Peltier Thermal Cycler (MJ Research, Rayne Brauntree, Essex, UK).
TaqMan® probes and primers for the genes of interest were designed using primer design software Primer Express™ (PE Applied Biosystems) and optimized for use. The forward primers, reverse primers and probes used are summarized in Table 1. The final optimized concentrations of forward primer, reverse primer and probe for all of the target genes were 900 nmol/l, 900 nmol/l and 100 nmol/l, respectively, except for CD14, for which the concentrations were 300 nmol/l, 300 nmol/l and 100 nmol/l. Standard curves for each individual target amplicon were constructed using sheared rat genomic DNA (BD Biosciences, Cowley, Oxford, UK). All PCR assays were performed in duplicate, and results are represented by the mean values of copy no./50 ng cDNA. Ubiquitin [17] was used as a housekeeping gene against which all samples were normalized.
Data presentation
The data included in Table 2 show the mean fold change (Delta) increase or decrease in gene expression in joints from SWC-injected rats compared with the expression in the corresponding PBS control group, along with the P value. As selection criteria to present the most relevant genes, a cutoff of 1.8-fold increased/decreased expression and P < 0.01 were arbitrarily chosen. Gene expression profile plots (Fig. 6) represent data from Affymetrix Rat Genome RAE230A GeneChip® and real-time RT-PCR TaqMan® as the mean of signal intensity or the mean of normalized copy no./50 ng cDNA for all the samples from the same group (four to five), respectively.
Results
Time course of inflammation in the SCW-induced arthritis model
Intra-articular injection of SCW resulted in increased ankle swelling that peaked 24 hours after injection (day -13), followed by a gradual reduction by day 0 (Fig. 1). At this time point intravenous challenge with SCW led to reactivation of the inflammatory response, which peaked 72 hours thereafter (day 3). Animals injected intra-articularly with PBS (vehicle in which the SCW was suspended) were used as control groups at each specific time point. Another group of naïve animals (noninjected rats) was used to assess a possible inflammatory response due to the intra-articular injection alone.
Gene expression profiling in SCW-induced arthritis
Analysis of RAE230A GeneChip® microarray data identified about 9000 probes (5479 upregulated and 3898 downregulated) that were differentially expressed to a highly significant degree (P < 0.01) in arthritic rat joints from the time course study. After applying selection criteria (Delta > 1.8 and P < 0.01), 631 of the dysregulated probes had well characterized full-length sequences in databases (441 upregulated and 190 downregulated) and 697 were unknown (ESTs; 444 upregulated and 253 downregulated). These genes are too numerous to describe in detail, and therefore we present a selected list of upregulated genes in Table 2 and Fig. 2, and a selection of downregulated genes based on the ontologies that reflect the major changes occurring in arthritic animals (Fig. 3). ESTs were excluded from Table 2 and from subsequent clustering analysis. See Additional file 1, which contains all genes that were upregulated and downregulated.
Principal component analysis and hierarchical clustering
An overview of the experimental RAE230A GeneChip® data was obtained using PCA (graphs not shown) [13] and agglomerative hierarchical clustering [14]. Both two-dimensional analyses identified day -13.8 (4 hours after intra-articular injection of SCW), day -13 and day 3 as the time points at which the greatest changes in gene expression in arthritic joints occurred in comparison with corresponding PBS control groups. The results from the hierarchical clustering are shown for visual inspection as a coloured heat map in Fig. 4. As shown on the x-axis (panel at the top of Fig. 4), the majority of the PBS samples clustered together, except the PBS samples from day -13.8, which clustered close to the SCW-injected animals from day 3. This observation indicated the presence of a mild inflammatory response in joints from rats killed 4 hours after the initial intra-articular injection of PBS, when compared with expression levels in joints from naïve animals or the PBS samples from later time points.
PCA and hierarchical clustering analysis allowed us to identify two outliers corresponding to arthritic animals from day 3, which did not show any sign of measurable inflammation after intravenous challenge. Both samples were excluded from subsequent mean or Delta calculations.
Identification of different patterns of gene expression
The selected 631 dysregulated genes (P < 0.01 and Delta > 1.8) were analyzed using Spotfire® profile search analysis and K-means clustering [15], allowing the identification of different patterns and levels of gene expression throughout the time course of disease. As shown in Fig. 5, the upregulated genes were grouped into seven clusters (C-1 to C-7) according to their kinetics of expression. Thus, all genes exhibiting similar patterns of expression at the analyzed time points were grouped into the same cluster (e.g. C-1 for those genes whose expression reached a peak on day -13.8). These genes were also sorted into three K-means clusters according to their level of expression (low, medium and high). The cluster number to which each gene belongs is summarized in Table2.
Interestingly, the expressions of different markers for cell types associated with the pathogenesis of RA were found to be upregulated throughout the time course of SCW-induced arthritis. These markers were grouped into different clusters as follows: C-2 = CD44 (leucocytes, erythrocytes); C-3 = CD2 (T cell, natural killer [NK] cells), E-selectin (SELE; activated endothelial cells); C-4 = L-selectin (SELL; lymphocytes, monocytes and NK cells); C-5 = CD14 (monocytes), ICAM1 (endothelial cells), α M integrin (ITGAM or CD11b; granulocytes, monocytes, NK cells), P-selectin (SELP; endothelial cells, activated platelets), lipocalin 2 (LCN2; neutrophils); C-6 = CD74 (B cells, monocytes), CD38 (activated T cells, plasma cells), CD8a (cytotoxic/suppressor T cells, NK cells); and C-7 = CD3d (T cells), CD4 (helper–inducer T cells). The different temporal expression of these markers highlights that expression levels for CD3d and CD4 were significantly upregulated only at day 3 after challenge, in contrast to CD2 and E-selectin, whose expression was found to be upregulated only at day -13. The rest of the markers exhibited significant fold changes in gene expression at both phases of disease (4 hours after intra-articular injection of SCW, day -13 and day 3 after challenge), except CD8a, CD74 and CD38, which were found to be upregulated at a later time point in the pre-reactivation phase (day -13). Only CD44 was not found to be upregulated on day 3 after challenge. Lipocalin 2, αM integrin and CD8a exhibited the greatest fold changes in gene expression.
Functional grouping of dysregulated genes
In order to establish functional annotations for the selected dysregulated genes, the biological processes and molecular functions of the genes were investigated using different databases. This search identified 19 ontologies for the upregulated genes, allowing us to organize them according to their major functions (Table 2 and Fig. 2). Because of space limitations in the manuscript, we could not include all of the upregulated genes in Table 2 and Fig. 2. The genes not included were involved in blood coagulation, catabolism, defence response, G-protein-coupled receptor protein signalling pathways, metabolism and protein modification, or were genes with unknown functions (for more information, please see Additional file 1). A hallmark of RA is infiltration of leucocytes into synovial tissue mediated by a complex network of cytokines, adhesion molecules and chemoattractants [18].
Interestingly, most of the genes exhibiting the greatest fold increase in gene expression (Delta > 5) on days -13.8, -13 or 3 were involved in chemotaxis. These included several CC chemokine ligands (CCLs; CCL20, CCL2 [also called SCYA2 or MCP-1]), CXC chemokine ligands (CXCLs; CXCL2, CXCL6 and GRO1), CC chemokine receptors (CCRs; CCR1, CCR2, CCR5), CXC chemokine receptors (CXCRs; CXCR2) and a recently characterized cytokine called chemokine-like factor 1 [19].
Our results also showed marked upregulation (Delta > 5) for numerous genes that are involved in the immune and/or inflammatory response, such as IL-1β, IL-6, TNF-α, TNFRSF1b, IL-1Rn, NOS2, CD8a, VAV1, LST1 (leukocyte specific transcript 1), LCP2 (lymphocyte cytosolic protein 2), FCGR2 (Fc receptor, IgG, low affinity Iib), PTGES (microsomal prostaglandin E synthase-1) and the major histocompatibility complex (MHC) class Ib gene (RTAW2). Other components of the MHC such as MHC class II (HLA-DMA and HLA-DMB) and MHC class Ib RT1.S3 genes were also found to be upregulated in this model. Genes participating in cell adhesion such as TNFIP6, FCNB (ficolin B), CSPG2 (versican), ICAM1 and αM integrin (ITGAM) also exhibited a significant fold increase in gene expression (Delta > 5). Among other genes, some mediators controlling extracellular matrix (ECM) turnover and breakdown under normal and disease conditions, including five matrix metalloproteinases (MMPs; MMP-3, -12, -13, -14 and -23a), the aggrecanase ADAMTS-1, tissue inhibitor of metalloproteinases (TIMP)1, and the secretory leucocyte protease inhibitor (SLPI) were also found to be significantly upregulated in arthritic joints. The majority of the downregulated genes were associated with regulation of metabolism, myogenesis, or regulation of muscle development and transport (Fig. 3).
Differentially expressed genes: QTL association
From the 441 selected genes that were upregulated during SCW-induced arthritis, we selected a list of 20 genes that exhibited a greater than fivefold change in gene expression and that had not previously been linked to autoimmune arthritis. To further investigate the possibility that these genes play a role in arthritis pathogenesis, their rat chromosomal locations and the locations of their human orthologues were identified and compared with those of rat and human QTLs for autoimmune diseases. Interestingly, 10 of these genes were found to be located in chromosomal regions that mapped to rat and/or human QTLs previously reported to be associated with inflammation, arthritis, or autoimmune diseases, such as systemic lupus erythematosus, multiple sclerosis, allergic rhinitis and asthma (Table 3).
Analysis of expression profiles of specific transcripts
In order to validate microarray data, mRNA expression levels for a subset of genes were quantified by real-time RT-PCR TaqMan® analysis. As shown in Fig. 6, there was a significant correlation (Pearson product moment correlation coefficient r > 0.9 and P < 0.01) between the gene expression profiles for the proinflammatory cytokines IL-1β, TNF-α and IL-6, the chemokine GRO1 and the cell markers CD14 and CD3, when microarray data were compared with RT-PCR TaqMan® data. Although the fold changes in gene expression calculated using data from both methods were not exactly the same (probably due to differences in the sensitivities of the assays), the quantitative real-time RT-PCR TaqMan® method verified the results of the gene array analysis.
Discussion
The temporal expression of multiple disease-associated genes with potential pathophysiological roles in the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat has not previously been fully addressed. The present study analyzed gene expression profiles in rat joints with SCW-induced arthritis using RAE230A GeneChip® oligonucleotide microarray (Affymetrix Inc.). We chose to profile gene expression in whole ankle joint tissues, which comprises heterogeneous cell types, with the aim of gaining a global insight into the molecular changes associated with arthritis pathology in this model. Analysis of the time course data generated by microarray identified 631 genes (441 upregulated and 190 downregulated) with full-length sequences in databases that were significantly differentially expressed (Delta > 1.8 and P < 0.01). Our experimental design (time course study) and use of K-means cluster analysis allowed us to identify specific patterns of gene expression for the different dysregulated genes, highlighting the importance of performing kinetic studies to identify the time point at which a particular gene is maximally expressed. Thus, these gene expression data indicate optimal times for measuring potential disease biomarkers in rat joints, and our approach offers a useful tool with which to investigate the clinical efficacy and mechanism of action of novel therapeutic agents in rat SCW-induced arthritis.
Changes in gene expression may reflect regulation at the mRNA level or changes in the number of cells (proliferation or infiltration) that synthesize these mRNAs. Thus, optimally, microarray analysis should be conducted in isolated populations of cells so that differential gene expression may be directly correlated with transcription of the genes. However, complex diseases such as RA involve extensive tissue injury, and not all of the cell types that contribute to RA pathogenesis have been identified. Hence, analysis of the damaged tissue, rather than analysis of an isolated cell type, increases the probability that differential gene expression will be examined in those cells that are important in RA pathogenesis. In the present study we conducted a global analysis of coordinated gene expression in injured tissue. Further bioinformatic analysis of the data to examine cell markers, and genes whose expression may correlate with them, in combination with analysis of the cell populations present in the arthritic joint using immunohistochemistry or fluorescence activated cell sorting techniques, would be required to corroborate the differential gene expression of a particular gene of interest. Previous studies have already shown that cell-specific gene expression patterns can indicate the presence of immune cells [20]. RAE230A GeneChip® oligonucleotide microarray analysis identified the expression of different markers for cell types associated with the pathogenesis of RA. Based on the level of gene expression and Delta values detected for the different markers, our results suggest that the main cell types present in arthritic joints in this model are T cells, neutrophils, monocytes/macrophages and B cells, confirming previous descriptions of the joint cell composition in this model [6,21].
Gene expression profiling of arthritic rat joints revealed a spectrum of genes exhibiting extensive inflammatory activity, infiltration of activated cells, angiogenesis, regulation of apoptosis and ECM remodelling activities. Most of the genes found to be upregulated in SCW-induced arthritic joints have also been reported to be highly expressed in human RA synovial tissue [22,23] or in joints from other rodent experimental arthritis models [10,11,24,25]. The upregulated expression of TNF-α, IL-1α, IL-1β, IL-4R, P-selectin, MIP-1α (CCL3), MCP-1 (CCL2), NOS2 and NOS3 [6-8] demonstrated in the present study is in agreement with previous observations of the dependency of the rat SCW-induced arthritis model on these mediators. The SLPI has previously been reported to be upregulated in arthritic joints and to mediate tissue destruction and inflammation in a rat model of arthritis induced by intraperitoneal injection of SCW [26]. Similar results were found in our study, because significant upregulation of SLPI gene expression was observed during both phases of the disease. Additionally, previous studies have shown that nuclear factor-κB (NF-κB) is activated in the synovium of rats with SCW-induced arthritis and that inhibition of the activity of this transcription factor enhances synovial apoptosis, which is consistent with the potential involvement of NF-κB in synovial hyperplasia [27]. In accord with these observations, the microarray data showed early upregulation of genes involved in the NF-κB signalling pathway, such as NF-κB1 (p50 or p105), NFKBIA (IκBα), TNF-α, TNFRSF1a and TNFRSF1b, suggesting a possible regulatory role of NF-κB in the transcription of genes that mediate disease progression in SCW-induced arthritis.
Histopathological studies in arthritic rat joints from the reactivation SCW-induced arthritis model have shown that only moderate histological changes in articular cartilage, with few erosive effects on bone, occur at early stages in the flare reaction (day 3), whereas evident cartilage degradation is observed at later time points (20 days after intravenous challenge with SCW) [4]. The microarray data suggest that tissue remodelling is an active process in this model because abundant expression of collagen-related genes (Col5A2, Col5A3, Col12A1 and Col18A1), enzymes that degrade matrix molecules such as MMPs and the aggrecanase ADAMTS-1 (a disintegrin-like and metalloproteinase with thrombospondin type 1 motif, which is capable of cleaving versican), together with other genes that control ECM turnover and breakdown (TIMP1, PLAU [plasminogen activator, urokinase], PLAU receptor [PLAUR]), were found to be upregulated in arthritic joints. MMP-3 (stromelysin) appears to be pivotal in the activation of collagenases, whereas MMP-13 is crucial in collagen breakdown [28]. The PLAU/PLAUR system plays a critical role in cartilage degradation during osteoarthris by regulating pericellular proteolysis mediated by serine proteases [22,29]. The complement system has also been reported to participate in tissue injury during inflammatory and autoimmune diseases [30], and ficolins can initiate the lectin pathway of complement activation through attached serine proteases (Mannan-binding lectin serine proteases [MASPs]) [31]. Interestingly, the microarray data revealed significant upregulation of the first complement component C1, which exerts collagenolytic activity in addition to the role it plays in the classic cascade [29]. In addition, upregulation of the expression of C2, C3, ficolin B (FCNB) and MASP1 was also noted, supporting the concept that activation of the complement system, together with the imbalance between MMPs, TIMPs and other related molecules, could mediate cartilage destruction in this experimental model of RA.
In our analysis we also identified 10 genes that are differentially expressed in arthritic joints and that that map to genomic regions previously reported to be QTLs for autoimmune diseases. Although it is premature to suggest that the 10 genes are candidates for these QTLs, our observations suggest that expression of these genes may influence the onset, severity and/or susceptibility to arthritis in this animal model. Of particular interest is KDAP (napsin) because of the high fold increase in gene expression observed in arthritic joints from SCW-injected animals (D = 48.2 on day 3). This aspartic protease was shown to be expressed in kidney, lung and lymphoid organs of mice [32], and it has been suggested that it functions as a lysosomal protease involved in protein catabolism in renal proximal tubules [33]. However, little is known about the role of KDAP in other organs and tissues. Interestingly, human KDAP resides on chromosome 19q13.3–19q13.4, a region previously identified to be involved in susceptibility to autoimmune diseases, including systemic lupus erythematosus, multiple sclerosis and insulin-dependent diabetes mellitus [34,35]. Our results show, for the first time, that KDAP gene expression is upregulated in experimental arthritis tissue, and suggest that further characterization is required to unravel the biological/pathological activities of this gene in RA.
The microarray data also revealed high upregulation in runt-related transcription factor 1 (RUNX1) and a group of transporter genes (SLC11A1, SLC13A3, SLC1A3, SLC21A2 [MATR1], SLC28A2, SLC29A3, SLC5A2 and SLC7A7), from which the prostaglandin transporter gene MATR1 exhibited the greatest upregulation on day 3 after intravenous challenge with SCW. The rat MATR1 gene maps to the type II collagen induced arthritis severity QTL6 (Cia6) [36], and its human orthologue is located within autoimmune disease QTLs for asthma, psoriasis and atopic dermatitis [37-39]. Several authors reported linkage of SLC11A1 (also named NRAMP1) to human RA [40-42]. The Z-DNA forming polymorphic repeat in the RUNX1-containing promoter region of human SLC11A1 may contribute to the differing allelic associations observed with infectious versus autoimmune disease susceptibility [43]. Recent studies reported that regulation of expression of organic cation transporter gene SLC22A4 by RUNX1 is associated with susceptibility to RA [44]. Other transporter genes (SLC12A8 and SLC9A3R1) have also been linked to susceptibility to other autoimmune diseases such as psoriasis [45]. These observations together suggest that RUNX1 and the transporter genes found to be differentially expressed in arthritic joints may contribute to arthritis susceptibility and to the inflammatory processes that mediate the pathology of this model.
Conclusion
The present study identified the temporal gene expression profiles of hundreds of genes, including cytokines, chemokines, adhesion molecules, transcription factors, apoptotic and angiogenesis mediators, whose expression is associated with onset and progression of arthritis pathology in rat joints from the reactivation model of SCW-induced arthritis in Lewis (LEW/N) rat. This transcript profiling offers not only the optimal kinetics of expression for different potential disease biomarkers, but it also improves our understanding of the molecular events that underlie the pathology in this animal model of RA. In addition, although the majority of genes found to be differentially expressed in this model were previously associated with human RA, further genes not previously linked to autoimmune diseases were identified, providing a resource for future research and for the development of new therapeutic targets.
Abbreviations
ANOVA = analysis of variance; CCL = CC chemokine ligand; CCR = CC chemokine receptor; CXCL = CXC chemokine ligand; CXCR = CXC chemokine receptor; ECM = extracellular matrix; EST = expressed sequence tag; IL = interleukin; MCP = monocyte chemoattractant protein; MHC = major histocompatibility complex; MIP = macrophage inflammatory protein; MMP = matrix metalloproteinase; NF-κB = nuclear factor-κB; NK = natural killer; NOS = nitric oxide synthase; PBS = phosphate-buffered saline; PCA = principal component analysis; PCR = polymerase chain reaction; PG-PS = peptidoglycan–polysaccharide; QTL = quantitative trait locus; RA = rheumatoid arthritis; RT = reverse transcription; SCW = streptococcal cell wall; SLPI = secretory leucocyte protease inhibitor; TIMP = tissue inhibitor of matrix metalloproteinase; TNF = tumour necrosis factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RI carried out the study design, in vivo experiments, total RNA extractions, RT-PCR analysis of data and manuscript preparation. CC and SG performed the microarray experiments and statistical analysis of the array data. MD and PL carried out the study design and collaborated in the preparation of the manuscript.
Supplementary Material
Additional File 1
Excel spreadsheets summarizing all of the genes upregulated (Delta > 1.8 and P < 0.01) and downregulated (Delta < 1.8 and P < 0.01) in ankle joints from SCW-induced arthritis in Lewis (LEW/N) rats on days -13.8 (4 hours after intra-articular injection of SCW), -13 and 3. Data are expressed as the mean fold increase in gene expression (D = Delta) in SCW-injected animals compared with the expression in the corresponding PBS control group, along with P values.
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Acknowledgements
The authors wish to acknowledge Jacqueline Buckton for sharing her expertise on the animal model experiments, and Alan Lewis and Ramu Elango for bioinformatics support. Dr Inmaculada Rioja is supported by an EU Postdoctoral Marie Curie Fellowship HPMI-CT-1999-00025.
Figures and Tables
Figure 1 Schematic representation of the experimental design for the time course study in the reactivation model of streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rats. The inflammatory response is represented as the change in ankle diameter (mm) relative to the starting diameter. Data are expressed as means ± standard error (four to five animals/group). Intra-articular (i.a.) injection of SCW resulted in increased ankle swelling that peaked 24 hours after injection (day -13) followed by a gradual reduction by day 0. At this time point, intravenous (i.v.) challenge with SCW led to reactivation of the inflammatory response, which peaked 72 hours thereafter (day 3). Animals injected with a suspension of SCW (continuous line) in PBS or with PBS alone (dashed line; five animals/group) were killed on the days indicated, and joints taken and processed for gene expression profiling analysis and mRNA quantification by GeneChip® microarray and real-time RT-PCR TaqMan®, respectively. A group of naïve noninjected animals (n = 4) was also included in the study to assess basal expression levels of the analyzed genes.
Figure 2 Representative graph of genes that were upregulated (Delta > 1.8 and P < 0.01) in arthritic joints from streptococcal cell wall (SCW)-induced arthritis model on day -13.8 (4 hours after systemic challenge), day -13 and day 3. The graphs represent the fold increase in gene expression (Delta) and the name of the genes associated with the following ontologies: apoptosis (A; red bars), regulation of cell cycle and cell proliferation (B; blue bars), transport (C; green bars) and regulation of transcription, DNA-dependent (D; yellow bars).
Figure 3 Downregulated genes (Delta < -1.8 and P < 0.01) in arthritic joints from streptococcal cell wall (SCW)-induced arthritis model on day 3 after systemic challenge. This graph shows the fold decrease in gene expression (Delta) on day 3 and the name of the downregulated genes associated with the following ontologies: metabolism (E; red bars), regulation of muscle development (F; blue bars) and transport (G; green bars).
Figure 4 Heat map diagram of differential gene expression in joints from the time course study in the streptococcal cell wall (SCW)-induced arthritis in Lewis (LEW/N) rat. Gene expression data were obtained using Affymetrix Rat Genome RAE230A GeneChip®. The cluster diagram represents 631 differentially expressed probes with P < 0.01 and Delta > 1.8. Each column represents a single joint tissue and each row represents a single gene. Expression levels are coloured green for low intensities and red for high intensities (see scale at the top left corner). At the top of the cluster diagram is an enlarged panel including the names and hierarchical clustering order of the individual samples analyzed. Red names are joint tissues from SCW-injected animals, indicating the corresponding time point of sample collection, and blue names are the samples from the phosphate-buffered saline (PBS) control groups. As shown, the major changes in gene expression occurred in samples corresponding to arthritic animals from days -13.8 (4 hours after intra-articular injection of SCW), -13 and 3. N, naïve animals.
Figure 5 Temporal gene expression profiles in the reactivation model of streptococcal cell wall (SCW)-induced arthritis in rat identified using Spotfire® profile search analysis. The seven different clusters identified are termed C-1 to C-7. Each graph shows the characteristic pattern of expression throughout the time course of disease for a representative gene from the defined cluster. Results are expressed as the mean of the signal intensity of gene expression for each group (four to five samples/group). The number of the cluster to which each gene belongs is included in Table 2. The time course of inflammation, expressed as change in ankle diameter (mm) relative to the starting diameter, is shown in the upper left panel. N, naïve; PBS, phosphate-buffered saline.
Figure 6 Confirmation of the expression levels of six of the highly differentially expressed genes highlighted in Table 2 by real-time RT-PCR TaqMan® analysis. The graphs compare the gene expression profiles for IL-1β, tumour necrosis factor (TNF)-α, IL-6, GRO1, CD14 and CD3 obtained using two different methods: Affymetrix Rat Genome RAE230A GeneChip® (filled squares) and real-time RT-PCR TaqMan® analysis (open squares). Data are expressed as the mean of signal intensity or the mean of copy no./50 ng cDNA normalized against the housekeeping gene ubiquitin, for all of the samples from the same group (four to five). The Pearson product moment correlation coefficient (r) for each comparison is given. PBS, phosphate-buffered saline; SCW, streptococcal cell wall.
Table 1 TaqMan® probes and primers for the genes of interest
Gene of interest Forward primer Reverse primer Probe
IL-1β 5'-CACCTCTCAAGCAGAGCACAG 5'-GGGTTCCATGGTGAAGTCAAC 5'-6-FAM-TGTCCCGACCATTGCTGTTTCCTAGG-TAMRA
IL-6 5'-CAAGACCATCCAACTCATCTTG 5'-CACAGTGAGGAATGTCCACAAAC 5'-6-FAM-TCGGCAAACCTAGTGTGCTATGCCTAAGCA-TAMRA
TNF-α 5'-CCAGGTTCTCTTCAAGGGACAA 5'-CTCCTGGTATGAAATGGCAAATC 5'-6-FAM-CCCGACTATGTGCTCCTCACCCACA-TAMRA
GRO1 5'-TGTGTTGAAGCTTCCCTTGGA 5'-TGAGACGAGAAGGAGCATTGGT 5'-6-FAM-TGTCTAGTTTGTAGGGCACAATGCCCT-TAMRA
CD14 5'-GGACGAGGAAAGTGTCCGCT 5'-AGGTACTCCAGGCTGCGACC 5'-6-FAM-TTCTATGCGCGGGGGCGGAA-TAMRA
CD3 5'-GGATGGAGTTCGCCAGTCAA 5'-GGTTTCCTTGGAGACGGCTG 5'-6-FAM-ACAGGTCTACCAGCCCCTCAAGGACCG-TAMRA
Ubiquitin 5'-CGAGAACGTGAAGGCCAAGA 5'-GGAGGACAAGGTGCAGGGTT 5'-6-FAM-CCCCTGACCAGCAGAGGCTCATCTTTG-TAMRA
IL, interleukin; TNF, tumour necrosis factor.
Table 2 Genes upregulated in ankle joints from SCW-induced arthritis in Lewis (LEW/N) rats
Accession no. Gene name Day -13.8 Day -13 Day 3 C I
Delta P Delta P Delta P
Angiogenesis
NM_030985 AGTR1 _ _ _ _ 4.0 2.3E-05 7 L
AI639162 ANGPT1 _ _ _ _ 1.8 8.5E-08 7 L
NM_031012 ANPEP _ _ _ _ 2.2 2.9E-18 7 M
AI101782 COL18A1 _ _ _ _ 2.9 6.3E-26 7 L
AI170324 FIGF _ _ 1.6 9.4E-04 2.4 4.6E-11 6 L
NM_012620 SERPINE1 6.6 2.3E-06 _ _ 27.6 0.0E+00 4 L
Cell adhesion
NM_012830 CD2 _ _ 2.1 2.9E-04 _ _ 3 L
NM_054001 CD36L2 _ _ _ _ 2.6 5.4E-05 7 L
AF065147 CD44 2.1 2.1E-08 1.6 1.1E-03 _ _ 2 M
BE108345 COL12A1 _ _ _ _ 2.5 4.6E-43 7 M
AI172281 COL5A2 _ _ _ _ 2.0 9.3E-06 7 H
NM_021760 COL5A3 _ _ _ _ 3.1 6.9E-27 7 M
AF084544 CSPG2 2.3 2.4E-03 2.1 5.8E-04 8.9 7.2E-37 5 L
NM_053719 EMB _ _ _ _ 3.5 1.7E-19 7 L
NM_053634 FCNB 8.7 3.0E-28 8.0 1.1E-17 29.0 0.0E+00 5 M
AI236745 GALNT1 _ _ _ _ 2.9 0.0E+00 7 L
NM_133298 GPNMB _ _ 2.2 3.6E-12 2.5 0.0E+00 6 H
NM_012967 ICAM1 8.9 0.0E+00 4.4 7.2E-09 4.0 2.2E-11 5 L
AF268593 ITGAM 2.0 2.6E-03 3.8 1.7E-15 6.4 3.3E-18 5 L
BI296880 ITGB3 1.5 7.0E-03 _ _ 2.1 2.3E-03 4 L
AF003598 ITGB7 2.0 8.9E-14 1.6 1.2E-03 1.9 1.1E-06 5 L
U56936 KLRB1B _ _ _ _ 3.0 6.4E-03 7 L
NM_022393 MGL _ _ 2.1 7.3E-08 2.1 2.3E-11 6 L
U72660 NINJ1 _ _ _ _ 1.8 6.6E-28 7 M
BE097805 PCDHGC3 _ _ 1.8 3.0E-03 _ _ 3 L
AJ299017 RET _ _ _ _ 2.8 1.6E-08 7 L
AF071495 SCARB1 _ _ _ _ 1.8 6.9E-03 7 L
L25527 SELE _ _ 3.1 3.6E-04 _ _ 3 L
D10831 SELL 1.6 9.3E-05 _ _ 1.8 6.9E-06 4 L
BI296054 SELP 1.8 7.1E-08 1.9 2.6E-07 2.2 4.2E-13 5 L
AI176034 TNC _ _ _ _ 2.6 3.7E-26 7 M
AF159103 TNFIP6 2.2 6.2E-04 2.2 3.1E-05 5.0 1.3E-21 5 L
NM_031590 WISP2 _ _ _ _ 2.6 0.0E+00 7 M
Chemotaxis
NM_053619 C5AR1 1.6 5.0E-06 2.4 9.6E-21 2.7 2.8E-32 5 M
NM_019205 CCL11 3.9 2.3E-03 3.7 2.3E-07 _ _ 2 L
NM_057151 CCL17 2.2 1.1E-05 _ _ _ _ 1 L
AF053312 CCL20 8.3 7.3E-19 10.2 2.8E-10 15.5 3.5E-32 5 L
U22414 CCL3 15.3 1.7E-19 3.2 3.8E-05 2.1 1.1E-08 5 L
U06434 CCL4 6.0 1.5E-17 _ _ _ _ 1 L
NM_031116 CCL5 _ _ 2.6 5.9E-11 2.0 1.1E-03 6 L
NM_020542 CCR1 5.2 1.4E-15 2.1 3.0E-05 2.1 3.2E-03 5 L
NM_021866 CCR2 5.1 2.7E-07 3.3 2.9E-05 6.9 2.7E-13 5 L
NM_053960 CCR5 6.2 4.3E-19 6.0 1.8E-09 6.0 1.4E-10 5 L
D87927 CINC2 3.5 7.0E-03 _ _ _ _ 1 L
AF253065 CKLF1 3.3 6.3E-09 3.0 2.7E-07 8.2 8.6E-08 5 L
NM_022218 CMKLR1 _ _ 2.5 3.4E-03 _ _ 3 L
U22520 CXCL10 3.2 4.4E-09 2.5 9.0E-03 1.4 1.3E-03 5 L
NM_053647 CXCL2 38.7 1.6E-07 2.3 9.1E-03 2.6 1.0E-03 5 L
NM_022214 CXCL6 2.2 2.3E-04 _ _ 7.5 3.2E-06 4 L
NM_017183 CXCR2 10.6 1.5E-07 3.6 1.3E-03 _ _ 2 L
NM_053415 CXCR3_V1 _ _ _ _ 1.9 9.5E-04 7 L
AA945737 CXCR4 1.6 1.7E-03 1.7 3.9E-04 3.4 2.7E-15 5 L
NM_030845.1 GRO 17.1 0.0E+00 23.0 2.4E-04 19.8 1.8E-12 5 L
NM_053321 PTAFR _ _ 2.5 2.0E-03 _ _ 3 L
NM_031530 SCYA2 3.4 6.0E-26 3.2 1.8E-16 6.0 0.0E+00 5 M
Complement activation
D88250 C1S _ _ 1.6 4.4E-03 1.8 7.5E-22 6 M
_ C2 6.9 9.20E-42 3.5 1.28E-11 16.8 0.0E+00 5 L
NM_016994.1 C3 2.7 2.0E-10 3.0 5.4E-12 10.4 0.0E+00 5 L
AI169829 MASP1 _ _ _ _ 2.4 8.5E-08 7 L
Immune response/inflammatory response
XM_215303 RT1.S3 _ _ 2.0 0.0012 1.6 1.5E-03 6 L
AF307302 BTNL2 _ _ 2.1 1.0E-15 3.2 0.0E+00 6 M
NM_021744 CD14 2.8 7.8E-18 2.0 4.4E-06 1.7 7.3E-05 5 M
NM_012705 CD4 _ _ _ _ 1.8 1.3E-07 7 L
NM_013069 CD74 _ _ 2.2 3.5E-18 2.7 1.1E-31 6 H
NM_031538 CD8a _ _ 9.5 2.7E-03 10.9 6.2E-07 6 L
BI282755 EDG3 _ _ _ _ 2.1 5.9E-03 7 L
X73371 FCGR2 3.1 1.4E-20 3.8 2.4E-08 6.5 0.0E+00 5 L
NM_053843 FCGR3 2.2 3.3E-15 2.0 2.8E-12 2.6 0.0E+00 5 M
NM_133624 GBP2 3.4 6.2E-35 _ _ 1.5 3.8E-06 4 L
AF176534 HFE _ _ _ _ 1.8 6.6E-03 7 L
XM_215347 HLA-DMA _ _ 2.0 1.22E-14 2.7 0.0E+00 6 L
_ HLA-DMB _ _ 2.0 5.42E-15 3.2 1.1E-13 6 M
NM_022605 HPSE _ _ _ _ 1.9 3.0E-09 7 L
NM_133533 IGB _ _ _ _ 2.9 8.4E-09 7 L
NM_053374 IGIFBP _ _ 2.2 7.2E-03 _ _ 3 L
AJ245643 IL1a 2.7 1.2E-03 _ _ _ _ 1 L
NM_031512 IL1b 22.0 1.1E-30 9.5 4.5E-15 5.7 1.6E-35 5 L
NM_053953 IL1R2 2.5 1.9E-15 _ _ _ _ 1 L
NM_022194 IL1RN 7.4 2.8E-03 _ _ _ _ 1 L
NM_012589 IL6 10.0 7.3E-17 20.7 7.9E-04 21.4 5.5E-17 5 L
NM_013110 IL7 _ _ _ _ 2.8 2.4E-04 7 L
NM_012591 IRF1 2.9 4.7E-13 2.6 2.8E-08 3.3 2.0E-13 5 L
NM_130741 LCN2 2.4 1.7E-09 3.4 9.8E-12 13.2 0.0E+00 5 M
BF282471 LCP2 2.6 2.2E-04 3.3 2.4E-04 6.2 4.4E-06 5 L
NM_022634 LST1 4.9 6.2E-14 6.3 6.4E-14 16.7 2.0E-36 5 L
NM_031634 MEFV 2.7 1.9E-07 _ _ 1.9 7.4E-05 4 L
X52711 MX1 _ _ 2.8 4.9E-07 1.9 2.1E-15 6 L
NM_134350 MX2 _ _ 2.9 5.9E-04 _ _ 3 L
NM_053734 NCF1 _ _ 2.0 9.4E-16 2.0 3.7E-06 6 L
AA858801 NFKB1 2.1 1.1E-12 _ _ _ _ 1 M
AW672589 NFKBIA 2.5 5.2E-36 _ _ _ _ 1 M
L12562 NOS2A 6.0 1.9E-05 _ _ _ _ 1 L
Z18877 OAS1 1.6 8.4E-06 2.4 3.8E-06 1.8 3.7E-06 5 L
NM_053288 ORM1 _ _ 2.0 7.0E-04 3.1 9.8E-19 6 L
NM_031713 PIRB 2.4 3.9E-06 2.5 2.6E-06 3.3 7.6E-10 5 L
AF349115 PPBP _ _ _ _ 3.2 8.5E-03 7 L
NM_080767 PSMB8 1.5 3.0E-03 2.3 2.0E-09 3.3 0.0E+00 5 L
AI599350 PSMB9 2.0 2.0E-07 2.1 7.4E-09 3.7 2.3E-25 5 L
AB048730 PTGES 8.2 8.1E-40 3.9 1.0E-04 2.4 6.7E-04 5 L
NM_012645 RT1Aw2 _ _ 3.3 0.000334 5.4 2.7E-10 6 L
X57523.1 TAP1 1.6 2.8E-04 1.6 9.8E-03 2.4 5.6E-07 5 L
NM_021578 TGFB1 _ _ 2.1 8.4E-06 2.6 1.7E-10 6 L
AA819227 TNF 11.1 1.3E-27 2.5 1.9E-04 _ _ 2 L
BM390522 TNFRSF1b 14.3 8.2E-19 3.7 4.2E-06 8.0 3.5E-06 5 L
NM_012759 VAV1 4.6 7.1E-05 7.6 1.2E-07 10.8 1.2E-12 5 L
Proteolysis and peptidolysis
NM_024400 ADAMTS1 3.1 9.2E-16 2.1 7.0E-04 3.5 1.3E-16 5 L
AA849399 CTSZ 1.6 6.4E-08 1.5 8.9E-12 3.4 1.6E-33 5 M
NM_012582 HP 2.1 4.8E-20 _ _ 1.7 5.5E-05 4 L
NM_031670 KDAP 18.8 8.7E-23 6.6 5.0E-07 48.2 2.3E-37 5 L
AF154349 LGMN _ _ 2.1 1.8E-06 2.8 0.0E+00 6 M
NM_053963 MMP12 _ _ 4.1 8.6E-05 7.7 8.2E-13 6 L
M60616.1 MMP13 _ _ _ _ 2.0 4.7E-08 7 M
X83537 MMP14 _ _ _ _ 1.8 2.1E-17 7 H
NM_053606 MMP23A _ _ _ _ 2.1 1.6E-11 7 L
NM_133523 MMP3 2.9 5.7E-29 2.7 1.4E-12 9.3 0.0E+00 5 H
AI102069 NSF _ _ 1.7 8.1E-03 1.8 3.9E-04 6 L
BF549923 PCSK5 _ _ 1.8 1.5E-03 3.4 7.5E-21 6 L
X63434 PLAU _ _ _ _ 1.8 2.9E-14 7 M
AF007789 PLAUR 6.2 4.6E-04 _ _ 4.9 7.2E-03 4 L
NM_053372 SLPI 2.6 8.3E-09 2.6 5.4E-22 7.0 0.0E+00 5 M
NM_053819 TIMP1 2.2 0.0E+00 1.8 5.9E-09 6.4 0.0E+00 5 H
NM_053299 UBD _ _ _ _ 4.7 9.1E-04 7 L
Signal transduction
NM_019285 ADCY4 _ _ _ _ 2.3 3.5E-05 7 L
BF285345 ARRB2 _ _ 1.8 2.5E-05 2.5 6.5E-20 6 L
NM_057196 BAIAP2 _ _ 4.4 5.5E-03 _ _ 3 L
NM_012766 CCND3 _ _ _ _ 2.1 6.0E-08 7 L
NM_013169 CD3d _ _ _ _ 3.9 3.7E-07 7 L
AF065161 CISH 2.5 1.1E-03 _ _ _ _ 1 L
NM_031352 DBNL _ _ 1.7 6.6E-11 1.8 1.2E-04 6 L
BI278868 EPIM _ _ _ _ 2.1 6.7E-03 7 L
NM_024147 EVL _ _ _ _ 3.6 8.0E-09 7 L
L02530 FZD2 _ _ _ _ 3.3 6.3E-07 7 L
NM_030829.1 GPRK5 _ _ _ _ 2.0 1.0E-04 7 L
U87863.1 HGS _ _ 1.9 3.6E-03 _ _ 3 L
AY044251 IL13RA1 _ _ _ _ 3.2 3.0E-08 7 L
AI178808 IL2RG 2.4 8.1E-14 2.5 10.0E-23 5.6 0.0E+00 5 L
NM_133380 IL4R _ _ 6.5 4.8E-05 7.5 3.4E-18 6 L
NM_017020 IL6R _ _ 1.8 2.4E-10 1.8 1.6E-12 6 L
NM_019311 INPP5D _ _ _ _ 1.9 7.9E-20 7 L
NM_012798 MAL 2.1 1.9E-04 _ _ _ _ 1 L
AW533194 MAPK10 2.4 5.7E-03 _ _ _ _ 1 L
AF411318 MT1A 2.6 2.4E-27 2.4 2.2E-04 3.4 6.0E-34 5 M
NM_012613 NPR1 _ _ _ _ 3.3 1.9E-03 7 L
U32497 P2RX4 _ _ 1.6 4.2E-15 1.9 2.5E-20 6 L
AF202733 PDE4B 2.4 1.1E-07 2.5 8.1E-04 2.3 2.5E-03 5 L
BE099769 PLAA _ _ 2.5 8.7E-03 _ _ 3 L
X04440 PRKCB1 _ _ _ _ 1.8 3.3E-08 7 L
AF254800 RAB0 _ _ _ _ 1.9 7.8E-04 7 L
NM_019250 RALGDS 1.9 8.1E-05 _ _ _ _ 1 L
NM_021661 RGS19 _ _ _ _ 1.8 1.8E-05 7 L
AF321837 RGS2 _ _ _ _ 2.3 2.4E-09 7 L
NM_053338 RRAD 7.0 5.2E-05 4.8 1.6E-05 4.0 6.5E-03 5 L
BE117558 SFRP1 _ _ _ _ 1.8 2.4E-08 7 M
BF389682 SOCS3 3.8 0.0E+00 2.0 1.2E-05 3.6 3.2E-33 5 L
NM_022230 STC2 _ _ 3.1 2.2E-03 _ _ 3 L
BG668493 STMN2 _ _ 2.3 2.6E-06 14.0 7.2E-42 6 L
U21683 SYK _ _ _ _ 1.8 2.1E-05 7 L
Genes upregulated (Delta > 1.8 and P < 0.01) on days -13.8 (4 hours after intra-articular injection of streptococcal cell wall [SCW]), -13 and 3 are grouped by their general ontology and clustered based on their similarity in terms of pattern of expression (C) and expression level (I). Data are expressed as the mean fold increase in gene expression (Delta) in SCW-injected animals as compared with expression in the corresponding phosphate-buffered saline (PBS) control group (four to five animals/group), along with the P value. C, number of clusters to which the gene corresponds (trend plots are given in Fig. 6); I, intensity of gene expression (L = low intensity [0–500], M = medium intensity [500–1500], H = high intensity [1500–4000]). A line (_) in the Delta or P cell indicates that the gene was not found to be differentially expressed at that particular time point.
Table 3 Upregulated genes (Delta > 5, P < 0.01) not previously reported to be associated with arthritis
Accession no. Gene Delta Rat CL Rat QTLs Human CL Human QTLs
NM_178144 AMIGO3 Nd/Nd/5.9 8q32 Cia6 3p21.31 Asthma
NM_130411 CORO1A 3.1/2.7/6.6 1q36 Pia11 16p12.1 Blau syndrome, asthma
NM_024381 GYK 6.7/Nd/Nd Xq22 Cia19 Xp21.3 Allergic rhinitis
NM_031670 KDAP 18.8/6.6/48.2 1q22 _ 19q13.3 Asthma, SLE, MS, SD
NM_569105 LCP2 2.6/3.3/6.2 10q12 Cia16, Pia15 5q33.1 RA, PDB, asthma, IBD, psoriasis, ATD
NM_021586 LTBP2 Nd/Nd/6.5 6q31 Pia3, Pia24 14q24 SLE, MODY3
NM_198746 Ly-49.9 Nd/2.0/5.6 4q42 Cia13, Cia24, Pia7, Pia23, Oia2, Oia7, Oia8, Ciaa4 12p13-p12 RA, allergic rhinitis, hypophosphataemic rickets
NM_022667 MATR1 1.7/1.9/5.7 8q32 Cia6 3q21 Atopic dermatitis, asthma, psoriasis
NM_133306 OLR1 8.3/2.8/3.7 4q42 Cia13, Cia24, Pia7, Pia23, Oia2, Oia7, Oia8, Ciaa4 12p13.2-p12.3 RA, hypophosphataemic rickets, allergic rhinitis
NM_053687 SLFN4 5.8/4.6/4.8 10q26 Cia16, Cia21, Cia22, Cia23, Oia4, Ciaa2 17q11.2-q21.1 SLE, MS
The rat chromosomal location and the chromosomal locations of the corresponding human orthologue were identified and mapped to quantitative trait loci (QTLs) previously associated with inflammation, arthritis and/or other autoimmune diseases. Delta values are given for the following time points: day -13.8/day -13/day 3. ATD, autoimmune thyroid disease; CIA, type II collagen-induced arthritis; Ciaa, CIA autoantibody; CL, chromosome location; IBD, inflammatory bowel disease; MOYD 3, maturity-onset diabetes of the young 3; MS, multiple sclerosis; Nd, not differentially expressed; Oia, oil-induced arthritis; PDB, Paget's disease of bone; PIA, pristane-induced arthritis; RA, rheumatoid arthritis; SD, spondylocostal dysostosis; SLE, systemic lupus erythematosus.
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| 15642130 | PMC1064886 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 19; 7(1):R101-R117 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1458 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14601564214310.1186/ar1460Research ArticleA model of inflammatory arthritis highlights a role for oncostatin M in pro-inflammatory cytokine-induced bone destruction via RANK/RANKL Hui Wang [email protected] Tim E [email protected] Carl D [email protected] Andrew D [email protected] Musculoskeletal Research Group, The Medical School, University of Newcastle, Newcastle upon Tyne, UK2 Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada2005 10 11 2004 7 1 R57 R64 21 7 2004 20 9 2004 5 10 2004 11 10 2004 Copyright © 2004 Hui et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is cited.
Oncostatin M is a pro-inflammatory cytokine previously shown to promote marked cartilage destruction both in vitro and in vivo when in combination with IL-1 or tumour necrosis factor alpha. However, the in vivo effects of these potent cytokine combinations on bone catabolism are unknown. Using adenoviral gene transfer, we have overexpressed oncostatin M in combination with either IL-1 or tumour necrosis factor alpha intra-articularly in the knees of C57BL/6 mice. Both of these combinations induced marked bone damage and markedly increased tartrate-resistant acid phosphatase-positive multinucleate cell staining in the synovium and at the front of bone erosions. Furthermore, there was increased expression of RANK and its ligand RANKL in the inflammatory cells, in inflamed synovium and in articular cartilage of knee joints treated with the cytokine combinations compared with expression in joints treated with the cytokines alone or the control. This model of inflammatory arthritis demonstrates that, in vivo, oncostatin M in combination with either IL-1 or tumour necrosis factor alpha represents cytokine combinations that promote bone destruction. The model also provides further evidence that increased osteoclast-like, tartrate-resistant acid phosphatase-positive staining multinucleate cells and upregulation of RANK/RANKL in joint tissues are key factors in pathological bone destruction.
boneIL-1oncostatin MRANKtumour necrosis factor alpha
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Introduction
Bone is an important skeletal extracellular matrix, and bone erosions are a major characteristic in rheumatoid arthritis (RA). The cytokines IL-1 and tumour necrosis factor (TNF) alpha play key roles in promoting joint inflammation, synovitis and cartilage/bone resorption [1,2]. These cytokines are overexpressed in RA cartilage and synovial membranes, and raised levels are found in synovial fluid and sera that correlate with disease activity and cartilage/bone destruction in RA [3-5]. Anti-IL-1 and TNF-α therapies in animal arthritis models and anti-TNF-α in humans with RA have been shown to significantly reduce arthritis incidence, inflammation and joint destruction [1,6-8], suggesting that the mediating pathways of joint damage are, at least in part, mediated by IL-1 and/or TNF-α.
Oncostatin M (OSM), a cytokine produced by activated T cells and macrophages, is structurally and functionally related to the IL-6 cytokine family. Raised levels of OSM are detected in synovial macrophages and synovial fluids of RA patients [9-11], and the levels correlate with markers of joint inflammation and destruction [3,10]. OSM causes joint inflammation, synovitis and structural damage in experimental animals [12,13]. Blockade of OSM ameliorates joint inflammation and cartilage damage in collagen-induced arthritis [14]. OSM has been found to enhance the differentiation and proliferation of osteoblasts during bone development and also induces the formation of osteoclasts and bone erosions [15-17]. These data indicate an important role for this cytokine in chronic joint inflammation and cartilage/bone damage. Furthermore, growing evidence from in vitro and in vivo studies suggests that OSM appears to be an important cofactor with other pro-inflammatory cytokines such as IL-1, TNF-α and IL-17 in mediating cartilage/bone destruction [9,18,19]. When these pro-inflammatory cytokines are overexpressed in combination with OSM in murine joints, a marked increase in damage to the joint tissues is observed [20,21].
RANKL is a TNF superfamily member and an essential mediator of osteoclastogenesis. It is produced from osteoblastic-stromal cells, synovial fibroblasts, chondrocytes and activated T lymphocytes [22,23]. This TNF-related cytokine and its receptor, RANK, are considered key factors in osteoclast differentiation, and RANK signalling is vital for osteoclast activation and survival [24,25]. RANKL binds directly to RANK on pre-osteoclasts and osteoclasts, initiating signal transduction that results in the differentiation of osteoclast progenitors as well as activation of mature osteoclasts, and therefore is implicated in the osteoclastogenic process in erosive arthritis [22,24].
The biological activity of RANKL is regulated by the soluble decoy receptor osteoprotegrin (OPG), a TNF-receptor superfamily member that is secreted by stromal cells and osteoblasts [26]. OPG competitively inhibits RANKL binding to RANK on the cell surface of osteoclast precursor cells and mature osteoclasts, thus inhibiting the osteoclastogenic actions of RANKL [27]. The levels of OPG and RANKL in osteoblastic and stromal cells are often reciprocally regulated in vitro and in vivo by bone active cytokines and hormones [28]. Excessive production of RANKL and/or deficiency of OPG may therefore contribute to the increased bone resorption typified by the focal bone erosions and peri-articular bone loss in RA.
We have recently shown in a murine model that OSM in combination with IL-1 or TNF-α synergistically promoted inflammation and cartilage degradation and increased matrix metalloproteinase expression [20,21]. Since bone erosions are also a major pathological feature of RA, we examined the effects of these cytokine combinations on bone in this model. In the present study we confirm that OSM exacerbates the effects of both IL-1 and TNF-α with respect to bone breakdown, osteoclast formation and the expression of RANK/RANKL, and further confirm that this rapid model of inflammatory arthritis is suitable for studies of RA.
Materials and methods
Adenoviral vectors and delivery of cytokines
Replication-defective recombinant adenoviruses engineered to overexpress murine IL-1β, TNF-α and OSM were as described previously [11,20,21], as was the empty control vector (Add170) [11]. Previous studies have validated these adenoviruses as an effective means of cytokine overexpression in synovial tissues [13,15,20,21]. All animal studies were compliant with the Canadian Council on Animal Care guidelines and were approved by the Animal Research Ethics Board at McMaster University, Canada.
C57BL/6 mice were purchased and housed until 12–14 weeks old. Mice were injected intra-articularly with adenovirus (5 × 106 plaque-forming units [pfu]/vector/joint) or PBS as previously described [13,20]. Briefly, anaesthesia was maintained with isofluorane, knees were swabbed with 70% ethanol and a 5 μl volume (treatment) was injected into the synovial space. The contralateral knee was treated with control vector or with PBS. One knee (n = 4 mice per treatment) was injected with combinations of vectors or with each vector alone combined with control vector to ensure that the total dose of vector was equivalent for each knee (1 × 107 pfu/joint). The animals were sacrificed at day 7 after administration.
Histology and histopathological scoring
The whole knee joints were dissected away from the limbs and were fixed with 7% formaldehyde in phosphate buffer (pH 7.4) overnight. Subsequently, joints were decalcified in 10% EDTA in phosphate buffer (pH 7.4) for 10 days at 4°C, and were then processed for paraffin embedding and sectioning (5 μm). Sections were stained with H&E. Bone damage was rated 0–5 (0 = normal, to 5 = severely affected) according to the following semiquantitative rating scale [29]: 0, none; 1, minimal (not readily apparent on low magnification); 2, mild (more numerous areas of resorption but not readily apparent on low magnification); 3, moderate (obvious foci of resorption, numerous osteoclasts); 4, marked (large erosions extending into bone cortices, more numerous osteoclasts); and 5, extensive erosions (markedly disrupted joint architecture). All scoring was performed blind with respect to the specific treatment.
Immunohistochemistry and tartrate-resistant acid phosphatase staining
Immunohistochemistry was performed with anti-RANK and anti-RANKL polyclonal antibodies (Santa Cruz Biotechnology, Santa Cruz, CA, USA), using the VECTASTAIN Elite ABC Kit PK 6101 (Vector, Burlingame, CA, USA). Formalin-fixed paraffin sections were deparaffinized, rehydrated and incubated with 10 mM sodium citrate, pH 6.0, for 2 hours at room temperature, and then with 3% H2O2 for 15 min. Thereafter, the sections were blocked with 1.5% normal sheep serum for 30 min, and were then incubated with primary antibody directed against RANK (rabbit polyclonal antibody raised against the epitope corresponding to amino acids 317–616 mapping at the carboxy terminus of RANK of human origin [H-300]) or against RANKL (rabbit polyclonal antibody raised against the epitope corresponding to amino acids 46–317 of RANKL of human origin [FL-317]) for 90 min at room temperature. After rinsing, sections were incubated with biotinylated secondary antibody for 30 min followed by avidin–biotin complex for 30 min according to the manufacturer's instructions (Vector). The signals were developed by 3,3'-diaminobenzidine tetrahydrochloride chromogen solution (DAKO Ltd, Ely, UK) and were counterstained with hematoxylin. A rabbit IgG antibody (X0936; Dako, Carpinteria, CA, USA) was used as a specificity control that gave no positive staining (data not shown).
Tartrate-resistant acid phosphatase (TRAP) enzyme was detected in paraffin sections (5 μm thick) using a commercial acid phosphatase leukocyte kit (Sigma, St Louis, MO, USA) according to the manufacturer's protocol.
Statistical analysis
All data are presented as the mean ± standard error of the mean. Statistical significance was assessed by the two-tailed unpaired Student's t test for comparisons between the means of two groups. P ≤ 0.05 was considered significant.
Results
Intra-articular overexpression of OSM in combination with either IL-1 or TNF-α induces bone damage
The morphology of H&E-stained sections from all treated joints (5 × 106 pfu/vector/joint) was assessed, and indicated that the contralateral joints treated with the control vector showed no evidence of bone damage (Fig. 1a). Administration of each cytokine alone caused a moderate synovial hyperplasia and bone erosions (Fig. 1b,1c,1d).
For the OSM + IL-1 combination (Fig. 1e,1f), pronounced bone destruction was observed with severe synovial hyperplasia and soft tissue inflammation. Prominent features of this arthritic lesion were the marked bone erosion, especially in the area of the bone and cartilage junction where marked synovial proliferation was seen with evidence of significant angiogenesis (Fig. 1e). Some areas of bone erosion and disconnection were seen with evidence of marked synovial invasion and multinucleated cells (Fig. 1e,1f). Similar results were obtained for the OSM + TNF-α combination (Fig. 1g,1h). A number of channels developing links between the synovial tissue and the bone marrow were seen (Fig. 1g) with focal bone erosions (Fig. 1h).
Semiquantitative evaluations of bone damage [29] indicated an increase in the severity and extent of bone erosions with both of the cytokine combinations compared with the individual cytokines alone (P < 0.05) (Fig. 2).
Intra-articular delivery of OSM with either IL-1 or TNF-α leads to an increase in TRAP-positive cells
No TRAP-positive staining cells were found in the control joints (Fig. 3a), but there was evidence of TRAP staining at the synovium–bone interface in joints treated with OSM, IL-1 or TNF-α (Fig. 3b,3c,3d, respectively).
When OSM was combined with IL-1 a substantial increase in the number of TRAP-positive staining cells was found at the leading edge of the synovium–bone interface (Fig. 3e), at sites within the synovium (Fig. 3f) and at the pannus–subchondral bone junction (Fig. 3g). These cells were often interposed between the bone surface and the 'erosive front' of the synovium and bone (Fig. 3h). At many sites of focal bone erosion (as in Fig. 1), TRAP-positive multinucleated cells were seen at the erosion front and in the synovium (Fig. 3e,3f), as well as in erosion pits in the bone (Fig. 3h). Furthermore, TRAP-positive staining cells were also seen in the area of cartilage/bone junctions and also in the pannus and synovium away from the eroded bone surface (Fig. 3f,3g).
Similar results were seen in the joints treated with OSM + TNF-α where TRAP-positive cells were aligned on the bone surface (Fig. 3i,3j,3k,3l). Multinucleated cells were also seen at the bone surface (Fig. 3i,3j) and in deep pockets where bone was eroded (Fig. 3k). TRAP-positive cells were also seen within the synovial tissue and the area of cartilage/bone junctions (Fig. 3l).
Intra-articular delivery of OSM with IL-1 or TNF-α elevates RANK and RANKL expression
There was little or no RANK-positive staining in synovial tissues taken from control joints (Fig. 4a). Increased RANK expression associated with inflammatory and synovial cells was observed following treatment with TNF-α (Fig. 4b), and similar levels of expression were observed for OSM and IL-1 (data not shown). RANK expression was increased further, especially at the bone erosion fronts, when OSM was combined with IL-1 or TNF-α (Fig. 4c,4d). Interestingly, RANK appeared to be expressed as a gradient from the synovial tissue where increased numbers of RANK-positive cells were observed close to the cortical bone and periosteum of the patella, the femur and the tibia. Diffuse RANK staining in the superficial layer of cartilage was seen for the joints treated with each of the vectors separately (see Fig. 4b; some data not shown), and this staining was more intense in the joints treated with OSM + IL-1 or with OSM + TNF-α (Fig. 4e,4f).
No RANKL staining was evident in the control joints, either in the cartilage (Fig. 4g) or in the synovium (data not shown). Treatment with all the individual vectors alone induced a similar positive staining for RANKL in synovial cells and in the infiltrating (inflammatory) cells (Fig. 4h). There was a marked increase in RANKL expression, consistent with the increase in inflammatory cells and synovial inflammation, in the joints treated with the combinations of OSM + IL-1 (Fig. 4i) or OSM + TNF-α (Fig. 4j). Within the articular cartilage there was very diffuse RANKL staining for both the control and each individual cytokine vector alone (data not shown), while strong RANKL expression was seen for both cytokine combinations close to the articular surface (Fig. 4k,4l).
Discussion
In the present study, we demonstrate for the first time that overexpression of OSM in combination with either IL-1 or TNF-α causes profound bone damage with osteoclast formation and activation, and increased expression of RANK/RANKL in inflammatory cells, in inflamed synovium, in articular cartilage and at the invading front of bone erosions.
It has been long recognized that pro-inflammatory cytokines are intimately associated with bone destruction during RA. IL-1, TNF-α and OSM have all been reported to induce joint inflammation and cartilage/bone destruction in vitro and in vivo [1,2,9,13]. Elevated levels of these cytokines are found in the synovial fluid of RA patients, and these levels correlate with disease activity and cartilage/bone destruction [2,5,9,12]. Recent studies indicate that cytokines such as TNF-α and IL-1 are likely to synergize with RANKL to promote bone loss [30-33]. Indeed, TNF-α stimulates differentiation of osteoclast precursors after priming by <1% of the amount of RANKL normally required to induce osteoclast formation [31]. TNF-α induces IL-1 production, and both IL-1 and RANKL function as osteoclast survival/activation factors [33]. In the inflammatory arthritides such as RA, TNF-α and IL-1 may therefore promote bone loss by amplifying RANKL effects. This is exemplified in transgenic mice overexpressing the human TNF-α gene; these mice exhibit an erosive arthritis, which is improved by administration of OPG, of neutralizing anti-TNF-α antibodies, or of the bisphosphonate pamidronate [34]. OSM-induced expression of RANK/RANKL in a murine arthritis model has also been reported [15].
In the present study, the combination of OSM with TNF-α significantly induced RANKL expression in inflammatory cells, in inflamed synovium and in articular chondrocytes. A number of factors contribute to arthritic cartilage/bone destruction in RA, including the proliferation of synovial cells, the influx and interaction of inflammatory cells, and the maintenance of a destructive fibroblastic phenotype, which result in the final loss of cartilage and bone. Indeed, CD14+ monocytes/macrophages have been shown to be osteoclast precursors within the inflamed synovium that promote bone resorption following differentiation [35]. In support of this we found evidence of high numbers of TRAP-positive multinucleate cells in the synovial tissues of mice treated with OSM + IL-1 or with OSM + TNF-α combinations.
As well as inducing marked synovial hyperplasia, angiogenesis and inflammation as described previously [20,21], marked bone erosions were also evident. Active synovial cells can cause bone erosions as well as produce factors that can themselves induce synovial proliferation, inflammation, osteoclast formation and activation. Angiogenesis contributes to synovial growth and leukocyte recruitment, thus potentiating disease progression [36]. The matrix metalloproteinases released by active synovial cells are also involved in angiogenesis, tissue invasion and inflammatory cell migration [37], as well as in osteoclast activation, migration and bone resorption [38,39]. The high levels of RANK/RANKL expression, the synovial hyperplasia, the angiogenesis and the osteoclast activity that the OSM + IL-1 or OSM + TNF-α treatments induced was associated with pronounced bone damage in this murine model with a similar pathology to that of active RA. Our previous studies have shown that these cytokine combinations upregulate matrix metalloproteinases [20,21].
TRAP is used as a molecular marker enzyme for chondroclast/osteoclast differentiation, the function of which is considered to relate to cartilage/bone resorption [40]. The cytokine combinations used in this study induced an increased number of TRAP-positive staining multinucleated cells at the pannus and cartilage/bone junctions compared with joints injected with the cytokines alone. The expression of TRAP activity by the multinucleated cells located within erosion pits and bone disconnection sites provides strong evidence of their osteoclast-like nature. The mechanism of induction of TRAP-positive multinucleated cells could be related to the marked induction of RANK/RANKL expression and the interactions between osteoblast and osteoclast precursor cells that is crucial for osteoclast development [15]. We also found that treatment with OSM increased the numbers of TRAP-positive cells at the invading front of bone erosion sites and at the bone surface, as well as in the synovium. This differs from a recent report that OSM induced synovial inflammation and increased the expression of IL-6, RANK and RANKL, but did not stimulate osteoclast activity [15]. The same authors also found marked growth plate damage, and determined that OSM-induced inflammation and proteoglycan depletion were IL-1 dependent [41].
Conclusion
Using gene transfer technology we have provided evidence of a murine model with an aggressive pathological phenotype that closely resembles RA in terms of inflammation, angiogenesis, and cartilage and bone destruction. These data further highlight the pro-inflammatory nature of OSM and confirm a potential role for these potent cytokine combinations in the joint destruction characteristic of inflammatory arthritic diseases.
Abbreviations
H&E = haematoxylin and eosin; IL = interleukin; OPG = osteoprotegrin; OSM = oncostatin M; PBS = phosphate buffered saline; pfu = plaque-forming units; RA = rheumatoid arthritis; RANK = receptor activator of nuclear factor kappa B; RANKL = receptor activator of nuclear factor kappa B ligand; TNF = tumour necrosis factor; TRAP, tartrate-resistant acid phosphatase.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CDR supplied and administered the adenoviral vectors. WH performed the tissue sectioning and staining experiments. ADR drafted the manuscript and, with TEC, conceived of the study and participated in its design and coordination.
Acknowledgements
The authors thank Dr Daniel C Anthony (University of Southampton, UK) for provision of the vector expressing IL-1. They are indebted to Dr Chris Morris (Newcastle) for the use of his histology facilities. This work was supported by the Arthritis Research Campaign, the Dunhill Medical Trust, the Arthritis Society (Canada), Hamilton Health Sciences Corporation and St Joseph's Hospital, Hamilton (Ontario, Canada).
Figures and Tables
Figure 1 Bone damage caused by oncostatin M (OSM) in combination with either IL-1 or tumour necrosis factor alpha (TNF-α) in murine joints. Adenovirus vectors overexpressing murine OSM, IL-1 or TNF-α were injected intra-articularly into the right knee joints of mice at 5 × 106 plaque-forming units [pfu]/vector/joint. The left knee joints were injected with the empty control vector. All joints received a total of 1 × 107 pfu/joint, and all animals were sacrificed at day 7 following administration. Sections (5 μm) were stained with H&E. (a) Control, (b) OSM, (c) IL-1, (d) TNF-α, (e), (f) OSM + IL-1, and (g), (h) OSM + TNF-α. The joints showed a moderate (b–d) to severe (e–h) synovial hyperplasia and an infiltration of inflammatory cells. Marked synovial hyperplasia with angiogenesis was also seen with bone erosions (arrows) and bone fractures, with evidence of synovial invasion (e and g). b, bone; bm, bone marrow; f, bone fracture; m, muscle; s, synovial cells; v, blood vessel. (a)–(e), (g) Bar = 50 μm; (f), (h) bar = 20 μm.
Figure 2 Quantitative analysis of bone damage. Mice (n = 4 for each treatment group) were injected with the adenoviral vectors expressing oncostatin M (OSM), IL-1 or tumour necrosis factor alpha (TNF-α) as described in Fig. 1. Sections (5 μm) were stained with H&E and were scored for bone damage as described in Materials and methods. The values represent the mean ± standard error of the mean for each treatment group (the control scored 0). The total scores are the combined scores of the four mice in each treatment group. Statistical differences between each treatment within experiments were determined using Student's t test: * P ≤ 0.05.
Figure 3 Increased tartrate-resistant acid phosphatase (TRAP)-positive staining following treatment with oncostatin M (OSM) with either IL-1 or tumour necrosis factor alpha (TNF-α) in murine joints. Mice were injected intra-articularly with adenoviral vectors as described in Fig. 1, and the animals were sacrificed at day 7 following administration. (a) No significant TRAP-positive cells outside the bone marrow were seen in control joints. Some TRAP-positive staining cells were located at the synovium-bone interface in (b) OSM-treated joints, (c) IL-1-treated joints and (d) TNF-α-treated joints. In both the (e)–(h) OSM + IL-1 and (i)–(l) OSM + TNF-α combinations, significant TRAP-positive staining was located at the front of the synovium–bone and the pannus–subchondral bone junctions, which were interposed between the bone surface and the 'erosive front' of the synovium (e, i and j). At many sites of focal bone erosions (arrows), TRAP-positive multinucleated cells were seen at the erosion front within the synovium (e, h–j), and within erosion pits in the bone (h and k). Furthermore, TRAP-positive staining cells were also seen at the cartilage/bone junction (l). b, bone; bm, bone marrow; c, cartilage; f, fracture; m, muscle; mn, multinucleated cells; s, synovial cells. (a)–(d), (g)–(l) Bar = 50 μm; (e), (f) bar = 20 μm.
Figure 4 Intra-articular overexpression of oncostatin M (OSM) with either IL-1 or tumour necrosis factor alpha (TNF-α) increases RANK/RANKL expression in murine joints. Mice were injected intra-articularly with adenoviral vectors as described in Fig. 1, and the animals were sacrificed at day 7 following administration. Sections (n = 4 per treatment group) were immunolocalized with antibodies specific for (a)–(f) RANK or (g)–(l) RANKL with haematoxylin counterstaining. No positive staining (brown) was observed in the controls (a and g), while similar patterns of RANK and RANKL staining were observed in the synovium and inflammatory cells in joints treated with a single cytokine (representative data are shown: (b) TNF-α, (h) IL-1). This expression was further enhanced by the combinations of OSM + IL-1 (c, e, i, k) or OSM + TNF-α (d, f, j, l), especially at sites of bone erosion (arrows). A marked increase in RANK and RANKL expression was also seen in the articular chondrocytes for both cytokine combinations (e, f, k, l). b, bone; bm, bone marrow; c, cartilage; i, inflammatory cells; s, synovial cells. Bar = 50 μm.
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| 15642143 | PMC1064887 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 10; 7(1):R57-R64 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1460 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14611564214410.1186/ar1461Research ArticleSerum cathepsin K levels of patients with longstanding rheumatoid arthritis: correlation with radiological destruction Skoumal Martin [email protected] Günther 1Kolarz Gernot 1Hawa Gerhard 3Woloszczuk Wolfgang 4Klingler Anton 51 Institut für Rheumatologie der Kurstadt Baden in Kooperation mit der Donauuniversität Krems, Austria2 Rheumasonderkrankenanstalt der SVA der gewerblichen Wirtschaft, Baden, Austria3 Biomedica Medizinprodukte GmbH & CO KG, Vienna, Austria4 L Boltzmann Institut für experimentelle Endokrinologie, Vienna, Austria5 Theoretical Surgery Unit, Department of General and Transplant Surgery, University Hospital, Innsbruck, Austria2005 10 11 2004 7 1 R65 R70 16 8 2004 22 9 2004 3 10 2004 11 10 2004 Copyright © 2004 Skoumal et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is cited.
Cathepsin K is a cysteine protease that plays an essential role in osteoclast function and in the degradation of protein components of the bone matrix by cleaving proteins such as collagen type I, collagen type II and osteonectin. Cathepsin K therefore plays a role in bone remodelling and resorption in diseases such as osteoporosis, osteolytic bone metastasis and rheumatoid arthritis. We examined cathepsin K in the serum of 100 patients with active longstanding rheumatoid arthritis. We found increased levels of cathepsin K compared with a healthy control group and found a significant correlation with radiological destruction, measured by the Larsen score. Inhibition of cathepsin K may therefore be a new target for preventing bone erosion and joint destruction in rheumatoid arthritis. However, further studies have to be performed to prove that cathepsin K is a valuable parameter for bone metabolism in patients with early rheumatoid arthritis.
bone remodellingcathepsin Kosteoclast activationrheumatoid arthritis
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Introduction
Progressive bone and cartilage destruction in arthritic joints leads to irreversible joint destruction, and subsequently to functional declines and work disability [1,2]. New biomarkers such as cartilage oligomeric matrix protein [3,4], osteoprotegerin [5-7] or receptor activator of NF-κB ligand [8-10] have been developed to describe the local bone and cartilage process in affected joints.
Cathepsin K is a cysteine protease that plays an essential role in osteoclast function and in the degradation of protein components of the bone matrix. It is produced by bone resorbing macrophages and synovial fibroblasts, and it cleaves proteins such as collagen type I, collagen type II and osteonectin [11]. Cathepsin K therefore plays a role in bone remodelling and resorption in diseases such as osteoporosis, osteolytic bone metastasis and rheumatoid arthritis (RA) [12,13].
Cathepsin K is a tissue-specific protease associated with pycnodysostosis, a rare genetic disorder that manifests itself in bone abnormalities such as short stature, acroosteolysis of distal phalanges and skull deformities [14,15]. Cathepsin K knockout mice develop an osteopetrosis. Inhibition of cathepsin K may therefore prevent bone resorption, as could be demonstrated in bone metastasis from breast cancer [16]. Osteoprotegerin has been shown to inhibit the expression of cathepsin K, the main enzyme involved in bone resorption.
The aim of this study was to measure serum levels of cathepsin K in RA and to prove that cathepsin K is a parameter of bone remodelling and resorption in a nonselected cohort of patients with longstanding RA. This patient group shows a variation of age, inflammatory level and Larsen score. We divided this cohort into different groups, according to age, inflammatory level, disease-modifying antirheumatic drug (DMARD) therapy, radiological progression and disease activity, to verify cathepsin K as an age-independent and laboratory inflammatory parameter-independent protease.
Materials and methods
Serum levels of cathepsin K were measured in the sera of 100 patients suffering from RA according to the criteria of the American Rheumatism Association [17]. Clinical and laboratory data are presented in Tables 1 and 2. The control group consisted of nonselected healthy blood donors from a central blood bank (n = 50; 21 female, 29 male) aged 18–65 years.
Most of the patients received DMARDs. The most frequently used DMARD was methotrexate, followed by leflunomide, sulfasalzopyrine and gold. Furthermore, azathioprine and chloroquine but no biological therapy were prescribed (Table 3).
Each examination consisted of a full interview, the assessment of functional disability and a standardised physical examination, which included a joint examination for tenderness (Ritchie score), pain on motion, soft tissue swelling, 44-swollen joint count and swollen proximal interphalangeal score [18,19].
The disease activity of RA was measured by the disease activity score (≤ 2.4, low activity; > 2.5 and ≤ 3.7, mean activity; > 3.7, high activity). The radiological progression in RA was calculated by the Larsen score [20].
The blood examination at each visit consisted of the determination of cathepsin K, the erythrocyte sedimentation rate, the haemoglobin level, the thrombocyte count, the serum rheumatoid factor (RapiTex® RF; Dade Behring, Liederbach, Germany), antinuclear antibodies (indirect immunfluorescent technique, ANA Fluor Kit 240®; Diasorin, Stillwater, MN, USA) and C-reactive protein (CRP) (Rheumajet CRP®; Biokit, Barcelona, Spain).
The variables of age, sex, duration of disease, visual analogue scale of general health and morning stiffness, treatment with DMARDs and reason for their discontinuation, and the Steinbrocker stage [21] were also recorded. Serum was obtained in the morning from the routinely taken blood samples and was centrifuged immediately. The samples were kept at -80°C prior to determination of cathepsin K. The serum used for the measurement of cathepsin K was the remainder from routinely drawn blood examinations on the day of hospitalisation; no examination was performed only for quantification of cathepsin K. Clinical data were used from a database developed for the long-term observation of patients with RA in our clinic.
An enzyme immunoassay for cathepsin K developed by Biomedica Austria (Vienna, Austria) was used. The Cathepsin K test kit is an enzyme immunoassay designed to determine cathepsin K directly in biological fluids (serum, plasma, cell culture supernatants). The ELISA used in this study is based on antibodies specific for amino acids 1–20 and amino acids 196–210 of the mature enzyme. The antibodies were produced by immunisation of sheep with peptides of that amino acid sequence coupled to Keyhole Limpet Hämocyanine (primary immunisation, 0.5 mg; boost, 0.25 mg). Antisera were purified using the biotinylated peptides coupled to streptavidine sepharose (Amersham-Pharmacia Biotech Ltd, Little Chalfont, UK). A synthetic cathepsin K (Pichem GmbH, Graz, Austria) was used as the calibrator. Signal generation was accomplished by labelling with horseradish peroxidase.
Briefly, the assay procedure consisted of incubating 50 μl sample with 200 μl horseradish peroxidase-labelled detection antibody on capture antibody precoated plates overnight at room temperature. After a washing step to remove unbound detection antibody, tetramethyl benzidine was added as the substrate. The reaction was stopped after 30 min by adding 50 μl of 0.9% H2SO4. The yellow colour that is directly proportional to the amount of cathepsin K present in the sample was measured on a standard microplate reader at 450 nm with 620 nm as the reference. The detection limit of the assay was calculated as 1.1 pmol/l (0 standard + 5 × standard deviation).
No cross-reactivity to cathepsin E, cathepsin D, cathepsin B and cathepsin L or rheumatoid factors was detected.
Statistical methods included Spearman correlation analysis, the Wilcoxon two-sample test the Kruskal–Wallis test and analysis of variance, if appropriate. P < 5% was considered statistically significant.
Results
The cathepsin K serum levels of the patients with RA (median first–third quartile range, 54.8 pmol/l) compared with the healthy control group (median first–third quartile range, 12.7 pmol/l) were significantly elevated (P = 0.0003) (Table 4).
The Larsen score ranged from 0 to 164 (median score, 39). The Spearman rank correlation showed a statistically significant correlation between cathepsin K and the Larsen score (P = 0.004). The highest levels of cathepsin K were observed in patients with the highest Larsen scores. We divided the cohort into three Larsen groups with equal numbers of patients (Larsen score < 18 points, Larsen score between 19 and 74 points, and Larsen score ≥ 75 points). Cathepsin K levels showed an increase with the augmentation of radiological destruction (P = 0.035) (Table 5).
Cathepsin K seems to be independent or only weakly correlated with laboratory inflammation parameters. It was not associated with CRP (P = 0.27), but weak correlations were found with the erythrocyte sedimentation rate (P = 0.03) and the disease activity score of the whole cohort (P = 0.04). However, the division of the disease activity score into three groups with low activity, medium activity and high activity did not show any difference. We could not find any correlation with sex and age (whole group/division into two patient groups ≤ 65 years and ≥ 66 years, P = 0.32), whereby the two groups were comparable in disease activity (3.53 versus 3.12), laboratory parameters (CRP, 25.4 mg/l versus 25.9 mg/l), clinical score (Ritchie score, 14 versus 9) and radiological score (Larsen score, 47 versus 62).
The most frequently used DMARD was methotrexate (n = 42), followed by leflunomide (n = 10) and sulfasalzine (n = 10). Twenty-two patients had no DMARD at the time of examination (Table 3). The lowest cathepsin K levels were evident in the leflunomide group, but no significant difference between these groups could be demonstrated.
Discussion
Bone resorption and formation is a well-balanced system and is mediated by osteoclasts. Cathepsin K is essential for bone resorption, which depends on the production of cathepsin K by osteoclasts and its secretion into the extracellular department. This leads to a degradation of the organic matrix between the osteoclasts and the bone surface [22]. In vivo the activation of cathepsin K occurs intracellularly, before secretion into the resorbing lacunae and the onset of bone resorption, whereby local factors may regulate the processing of procathepsin K to mature cathepsin K [23]. In accordance with this, synovial fibroblasts are also involved in joint destruction and in the pathogenesis of RA. Hou and colleagues found that cathepsin K has a potent aggrecan-degrading activity, whereby the aggrecan cleavage products increase the collagenolytic effects of this protease on collagen type I and type II. They were able to show that cathepsin K is also a critical protease in cartilage degradation by synovial fibroblasts [24]. Increased expression of cathepsin K around lymphocytic infiltrates in synovial tissue seems to facilitate the movement of mononuclear cells through the perivascular matrix [25]
Proinflammatory cytokines such as IL-1β and tumour necrosis factor alpha influence the expression of cathepsin K. Its overexpression in the rheumatoid synovium, induced by IL-1β and tumour necrosis factor alpha due to the increase of cathepsin K-expressing cells, proves this protease to be a valuable tool for bone research, and cathepsin K also may become a new and highly specific biomarker for RA [26].
Votta and colleagues demonstrated high levels of cathepsin K expression in osteoclasts at sites of extensive bone loss. According to this, they developed a peptide aldehyde inhibitor of cathepsin K that inhibits osteoclast-mediated bone resorption in foetal rat long bone organ cultures and even in a human osteoclast-mediated assay in vitro. This inhibitor leads to a significantly reduced bone loss [27]. Furthermore, structure activity studies on a series of reversible ketoamide-based inhibitors of cathepsin K have led to the identification of potent and selective inhibitors [28].
Wittrant and colleagues demonstrated osteoprotegerin to be an inhibitor of cathepsin K. Osteoprotegerin is an osteoblast-secreted decoy receptor that inhibits osteoclast differentiation and activation. Human osteoprotegerin inhibits cathepsin K and tartrate-resistant acid phosphatase, both osteoclast markers, but stimulates the expression of tissue inhibitor of metalloproteinases-1 [29]. These results are a further step in the development of new therapies for the prevention of bone destruction.
In the synovium of RA, the cathepsin K protein was localised in synovial fibroblasts, stromal multinucleated giant cells and CD68+ macrophage-like synoviocytes. Highly interesting is the expression of cathepsin K by fibroblasts and giant cells at sites of cartilage erosions. This was two to five times higher compared with osteoarthritic synovium. In normal synovium, cathepsin K expression was not increased and was restricted to fibroblast like cells [26,30-32]. The overexpression of cathepsin K in RA synovia proves that this protease is responsible for the degradation of articular tissue in rheumatoid joints and in normal synovial tissue.
To our knowledge, no study has previously investigated the serum levels of cathepsin K in RA. Our results demonstrate that cathepsin K is elevated in the serum of patients with RA compared with that of a healthy control group (Table 4). The upregulation of cathepsin K and the correlation with the Larsen score as a parameter for radiological changes (Table 5) mirrors the destruction of bone structures in inflammatory diseases like RA. The measurement of cathepsin K seems an inexpensive tool that is independent of CRP and shows only a weak correlation with the erythrocyte sedimentation rate.
Further studies should investigate whether elevated cathepsin K levels precede osseous destruction or whether they occur as result of them. In the first case, determination of cathepsin K could be an important additional tool to decide on aggressive forms of disease-modifying antirheumatic therapies.
Conclusion
This is the first study that demonstrates increased cathepsin K levels in the serum of patients with RA. As could be shown in the synovia of RA, the elevated serum levels of this protease are significantly correlated with the joint destruction, which in this study was assessed by the Larsen score. Cathepsin K seems to be a valuable parameter for the assessment of bone metabolism in patients with established RA and its measurement will probably contribute to developing targeted therapies for the prevention of further bone destruction. However, more studies need to be performed to verify the presence of cathepsin K in patients with early RA and its value as a prognostic factor for bone destruction in RA
Abbreviations
CRP = C-reactive protein; DMARD = disease-modifying antirheumatic drug; ELISA = enzyme-linked immunosorbent assay; IL = interleukin; NF = nuclear factor; RA = rheumatoid arthritis.
Competing interests
Dr G Hawa and Prof. W Woloszczuk are members of BIOMEDICA who developed the Cathepinsin K kit, but they did not receive any financial benefits.
Authors' contributions
MS is the corresponding author, and GH and GK are coauthors of the manuscript. GH and WW developed the cathepsin K ELISA kit. AK performed the statistical analysis.
Figures and Tables
Table 1 Clinical parameters of 100 rheumatoid arthritis (RA) patients
Disease duration (years) Age at manifestation (years) Age (years) Morning stiffness (min) Ritchie score Larsen score 44 swollen joint count Disease activity score
Mean 11.7 52.0 62.9 31.9 11.3 54.8 7.4 3.3
Minimum 0.5 18.0 20.0 0.0 0.0 0.0 0.0 0.4
Maximum 56.0 75.0 83.0 130.0 42.0 164.0 28.0 6.0
Standard deviation 11.6 12.4 11.0 37.8 10.3 49.5 6.8 1.4
Median 8.0 53.0 63.0 15.0 10.0 38.0 6.0 3.6
Table 2 Laboratory parameters of 100 rheumatoid arthritis (RA) patients
Rheumatoid factor (U/l) Erythrocyte sedimentation rate (mm/hour) C-reactive protein (mg/l) Leucocytes (g/l) Cathepsin K (pmol/l)
Mean 298.8 30.3 25.0 7.2 304.7
Standard deviation 1142.3 20.9 23.4 2.2 681.0
Median 27.0 30.0 20.0 7.0 54.8
Table 3 Distribution of disease-modifying antirheumatic drug in 100 rheumatoid arthritis patients
Disease-modifying antirheumatic drug
None Methotrexate Leflunomide Sulfasalazopyrine Gold Chloroquine Others
Number 22 42 10 10 6 4 6
Table 4 Correlations of cathepsin K with clinical, laboratory and radiological parameters
Mean Standard deviation n Coeffficient Probability > |r|
Variable 1
Cathepsin K 304.66 677.607
Variable 2
Age (years) 62.89 11.1581 100 0.0543 0.5915
Rheumatoid factor (U/l) 298.818 1136.49 100 0.4761 < 0.0001
Morning stiffness (min) 32 38.7233 100 0.1320 0.1905
Erythrocyte sedimentation rate (mm/hour) 30.27 20.8404 100 0.2200 0.0279
C-reactive protein (mg/l) 24.96 23.5032 100 0.1121 0.2670
Ritchie score 11.31 10.3394 100 0.1353 0.1797
Proximal interphalangeal score 1.51 2.69865 100 0.2560 0.0101
Disease activity score 3.33283 1.43345 100 0.2093 0.0376
Larsen score 54.77 49.3335 100 0.2856 0.0040
Table 5 Increase of cathepsin K levels with the augmentation of the Larsen score
Larsen group Larsen score Cathepsin K Kruskal–Wallis test
n Minimum Median Maximum Minimum Median Maximum
< 18 32 0.0 7.5 17.0 0.0 26.5 3352.0
≥ 19 and < 74 34 18.0 38.0 67.0 0.0 70.9 1721.6
≥ 75 34 75.0 105.5 164.0 0.0 88.8 3453.2
Total 100 0.0 39.0 164.0 0.0 54.8 3453.2 P = 0.035
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| 15642144 | PMC1064888 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 10; 7(1):R65-R70 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1461 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14621564213110.1186/ar1462Research ArticleMMP-3 expression and release by rheumatoid arthritis fibroblast-like synoviocytes induced with a bacterial ligand of integrin α5β1 Zeisel Mirjam B [email protected] Vanessa A [email protected] Dominique [email protected] Jean [email protected] Inserm 392, Infection et Inflammation, Université Louis Pasteur de Strasbourg, Faculté de Pharmacie, 74 route du Rhin, 67400 Illkirch, France2 Département de Rhumatologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France2005 24 11 2004 7 1 R118 R126 26 6 2004 18 8 2004 17 9 2004 12 10 2004 Copyright © 2004 Zeisel et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Fibroblast-like synoviocytes (FLSs) play a major role in the pathogenesis of rheumatoid arthritis (RA) by secreting effector molecules that promote inflammation and joint destruction. How these cells become and remain activated is still elusive. Both genetic and environmental factors probably play a role in transforming FLSs into inflammatory matrix-degrading cells. As bacterial products have been detected in the joint and shown to trigger joint inflammation, this study was undertaken to investigate whether a bacterial ligand of integrin α5β1, protein I/II, could contribute to the aggressive behavior of RA FLSs. Protein I/II is a pathogen-associated molecular pattern (PAMP) isolated from oral streptococci that have been identified in the joints of RA patients. The response of RA and osteoarthritis FLSs to protein I/II was analyzed using human cancer cDNA expression arrays. RT-PCR and pro-MMP-3 (pro-matrix metalloproteinase) assays were then performed to confirm the up-regulation of gene expression. Protein I/II modulated about 6% of all profiled genes. Three of these, those encoding IL-6, leukemia inhibitory factor, and MMP-3, showed a high expression level in all RA FLSs tested, whereas the expression of genes encoding other members of the cytokine or MMP-family was not affected. Furthermore, the up-regulation of MMP-3 gene expression was followed by an increase of pro-MMP-3 release. The expression of interferon regulatory factor 1 and fibroblast growth factor-5 was also up-regulated, although the expression levels were lower. Only one gene, that for insulin-like growth factor binding protein-4, was down-regulated in all RA FLSs. In contrast, in osteoarthritis FLSs only one gene, that for IL-6, was modulated. These results suggest that a bacterial ligand of integrin α5β1 may contribute to the aggressive behavior of RA FLSs by inducing the release of pro-inflammatory cytokines and a cartilage-degrading enzyme, such as IL-6 and MMP-3, respectively.
fibroblast-like synoviocytesintegrin α5β1MMP-3PAMP
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Introduction
Fibroblast-like synoviocytes (FLSs) appear to play a major role in the pathogenesis of rheumatoid arthritis (RA). These cells are characterized by pannus formation, cartilage invasion, and secretion of effector molecules, including cytokines and chemokines, that act on various cells to promote inflammation [1-3]. Recent experiments have shown that although FLSs were able to secrete large amounts of IL-6 and IL-8, they failed to release significant amounts of TNF-α, IL-1, IL-15, and IL-18 [4,5]. They showed a dissociated pattern of cytokine mRNA and protein expression, suggesting the existence of post-transcriptional regulation. FLSs are also the principal promoters of joint destruction [3], either through the release of proteolytic enzymes such as matrix metalloproteinases (MMPs), or indirectly through the stimulation of osteoclastogenesis. FLSs are the source of a broad range of MMPs, including MMP-1, MMP-13, and MMP-3; the last of these degrades different types of collagen and proteoglycans and activates other MMPs such as MMP-2 and MMP-9.
How these cells become and remain activated is still not known. Numerous factors may account for the permanent changes observed in FLS functions. To date it is very difficult to define whether FLSs are changed in response to genetic modifications, such as mutations or microsatellite instability, or in response to environmental factors. Both factors probably play a role in transforming FLSs into invading, inflammatory, matrix-degrading cells. Exposure to an inflammatory environment may even result in DNA damage [6]. On the other hand, genetic modifications may contribute to inflammation: it has been shown that a decrease of the expression of p21, an inhibitor of the cyclin-dependent kinases which is regulated by p53, activates activating protein-1, leading to enhanced cytokine and MMP synthesis in RA FLSs [7,8].
Regarding environmental factors, there has been considerable interest in a possible role of innate immunity in the initiation and perpetuation of inflammation during RA. Bacterial products – also called pathogen-associated molecular patterns (PAMPs) – such as lipopolysaccharide (LPS), peptidoglycan, and bacterial DNA have been detected in the joint [9,10] and were shown to trigger joint inflammation: in fact, oral administration of LPS exacerbates collagen-induced arthritis in mice, and intra-articular injection of CpG oligonucleotides and of peptidoglycan leads to transient arthritis in mice [11-13]. As DNA and rRNA from a wide variety of bacterial species have been identified in the joints of RA patients [9,14,15], it has been suggested that joint inflammation could be triggered by PAMPs common to various microorganisms interacting with cellular pattern-recognition receptors (PRRs). FLSs express a large number of PRRs, such as Toll-like receptor (TLR)-2, TLR-4, and TLR-9 [16], as well as numerous integrins. Their interaction with a variety of PAMPs may contribute to the aggressive phenotype of RA FLSs.
We reported previously that interaction of protein I/II, a cell wall component of oral streptococci, with FLSs triggers, through integrin α5β1, the production and release of inflammatory mediators such as IL-6 and IL-8 but not of TNF-α, IL-1, or IL-18 [17-19]. This cytokine synthesis involves extracellular signal-regulated kinase 1/2 and c-Jun N-terminal kinases, as well as activating protein-1-binding activity and nuclear translocation of nuclear factor κB [20]. Oral streptococci have been identified in the joints of RA patients [15] and have been shown to exacerbate collagen-induced arthritis in mice [21]. This study was undertaken to investigate whether a PAMP such as protein I/II could contribute to the aggressive behaviour of RA FLSs. Using cDNA array analysis, we show that this cell wall component triggers the synthesis of MMP-3 by FLSs and may therefore contribute to joint destruction. Furthermore, the genes modulated by protein I/II are mainly involved in cell signaling, protein turnover, and cellular communication, suggesting that protein I/II may contribute to the aggressive behavior of FLSs.
Materials and methods
Reagents
Cell-culture media (RPMI 1640 and M199), FCS, penicillin, streptomycin, amphotericin B, Taq DNA polymerase, dNTPs, and primers were from Invitrogen (Cergy-Pontoise, France). Cell-culture media never had an endotoxin level above 0.04 ng/mL, as tested by the Limulus chromogenic assay. LPS from Escherichia coli O55:B5, polymyxin B, and type XI collagenase were obtained from Sigma (Saint Quentin Fallavier, France). The First Strand cDNA synthesis kits and 32P dATP were from Amersham Pharmacia Biotech (Saclay, France). A Nucleospin RNA II extraction kit was from Macherey-Nagel (Souffelweyersheim, France). Atlas human cancer cDNA expression arrays were from Clontech (Ozyme, Saint Quentin Yvelines, France). BINDAZYME™ ProMMP-3 enzyme immunoassay kit was from The Binding Site (Saint Egreve, France). The SYBR®Green PCR master mix was from Applied Biosystems (Courtaboeuf, France). Buffers were prepared with apyrogenic water obtained from Braun Medical (Boulogne, France).
Cell culture
Human FLSs were isolated from RA synovial tissues from three patients at the time of knee joint arthroscopic synovectomy, as described previously [22]. The diagnoses conformed to the revised criteria of the American College of Rheumatology [23]. Human FLSs were also isolated from osteoarthritis (OA) synovial tissues from three patients who were having joint replacements. FLS cultures were performed as previously described [20]. Briefly, tissues were minced, digested with 1 mg/mL collagenase in serum-free RPMI 1640 for 3 hours at 37°C, centrifuged (130 g for 10 minutes at 4°C) and resuspended in M199-RPMI 1640 (1:1) containing 2 mm l-glutamine, penicillin (100 IU/mL), streptomycin (100 μg/mL), amphotericin B (0.25 μg/mL), and 20% heat-inactivated FCS (complete medium). After overnight culture, nonadherent cells were removed and adherent cells were cultured in complete medium. At confluence, cells were trypsinized and passaged in 75-cm2 culture flasks in complete medium containing 10% heat-inactivated FCS. Experiments were performed between the third and the ninth passages, during which time cultures were a homogeneous population of fibroblastic cells, negative for CD16 as determined by FACS analysis. Before activation experiments, cells were deprived of serum for 24 hours, and then the appropriate stimuli diluted in serum-free RPMI 1640 with antibiotics were added. To eliminate the possibility that the observed effects were due to LPS contamination, all the experiments were performed in the presence of polymyxin B (2 μg/ml). Cell numbers and cell viability were assessed using the MTT test (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test) as described elsewhere [24].
Purification of protein I/II
Recombinant protein I/II of Streptococcus mutans OMZ 175 was purified from pHBsr-1 transformed E. coli cell extract by gel filtration and immunoaffinity chromatography as described elsewhere [25]. The purity of the protein was checked by SDS–PAGE after staining with Coomassie blue. Protein I/II migrated as a single band having an apparent molecular weight of 195 kDa.
Stimulation of cells for total RNA extraction
FLSs (3 ×106 cells) were stimulated with 600 μL of serum-free RPMI 1640 containing protein I/II (125 pm final concentration). After a 4-hour incubation period, cells were centrifuged (130 g for 10 min at 4°C), and total RNA was extracted from cell pellets using the Nucleospin RNA II extraction kit in accordance with the manufacturer's instructions.
Stimulation of cells for pro-MMP-3 assay
FLSs (5 ×103 cells) were grown to confluence in 96-well plates (7–10 days) and then stimulated with 200 μL of serum-free RPMI 1640 containing protein I/II (125 pm final concentration). After an 18-hour incubation period, a heterologous two-site sandwich ELISA was used to estimate pro-MMP-3 release in the culture supernatants.
Gene expression profile analysis and quantification
The gene expression profile was examined using Atlas human cancer cDNA expression arrays, consisting of nylon membranes spotted in duplicate with cDNA fragments of 588 known genes. These genes are classified into several functional groups, such as oncogenes and tumor suppressors, growth factors and receptors, and regulators of cell adhesion, angiogenesis, and cell cycle (listed at ). Total RNA (2 μg) was converted into 32P-labeled first-strand cDNA following the protocol provided by the manufacturer. Briefly, total RNA was reverse-transcribed using MMLV (Moloney murine leukemia virus) reverse transcriptase and 32P-labeled dATP in a PCR thermal cycler set at 50°C for 25 min. Labeled cDNA probes were then purified from unincorporated 32P-labeled nucleotides and small (<0.1 kb) cDNA fragments using column chromatography. Probes (2–10 × 106 cpm) were freshly applied to cDNA array membranes to hybridize overnight at 68°C in continuously agitated roller bottles. After hybridization, membranes were washed four times, for 30 min each, at 68°C with 2 × SSC (standard saline citrate), 1% SDS, and once with 0.1 × SSC, 0.5% SDS, followed by one wash with 2 × SSC at room temperature. Array membranes were wrapped in plastic and exposed to a phosphor screen for 1–5 days, depending on the radiation intensity of the bound fragments. After image acquisition on a Storm phosphor imager (ImageQuant, Molecular Dynamics, Sunnyvale, CA, USA), spots were quantified using the AtlasImage 2.0 Software (Clontech), developed specifically for analysis of the Atlas cDNA expression arrays. All spots were individually checked by hand to ensure accuracy of the detection method. A dot was considered to be positive if it was well located and at least three times the local background level. Spots of poor quality were not included. For each patient, the relative expression level of each gene between protein-I/II-stimulated and control FLSs was evaluated and standardized based on expression levels of housekeeping genes included on each array.
RT-PCR reactions
Total RNA (2 μg) isolated from FLSs was reverse-transcribed using the First Strand cDNA Synthesis Kit in accordance with the manufacturer's instructions. Total RNA (8 μL) was mixed with 5 μl of the bulk reaction mix, 1 μl of DTT (200 mm), and 1 μl of the Not I-d(T)18 bifunctional primer (0.2 μg/ml). The reaction was carried out for 1 hour at 37°C. Real-time PCR was performed in 96-well plates in a total volume of 25 μl using the SYBR®Green PCR master mix (containing SYBRGreen dye, AmpliTAq Gold® DNA Polymerase, dNTPs with dUTP, passive reference and optimized buffer components) and gene-specific primers (250 nm): MMP-3 1) 5' GCA GTT TGC TCA GCC TAT CC 3' and 2) 5' GAG TGT CGG AGT CCA GCT TC 3' [26]; GAPDH (glyceraldehyde-3-phosphate dehydrogenase), 1) 5' AGC AAT GCC TCC TGC ACC ACC AAC 3' and 2) 5' CCG GAG GGG CCA TCC ACA GTC T 3' [27]. After incubation at 50°C for 10 min and at 95°C for 10 min, samples were subjected to 40 rounds of amplification for 15s at 95°C, 15s at 56°C, and 40s at 72°C using the AbiPrism 7700 Sequence Detection System (Applied Biosystems). Amplification products were detected as an increased fluorescent signal of SYBR®Green during the amplification cycles. Results were obtained using SDS Software (Applied Biosystems, Foster City, CA, USA) and evaluated using Excel (Microsoft). Primer efficiency was calculated for MMP-3 and GAPDH using the standard curve method in accordance with the supplier's recommendations. As the MMP-3 and GAPDH amplifications were about equally efficient, the relative expression levels of the MMP-3 gene were evaluated using the 2-ΔΔCT method as described by Applied Biosystems. Additionally, to confirm the amplification specificity of each gene product, the PCR products were subjected to a melting-curve analysis.
Pro-MMP-3 assay
Pro-MMP-3 levels in cell-culture supernatants were determined using the BINDAZYME™ pro-MMP-3 Enzyme Immunoassay Kit in accordance with the manufacturer's instructions. Briefly, duplicate samples were added to wells coated with anti-pro-MMP-3 and were then incubated with anti-pro-MMP-3 peroxidase conjugate; the peroxidase substrate (TMB) was then added. After the reaction had been stopped by the addition of phosphoric acid (3 m), the optical density of each well was read at 450 nm. Pro-MMP-3 levels were calculated using a standard curve.
Statistical analysis
Values are represented as means ± standard error of the mean. The significance of the results was analyzed using Student's two-tailed t-test. Values of P < 0.05 were considered significant.
Results
Gene expression in RA and OA FLSs after a stimulation with protein I/II
In this study we used a gene array technique to further investigate the cellular response of FLSs to protein I/II. As we were particularly interested in genes that could contribute to the aggressive behavior of RA FLSs, we chose Atlas human cancer cDNA expression arrays containing genes involved in cell-cycle regulation, cellular adhesion, and inflammation. In parallel to RA FLSs, we used OA FLSs in order to study the protein-I/II-induced response of FLSs isolated from a noninflammatory arthropathy. After a 4-hour incubation in the presence or absence of 125 pm protein I/II, the total RNA from FLSs of three RA and three OA patients was extracted, and radiolabeled cDNA probes were subsequently hybridized to the cDNA arrays. Figure 1 shows the expression patterns of FLSs isolated from one RA and one OA patient in nonstimulated controls after 4 hours (Fig. 1a,1c) and after stimulation with protein I/II for 4 hours (Fig. 1b,1d). The products of some differentially expressed genes (e.g. the gene for IL-6) are easily observed. The relative expression level of each gene in protein-I/II-stimulated FLSs versus control FLSs was evaluated with the AtlasImage 2.0 Software.
When a cut-off of a threefold change in mRNA expression was used, 28 genes were up-regulated and 5 genes were down-regulated in a least one of the three RA FLSs tested (Tables 1 and 2). These modulated genes account for about 6% of all profiled genes. Among the genes up-regulated by protein I/II in all three RA FLSs, two genes encoding proinflammatory cytokines, IL-6 and leukemia inhibitory factor, showed a strong expression level. The expression of other cytokine genes included on the array, such as TNF-α or IL-1, was not affected by protein I/II stimulation (IL-8 was not represented on the array). Protein I/II also strongly up-regulated the expression of MMP-3 (stromelysin 1), whereas the expression of genes encoding other members of the MMP family was not modified. Furthermore, a transcription factor, interferon regulatory factor 1 (IRF-1), as well as a growth factor, fibroblast growth factor-5 (FGF-5), were up-regulated by protein I/II in the three RA FLSs, although the expression levels were lower. Besides this group of genes which were strongly up-regulated in all RA FLSs, the expression of other genes was more heterogeneous and varied between the different RA FLS cultures. Among these genes, mainly involved in protein turnover and in cell signaling and communication, two genes, encoding CDC27Hs protein and interferon γ antagonist, were strongly up-regulated and the expression of three more genes (those encoding integrin αE, vimentin, and transmembrane protein SEX) was less intensely up-regulated in two of the three RA FLSs.
Interestingly, the expression of a few genes was down-regulated by protein I/II and only one gene was down-regulated in all three RA FLSs tested, namely, that for insulin-like growth factor binding protein-4. This member of the family of insulin-like growth factor binding proteins, which act as carriers and regulators of insulin-like growth factor, is believed to play an important role in maintaining the equilibrium between synthesis and degradation of tissue matrix molecules.
In OA FLSs, the number of genes modulated within 4 hours by protein I/II was much lower than in RA FLSs: protein I/II up-regulated the expression of only one gene, namely IL-6, in all OA FLSs tested. Other genes were up-regulated but with a high variability among FLSs cultures (Table 3). A very interesting finding was that one of the three OA FLS cultures (from patient 3) showed a gene expression pattern that was very close to the patterns obtained with protein-I/II-stimulated RA FLSs. Clinical investigations revealed that this patient had a secondary arthropathy of the knee occurring during a long remission phase of RA. This patient was therefore excluded from further experiments.
The up-regulation of IL-6 gene expression found in both RA and OA FLSs in response to protein I/II has been previously shown to be followed by an increase of IL-6 release from these cells (MB Zeisel and colleagues, unpublished data) [20]. Since we were primarily interested in genes that could contribute to the aggressive, invasive behavior of RA FLSs, the marked up-regulation of the MMP-3 gene, which plays a major role in cartilage degradation, prompted us to further study its expression in FLSs stimulated with protein I/II.
Confirmation of the differential expression of MMP-3 by RT-PCR analysis
In order to confirm the up-regulation of the expression of MMP-3 gene in RA FLSs by protein I/II, we performed real-time PCR analysis for MMP-3. Total RNA was extracted from FLSs isolated from three RA FLSs stimulated with protein I/II or left untreated, reverse-transcribed, and then amplified by real-time PCR. MMP-3 mRNA showed a mean 36-fold up-regulation after protein I/II stimulation of RA FLSs in comparison with control cells, confirming the results obtained with the cDNA array experiments.
Confirmation of the differential expression of MMP-3 by pro-MMP-3 assay
To determine whether the up-regulation of the expression of MMP-3 gene by protein I/II stimulation in RA FLSs resulted in changes in gene translation and thereby contributed to an increase in the release of pro-MMP-3, we next measured the level of pro-MMP-3 in culture supernatants from control and RA FLSs that had been stimulated with protein I/II for 18 hours. As MMP-3 is first synthesized as a proenzyme (pro-MMP-3), which is subsequently cleaved to generate active MMP-3, we chose to assay pro-MMP-3 levels, as the activation process might not occur in our in vitro cultures. Figure 2 shows that control RA FLSs basally released pro-MMP-3 (1.4 ± 0.4 ng/mL) and that stimulation with protein I/II induced an increase in pro-MMP-3 secretion (3.1 ± 0.4 ng/mL, P < 0.01). Control OA FLSs basally released pro-MMP-3, but protein I/II stimulation did not significantly increase pro-MMP-3 secretion from these cells (Fig. 2). To confirm the activation of FLSs, we assayed IL-6 and IL-8 levels in the same supernatants used for pro-MMP-3 assay, and found that protein I/II induced a significant increase in the release of these cytokines from both RA and OA FLSs (data not shown).
Discussion
In this study, we analyzed gene expression changes in RA FLSs after stimulation with protein I/II, a ligand of integrin α5β1, a PRR highly expressed on FLSs. We chose to study the early induction of genes, within 4 hours of protein I/II stimulation, in order to analyze the direct effects of protein I/II, but we cannot rule out that some genes can be modulated in an autocrine fashion by some FLS-secreted mediators. Our results showed that stimulation of FLSs through this PAMP/PRR pathway induced the expression of several genes that may be involved in inflammation, matrix degradation, and invasiveness.
Protein I/II strongly up-regulated the expression of genes encoding IL-6 and leukemia inhibitory factor in RA FLSs. Although a great many interleukins (IL-1 to IL-17 except IL-8 and IL-16) as well as members of the TNF family were represented on the cDNA arrays, the expression of genes for only these two cytokines was modulated by protein I/II. IL-6 and leukemia inhibitory factor, which both belong to the same family of cytokines, are known to participate substantively in the pathogenesis of murine models of arthritis as well as RA. In fact, they contribute to cartilage degradation by inducing proteoglycan resorption and inhibiting proteoglycan synthesis [28].
Elevated expression of MMP-3 gene as well as pro-MMP-3 secretion in response to protein I/II were also found in RA FLSs, whereas the expression of other MMPs (MMP-1 to MMP-3 and MMP-7 to MMP-18) present on the arrays were not affected by protein I/II stimulation. FLSs have been reported to express different kinds of MMPs, but, as was found for cytokines, protein I/II seemed to up-regulate only a limited number of genes encoding matrix-degrading enzymes. Interestingly, the only MMP up-regulated by protein I/II is MMP-3, which is one of the major MMPs involved in cartilage destruction, and it has also been associated with an increased invasive potential of cells [29].
Kyburz and colleagues found similar cytokine and MMP expression profiles (i.e. IL-6, IL-8, and MMP-3) after stimulation of RA FLSs with the TLR-2 ligand peptidoglycan [16]. Peptidoglycan is one of the bacterial components that have been detected in the synovial cavity [9] and have been shown to induce an inflammatory response in the joint [13]. In contrast to our results, peptidoglycan not only induced the expression of MMP-3 but also seemed able to up-regulate the expression of other MMPs, such as MMP-1, MMP-9, and MMP-13 [16]. Furthermore, this bacterial component up-regulated the expression of several chemokine genes, including RANTES (regulated upon activation, normal T-cell expressed and secreted) and MCP-1 (monocyte chemoattractant protein-1) [30], but these genes were not represented on the arrays used in this study. These data indicate that various PRRs present on FLSs may contribute in different ways to the aggressive behavior of RA FLSs.
In this study, we also evaluated how FLSs isolated from OA patients responded to protein I/II. In our cDNA analysis, protein I/II induced strong IL-6 production in both RA and OA FLSs. In contrast to RA FLSs, OA FLSs did not express leukemia inhibitory factor or MMP-3. Our results are in contrast with those of Pierer and colleagues, who did not find a significant difference between RA and OA FLSs regarding MMP-3 and chemokine expression after stimulation with peptidoglycan. However, they found a higher chemokine level in the synovial fluid of RA patients than in OA patients [30]. The differences we noted between the gene expression profiles of protein-I/II-stimulated RA and OA FLSs could be due to particular features of RA FLSs but are probably not due to the absence of integrin α5β1, which is expressed on both RA and OA FLSs in vitro (data not shown).
Moreover, we have shown that protein I/II induced the expression of the transcription factor IRF-1, the growth factor FGF-5, and vimentin in RA but not OA FLSs. IRF-1 activates transcription from genes that play a role in inflammation, such as cyclooxygenase-2 and caspase 1, but also in apoptosis or cell growth inhibition [31]. In contrast to IRF-1, FGF-5 and vimentin are associated with increased cell growth, motility, and invasiveness [32,33]. Protein I/II seems thus able to act on FLSs apoptosis/proliferation by inducing different genes, although their effects may be opposite.
These PAMP–PRR interactions are probably not sufficient to explain activation of FLSs in RA and it is evident that additional factors, such as genetic polymorphisms, play an important role, because bacterial cell wall components, such as peptidoglycan, have also been found in joint tissues from OA patients but do not induce the inflammatory response occurring in RA. However, bacterial components probably have an important role in perpetuating joint inflammation. In support of this hypothesis, Choe and colleagues showed that innate immune functions via TLR-4 may perpetuate inflammatory mechanisms and bypass the need for IL-1 in chronic joint inflammation. In fact, in the transgenic K/BXN mice, IL-1 plays a key role in joint swelling and destruction, but administration of the TLR-4 ligand LPS along with arthritogenic serum from K/BXN mice resulted in joint swelling and destruction in IL-1R-deficient mice but not in MyD88-deficient mice [34].
Further experiments will be necessary to define the mechanisms underlying this PAMP-specific response of RA FLSs.
Conclusion
Our results suggest that a bacterial ligand of integrin α5β1 may contribute to the aggressive behavior of RA FLSs by inducing the release of pro-inflammatory cytokines, such as IL-6, and a cartilage-degrading enzyme, MMP-3.
Abbreviations
ELISA = enzyme-linked immunosorbent assay; FACS = fluorescence-activated cell sorter; FCS = fetal calf serum; FGF = fibroblast growth factor; FLS = fibroblast-like synoviocyte; GAPDH = glyceraldehyde-3-phosphate dehydrogenase; IL = interleukin; IRF = interferon regulatory factor; LPS = lipopolysaccharide; MMP = matrix metalloproteinase; OA = osteoarthritis; PAMP = pathogen-associated molecular pattern; PCR = polymerase chain reaction; PRR = pattern-recognition receptor; RA = rheumatoid arthritis; RT = reverse transcriptase; SSC = standard saline citrate; TLR = Toll-like receptor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MBZ carried out the study and drafted the manuscript. VAD participated in the study. DW and JS coordinated the study. All authors read and approved the manuscript.
Acknowledgements
We would like to thank Patrick Garnero for pro-MMP-3 assay and Claude Philippe for technical assistance with real-time PCR. We are grateful to Francis Raul, in whose laboratory cDNA array experiments were performed. MBZ is supported by a grant from the French Ministry of Research and Technologies, and VAD, by grants from Région Alsace, Amgen, and Wyeth Lederle. This work was supported in part by a grant from the CAMPLP(Caisse d'assurance maladie des professions liberals de province).
Figures and Tables
Figure 1 Gene expression patterns in rheumatoid arthritis (RA) and osteoarthritis (OA) fibroblast-like synoviocytes (FLSs). Total RNA of control RA (a) and OA FLSs (c) and of RA (b) and OA FLSs (d) stimulated for 4 hours with protein I/II were first reverse-transcribed and 32P-labeled and then hybridized to Atlas human cancer cDNA expression arrays. Arrows indicate double spots representing IL-6.
Figure 2 Protein-I/II-induced release of pro-matrix metalloproteinase (pro-MMP-3) from rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLSs) and osteoarthritis (OA) FLSs. The level of pro-MMP-3 was determined in culture supernatants from control RA FLSs stimulated for 18 hours with protein I/II (n = 3) and OA FLSs (n = 3), using heterologous two-site sandwich ELISA. ELISA was performed in duplicate. Experiments were performed in duplicate. Values are expressed as means ± standard error of the mean. Statistical comparison of protein-I/II-stimulated and control samples was performed using the t-test (**P < 0.01; NS, not significant).
Table 1 Up-regulated genes in rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLSs) stimulated with protein I/II. Confluent RA FLSs were stimulated with protein I/II for 4 hours or left untreated as controls. Total RNA was isolated, reverse-transcribed into 32P-labeled cDNA, and hybridized to Atlas human cancer cDNA expression arrays. The values are expression ratios (protein I/II/ control).
RA patient
Protein product of gene GenBank accession no. 1 2 3
Matrix metalloproteinase 3 X05232 5.0 190.3 6.0
IL-6 X04602 14.6 65.0 4.0
Leukemia inhibitory factor X13967 10.8 15.9 5.0
Interferon regulatory factor 1 X14454 9.4 15.3 3.4
Fibroblast growth factor-5 M37825 3.5 2.9 5.7
CDC27Hs protein U00001 7.4 - 10.6
Interferon gamma antagonist A25270 5.4 - 13.0
Integrin αE L25851 3.9 - 3.5
Vimentin X56134 3.2 - 3.7
Transmembrane protein SEX X87852 3.8 - 3.0
TRK-T3 X85960 - - 7.3
Collagen I α2 subunit X55525 5.7 - -
Transforming growth factor β2 M19154 - - 4.8
Cell division protein kinase 4 M14505 4.7 - -
Bone morphogenetic protein 2A M22489 - 4.5 -
Rho-related GTP-binding protein RhoE X95282 4.3 - -
Cyclin H U11791 4.3 - -
Tyrosine-protein kinase receptor UFO M76125 3.9 - -
Tissue inhibitor of metalloproteinase 1 X03124 3.9 - -
BIGH3 M77349 3.8 - -
Ras-related C3 botulinum toxin substrate 1 M29870 3.6 - -
Transforming protein RhoA H12 L25080 3.5 - -
CD9 antigen M38690 3.5 - -
Notch homolog M99437 3.4 - -
Integrin β8 M73780 3.4 - -
Secreted protein acidic and rich in cysteine J03040 3.2 - -
α2-Macroglobulin receptor X13916 3.1 - -
Fibronectin 1 X02761 3.0 - -
-, not found or <3.0.
Table 2 Down-regulated genes in rheumatoid arthritis (RA) fibroblast-like synoviocytes (FLSs) stimulated with protein I/II. Confluent RA FLSs were stimulated with protein I/II for 4 hours or left untreated as controls. Total RNA was isolated, reverse-transcribed into 32P-labeled cDNA, and hybridized to Atlas human cancer cDNA expression arrays. The values are expression ratios (protein I/II / control).
RA patient
Protein product of gene GenBank accession no. 1 2 3
Insulin-like growth factor binding protein-4 M62403 0.1 0.2 0.2
Insulin-like growth factor binding protein-5 M65062 - 0.2 -
Cyclin-dependent kinase 4 inhibitor D U40343 - - 0.3
Ubiquitin-conjugating enzyme E2A M74524 - - 0.3
Methallothionein III D13365 0.3 - -
Table 3 Up- and down-regulated genes in osteoarthritis (OA) fibroblast-like synoviocytes (FLSs) stimulated with protein I/II. Confluent OA FLSs were stimulated with protein I/II for 4 hours or left untreated as controls. Total RNA was isolated, reverse-transcribed into 32P-labeled cDNA, and hybridized to Atlas human cancer cDNA expression arrays. The values are expression ratios (protein I/II / control).
OA patient
Protein product of gene GenBank accession no. 1 2 3a
Up-regulated
IL-6 X04602 3.0 3.8 6.8
Interferon-gamma receptor β subunit U05875 5.8 - 7.7
Interferon regulatory factor 1 X14454 - 11.0 5.5
Leukemia inhibitory factor X13967 4.1 - 5.0
Matrix matalloproteinase 3 X05232 - - 13.3
Placenta growth factors 1+2 X54936 - - 4.6
Glia maturation factor β M86492 - - 4.5
Transforming growth factor β2 M19154 - - 4.5
Growth inhibitory factor D13365 - - 3.4
Rho-related GTP-binding protein RhoE X95282 - - 3.2
Vascular endothelial growth factor receptor 1 X51602 - - 3.2
Collagen VI α3 subunit X52022 - - 3.2
Early growth response protein 1 X52541 - - 3.0
TRK-T3 X85960 - - 3.0
Down-regulated
Vimentin X56134 - 0.3 -
Transmembrane protein SEX X87852 - 0.3 -
Integrin β8 M73780 - 0.3 -
aClinical investigations revealed that, besides OA, patient 3 (results in bold type) also had RA.
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| 15642131 | PMC1064889 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 24; 7(1):R118-R126 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1462 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14631564214110.1186/ar1463Research ArticleQuantitative ultrasonic assessment for detecting microscopic cartilage damage in osteoarthritis Hattori Koji [email protected] Ken [email protected] Yusuke [email protected] Yoshinori [email protected] Department of Orthopaedic Surgery, Nara Medical University, Kashihara, Nara, Japan2 Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan2005 16 11 2004 7 1 R38 R46 4 8 2004 1 10 2004 9 10 2004 16 10 2004 Copyright © 2004 Hattori et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Osteoarthritis (OA) is one of the most prevalent chronic conditions. The histological cartilage changes in OA include surface erosion and irregularities, deep fissures, and alterations in the staining of the matrix. The reversibility of these chondral alterations is still under debate. It is expected that clinical and basic science studies will provide the clinician with new scientific information about the natural history and optimal treatment of OA at an early stage. However, a reliable method for detecting microscopic changes in early OA has not yet been established. We have developed a novel system for evaluating articular cartilage, in which the acoustic properties of the articular cartilage are measured by introducing an ultrasonic probe into the knee joint under arthroscopy. The purpose of this study was to assess microscopic cartilage damage in OA by using this cartilage evaluation system on collagenase-treated articular cartilage in vivo and in vitro. Ultrasonic echoes from articular cartilage were converted into a wavelet map by wavelet transformation. On the wavelet map, the maximum magnitude and echo duration were selected as quantitative indices. Using these indices, the articular cartilage was examined to elucidate the relationships of the ultrasonic analysis with biochemical, biomechanical and histological analyses. In the in vitro study, the maximum magnitude decreased as the duration of collagenase digestion increased. Correlations were observed between the maximum magnitude and the proteoglycan content from biochemical findings, and the maximum magnitude and the aggregate modulus from biomechanical findings. From the histological findings, matrix staining of the surface layer to a depth of 500 μm was closely related to the maximum magnitude. In the in vivo study, the maximum magnitude decreased with increasing duration of the collagenase injection. There was a significant correlation between the maximum magnitude and the aggregate modulus. The evaluation system therefore successfully detected microscopic changes in degenerated cartilage with the use of collagen-induced OA.
cartilageevaluationosteoarthritisultrasoundwavelet transformation
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Introduction
Osteoarthritis (OA), also referred to as degenerative joint disease, is one of the most prevalent chronic conditions. It consists of a general progressive loss of articular cartilage, remodeling and sclerosis of the subchondral bone, and the formation of subchondral bone cysts and marginal osteophytes. In particular, the degenerative processes of articular cartilage can be accelerated by a single traumatic event, multiple repetitive loads, or local chemical and mechanical factors [1]. The histological changes that occur in cartilage in OA are a striking feature of the disease. The earliest alterations include surface erosion and irregularities, deep fissures and alterations in the staining of the matrix. The reversibility of these chondral alterations is still under debate [2]. It is expected that clinical and basic science studies will provide the clinician with new scientific information about the natural history and optimal treatment of OA at an early stage. However, a reliable method for detecting microscopic changes in early OA has not yet been established.
We previously developed a novel system for evaluating articular cartilage, in which the acoustic properties of articular cartilage are measured by introducing an ultrasonic probe into the knee joint under arthroscopy [3,4]. The analysis system is based on wavelet transformation of the reflex echogram from articular cartilage. In detail, reflex echograms from many articular cartilage samples were transformed into wavelet maps by wavelet transformation and examined in detail. The results revealed two quantitative parameters on the wavelet maps that could be used as indices for the quantitative assessment of articular cartilage, namely the maximum magnitude and the echo duration at the 95% interval of the maximum magnitude. Macroscopic articular cartilage degeneration would result in a decreased magnitude and prolonged echo duration, as indicated by the L-shaped distribution obtained with human cadaver cartilage. However, the point at which this system can detect microscopic changes in articular cartilage degeneration is unknown. If our evaluation system can detect microscopic changes in cartilage in vivo and in vitro, it may provide a means to solve the problem of whether or not microscopic damage in OA is reversible. Moreover, this system will provide new information about the natural history and treatment of OA.
The purpose of this study was to investigate the clinical usefulness of our system for evaluating microscopic damage in OA. We therefore evaluated articular cartilage with no visible disruption in collagenase-induced experimental OA, using our system to assess the microscopic damage. The present study was also performed to investigate the correlation between ultrasonic examination and biomechanical or biochemical examination. The goal of our study was to further elucidate the processes of articular cartilage degeneration with the use of our ultrasonic evaluation system.
Materials and methods
In vitro study
Pig osteochondral plugs (diameter 5 mm; n = 77) were prepared for this study. The pig cartilage was delivered intact within 6 hours of slaughter, and the knee joints were stored at less than -30°C until use. During the preparation, the knee joints were first thawed in saline at 20°C and the joint cartilage was then exposed. Osteochondral plugs were excised from a flat area of the cartilage with a metal punch. The osteochondral samples were subsequently digested in PBS (Invitrogen Corporation, Carlsbad, CA, USA) containing 30 U/ml collagenase type ΙΙ (Worthington Biochemical Corporation, Lakewood, NJ, USA) at 37°C for 1, 2, 4, 8, 16 and 24 hours. Cartilage samples in PBS alone at 37°C were used as controls. After digestion, all the samples in each group (n = 11) were examined by ultrasonic evaluation. Four samples in each group were prepared for mechanical testing by cartilage indentation. Four samples of each group were used for biochemical examination and were separated from the bone with the use of an autopsy saw. Three samples in each group were prepared for histological analysis.
Ultrasonic analysis
Our evaluation method was described in detail in a previous manuscript [3], and is illustrated in Fig. 1. In brief, during arthroscopic examination, ultrasonic evaluation was performed by using an ultrasonic probe with a transducer fixed to the tip. The transducer (Panametrics Japan Co. Ltd., Tokyo, Japan) was small (diameter 3 mm; thickness 3 mm) and used a flat ultrasonic wave (center frequency 10 MHz). Ultrasonic echoes from the cartilage surface were converted into a wavelet map by wavelet transformation. The wavelet transformation (W(a,b)) of the reflex echogram (f(t)) is expressed by
where Ψ(t) is the mother wavelet function.
For the mother wavelet function, Gabor's function was selected. The right side of Fig. 1 shows a typical ultrasonic echogram (upper) and wavelet map (lower) of an intact articular cartilage surface in vitro. The wavelet map shows a two-dimensional map whose x-axis and y-axis represent time and frequency, respectively, and the magnitude is indicated by the gray scale. As quantitative indices we used the maximum magnitude and echo duration, which was defined as the length of time that included 95% of the echo signal. These indices were calculated automatically by a computer. Articular cartilage was evaluated in vivo and in vitro with these two indices.
Biochemical analysis
The cartilage samples were freeze-dried overnight after measuring the wet weight. The dry weight of the samples was then measured, and the amount of water was calculated. The water content of the cartilage was determined as a percentage by using the following equation: 100 × (wet weight - dry weight)/wet weight. The samples were digested with papain (Sigma Chemical Co., St Louis, MO, USA) (40 μg/ml in 20 mM ammonium acetate, 1 mM EDTA, 2 mM dithiothreitol) for 48 hours at 65°C and then stored at -20°C until analysis. Aliquots of the digests were assayed separately for the proteoglycan and collagen contents. The proteoglycan content was estimated by quantifying the amount of sulfated glycosaminoglycans with the use of a dimethylmethylene blue dye binding assay (Polyscience Inc., Washington, PA, USA) and spectrophotometry (wavelength 525 nm). A standard curve for the analysis was generated with bovine trachea chondroitin sulfate A (Sigma). The collagen content was estimated by determination of the hydroxyproline content. Aliquots of the papain digest were hydrolyzed at 110°C in 6 M HCl for 18 hours. The hydroxyproline content of the resulting hydrolyzate was determined by the chloramine-T/Ehrlich reagent assay and spectrophotometry (wavelength 561 nm). A standard curve for this analysis was generated with L-hydroxyproline (Sigma).
Biomechanical analysis
A custom-made indentation testing device was used for mechanical testing to determine the creep and recovery behavior of the osteochondral samples. The samples were mounted on stainless steel plates with cyanoacrylate cement such that the rigid porous indenter tip was perpendicular to the test site on the cartilage surface. The porous indenter was made of titanium alloy particles (Ti-6Al-4V; diameter 75–180 μm). The porous permeable indenter tip (diameter 1.5 mm) was ultrasonically cleaned before testing to ensure ease of fluid flow from the specimen into the tip. The displacement of the indenter was measured using a laser measurement sensor (LB040/LB-1000; Keyence Corporation, Osaka, Japan). After equilibration under a tare load (0.0098 N), the test load (0.0098 N) was applied and the osteochondral specimen was allowed to creep to equilibrium. Equilibrium was determined as being when no further variations occurred in the observed creep value for 20 min. After creep equilibrium had been achieved, the test site was unloaded and the recovery was observed. The cartilage thickness was then measured at an exact location and orientation site with a penetrating steel needle probe. The aggregate modulus, Ha, was determined from the equilibrium stress–strain data as described by Mow and colleagues [5,6].
Histological analysis
The cartilage samples were fixed in 10% formalin, decalcified in EDTA and then embedded in paraffin. Sagittal sections of 5 μm thickness were prepared from the center of the samples and stained with Safranin-O.
In vivo study
The experimental OA model used in this study was created by intra-articular injection of collagenase into rabbit knee joints as reported by Kikuchi and colleagues [7]. Collagenase type ΙΙ (Worthington Biochemical Corporation) was dissolved in saline (530 U/ml), filtered with a 0.22 μm pore-size membrane, and used for the intra-articular injection. Japanese white adult rabbits (male, weight 3.0–5.5 kg; n = 24) were anesthetized with a mixture of ketamine (50 mg/ml) and xylazine (20 mg/ml) at a ratio of 2:1, by means of a dose of 1 ml/kg body weight injected intramuscularly into the gluteal muscle. After both knee joints had been shaved and sterilized, 0.5 ml of collagenase solution was injected intra-articularly into the right knee joint and/or saline was injected into the left knee joint as a control. The injection was performed twice, on days 1 and 4 of the experiment. The rabbits were returned to their cages and allowed to move freely without joint immobilization. For each experiment, four rabbits were killed at 0, 1, 4, 8, 12 and 16 weeks after the start of the experiment with an overdose of phenobarbital sodium salt, although two rabbits were discounted from the study because of a bacterial infection and a patellar dislocation, respectively. All the remaining knee joints were opened and the cartilage surfaces were observed macroscopically and photographed. The knee joint was dissected free from all soft tissues and the tibia was removed. The distal femur was cut proximally to the patellofemoral joint and cartilage samples were taken. Ultrasonic and biomechanical analyses were performed on the medial femoral condyle. For histological analysis, the lateral femoral condyles of the cartilage samples were fixed in 10% formalin, decalcified in EDTA and then embedded in paraffin. Sagittal sections of 5 μm thickness were prepared from the center of the samples and stained with Safranin-O. This study was approved by the Nara Medical University Ethics Committee.
Statistical analysis
All data in this study are reported as means ± SD. The changes in the maximum magnitude, echo duration, water content, chondroitin sulfate content, hydroxyproline content and aggregate modulus with respect to the collagenase treatment duration were analyzed by one-way analysis of variance. Pearson correlations were performed to determine the associations between the ultrasonic data and the biochemical or biomechanical data. The significance level was set at P < 0.05.
Results
In vitro study
Ultrasonic measurement
The maximum magnitude decreased as the duration of collagenase digestion increased. There was a rapid decrease in the maximum magnitude after 8 hours of digestion in comparison with the control, and then a gradual decrease from 8 to 24 hours (Fig. 2a). There was no significant change in echo duration over the time course of digestion (Fig. 2b).
Biochemical measurement
The water content gradually increased over the time course of collagenase digestion (Fig. 3a). At the same time, the chondroitin sulfate content decreased rapidly with increasing duration of digestion. There was a rapid decrease in the chondroitin sulfate content after 8 hours of digestion and then a gradual decrease from 8 to 24 hours (Fig. 3b). There was a significant correlation between maximum magnitude and chondroitin sulfate content (R2 = 0.6164, P < 0.01) (Fig. 3c). There was very little change in hydroxyproline content during collagenase digestion (Fig. 3d). There was no significant correlation between maximum magnitude and hydroxyproline content (R2 = 0.069, P = 0.176) (Fig. 3e).
Biomechanical measurement
The aggregate modulus rapidly decreased during the first 4 hours of collagenase digestion, but there was no subsequent change from 4 to 24 hours (Fig. 4a). There was a significant correlation between maximum magnitude and aggregate modulus (R2 = 0.739, P < 0.01) (Fig. 4b).
Histological findings
Representative sections of collagenase-digested cartilage stained with Safranin-O are shown in Fig. 5. In control cartilage, the Safranin-O staining of the extracellular matrix appeared almost homogeneous. After 1 hour of digestion, the superficial layer showed slight changes in the Safranin-O staining. After 8 hours of digestion, the surface layer to a depth of 500 μm was not stained with Safranin-O. Over the course of degeneration time, Safranin-O staining became less intense in the deeper layers.
In vivo study
Macroscopic and histological findings
Figure 6 shows the macroscopic and histological findings of the collagenase-injected articular cartilage. Macroscopically, cartilage surface changes were not detected on either femoral condyle of the rabbits. Histologically, chondrocyte cluster formation was seen and the surface layer was not stained with Safranin-O at 4 weeks after injection. Several fissures were observed in the surface area at 8 weeks after injection.
Ultrasonic measurement
The maximum magnitude decreased with increasing time after collagenase injection. There was a rapid decrease in the maximum magnitude at 4 weeks after injection, in comparison with control samples (Fig. 7a). However, there was no significant decrease in echo duration after injection (Fig. 7b).
Biomechanical measurement
In the same manner as for the in vitro study, the relationship between the maximum magnitude and the aggregate modulus was investigated: there was a significant correlation (R2 = 0.5173, P < 0.05) (Fig. 8).
Discussion
The results of this study indicate that ultrasonic examination is promising as a minimally invasive method of evaluating microscopic damage in OA at an early stage. To evaluate microscopic damage to articular cartilage, reflex echoes from the cartilage were transformed into a wavelet map, and the echo duration and maximum magnitude were calculated and used as quantitative indices of cartilage degeneration. According to this study, the maximum magnitude was shown to reflect the proteoglycan content from biochemical analysis, the aggregate modulus from biomechanical analysis and the decrease in Safranin-O staining of the cartilage surface from histological analysis.
There are numerous clinical methods of grading the degenerative changes and injuries to articular cartilage at the time of surgery or arthroscopy with direct observation of the cartilage surface [8-10]. The overall observation from macroscopic findings and probing is that cartilage lesions vary in location, depth, size and shape. In addition, it is well established that probing cannot evaluate the cartilage condition quantitatively. As a quantitative method that could replace probing, attempts have been made to evaluate cartilage using magnetic resonance imaging, but such in situ evaluation has been performed only in experimental trials [11-13]. Cartilage biopsy and histological examination have been performed to evaluate articular cartilage clinically. However, it is still difficult to measure the degree of cartilage degeneration in a non-destructive manner. Therefore, further developments in diagnostic techniques are required for in situ evaluation.
Several different approaches have been investigated to improve the techniques for diagnosing the condition of cartilage, including optical coherence tomography [14], electromechanical evaluation [15], mechanical indentation [16], ultrasonic evaluation [17,18] and ultrasonic indentation [19-21]. Most of these approaches are still under development and only a few devices have been used successfully for cartilage evaluation during clinical investigations.
Ultrasonic indentation methods are capable of determining the cartilage thickness and deformation, and can therefore be used to determine the Young's modulus of articular cartilage. In a clinical context, Lyyra and colleagues [19] reported the efficacy of an ultrasonic indentation instrument under arthroscopic control for the quantification of cartilage stiffness, as evaluated with three human cadaver knees. This might prove to be suitable for clinical use, but the rod of the instrument (5 mm in diameter) is too thick to evaluate the cartilage in all regions of knee joints or the cartilage in ankle and wrist joints [21]. In contrast, our ultrasonic probe is so small (4 mm wide and 2.5 mm thick) that we can evaluate living human joint cartilage under arthroscopy. Moreover, we have reported clinically relevant data obtained from living human cartilage in situ [4].
Ultrasonic measurement under arthroscopy has three merits in comparison with arthroscopic indentation. The first is that the possibility of tissue damage caused by the measurement device can be completely excluded owing to the non-contact measurement. The second is that the evaluation system can predict the histological findings of cartilage on the basis of studies in experimental animal models [22,23]: hyaline cartilage has a higher maximum magnitude than fibrous tissue, whereas imperfectly regenerated cartilage has a lower maximum magnitude, even when only fibrous tissue and fibrocartilage are present in the superficial layer of the repaired tissue. The third is that the ultrasonic probe used in the evaluation is so small that it should be useful not only for articular cartilage in the knee joint but also for that in the wrist and ankle joints under arthroscopy.
Before this investigation, the maximum magnitude and echo duration were used as quantitative indices of degenerated cartilage, but it was not known what the indices were closely related to [3]. However, this study using a collagenase-induced OA model clarified the significance of the maximum magnitude. From an acoustic point of view, the maximum magnitude is a modification of the echo reflection from the cartilage surface, and hence differences in the surface reflection indicate significant alterations in the acoustic impedance among degenerated cartilage samples. From the histological findings, the matrix staining of the surface layer to a depth of 500 μm was closely related to the maximum magnitude. From a biochemical point of view, the proteoglycan content was more related to the maximum magnitude than the type ΙΙ collagen content. The collagen content showed little change after collagenase digestion in this study, although the collagen meshwork is widely known to be the main reflector of ultrasound and the source of ultrasound backscatter [24-26]. However, the apparent inconsistency between these observations and our results would be due to differences between the reflex echoes from flat ultrasound and focal ultrasound. From a biomechanical point of view, the maximum magnitude was related to the aggregate modulus from the mechanical properties of the articular cartilage. Therefore, the maximum magnitude reveals microstructural changes in degenerated cartilage and can provide diagnostically important information about the degenerated cartilage.
In this study, the echo duration showed no change over the time course of collagenase digestion. From the histological findings, the cartilage surface was smooth after collagenase digestion in the in vitro study and had several fissures only at 8 weeks after the collagenase injection. According to the previous human cadaver study, the echo duration becomes longer with macroscopic roughening of the cartilage surface due to wear [3]. Moreover, Myers and colleagues showed that the width of the echo band can be related to the depth of fibrillation in the macroscopic degenerative cartilage surface [27]. The echo duration is therefore closely related to the macroscopic fibrillation of articular cartilage.
There are three limitations to this study. First, the cartilage samples in this study were not human OA cartilage but collagenase-treated articular cartilage. However, OA-like changes were observed in the experimental animals after induction by intra-articular injection of collagenase, and enzyme-induced OA models are also used to investigate the pathogenesis of OA. Second, our evaluation system could not detect any microscopic roughness of the articular cartilage by using the index of echo duration. To detect this histological change, high-frequency ultrasound might be required. Finally, we did not detect the progression of cartilage degeneration in living humans. However, we have reported relevant clinical acoustic data from human cartilage in situ under arthroscopy. Further studies are therefore needed to determine whether this evaluation system will be beneficial for studying the pathogenesis of OA.
Conclusion
Ultrasonic evaluation using a wavelet map can support the evaluation of microscopic damage of articular cartilage in OA. The evaluation system is suitable for clinical use under arthroscopy. This evaluation successfully predicted the histological findings of degenerated cartilage with the use of a collagen-induced OA model. We believe that our findings offer the potential for standardized evaluation as an adjunct to further research in this field, which will lead to a reliable method for the quantification of articular cartilage treatments.
Abbreviations
OA = osteoarthritis.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KH conceived the study, participated in its design and performed all the experiments. KI and YM performed biomechanical studies. YT participated in the design of the study and participated in the in vivo study. All authors read and approved the final manuscript.
Acknowledgements
We thank Syoji Mizuno and Tetsuro Maejima for their help in the biochemical analysis.
Figures and Tables
Figure 1 Schematic illustration of the articular cartilage analysis and measurement methods of the cartilage samples. A reflex echogram of articular cartilage and a wavelet map are shown. The maximum magnitude is indicated by the gray scale and the echo duration is defined as the length of time for which 95% of the echo signal is detected.
Figure 2 Time courses of the maximum magnitude (P < 0.01) (a) and echo duration (P = 0.14) (b) in collagenase-digested pig articular cartilage. Values are means ± SD.
Figure 3 Time courses of the water content (P < 0.01) (a), chondroitin sulfate content (P < 0.01) (b) and hydroxyproline content (P = 0.23) (d) in collagenase-digested articular cartilage. Values are means ± SD. The relationships between the maximum magnitude and the chondroitin sulfate content (c) and the maximum magnitude and the hydroxyproline content (e) are also shown.
Figure 4 Time course of the aggregate modulus (P < 0.01) (a) in collagenase-digested articular cartilage. The relationship between the maximum magnitude and the aggregate modulus (b) is also shown.
Figure 5 Photomicrographs of pig articular cartilage after 1 hour (a), 4 hours (b), 8 hours (c) and 16 hours (d) of collagenase digestion (Safranin-O stain; magnification × 4).
Figure 6 Macroscopic findings of rabbit articular cartilage at 1 week (a), 4 weeks (b) and 8 weeks (c) after collagenase injection. Photomicrographs of rabbit articular cartilage at 1 week (d), 4 weeks (e) and 8 weeks (f) after collagenase injection are also shown (Safranin-O staining; magnification × 4).
Figure 7 Time courses of the maximum magnitude (P < 0.01) (a) and echo duration (P = 0.55) (b) in collagenase-injected rabbit articular cartilage. Values are means ± SD.
Figure 8 Correlation between the maximum magnitude and the aggregate modulus in collagenase-injected articular cartilage.
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| 15642141 | PMC1064890 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Nov 16; 7(1):R38-R46 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1463 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14641564213310.1186/ar1464Research ArticleTumor necrosis factor alpha and epidermal growth factor act additively to inhibit matrix gene expression by chondrocyte Klooster Aaron R [email protected] Suzanne M [email protected] CIHR Group in Skeletal Development and Remodeling, Department of Anatomy and Cell Biology, The University of Western Ontario, London, Ontario, Canada2005 29 11 2004 7 1 R127 R138 26 7 2004 23 9 2004 8 10 2004 22 10 2004 Copyright © 2004 Klooster and Bernier., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
The failure of chondrocytes to replace the lost extracellular matrix contributes to the progression of degenerative disorders of cartilage. Inflammatory mediators present in the joint regulate the breakdown of the established matrix and the synthesis of new extracellular matrix molecules. In the present study, we investigated the effects of tumor necrosis factor alpha (TNF-α) and epidermal growth factor (EGF) on chondrocyte morphology and matrix gene expression. Chondrocytes were isolated from distal femoral condyles of neonatal rats. Cells in primary culture displayed a cobblestone appearance. EGF, but not TNF-α, increased the number of cells exhibiting an elongated morphology. TNF-α potentiated the effect of EGF on chondrocyte morphology. Individually, TNF-α and EGF diminished levels of aggrecan and type II collagen mRNA. In combination, the effects of TNF-α and EGF were additive, indicating the involvement of discrete signaling pathways. Cell viability was not compromised by TNF-α or by EGF, alone or in combination. EGF alone did not activate NF-κB or alter NF-κB activation by TNF-α. Pharmacologic studies indicated that the effects of TNF-α and EGF alone or in combination were independent of protein kinase C signaling, but were dependent on MEK1/2 activity. Finally, we analyzed the involvement of Sox-9 using a reporter construct of the 48 base pair minimal enhancer of type II collagen. TNF-α attenuated enhancer activity as expected; in contrast, EGF did not alter either the effect of TNF-α or basal activity. TNF-α and EGF, acting through distinct signaling pathways, thus have additive adverse effects on chondrocyte function. These findings provide critical insights into the control of chondrocytes through the integration of multiple extracellular signals.
chondrocyteepidermal growth factorextracellular matrixsignalingtumor necrosis factor alpha
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Introduction
The role of epidermal growth factor (EGF) in the development of articular cartilage and the pathogenesis of arthritis is poorly understood. During development, EGF produced by the apical ectodermal ridge promotes the outgrowth of the limb bud mesoderm; however, migration away from the apical ectodermal ridge and downregulation of EGF expression in the mesodermal cells is necessary for differentiation of this cell population into chondrocytes [1]. We previously found that EGF encourages expansion of early committed chondrocytes but prevents the expression of link protein and aggrecan [2], two extracellular matrix components that are necessary for proper cartilage organization [3]. Proteoglycan accumulation is inhibited following treatment of mature articular chondrocytes with EGF in a monolayer or an organ culture [4,5]. We recently demonstrated an increase in proton efflux from chondrocytes treated with EGF resulting in localized acidification of the microenvironment that may contribute to altering both responsiveness of chondrocytes to extracellular stimuli and the activity of matrix metalloproteinases [6]. EGF is detectable in the synovial fluid of rheumatoid arthritis patients and influences the growth of synovial cells [7]. However, the effects on cartilage of EGF, alone or in conjunction with other mediators associated with inflammation, are poorly characterized.
Among the inflammatory mediators associated with joint diseases, tumor necrosis factor alpha (TNF-α) is well established as a key mediator in the progression of cartilage degeneration. High levels of TNF-α are detected in the synovial lining of rheumatic joints and in chondrocytes of osteoarthritic joints [8]. TNF-α promotes further expression of cytokines and chemokines by synovial cells and chondrocytes, thereby sustaining a renewal of local inflammatory mediators (reviewed in [9,10]). The presence of TNF-α correlates with a general loss of cartilage matrix molecules, such as type II collagen and aggrecan, due to increased production of matrix metalloproteinases and a reduction in synthesis of matrix molecules [11]. We recently demonstrated that activation of the NF-κB and mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signaling pathways contributes to the TNF-α-mediated reduction of transcription of the type II collagen and link protein genes, as well as to a reduction in the steady-state mRNA levels of these key extracellular matrix components [12]. In rheumatic joints, elevated levels of EGF in the synovial fluid contribute to hyperplasia of the synovial lining, where synovial cells display increased expression of the EGF receptor ErbB-2 (also known as c-neu or HER2) [7,13,14] and amplify IL-1-mediated release of prostaglandin E2 from synovial cells [15]. However, the combined effects of EGF and TNF-α have not been investigated previously.
The objective of the present study was to determine whether EGF potentiates the response of chondrocytes to TNF-α. We investigated changes in chondrocyte morphology and function. The expression of type II collagen that is responsible for the structural integrity of articular cartilage and aggrecan that imparts resilience to the tissue were used as measures of chondrocyte function. Co-administration of TNF-α and EGF in the present study resulted in a marked increase in the proportion of elongated cells and an additive decrease in matrix gene expression. These changes in morphology and gene expression were found to be controlled in part by the MAPK pathway. Furthermore, EGF exerts its effects on matrix gene expression through a pathway independent of Sox-9.
Materials and methods
Primary cell culture
Articular chondrocytes were isolated from the distal femoral condyles of 1-day-old Sprague–Dawley rats (Charles River, St Hyacinthe, QC, Canada) as previously described [12]. The Animal Use Subcommittee of the University of Western Ontario Council on Animal Care approved the use of rats for these studies. Cells were plated at a density of 4.25 × 104 cells/cm2 on tissue culture-treated plates (Falcon; BD Biosciences, Mississauga, ON, Canada) and cultured in RPMI 1640 media supplemented with 5% fetal bovine serum, 100 U/ml penicillin, 100 U/ml streptomycin and 10 mM HEPES (Invitrogen Life Technologies Inc., Burlington, ON, Canada). Culture media was replaced every 3 days. Culture medium was replaced with serum-free medium 16–20 hours prior to experiments.
Primary chondrocyte cultures were treated with TNF-α (30 ng/ml; Sigma Aldrich, Oakville, ON, Canada), with EGF (10 ng/ml; Sigma Aldrich) or with vehicle (phosphate-buffered saline + 0.01% bovine albumin; Roche Diagnostics, Laval, QC, Canada) in serum-free medium. These concentrations were previously found to elicit maximal responses from these cells [6,12]. For analysis of signaling pathways, cells were treated prior to addition of TNF-α or EGF with pharmacologic inhibitors including 2-[1-(3-dimethylaminopropyl)-1H-indol-3-yl]-3-(1H-indol-3-yl)-maleimide (10 μM bisindolylmaleimide [BIS] I, protein kinase C [PKC] inhibitor), or 2,3-bis(1H-indol-3-yl)-N-methylmaleimide (10 μM BIS V, inactive analog of BIS I), 1,4-diamino-2,3-dicyano-1,4-bis(2-aminophenylthio)-butadiene (10 μM U0126, mitogen-activated protein kinase kinase 1 and 2 [MEK1/2] inhibitor; Promega, Madison, WI, USA), and 1,4-diamino-2,3-dicyano-1,4-bis(methylthio)-butadiene (10 μM U0124, inactive analog of U0126). BIS I was used at a concentration that was greater than 500 times the inhibitory concentration 50% for conventional PKCs and twice the inhibitory concentration 50% for PKCζ. U0126 was used at a concentration previously found to be effective for inhibiting the phosphorylation of ERK1/2 [12]. The pharmacologic agents were obtained from EMD Biosciences (Calbiochem, La Jolla, CA, USA) unless otherwise stated.
Imaging
Digital images of confluent monolayers were obtained using a Sony Power HAD 3CCD mounted onto a Nikon TMS inverted phase-contrast microscope (20 × objective magnification) (Nikon Canada Inc., Mississauga, ON, Canada). Images were acquired with NorthernEclipse V.5 software (Empix, Mississauga, ON, Canada). For the present study, an elongated cell was defined as having a predominant axis length exceeding three times the maximum width of the cell. The number of elongated cells per field of view (1.376 mm2) was counted and averaged.
RNA extraction and northern blot analysis
Total RNA was collected from cells using the acid–guanidium–phenol–chloroform extraction method (Trizol; Invitrogen Life Technologies Inc.), according to the manufacturer's instructions. RNA was quantified by ultraviolet spectrophotometry. Total RNA (10 μg) was resolved on a 1.1% agarose gel containing formaldehyde. Equivalent loading of samples was verified by ethidium bromide staining before RNA was transferred to Nytran membranes (Schleicher & Schuell, Keene, NH, USA). RNA was fixed to the Nytran membrane by incubation at 80°C for 2.5 hours under vacuum. cDNA probes corresponding to the mouse C-propeptide of type II collagen (pKN225) [16], to 18S rRNA (DECAtemplate 18S mouse; Ambion, Austin, TX, USA), and to the C-terminus of rat aggrecan [17,18] were labeled with [α32P]dCTP (3000 Ci/mmol; Perkin Elmer, Woodbridge, ON, Canada) by a random-primed oligonucleotide method (Prime-a-gene labeling kit; Promega). Membranes were hybridized with cDNA probes and processed as described previously [19].
Preparation of cell extracts and immunoblotting
Cell extracts were prepared as described previously [12]. Equivalent amounts of protein (15–30 μg) were resolved by electrophoresis on 7.5% polyacrylamide-SDS gels. Protein was transferred to nitrocellulose membrane (Schleicher & Schuell) by electroblotting. Transfer and equivalent loading was verified by subsequent staining with Ponceau Red (3-hydroxy-4-(2-sulfo-4-[4-sulfophenylazo]-phenylazo)-2,7-napthalenedisulfonic acid) [20]. Immunoblotting was performed by blocking the membrane for 1 hour with 5% non-fat milk (Carnation, North York, ON, Canada)/TBS 0.5% Tween. Membranes were incubated with antibodies for poly(ADP ribose) polymerase (PARP) (Santa Cruz Biotechnology, Santa Cruz, CA, USA), phospho-specific ERK1/2 (Anti-active MAPK; Promega) or ERK1 and ERK2 (Santa Cruz Biotechnology) according to the manufacturer's instructions. Target signals were detected with SuperSignal West Pico Chemiluminescent Substrate (Pierce Biotechnology Inc., Rockford, IL, USA) and exposure to Hyperfilm ECL (Amersham Biosciences, Baie d'Urfé, QC, Canada).
Apoptosis analysis
Cells were seeded on Permanox chamber slides (Nalge Nunc, Naperville, IL, USA) at a density of 550 cells/mm2. Following treatment with factors, slides were fixed with 4% formalin solution. Apoptosis was assessed by the terminal deoxynucleotidyltransferase end-labeling with fluorescein-dUTP (TUNEL) method (Roche Diagnostics) as described in the manufacturer's instructions. Positive controls were treated for 10 min with DNAse I (Roche Diagnostics) to induce DNA breaks. Fluorescein activity was imaged by laser scanning confocal microscopy (LSM 510 Meta; Carl Zeiss Microscopy, Jena, Germany).
MTT assay for cell viability
Cell viability was analyzed using the Cell Proliferation Kit I (MTT; Roche Diagnostics) following the manufacturer's instructions. Cells were seeded on 96-well plates at 400 cells/mm2, were cultured for 5 days and were then treated with TNF-α, or with EGF, or with TNF-α + EGF for an additional 24 hours. The colorimetric reaction was read on a μQuant spectrophotometer (Bio-Tek Instruments, Winooski, VT, USA) at 550 nm and 690 nm. The reading at 690 nm was used as a reference wavelength to calculate a corrected absorbance (A550 – A690).
Transfections and luciferase reporter analysis
Chondrocytes were transfected with reporter constructs for NF-κB (Clontech, Palo Alto, CA, USA) or the type II collagen enhancer region (pGl3 4 × 48; a kind gift from Dr TM Underhill, The University of British Columbia, Vancouver, BC, Canada) [21]. Briefly, per transfection reaction, 0.1 μg reporter DNA and 2 ng PRL-SV40, a constitutively expressed renilla luciferase plasmid for monitoring transfection efficiency, were incubated with Fugene 6 transfection reagent (Roche Diagnostics). The mixture was added to a well of a 48-well plate and overlayed with 3.5 × 104 cells in serum-free culture medium. After 5 hours, medium containing serum was added to the wells. The following day, cells were treated with TNF-α (30 ng/ml), with EGF (10 ng/ml), with a combination of both or with vehicle in serum-free medium for 24 hours. The cells were lysed with 1 × Reporter Lysis Buffer (Promega) and luciferase activity quantified using the Dual Luciferase Assay System (Promega).
Nuclear extract preparation and electrophoretic mobility shift assays
Isolation of nuclear extracts and the electrophoretic mobility shift assay were performed as previously described [12,22]. The double-stranded oligonucleotide containing the κB recognition sequence was purchased from Santa Cruz Biotechnology.
Densitometry and statistical analysis
All data are representative of at least three independent experiments. Bands appearing on exposed film were analyzed using Kodak Digital Science software (Eastman Kodak, Rochester, NY, USA). Relative expression levels of type II collagen mRNA and aggrecan mRNA were standardized to the expression levels of 18S rRNA. One-way analysis of variance or repeated-measures analysis of variance followed by Tukey–Kramer post-test comparisons was performed to determine the statistical significance of differences among means (GraphPad Prism version 3.00; GraphPad Software, San Diego, CA, USA).
Results
Effects of TNF-α and EGF on chondrocyte morphology
The cellular morphology reflects the differentiation status of cells such as chondrocytes. For example, a change from a rounded to a more elongated morphology in response to EGF by CFK2 chondrocytic cells is associated with a diminished onset of expression of aggrecan and link protein gene [2]. To determine whether the morphology of primary chondrocytes expressing the matrix was affected by TNF-α or EGF, live cultures were examined by phase-contrast microscopy (Fig. 1a) and the number of elongated cells per field was quantified (Fig. 1b).
Previous studies established concentrations for TNF-α (30 ng/ml) [12] and EGF (10 ng/ml) [6] for maximal activation of signaling pathways in primary chondrocytes. Following a 24-hour treatment with vehicle (control) or TNF-α, the monolayers exhibited a 'cobblestone' appearance. In contrast, treatment with EGF promoted cell elongation, a change that was significantly potentiated by the presence of TNF-α. The distribution and arrangement of actin filaments were analyzed by phalloidin labeling. An increase in stress fibers was observed in elongated cells; however, the density of cells and prevalence of filamentous actin throughout the monolayer precluded any further quantitative analysis (data not shown).
Effects of TNF-α and EGF on levels of aggrecan and type II collagen mRNA
We previously demonstrated that TNF-α reduces transcriptional expression of type II collagen and link protein genes [12]. In the present study, we characterized the effect of TNF-α on aggrecan mRNA levels and determined whether EGF altered type II collagen and aggrecan mRNA levels in the presence or absence of TNF-α. Cultures were treated with TNF-α or EGF individually or in combination (TNF-α + EGF) and the levels of aggrecan and type II collagen mRNA were analyzed (Fig. 2). Following 24 hours of treatment with TNF-α, levels of aggrecan and type II collagen mRNA were decreased by 42 ± 4% and 39 ± 2%, respectively. EGF alone decreased levels of aggrecan and type II collagen mRNA by 44 ± 5% and 42 ± 4%, respectively. Treatment of chondrocytes with TNF-α + EGF resulted in additive losses of aggrecan and type II collagen mRNA (93 ± 2% and 79 ± 4%, respectively). Treatment with TNF-α for 4 hours prior to the addition of EGF for the remainder of the 24 hours resulted in comparable decreases in levels of aggrecan and type II collagen mRNA (89 ± 2% and 81 ± 7%, respectively; data not shown). The combination of TNF-α and EGF therefore produces an additive decrease in both aggrecan and type II collagen mRNA levels, suggestive of discrete signals regulating mRNA expression by each factor.
TNF-α and EGF do not alter the extent of apoptosis in the chondrocyte culture
Cultures treated with TNF-α, with EGF or with TNF-α + EGF were assessed for evidence of apoptosis using an early marker, PARP (Fig. 3a). PARP is a 116 kDa protein involved in DNA repair [23] that is cleaved as part of the caspase cascade initiated in cells undergoing apoptosis. Cell extracts were immunoblotted for the presence of intact and cleaved forms of PARP. Neither loss of intact PARP nor the appearance of cleaved moieties (85 kDa) was detected following 24 hours of treatment with TNF-α, with EGF or with TNF-α + EGF. Interestingly, TNF-α + EGF increased the amount of PARP present in the chondrocytes. To confirm the lack of apoptosis in factor-treated cultures, the presence of DNA strand breaks was evaluated by in situ labeling (TUNEL) (Fig. 3b). TUNEL labeling was not detected following any of the treatments.
Cell viability was also assessed using the MTT assay (Fig. 4). TNF-α did not significantly alter cell viability after 24 hours. EGF caused an increase in metabolism of the tetrazolium salt at 24 hours that was not, however, changed significantly by co-addition of TNF-α, probably reflecting an increase in chondrocyte number. These results suggest that reduction in aggrecan and type II collagen mRNA levels induced by TNF-α and EGF are not correlated with initiation of programmed cell death (Fig. 3) or a decrease in cell number (Fig. 4).
EGF does not alter NF-κB activation by TNF-α
Several signaling pathways known to mediate the effects of TNF-α and EGF were next investigated. We previously demonstrated that primary articular chondrocytes treated with TNF-α exhibit sustained activation of NF-κB at 24 hours, and that NF-κB partially mediated the reduction in type II collagen mRNA induced by TNF-α [12]. To assess whether changes in NF-κB activity contribute to the observed decrease in aggrecan and type II collagen mRNA, chondrocytes were transfected with a κB-driven reporter to detect functional activation of NF-κB (Fig. 5a). As expected, TNF-α significantly increased reporter levels. In contrast, EGF did not activate NF-κB or alter activation of NF-κB by TNF-α. Furthermore, sustained NF-κB activation induced by TNF-α was unchanged by EGF as determined by the electrophoretic mobility shift assay (Fig. 5b). The heightened decrease in aggrecan and type II collagen mRNA induced by TNF-α + EGF was therefore not the result of altered NF-κB activation.
Inhibition of PKC does not prevent reduction in levels of aggrecan or type II collagen mRNA by TNF-α and EGF
TNF-α and EGF have been found to activate PKC in other cell types. The role of PKC signaling in the reduction of aggrecan and type II collagen mRNA by TNF-α and EGF was examined using a pharmacologic inhibitor of PKC (Fig. 6). Cultures were pretreated with the PKC inhibitor BIS I at a concentration known to inhibit activation of several PKC isoforms, specifically PKCα, PKCβI, PKCβII, PKCγ, PKCδ, PKCε, and PKCζ [24,25], or with BIS V, an inactive analog of BIS I. TNF-α and/or EGF were added and the mRNA levels were analyzed by northern blot. Pretreatment with either BIS I or BIS V did not prevent the reduction in levels of aggrecan and type II collagen mRNA by TNF-α, by EGF or by TNF-α + EGF. Activation of PKC thus does not appear to be involved in the regulation of matrix gene expression by TNF-α and EGF. Neither BIS I nor BIS V treatment alone significantly altered the levels of aggrecan and type II collagen mRNA.
Changes in cell morphology induced by EGF or the combination of TNF-α and EGF are suppressed by inhibition of MAPK
EGF is a well-characterized activator of the MAPK/ERK pathway [26]. We investigated whether the changes observed in cell morphology were dependent on the MAPK/ERK pathway. Chondrocytes were treated with the selective inhibitor of MEK1/2 activation, U0126, at a concentration previously found to inhibit ERK1/2 phosphorylation in these cells [12], or the inactive analog U0124 followed by TNF-α and/or EGF or vehicle. After 24 hours, the chondrocytes treated with U0124 or with U0126 followed by treatment with either vehicle or TNF-α exhibited similar morphology (Fig. 7a) to that observed in the absence of pharmacological agents (Fig. 1).
The number of elongated cells per field was also counted (Fig. 7b). The number of elongated cells induced by EGF and by TNF-α + EGF was markedly reduced following pretreatment with U0126. Cultures treated with U0124 followed by EGF or by TNF-α + EGF exhibited changes in the number of elongated cells comparable with vehicle-pretreated cultures. Changes in morphology in response to EGF and to TNF-α + EGF are thus dependent on a MEK1/2-regulated process.
Inhibition of the MAPK pathway prevents TNF-α and EGF-mediated loss of aggrecan and type II collagen mRNA
We previously demonstrated that activation of the MAPK signaling cascade contributed to a reduction in type II collagen mRNA levels [12]. The involvement of the MAPK/ERK pathway in the reduction in aggrecan and type II collagen mRNA levels by EGF and TNF-α was investigated. Cells were pretreated with U0124 or U0126 followed by the addition of TNF-α and/or EGF or vehicle for 24 hours (Fig. 8). Cultures treated with the inactive inhibitor exhibited no change in either the basal levels of mRNA (data not shown) or the extent of reduction in aggrecan or type II collagen mRNA levels from that of untreated cultures (Fig. 2). U0126 prevented the losses mediated by the individual factors and partially protected against the effect of TNF-α and EGF in combination.
To determine the MAPK responsiveness of chondrocytes to the combination of these factors, the phosphorylation of ERK1/2 was assessed. Cell extracts were collected from cultures treated for 4 hours with vehicle or with TNF-α prior to the addition of EGF, and were immunoblotted with antibody specific for phosphorylated forms of ERK1/2 (Fig. 9). In a previous study [12], we demonstrated phosphorylation of ERK1/2 within 15 min of the addition of TNF-α. In the present study, we found that phosphorylation of ERK1/2 returned to control levels after 4 hours of treatment with TNF-α. Phosphorylation of both ERK1 and ERK2 by EGF was apparent after 5 min and had not diminished by 30 min in the vehicle-treated cells. As both simultaneous and sequential addition of TNF-α and EGF produced comparable reductions in matrix gene mRNA levels, the MAPK response to EGF was assessed in the presence or absence of a 4-hour TNF-α pretreatment. Cultures that received TNF-α pretreatment followed by EGF had levels of ERK1/2 phosphorylation comparable with those cultures treated with EGF alone. An increase in the level of phosphorylation therefore did not contribute to the greater loss of matrix gene mRNA expression.
Effects of EGF on type II collagen mRNA are independent of the Sox-9 response region of the type II collagen enhancer
Expression of both type II collagen and aggrecan genes is regulated by a transcriptional complex containing members of the Sox family, namely Sox-5, Sox-6, and Sox-9 [27,28]. A Sox-9 regulatory element resides in the type II collagen enhancer. To determine whether the reduction in type II collagen mRNA levels by EGF involves the minimal enhancer region, chondrocytes were transfected with a reporter construct for this 48 base pair region [21] and were treated with TNF-α and/or EGF or with vehicle (Fig. 10). As previously demonstrated [12], TNF-α markedly reduced activity at this regulatory region consistent with impairment of Sox-9 binding or activity. In contrast, EGF did not alter the activity of the enhancer region and the effect of TNF-α + EGF was not significantly different from that of TNF-α alone. These results indicate that the EGF-mediated reduction in levels of type II collagen mRNA is independent of the minimal enhancer regulatory region and, therefore, probably independent of Sox-9 regulation.
Discussion
The morphology of cells is regulated by extracellular signals including soluble mediators and a matrix. Moreover, a relationship exists between the morphology and the state of differentiation. In the present study we investigated the effects of TNF-α, a factor that did not alter cell morphology, and the effects of EGF, a factor that induced a change in cell morphology. It is well established that removal of chondrocytes from their environment rich in extracellular matrix to a two-dimensional culture causes a change in morphology from rounded/cuboidal to more flattened and spread cells [29]. These morphological changes are accompanied by changes in the organization of the actin cytoskeleton [30]. Coincident with the change in chondrocyte shape is a loss of expression of phenotypic markers such as type II collagen and aggrecan [29,31,32], a phenomenon referred to as dedifferentiation. In addition, non-matrix factors can influence the organization of the actin cytoskeleton and can have profound effects on differentiation of chondrocytes. For example, bone morphogenetic protein-7 and IL-1 promote and restrict chondrogenesis, respectively, through changes in the distribution of focal adhesion proteins that are essential components of the cytoskeletal complexes. Their induction or their repression, respectively, of type II collagen gene expression involves altering the organization of the actin cytoskeleton [33]. In the present study, however, while only EGF induced a notable change in cell morphology, both TNF-α and EGF brought about comparable reductions in the mRNA levels of cartilage matrix genes. Morphological changes may thus be linked to expression of a differentiated phenotype for some inflammatory mediators.
Cell survival is essential for ensuring ongoing homeostatic maintenance of cartilage and for bringing about repair to damaged cartilage. Maintaining integrity of the nuclear material is critical, and the repair of damaged DNA is dependent on PARP. When a cell initiates apoptosis, PARP is targeted by caspase 3 and caspase 7, and is cleaved, rendering the enzyme inactive (properties of PARP are reviewed in [34,35]). Furthermore, in a caspase-independent manner, overactivation of PARP can lead to cell death through the release of apoptosis-inducing factor [35]. In the present study, apoptosis was not initiated by TNF-α and/or EGF as there was no cleavage of PARP and no evidence of DNA fragmentation (TUNEL staining). TNF-α and EGF separately had no effect on levels of PARP; however, when TNF-α and EGF were combined, increased levels of PARP were found. It is not clear whether the increase in PARP is due to increased de novo synthesis or to prevention of turnover. A similar phenomenon has been observed in retinal tissue following ischemia-reperfusion injury [36], another situation in which multiple inflammatory mediators are present. The upregulation of PARP by the combination of TNF-α and EGF suggests a protective response by chondrocytes as a certain cellular threshold for tolerance is exceeded. Furthermore, PARP can mediate transcriptional suppression through direct interaction with promoter DNA or modification of regulatory transcription factors such as NF-κB. Further investigation would be needed to determine whether PARP is involved in the reduced mRNA levels of type II collagen and aggrecan.
TNF-α and EGF activate several intracellular signaling pathways through their respective receptors or via cross-talk of pathway components. The concentrations of factors used in this study are sufficient to elicit maximal responses in these chondrocytes [6,12]. The additive nature of the decrease in aggrecan and type II collagen mRNA suggests the involvement of at least two signaling pathways activated by TNF-α and EGF. We have previously shown that TNF-α does not activate p38 in this system [12]. Furthermore, NF-κB activity has been implicated in mediating the effects of TNF-α and IL-1β on the expression of type II collagen [12,37]. Disruption of the actin cytoskeleton with cytochalasin D or with latrunculin B results in an increase in NF-κB activation [38]. Although inducing a change in morphology, EGF did not alter the activity of NF-B, either basal or that induced by TNF-α. Similarly, PKC is typically activated in response to TNF-α or EGF and can mediate an activation of MAPK signaling [39,40]. In the present study, however, inhibition of several isoforms of PKC did not alter the observed losses in aggrecan and type II collagen mRNA. The pharmacological inhibitor of MEK1/2 suppressed mRNA loss and changes in cell morphology. The MAPK/ERK pathway is thereby at least partially involved in regulating the aggrecan and type II collagen genes and in remodeling of the cytoskeleton in response to factors present during inflammation.
The MAPK/ERK pathway plays an important role in directing alterations of the cytoskeleton. For example, constitutively active MAPK induces morphological changes in fibroblasts, coinciding with disruption of stress fibers and disappearance of focal adhesions [41]. The MEK/ERK pathway is crucial in the control of hepatocyte cell morphology and cell cycle in response to EGF [42]. In chondrocytes, the MAPK/ERK pathway may have dual function in controlling the alteration in gene expression during cartilage degeneration and cytoskeletal remodeling. Induction of dedifferentiation may be a consequence of proliferation induced by growth factors, a process involving MAPK that may shift the balance away from differentiated phenotype towards amplification of the population. When both TNF-α and EGF are present, inhibition of MEK1/2 failed to completely prevent a reduction in mRNA levels of matrix components. The level of ERK1/2 phosphorylation induced by EGF was not altered in the presence of TNF-α, suggesting that MEK1/2 activity was also not altered and could be fully inhibited by the concentration of U0126 used. Taken together, these data suggest that although blockade of MEK1/2 can prevent the loss of aggrecan and type II collagen mRNA by TNF-α and EGF individually, additional signals beyond the MAPK pathway are probably involved when the factors are combined.
The intracellular signals that control matrix gene expression elicit their effects through regulation of gene transcription or through post-transcriptional modification and turnover of gene products (i.e. stability of mRNA). A key molecule involved in the transcriptional regulation of both type II collagen and aggrecan is Sox-9 [28,43,44]. Sox-9 acts by binding to enhancer regions of the type II collagen gene and to regulatory regions of the aggrecan gene to drive promoter activity [43]. Although the exact mechanism of loss of aggrecan mRNA in response to TNF-α and EGF remains unclear, the lack of change in activity at the type II collagen enhancer in response to EGF suggests that changes in Sox-9 activity do not mediate the EGF effects. Furthermore, these results together with the fact that the MAPK/ERK pathway was activated by EGF in this system suggest that this region is independent of MAPK/ERK activity. There are, however, alternate sites within these genes that may govern expression, such as SP1/SP3, C-Krox, and Stat1 [45-48]. In addition, activation of signaling pathways can increase synthesis of proteins responsible for the breakdown of existing mRNA, thereby increasing mRNA turnover. In this regard, we previously demonstrated that TNF-α reduces levels of type II collagen mRNA by approximately 40% when transcription was fully inhibited pharmacologically [12]. The additional loss of mRNA species following the treatment with both EGF and TNF-α in the present study suggests that intracellular signals target regulatory elements external to the enhancer-like sequence or stability of the mRNA.
Conclusion
In this study, changes in chondrocyte phenotype and function were determined following treatment with TNF-α and EGF, mediators that contribute to sustaining the inflammatory processes associated with arthritis. The effects of this combination of factors have not been explored previously in cartilage or in other tissues. The expression of matrix genes critical for maintaining structural and functional integrity of cartilage was downregulated additively by TNF-α and EGF through mechanisms that involved at least two signals convergent on matrix gene regulation. Multiple inflammatory mediators can therefore profoundly reduce chondrocyte function and contribute to the progression of cartilage degeneration through several distinct signaling events.
Abbreviations
BIS = bisindolylmaleimide; EGF = epidermal growth factor; ERK = extracellular signal-regulated kinase; IL = interleukin; MAPK = mitogen-activated protein kinase; MEK1/2 = mitogen-activated protein kinase kinase 1 and 2; NF = nuclear factor; PARP = poly(ADP ribose) polymerase; PKC = protein kinase C; TNF-α = tumor necrosis factor alpha; TUNEL = terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling; U0124 = 1,4-diamino-2,3-dicyano-1,4-bis(methylthio) butadiene; U0126 = 1,4-diamino-2,3-dicyano-1,4-bis[2-aminophenylthio] butadiene.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ARK, an MSc candidate, participated in the design of the study, performed all the experiments and analysis, prepared the figures, and contributed to the writing of the manuscript. SMB conceived of the study, participated in its design and analysis, and prepared and revised the manuscript. Both authors read and approved the final manuscript.
Acknowledgements
This work is supported by an operating grant from the Canadian Institutes of Health Research and the Institute of Musculoskeletal Health and Arthritis (IMH 14095). The authors would like to thank Dr S Jeff Dixon for critical review of the manuscript.
Figures and Tables
Figure 1 Tumor necrosis factor alpha (TNF-α) enhances elongated cell morphology induced by epidermal growth factor (EGF). Confluent monolayers of chondrocytes were treated with vehicle, TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF for 24 hours. (a) Cell morphology was observed by phase contrast microscopy. Arrowheads indicate spinous processes that appear following incubation with EGF or TNF-α + EGF. An elongated cell is defined as having a predominant axis with a length exceeding three times the maximum width of the cell. Digital images of live cultures were captured at 20 × objective magnification. Bar = 100 μm. Images shown are representative of three independent experiments. (b) The total number of elongated cells per field (1.376 mm2) were counted, averaged for at least three independent experiments (n = 3–5), and analyzed by analysis of variance. a Significant difference from control (P < 0.01), b significant difference from control (P < 0.001) and significant difference from EGF-treated cells (P < 0.01).
Figure 2 Tumor necrosis factor alpha (TNF-α) + epidermal growth factor (EGF) results in additive reduction in levels of aggrecan and type II collagen mRNA. Confluent monolayers of chondrocytes were treated for 24 hours with vehicle (CNTL), TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF (n = 12). Levels of (a) aggrecan and (b) type II collagen mRNA were analyzed by northern blot of total RNA (10 μg). Changes in levels of (c) aggrecan and (d) type II collagen mRNA were quantified by densitometry. Levels were normalized to levels of 18S rRNA and data are expressed as the percentage of control ± standard error of the mean. a Significant difference from control (P < 0.001), b significant difference from TNF-α-treated and EGF-treated populations (P < 0.001).
Figure 3 Apoptosis is not observed following tumor necrosis factor alpha (TNF-α) and/or epidermal growth factor (EGF) treatment. Confluent monolayers of chondrocytes were treated with vehicle, TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF for 24 hours. (a) Early stages of apoptosis were assayed by immunoblot with an antibody specific for intact and cleaved forms of poly(ADP ribose) polymerase (PARP). No cleavage of PARP (i.e. appearance of a band at 89 kDa) was detected following any of the treatments. Blot shown is representative of three independent experiments. (b) Apoptosis-induced DNA strand breaks were examined by in situ labeling (terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling [TUNEL]) and imaged using confocal microscopy. No TUNEL labeling was detected with any of the treatments. Cells treated with DNAse I to induce DNA breaks served as a positive control. Bar = 50 μm. Images are representative of three independent experiments.
Figure 4 Cell viability is maintained in the presence of tumor necrosis factor alpha (TNF-α) and/or epidermal growth factor (EGF). Subconfluent monolayers of chondrocytes were treated with vehicle, TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF for 24 hours. Cell viability was assessed by MTT assay. Data shown are combined from four independent experiments, presented as the mean corrected absorbance ± standard error of the mean analyzed by repeated-measures analysis of variance. a Significant difference from control (P < 0.01, n = 4). OD, optical density.
Figure 5 Epidermal growth factor (EGF) does not alter NF-κB activation by tumor necrosis factor alpha (TNF-α). (a) Chondrocytes transfected with a κB reporter were treated with TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF. Reporter activity was assessed after 24 hours. Data were corrected for a constitutively expressed reporter (pSV40-RL), presented as mean relative luciferase units (RLU) ± standard error of the mean, and analyzed using one-way analysis of variance followed by a Tukey–Kramer post-test. a Significant difference from basal κB activity at P < 0.001 and no significant difference between TNF-α and TNF-α + EGF. (b) The effect of EGF on sustained NF-κB activity induced by TNF-α was determined by treating confluent chondrocytes with TNF-α (30 ng/ml) alone for 24 hours, EGF (10 ng/ml) alone for 20 hours, or TNF-α for 4 hours followed by the addition of EGF (10 ng/ml) for co-treatment for a further 20 hours. Nuclear extracts were prepared and analyzed by electrophoretic mobility shift assay for activation of NF-κB. The reporter assay and band shift shown are representative of three independent experiments.
Figure 6 Inhibition of protein kinase C does not prevent reduction in levels of aggrecan or type II collagen mRNA by tumor necrosis factor alpha (TNF-α) and epidermal growth factor (EGF). Confluent monolayers of chondrocytes were pretreated with 10 μm bisindolylmaleimide (BIS) I or the structurally related, nonfunctional analog BIS V (10 μm) for 15 min, followed by treatment with TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF in combination for 24 hours. Levels of (a) aggrecan and (b) type II collagen mRNA were determined by northern blot analysis of total RNA (10 μg). Levels were normalized to levels of 18S rRNA and data are expressed as the percentage of respective controls ± standard error of the mean (n = 4–6). a Significant difference from respective control populations (P < 0.05), b significant difference from populations treated individually with TNF-α or EGF (P < 0.001).
Figure 7 Changes in cell morphology induced by the combination of tumor necrosis factor alpha (TNF-α) + epidermal growth factor (EGF) are partially reduced by inhibition of MEK1/2. (a) Confluent monolayers of chondrocytes were pretreated with U0124 (10 μM) or U0126 (10 μM) for 15 min followed by 24-hour treatment with TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF. Digital images of live cultures were captured at 20 × objective magnification. Bar = 100 μm. Images are representative of two independent experiments. (b) An elongated cell is defined as having a predominant axis with a length exceeding three times the maximum width of the cell. The total number of elongated cells per field (1.376 mm2) were counted, averaged for three independent experiments (n = 3), and analyzed by analysis of variance. a Significant difference from respective control (P < 0.05), b significant difference from respective control (P < 0.001), c significant difference from U0124 + EGF-treated cells (P < 0.01), d significant difference from U0124 + EGF-treated cells (P < 0.05), e significant difference from U0124 + TNF-α + EGF-treated cells (P < 0.001).
Figure 8 Inhibition of the mitogen-activated protein kinase pathway prevents tumor necrosis factor alpha (TNF-α) and epidermal growth factor (EGF)-mediated loss of aggrecan and type II collagen mRNA. Confluent chondrocytes were pretreated with U0124 (10 μm, inactive analog of U0126) or U0126 (10 μm, a MEK1/2 inhibitor), for 15 min, followed by treatment with TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF for 24 hours. Levels of (a) aggrecan and (b) type II collagen mRNA were assessed by northern blot analysis of total RNA (10 μg). Levels were normalized to levels of 18S rRNA and data are expressed as the percentage of respective control ± standard error of the mean (n = 5). a Significant difference from respective control (P < 0.001), b significant difference from cultures treated individually with TNF-α or EGF (P < 0.01), c significant difference from cultures treated with U0124 followed by addition of TNF-α + EGF (P < 0.05).
Figure 9 Comparable levels of extracellular signal-regulated kinase (ERK)1/2 phosphorylation are observed in chondrocytes treated with epidermal growth factor (EGF) alone or in combination with tumor necrosis factor alpha (TNF-α). Confluent monolayers of chondrocytes were treated for 4 hours with TNF-α (30 ng/ml) followed by (a) 15 min treatment or (b) 30 min treatment with EGF (10 ng/ml). Phosphorylation of ERK1/2 was determined by immunoblot assay using phospho-specific ERK1/2 antibody and ERK1 antibody (antibody against ERK1 is cross-reactive for ERK2). Blots shown are representative of three independent experiments.
Figure 10 Epidermal growth factor (EGF) and tumor necrosis factor alpha (TNF-α) differentially regulate the activity of the type II collagen enhancer. Chondrocytes were co-transfected with the type II collagen enhancer luciferase reporter and the pSV40-RL constructs. The transfected cells were treated with vehicle (CNTL), TNF-α (30 ng/ml), EGF (10 ng/ml) or TNF-α + EGF, and the reporter activities were analyzed after 24 hours. Values of relative luciferase expression (corrected for transfection efficiency) were compared using one-way analysis of variance followed by a Tukey–Kramer post-test, and are presented as the mean ± standard error of the mean. a Significantly different from others at P < 0.001. Data are representative of three independent experiments. RLU, relative luciferase units.
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| 15642133 | PMC1064891 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Nov 29; 7(1):R127-R138 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1464 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14651564213510.1186/ar1465Research ArticleCirculating tumour necrosis factor-α bioactivity in rheumatoid arthritis patients treated with infliximab: link to clinical response Marotte Hubert 1Maslinski Wlodzimierz 2Miossec Pierre [email protected] Departments of Immunology and Rheumatology and Unité Mixte Hospices Civils de Lyon-BioMérieux, Hôpital Edouard Herriot, Lyon, France2 Institute of Rheumatology, University of Warsaw, Warsaw, Poland2005 1 12 2004 7 1 R149 R155 14 4 2004 7 5 2004 18 9 2004 25 10 2004 Copyright © 2004 Marotte et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Our objective was to clarify the heterogeneity in response to infliximab treatment in rheumatoid arthritis (RA); to this end, a bioassay was designed to explore the contribution of circulating tumour necrosis factor (TNF)-α bioactivity and its possible link to response. The bioassay is based on the induction of IL-6 and osteoprotegerin (OPG) production by synoviocytes in response to TNF-α. RA synoviocytes were cultured with TNF-α (5 ng/ml) and 42 RA plasma samples collected just before starting therapy. Levels of IL-6 and OPG were measured in supernatants. In 20 of the patients, plasma samples collected before and 4 hours after the first and the ninth infusions were tested in the same way. Plasma concentrations of TNF-α and p55 and p75 soluble receptors were measured using ELISA. TNF-α induced IL-6 and OPG production by synoviocytes, which was further increased with patient plasma dilutions and inhibited by infliximab. With plasma samples obtained before the first infusion, the IL-6-induced production was greater in patients with a good clinical response than in the poor responders (44.4 ± 23.3 ng/ml versus 27.4 ± 20.9 ng/ml; P = 0.05). This high circulating TNF-α bioactivity was strongly inhibited with the first infliximab infusion. The difference between IL-6 levels induced with plasma samples obtained before and 4 hours after the first infusion was greater in patients with a good clinical response (40.0 ± 23.7 ng/ml versus 3.4 ± 10.0 ng/ml; P = 0.001). Similar findings were obtained for OPG production (7.0 ± 6.2 ng/ml versus 0.0 ± 3.0 ng/ml; P < 0.05). Levels of circulating TNF-α bioactivity were predictive of clinical response to TNF-α inhibition, confirming a key role for TNF-α in these RA patients.
TNFInfliximabBioactivityResponseTreatment
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Introduction
Rheumatoid arthritis (RA) is a chronic disease characterized by synovial inflammation that leads to progressive joint damage. Knowledge concerning the role played by cytokines in mediating cell–cell interactions in rheumatoid synovium has led to the rational development of treatment with anticytokine agents. Among these proinflammatory cytokines, tumour necrosis factor (TNF)-α has emerged as a major therapeutic target, based on clinical studies with biological inhibitors such as monoclonal antibodies and soluble receptors. In large proportions of patients, TNF-α inhibitors strongly reduced symptoms of synovitis, biological markers of inflammation and bone destruction [1-4]. However, the improvement varied between patients.
In an attempt to explain these differences between patients, we explored whether heterogeneity exists in the contribution of circulating TNF-α bioactivity, with the hypothesis that patients with higher levels of bioactive TNF-α would be more sensitive to the systemic administration of a specific inhibitor. Such circulating TNF-α activity would reflect local joint production. The goal of the present study was to evaluate circulating TNF-α bioactivity in RA patients before infliximab treatment and to assess its acute modulation by infliximab. Indeed, the remaining TNF-α activity would represent the difference between total TNF-α and its fraction bound to specific and nonspecific inhibitors. Therefore, a bioassay was developed using the properties of synoviocytes to produce IL-6 and osteoprotegerin (OPG) in response to TNF-α [5,6]. Finally, we looked for a possible link between changes in OPG and IL-6 levels and the rate of clinical improvement during infliximab treatment.
Methods
Patients
Forty-two patients with RA (35 women and 7 men, median age 46.8 years [range 20–67 years], disease duration 9.0 years [range 1–31 years]), diagnosed according to the revised criteria of the American College of Rheumatology (ACR) [7], were enrolled. Rheumatoid factor was present in 31 of the patients. All received infliximab according to the ATTRACT (Anti-TNF Trial in RA with Concomitant Therapy) protocol at 3 mg/kg every 8 weeks, combined with methotrexate [8]. The following indices were measured: tender joint count, swollen joint count, patient's assessment of pain, patient's global assessment of disease activity, physician's global assessment of disease activity, the Disability Index of the Health Assessment Questionnaire, serum levels of C-reactive protein and erythrocyte sedimentation rate. ACR response was recorded at 54 weeks [9]. RA patients were divided into two groups: good responders, with an ACR response equal to or greater than 50 (n = 24); and poor responders, with an ACR response equal to or less than 20 (n = 18). EDTA-treated venous blood was collected before infliximab therapy in all patients (n = 42). In 20 patients, blood samples were collected during infliximab treatment before and 4 hours after the first and ninth infusions. Plasma samples obtained by centrifugation were stored at -20°C and thawed before use. The main characteristics of the patients are summarized in Table 1.
Bioassay for circulating TNF-α bioactivity
A functional assay for TNF-α activity was designed using the ability of RA synoviocytes to produce IL-6 and OPG in response to TNF-α [5]. To isolate synoviocytes, RA synovial tissues were finely minced and digested with 4 mg/ml collagenase (Worthington, Freehold, NJ, USA) in phosphate-buffered saline (Life Technologies, Grand Island, NY, USA). These synovium pieces were obtained from RA patients undergoing joint replacement.
Synoviocytes were used at passages four to eight. RA synoviocytes (104 cells/well) were cultured in 96-well plates (Falcon, Lincoln Park, NJ, USA) in a final volume of 200 μl in minimum essential medium (Life Technologies) supplemented with 10% heat-inactivated foetal calf serum (Life Technologies), 25 000 UI penicillin, 25 000 μg streptomycin and 250 μg fungizone.
TNF-α (0–10 ng/ml) was added to RA synoviocytes with or without infliximab (10 μg/ml). Then, TNF-α (5 ng/ml) was combined with 20 μl plasma per well in order to increase the sensitivity of the synoviocyte response. Plasma samples were collected just before the first infusion. In addition, RA synoviocytes were stimulated with plasma samples collected before and 4 hours after the first and the ninth infusions from 20 RA patients. TNF-α (5 ng/ml), with or without infliximab (10 μg/ml), was preincubated with these four plasma samples for 1 hour before being added to the culture.
IL-6 and OPG production were measured by ELISA in 48-hour supernatants, as previously described [5,6]. At baseline, in the 20 patients with a 1-year follow up, plasma concentrations of TNF-α p55 and p75 soluble receptors were measured using commercial ELISA kits (Biosource, Camarillo, CA, USA), in accordance with the manufacturer's instructions.
Statistical analysis
Statistical analysis was performed using the Statview software (Abacus Concept Inc., Cary, NC, USA). Means were compared using a nonparametric test. Spearman's correlation was used to determine a relationship between the changes in IL-6 and OPG levels and those of TNF-α or TNF soluble receptors detected by ELISA. χ2 test was performed to detect differences between different subsets.
Results
Principle of the bioassay
The principle of the bioassay, as shown in Fig. 1, is based on the ability of TNF-α to induce IL-6 and OPG production by RA synoviocytes. With TNF-α concentrations ranging from 0.1 to 100 ng/ml, IL-6 production by synoviocytes increased in a dose-dependent manner. Addition of infliximab at 10 μg/ml completely inhibited the effect of TNF-α at 1 ng/ml, and reduced that of TNF-α at 10 ng/ml by 74% (32.9 ng/ml without versus 8.5 ng/ml with infliximab; Fig. 1a). Similar studies were performed for OPG production. As for IL-6, OPG production by synoviocytes increased in a dose-dependent manner in response to TNF-α (Fig. 1b). Maximal concentrations of IL-6 and OPG were in the same range up to 35 ng/ml. With regard to IL-6, addition of infliximab inhibited OPG production induced by TNF-α at 10 ng/ml.
Addition of plasma further increased the effect of exogenous TNF-α. With RA plasma concentrations ranging from 0% to 20% used alone (n = 4), IL-6 production by synoviocytes increased in a dose-dependent manner (Fig. 2a). This effect was further increased when exogenous TNF-α at 5 ng/ml was added. The greatest effect was observed with 10% plasma. An inhibitory effect was often observed with concentrations of plasma at 20%. In further experiments, 5 ng/ml TNF-α and 10% plasma concentration were used. With RA plasma samples collected before infliximab therapy used alone, IL-6 production was 6.5 ± 4.8 ng/ml (n = 20).
The contribution of TNF bioactivity is shown in Fig. 2b. Combination of 10% concentration of RA plasma with TNF-α at 5 ng/ml increased IL-6 production to 43.5 ng/ml. This effect was inhibited by infliximab (6.5 ng/ml), demonstrating the specificity for TNF-α.
Effect of patient plasma samples collected just before the first infliximab infusion
Samples were obtained from 42 patients before the first infliximab infusion. The levels of IL-6 produced by stimulation with 10% plasma and TNF-α (5 ng/ml) are shown in Fig. 3, stratified by ACR response observed at week 54. Levels were higher for the good responders (ACR response ≥ 50) than for poor responders (ACR ≤ 20; 44.4 ± 23.3 ng/ml versus 27.4 ± 20.9 ng/ml; P = 0.05).
Circulating TNF-α bioactivity modulation by the first infliximab infusion
For 20 of the patients, samples were collected before and after the first and the ninth infusions. There was no significant clinical difference between the 20 patients and the rest of the 42 patient cohort (Table 1).
The high IL-6-inducing activity found in samples before treatment was strongly reduced in samples obtained 4 hours after the first infusion, following TNF-α /anti-TNF-α complex formation induced by the infliximab infusion (Fig. 4a,4b). This reduction demonstrates the contribution of TNF-α to the activity following in vivo administration of the TNF-α inhibitor. This pattern of reduction was associated with a very good clinical response at 54 weeks for all patients (ACR 50; n = 11) except one (ACR 20). Conversely, no modulation in circulating bioactivity was observed in patients with a low level of TNF-α bioactivity (Fig. 4c). This pattern was associated with a poor clinical response (ACR <20) except in one patient (ACR 50).
The difference between the IL-6 levels induced with TNF-α at 5 ng/ml and plasma obtained before and 4 hours after the first infusion correlated with good clinical response (40.0 ± 23.7 ng/ml versus 3.4 ± 10.0 ng/ml; P = 0.001; Fig. 5a). Similarly, OPG levels were measured in the same supernatants. With regard to IL-6, the difference between the levels of OPG with plasma samples obtained before and 4 hours after the first infliximab infusion correlated with good clinical response (7.0 ± 6.2 ng/ml versus 0.0 ± 3.0 ng/ml; P < 0.05; Fig. 5b). A positive correlation was observed between changes in IL-6 and OPG production (n = 20; r = 0.843; P < 0.001).
Circulating TNF-α bioactivity modulation by the ninth infusion
Patients with high circulating bioactivity at the first infusion could be separated into two subsets. For the first group, before the ninth infusion, no circulating bioactivity was detected (Fig. 4a). This pattern was associated with very good clinical response at 54 weeks in all patients (ACR 50; n = 6). In the second group, circulating bioactivity was still present before the ninth infusion but remained sensitive to further infliximab administration (Fig. 4b). With this pattern, five out of six patients had an ACR 50 response but one had a poor response (ACR ≤ 20). No modulation in circulating bioactivity was observed in patients with low circulating TNF-α bioactivity before infliximab therapy (n = 8; Fig. 4c). A link was observed between these patterns and the clinical response (χ2 = 16.6; P < 0.001).
Various clinical and biological parameters were analyzed according to these patterns. Before treatment, no difference was observed between joint counts, erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor positivity, or Disease Activity Score 28 (5.4 ± 0.9 in good responders versus 5.2 ± 1.4 in poor responders; P = 0.6).
Absence of correlation between circulating TNF-α bioactivity and ELISA levels of TNF-α or soluble receptors
Levels of TNF-α and of soluble receptors (p55 and p75) were measured in plasma samples obtained at baseline from 20 RA patients. The mean TNF-α level was 144.87 ± 130.36 pg/ml in good responders and 153.75 ± 132.93 pg/ml in poor responders (P = 0.97). Similar results were observed with p55 (2534 ± 1074 pg/ml versus 2436 ± 953 pg/ml; P = 0.57) and p75 (3054.8 ± 673.8 pg/ml versus 2332.2 ± 921.3 pg/ml; P = 0.84) soluble receptors in good responders versus poor responders. No correlation was observed between circulating TNF-α bioactivity and TNF-α or soluble receptors, or the difference between TNF-α and p55 and p75 levels. Similarly, no negative correlation was observed between levels of circulating TNF-α bioactivity and of p55 or p75 soluble receptors.
Discussion
Prediction of the response of RA to treatment remains a hot topic. There is no evidence that simple determination of plasma TNF-α levels by ELISA allows such prediction for treatment with TNF-α inhibitors. However, it still makes sense that patients producing high levels of TNF-α will be more sensitive to TNF-α inhibition.
A system was established to evaluate the circulating TNF-α-related bioactivity in plasma. Exogenous TNF-α alone stimulates IL-6 production and this effect can be abrogated by first incubating TNF-α with infliximab before exposure to the synovial cells. When plasma is combined, cells respond to free TNF-α and not to inactive TNF-α bound to specific soluble receptors and to other less specific binding sites on proteins. This bioassay was based on the IL-6 production induced by the combination of TNF-α and plasma. Addition of exogenous TNF-α was used to detect the presence of circulating inhibitors. Such inhibitors in plasma are probably involved in the lower effect of 20% plasma concentration as compared with the effect seen with a 10% concentration (Fig. 2a).
When the system was applied to explain part of the heterogeneity in treatment response, a third of the patients showed strong and prolonged inhibition in circulating TNF-α bioactivity, suggesting the critical contribution of systemic TNF-α in these patients. In another third of the patients circulating TNF-α bioactivity was inhibited by infliximab infusion for a short time, because bioactive TNF-α reappeared but was again inhibited by the next infliximab infusion. This profile suggested partial inhibition, although the clinical benefit was still very significant. In two thirds of the samples, the bioassay measured a strong inhibition of circulating TNF-α-related activity during the first infusion. The link between strong anti-TNF-α activity induced by the first infusion and the good clinical response confirms the key role played by TNF-α in approximately two thirds of the RA patients. This result is in accord with results from the ATTRACT study [8]. In contrast, in the last third of the patients the assay suggested no contribution or a reduced contribution of TNF-α bioactivity either before or after the first infusion of infliximab. All plasma samples from these patients inhibited the IL-6 production usually induced by TNF-α. This profile suggests that these patients may have a high level of innate neutralizing TNF-α activity and/or no circulating active TNF-α. Patients with this pattern exhibited a poor clinical response, suggesting that their RA was not much driven by TNF-α activity alone but probably by other cytokines or mechanisms.
The heterogeneity in these patterns may be explained by higher disease activity for responders than nonresponders, but no difference was observed in clinical and biological parameters before treatment. One way to view the difference among patients with high circulating TNF-α bioactivity initially is the link observed between higher trough concentrations of infliximab in RA patient serum and good response and a reduced progression of radiographic joint damage [10]. This latter study suggested that RA patients with a poor clinical response tend to eliminate infliximab more rapidly from their circulation.
RA synoviocytes produce higher levels of OPG than do peripheral blood mononuclear cells or synovial fluid mononuclear cells, but they do not produce soluble receptor activator of nuclear factor-κB ligand [5]. Accordingly, we focused on OPG, which is produced in response to TNF-α. Similar results were observed for IL-6 and OPG production, although changes in IL-6 levels appeared more sensitive. This is related to the very low levels of IL-6 produced by resting synoviocytes. Accordingly, the predictive value of changes in IL-6 was better than that of OPG. Our findings extended previous studies indicating a correction in high OPG serum levels in RA patients treated with infliximab [5].
No correlation was observed between circulating TNF-α bioactivity and its protein concentration in plasma measured by ELISA. Circulating TNF-α bioactivity levels could not be calculated as free protein TNF-α taking into account the levels of TNF-α and soluble receptors (p55 and p75) measured by ELISA. This discrepancy further indicates the usefulness of a bioassay when function is the key. Such complexity has been observed when trying to explain loss of infliximab response by the induction of anti-mouse antibodies [11]. Once again, only the demonstration of inhibitory activity in vivo would allow such a conclusion to be drawn.
Conclusion
In conclusion, this bioassay was able to predict correctly the clinical response in 69% of cases (29/42). Taking into account the effect of the first infusion increased the value to 90%. However, a simplified assay would need to be designed for routine application.
Abbreviations
ACR = American College of Rheumatology; ELISA = enzyme-linked immunosorbent assay; IL = interleukin; OPG = osteoprotegerin; RA = rheumatoid arthritis; TNF = tumour necrosis factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
HM conducted the experiments and wrote the paper. WM took OPG measurements. PM directed the research.
Figures and Tables
Figure 1 Principle of the bioassay. Rheumatoid arthritis synoviocytes (104 cells/well) were cultured in 96-well plates and stimulated with increasing doses of tumour necrosis factor (TNF)-α (0–100 ng/ml). Levels of (a) IL-6 and (b) osteoprotegerin (OPG) were measured in 48-hour supernatants. Infliximab at 10 μg/ml was preincubated for 1 hour with TNF-α before its addition to the culture.
Figure 2 Pattern of plasma tumour necrosis factor (TNF) bioactivity in rheumatoid arthritis (RA) patients and healthy individuals. Using the ability of TNF-α to stimulate RA synoviocytes, TNF-α (5 ng/ml) was combined with increasing concentrations of plasma from RA patients (0–20%). (a) Four RA plasma samples were obtained before infliximab therapy. (b) RA plasma samples (n = 2) were used at 10% dilution. IL-6 production was measured by ELISA in 48-hour supernatants.
Figure 3 Link between circulating tumour necrosis factor (TNF)-α bioactivity and clinical response. Using the ability of TNF-α to stimulate rheumatoid arthritis synoviocytes, TNF-α (5 ng/ml) was combined with 20 μl plasma per well in order to increase the sensitivity of synoviocyte response. Levels of circulating TNF-α bioactivity were estimated with plasma samples obtained before infliximab therapy and separated according to American College of Rheumatology (ACR) clinical response (good or poor) at 54 weeks (n = 42). A good clinical response was defined as an ACR 50 response or better (n = 24).
Figure 4 Patterns of plasma tumour necrosis factor (TNF)-α bioactivity in rheumatoid arthritis (RA) patients treated with infliximab. Using this bioassay three patterns were observed and linked to the clinical response in the 20 patients with a 1-year follow up. (a) The first pattern showed good ability of the plasma to induce IL-6 production before infliximab therapy, followed by complete inhibition 4 hours after the first infusion. Plasma samples before the ninth infusion had low IL-6-inducing activity. This pattern was associated with an American Colege of Rheumatology (ACR) 50 response at 54 weeks (n = 6). (b) In the second pattern the effect of the first infusion was similar to that in the first pattern. However, high IL-6 inducing activity was still present before the ninth infusion but remained sensitive to treatment. In this pattern, five patients had an ACR 50 response but one had no response. (c) In the last pattern the first plasma samples had a moderate or no effect on IL-6 production. This activity was not sensitive to infliximab infusion. All patients (n = 8) had a poor clinical response (ACR <20). A link was observed between these patterns and clinical response (χ2 = 16.6; P < 0.001).
Figure 5 Link between changes in IL-6 and osteoprotegerin (OPG) production by rheumatoid arthritis (RA) synoviocytes and clinical response. Changes, expressed as ΔIL-6 and ΔOPG production by RA synoviocytes before and 4 hours after the first infliximab infusion, are separated according to the American College of Rheumatology (ACR) clinical response (good or poor) at 54 weeks (n = 20). A good clinical response was defined as an ACR 50 response or better (n = 12).
Table 1 Patient characteristics
Characteristic All RA patients (n = 42) RA patients with a 1-year follow up (n = 20) P
Age (years) 46.81 ± 10.78 48.05 ± 9.47 0.51
Sex (% female) 83 ± 11.27 70 ± 20.08 0.38
Disease duration (years) 8.98 ± 8.39 8.80 ± 8.01 0.82
Previous DMARD treatment (n) 2.78 ± 1.41 2.70 ± 2.70 0.47
Swollen joint count (0–28) 4.57 ± 3.16 4.55 ± 2.70 0.43
Tender joint count (0–28) 9.21 ± 5.78 8.35 ± 6.07 0.80
DAS28 score 5.33 ± 1.12 5.31 ± 1.19 0.76
ESR (mm/hour) 37.59 ± 23.18 37.45 ± 23.71 0.91
CRP (mg/l) 26.93 ± 24.87 31.30 ± 27.24 0.65
Values are expressed as mean ± standard deviation. CRP, C-reactive protein; DAS28, Disease Activity Score 28; DMARD, disease-modifying antirheumatic drug; ESR, erythrocyte sedimentation rate; RA, rheumatoid arthritis.
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| 15642135 | PMC1064892 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Dec 1; 7(1):R149-R155 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1465 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14661564213810.1186/ar1466Research ArticleLocal expression of matrix metalloproteinases, cathepsins, and their inhibitors during the development of murine antigen-induced arthritis Schurigt Uta [email protected] Nadine [email protected]ückel Marion [email protected] Christina [email protected] Bernd [email protected]äuer Rolf [email protected] Institute of Pathology, Friedrich Schiller University, Jena, Germany2 Institute of Biochemistry I, Friedrich Schiller University, Jena, Germany2005 10 12 2004 7 1 R174 R188 7 6 2004 3 9 2004 23 9 2004 26 10 2004 Copyright © 2004 Schurigt et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cartilage and bone degradation, observed in human rheumatoid arthritis (RA), are caused by aberrant expression of proteinases, resulting in an imbalance of these degrading enzymes and their inhibitors. However, the role of the individual proteinases in the pathogenesis of degradation is not yet completely understood. Murine antigen-induced arthritis (AIA) is a well-established animal model of RA. We investigated the time profiles of expression of matrix metalloproteinase (MMP), cathepsins, tissue inhibitors of matrix metalloproteinases (TIMP) and cystatins in AIA. For primary screening, we revealed the expression profile with Affymetrix oligonucleotide chips. Real-time polymerase chain reaction (PCR) analyses were performed for the validation of array results, for tests of more RNA samples and for the completion of the time profile. For the analyses at the protein level, we used an MMP fluorescence activity assay and zymography. By a combination of oligonucleotide chips, real-time PCR and zymography, we showed differential expressions of several MMPs, cathepsins and proteinase inhibitors in the course of AIA. The strongest dysregulation was observed on days 1 and 3 in the acute phase. Proteoglycan loss analysed by safranin O staining was also strongest on days 1 and 3. Expression of most of the proteinases followed the expression of pro-inflammatory cytokines. TIMP-3 showed an expression profile similar to that of anti-inflammatory interleukin-4. The present study indicates that MMPs and cathepsins are important in AIA and contribute to the degradation of cartilage and bone.
Affymetrix oligonucleotide chipscathepsinscytokinesmatrix metalloproteinasesmurine antigen-induced arthritis
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Introduction
Rheumatoid arthritis (RA) is a chronic destructive autoimmune disease characterized by the inflammation and progressive destruction of distal joints. The initial histological features of RA are characterized by synovial lining hyperplasia, excessive angiogenesis and the accumulation of polymorphonuclear and mononuclear cells in the synovium [1,2].
The etiology of RA is still unknown, but the degradation of cartilage and bone observed in RA is caused by an increased expression of proteinases, resulting in an imbalance of these degrading enzymes and their inhibitors [3,4]. Proteinases have a pivotal function in endogenous angiogenesis, antigen presentation and pathological remodeling of cartilage and bone [5-7]. For an understanding of the pathogenesis of RA, it is important to investigate the time profiles of expression of proteinases and proteinase inhibitors during the development of arthritis and their relationship to cytokine expression.
It has been suggested that the immune hyper-responsiveness in RA tissues is triggered by an unknown joint-specific antigen. Antigen-induced arthritis (AIA) in mice is an experimental model for RA, in which arthritis is induced by systemic immunization with the antigen methylated bovine serum albumin (mBSA) in complete Freund's adjuvant, followed by a single intra-articular injection of the antigen into the knee joint cavity [8]. The development of chronic arthritis is visible for several weeks. The advantage of AIA over other experimental arthritis models consists in the exactly defined stages of the development of arthritis elicited by antigen injection into the knee joint cavity. After this initiation of AIA, it is possible to distinguish between the acute stage from day 0 to day 7, characterized by inflammatory processes, and the subsequent chronic stage with pannus formation and joint destruction.
As in RA, breakdown of articular cartilage and bone in AIA results from the overexpression of proteinases and deregulation of the balance between proteinases and their inhibitors. We investigated the expression patterns of matrix metalloproteinases (MMPs), tissue inhibitors of matrix metalloproteinases (TIMPs), cathepsins and cystatins by Affymetrix oligonucleotide microarray technology in combination with real-time polymerase chain reaction (PCR), to identify the mediators that were differentially expressed in murine AIA. We were able to follow the expression in murine knee joints from the induction of arthritis to the development of the chronic phase. Additionally, we studied the expression profiles of different cytokines on mRNA and protein level. To complete our oligonucleotide chip and PCR results, we investigated MMP expression and activity by fluorescence assays and zymography.
Materials and methods
Animals
Female C57Bl/6 mice (age 7–9 weeks) were obtained from the Animal Research Facility of Friedrich Schiller University, Jena, Germany, and Charles River Laboratories, Sulzfeld, Germany, respectively. They were kept under standard conditions in a 12 hours:12 hours light:dark cycle and fed with standard pellets (Altromin no. 1326, Lage, Germany) and water ad libitum. All animal studies were approved by the governmental committee for animal protection.
Immunization and arthritis induction
Mice were immunized on days – 21 and – 14 by subcutaneous injections of 100 μg of mBSA (Sigma, Deisenhofen, Germany) in 50 μl of saline solution, emulsified in 50 μl of complete Freund's adjuvant (Sigma), adjusted to a concentration of 2 mg/ml heat-killed Mycobacterium tuberculosis strain H37RA (Difco, Detroit, MI, USA). In addition, intraperitoneal injections of 2 × 109 heat-killed Bordetella pertussis bacteria (Chiron Behring, Marburg, Germany) were performed on the same days. Arthritis was induced on day 0 by injection of 100μg of mBSA in 25 μl of saline solution into the right knee joint cavity.
Joint swelling
For clinical monitoring of AIA development, the joint diameters were analyzed before (nonarthritic mice, immunized with mBSA: day 0) and at various times after AIA induction (days 1, 3, 7, 14 and 21). The joint swelling was measured with an Oditest vernier caliper (Kroeplin Längenmesstechnik, Schluechtern, Germany). Joint swelling was expressed as the difference in diameter (mm) between the right knee joint on days 1, 3, 7, 14 and 21, and the same knee joint on day 0 before arthritis induction. For measurement of joint swelling, the mice were anesthetized by ether inhalation.
Preparation of total RNA
The expression of mRNA for proteinases and cytokines was analyzed for each individual animal. Arthritic and control mice were anesthetized with ether and killed by cervical dislocation. Then right knee joints (where arthritis was induced) were dissected and skinned. The muscle tissue was removed and the bony parts of knee joints were prepared, including the joint capsules with synovial tissue. The RNA in the knee joint was stabilized in RNAlater (Qiagen, Hilden, Germany), in accordance with the instructions in the manual. After incubation of the joints for 12 hours at 4°C in RNAlater, the samples were transferred to -80°C. The joints were mechanically disrupted by milling with a dismembrator U (Braun AG, Meiningen, Germany) for 15 s at 2000 Hz, followed by cooling the vessel in liquid nitrogen for 1 min. This procedure was repeated six or seven times. The tissue powder was rapidly transferred into 2 ml of cold TRIzol (Invitrogen, Carlsbad, CA, USA), immediately followed by dispersion for 1 min with a Polytron 1200 CL (Kinematica AG, Littau/Luzern, Switzerland). After homogenization and mechanical disruption, the RNA was extracted with TRIzol in accordance with the manual.
Microarray data analysis
The MG_U74Av2 oligonucleotide chip (array A; Affymetrix, Santa Clara, CA, USA), representing all functionally characterized sequences (about 6000 genes) in the Mouse UniGene database (Build 74) and additionally about 6000 expressed sequence tag clusters, was used to analyze the gene expression in murine arthritic knee joints during AIA.
We hybridized six chips with samples from two individual animals from each investigated time point (two at day 0 [control], two at day 3 [acute phase] and two at day 14 [chronic phase]). RNA labelling, hybridization and scanning of gene chips were performed in accordance with the supplier's instruction. Expression levels were calculated with the commercially available software MAS 5.0 provided by Affymetrix. Normalization of the signal was based on the expression of the housekeeping gene β-actin. Expression in the acute and chronic inflamed knee joints was compared with expression in the knee joints of control animals at day 0.
DNase treatment and complementary DNA (cDNA) synthesis for real-time PCR
The DNase treatment was performed by DNA Free™ kit (Ambion, Woodward, Austin, TX, USA) in accordance with the manufacturer's instructions. Total RNA (5 μg) was digested with 1 μl of DNase I (1 U/μl) and 2 μl of 10 × DNase I buffer in a volume of 20 μl. Supernatant (15 μl), containing the DNase-treated RNA, was transferred into a fresh 0.5 ml PCR tube. The RNA was denatured by incubation at 65°C for 15 min. After incubation on ice for 5 min, the reverse transcriptase (RT) reaction was completed by adding 35 μl of RT reaction mix, containing 1 μl of Superscript RT (200 U/μl; Invitrogen), 10μl of 5 × RT buffer (Invitrogen), 5 μl of 0.1 M dithiothreitol (Invitrogen), 5 μl of dNTPs (10 mM; Promega, Mannheim, Germany), 2 μl of poly(T) primer (T30VN; 50μmol), and 12 μl of distilled water.
Reverse transcription was performed at 42°C for 1 hour, after which the cDNA was precipitated with ethanol. The precipitated and air-dried cDNA was resuspended in 50 μl of distilled water.
Real-time PCR analyses
The oligonucleotide microarray data of interesting proteinases that could have a pivotal role in AIA were independently validated by real-time PCR. Cytokine expression at the mRNA level was also determined with this technique. Semiquantification of proteinase and cytokine expression by real-time PCR was performed with the Rotorgene 2000 (LTF Labortechnik, Wasserburg/Bodensee, Germany). The β-actin gene served as an endogenous control to normalize the differences in the amount of cDNA in each sample. SYBR Green I dye (Sigma) was supplied at 10,000 × concentration in dimethylsulfoxide. The enzyme Hot Star Taq (Qiagen) with the supplied reaction buffer was used for the PCR reaction. The reaction was performed in a volume of 25 μl, consisting of 2.5 μl of 10 × PCR buffer, 2.0–2.5 μl of MgCl2 (25 mM), 0.5 μl of dNTPs (10 mM each; Promega), 0.5 μl of Hot Star Taq (5 U/μl), 0.5 μl of primer mix (20 μM each) and 2.5 μl of 1 × SYBR Green I solution.
A standard curve was prepared by serial dilution of plasmid DNA (Vector pCR® 2.1-TOPO®; Invitrogen), containing the cDNA of the analyzed gene. The cloned fragment was identical in sequence and length with the PCR product. All samples that had to be compared for expression differences were run in the same assay as duplicates together with the standards. After completion of PCR amplification, data were analyzed with Rotorgene Software version 4.4. Data were initially expressed as a threshold cycle and are expressed as fold increases in gene expression in mice on day 0 compared with the expression on the other days investigated. The mean value of day 0 was set at 100%. After amplification was complete, the PCR products were analyzed by agarose gel electrophoresis. The primers used and the resulting PCR product sizes are given in Table 1.
Preparation of joint extracts
Arthritic and control mice were anesthetized with ether and killed by cervical dislocation. Knee joints were dissected, skinned and snap-frozen in propane/liquid nitrogen, and stored at -80°C until further analysis. Joint extract was obtained as described by Smith-Oliver and colleagues [9]. The frozen joints were ground under liquid nitrogen with mortar and pestle. The powdered tissue was transferred to a glass homogenizer, and exactly 2 ml of sterile saline solution was added. The powder suspension was homogenized by hand for 2 min and centrifuged for 20 min at 1500 × g and 4°C. The supernatant was spun again for 10 min at 3000 × g, and the resulting supernatant was aliquoted and frozen at -70°C. Protein concentration was determined by bicinchoninic acid assay (Pierce, Rockford, IL, USA).
Cytokine analysis in joint extracts
Concentrations of cytokines in joint extracts were determined by sandwich enzyme-linked immunosorbent assay (ELISA) in accordance with standard procedures, with the following antibody pairs: MP5-20F3 and MP5-32C11 (interleukin-6 [IL-6]; BD Biosciences, Palo Alto, CA, USA), R4-6A2 and XMG1.2 (interferon-γ [IFN-γ]; BD Biosciences), G281-2626 and MP6-XT3 (tumor necrosis factor-α [TNF-α]; BD Biosciences), MAB401 and BAF401 (IL-1β; R&D Systems, Minneapolis, MN, USA), BVD4-1D11 and BVD6-24G2 (IL-4; BD Biosciences). The second antibody of each antibody pair was biotinylated. Bound antibodies were detected with streptavidin-coupled peroxidase using o-phenylene diamine with 0.05% H2O2 as substrate. Optical density was measured at 492 nm in a microtiter plate-reader, model 16 598 (SLT Lab instruments, Crailsheim, Germany).
MMP analysis in joint extracts by zymography
For analysis of proteolytic capacity, joint extracts were diluted to a final protein concentration of 1 mg/ml, mixed with sample buffer containing sodium dodecyl sulfate (SDS), glycerol and bromophenol blue. Equal amounts of each sample were separated on an SDS-polyacrylamide gel (7.5%) containing 0.8 mg/ml gelatin (Merck, Darmstadt, Germany). After SDS-polyacrylamide gel electrophoresis, the gels were washed twice with 2.5% Triton X-100 for 30 min to remove SDS, then twice with distilled water and were finally equilibrated with incubation buffer (100 mM Tris/HCl, 30 mM CaCl2, 0.01% NaN3). The gel was then incubated in incubation buffer for 20 hours at 37°C. Staining of protein was performed with Coomassie Blue solution (10 ml of acetic acid, 40 ml of distilled water, 50 ml of methanol, 0.25% Coomassie Blue G250 [SERVA, Heidelberg, Germany]) for 40 min. Destaining was performed in methanol/acetic acid/distilled water (25:7:68, by vol.). After staining, white bands on blue gels indicate enzyme species. We used human pro-MMP-2 and pro-MMP-9 as controls (Novus Molecular Inc., San Diego, CA, USA)
Determination of MMP activity in joint extract
The synthetic peptide Mca-Pro-Leu-Gly-Leu-Dap(Dnp)-Ala-Arg-NH2 (no. M-1895; Bachem, Heidelberg, Germany; Mca stands for (7-methoxycoumarin-4-yl)acetic acid, Dap for L-2,3-diaminopropionic acid, and Dnp for 2,4-dinitrophenyl) was used to quantify the activity of MMPs in joint extracts. This substrate can be cleaved by different MMPs (MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12, MMP-13, MMP-14, MMP-17, MMP-25 and MMP-26). This fluorogenic peptide is a very sensitive substrate for continuous assays and for the in situ determination of matrix metalloproteinase activity. Cleavage at the Gly-Leu bond separates the highly fluorescent Mca group from the efficient Dnp quencher, resulting in an increase in fluorescence intensity.
Joint extracts were diluted with phosphate-buffered saline to give a final protein concentration of 1 mg/ml, then incubated with 5 μM substrate at 37°C in incubation buffer (100 mM Tris-HCl, 30 mM CaCl2, pH 7.6) for 3 hours. Active and free (not inhibited by TIMPs) MMPs cleaved the quenched substrate, which led to an increase in fluorescence at 390 nm (λex = 330 nm). The fluorescence was serially measured in black microtiter plates (Greiner, Solingen, Germany) with a fluorescence reader (FLUOstar Galaxy; BMG, B&L Systems, AS Maarssen, Netherlands). To estimate the total MMP activity, latent MMPs were activated by incubation with 2 mM aminophenylmercuric acetate (APMA; Sigma) for 15 min before the addition of substrate. The subsequent fluorescence signal after activation with APMA reflects the sum of the pro-forms of MMPs and the active forms of MMPs, which are not inhibited by TIMPs.
Histological evaluation of paraffin sections
Mice were killed on days 0, 1, 3, 7, 14 and 21 (two animals per group) and the knees were dissected and fixed in 4% phosphate-buffered paraformaldehyde. Fixed joints were decalcified in 20% EDTA (Sigma), dehydrated and embedded in paraffin wax. Frontal sections (2 μm) of the whole knee joint were stained with safranin O (counterstained with hematoxylin) for microscopic examination. Proteoglycans of cartilage are stained red. Safranin O staining was used to reflect cartilage proteoglycan depletion during the development of experimental arthritis.
Statistics
Analyses were performed with the statistical software SPSS/Win version 10.0 (SPSS Inc., Chicago, IL, USA). Data were analyzed with the Mann–Whitney U-test. After induction of arthritis, each group of animals was compared with the control day 0 group and the control normal group, respectively. For each test, P ≤ 0.05 was considered to be statistically significant.
Correlations according to Pearson and Spearman's rho were also made with SPSS/Win version 10.0.
Results
Joint swelling
Joint swelling reached its maximum on day 1 of arthritis in the acute phase, and gradually declined until day 7 in the beginning chronic phase of AIA (Fig. 1).
Affymetrix chip and real-time PCR analyses
Primarily, we screened the expression patterns during the course of AIA by MG_U74Av2 Affymetrix oligonucleotide chips (array A) to investigate the role of proteinases and their inhibitors in the development of arthritis. We compared the expression at the mRNA level in arthritic knee joints of two animals on day 3 and two animals on day 14 with the expression in knee joints of two control mice on day 0. By oligonucleotide chip technology we obtained a first impression of which MMPs, TIMPs, cathepsins and cystatins were strongly and differentially expressed in murine knee joints (Table 2). We identified 5 MMPs (MMP-3, MMP-8, MMP-9, MMP-13 and MMP-14), 3 TIMPs (TIMP-1, TIMP-2 and TIMP-3), 11 cathepsins (cathepsins B, C, D, E, F, G, H, K, L, S and Z) and 3 cystatins (cystatins B, F and C) that were highly expressed at the mRNA level in murine joints during AIA and resulted in a present call as a detection signal analyzed by MAS 5.0 (Table 2). Many proteinases and inhibitors were very weakly expressed and resulted in an absent call. Among the strongly expressed proteinases and inhibitors, some MMPs, cathepsins, TIMPs and cystatins showed interesting expression patterns, which seemed to be connected with arthritis development. We used real-time PCR to validate the expression of these molecules by an independent method. We confirmed the oligonucleotide chip data for most of the proteinases investigated (except cathepsin L; Fig. 2). It was possible to investigate additional time points of AIA and to study the expression behavior more exactly by this second molecular biological method.
Of all the proteinases investigated by chip analysis and real-time PCR, the matrix metalloproteinase MMP-3 was heavily induced in the acute phase of AIA and showed the most impressive increase of expression on days 1 and 3 (Fig. 2a). Its expression level decreased in the chronic phase in comparison with the acute phase but remained significantly elevated in chronically inflamed knee joints (days 14 and 21). MMP-13 was non-significantly overexpressed on day 1 compared with day 0. No significant changes in expression of MMP-13 mRNA were found for days 3, 7, 14 and 21.
Although oligonucleotide chip technology showed MMP-12 (macrophage elastase) to be very weakly expressed (absent call; Table 2), we investigated its expression by real-time PCR, because macrophages are important in RA. MMP-12 was very strongly and significantly overexpressed in the course of AIA after arthritis induction (Fig. 2a). The significantly increased expression of MMP-12 was observed until day 21 in the chronic phase.
Gelatinase B (MMP-9) showed little change in expression at the mRNA level (Fig. 2a). The transcription was weakly but not significantly downregulated in the acute phase at the mRNA level. MMP-2 (absent call on the array) was significantly downregulated at the mRNA level on day 1 in comparison with day 0 (Fig. 2a), reaching the day 0 level again on day 3 and increasing until day 21 of the chronic phase. MMP-2 was non-significantly overexpressed at the mRNA level on day 21 in comparison with day 0.
Next we investigated in more detail the differential expression of strongly expressed cathepsins by real-time PCR. After an analysis of the array data we found some candidate genes that might have a pivotal function in AIA. We analyzed cathepsins B, H, K, L and S more thoroughly by real-time PCR. The cathepsins B and H were non-significantly overexpressed in the acute phase. The mRNAs for cathepsins L and S were significantly elevated on day 3 in murine knee joints, where arthritis was induced (Fig. 2b). In contrast to MMP-3 overexpression with the highest peak on day 1, the overexpression of cathepsins was strongest on day 3.
Because cathepsin K was strongly expressed in murine knee joints and has an essential role in joint destruction, we investigated its expression pattern by real-time PCR (Fig. 2b) and found a significant downregulation at the mRNA level on day 1 but no alteration of expression at the mRNA level on the other days in the course of AIA.
Not only were MMPs and cathepsins differentially expressed after arthritis induction, but the expression of their inhibitors, the TIMPs and cystatins, was also changed in the course of AIA, as analyzed by Affymetrix oligonucleotide chips (Table 2). We performed real-time PCR for TIMP-1, TIMP-3 and cystatin B. TIMP-1 was the most strongly overexpressed (Fig. 3a). TIMP-3 showed a totally different expression profile from that of TIMP-1 (Fig. 3a): its expression was downregulated on day 1, increased from day 1 to day 7 and returned on day 14 to the level of control mice at day 0. The expression of cystatin B was increased about twofold on day 3 of the acute stage (Fig. 3a). Like the expression of cathepsins, the expression of cystatin B was strongest on day 3 (Fig. 3a).
MMP measurement by fluorescence activity assay and zymography
Fluorescence assay data of MMP activity in arthritic knee joints showed elevated latent MMP concentrations in the acute phase of AIA, in comparison with healthy control mice (Fig. 4a). However, active MMP molecules, not bound by inhibitor molecules, were not significantly elevated in arthritic knee joints.
Zymography of total protein knee joint extracts was performed for the different time points of AIA (Fig. 5). By gelatin zymography, bands could be seen at about 105 kDa (MMP-9) and 66 kDa (MMP-2). Protein solution from arthritic knee joints showed elevated gelatinolytic activity. In contrast to mRNA studies, zymography showed an increased expression of MMP-9 (105 kDa) in the course of AIA in comparison with day 0 (Fig. 5).
Cytokine expression during AIA
Because the production and secretion of proteinases and their inhibitors are regulated by cytokines, we investigated the expression of different cytokines at the mRNA and protein levels.
Several cytokines were differentially expressed at the mRNA level. We showed an altered expression for the pro-inflammatory cytokines IFN-γ, IL-6, TNF-α, IL-1β and IL-17 (Fig. 2c). IFN-γ was significantly elevated at the mRNA level on day 1 in the acute phase of inflammation. On day 7 it was significantly decreased at the mRNA level in comparison with day 0. IL-1β was non-significantly increased on day 1. IL-6 was significantly elevated at the mRNA level from day 1 to day 3, compared with day 0 mice at the mRNA level. Expression of IL-17 was non-significantly elevated at the mRNA level in the arthritic knee joint on days 1 and 3 of the acute phase. TNF-α expression was not significantly elevated at the mRNA level on day 1 of the acute phase or on day 21 of the chronic phase. The anti-inflammatory cytokine IL-4 was non-significantly decreased on day 1 at the mRNA level (Fig. 3b). Afterwards the mRNA expression of IL-4 increased until day 7 and returned to the starting level until day 14 of the chronic phase.
The expression patterns of these cytokines (exception IL-17) were also confirmed at the protein level by ELISA in murine knee joints after the induction of arthritis (Fig. 6). IFN-γ was significantly elevated at the protein level on days 1 and 3 of the acute phase. IL-1β was significantly elevated at the protein level from the induction of arthritis during the acute phase (days 1 and 3) until the beginning of the chronic phase of inflammation (day 7). IL-6 was also significantly increased in joint extracts on days 1 and 3. TNF-α expression was upregulated at the protein level on days 1 and 3 in the acute phase. In contrast to the mRNA level, no second increase in TNF-α expression was found. The anti-inflammatory cytokine IL-4 was downregulated on days 1, 3 and 7 at the protein level.
Correlations between cytokine and proteinase expression
The expression of TIMP-3 in knee joints was significantly correlated with IL-4 expression (correlation according to Spearman's rho, P ≤ 0.01; Fig. 3b). At the protein level, total MMP activity was significantly correlated with IL-1β protein concentration in knee joints (correlation according to Pearson P ≤ 0.005) (Fig. 4b).
Safranin O staining
Safranin O staining reflects the proteoglycan content of cartilage. Proteoglycan loss is an early marker of cartilage destruction. The staining intensity of proteoglycans was very weak during the acute stage of AIA on days 1 and 3, corresponding to the days of highest proteinase expression, in comparison with control joints on day 0 (Fig. 7).
Discussion
The aim of the present study was to investigate the contribution of various proteinases to bone and cartilage degradation during the development of AIA, an experimental model of human RA.
MMPs are believed to be pivotal enzymes in the invasion of articular cartilage by synovial tissue in RA [10,11]. However, the role of individual MMPs in the pathogenesis of arthritis must be determined to identify specific targets for joint protective therapies. MMP-3 (stromelysin-1) was the proteinase that showed the highest differences in expression during the course of AIA. Our observation concurs with that of van Meurs and colleagues [12] who noted a fast upregulation of mRNA for stromelysin during acute flare-up of murine AIA. The overproduction of MMP-3 has a role not only in the direct lysis of collagen but also in the activation of other proteinases [13]. MMP-13 (collagenase 3) showed a non-significantly increased expression on day 1 of the acute phase of AIA. The significance of these results must be shown by further experimental analyses. However, Wernicke and colleagues [14] demonstrated that MMP-13 mRNA was induced at sites of cartilage erosion in synovial fibroblasts co-implanted with normal cartilage in non-obese diabetic/severe combined immunodeficient mice. In contrast to MMP-13, the macrophage elastase (MMP-12) was abundantly increased at all investigated time points in AIA. Janusz and colleagues [15] showed that murine MMP-12 degrades cartilage proteoglycan with an efficiency about equal to human MMP-12 and matrilysin (MMP-7) and twice that of stromelysin-1 (MMP-3).
The gelatinases (MMP-2/gelatinase A and MMP-9/gelatinase B) are overproduced in joints of patients with RA [16-18]. Because of their degradative effects on the extracellular matrix, the family of gelatinases has been believed to be important in progression and cartilage degradation in this disease, although their precise roles still are to be defined. Itoh and colleagues [19] showed in the model of antibody-induced arthritis that MMP-9 knockout mice displayed milder arthritis than their wild-type littermates. Surprisingly, they found that MMP-2 knockout mice display a more severe arthritis than wild-type mice. These results indicate a suppressive role of MMP-2 and a pivotal role of MMP-9 in the development of inflammatory joint disease. In contrast to most of the investigated proteinases in our study, MMP-9 showed a decreased, nearly unaffected, expression at the mRNA level. For pro-MMP-9, we detected by zymography an increased protein content in knee joints during the development of experimental arthritis. The contradiction between high MMP-9 expression at the protein level and a decreased mRNA level in our experiments can be explained by the paracrine mechanism postulated by Dreier and colleagues [20]. MMP-2 showed a significantly decreased expression on day 1. A similar regulation mechanism for MMP-2 expression as for MMP-9 has not yet been described.
Usually, metalloproteinases have been favored as potential matrix-degrading enzymes over cysteine proteinases because of an activity optimum at neutral pH. However, an increasing number of expression analyses of synovial tissues and fluids from patients with RA [21-27], and studies with models of experimental arthritis [28,29], have shown the pivotal role of cathepsins in arthritis development. Extracellular cathepsin S shows an elastin-degrading and proteoglycan-degrading activity at neutral pH [30-32]. Cathepsin B, secreted by invading tumor cells, can degrade collagen and elastin [33]. The role of cathepsin B, for instance in activation of pro-MMP-3 and its regulation in arthritis, was recently reviewed by Yan and Sloane [34]. However, the expression and function of most of the cathepsins in RA are unknown. Our results support the hypothesis that cathepsins participate in matrix-degrading processes. We detected strong increases in cathepsin S and L mRNA in the acute phase of AIA. Cathepsin B and H were non-significantly elevated on day 3 after the induction of experimental arthritis.
Current interest is focused on the role of cathepsin K in RA. Besides its expression in osteoclasts and related chondroclasts as well as in multinucleated giant cells, cathepsin K has also been described in mononuclear cells that might act as precursor cells for osteoclasts, in macrophages and in various epithelial cells. In situ hybridization experiments have demonstrated cathepsin K expression in synoviocytes of patients with RA at sites of synovial destruction [35,36]. The overexpression of cathepsin K as shown in RA and collagen-induced arthritis of mice could not be confirmed in AIA at the mRNA level. We found a significant decrease in the expression of cathepsin K on day 1, when IFN-γ was expressed at its highest level. A feedback mechanism, as described for MMP-9, can be excluded, because Kamolmatyakul and colleagues [37] demonstrated that IFN-γ simultaneously downregulates cathepsin K expression in osteoclasts at the protein and mRNA levels.
The cytokines, especially the pro-inflammatory cytokines such as IFN-γ, TNF-α, IL-1, IL-6 and IL-17, also have a pivotal role in the pathology of RA. They are found in large quantities in RA synovium and synovial fluid [38-42]. Furthermore, cytokine knockout and transgenic mice showed a changed susceptibility for arthritis in animal models for RA [43-46]. A variety of cytokines including IL-1, TNF-α, and IL-17 increase the production and secretion of MMPs and cathepsins in fibroblasts [21,47-49]. In our studies, the local induction of AIA led to an impressive increase in IFN-γ, TNF-α, IL-1β and IL-6 in the joints. The latent MMP concentration at the protein level significantly correlated with the IL-1β concentration in joint extracts. We obtained very precise expression profiles for the cytokines and proteinases under investigation by using real-time PCR. At the transcriptional level, the increase in many MMPs and cathepsins is correlated with the highest expression levels of pro-inflammatory cytokines on days 1 and 3. The downregulation of cathepsin K on day 1 could be another example of the fact that the time course of cytokine expression determines the time course of proteinase and inhibitor expression.
In contrast to pro-inflammatory cytokines, IL-4 is one of the major cytokines that are able to protect against severe cartilage destruction during experimental arthritis. IL-4 treatment can inhibit IL-1 mRNA levels in the synovium [50,51]. IL-4 overexpression in the arthritic knee joint strongly and locally inhibited the mRNA expression of MMP-3 in mice with collagen-induced arthritis [50]. In addition, Borghaei and colleagues [52] have shown that IL-4 suppresses the IL-1-induced transcription of collagenase 1 (MMP-1) and stromelysin-1 (MMP-3) in human synovial fibroblasts. Van Lent and colleagues [53] reported that IL-4 blocks the activation step for latent MMPs within the cartilage layer. In the course of AIA we have seen a downregulation of IL-4 on day 1 at the mRNA level, after which IL-4 mRNA expression increased until day 7. Together with the fact that IFN-γ is significantly downregulated on day 7, this might be the reason for the decreased expression of MMP-3 on day 7 at the mRNA level in our study.
The biological activity of the proteinases can be regulated transcriptionally and post-transcriptionally by different TIMPs and by activation steps by the proteolytic cleavage of latent forms. Van der Laan and colleagues [54] demonstrated that the degradation and invasion of cartilage by rheumatoid synovial fibroblasts can be inhibited by gene transfer of TIMP-1 and TIMP-3. We have demonstrated in our analyses that TIMP-1 and TIMP-3 were differentially expressed in AIA. On account of the high similarity of expression profiles, TIMP-3 transcription seems to be directly or indirectly associated with IL-4 expression. Currently, there is no experimental evidence that IL-4 upregulated the TIMP-3 mRNA expression, but a similar mechanism to the upregulation of TIMP-2 by IL-4 in dermal fibroblasts described by Ihn and colleagues [55] cannot be excluded. TIMP-1 shows an expression profile very similar to that of MMP-3, being highly overexpressed during AIA. The overexpression of TIMP-1 at the mRNA level in our analysis is smaller and appears later than MMP-3 overexpression in arthritic knee joints. Hegemann and colleagues [56] demonstrated in canine rheumatoid arthritis that the amount of TIMP-1 was not sufficient to block the increased MMP-3 activity. We showed also in murine AIA, by safranin O staining of articular cartilage, that the balance between proteinase and inhibitor expression is disturbed and results in cartilage depletion.
Conclusion
This study was designed to detect proteinases and proteinase inhibitors that contribute to pathogenic processes in the development of experimental arthritis. We have been able to show that several MMPs, cathepsins and proteinase inhibitors are differentially expressed during the course of AIA. MMP-3, with the highest expression differences, seemed to have the major role in AIA development. We were able to show a correlation between, on the one hand, proteinase activity and proteinase inhibitor expression at the mRNA level and, on the other, cytokine expression. The mRNA data were manifested at the protein level by zymography and activity assays. Safranin O staining showed that the balance between proteinase and inhibitor expression is disturbed.
Abbreviations
AIA = antigen-induced arthritis; APMA = aminophenylmercuric acetate; ELISA = enzyme-linked immunosorbent assay; IFN = interferon; IL = interleukin; mBSA = methylated bovine serum albumin; MMP = matrix metalloproteinases; PCR = polymerase chain reaction; RA = rheumatoid arthritis; RT = reverse transcriptase; SDS = sodium dodecyl sulfate; TIMP = tissue inhibitor of matrix metalloproteinases; TNF = tumor necrosis factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
US and NS performed the Affymetrix chip analysis. US, NS and CP analysed the MMP, cathepsin and cytokine expression by real-time PCR. MH performed the zymography, the MMP activity assay and the cytokine ELISAs. US wrote the manuscript. BW and RB supervised the project and gave helpful comments about the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Uta Griechen, Cornelia Hüttich and Renate Stöckigt for excellent technical assistance, and Erich Schurigt for proofreading the manuscript. This work was supported by the Deutsche Forschungsgemeinschaft (grants Br 1372/5-1 and Wi 1102/8-1) and the Interdisciplinary Centre for Clinical Research (IZKF) Jena.
Figures and Tables
Figure 1 Course of joint swelling in antigen-induced arthritis. Joint swelling was expressed as the difference in diameter (mm) between the right arthritic knee joint on days 1, 3, 7, 14 and 21 and the same knee joint on day 0 before arthritis was induced. Results are expressed as means and SEM for at least five individual animals per group.
Figure 2 Real-time polymerase chain reaction (PCR) analyses of matrix metalloproteinase (MMP), cathepsin and pro-inflammatory cytokine expression in murine arthritic knee joints. Total RNA was isolated from murine right knee joints before (d0) and after (d1–d21) arthritis induction. mRNA samples from three (n = 3) (days 1, 7 and 21) or five (n = 5) (days 0, 3 and 14) animals were analyzed by real-time PCR with Rotorgene 2000. The mean of day 0 expression was set at 100%. The housekeeping gene β-actin was used for normalization of expression. Significant changes in comparison with day 0 expression are indicated by asterisks (increased expression) [(*) P ≤ 0.1, * P ≤ 0.05, ** P ≤ 0.01] or hash signs (decreased expression) [(#) P ≤ 0.1, # P ≤ 0.05]. Results are expressed as means and SEM. (a) MMP expression; (b) cathepsin expression; (c) pro-inflammatory cytokine expression. IFN, interferon; IL, interleukin; TNF, tumor necrosis factor.
Figure 3 Real-time polymerase chain reaction (PCR) analyses of tissue inhibitor of metalloproteinases (TIMP)-1, TIMP-3, cystatin B and anti-inflammatory interleukin (IL)-4 expression in murine knee joints during the course of antigen-induced arthritis. Total RNA was isolated from murine right knee joints before (d0) and after (d1–d21) arthritis induction. After reverse transcription of mRNA, the expression of TIMP-1, TIMP-3, cystatin B and IL-4 was measured by real-time PCR. The housekeeping gene β-actin was used for normalization of expression. Three (n = 3) (days 1, 7 and 21) or five (n = 5) (days 0, 3 and 14) animals were analyzed by real-time PCR with Rotorgene 2000 (except IL-4, for which n was 3 for all investigated days). The mean of day 0 expression was set at 100%. Results are expressed as means and SEM. Significant changes in comparison with day 0 animals are indicated by asterisks (* P ≤ 0.05, ** P ≤ 0.01). (a) TIMP-1, TIMP-3 and cystatin B mRNA expression, and (b) anti-inflammatory IL-4 mRNA expression in knee joints significantly correlates with TIMP-3 mRNA expression according to Spearman's rho (P ≤ 0.01).
Figure 4 Matrix metalloproteinase (MMP) fluorescence activity assay in joint extracts at different points of time in antigen-induced arthritis (AIA). (a) The contents of the active forms of MMPs and the pro-forms of MMPs (after activation with aminophenylmercuric acetate) in joint extracts were measured by fluorescence assay with the synthetic peptide substrate Mca-Pro-Leu-Gly-Leu-Dap(Dnp)-Ala-Arg-NH2. This substrate can be cleaved by different MMPs (MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12, MMP-13, MMP-14, MMP-17, MMP-25 and MMP-26). An increase in the MMP content of the pro-form during the acute phase of AIA was seen. Results are expressed as means and SEM for three animals per group (n = 3). Significant changes in comparison with normal animals (N) are indicated by asterisks (* P ≤ 0.05). (b) IL-1β protein content and total MMP activity (sum of pro-form and active form) in joint extracts is significantly correlated according to Pearson (P < 0.005).
Figure 5 Detection, by gelatin zymography, of pro-matrix metalloproteinase-2 (pro-MMP-2) and pro-MMP-9 expression in joint extracts at different time points after the induction of antigen-induced arthritis. Joint extracts were prepared at days 0, 1, 3, 7 and 21 and from normal mice (N) (three individual animals per day). The protein concentration was determined by bicinchoninic acid protein assay, and equal amounts of protein were applied to each lane. The zymography was developed and stained as described in Materials and Methods. The picture shows a representative gel of three comparable experiments. S, standard (human pro-MMP-2, [72 kDa] and pro-MMP-9 [92 kDa]).
Figure 6 Cytokine concentrations in joint extracts at different time points after the induction of antigen-induced arthritis. The pro-inflammatory cytokines interferon (IFN)-γ, interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α and the anti-inflammatory cytokine IL-4 were determined by enzyme-linked immunosorbent assay (ELISA) sandwich technique (see Materials and Methods) and were calculated in relation to the protein concentration. Results are expressed as means and SEM for at least three animals per group. Significant changes in comparison with normal animals (N) are indicated by asterisks: (*) P ≤ 0.1, * P ≤ 0.05, ** P ≤ 0.01. n.d., cytokine content was not detectable by ELISA.
Figure 7 Proteoglycan (PG) degradation in articular cartilage between femur and patella. Sections were stained with safranin O and counterstained with hematoxylin (see Materials and Methods) to determine the proteoglycan contents in murine knee joints before and after the induction of antigen-induced arthritis (AIA). Proteoglycans in cartilage were stained red by safranin O. The proteoglycan content in sections before and after arthritis induction reflects the cartilage degradation during the development of AIA. A, articular cartilage; F, femur; I, infiltrate; P, patella. The proteoglycan content in articular cartilage was decreased on days 1 and 3 in comparison with control day 0 animals. Original magnification × 100.
Table 1 Real-time polymerase chain reaction primers for analysis of proteinase, proteinase inhibitor and cytokine mRNA expression
Gene Forward primer Reverse primer Product size (base pairs)
IFN-γ 5'-gcg tca ttg aat cac acc tg-3' 5'-gac ctg tgg gtt gtt gac ct-3' 104
IL-1β 5'-cag gca ggc agt atc act ca-3' 5'-atg agt cac aga gga tgg gc-3' 140
IL-4 5'-agc tgc aga gac tct ttc gg-3' 5'-tgc tct tta ggc ttt cca gg-3' 111
IL-6 5'-ccg gag agg aga ctt cac ag-3' 5'-cag aat tgc cat tgc aca ac-3' 134
IL-17 5'-cct aag aaa ccc cca cgt tt-3' 5'-ttc ttt tca ttg tgg agg gc-3' 129
TNF-α 5'-acg gca tgg atc tca aag ac-3' 5'-gtg ggt gag gag cac gta gt-3' 116
MMP-2 5'-agc gtg aag ttt gga agc at-3' 5'-cac atc ctt cac ctg gtg tg-3' 105
MMP-3 5'-tgg aga tgc tca ctt tga cg-3' 5'-gcc ttg gct gag tgg tag ag-3' 120
MMP-9 5'-cat tcg cgt gga taa gga gt-3' 5'-att ttg gaa act cac acg cc-3' 118
MMP-12 5'-ttt gga gct cac gga gac tt-3' 5'-cac gtt tct gcc tca tca aa-3' 116
MMP-13 5'-agt tga cag gct ccg aga aa-3' 5'-ggc act cca cat ctt ggt tt-3' 105
TIMP-1 5'-ggt gtt tcc ctg ttt atc-3' 5'-tag ttc ttt att tca cca tct-3' 254
TIMP-3 5'-ttg ggt acc ctg gct atc ag-3' 5'-agg tct ggg ttc agg gat ct-3' 132
Cathepsin B 5'-gga gat act ccc agg tgc aa-3' 5'-ctg cca tga tct cct tca ca-3' 121
Cathepsin H 5'-gg cag agc ctc aga att gc-3' 5'-act ggc gaa aca aca ttt gc-3' 109
Cathepsin K 5'-ggg cca gga tga aag ttg ta-3' 5'-cac tgc tct ctt cag ggc tt-3' 106
Cathepsin L 5'-atc ccc aag tct gtg gac tg-3' 5'-tca gtg aga tca gtt tgc cg-3' 145
Cathepsin S 5'-aga gaa ggg ctg cgt cac t-3' 5'-gat atc agc ttc ccc gtt ttc ag-3' 117
Cystatin B 5'-tgc tga caa ggt cag acc ac-3' 5'-gca acc acg tcc tac att ca-3' 133
β-actin 5'-cca cag ctg aga ggg aaa tc-3' 5'-tct cca ggg agg aag agg at-3' 108
IFN, interferon; IL, interleukin; MMP, matrix metalloproteinase; TIMP, tissue inhibitor of metalloproteinases; TNF, tumor necrosis factor.
Table 2 Results of oligonucleotide chip analyses for matrix metalloproteinase (MMP), cathepsin, tissue inhibitor of metalloproteinases (TIMP) and cystatin expression
Gene Probe set ID First screening set Second screening set
Day 0 Day 3 Day 14 Day 0 Day 3 Day 14
S D S D S D S D S D S D
MMP-2 161509_at 34 A 17,2 A 6,2 A 14,5 A 19,2 A 31,8 A
MMP-2 95663_at 22,8 A 23,6 A 12,1 A 25,6 A 33,9 A 22,8 A
MMP-3 98833_at 35,6 P 97,4 P 38,5 P 35 P 424,5 P 84,3 P
MMP-7 162318_r_at 24,2 A 4 A 3,9 A 3,8 A 11,7 A 2,2 A
MMP-7 92917_at 3 A 5,4 A 2,6 A 4,1 A 1,8 A 2,7 A
MMP-8 94769_at 940,1 P 830,2 P 823,8 P 998,7 P 778,2 P 1006,5 P
MMP-9 162369_f_at 237 P 170,6 P 175,5 P 172,1 P 179,4 P 147,1 P
MMP-9 99957_at 2481,1 P 2287,7 P 1993,7 P 1838,9 P 2177 P 2264,3 P
MMP-10 94724_at 16 A 1,4 A 2 A 2 A 5,5 A 0,9 A
MMP-11 100016_at 42,3 A 47,7 M 26,1 A 40,1 A 30,5 A 53 A
MMP-12 95338_s_at 20,1 A 38,1 A 7 A 19,7 A 21,2 A 8,1 A
MMP-12 95339_r_at 33 A 81,9 A 32,6 A 15,2 A 71,8 A 30 A
MMP-13 161219_r_at 64,5 P 61,4 M 62,9 A 56,6 A 69,8 P 35,1 A
MMP-13 100484_at 1202,4 P 927 P 843,5 P 1510,5 P 1373,3 P 1184,2 P
MMP-14 160118_at 796,5 P 876,4 P 717,9 P 607,6 P 611,8 P 753,3 P
MMP-15 93612_at 40,7 A 9,4 A 7,3 A 24,6 A 11,5 A 15,7 A
MMP-16 98280_at 12,7 A 3,7 A 5,3 A 8,6 A 8,8 A 5,5 A
MMP-17 92461_at 35,4 A 43,4 M 26,3 A 35,4 A 56,1 A 43 A
MMP-24 160665_at 4,4 A 3,1 A 2,5 A 5,2 A 4,4 A 3,8 A
TIMP-1 101464_at 525,5 P 617,2 P 427,7 P 383,4 P 652,7 P 407,3 P
TIMP-2 93507_at 147,6 P 122,9 A 126,3 A 152,4 P 188,1 P 131,3 A
TIMP-3 160519_at 367,3 P 451,3 P 484,6 P 553,2 P 518,8 P 521 P
Cathepsin B 92256_at 78,3 P 113,1 P 46,5 P 102,5 P 104,8 P 88,9 P
Cathepsin B 94831_at 2150,4 P 2480,2 P 2244 P 1666,8 P 2013 P 1694 P
Cathepsin B 95608_at 119,6 P 96 M 117,5 P 156,3 P 121,7 P 100,7 P
Cathepsin C 101019_at 286,7 P 188,5 P 192,6 P 298,7 P 339,5 P 197,6 P
Cathepsin C 101020_at 145,8 P 97 P 101,6 P 150,6 P 251,5 P 126 P
Cathepsin C 161251_f_at 42,8 P 53,2 A 42,8 M 64,2 A 52,2 A 68,8 A
Cathepsin D 93810_at 1934 P 2280,4 P 1968,3 P 1551,4 P 1526 P 1994,3 P
Cathepsin E 104696_at 853,1 P 1383,5 P 1680,7 P 1619,9 P 1367,1 P 1368,3 P
Cathepsin F 97336_at 169 P 196,9 P 186,6 P 149,5 P 136 P 181,7 P
Cathepsin G 92924_at 1329 P 931 P 952,8 P 813,1 P 860,2 P 910,6 P
Cathepsin H 94834_at 506,5 P 565,4 P 399 P 319,9 P 399,2 P 398,2 P
Cathepsin K 160406_at 1312,8 P 1256,8 P 1296,1 P 1048,2 P 971,3 P 1202,5 P
Cathepsin L 101963_at 327,5 P 237,1 P 251,3 P 385,8 P 359,1 P 308,2 P
Cathepsin S 98543_at 1184,2 P 2003,7 P 1208,8 P 1549,8 P 2321,3 P 1349,6 P
Cathepsin W 92214_at 11,6 A 7,4 A 23,1 A 38,7 A 17,8 A 44 A
Cathepsin Z 92633_at 273,1 P 278,4 P 215,8 P 274,8 P 228 P 265,3 P
Cathepsin 7 97678_r_at 0,5 A 0,4 A 2,6 A 10,8 A 4 A 1 A
Cystatin B 100581_at 895,4 P 964,3 P 881,6 P 896,3 P 1017,8 P 760,4 P
Cystatin F 102638_at 360,4 P 375,4 P 280,4 P 343 P 334,3 P 366,8 P
Cystatin 8 103245_at 11,7 A 24,7 P 8,6 A 10,1 A 10,8 A 11,3 A
Cystatin 9 103296_at 5,2 A 12,8 A 6,4 A 25,2 A 2,5 A 32,4 A
Cystatin C 161522_i_at 130,6 P 180,2 P 201,9 P 105,7 P 91,1 P 215,5 P
Cystatin C 99586_at 2978,9 P 2793,1 P 3150,2 P 2467,9 P 2313,2 P 2982,7 P
β-actin M12481_5_at 9831 P 7177,8 P 7669,6 P 9942,5 P 9216,7 P 16027,1 P
β-actin M12481_M_at 10941 P 11693 P 10336,4 P 11128 P 11603,2 P 19708,7 P
β-actin M12481_3_at 12579 P 15514 P 12970,5 P 14876 P 12678,2 P 21765,3 P
The MG_U74Av2 Affymetrix oligonucleotide chip (array A) was used to analyze gene expression in murine arthritic knee joints during the course of antigen-induced arthritis (see Materials and Methods). We hybridized six chips with samples from two individual animals for each point of time (first screening set, days 0, 3 and 14; second screening set, days 0, 3 and 14). The table contains the following information: analyzed genes with Affymetrix probe set IDs, signal intensity (S) and detection information (D), with an absent call indicated by A, a marginal call by M and a present call by P. Probe sets with a present call (P) for all investigated days are indicated in bold.
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| 15642138 | PMC1064893 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Dec 10; 7(1):R174-R188 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1466 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14701564213410.1186/ar1470Research ArticleIncreased interleukin-17 production via a phosphoinositide 3-kinase/Akt and nuclear factor κB-dependent pathway in patients with rheumatoid arthritis Kim Kyoung-Woon [email protected] Mi-La [email protected] Mi-Kyung [email protected] Chong-Hyeon [email protected] Sung-Hwan [email protected] Sang-Heon [email protected] Ho-Youn [email protected] Department of Medicine, Division of Rheumatology, The Center for Rheumatic Diseases, and The Rheumatism Research Center (RhRC), Catholic Research Institutes of Medical Sciences, Catholic University of Korea, Seoul, Korea2005 29 11 2004 7 1 R139 R148 27 4 2004 19 5 2004 18 10 2004 3 11 2004 Copyright © 2004 Kim et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Inflammatory mediators have been recognized as being important in the pathogenesis of rheumatoid arthritis (RA). Interleukin (IL)-17 is an important regulator of immune and inflammatory responses, including the induction of proinflammatory cytokines and osteoclastic bone resorption. Evidence for the expression and proinflammatory activity of IL-17 has been demonstrated in RA synovium and in animal models of RA. Although some cytokines (IL-15 and IL-23) have been reported to regulate IL-17 production, the intracellular signaling pathways that regulate IL-17 production remain unknown. In the present study, we investigated the role of the phosphoinositide 3-kinase (PI3K)/Akt pathway in the regulation of IL-17 production in RA. Peripheral blood mononuclear cells (PBMC) from patients with RA (n = 24) were separated, then stimulated with various agents including anti-CD3, anti-CD28, phytohemagglutinin (PHA) and several inflammatory cytokines and chemokines. IL-17 levels were determined by sandwich enzyme-linked immunosorbent assay and reverse transcription–polymerase chain reaction. The production of IL-17 was significantly increased in cells treated with anti-CD3 antibody with or without anti-CD28 and PHA (P < 0.05). Among tested cytokines and chemokines, IL-15, monocyte chemoattractant protein-1 and IL-6 upregulated IL-17 production (P < 0.05), whereas tumor necrosis factor-α, IL-1β, IL-18 or transforming growth factor-β did not. IL-17 was also detected in the PBMC of patients with osteoarthritis, but their expression levels were much lower than those of RA PBMC. Anti-CD3 antibody activated the PI3K/Akt pathway; activation of this pathway resulted in a pronounced augmentation of nuclear factor κB (NF-κB) DNA-binding activity. IL-17 production by activated RA PBMC is completely or partly blocked in the presence of the NF-κB inhibitor pyrrolidine dithiocarbamate and the PI3K/Akt inhibitor wortmannin and LY294002, respectively. However, inhibition of activator protein-1 and extracellular signal-regulated kinase 1/2 did not affect IL-17 production. These results suggest that signal transduction pathways dependent on PI3K/Akt and NF-κB are involved in the overproduction of the key inflammatory cytokine IL-17 in RA.
interleukin-17nuclear factor κBPI3K/Akt pathwayperipheral blood mononuclear cellsrheumatoid arthritis
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Introduction
Rheumatoid arthritis (RA) is characterized by infiltrations of macrophages and T cells into the joint, and synovial hyperplasia. Proinflammatory cytokines released from these cells are known to be important in the destruction of joints in RA [1]. The favorable clinical benefits obtained with inhibitors of tumor necrosis factor (TNF)-α) and interleukin (IL)-1 suggest that the blockade of key inflammatory cytokines has been the important issue in the development of new therapeutic applications [2].
A little over a decade ago, the primacy of T cells in the pathogenesis of autoimmune disease such as RA was undisputed because they are the largest cell population infiltrating the synovium. However, a series of studies demonstrated paucity of T cell-derived cytokines such as IL-2 and interferon-γ in the joints of RA, whereas macrophage and fibroblast cytokines including IL-1, IL-6, IL-15, IL-18 and TNF-α were abundant in rheumatoid synovium. This paradox has questioned the role of T cells in the pathogenesis of RA [3]. Because we have already demonstrated the enhanced proliferation of antigen specific T cells, especially to type II collagen, and the skewing of T helper type 1 (Th1) cytokines in RA [4], the role of T cells needs to be elucidated in different aspects.
IL-17 is one of the inflammatory cytokines secreted mainly by activated T cells, which can induce IL-6 and IL-8 by fibroblasts [5]. This cytokine is of interest for two major reasons: first, similarly to TNF-α and IL-1, IL-17 has proinflammatory properties; second, it is produced by T cells [6]. Recent observations demonstrated that IL-17 can also activate osteoclastic bone resorption by the induction of RANKL (receptor activator of nuclear factor κB [NF-κB] ligand), which is involved in bony erosion in RA [7]. It also stimulates the production of IL-6 and leukemia inhibitory factor by synoviocytes, and of prostaglandin E2 and nitric oxide by chondrocytes, and has the ability to differentiate and activate the dendritic cells [8-10]. Levels of IL-17 in synovial fluids were significantly higher in patients with RA than in patients with osteoarthritis (OA), and it was produced by CD4+ T cells in the synovium [11,12].
IL-15, secreted from activated macrophages, has been reported to be an important trigger of IL-17 production in RA peripheral blood mononuclear cells (PBMC) by cyclosporine and steroid sensitive pathways [13]. Recently, Happel and colleagues also showed that IL-23 could be an efficient trigger of IL-17 production from both CD4+ and CD8+ T cells [14].
Although the contribution of IL-17 in joint inflammation in RA has been documented in earlier studies [12,15,16], the intracellular signal transduction pathway for IL-17 production remains uncertain. In the present study we used various stimuli to investigate IL-17 production in PBMC of patients with RA and its signaling transduction pathway.
We found that the intracellular signaling pathway involving phosphoinositide 3-kinase (PI3K)/Akt and NF-κB might be involved in the overproduction of the key inflammatory cytokine IL-17 in RA. These results might provide new insights into the pathogenesis of RA and future directions for new therapeutic strategies in RA.
Materials and methods
Patients
Informed consent was obtained from 24 patients (5 men and 19 women) with RA who fulfilled the 1987 revised criteria of the American College of Rheumatology (formerly the American Rheumatism Association) [17]. The age of the patients with RA was 50 ± 8 (mean ± SEM) years (range 23–71 years). All medications were stopped 48 hours before entry to the study. Comparisons were made with 14 patients with OA (3 men and 11 women) and with 14 healthy controls (3 men and 11 women) who had no rheumatic diseases. The mean ages of the patients with OA and the healthy controls were 50 ± 8 years (range 34–68 years) and 30 ± 6 years (range 24–57 years). Informed consent was obtained, and the protocol was approved by the Catholic University of Korea Human Research Ethics Committee.
Reagents
Recombinant IL-17, IL-18, IL-15, monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein (MIP)-1α, MIP-1β, IL-6 and IL-8 were purchased from R & D systems (Minneapolis, MN, USA). Recombinant transforming growth factor (TGF)-β was purchased from Peprotech (London, UK). Recombinant TNF-α and IL-1 were purchased from Endogen Inc. (Cambridge, MA, USA). Cyclosporin A was provided by Sandos Ltd. (Basel, Switzerland). Phytohemagglutinin (PHA), pyrrolidine dithiocarbamate (PDTC), rapamycin, dexamethasone and curcumin were all obtained from the Sigma Chemical Co. (St Louis, MA, USA). Anti-CD3 monoclonal antibody and anti-CD28 monoclonal antibody were obtained from BD Biosciences (San Diego, CA, USA). LY294002, SB203580, FK506, wortmannin and PD98059 were obtained from Calbiochem (Schwalbach, Germany).
Production of IL-17 by T cell receptor activation, cytokines or chemokines
PBMC were prepared from heparinized blood by Ficoll-Hypaque (SG1077) density-gradient centrifugation. Cell cultures were performed as described previously [18]. In brief, the cell suspensions were adjusted to a concentration of 106/ml in RPMI 1640 medium supplemented with 10% fetal calf serum, 100 U/ml penicillin, 100 mg/ml streptomycin and 2 mM L-glutamine. Cell suspension (1 ml) was dispensed into 24-well multi-well plates (Nunc, Roskilde, Denmark), and incubated for 24 hours at 37°C in 5% CO2. Subsequently, various concentrations of cyclosporin A (10–500 ng/ml) were added to the medium and cells were incubated for 24 hours. To each well was added FK506, rapamycin, curcumin, PDTC, LY294002, SB203580, PD98059, dexamethasone or wortmannin. After incubation for 24 hours (unless otherwise stated), cell-free media were collected and stored at -20°C until assayed. All cultures were set up in triplicate, and results are expressed as means ± SEM.
CD4+ T-cell isolation by MACS
Anti-CD4 microbeads were used essentially as recommended by the manufacturer (Miltenyi) [19]. PBMC were resuspended in 80 μl of FBS staining buffer. Anti-CD4 microbeads (20 μl) were added and incubated for 15 min at 6–12°C. Saturating amounts of fluorochrome-conjugated antibodies were added for a further 10 min. Cells were diluted in 2.5 ml of FBS staining buffer, pelleted, resuspended in 500 μl and magnetically separated, usually on an AutoMACS magnet fitted with a MACS MS column. Flow-through and two 1 ml washes were collected as the negative fraction. Enriched cells were collected in two 0.5 ml aliquots from the column after removal from the magnet. Alternatively, cells stained with anti-CD4–phycoerythrin were washed, magnetically labeled with anti-phycoerythrin microbeads (20 μl added to 80 μl of cell suspension; 15 min, 6–12°C), and magnetically separated as described above. The purity of cells was assessed by flow cytometric analysis of stained cells on a FACS Vantage sorter. Most (more than 97%) of the isolated cells had the CD4 T cell marker.
Enzyme-linked immunosorbent assay of IL-17
IL-17 in culture supernatants was measured by sandwich enzyme-linked immunosorbent assay as described previously [20]. In brief, a 96-well plate (Nunc) was coated with 4 μg/ml monoclonal antibodies against IL-17 (R & D Systems) at 4°C overnight. After blocking with phosphate-buffered saline/1% bovine serum albumin (BSA)/0.05% Tween 20 for 2 hours at room temperature (22–25°C), test samples and the standard recombinant IL-17 (R & D Systems) were added to the 96-well plate and incubated at room temperature for 2 hours. Plates were washed four times with phosphate-buffered saline/Tween 20, and then incubated with 500 ng/ml biotinylated mouse monoclonal antibodies against IL-17 (R & D Systems) for 2 hours at room temperature. After washing, streptavidin–alkaline phosphate–horseradish peroxidase conjugate (Sigma) was incubated for 2 hours, then washed again and incubated with 1 mg/ml p-nitrophenyl phosphate (Sigma) dissolved in diethanolamine (Sigma) to develop the color reaction. The reaction was stopped by the addition of 1 M NaOH and the optical density of each well was read at 405 nm. The lower limit of IL-17 detection was 10 pg/ml. Recombinant human IL-17 diluted in culture medium was used as a calibration standard, ranging from 10 to 2000 pg/ml. A standard curve was drawn by plotting optical density against the log of the concentration of recombinant cytokines, and used for determination of IL-17 in test samples.
Quantification of IL-17 mRNA by semiquantitative reverse transcription–polymerase chain reaction
PBMC were incubated with various concentrations of anti-CD3 in the presence or absence of inhibitors (LY294002, PDTC). After 16 hours of incubation, mRNA was extracted with RNAzol B (Biotex Laboratories, Houston, TX, USA) in accordance with the manufacturer's instructions. Reverse transcription of 2 μg of total mRNA was performed at 42°C using the Superscript™ reverse transcription system (Takara, Shiga, Japan). PCR amplification of cDNA aliquots was performed by adding 2.5 mM dNTPs, 2.5 U of Taq DNA polymerase (Takara) and 0.25 μM of sense and antisense primers. The reaction was performed in PCR buffer (1.5 mM MgCl2, 50 mM KCl, 10 mM Tris-HCl, pH 8.3) in a total volume of 25 μl. The following sense and antisense primers for each molecules were used: IL-17 sense, 5'-ATG ACT CCT GGG AAG ACC TCA TTG-3'; IL-17 antisense, 5'-TTA GGC CAC ATG GTG GAC AAT CGG-3'; glyceraldehyde-3-phosphate dehydrogenase (GAPDH) sense, 5'-CGA TGC TGG GCG TGA GTA C-3'; GAPDH antisense, 5'-CGT TCA GCT CAG GGA TGA CC-3'. Reactions were processed in a DNA thermal cycler (Perkin-Elmer Cetus, Norwalk, CT, USA) through cycles for 30 s of denaturation at 94°C, 1 min of annealing at 56°C for GAPDH and IL-17, followed by 1 min of elongation at 72°C. PCR rounds were repeated for 25 cycles each for both GAPDH and IL-17; this was determined as falling within the exponential phase of amplification for each molecule. The level of mRNA expression was presented as a ratio of IL-17 PCR product over GAPDH product.
Western blot analysis of Akt, phosphorylated Akt and IκB-α
PBMC were incubated with anti-CD3 (10 μg/ml) in the presence or absence of LY294002 (20 μM). After incubation for 1 hour, whole cell lysates were prepared from about 107 cells by homogenization in the lysis buffer, and centrifuged at 14,000 r.p.m. (19,000 g) for 15 min. Protein concentrations in the supernatants were determined with the Bradford method (Bio-Rad, Hercules, CA, USA). Protein samples were separated by 10% SDS–PAGE and transferred to a nitrocellulose membrane (Amersham Pharmacia Biotech, Uppsala, Sweden). For western hybridization, membrane was preincubated with 0.1% skimmed milk in TBS-T buffer (0.1% Tween 20 in Tris-buffered saline) at room temperature for 2 hours, then primary antibodies against Akt, phosphorylated Akt and IκB-α (Cell Signaling Technology Inc., Beverly, MA, USA), diluted 1:1000 in 5% BSA/TBS-T, were added and incubated overnight at 4°C. After washing four times with TBS-T, horseradish peroxidase-conjugated secondary antibodies were added and allowed to incubate for 1 hour at room temperature. After TBS-T washing, hybridized bands were detected with the enhanced chemiluminescence (ECL) detection kit and Hyperfilm-ECL reagents (Amersham Pharmacia).
Gel mobility-shift assay of NF-κB binding site
Nuclear proteins were extracted from about 5 × 106 PBMC. Oligonucleotide probes encompassing the NF-κB binding site of the human IL-17 promoter (5'-ATG ACC TGG AAA TAC CCA AAA TTC-3') were generated by 5'-end labeling of the sense strand with [γ-32P]dATP (Amersham Pharmacia) and T4 polynucleotide kinase (TaKaRa). Unincorporated nucleotides were removed by NucTrap probe purification columns (Stratagene, La Jolla, CA, USA). Nuclear extracts (2 μg of protein) were incubated with radiolabeled DNA probes (10 ng; 100,000 c.p.m.) for 30 min at room temperature in 20 μl of binding buffer consisting of 20 mM Tris-HCl, pH 7.9, 50 mM KCl, 1 mM dithiothreitol, 0.5 mM EDTA, 5% glycerol, 1 mg/ml BSA, 0.2% Nonidet P40 and 50 ng/μl poly(dI-dC). Samples were subjected to electrophoresis on nondenaturing 5% polyacrylamide gels in 0.5 × Tris-borate-EDTA buffer (pH 8.0) at 100 V. Gels were dried under vacuum and exposed to Kodak X-OMAT film at -70°C with intensifying screens. Rabbit polyclonal antibodies against NF-κB subunits p50, p65 and c-Rel were from Santa Cruz Biotechnology (Santa Cruz, CA, USA).
Cell viability (Trypan blue dye exclusion assay)
For cell viability assays, the trypan blue dye exclusion method was used to evaluate the potential of direct cytotoxic effect of inhibitors on cells. After incubation for 24 hours, the cells were harvested and the percentage cell viability was calculated with the formula 100 × (number of viable cells/number of both viable and dead cells) [21].
Statistical analysis
Data are expressed as means ± SEM. Statistical analysis was performed with Student's t-test for matched pairs. P values less than 0.05 were considered significant.
Results
IL-17 production in PBMC from patients with RA, patients with OA and normal individuals
PBMC were separated and cultured with PHA (5 μg/ml) from patients with RA, patients with OA, and age-matched normal controls; IL-17 levels were then determined in the culture supernatants (Fig. 1). Although the amounts of basal IL-17 secretion were not different between RA, OA and normal controls (62 ± 31, 43 ± 19 and 43 ± 10 pg/ml, respectively), the IL-17 production stimulated by PHA was significantly higher in RA PBMC than in those from OA and controls (768 ± 295 versus 463 ± 211 pg/ml [P < 0.05] and 241 ± 29 pg/ml [P < 0.001]).
Increased IL-17 production in PBMC of patients with RA by anti-CD3 and/or anti-CD28, and PHA
Because IL-17 was already known from earlier reports to be produced mainly by activated T cells, we investigated the effect of different concentrations of anti-CD3 (1, 5 and 10 μg/ml) as a T cell activation, which showed a dose-dependent increase in IL-17 levels (data not shown). On the basis of this, we chose 10 μg/ml as a stimulation concentration for anti-CD3. As shown in Table 1, anti-CD3 significantly upregulated IL-17 production up to 3.7-fold, and the combination of anti-CD28 and anti-CD3 produced more IL-17 (approximately 1.3-1.5-fold) than anti-CD3 alone. Furthermore, when incubated with T cell mitogens such as PHA, increased IL-17 production was more pronounced than with anti-CD3 and anti-CD28 (588 ± 85 versus 211 ± 1 pg/ml; P < 0.05).
Regulation of IL-17 production in RA PBMC by inflammatory cytokines and chemokines
Because RA PBMC include several cell types in addition to T cells, some inflammatory cytokines released from macrophages and other lymphocytes might have affected the production of IL-17 from T cells. To evaluate the effects of inflammatory cytokines released by activated PBMC, we tested the effects of several cytokines and chemokines on IL-17 production. We detected an increase in IL-17 level after stimulation with IL-15 (10 ng/ml), whereas with IL-1β (10 ng/ml), TNF-α (10 ng/ml), IL-18 (10 ng/ml) or TGF-β (10 ng/ml) the levels in IL-17 were unchanged (Fig. 2a). When treated with MCP-1 (10 ng/ml) or IL-6 (10 ng/ml), significant upregulations of IL-17 proteins were observed (62 ± 42 and 50 ± 10 versus 31 ± 11 pg/ml, respectively; P < 0.05), whereas none was observed with IL-8 (10 ng/ml), MIP-1α (10 ng/ml) or MIP-1β (10 ng/ml) (Fig. 2b).
Inhibition of IL-17 production by signal transduction inhibitors and anti-rheumatic drugs
Having observed the increased IL-17 production in RA PBMC, it was important to know which signal transduction pathways were involved. As illustrated in Fig. 3, an significant decrease in anti-CD3-induced IL-17 production was observed when co-incubated with NF-κB inhibitor, PDTC and dexamethasone in comparison with anti-CD3 alone (38 ± 5 and 54 ± 11 versus 98 ± 19 pg/ml, respectively; P < 0.05).
LY294002 and wortmannin, as an inhibitor of PI3K, also markedly inhibited the anti-CD3-induced IL-17 production in RA PBMC (98 ± 19 versus 38 ± 10 pg/ml [P < 0.005] and 48 ± 4 pg/ml [P < 0.05], respectively).
The calcineurin inhibitors cyclosporin A and FK506 also downregulated the IL-17 secretion as well as the mitogen-activated protein kinase (MAPK) p38 inhibitor SB203580 did, whereas rapamycin and PD98059 had no effect on IL-17 levels (Fig. 3). To evaluate the possibility of non-specific inhibition by the drug at high concentrations, we observed the dose response of PDTC and LY294002 for the inhibition of IL-17 production in PBMC. There were dose-dependent inhibitions of IL-17 production with chemical inhibitors (Fig. 4a). The other inhibitors in addition to PDTC and LY294002 showed the same pattern of inhibition. Cytotoxic effects on PBMC by the chemical inhibitors at experimental concentrations were not observed (Fig. 4b).
IL-17 mRNA expression in RA PBMC
To see whether enhanced IL-17 production could be regulated at a transcriptional level, semi-quantatitive reverse transcription–polymerase chain reaction was performed. We observed a dose-dependent increase in IL-17 mRNA transcripts after stimulation with anti-CD3; this was inhibited by the PI3K inhibitor LY294002 and by the NF-κB inhibitor PDTC (Fig. 5).
Activation of PI3K/Akt signal transduction pathway on IL-17 production by anti-CD3
To determine downstream effector molecules of the PI3K pathway, we evaluated the activation of Akt by western blotting. As shown in Fig. 6, at 10 min of incubation with anti-CD3 (10 μg/ml) or LY294002 (20 μM), no difference in the amounts of phosphorylated Akt was observed. However, after 30 min of incubation, phosphorylated Akt increased (lane 2), and the effect of inhibition by LY294002 (lane 3) reached a peak at 60 min, lasting to 120–240 min. In contrast, non-phosphorylated Akt and β-actin remained unchanged regardless of incubation time. PHA, concanavalin A and IL-15 also demonstrated the same effect on phosphorylated Akt as shown with anti-CD3, which was an inhibition by wortmannin and PDTC as well as by LY294002 (data not shown).
Activation of the NF-κB and activator protein-1 (AP-1) pathway in the IL-17 promoter region
To investigate further the intracellular signaling pathway activated by anti-CD3 plus anti-CD28, concanavalin A, PHA and IL-15, and responsible for inducing IL-17 expression, we performed an electrophoretic mobility-shift assay (EMSA) of NF-κB recognition sites in the promoters of IL-17. As shown in Fig. 7a, nuclear extracts from RA PBMC stimulated with anti-CD3 plus anti-CD28 (lane 2) demonstrated increased binding of NF-κB to IL-17 promoters in comparison with that of controls (lane 1). A supershift assay demonstrated shifted bands in p65 and p50 (lanes 3 and 4) not in c-Rel (lane 5). In normal PBMC the same pattern was observed, but the degree of NF-κB activation by anti-CD3 plus anti-CD28 was less intense than that in RA PBMC (Fig. 7b). To confirm the link between PI3K activity and NF-κB, we performed EMSA to determine the NF-κB binding activity after treatment with both LY294002 and PDTC. Both agents block NF-κB DNA-binding activity in the IL-17 promoter (Fig. 7c). Western blotting for IκB-α showed inhibition of degradation of IκB-α by LY294002 and PDTC at the same time (Fig. 7c). In contrast, the AP-1 pathway was not activated by stimulation with anti-CD3 plus anti-CD28 (data not shown), demonstrating that NF-κB is the main intracellular signaling pathway in IL-17 production by activated PBMC from patients with RA.
Discussion
IL-17 was first described as a T cell product with proinflammatory properties [5,22]. RA is characterized by hyperplasia of synovial lining cells and an intense infiltration by mononuclear cells [23]. Proinflammatory cytokines such as IL-1 and TNF-α are abundant in rheumatoid synovium, whereas the T cell-derived cytokines, especially IL-4 and interferon-γ, have often proved difficult to detect in RA synovium [24]. Although T cells may have a role in the augmentation of rheumatoid synovial inflammation, the lack of T cell-derived cytokines has limited its importance. In this respect, IL-17 is appealing because it has been described as a T cell-derived cytokine with proinflammatory properties.
In our studies, we tried to evaluate how IL-17 production is regulated in RA PBMC, and which signaling pathway it used. Levels of IL-17 were found to be higher in RA synovial fluid than in OA synovial fluid [15]. However, there are few data available on the agents that stimulate IL-17 production in RA, although the highest level of IL-17 production can be achieved by anti-CD3/anti-CD28 stimulation in healthy individuals [25]. In our experiments, PHA as mitogens, as well as anti-CD3/anti-CD28 for signaling through the T cell receptor, increased IL-17 production from RA PBMC in a dose-dependent manner. We found, by a cell proliferation assay (data not shown), that this upregulation of IL-17 might be due to increased cellular activity rather than to cellular proliferation.
IL-17 is produced mainly by activated CD4+ T cells, especially for Th1/Th0 cells, not the Th2 phenotype [26]. However, it can also be produced by CD8+ T cells via an IL-23 triggering mechanism in Gram-negative pulmonary infection [14]. In addition, IL-17 production was significantly augmented by T cells recognizing type II collagen in a collagen-induced arthritis model [27]. A complex interaction between cells in inflamed RA joints might produce a variety of proinflammatory cytokines and chemokines, which also activate other cells in the joints. For example, IL-17 stimulates rheumatoid synoviocytes to secrete several cytokines such as IL-6, IL-8 and tumor necrosis factor-stimulated gene 6 as well as prostaglandin E2 in vitro [12,28,29]. There are as yet few data available on the agents that stimulate IL-17 production in RA, although some cytokines (IL-15 and IL-23) have been known to regulate IL-17 production [13,14]. We therefore investigated the in vitro production of IL-17 in RA PBMC responding to a variety of cytokines/chemokines and mitogens as well as T cell receptor (TCR) ligation using anti-CD3/anti-CD28. Our studies demonstrated that IL-15 and MCP-1 as well as TCR ligation significantly increased the production of IL-17 in RA PBMC. Adding IL-15 or MCP-1 to TCR ligation augmented IL-17 production more markedly. In contrast, IL-1 and TNF-α, which are known to have proinflammatory properties and to be increased in RA joints, did not affect IL-17 production. Our data were consistent with a recent report that IL-15 triggered in vitro IL-17 production in PBMC, but TNF-α did not do so [13]. Although there were no data that MCP-1 directly induces T cell activation, it might exert effects indirectly on T cells through the activation of monocytes/macrophages in PBMC cultures. As reported for normal individuals [25], T cell activation through anti-CD3/anti-CD28 also increases IL-17 induction in RA PBMC.
Although the signaling pathway for the induction of cytokines/chemokines by IL-17 has been documented widely [8,30,31], no data have been available on how IL-17 production can be regulated by certain signaling pathways. By using signal transduction inhibitors, we therefore examined which signaling pathway was mainly involved in the induction of IL-17 in RA PBMC.
We identified that anti-CD3-induced IL-17 production in RA PBMC was significantly hampered by the PI3K inhibitor LY294002 and the NF-κB inhibitor PDTC to comparable levels of basal production without stimulation. We also found that anti-CD3-induced IL-17 production was downregulated by the addition of SB203580, a p38 MAPK inhibitor. It is interesting that a series of evidence supports crosstalk between NF-κB and p38. In myocytes, IκB kinase-β is activated by p38 [32], and the activated p38 can stimulate NF-κB by a mechanism involving histone acetylase p300/CREB-binding protein [33]. Our results revealed that p38 MAPK activation was not affected by LY294002, whereas NF-κB binding activity was decreased by LY294002, which provided the evidence for a p38 MAPK pathway independent of PI3K activation. The direct relationship between p38 and NF-κB for IL-17 production needs to be studied in future experiments.
The search for a downstream pathway of PI3K seemed to have a maximal response of Akt activation at 1 hour and a gradual loss of activity at 2 hours. The fact that Akt is phosphorylated upon anti-CD3 stimulation suggests the possible involvement of PI3K in the induction of IL-17 in RA. In view of the fact that NF-κB was also activated by anti-CD3/anti-CD28, IL-15 or mitogens in our experiments, it is most likely that the NF-κB pathway is also actively involved in the induction of IL-17 in RA PBMC. In contrast, the AP-1 signal transduction pathway, another important signaling pathway for cytokines/chemokines, was not activated in our experiments (data not shown). Although PI3K and its downstream kinase Akt in association with NF-κB have been reported to deliver activating signals in many cell types, the data on the signal inducing IL-17 are lacking. Our data clearly demonstrated that PI3K/Akt and resultant NF-κB activation could be an important arbitrator of the upregulation of IL-17 in RA, on the basis of our experiments showing simultaneous blocking of NF-κB binding activity in the IL-17 promoter by PDTC and LY294002. Considering its proinflammatory activities and successful induction of anti-IL-17 for ameliorating arthritis in animal models [2,6,34-36], understanding the IL-17 signaling pathway is an important element of developing new targeted therapies in RA.
Conclusions
We have detected a more pronounced production of IL-17 from RA PBMC in response to IL-15 and MCP-1 as well as stimulation by anti-CD3/anti-CD28. We have also shown that upregulation of IL-17 by activated T cells in patients with RA could be the result of activation via the PI3K/Akt pathway with resultant NF-κB activation. Our data provide insights into cellular mechanisms of the regulation of IL-17 production in RA, and highlight the role of T cells, which has hitherto been neglected in RA pathogenesis. Together with recent data on the successful introduction of anti-IL-17 in RA, our results have added information for the future molecular targeting of new therapeutic applications in RA.
Abbreviations
AP-1, activator protein-1; BSA = bovine serum albumin; EMSA = electrophoretic mobility-shift assay; GAPDH = glyceraldehyde-3-phosphate dehydrogenase; IL = interleukin; MAPK = mitogen-activated protein kinase; MCP-1 = monocyte chemoattractant protein-1; MIP = macrophage inflammatory protein; NF-κB = nuclear factor κB; OA = osteoarthritis; PBMC = peripheral blood mononuclear cells; PDTC = pyrrolidine dithiocarbamate; PHA = phytohemagglutinin; PI3K = phosphoinositide 3-kinase; RA = rheumatoid arthritis; TGF = transforming growth factor; Th = T helper; TNF = tumor necrosis factor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KWK performed the cellular immune response studies and participated in the immunoassays. MLC participated in the design of the study and performed the statistical analysis. MKP participated in the isolation of the cells. CHY drafted the manuscript. SHP participated in the molecular biology and in the PCR. SHL conceived the study, participated in its design and coordination and helped to draft the manuscript. HYK helped to draft the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This study was supported by SRC grant R11-2002-098-04002-0 from the Korea Science and Engineering Foundation (KOSEF) to the Rheumatism Research Center at the Catholic University of Korea, Seoul.
Figures and Tables
Figure 1 Levels of interleukin (IL)-17 production in peripheral blood mononuclear cells from patients with rheumatoid arthritis (RA; n = 24), patients with osteoarthritis (OA) (n = 14) and normal individuals (n = 14). Each peripheral blood mononuclear cell was stimulated for 24 hours with or without phytohemagglutinin (PHA; 5 μg/ml). IL-17 was measured in culture supernatants by sandwich enzyme-linked immunosorbent assay. Data are expressed as means and SEM. One representative result of five independent experiments is shown. Student's t-test was used to compare each group. *, P < 0.05; **, P < 0.001.
Figure 2 Production of interleukin (IL)-17 by peripheral blood mononuclear cells (PBMC) from patients with rheumatoid arthritis (RA). (a) Production of IL-17 by cytokine induction. PBMC from patients with RA were stimulated for 24 hours with IL-15 (10 ng/ml), IL-1β (10 ng/ml), tumor necrosis factor-α (TNF-α; 10 ng/ml), IL-18 (10 ng/ml) and transforming growth factor-β (TGF-β; 10 ng/ml). Levels of IL-17 were measured in culture supernatants by enzyme-linked immunosorbent assay. Each value represents the mean and SEM of three independent experiments. (b) Production of IL-17 by chemokine induction. PBMC were cultured in the presence of monocyte chemoattractant protein-1 (MCP-1; 10 ng/ml), macrophage inflammatory protein-1α (MIP-1α; 10 ng/ml), MIP-1β (10 ng/ml), IL-6 (10 ng/ml) and IL-8 (10 ng/ml). *, P < 0.05.
Figure 3 Effects of protein kinase inhibitors and anti-rheumatic drug on anti-CD3 triggered interleukin (IL)-17 production by peripheral blood mononuclear cells (PBMC) from patients with rheumatoid arthritis. PBMC pretreated for 1 hour with pyrrolidine dithiocarbamate (PDTC; 300 μM), curcumin (10 μM), LY294002 (20 μM), wortmannin (200 nM), Cyclosporin A (500 ng/ml), dexamethasone (DEX; 100 nM), FK506 (100 ng/ml), rapamycin (10 ng/ml), SB203580 (10 nM) or PD98059 (20 μM) in combination with anti-CD3 antibody (5 μg/ml). Culture supernatant was assayed for IL-17 as described in the Materials and methods section. Each value represents the mean and SEM of three independent experiments. *, P < 0.05; **, P < 0.005.
Figure 4 Dose-dependent effects of LY294002 or pyrrolidine dithiocarbamate (PDTC) in peripheral blood mononuclear cells (PBMC) from patients with rheumatoid arthritis (RA). (a) Effect of inhibitors on interleukin (IL)-17 release by anti-CD3-stimulated PBMC from patients with RA. (b) Effects of LY294002 or PDTC on PBMC viability. PBMC were cultured at a concentration of 2 × 105 cells per well with medium, anti-CD3, anti-CD3 and LY294002 or PDTC under the conditions described in the Materials and methods section. After 24 hours of treatment, cell viability was assessed by the trypan blue dye exclusion method and expressed as a percentage with the formula 100 × (number of viable cells/number of both viable and dead cells).
Figure 5 Effects of LY294002 or pyrrolidine dithiocarbamate (PDTC) on anti-CD3 antibody-triggered interleukin (IL)-17 mRNA expression by peripheral blood mononuclear cells (PBMC) from patients with rheumatoid arthritis. PBMC were cultured with medium only (lane 1), anti-CD3 antibody (1 μg/ml; lane 2), anti-CD3 antibody (10 μg/ml; lane 3), anti-CD3 antibody (10 μg/ml) plus LY294002 (20 μM; lane 4) or anti-CD3 antibody (10 μg/ml) plus PDTC (300 μM; lane 5) for 12 hours; lane 6 shows a negative control. Total RNA (2 μg) was used for cDNA synthesis in a volume of 20 μl; 1 μl of the synthesized cDNA was used for reverse transcription–polymerase chain reaction as described. PCR reaction product (25 μl) was separated on an agarose gel containing ethidium bromide. The relative intensities of the bands were revealed under UV radiation.
Figure 6 Activation of phosphorylated Akt after interleukin (IL)-17 induction by anti-CD3 antibody, and its inhibition by LY294002. Peripheral blood mononuclear cells were cultured with medium only (lane 1), anti-CD3 antibody (10 μg/ml; lane 2) or anti-CD3 antibody (10 μg/ml) plus LY294002 (20 μM; lane 3) for 10–120 min. Cell lysates were analyzed for Akt activation by western blot analysis of total and Ser473-phosphorylated Akt (P-Akt) using specific antibodies. Levels of phosphorylated Akt were compared at each time point, after normalization to Akt and β-actin in the same sample. A representative example of three separate experiments is shown.
Figure 7 Effects of anti-CD3 plus anti-CD28 on NF-κB complex in nuclear extracts of rheumatoid arthritis (RA) peripheral blood mononuclear cells (PBMC) and normal PBMC. (a) NF-κB activity in the absence (lane 1) or presence (lane 2) of anti-CD3 plus anti-CD28 antibody; supershift assay of NF-κB site with antibodies against p65 (lane 3), p50 (lane 4) and c-Rel (lane 5). PBMC from patients with RA were stimulated with anti-CD3 plus anti-CD28 and were used for the supershift assay. (b) NF-κB activity in the absence (lane 1) or presence (lane 2) of anti-CD3 antibody plus anti-CD28 in normal PBMC. (c) NF-κB activity in the absence (lane 1) or presence (lane 2) of anti-CD3, anti-CD3 plus LY294002 (lane 3) or anti-CD3 plus pyrrolidine dithiocarbamate (PDTC; lane 4). Arrows denote a labeled oligonucleotide band shifted after NF-κB binding. The lower panel shows an immunoblot for IκB-α and actin at the same time.
Table 1 Production of interleukin-17 in response to anti-CD3 and mitogens by peripheral blood mononuclear cells and T cells from patients with rheumatoid arthritis
RA cells Stimulation Interleukin-17 (pg/ml)
PBMC None 42 ± 11
Anti-CD3 155 ± 24
Anti-CD3 + anti-CD28 211 ± 1
PHA 588 ± 85
T cells None 30 ± 10
Anti-CD3 94 ± 41
PHA 122 ± 73
Rheumatoid arthritis (RA) peripheral blood mononuclear cells (PBMC) were stimulated for 24 hours with anti-CD3 (10 μg/ml) plus anti-CD28 antibody (1 μg/ml), phytohemagglutinin (PHA; 5 μg/ml), or none of these (medium only). RA T cells were stimulated for 24 hours with anti-CD3 (10 μg/ml) and PHA (5 μg/ml). The levels of interleukin-17 were measured in culture supernatants by enzyme-linked immunosorbent assay. Results are means ± SEM of three independent experiments.
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| 15642134 | PMC1064895 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Nov 29; 7(1):R139-R148 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1470 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14711564213610.1186/ar1471Research ArticleEarly and stable upregulation of collagen type II, collagen type I and YKL40 expression levels in cartilage during early experimental osteoarthritis occurs independent of joint location and histological grading Lorenz Helga [email protected] Wolfram [email protected] Mate 1Steck Eric [email protected] Wiltrud [email protected] Division of Experimental Orthopedics, University of Heidelberg, Germany2005 7 12 2004 7 1 R156 R165 20 8 2004 13 10 2004 6 11 2004 10 11 2004 Copyright © 2004 Lorenz et al., licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
While morphologic and biochemical aspects of degenerative joint disease (osteoarthritis [OA]) have been elucidated by numerous studies, the molecular mechanisms underlying the progressive loss of articular cartilage during OA development remain largely unknown. The main focus of the present study was to gain more insight into molecular changes during the very early stages of mechanically induced cartilage degeneration and to relate molecular alterations to histological changes at distinct localizations of the joint. Studies on human articular cartilage are hampered by the difficulty of obtaining normal tissue and early-stage OA tissue, and they allow no progressive follow-up. An experimental OA model in dogs with a slow natural history of OA (Pond–Nuki model) was therefore chosen. Anterior cruciate ligament transection (ACLT) was performed on 24 skeletally mature dogs to induce joint instability resulting in OA. Samples were taken from different joint areas after 6, 12, 24 and 48 weeks, and gene expression levels of common cartilage molecules were quantified in relation to the histological grading (modified Mankin score) of adjacent tissue. Histological changes reflected early progressive degenerative OA. Soon after ACLT, chondrocytes responded to the altered mechanical conditions by significant and stable elevation of collagen type II, collagen type I and YKL40 expression, which persisted throughout the study. In contrast to the mild to moderate histological alterations, these molecular changes were not progressive and were independent of the joint localization (tibia, femur, lateral, medial) and the extent of matrix degeneration. MMP13 remained unaltered until 24 weeks, and aggrecan and tenascinC remained unaltered until 48 weeks after ACLT. These findings indicate that elevated collagen type II, collagen type I and YKL40 mRNA expression levels are early and sensitive measures of ACLT-induced joint instability independent of a certain grade of morphological cartilage degeneration. A second phase of molecular changes in OA may begin around 48 weeks after ACLT with altered expression of further genes, such as MMP13, aggrecan and tenascin. Molecular changes observed in the present study suggest that dog cartilage responds to degenerative conditions by regulating the same genes in a similar direction as that observed for chondrocytes in late human OA.
ACLTcartilagegene expressionhistologyosteoarthritis
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Introduction
Osteoarthritis (OA) is a disease with a high prevalence, and it occupies a very important place in orthopedic surgery. It is characterized by progressive degeneration of articular cartilage and damage to subchondral bone. While macroscopic, histological and biochemical features of OA have been extensively studied [1-4], the molecular changes in chondrocyte metabolism underlying the pathophysiological process of cartilage degeneration remain largely unknown. Studies on human articular cartilage are hampered by the difficulty of obtaining normal tissue and early-stage OA tissue. For this reason a number of animal models have been developed in which cartilage degeneration is induced by causing permanent joint instability [5-7]. In the Pond–Nuki model in dogs, anterior cruciate ligament transection (ACLT) leads to joint laxity and altered mechanical loading in the knee joint, resulting in cartilage degeneration over time. This model has the advantage of a fairly slow natural history of OA, since full-thickness loss of articular cartilage does not develop until about 4–5 years after ACLT. The resulting degenerative changes in cartilage and synovial tissue thus closely resemble those in natural canine OA and human OA [1,4,6,8].
Recent studies have examined the gene expression levels of collagen type (col) II, aggrecan and small proteoglycans in dogs [9-11] and of several cartilage-related and OA-related molecules in rabbits [12-14] over time after ACLT. However, most of these studies used insensitive methods of RNA detection (northern blot) [9,11], or the methods were only semiquantitative [13,14] or failed to relate specific gene expression to the basic expression level of housekeeping genes [9,10]. This may be one reason why contradictory results have been obtained; for example in rabbits, in which no alteration of col II gene expression [12,13] or upregulation at region-specific sites [14] is reported. Other matrix molecules, such as aggrecan and fibromodulin, seem to be altered only at isolated time points [13,14]. Col II expression in dogs was reported to be higher in ACLT-treated knees than in control knees. These changes in col II expression progressed in one study [9] and decreased in another one [10], while the elevation of aggrecan was stable.
The histological appearance of OA cartilage makes it obvious that the severity of changes may vary with the location in the joint. It has been demonstrated in the rabbit knee joint that there are differences in RNA levels between different cartilage regions, which emphasizes that it may be risky to pool samples from distinct regions of the knee for molecular analysis [15]. Nonetheless, the difficulty of extracting sufficient RNA from a limited quantity of cartilage has hampered attempts to find direct correlations between the histological appearance of the cartilage and the metabolism of the chondrocytes in this region. For this reason, histological information has either not been considered at all [9,10] or has been derived from separate animals [12,14] in earlier gene expression studies, although region-specific histological progression of cartilage degeneration was reported in the rabbit ACLT model [14].
The aim of the present study was to perform a highly sensitive and quantitative molecular analysis of the response of articular cartilage to increased joint instability and altered mechanical loading, and thus to gain more insight into the very early stages of mechanically induced cartilage degeneration. The major objective was to focus on local aspects of gene expression with reference to the histological grading of cartilage degeneration. By improving RNA yields from small cartilage samples and selecting highly sensitive methods for quantitative gene expression analysis, we studied alterations in chondrocyte metabolism side by side with the histological appearance of cartilage degeneration in adjacent tissue. The Pond–Nuki model was chosen because of its close similarity to human OA, and the gene expression levels of six OA-related molecules were followed longitudinally over 48 weeks.
Methods
Animals
Twenty-four skeletally mature beagle dogs were each assigned randomly to one of four experimental groups. The animals' ages ranged from 1 to 2 years (average, 17 months) and the animal body mass was 15–22 kg (average, 19 kg). The dogs were cared for according to the guidelines of the local Council of Animal Care. ACLT was performed on each dog's left knee as described elsewhere [7], with the right knee serving as the control. After 3 days in individual indoor kennels the animals were allowed to move about freely in groups in outdoor pens. The dogs showed no abnormalities in posture and movement before surgery and at euthanasia. Dogs in the four groups were euthanized after 6, 12, 24 and 48 weeks, respectively.
Preparation of specimens
Samples were taken within 2 hours after euthanasia. A full-thickness sample about 5 × 2 mm2 in area, including cartilage and bone, was excised with a hammer and chisel from the lateral and the medial femoral condyle and from the lateral and the medial tibial plateau. For molecular analysis, articular cartilage was shaved off the articular surface 2–4 mm around the site of the histological samples and was immersed in liquid nitrogen. Peripheral areas of cartilage were not included.
Histology
Samples were fixed in 4% formalin, embedded in paraffin, cut into 3-μm-thick slices and were stained with Safranin O. Specimens were analyzed for the degree of histological change using the Mankin score [16] modified as previously published [17]. All sections were graded by three independent observers blinded to the group, and median scores were determined for statistical analysis.
Gene expression analysis
After measurement of the frozen tissue mass, cartilage samples were pulverized in a freezer mill (Dismembrator S; Braun Biotech, Melsungen, Germany). Messenger RNA was extracted from the powder using oligo(dT)-coated beads (Dynabeads; Dynal, Oslo, Norway) according to the manufacturer's instructions, and was quantified and tested for quality by measurement of the optical density at 280 and 260 nm in a NanoDrop ND100 photometer (Kisker, Steinfurt, Germany). First-strand cDNA was generated using reverse transcriptase (SuperScript II; Invitrogen Life Technologies, Karlsruhe, Germany) and oligo(dT) primers. The cDNA was further purified using a commercially available kit (PCR purification kit; Qiagen, Hilden, Germany). In a pilot study, tissue requirements had been minimized to confine the analysis to the close proximity around the histological tissue sample. About 50 mg tissue was sufficient for quantitative analysis of up to 10 genes.
Canine-specific PCR primers for GAPDH, col I, col II, aggrecan and MMP13 were designed on the basis of gene bank information. For tenascinC, YKL39 and YKL40 degenerated primers were applied on canine chondrocyte cDNA to obtain specific DNA fragments. Amplified fragments were purified and sequenced, and specific primers were designed based on this sequence: GAPDH, 5'-GATTGTCAGCAATGCCTCCT-3' and 5'-GTGGAAGCAGGGATGAT-GTT-3' ; col I A1, 5'-GAGAAAGAGGCTTCCCTGGT-3' and 5'-AGGAGAACCATCTCGTCCAG-3' ; col II A1, 5'-TGAATGGAAGAGCGGAGACT-3' and 5'-CCACCATTGAT-GGTTTCTCC-3' ; YKL40, 5'-TCTGTTGGAGGATGGAGCTT-3' and 5'-CAGCCTTCATTTCCTTGACC-3' ; MMP13, 5'-CAGAGCGCTACCTGAAATCC-3' and 5'-CATTGTACTCGCCCACATCA-3' ; aggrecan, 5'-ACCCCT-GAGGAACAGGAGTT-3' and 5'-GTGCCAGATCATCACCACAC-3'; tenascinC, 5'-AGGGGGTCTTCGACAGTTTT-3' and 5'-CATGGCTGTTGTTGCTATGG-3'.
Quantitative reverse transcriptase-PCR was performed in a LightCycler (Roche Diagnostics, Mannheim, Germany) with optimized parameters according to the Operator's Manual (version 3.5; Roche). Melting curves were checked for correctness and the size of the fragments was verified on agarose gels. In order to obtain a GAPDH standard curve, dilutions of GAPDH cDNA in a range from 3 × 10-6 to 3 ng were subjected to LightCycler analysis. The absolute amount of GAPDH mRNA contained in each cartilage sample was obtained after LightCycler analysis by deduction from the GAPDH standard curve. A GAPDH standard was included in every LightCycler run, and the expression of each gene was normalized to the mRNA level of the housekeeping gene GAPDH in the corresponding sample (set as 100% GAPDH).
Statistics
The mean and standard deviation of each variable were computed. The median and interquartile range were also calculated. A two-way analysis of variance was used to control for the side factor and for the time factor. The side factor was analyzed as the paired measure. Post-hoc tests were also performed to compare time points. Significant changes were depicted. Post-hoc test results were calculated (Scheffé tests) and a two-tailed P ≤ 0.05 was considered significant. An explorative Mann–Whitney U-test and the Wilcoxon test were chosen to evaluate differences between two groups without alpha adjustment. Data analysis was performed with SPSS for Windows 11.0.1 (SPSS Inc., Chicago, IL, USA).
Results
The age and body mass of the animals were similar in all experimental groups. All but one of the dogs, which was in the 24-week group, were male. One animal had to be excluded after euthanasia following severe injury, so that in the 12-week group only five animals were evaluated instead of six animals. There were no surgical complications, and none of the dogs showed clinical signs of OA, such as altered posture or motion. The animals did not show partially or totally restricted use of the ACLT-treated extremity. When opened, the joints were found to show no macroscopic signs of inflammation or cartilage degeneration at 6 and 12 weeks. In those joints opened 24 weeks after ATLC surgery incipient cartilage discoloration and softening were seen, which were slightly more frequent and pronounced at 48 weeks. Most dogs had an increased volume of synovial fluid in treated knees at the two later time points.
Histological examination
Histological features observed in cartilage from ACLT-treated knees and from control knees were similar to those described previously [3,4,18-20]. We found slight changes of the cartilage surface and chondrocytes at 6 weeks after surgery but such changes appeared also in part of the control samples. Decreased Safranin O staining and loss of zonal structure were noticeable in some specimens 12 weeks after ACLT, and were more pronounced in joints examined 48 weeks after surgery. Chondrocyte clustering and fissures never extending beyond the transitional zone were observed at 24 and 48 weeks, which never extended beyond the transitional zone. Variations in histopathological scores were evident within each group, indicating variations in the progression of osteoarthritic changes over time (Fig. 1). Median modified Mankin scores at 48 weeks (12.4) were significantly higher than those at 6 weeks (7.4) (P = 0.036), confirming progressive degeneration of cartilage in ACLT-treated knees. There was no obvious difference between the lateral and the medial compartments of the tibia or of the femur (48 weeks) and no progressive changes were obvious in the control knees over time. In summary, the ACLT-induced changes were progressive and consistent with early stages of OA.
Gene expression analysis
The concentration of mRNA per sample was determined and used to calculate the absolute amount of mRNA per milligram of tissue wet weight. The mRNA content was similar in all study groups, providing no evidence for major differences in RNA extraction efficiency, tissue water content or overall transcriptional activity of cells between groups. The absolute amount of GAPDH mRNA per total mRNA was determined for each sample by LightCycler analysis using a GAPDH standard curve. A considerable variability of GAPDH per microgram of mRNA was noted that, according to values of the control side (right knee) and the ACLT side (left knee), primarily reflected differences between individuals. Absolute amounts of GAPDH levels per microgram of mRNA determined in all samples were compared by variance analysis comprising the factors time, side and treatment. Post-hoc tests were also performed to analyze differences between all groups. Statistical analysis revealed no significant differences in GAPDH levels between any of the study groups (controls, ACLT, locations, time).
Analysis of tenascinC and the chitinase-like molecules YKL39 and YKL40 was included in the present study because they have been related to human OA [21-23]. Since canine DNA sequence information on tenascinC, YKL39 and YKL40 was not available from public databases, degenerated primers were designed on the basis of multiple sequence alignment. Resulting PCR fragments for tenascinC (923 bp) and for YKL40 (665 bp) were sequenced, and they showed 90% and 83% nucleotide identity with the corresponding human cDNA sequence, respectively. The deduced protein sequence of the canine YKL40 fragment had 83% identical and 91% similar amino acids to human YKL40, and was only 48% identical to human YKL39. In spite of intense primer design and PCR analysis, it was not possible to obtain any fragments for YKL39 from dog cartilage. The dog genome database [24] also contained no sequence information with high homology to human YKL39. Interestingly, in spite of eightfold sequence coverage of the mouse genome, no YKL39 sequence is available from mouse databases and there is no orthologous gene at the locus corresponding to the one where human YKL39 is located. This suggested that mouse and dog lack the YKL39 gene, while YKL40 is present in the human, mouse, and dog.
Early upregulation of col II, YKL40 and col I
Quantitative reverse transcriptase-PCR analysis of cartilage from the tibial plateau revealed significant upregulation of YKL40 and col II expression at all time points in ACLT-treated knees compared with the untreated joints. The median elevation was twofold to sevenfold for col II, and was threefold to 17-fold for YKL40 (Fig. 2a,2b). Gene expression of col I was significantly elevated at 12, 24 and 48 weeks after surgery in cartilage from the tibial plateau (Fig. 2c). Higher expression in OA samples was also evident at 6 weeks, but owing to a large standard deviation the difference did not reach statistical significance. There was no significant increase or decrease over time in the levels of col I or col II or of YKL40 in osteoarthritic cartilage. Femoral condyle samples were analyzed at 48 weeks after surgery. In keeping with our observations in the tibial plateau, expression of col II and of YKL40 was significantly higher in osteoarthritic cartilage than in control cartilage, while col I was highly variable (Table 1).
Aggrecan, MMP13 and tenascinC expression
While mRNA levels for aggrecan tended to be lower in OA samples than in control samples, tenascinC and MMP13 levels tended to be higher (Fig. 3). Overall, for all time points, osteoarthritic and control knees did not differ significantly in aggrecan, MMP13 or tenascinC expression. At 24 and 48 weeks, however, MMP13 was significantly higher in ACLT-treated knees than in normal knees (Fig. 3a). Aggrecan mRNA levels showed relatively wide variation between samples taken from animals in the same study group, and median values tended to be slightly lower in osteoarthritic samples than in control samples. At 48 weeks after ACLT surgery the difference was twofold, and it reached statistical significance (P = 0.009) (Fig. 3c). In addition, elevated levels of tenascinC were seen in OA samples at 48 weeks after surgery (Fig. 3b). mRNA levels of aggrecan, tenascinC and MMP13 in the femur samples (48 weeks) were unchanged in OA samples compared with controls (Table 1).
Region-specific differences in gene expression
Significantly higher expression of col II, YKL40 and col I was evident in osteoarthritic samples than in normal cartilage samples from both the lateral and the medial tibial plateau (Fig. 4a,4b,4c) at all time points studied. The results for joints examined at all time points were pooled since no time effects were evident for col II, col I and YKL40. On average, the levels of col II in osteoarthritic joints were fourfold those in normal joints in the lateral compartment (P < 0.001) and were sevenfold those in normal joints in the medial compartment (P <0.001). YKL40 was elevated to 6.5-fold normal values (P < 0.001) in the lateral compartment and to eightfold in the medial compartment (P < 0.001). Expression of col I in the lateral compartment of ACLT-treated knees was fourfold that in control joints (P = 0.004); levels in the medial compartment were 22-fold normal (P < 0.001). The baseline expression of several genes tended to be higher in the lateral compartment than in the medial tibial compartment, reaching significance for col II expression (3.5-fold, P = 0.045) and for col I expression (ninefold, P < 0.001) (Fig. 4a,4c).
A similar tendency was seen for basic YKL40 and aggrecan expression, but not for MMP13 and tenascinC expression. In spite of region-specific baseline expression differences, robust generalized molecular effects were seen after ACLT surgery in canine knee cartilage, indicating a consistent regulation of the chondrocytes' response to an altered mechanical loading.
No correlation between molecular changes and histological scoring
In order to correlate a particular histological outcome with gene expression independently of the study group or the location, samples adjacent to sections with extreme signs of chondrocyte cloning and with the highest (n = 8) or the lowest (n = 7) modified Mankin score in the ACLT-treated group were selected. The corresponding samples were compared with seven normal control samples (modified Mankin score 0–2). Comparison of extreme samples preselected for the highest histological scores (Fig. 5a) and the lowest histological scores (Fig. 5b) in the ACLT group will increase the statistical power to detect differences that would correlate with histological scoring. Nevertheless, we did not obtain molecular differences between these two ACLT groups, although both kept statistically significant molecular differences to the contralateral control group (Fig. 5c). Even samples with the lowest modified Mankin score in the ACLT group had already elevated col II (P = 0.004), col I (P = 0.007) and YKL-40 (P = 0.052) expression levels. Upregulation of col II, col I and YKL-40 was thus a very sensitive measure of cartilage degeneration that did not progress any further during the moderate advancement of cartilage degeneration examined in the present study.
Discussion
Although OA has been well studied, many of the basic molecular mechanisms underlying its development are still unknown. The use of an animal model opens up the possibility of studying the early stages of disease progression and the regional pattern of matrix degradation by comparing diseased and healthy joints in the same individual. The results presented in this report demonstrate that ACLT leads to early and robust upregulation of the extracellular matrix molecule col II and of YKL40 in knee cartilage, which is independent of the time lapse since surgery, of the particular joint region studied and of the structural appearance of the extracellular matrix in adjacent histological sections. In addition, some evidence for a later change of gene expression of MMP13, aggrecan and tenascinC was obtained, which differed significantly from that on the control side by 24 and/or 48 weeks after surgery.
Our data might be interpreted as indicative of an early phase of OA development characterized by upregulation of col II, col I and YKL40, which is followed by a second phase of OA progression characterized by further upregulation of MMP13 and tenascinC. From a clinical point of view, the possibility of differentiating successive stages of OA progression by means of early and late disease markers is quite an attractive prospect. According to our data, col II and YKL40 are good candidates for use as robust, early and sensitive markers of joint instability. Although tenascinC and MMP13 may appear on a list of potential marker genes characterizing a second phase of OA, their expression will deserve further attention since no difference between ACLT samples with the lowest and the highest modified Mankin grades is yet obvious for these molecules. Such a distinction would be expected since cartilage degeneration progresses with time. Since the latest time point studied in our dog model is about 2–3 years before full-thickness loss of articular cartilage can be anticipated, the median and late alterations of gene expression cannot be expected to have occurred. Longer studies will be required to decide whether regulation of tenascinC and MMP13 indicate a further stage of molecular alterations in the Pond–Nuki model of OA development.
One of the major objectives of this project was to focus on local aspects of gene expression with reference to the histological grading of cartilage degeneration. Most strikingly, upregulation of col II, col I and YKL40 was pronounced in ACLT-treated knees even if the histological grading of adjacent tissue was only weak above control level and there was no progression with OA development (Fig. 5). Differing levels of severity or 'doses' of injury have been suggested by others as a possible reason for regional changes in mRNA expression [9]. Our study, however, lends little credence to this suggestion [10,13]. Knee instability is very likely to increase inappropriate mechanical loading in many parts of the joint and, in keeping with this, col II and YKL40 expression rose in all locations after ACLT in our study. Surprisingly, we detected significant differences in col II and col I expression between the lateral and the medial tibial plateau already in normal control knees, while levels of the housekeeping gene GAPDH did not differ significantly among the compartments at any time point of the study. Given that mechanical forces are modulators of chondrocyte metabolism, even physiologic loading differences may influence basal expression levels of sensitive genes and be the explanation for this effect. This is in line with reports of upregulation of col II and col I synthesis in chondrocytes in response to cyclic loading in vitro [25,26].
Elevated col II and aggrecan mRNA levels were reported previously in early-stage and median-stage canine OA after ACLT [9,10]. In contrast to our data, however, either a progressive [9] or a declining [10] elevation of col II RNA was observed over time in those studies. Beside the fact that semiquantitative methods have been used in these previous studies, the normalization of gene expression data will strongly influence the results [27]. While others decided to express changes in mRNA expression on a per cell basis by referring data to the DNA content of the tissue, we referred specific mRNA expression levels to expression of the housekeeping gene GAPDH. The strength of our method is that we can detect a specific regulation of cartilage-relevant molecules beyond generalized effects that may occur after ACLT, such as the overall activation of chondrocyte metabolism. Its weakness is that there is a risk that the housekeeping gene itself may be regulated by ACLT. Among a selection of housekeeping genes, GAPDH correlated best with total RNA content per milligram of tissue in dog cartilage [28]. Thorough statistical analysis of our data provided no evidence for either a regulation of total mRNA per milligram of tissue or a regulation of GAPDH in response to ACLT at any time point of the study. Given that the very weak trend to higher GAPDH levels after ACLT would become significant when data are referred on a per cell basis, the upregulation of col II, col I, YKL40, MMP13 and tenascinC per cell would be even more pronounced.
Elevated release of proteoglycan and col II protein degradation products into body fluids of ACLT-treated dogs [29] and of patients with advanced knee OA [30,31] indicate that loss of such molecules from the cartilage matrix makes a major contribution to the development of OA. Chondrocytes may sense this loss and respond to it with upregulation of col II mRNA levels, but they did not adapt mRNA levels for the aggrecan core protein accordingly in the present study. Although there is evidence for enhanced sulfate incorporation into proteoglycans of ACLT-treated cartilage versus normal dog cartilage [32-34], the identity of these molecules remained unknown. Enhanced mRNA levels for small proteoglycans like biglycan, decorin and fibromodulin after ACLT [11] may explain such observations, but due to the small sample size they unfortunately could not be included in the present study.
Human chondrocytes secrete two distinct chitinase-like molecules, called YKL39 and YKL40. While YKL40 has been linked with tissue remodeling, with joint injury and with in situ inflammatory macrophages [35-40], clinical correlates of YKL39 expression remained unknown. Enhanced expression of YKL39, but not of YKL40, was demonstrated in severe human OA cartilage [41,42]. However, in spite of reasonable effort, we have not been able to detect YKL39 in canine chondrocytes and databases. The upregulation both of YKL40 in early stages of dog OA and of YKL39 in late-stage human OA suggests, however, that chitinase-like molecules may have some function in cartilage remodeling and are potentially interesting marker molecules for osteoarthritic joint disease.
Human late-stage osteoarthritic cartilage from joint replacement surgery subjected to cDNA array analysis showed significantly elevated levels of col II, col I, chitinase precursor, tenascin and several matrix metalloproteinases, including MMP13, while aggrecan levels remain unaltered [43]. Taking into account the high donor-dependent variability in human samples, the lower sensitivity of the cDNA array technique than of quantitative PCR and the different time frames studied, the overlap between molecular alterations in natural human OA and experimental canine OA is considerable. This indicates that chondrocytes in dog cartilage respond to degenerative conditions by regulating the same genes as chondrocytes in human OA, and in a similar direction.
Conclusion
In conclusion, upregulation of col II, col I and YKL40 was a very sensitive and robust response to the altered mechanical situation after ACLT surgery, which occurred quite independent of joint location and a certain grade of morphological cartilage degeneration. Levels did not progress any further during the moderate advancement of cartilage degeneration examined in the present study, and more progressed stages of cartilage degeneration may therefore rather be characterized by regulation of additional cartilage-relevant molecules like MMP13 and tenascinC.
We interpret the molecular alterations in natural human OA and experimental canine OA as considerable and we suggest that the Pond–Nuki model may be a suitable experimental model to unravel further basic anabolic and catabolic molecular mechanisms of relevance for human disease development.
Abbreviations
ACLT = anterior cruciate ligament transection; bp = base pair; col = collagen type; GAPDH = glyceraldehydes-3-phosphate dehydrogenase; OA = osteoarthritis; PCR = polymerase chain reaction.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
HL participated in the design of the study, coordinated and assisted surgery, carried out the molecular analysis, evaluated histology and drafted the manuscript. WW participated in the design of the study, performed surgery and evaluated histological samples. MI assisted with surgery and evaluated histological samples. ES participated in the design and evaluation of molecular analysis, and contributed to the manuscript. WR conceived of the study, participated in its design, and contributed to histological and molecular data analysis and to the manuscript. All authors read and approved the manuscript.
Acknowledgements
This work was supported by a grant from the research fund of the Stiftung Orthopädische Universitätsklinik Heidelberg. The authors thank Stephanie Kadel and Christoph Michalski for excellent technical assistance and Sven Schneider for statistical support. Furthermore, the authors wish to thank Katrin Goetzke and Regina Foehr for the histological preparation of samples.
Figures and Tables
Figure 1 Box plot of histological grading in anterior cruciate ligament transection-treated knees at different times after surgery. Demonstrated are the median, standard deviation and interquartile range of the modified Mankin score data. White boxes, lateral tibial plateau; gray boxes, medial tibial plateau. * P < 0.05.
Figure 2 (a) Collagen type (col) II, (b) YKL-40 and (c) col I expression in cartilage of experimental osteoarthritis (OA) at different times after surgery. Shown are the mean relative expression levels of mRNA summarized for lateral and medial tibial plateau. * P < 0.05, ** P < 0.001.
Figure 3 (a) MMP13, (b) tenascinC and (c) aggrecan expression in cartilage of experimental osteoarthritis (OA) at different times after surgery. Shown are the mean relative expression levels of mRNA summarized for lateral and medial tibial plateau. * P < 0.05, ** P < 0.001.
Figure 4 Local mRNA expression levels in cartilage of experimental osteoarthritis (OA): (a) collagen type (col) II, (b) col I, (c) YKL-40 and (d) aggrecan. Shown are mean relative expression levels of mRNA in lateral and medial tibial plateau summarized for 6, 12, 24 and 48 weeks. * P < 0.05, ** P < 0.001.
Figure 5 No correlation of molecular changes to the histological scoring. In order to directly correlate a certain histological outcome with gene expression independent of the study group or the location, samples adjacent to sections with (a) the highest (n = 8) or (b) the lowest (n = 7) modified Mankin score (mod. MS) in the anterior cruciate ligament transection-treated group were selected and were compared with (c) seven normal control samples with the lowest mod. MS in the study. Top, representative histological sample (magnification, 40 ×); bottom, corresponding mean and standard deviation of the mod. MS and of gene expression levels (% GAPDH expression). col., collagen type. a P < 0.05 versus (c), b no significant difference versus (b), c no significant difference versus (a), d P = 0.052 versus (c), e no significant difference versus (b) and (c), f no significant difference versus (a) and (c).
Table 1 mRNA expression levels of osteoarthritis-relevant genes in femoral condyles at 48 weeks
Group Collagen type II YKL-40 Collagen type I Aggrecan TenascinC MMP13
Anterior cruciate ligament transection 1359.4* (1875.9 ± 1680.0) 1.0* (2.2 ± 2.6) 0.6 (1.4 ± 1.9) 472.0 (547.2 ± 448.6) 2.7 (11.9 ± 29.8) 0.2 (0.5 ± 0.5)
Control 121.3 (164.2 ± 150.2) 0.4 (0.3 ± 0.2) 0.7 (6.7 ± 13.2) 372.5 (434.3 ± 225.1) 1.0 (2.9 ± 2.8) 0.2 (0.7 ± 1.0)
Data presented as median (mean ± standard deviation).
* P < 0.05 versus control.
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| 15642136 | PMC1064896 | CC BY | 2021-01-04 16:02:34 | no | Arthritis Res Ther. 2005 Dec 7; 7(1):R156-R165 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1471 | oa_comm |
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Arthritis Res TherArthritis Research & Therapy1478-63541478-6362BioMed Central London ar14731564213710.1186/ar1473Research ArticleJNK1 is not essential for TNF-mediated joint disease Köller Marcus [email protected] Silvia 2Redlich Kurt 1Ricci Romeo 3David Jean-Pierre 3Steiner Günter 12Smolen Josef S 12Wagner Erwin F 3Schett Georg [email protected] Department of Internal Medicine III, Division of Rheumatology, Medical University of Vienna, Austria2 CeMM, Center of Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria3 Research Institute of Molecular Pathology, Vienna, Austria2005 7 12 2004 7 1 R166 R173 4 8 2004 26 8 2004 14 10 2004 10 11 2004 Copyright © 2004 Köller et al.; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is cited.
Tumour necrosis factor (TNF) signalling molecules are considered as promising therapeutic targets of antirheumatic therapy. Among them, mitogen-activated protein kinases are thought to be of central importance. Herein, we investigate the role in vivo of TNF-α signalling through c-Jun N-terminal kinase (JNK)1 in destructive arthritis. Human TNF transgenic (hTNFtg) mice, which develop inflammatory arthritis, were intercrossed with JNK1-deficient (JNK1-/-) mice. Animals (n = 35) of all four genotypes (wild-type, JNK1-/-, hTNFtg, JNK1-/-hTNFtg) were assessed for clinical and histological signs of arthritis. Clinical features of arthritis (swelling and decreased grip strength) developed equally in hTNFtg and JNK1-/-hTNFtg mice. Histological analyses revealed no differences in the quantity of synovial inflammation and bone erosions or in the cellular composition of the synovial infiltrate. Bone destruction and osteoclast formation were observed to a similar degree in hTNFtg and JNK1-/-hTNFtg animals. Moreover, cartilage damage, as indicated by proteoglycan loss in the articular cartilage, was comparable in the two strains. Intact phosphorylation of JNK and c-Jun as well as expression of JNK2 in the synovial tissue of JNK1-/-hTNFtg mice suggests that signalling through JNK2 may compensate for the deficiency in JNK1. Thus, JNK1 activation does not seem to be essential for TNF-mediated arthritis.
arthritisJNK1TNF-α transgenic
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Introduction
Proinflammatory cytokines bind to their receptors on the plasma membrane and transmit the stimulatory effects to the nucleus via intracellular signalling molecules. Therefore, these cytokines are considered as promising therapeutic targets. Drugs specifically inhibiting such proteins are usually small molecules and are thought to open a new frontier in antirheumatic therapy along with newly arisen cytokine-blocking strategies. Among the many downstream molecules of cytokine signalling, mitogen-activated protein kinases (MAPKs) are of central importance in shuttling the signal of proinflammatory cytokines, such as IL-1 or tumour necrosis factor (TNF)-α, to their respective target tissues [1,2].
Cellular activation by TNF-α is a critical step in chronic synovial inflammation and progressive joint destruction. This is supported by the overwhelming effects of TNF blockade, which has revolutionized the therapy of rheumatoid arthritis (RA). It inhibits both inflammation and destruction of joints, in a majority of patients suffering from RA [3-5]. The hypothesis is supported by animal models in which specific overexpression of TNF-α is sufficient to cause chronic destructive arthritis [6]. Increased levels of this cytokine in the synovial fluid and tissue of RA patients have also been reported [7-9]).).
To design therapeutic tools that not only interfere with TNF signalling but also effectively block TNF-mediated inflammatory responses, it is essential to identify the major signalling targets of TNF in inflammatory joint disease. In fact, TNF-α signalling is a complex process, involving not only MAPKs but also other pathways including nuclear factor κB and the caspase cascade [10,11]. MAPKs are thought to be of central importance for mediating the proinflammatory effects of TNF-α. Interestingly, all three MAPK families – p38 protein kinase, extracellular signal-regulated kinase, and c-Jun N-terminal kinase (JNK) – are activated in the synovial membrane of RA patients, and TNF-α has the potential to signal through all of them [12]. Therefore, each of these different MAPKs is a possible therapeutic target.
We investigated the role of JNK1 in TNF-mediated inflammatory joint disease. Our findings show that the JNK1 signal pathway is not essential for the development of arthritis and joint destruction.
Materials and methods
Animals
The heterozygous human TNF transgenic (hTNFtg) mouse (strain: tg197; background: C57/BL6) has been described previously [6]. As reported elsewhere, mice of this strain develop destructive arthritis resembling RA within 4–6 weeks of birth [6,13]. JNK1-deficient (JNK1-/-) mice were generated as previously described [14]. The hTNFtg and JNK1-/- strains were intercrossed to obtain double mutant animals. F2 generations were used and all data were generated from littermates. A total of 35 mice (wt, n = 7; hTNFtg, n = 13; JNK1-/-, n = 6; and JNK1-/-hTNFtg, n = 9) of six different breedings were studied. This study was approved by the local ethical committee pf the Medical University of Vienna.
Clinical assessment
Arthritis was evaluated in a blinded manner as described in earlier reports [13]. Assessments were started when the mice were 5 weeks old and were repeated weekly. In brief, joint swelling was assessed using a clinical score graded from 0 to 3 (0, no swelling; 1, mild swelling of toes and ankle; 2, moderate swelling of toes and ankle; 3, severe swelling of toes and ankle). In addition, the grip strength of each paw was analysed on a wire 3 mm in diameter, using a score from 0 to -4 (0, normal grip strength; -1, mildly reduced grip strength; -2, moderately reduced grip strength; -3, severely reduced grip strength; -4, no grip strength at all). After cervical dislocation, the blood was withdrawn by heart puncture and the paws of all animals were dissected and preserved for histological analysis. The last evaluation was performed 10 weeks after birth.
Histological sections and histochemistry
A total of 26 mice (wt, n = 7; hTNFtg, n = 6; JNK1-/-, n = 6; and JNK1-/-hTNFtg, n = 7) were assessed histologically. Hind and front paws and right knee joints were fixed in 4.0% formalin overnight and then were decalcified in a 14% EDTA/ammonium hydroxide buffer at pH 7.2 (Sigma-Aldrich, St Louis, MO, USA) at 4°C until the bones were pliable. Serial paraffin sections (2 μm) were stained with H&E, or with toluidine blue for tartrate-resistant acid phosphatase (TRAP) activity. TRAP staining was performed as previously described [13]. For immunohistochemistry, deparaffinized, ethanol-dehydrated tissue sections were boiled for 2 min in 10 mM sodium citrate buffer (pH 6.0) using a 700-W microwave oven. Tissue sections were cooled to room temperature and then rinsed using detergent solution: 0.5% Tween in phosphate-buffered saline (PBS).
For quantification of inflammation, areas of H-&-E-stained sections were measured (5 sections/animal). The total area of inflammation for each single animal was calculated by evaluating all digital, carpal, and tarsal joints and the right knee joint. The same H-&-E-stained sections were analysed as described above for quantification of erosions. The number of osteoclasts was counted as described above analysing TRAP-stained serial sections. Cartilage breakdown (i.e., proteoglycan loss and matrix dissolution) was measured from toluidine-blue-stained serial sections by assessing cartilage according to the method of Joosten and colleagues [15]. Total and destained cartilage areas were measured and percentages of destained areas indicating low proteoglycan content were ascertained.
For immunohistochemistry, dewaxed, ethanol-dehydrated tissue sections were boiled for 2 min in 10 mM sodium citrate buffer (pH 6.0) using a 700-W microwave oven, then allowed to cool to room temperature and rinsed in detergent solution (0.5% Tween in PBS) for 10 min. Tissue sections were blocked for 20 min in PBS containing 20% rabbit serum and were then incubated for 1 hour at room temperature with the following antibodies (Abs): rat monoclonal antimacrophage (F4/80) Ab (Serotec Inc, Oxford, UK); diluted 1:80), rat monoclonal anti-CD3 Ab (Novocastra, Newcastle, UK; diluted 1:100), rat monoclonal anti-CD45R/B220 Ab (Pharmingen International, Oxford, UK) and rat monoclonal antineutrophil Ab (clone7/4, Serotec), mouse monoclonal antiphosphorylated-JNK Ab (clone G7, Santa Cruz Biotechnology, Santa Cruz, CA, USA), mouse monoclonal anti-JNK2 Ab (clone D2, Santa Cruz), and mouse monoclonal phospho-specific anti-c-Jun Ab (clone KM-1, Santa Cruz). The sections were rinsed, and then endogenous peroxidase was blocked with 0.3% hydrogen peroxide in Tris-buffered saline (10 mM Tris/HCl, 140 mM NaCl, pH 7.4) for 10 min. This was followed by 30 min of incubation with a biotinylated antirat IgG secondary Ab (Vector, Burlingame, CA, USA). Then, sections were incubated with the appropriate VECTASTAIN@ABC reagent (Vector) for another 30 min using 3,3'-diaminobenzidine (Sigma).
Statistical analysis
Data are shown as means ± standard deviation. Group mean values were compared using a two-tailed Student's t test.
Results
TNF-induced clinical signs of arthritis develop independently of JNK1
To study the role in vivo of JNK1 activation in TNF-mediated arthritis, we intercrossed hTNFtg with JNK1-/- mice. The offspring of all four genotypes (wt, hTNFtg, JNK1-/-, and JNK1-/-hTNFtg) were born at Mendelian frequency and were viable. To evaluate arthritis, we assessed joint swelling and grip strength weekly in all four genotypes. Neither wt nor JNK1-/- mice developed any signs of paw swelling, and both maintained normal grip strength (data not shown). In contrast, the hTNFtg animals developed joint swelling at 6 weeks of age, which increased to a maximum at week 10 (P < 0.05 in comparison with wt and JNK1-/-). In the JNK1-/-hTNFtg group, joint swelling started at age 7 weeks and increased significantly thereafter (P < 0.05 in comparison with wt and JNK1-/-). There was no significant difference between the hTNFtg and JNK1-/-hTNFtg mice at any time of the analysis (Fig. 1a). In addition, grip strength significantly decreased in both hTNFtg and JNK1-/-hTNFtg strains, but no significant difference between the two genotypes was found (Fig. 1b). Thus, overexpression of TNF-α induces clinical signs of arthritis also in the absence of JNK1.
Similar degrees of synovial inflammation and cellular composition of synovial inflammatory tissue in hTNFtg and JNK1-/-hTNFtg mice
We next more closely evaluated arthritis by quantitative and qualitative histological analysis of inflammatory tissue (Fig. 2). Animals of both control groups, wt and JNK1-/-, did not show any sign of joint inflammation or destruction. In contrast, hTNFtg mice not only developed intense inflammation but also showed multiple bone erosions. Comparable destructive changes were observed in joints from JNK1-/-hTNFtg mice. Quantitative analysis of the area of synovial inflammation revealed no significant differences between hTNFtg and JNK1-/-hTNFtg mice (Fig. 3a). Similarly, quantification of erosive changes was comparable in these two genotypes (Fig. 3b). Furthermore, immunohistochemical analysis revealed similar distributions of T cells, B cells, granulocytes, and macrophages within the synovial membranes of the two genotypes (Table 1).
Synovial osteoclast formation is not affected by the absence of JNK1
Since JNK1 is involved in osteoclast differentiation, we wanted to know whether the absence of JNK1 influences TNF-driven osteoclast formation in the inflamed joints. Staining for the osteoclast-specific enzyme TRAP revealed numerous osteoclasts within arthritic bone erosions in both hTNFtg and JNK1-/-hTNFtg mice (Fig. 4a). Quantification of osteoclasts revealed no significant difference between the two groups (Fig. 4b), reflecting that the absence of JNK1 does not alter the progression of joint destruction in conditions of TNF overexpression.
Proteoglycan content is reduced in cartilage of hTNFtg and JNK1-/-/hTNFtg mice
Lastly, we addressed whether the absence of JNK1 influences cartilage damage in TNF-α-induced arthritis. Quantitative assessment of proteoglycan loss by toluidine blue staining showed a marked reduction of proteoglycan content in both hTNFtg and JNK1-/-hTNFtg mice (Fig. 5a). But again, no significant difference in the percentage of the affected cartilage surface was detected between hTNFtg and JNK1-/-hTNFtg mice. In contrast, wt and JNK1-/- animals revealed virtually no alterations of articular cartilage (Fig. 5b). This suggests that TNF-α induces degradation of cartilage independently of JNK1.
Lack of JNK1 decreases expression of phosphorylated JNK but does not affect activation of c-Jun
Surprisingly, JNK1-/-hTNFtg mice developed destructive arthritis comparable to that of hTNFtg animals. Therefore, we assessed JNK signalling in animals of both genotypes. Histological staining of synovial membrane from JNK1-/- hTNFtg mice revealed significantly fewer cells expressing phosphorylated JNK than in hTNFtg animals (Fig. 6a; P < 0.05). The expression within the synovial membrane of JNK2 (Fig. 6b) or phosphorylated c-Jun (Fig. 6c) was similar in both groups that developed arthritis.
Discussion
In RA, cytokine-mediated cell activation leads to chronic synovial inflammation as well as bone and cartilage destruction. Evidence from basic and clinical research has established TNF-α as one of the major players in the pathogenesis of RA. The effects of TNF-α are mediated via membrane receptors which themselves activate intracellular messenger cascades, such as MAPK. JNKs are especially important, because of their ability to phosphorylate the activator protein (AP)-1 component c-Jun, making them critical regulators of transcription [16]. The JNK proteins include three different isoforms, of which JNK1 and (with even higher affinity) JNK2 phosphorylate c-Jun [17]. JNK2 is preferentially bound to c-Jun in unstimulated cells, whereas JNK1 becomes the major c-Jun-interacting kinase after cell stimulation [18]. Notably, JNK1 appears to be a key regulator of the differentiation of type 1 T helper cells in mice [19].
Like other MAPK pathways, JNK signalling is activated in the synovial tissue of RA patients [12,20-22]. Studies with cultured synovial fibroblast-like cells from RA patients have established proinflammatory cytokines, such as TNF-α and IL-1, as important activators of JNK signalling, and JNK2 is the dominant isoform in synovial cells [12,20-22]. In these cells, TNF-induced phosphorylation of JNK leads to the phosphorylation of c-Jun and finally activation of the transcription factor complex AP-1 [20]. Loss of either JNK1 or JNK2 suppresses AP-1 [21]. The JNK pathway is therefore involved in the regulation of genes coding for collagenases, chemoattractants such as macrophage chemoattractant protein-1, or the adhesion molecule E-selectin [23,24].
These data indicate that JNK1 is dispensable in TNF-mediated joint disease. Of interest, arthritis in hTNFtg mice is directly induced by overexpression of TNF, a potent trigger of the JNK pathway. Thus, it is surprising that even if disease is caused by the overexpression of a trigger of JNK-signalling, the absence of JNK1 is not essential for the development of the disease. However, the hTNFtg animal model has its limits, since it bypasses the autoimmune inductive phase, which is seen in other arthritis models such as collagen-induced arthritis or adjuvant arthritis, as well as in human disease. Thus, although these findings are relevant for TNF-mediated inflammation and connective tissue destruction, their implication for human RA should be seen with caution. Interestingly, the role of JNK has been investigated in autoimmune-based models of experimental arthritis in two outstanding studies. Treatment of rat adjuvant arthritis with SP600125, an inhibitor that affects kinase activity of both JNK1 and JNK2, was followed by a modest decrease of inflammation and paw swelling and an impressive inhibition of radiographic damage [21]. The influence of selective deletion of JNK2 was investigated in collagen-induced arthritis in mice. The severity of arthritis was even slightly increased, and histological evaluation showed a degree of synovial inflammation comparable to that in wt mice [25]. These latter findings suggest that selective inhibition of JNK2 is not effective to block destructive arthritis and raises the question whether JNK1 is a more promising target or effective inhibition of arthritis depends on the nonselective inhibition of JNK1 and JNK2. Our data extend this knowledge: the selective inhibition of JNK1 is not effective to block inflammation in TNF-driven arthritis. In fact, we show that even in the complete absence of JNK1, phosphorylation of JNK, although to a reduced amount, still occurs via engagement of JNK2. The latter is expressed in the synovial inflammatory tissue and its activation by TNF is sufficient to induce c-Jun transcription factor. Taken together, these findings from various experimental models of arthritis suggest that only the inhibition of both JNK isoforms, JNK1 and JNK2, may be a feasible approach to achieve a major blockade of synovitis and thus achieve therapeutic efficacy.
Development of local bone erosions in the joints affected by chronic arthritis depends on the presence of osteoclasts [13,26]. Interestingly, JNK1 plays an important role in osteoclastogenesis. Cells derived from JNK1-/- mice revealed an impaired differentiation of osteoclasts in vitro [27]. Thus, targeting of JNK1 could have been considered as a feasible approach to protect from inflammatory joint damage. Surprisingly, however, the degree of bone erosions as well as numbers of osteoclasts was not affected by the lack of JNK1, indicating that TNF-driven osteoclastogensis is independent of JNK1 in vivo.
In fact, matrix metalloproteinases substantially contribute to irreversible degradation of collagen. The IL-1-dependent induction of matrix metalloproteinases in chondrocytes is regulated through complex pathways including MAPKs, AP-1, and nuclear factor κB transcription factors [28]. Interestingly, lack of JNK2 led to a modest but significant reduction of cartilage damage in collagen-induced arthritis [25]. Therefore, we hypothesized that a reduction of cartilage damage could be expected after knocking out JNK1 in hTNFtg mice. However, much as with synovial inflammation and bone loss, no significant protection of articular cartilage has been observed in JNK1-/-hTNFtg, suggesting that JNK1 does not seem to be important in TNF-mediated cartilage destruction.
Conclusion
Taken together, these findings show that JNK1 is not essential for TNF-mediated joint disease. Specific inhibitors of JNK1 in mice probably work in conditions that are not primarily dependent on TNF-α. Moreover, an effective inhibition of synovitis and joint destruction may necessitate a combined blockade of the JNK isoforms or even additional MAPKs.
Abbreviations
Ab = antibody; AP-1 = activator protein-1; H&E = hematoxylin and eosin; hTNFtg = human tumour necrosis factor transgenic; IL-1 = interleukin-1; JNK = c-Jun N-terminal kinase; MAPK = mitogen-activated protein kinase; PBS = phosphate-buffered saline; RA = rheumatoid arthritis; TNF = tumour necrosis factor; TRAP = tartrate-resistant acid phosphatase; wt = wild-type.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MK carried out histological analyses and drafted the manuscript.
SH, EW, and GS conceived of the study and participated in its design and coordination.
KR carried out histological and statistical analyses.
RR participated in breeding of mice.
JD participated in the design of the study and breeding of mice.
GSt coordinated immunohistological analysis.
JS participated in the design of the study.
All authors read and approved the final manuscript.
Acknowledgements
This study was supported by the START Prize of the Austrian Science Fund (G Schett) and the Center of Molecular Medicine (CeMM) of the Austrian Academy of Sciences. The Research Institute of Molecular Pathology is supported by Boehringer Ingelheim.
Figures and Tables
Figure 1 Clinical course of arthritis in human tumour necrosis factor transgenic (hTNFtg) and JNK1-/-hTNFtg mice. Joint swelling (a) and grip strength (b) were assessed. No statistically significant differences between the two groups were detected. Vertical bars indicate standard deviation. Control animals (wild-type and JNK1-/-) showed no signs of arthritis (not shown). JNK, c-Jun N-terminal kinase.
Figure 2 Histological assessment of synovial inflammation and joint destruction. No signs of arthritis are seen in the tarsal joints of wild-type (wt) and JNK1 (c-Jun N-terminal kinase 1) knockout (JNK1-/-) mice, whereas severe inflammation (I) and numerous erosions (E) are observed in human TNF transgenic (hTNFtg) and intercrossed (JNK1-/-hTNFtg) mice. H-&-E-stained sections; magnification 50×.
Figure 3 Quantitative analysis of synovial inflammation and joint destruction. There were no differences in the extent of inflammatory tissue between human tumour necrosis factor transgenic (hTNFtg) and intercrossed (JNK1-/-hTNFtg) mice. Mean areas of inflammation (a) and erosions (b) were comparable in these two strains. Vertical bars indicate standard deviation. JNK, c-Jun N-terminal kinase.
Figure 4 Synovial osteoclasts in human tumour necrosis factor transgenic (hTNFtg) and JNK1-/-hTNFtg mice. As demonstrated by staining with tartrate-resistant acid phosphatase (a), there are abundant osteoclasts (OC) at the site of erosions in hTNFtg and intercrossed (JNK1-/-hTNFtg) mice, which all developed arthritis (magnification 100×). The number of osteoclasts was similar in the two animal groups (b). Vertical bars indicate standard deviation. JNK, c-Jun N-terminal kinase.
Figure 5 Evaluation of proteoglycan loss in human tumour necrosis factor transgenic (hTNFtg) and JNK1-/-hTNFtg mice. As shown by toluidine blue staining (a), arthritis leads to loss of proteoglycan (arrows) in the cartilage of hTNFtg and intercrossed JNK1-/-hTNFtg mice (magnification 100×). The area of early cartilage damage (b) was similar in the two groups with arthritis, whereas the joints of wild-type (wt) and JNK1 knockout (JNK1-/-) animals were unaffected. Vertical bars indicate standard deviation. JNK, c-Jun N-terminal kinase.
Figure 6 Analysis of JNK activation in human tumour necrosis factor transgenic (hTNFtg) and JNK1-/-/hTNFtg mice. Synovial inflammatory tissue of the mice were stained with antibodies against the phosphorylated form of JNK (a), total JNK2 (b), and the phosphorylated forms of c-Jun (c). Positive cells are stained in brown (arrows; magnification 400×). Results of quantification (percentages of positive cells) are given in separate bar charts. Vertical bars indicate standard deviation. JNK, c-Jun N-terminal kinase; p-c-Jun, phosphorylated c-Jun; p-JNK, phosphorylated JNK. 1 cm = (a) 40 μm, (b, c) 30 μm.
Table 1 Cellular composition (%) of cells infiltrating synovial tissue in two mouse strains with arthritis
Cells hTNFtg JNK1-/-hTNFtg
Macrophages 49.5 ± 7.8 50.5 ± 5.3
Granulocytes 17.7 ± 1.5 15.5 ± 2.5
T lymphocytes 5.0 ± 1.0 6.7 ± 0.6
B lymphocytes 6.3 ± 2.1 5.0 ± 1.8
Values are percentages (mean ± standard deviation) of positive cells among total cells in the synovial tissue. hTNFtg, human tumour necrosis factor transgenic; JNK, c-Jun N-terminal kinase.
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| 15642137 | PMC1064897 | CC BY | 2021-01-04 16:02:33 | no | Arthritis Res Ther. 2005 Dec 7; 7(1):R166-R173 | utf-8 | Arthritis Res Ther | 2,004 | 10.1186/ar1473 | oa_comm |
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Research
Effects of positive end-expiratory pressure on gastric mucosal perfusion in acute respiratory distress syndrome
Bruhn Alejandro [email protected]
Hernandez Glenn [email protected]
Bugedo Guillermo [email protected]
Castillo Luis [email protected]
1 Programa de Medicina Intensiva, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
2004
15 7 2004
8 5 R306R311
17 3 2004
20 4 2004
19 5 2004
10 6 2004
Copyright © 2004 Bruhn et al.; licensee BioMed Central Ltd.
2004
Bruhn et al.; licensee BioMed Central Ltd.
This is an Open Access article: verbartim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with article's original URL. (http://creativecommons.org/licences/by/2.0)
Introduction
Positive end-expiratory pressure (PEEP) improves oxygenation and can prevent ventilator-induced lung injury in patients with acute respiratory distress syndrome (ARDS). Nevertheless, PEEP can also induce detrimental effects by its influence on the cardiovascular system. The purpose of this study was to assess the effects of PEEP on gastric mucosal perfusion while applying a protective ventilatory strategy in patients with ARDS.
Methods
Eight patients were included. A pressure–volume curve was traced and ideal PEEP, defined as lower inflection point + 2 cmH2O, was determined. Gastric tonometry was measured continuously (Tonocap). After baseline measurements, 10, 15 and 20 cmH2O PEEP and ideal PEEP were applied for 30 min each. By the end of each period, hemodynamic, CO2 gap (gastric minus arterial partial pressures), and ventilatory measurements were performed.
Results
PEEP had no effect on CO2 gap (median [range], baseline: 19 [2–30] mmHg; PEEP 10: 19 [0–40] mmHg; PEEP 15: 18 [0–39] mmHg; PEEP 20: 17 [4–39] mmHg; ideal PEEP: 19 [9–39] mmHg; P = 0.18). Cardiac index also remained unchanged (baseline: 4.6 [2.5–6.3] l min-1 m-2; PEEP 10: 4.5 [2.5–6.9] l min-1 m-2; PEEP 15: 4.3 [2–6.8] l min-1 m-2; PEEP 20: 4.7 [2.4–6.2] l min-1 m-2; ideal PEEP: 5.1 [2.1–6.3] l min-1 m-2; P = 0.08). One patient did not complete the protocol because of hypotension.
Conclusion
PEEP of 10–20 cmH2O does not affect gastric mucosal perfusion and is hemodynamically well tolerated in most patients with ARDS, including those receiving adrenergic drugs.
acute respiratory distress syndrome
gastric mucosal perfusion
positive end-expiratory pressure
tonometry
See related commentary, http://ccforum.com/content/8/5/308
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Introduction
Recent studies have shown that lung protective strategies using low tidal volumes and high levels of positive end-expiratory pressure (PEEP) reduce mortality and are becoming standard practice in patients with acute respiratory distress syndrome (ARDS) [1,2].
Although PEEP improves arterial oxygenation, it can adversely affect systemic hemodynamics, reducing venous return and cardiac output. These effects are proportional to the PEEP level. Regional perfusion can also be affected by PEEP, independently of cardiac output changes. The splanchnic perfusion is particularly sensitive, and any reduction can compromise its barrier function, promote bacterial translocation, and contribute to the development of multiple organ failure [3]. In experimental models, PEEP has markedly decreased mesenteric and portal blood flow, despite only moderate reductions in cardiac output [4-8]. Similar results have been reported in patients without lung injury [9,10]. These effects are usually dose related, becoming more pronounced with PEEP levels around 20 cmH2O.
Kiefer reported that PEEP did not significantly alter splanchnic blood flow in six patients with acute lung injury [11]. Nevertheless, caution should be exercised in extending these results to clinical practice, because only hemodynamically stable patients without adrenergic drugs were studied, and PEEP levels never exceeded 14 cmH2O [12].
Our aim was to evaluate the effects of PEEP levels up to 20 cmH2O on gastric mucosal perfusion and systemic hemodynamics in mechanically ventilated patients with ARDS under hemodynamic support.
Methods
Patients
The study was approved by the Ethics Committee of the Medicine Faculty and was performed in the Surgical Intensive Care Unit of the Catholic University Hospital of Chile.
Adult mechanically ventilated patients were considered eligible for the study if they met the following criteria for ARDS during the 24 hours that preceded the study: acute onset of respiratory failure; diffuse bilateral infiltrates in the chest radiograph; a ratio of partial pressure of O2 (PaO2) to fraction of inspired oxygen (FiO2) of less than 200 mmHg; and a pulmonary arterial occlusion pressure less than 18 mmHg and no cardiac failure.
Hemodynamic monitoring included an arterial line and a pulmonary artery catheter (Baxter Edwards Critical-Care, Irvine, CA). Patients could be under vasopressor or inotropic support, but had to be hemodynamically stable for at least 3 hours before starting the protocol.
Patients were excluded if they had any of the following conditions: pregnancy, pre-existing respiratory dysfunction, cardiac index of less than 2.5 l min-1 m-2, or were receiving enteral nutrition.
Interventions
A nasogastric tonometer (TRIP® Tonometry Catheter 14F, with biofilter connector for TONOCAP™ Monitor) was inserted into the stomach and connected to air automated tonometry (TONOCAP™ Monitor; Datex-Engstrom, Helsinki, Finland). All patients were sedated with midazolam and morphine, and paralyzed with rocuronium. Neuromuscular relaxation was measured by a TOF watch® device. An intravenous 20 mg dose of famotidine was administered before starting the protocol. Patients were connected to volume-controlled mechanical ventilation (Servo 900 C; Siemens, Solna, Sweden). A pressure–volume curve was obtained for each patient by the airway occlusion technique [13], and ideal PEEP was defined as the lower inflection point + 2 cmH2O, or 12 cmH2O if no lower inflection point was found.
PEEP levels of 10, 15, 20 cmH2O, and ideal PEEP, with tidal volumes of 8 ml kg-1, were applied in four consecutive 30 min periods, respectively. Respiratory rate was modified to maintain end tidal CO2 within ± 10 mmHg of basal. All patients were receiving a constant infusion of 6% hetastarch before the beginning of the study (40–80 ml h-1). Cardiac output was optimized before and during the trial by determining the respiratory variation of systolic arterial pressure [14]. Whenever the variation was more than 10% a 100 ml bolus of 6% hetastarch was infused and the volume status was reassessed. No change in adrenergic support was allowed during the protocol. If hypotension (mean arterial pressure < 65 mmHg) persisted for more than 1 min, the protocol was stopped.
Measurements
At baseline, and at the end of each period, hemodynamic, ventilatory and tonometric measurements were performed, and arterial blood samples withdrawn. Hemodynamic records included mean arterial pressure, heart rate, cardiac output, pulmonary artery occlusion pressure, central venous pressure and left ventricular stroke work index. Cardiac output was measured by thermodilution as the average of three values obtained after injections of 10 ml of 5% dextrose in water at room temperature. Mean airway pressure, oxygenation index and PEEP levels were registered. Oxygenation index was calculated as mean airway pressure × FiO2 × 100/PaO2. The CO2 gap (gastric partial pressure of CO2 [pCO2] minus arterial pCO2) was calculated by comparing simultaneous measurements of tonometric gastric mucosal pCO2 and arterial pCO2.
Statistical analysis
Results are presented as median and range. The software Statview 5.0 was used to perform the statistical analysis. Nonparametric tests were used because of the small sample size. Data were analyzed with a Friedman test followed by a Wilcoxon signed-rank test if necessary. Results were considered statistically significant at P < 0.05.
Results
Eight patients with ARDS were enrolled. They had a median (range) age of 63.5 years (23–86), and an Acute Physiology and Chronic Health Evaluation II score of 14 (7–20) at admission to the intensive care unit. On the day of the study they had a median Sepsis-related Organ Failure Assessment (SOFA) [15] score of 10 (7–13). All patients fulfilled criteria for ARDS, as defined by the inclusion criteria, during the 24 hours before the study and they had been on mechanical ventilation for 32 (12–72) hours. They were being ventilated with a median PEEP level of 9 (4–12) cmH2O, they had a PaO2/FiO2 ratio of 235 (144–388) mmHg and their respiratory system compliance was 45 (27–60) ml per cmH2O. Seven patients had sepsis (two pneumonia and five extrapulmonary sepsis), and one a severe thoracic trauma. Of the septic patients, six were in septic shock. Characteristics of individual patients are shown in Table 1.
Table 1 Baseline characteristics of the patients
Patient Age (years) Sex Diagnosis APACHE II SOFA PaO2/FiO2 (mmHg) pH Bicarbonate (mEq/L) PEEP (cmH2O) Crs (ml/cmH2O) LIP (cmH2O) Vasopressors/inotropesa Outcome (S/NS)
1 55 M Hepatic lobectomy 14 13 144 7.38 25.4 10 51 10 NA 0.08 S
Dbt 3.3
2 23 F Peritonitis 20 10 388 7.36 23.5 8 32 10 NA 0.12 S
3 32 M Mucormycosis and sepsis 7 7 282 7.42 21.5 6 60 6 NA 0.09 S
4 68 F Acute pancreatitis 9 13 208 7.38 20.4 10 40 NL NA 0.2 NS
5 59 F Pneumonia and sepsis 16 8 197 7.28 25.5 10 55 NL NA 0.03;
Dp 6.8;
Dbt 3.4 S
6 68 M Thoracic trauma 14 10 289 7.36 21.6 4 37 13 NA 0.05 S
7 72 M Sepsis 17 9 263 7.25 13.8 4 50 8 Dbt 5.4 S
8 86 M Pneumonia and sepsis 14 12 150 7.37 20.3 12 27 13 NA 0.02 NS
APACHE, Acute Physiology and Chronic Health Evaluation; Crs, Respiratory system compliance; Dbt, dobutamine; Dp, dopamine; LIP, lower inflection point; NE, norepinephrine (noradrenaline); NL, no LIP found; NS, not significant; PEEP, positive end-expiratory pressure; S, significant; SOFA, Sepsis-related Organ Failure Assessment. aDoses are in μg kg-1 min-1.
No changes in cardiac index or in CO2 gap were found at any of the study periods (Table 2). Oxygenation index, mean arterial pressure, pulmonary mean arterial pressure, pulmonary artery occlusion pressure, central venous pressure and left ventricular stroke work index also remained stable through the study. Only mean airway pressure and PaO2/FiO2 ratio differed between periods, as expected. Five patients required a 100 ml bolus of hetastarch during the trial; in no patient was it necessary to repeat it. Individual changes in CO2 gap and cardiac index are presented in Figs 1 and 2, respectively. At baseline three patients had already a CO2 gap of more than 20 mmHg. After starting the protocol with 10 cmH2O PEEP, patient 6, who was previously being ventilated with 4 cmH2O PEEP, had a further increase in CO2 gap. When PEEP was increased from 10 to 15 cmH2O, six patients decreased their CO2 gap and two increased it. When PEEP was increased from 15 to 20 cmH2O, three patients increased their CO2 gap, three decreased it and in one patient it remained unchanged. Patient 4 did not complete the protocol because of moderate hypotension (mean arterial pressure 60 mmHg) when PEEP was increased to 20 cmH2O. This patient recovered after an increased dose of norepinephrine (noradrenaline) and a return of PEEP to baseline levels.
Table 2 Respiratory, hemodynamic and tonometric measurements
Parameter Baseline (n = 8) PEEP 10 (n = 8) PEEP 15 (n = 8) PEEP 20 (n = 7) Ideal PEEP (n = 7) P
PEEP (cmH2O) 9 (4–12) 10 15 20 12 (8–15)
Mean airway pressure (cmH2O) 13.2 (8–18.7) 14 (12–17) 19 (17–22.2) 24 (22–26.4) 16.2 (11.5–22.2) 0.0001a
OI (cmH2O per mmHg) 5.3 (2.9–12.4) 7 (3–14.5) 6.7 (4.1–12.3) 7 (5–12.3) 6.6 (2.9–12.3) 0.3
PaO2/FiO2 (mmHg) 235 (144–388) 210 (117–402) 285 (154–412) 333 (196–440) 243 (164–467) 0.0009b
PaCO2 (mmHg) 36 (31–54) 41 (28–63) 42 (31–66) 45 (32–60) 43 (28–52) 0.08
Cardiac index (l min-1 m-2) 4.6 (2.5–6.3) 4.5 (2.5–6.9) 4.3 (2–6.8) 4.7 (2.4–6.2) 5.1 (2.1–6.3) 0.08
LVSWI (g m m-2) 45 (22–71) 43 (22–60) 40 (14–60) 36 (15–58) 42 (14–66) 0.13
MAP (mmHg) 79 (74–103) 81 (69–99) 74 (69–97) 74 (66–93) 73 (69–96) 0.24
PAOP (mmHg) 16 (10–19) 17 (8–22) 17 (11–23) 18 (12–26) 14 (11–23) 0.22
CVP (mmHg) 14 (9–17) 15 (7–19) 15 (9–24) 15 (10–19) 12 (8–18) 0.27
CO2 gap (mmHg) 19 (2–30) 19 (0–40) 18 (0–39) 17 (4–39) 19 (9–39) 0.18
Results are presented as median (range). CVP, central venous pressure; CO2 gap, arterial partial pressure of CO2 [pCO2] minus gastric pCO2; FiO2, fraction of inspired oxygen; LVSWI, left ventricular stroke work index; MAP, mean arterial pressure; OI, oxygenation index, defined as mean airway pressure × FiO2 × 100/arterial pCO2; PaO2, partial pressure of O2; PaCO2, partial pressure of CO2; PAOP, pulmonary arterial occlusion pressure; PEEP, positive end-expiratory pressure. aP < 0.05 for all comparisons except baseline versus PEEP 10 and PEEP 10 versus ideal PEEP. bP < 0.05 for all comparisons except baseline versus PEEP 10, baseline versus PEEP 15, baseline versus ideal PEEP, and PEEP 15 versus ideal PEEP.
Six of the eight patients studied survived (75%). The median length of stay in the intensive care unit was 17 (10–34) days and the median duration of mechanical ventilation was 9 (5–34) days.
Figure 1 Individual changes in CO2 gap (gastric pCO2 minus arterial pCO2) with different positive end-expiratory pressure levels.
Figure 2 Individual changes in cardiac index with different positive end-expiratory pressure levels.
Discussion
Our results show that high PEEP levels (up to 20 cmH2O) do not compromise gastric mucosal perfusion, as assessed by tonometry, and do not affect systemic hemodynamics in most patients with ARDS. This is consistent with the findings of two other studies on the effects of PEEP on splanchnic perfusion in patients with ARDS. Nevertheless, in contrast with our study, neither of those studies included patients in septic shock or under adrenergic support [11,16].
Shock and cardiovascular dysfunction are frequently associated with ARDS. This is an important issue, because hemodynamic safety concerns could preclude the use of high or optimal PEEP levels in that setting, even if necessary. A major finding of our study is that PEEP levels up to 20 cmH2O can be well tolerated, even in patients with ARDS and septic shock. Nevertheless, our trial was relatively short and we cannot exclude the possibility that keeping high PEEP levels for a longer period might result in increased fluid requirements, which could be deleterious in the longer term.
Experimental and clinical research has demonstrated that in mechanically ventilated subjects without lung injury, PEEP decreases venous return and, secondarily, cardiac output [17-19]. In addition, Trager and colleagues showed that, in patients with acute respiratory failure associated with septic shock, high PEEP levels induced a decrease in cardiac output [20]. In contrast, we found no decrease in cardiac output in our patients tested with increasing PEEP levels when fluid administration was optimized according to respiratory variation in systolic arterial pressure. A similar result was reported by Kiefer and colleagues and by Akinci and colleagues [11,16]. Possible explanations for these contradictory results are a higher rate of fluid administration and the use of lower tidal volumes in the latter studies. Although we did not determine the upper inflection point of the pressure–volume curve, we think that by keeping tidal volume at 8 ml kg-1 any overdistension of the lungs was minimized. Lung volumes are a critical component of the hemodynamic effects of ventilation [21]. Thus, it seems that it is possible to preserve cardiac output in patients with ARDS, despite the use of high PEEP levels, by optimizing fluid administration and limiting tidal volumes.
Gastric mucosal perfusion, as assessed by CO2 gap, also remained unchanged during the PEEP trial. This is consistent with the results reported by Kiefer and Akinci in similar studies. In all these studies cardiac output remained unchanged [11,16]. In contrast, Trager reported, in a series of septic shock patients with acute respiratory failure, that an increase in PEEP from 5 to 15 cmH2O induced a decrease in cardiac output associated to a decrease in hepatic vein O2 saturation and in hepatic glucose production [20]. It therefore seems that by avoiding decreases in cardiac output, splanchnic perfusion can be preserved in the majority of the patients.
In spite of the fact that no significant changes in CO2 gap or cardiac index were found during the protocol, when looking at the individual data certain patients evidenced an adverse effect when their PEEP level was increased. Patient 4, who had an extrapulmonary ARDS, presented hypotension when 20 cmH2O PEEP was applied. In this case, no simultaneous records of cardiac output or CO2 gap could be made for safety reasons (we immediately proceeded to decrease PEEP level). Patient 6, who had a pulmonary ARDS and who before starting the study had a 30 mmHg CO2 gap while being ventilated with 4 cmH2O PEEP, presented a further deterioration in CO2 gap after starting the protocol with 10 cmH2O PEEP, which was not associated with a decrease in cardiac output. Thereafter, the CO2 gap remained unchanged despite increasing PEEP up to 20 cmH2O. These events suggest that not all patients with ARDS can tolerate high PEEP levels; if required, careful hemodynamic monitoring including assessment of regional perfusion should be applied.
One major limitation of our study is the small number of patients studied. Thus, a type II error cannot be excluded. We did not perform any a priori power analysis because we had no estimation of the possible magnitude of the effects that PEEP could have on gastric tonometry.
Another limitation is the rather moderate severity of ARDS in our study. Although all patients fulfilled criteria for ARDS during the 24 hours that preceded the study, at inclusion their PaO2/FiO2 ratio and their respiratory system compliance were only moderately decreased. Two recent papers provide an explanation for this observation [22,23]. They show in patients diagnosed with ARDS that after a few hours of treatment with PEEP or a high FiO2, more than half of the patients present a PaO2/FiO2 ratio of more than 200 mmHg. In addition, respiratory system compliance increased by more than 10 ml per cmH2O after 6 hours of treatment with PEEP [23]. At inclusion our patients had already been ventilated with a median PEEP level of 9 cmH2O for more than 12 hours, which could have explained the rather improved respiratory performance at baseline. In any event, this improvement demonstrated a less severe ARDS. It is possible that more severely compromised patients might present a lower tolerance to high PEEP levels.
Other limitation is that tonometry was the sole method used to assess gastric mucosal perfusion. Nevertheless, Elizalde and colleagues showed that gastric mucosal blood flow, measured by laser Doppler flowmetry and by reflectance spectrophotometry, is well correlated with gastric intramucosal acidosis in mechanically ventilated patients [24].
Conclusions
Our study supports the findings of previous studies suggesting that high PEEP levels do not affect splanchnic perfusion and are hemodynamically well tolerated in most patients with ARDS. Furthermore, our study shows that gastric mucosal perfusion can be well preserved while high PEEP levels are applied even in patients presenting cardiovascular dysfunction and receiving adrenergic support, which is a frequent occurrence in critical care. Nevertheless, two of the eight patients studied exhibited adverse effects during the PEEP trial, which highlights the importance of a close monitoring of systemic and regional perfusion while applying high PEEP levels to patients with ARDS. Future studies should assess the effects of PEEP on splanchnic perfusion in a longer term.
Key messages
• High PEEP levels do not affect gastric mucosal perfusion and are hemodinamically well tolerated in most patients with ARDS
Competing interests
None declared.
Abbreviations
ARDS = acute respiratory distress syndrome; CO2 gap = gastric pCO2 minus arterial pCO2; FiO2 = fraction of inspired oxygen; PaO2 = partial pressure of O2; pCO2 = partial pressure of CO2; PEEP = positive end-expiratory pressure.
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| 15469573 | PMC1065018 | CC BY | 2021-04-27 23:14:50 | no | Crit Care. 2004 Jul 15; 8(5):R306-R311 | utf-8 | Crit Care | 2,004 | 10.1186/cc2905 | oa_comm |
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