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Harm Reduct JHarm Reduction Journal1477-7517BioMed Central London 1477-7517-1-101554833110.1186/1477-7517-1-10Research"Tied together like a woven hat:" Protective pathways to Alaska native sobriety Mohatt Gerald V [email protected] S Michelle [email protected] Lisa [email protected] James [email protected] Kelly [email protected] Chase [email protected] University of Alaska, Box 757000, Fairbanks, Alaska, 99775-700, USA2 University of Washington, Box 351525, Seattle WA 98195, USA3 Metropolitan State University, 730 Hennepin Ave., Minneapolis, MN 55403-1897, USA4 Psychosocial Center for Refugees, University of Oslo, Boks 1072 Blinden, NO 0316, Oslo, Norway2004 17 11 2004 1 10 10 17 6 2004 17 11 2004 Copyright © 2004 Mohatt et al; licensee BioMed Central Ltd.2004Mohatt 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
The People Awakening Project (1RO1 AA 11446-03) had two purposes, completed in Phase I and Phase II of the project. The purpose of Phase I was to complete a qualitative study; the research objective was discovery oriented with the specific aim of identification of protective and recovery factors in Alaska Native sobriety. Results were used to develop a heuristic model of protective and recovery factors, and measures based on these factors. The research objective of Phase II was to pilot these measures and provide initial validity data.
Methods
Phase I utilized a life history methodology. People Awakening interviewed a convenience sample of 101 Alaska Natives who had either recovered from alcoholism (n = 58) or never had a drinking problem (n = 43). This later group included both lifetime abstainers (LAs) and non-problem drinkers (NPs). Life histories were transcribed and analyzed using grounded theory and consensual data analytic procedures within a participatory action research framework. Analyses were utilized to generate heuristic models of protection and recovery from alcohol abuse among Alaska Natives.
Results
Analyses generated a heuristic model of protective factors from alcohol abuse. The resulting multilevel and multi-factorial model describes interactive and reciprocal influences of (a) individual, family, and community characteristics; (b) trauma and the individual and contextual response to trauma, (c) experimental substance use and the person's social environment; and (d) reflective processes associated with a turning point, or a life decision regarding sobriety. The importance of cultural factors mediating all these protective processes is emphasized. For NPs, the resilience process drew from personal stores of self-confidence, self-efficacy, and self-mastery that derived from ability to successfully maneuver within stressful or potentially traumatizing environments. In contrast, for many LAs, efficacy was instead described in more socially embedded terms better understood as communal mastery. One style of mastery is more associated with individualistic orientations, the other with more collectivistic. Future research is needed regarding the generalizeability of this group difference.
Conclusions
Results suggest that preventative interventions should focus on intervening simultaneously at the community, family, and individual levels to build resilience and protective factors at each level. Of particular importance is the building of reflexivity along with other cognitive processes that allow the individual to think through problems and to reach a life decision to not abuse alcohol.
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Background
Many American Indian and Alaska Native people experience problems with alcohol abuse that lead to social, psychological, and physical problems [1-3]. Unfortunately, little is known about American Indian or Alaska Native people who live sober and healthy lives. This paper presents initial findings from the People Awakening Project (PA), a collaborative study involving the Alaska Native community, and Native and non-Native university researchers. The goal of PA was to provide an Alaska Native understanding of the sobriety process. In earlier work, we provided a detailed description of PA's focus on cultural and spiritual understandings of sobriety [4], and its use of participatory research methodologies with Alaska Natives [5]. Sobriety in the addiction literature is generally defined as total abstinence following a period of alcohol abuse and/or dependence. However, many Alaska Natives also consider life-long abstinence, as well as non-abusive or moderate use of alcohol, as examples of a sober lifestyle. PA has adopted this broader definition of sobriety.
Recent research on resilience identifies and describes protective factors that moderate risk and adverse environmental circumstances; this work has relevance to understanding the sobriety process [6-10]. Resilience is "a capacity that develops over time in the context of person-environment interactions" [11] (p. 517). Protective factors are those attributes that contribute to this capacity, and include those "individual characteristics or environmental conditions that help children and youth resist or otherwise counteract the stress to which they were exposed. They delay, suppress, or neutralize negative outcomes" [12] (p. 4). Protective factors can be grouped according to three broadly conceived categories [13-15]: (a) internal or dispositional attributes of the individual, such as sociability, intelligence, social competence, and internal locus of control; (b) familial attributes, such as warmth and closeness of affectional ties, and level of active emotional support within the family network; and, (c) contextual factors, such as social support, and characteristics of school, work and church settings.
Because protective factors include personality traits and family, community, and environmental characteristics, it is difficult to compile a universal list of factors appropriate to all groups of people in very diverse contexts, especially when the nature or the composition of those categories includes diverse cultural dimensions [16]. For example, self-efficacy is a commonly cited protective factor [13,14,17], but few studies describe the nature of self-efficacy and how it works to protect American Indians or Alaska Natives. Hobfoll, Jackson, Hobfoll, Pierce, and Young [18] expanded our understanding of how efficacy may differ in a collectivist culture. A measure of communal mastery developed for the Hobfoll et al. study, but not a standard self-efficacy measure [19], predicted lower depressive mood and anger among American Indian women in stressful situations. Research among other ethnically diverse populations, including work with indigenous people in Kauai [15], Asian-Americans [20], and culturally-diverse inner city populations [21,22] similarly highlight the importance of cultural factors in the understanding of protective processes.
Triadic Influence theory (TI) [23] provides a multi-level, multi-factorial model for understanding protective factors in sobriety that both integrates constructs from other theories on alcohol use and abuse, and provides a conceptual framework for interventions [24]. However, Petraitis, Flay, and Miller [25] noted that there has been limited research on protective factors within a TI framework associated with race and ethnicity. The limited existing research on the role of cultural factors within protective processes from substance abuse among American Indians and Alaska Natives has focused on cultural identity processes and has yielded mixed findings. Beauvais and Oetting's [26] review of research suggested high levels of cultural identification function as a protective factor from substance abuse among American Indian adolescents, and Schinke et al. [27]found bicultural skills training an effective preventive intervention against substance abuse for this population. However, other studies of cultural identity and substance abuse have found no relation [28], or a positive relationship for women [29]. Oetting, Donnermeryer, Trimble, and Beauvais [30] concluded that simple relationships between cultural identification and substance abuse are unlikely to be found given four potentially overlapping considerations. First, members of an ethnic group vary on level of cultural identification, which may effect conformity to substance use norms. Second, substance abuse may originate from norms socialized in the subculture and differ from those of the larger ethnic group. Third, cultural identification and substance use norms may differ in different contexts. Fourth, cultural identification may originate from primary socialization sources that are different than drug use norms.
Instead of attempting to study cultural factors through measurement of identification with Alaska Native culture, the narrative form of the qualitative study reported in this paper allows for an alternative approach involving the generation of hypotheses on ways in which specific culturally mediated processes are conceptualized as protective by the members of the culture themselves.
In summary, there is a need for research that examines the resilience experience of Alaska Natives who lead sober lives, and in particular, for research that includes an examination of the role of cultural factors in the protective process. In order to provide the rich description necessary to understand the range of experience and cultural processes of Alaska Natives who never drank abusively or who have recovered, qualitative methodologies are used. The goal of this study is to generate a theoretical model [31] of protection grounded in the experience of Alaska Native people that could inform the development of culturally anchored prevention approaches. Aligned with this goal, in this article we focus on Alaska Native pathways to the sobriety outcomes of abstinence and nonproblem alcohol use. Our analysis of the recovery group in this study is therefore restricted to identification of unique attributes within the abstinent and nonproblem drinking group not found among the recovery group. Future research will explore Alaska Native pathways of recovery from alcohol abuse.
Methods
Sample
A purposive sampling procedure was used. Selection criteria were established by the PA Coordinating Council, a statewide group consisting of Alaska Native community leaders, individuals involved with grassroots Alaska Native sobriety movement efforts, and Alaska Native substance abuse services providers, who functioned as co-researchers in the participatory methodology. The Council distinguished three groups of interest: (1) lifetime abstainers (LAs) defined as individuals who have never drank more than two drinks per year, (2) non-problem drinkers (NPs) who report drinking alcohol with no problem and score less than 12 on the lifetime total consequences score of the Drinkers Inventory of Consequences for Alaska Natives (DrInC-AN)-a culturally adapted version of the Drinkers Inventory of Consequences (30), and (3) five years or greater of sobriety (5+) who identified themselves as recovered after a serious problem with alcohol, scored greater than 12 on the DrInC-AN lifetime total consequences score, and reported abstinence for at least five years. The project goal for Phase I was to select 36 participants with equal representation from the five Alaska Native tribal groups-Aleut/Alutiq, Athabascan, Inupiaq, Tlingit/Haida/Tsimshian, and Yup'ik/Cup'ik,-balanced by gender, age, and sobriety group status, and to oversample 12 additional interviews from the Yup'ik because Phase II measurement development would focus on this group. PA utilized nomination and snowball procedures to identify potential participants. Age representation was categorized into three age groups: 21 to 30, 30 to 55, and 56 and over. These age ranges were selected by the Council as indicative of culturally significant age ranges, marking indigenous age transitions from young adulthood to middle adulthood to elder. The Council selected these three sobriety categories to maximize our ability to discover potential protective factors as well as recovery factors, together which would define broadly resilience factors used by Alaska Natives in dealing with alcohol. Consultants from the respective tribal communities, the regional non-profit corporations, area health service providers, and other Native political organizations nominated individuals for participation, who then nominated others. Additionally, radio shows, advertisements, and newspaper articles solicited volunteers. This yielded 152 volunteers. Because our Council indicated it would be culturally inappropriate to not interview people following their offer to tell their life story to the project, PA offered interviews to all volunteers, and 101 completed the entire interview process. The results presented here analyze 37 long life history interviews and 14 briefer interviews on sobriety experiences. These participants were distributed across tribal group affiliation (Aleut/Alutiq-6, Athabascan-7, Inupiaq-6, Tlingit/Haida/Tsimshian-6, Yup'ik/Cup'ik-26), and the three sobriety types: LA - 10, NP - 19, and 5+ - 22, with proportional representation of the long life histories by gender and age in each sobriety category. In addition to over-sampling from the Yup'ik cultural group for life history interviews, 14 Yup'ik briefer interviews are included in this analysis in order to maximize the generalizeability of the findings to this cultural group, as the next phases of PA involve the development of measurement instruments and preventative interventions in regions of Alaska that include a Yup'ik majority.
Sixty-two percent of participants spoke English as a first language and 48% their indigenous language. Eighty-two percent had been married at one time, with the average length of marriage being 10 years. At the time of the interviews 57% remained married. Participants' immediate families averaged 3 children. Participant incomes ranged from below $10,000 to over $100,000 per annum with the mean at $46,800. Most participants had graduated from high school (84%) and education ranged from no school to doctoral degrees. Of those who had recovered from alcohol abuse/dependence, mean years of sobriety was 17.5 years.
Procedures
PA was approved by the Institutional Review Board at the University of Alaska Fairbanks prior to participant enrollment. Nominees were contacted initially by phone, the purpose and structure of the interviews was described, and participation invited. Preference for location of interview, gender of interviewer, indigenous language or English interviewer, and interviewer that they knew or did not know was established. Interviewers were trained in the interview protocol, including protection of human participant procedures, prior to this contact.
Life history interviews followed an open-ended for long life histories (LLH) or semi-structured format for brief life stories (BLS). The mean for LLH was 173.5 minutes (SD = 87.5), median was 159.5, and mode was 141.9. For BLS the mean was 119.5 minutes (SD = 49.5), median was 110, and mode was 106.5. Range for LLH were 20 to 452 minutes and for BLS were 45 to 272 minutes. The interview protocol elicited lifespan information with a focus on what the person considered most important in their process of sobriety. The intent was to garner rich detail about each person's life story. Briefer interviews were semi-structured. Questions addressed specific issues including the role of culture, spirituality, role models, parenting, and the methods of coping that individuals utilized to either not abuse alcohol or to recover. However, it is important to note that Alaska Native narrative patterns [32] at times overrode the distinction between these interview types and participants often responded to both formats similarly in time duration and style of discourse. Many participants tended to respond to either question format with a narrative, and did not distinguish more structured questions from less structured ones, e.g. "When did you first drink and what was your experience like?" in contrast to, "Tell me about your life in as much detail as possible from whatever point that you wish?" would often be answered in the same way and expanded upon equally. Our sense was that older participants in particular would often respond to either type of question by telling their entire life story. Additionally, we noted the length of the interviews also often varied by the experience of the interviewer and/or how the interviewer responded to the content of the interviews. For example, some interviewers felt it was best to close off interviews that began to bring out too much emotional material, whereas others with more clinical experience were more comfortable in moving through emotional material, framing and containing it, and then move on to other material. Interviews were recorded digitally using mini-disk recorders. At interview conclusion, participants completed a demographic questionnaire and the DrInC-AN,
Analysis
Our analytic approach combined elements of grounded theory analysis [31] with recent methodological advances in team-based coding and analysis [33] and consensual qualitative data analysis [34]. Interviews were verbatim transcribed, reviewed by the interviewer, then, in the case of the life history interviews, the transcript was mailed to and reviewed by the participant for accuracy, additions, or changes. The following describes the analytic process from which a heuristic model of protective factors in Alaska Native sobriety emerged. Although the analytic structure is presented in stages for exposition of its elements, the analysis in practice functioned in an iterative process through multiple passes through stages, involving continual reassessment of inferences and analyses.
Step 1: Memoing
Each analysis team member memoed the recordings of assigned interviews while also making additions and corrections to the transcripts for fidelity to the recorded interview. Memoing entailed three steps: (1) open coding identify possible codes, (2) connecting codes through overarching themes, and (3) documenting how codes and themes fit possible theories of protection. Team members then read all memos. Additionally, some of the team members shared their memos with the participant to gather feedback on the accuracy of their perceptions. Changes to the coding and analysis were made to reflect the perceptions of the participant. Most participants made no changes to the transcripts or small changes to the transcripts. A small number made changes by adding material or deciding to delete material, e.g. a number of individuals dropped names of people that were in the interview. A few added material that they had remembered. We gave the participants their verbatim transcripts (with all pauses, false starts, "ahs", etc.) and discovered participants were often embarrassed by their unedited nature. We learned immediately we needed to explain the nature of the transcription process and its intent, and that their interviews would not be published in such a form (participants' interview transcripts were confidential, but several participants expressed a cultural value in their desire to have their interviews made available to others who may be struggling with alcohol themselves and find them helpful). An initial set of codes and overarching themes or domains under which the codes clustered was identified and then systematized in an initial draft coding manual.
Step 2: Open coding and coding manual development
Two research team members continued to read and open-code interviews. The team met periodically with Gerald V. Mohatt, Principal Investigator, who also coded a number of transcripts, to discuss coding discrepancies and refine coding rules. The goal at this stage was inclusive not exclusive, and to add as many codes as possible; therefore, we did not limit ideas. We spent much time operationalizing definitions in order to ensure that each code was clearly distinguished from others and could be reliably scored using the codebook criteria. This was done through hours of discussion, with final agreement regarding the definition of each code arrived at between the PI, the research Project Director, and at least one of the Co-Investigators or research assistants. This process resulted in 220 separate codes organized under 25 hierarchical domains. Coding reliability was enhanced in the revised coding manual through development of definitions for each code, along with examples of the code in use and decision rules where appropriate.
Step 3: Coding/content analysis and codebook refinement
The research team trained coders to code using AnSWR software [35] and content analyze the remaining transcripts. Inter-coder reliability between coders was assessed on every seventh transcript. What represents adequate inter-coder reliability in qualitative research continues to provoke divergent viewpoints in the literature. Miles and Huberman [36](p. 64) suggest that final inter-coder agreement in qualitative data analysis should approach or exceed 90%, though Stein[37] recently published a study where she used less than 80% agreement. Moreover, simple proportions do not account for the possibility that coders might agree due to chance, which is a function of the frequency or infrequency with which a code appears [38] and therefore provide a biased over-estimate of the true level of agreement. To correct for this, we used the kappa statistic [39]. Carey, Morgan, & Oxtoby [40] judged that a kappa less than .90 indicated a problem with agreement in the way a code was being used in qualitative research. However, insistence upon very high levels of reliability can also have the effect of diminishing validity [41], and this is a particular concern in discovery-based research such as that of the present study. Therefore, we adopted minimum criteria for the 25 hierarchical categories of kappa .90 or greater, and coding of the 220 lower level categories of no less than .60. Kappas ranged from .60 to .81 for all lower level categories, and all hierarchical categories were at .90 or above. The team continued to reconcile divergences in coding, refine coding categories, open code, and revise the codebook. Previously coded transcripts were recoded, using the revised codebook.
Step 4: Cultural auditing
The team submitted a sample of transcripts to the PA Coordinating Council as part of a cultural auditing procedure. The co-researcher role of this Council, which included members of all five Alaska Native tribal groups interviewed by the project is described elsewhere [5]. The Council collectively open-coded five transcripts from participants selected from all three sobriety groups. Council members coded the transcript of a participant from their own cultural group. The Council convened to discuss their coding and address specific research team questions; such as, have we identified and labelled the codes appropriately. This cultural auditing process moved the team forward in understanding the narratives from a more culturally grounded perspective.
For example, Council members understood "being a role model" within the context of the cultural value of contributing to the good of the family or community, and not merely in terms of individual achievement. The Council also indicated that we should add codes such as shame, praise, and pride to our coding system, and elaborated on their definitions. An overall comparison of the coding and domains generated by the Council with those of the research team displayed high levels of consistency, along with selected important divergences which were discussed to mutual understanding, then adopted by the research coding team.
Step 5: Generating theories through a consensual analytic process
Team members next identified how coded segments clustered and interacted, generating potential theories on protective factors through comparison of the life histories of LAs and NPs to 5+ individuals. The team discussed multiple theories, and reconciled potential theories to case histories of non-agreement through revision or abandonment of the theory.
Step 6: Developing and refining a theoretical pathway to sobriety
Team discussions were summarized and synthesized by the principal investigator into competing models. The team reread transcripts, discussed and refined models, converging on one model that best fit the majority of transcripts, which was then presented to the PA Coordinating Council. The Council added refinements and culturally grounded elaborations to this model.
Step 7: Doubling back
The team re-read transcripts and reassessed the model, refining and elaborating elements until consensus that the full set of transcripts supported the model. As part of this process the team enlisted the Cuiliat Group of Yup'ik speakers, who were our cultural consultants, and would also assist us in the Phase II measurement development. Translating each of the protective factors into Yup'ik forced us to clarify definitions and ensured that they differentiated culturally specific dimensions of each protective factor. For example, from this process the importance of collective group factors became clearer.
Methods for Verification
In qualitative research, the analogue for validity in quantitative research is often termed credibility, which can be defined through (1) the confidence that can be placed in the data and analysis [42,43], (2) how well the conclusions from the data analysis are grounded and supported in the data [44], and (3) the degree to which the descriptions and analyses provide an understanding of the experience studied [45]. In this study, several methods [36] were used to enhance the credibility of the findings: prolonged engagement with the participants resulting in rich, thick description; initial memoing of each narrative prior to coding; confirmation of the narrative and its transcription, and of the memoing, through checks with the study participants; team data coding with ongoing reliability checks and refinement of the coding system; triangulation through the use of multiple data sources and multiple co-researcher perspectives; negative case analysis, or the examination of events and perceptions that did not fit emerging themes; cultural auditing of the coding and interpretative process; and team-based consensual analytic processes. Examples of triangulation included sending transcripts and memoing to the participant,, discussion of the memoing and transcripts with the Council, and the parallel discussions within the research team, which provided three typically converging perspectives on the analysis, along with recognition and discussion of discrepancies whenever they occurred, to the point of mutual understanding, and resolution and agreement. Depending upon the specific theme that was divergent, action could involve reworking of the coding theme to make it more congruent, dropping the theme as an unreliable code,, or addition of a new theme that was not seen by the research coding team, but was identified by others who analyzed the transcripts. Given the multiple cultural perspectives, this provided rich, deep, and inclusive coding categories allowing for the generation of multiple hypotheses regarding themes and the connections between them in the life stories.
Generalizability
The research aim of the PA study was discovery-based, and not proof through hypothesis testing and falsification. Our objective was to characterize the types of protective factors utilized within this purposive sample, and not to generalize to all Alaska Natives or American Indians. Our goal was to generate a heuristic theory that would suggest testable hypotheses that could later be investigated in a larger, population based study, using measures developed in Phase II. We also hoped to offer ideas to services programs regarding variables that they could test for effectiveness in prevention or treatment.
Results
Using the above process we first identified a set of factors protective from alcohol abuse. We use the direct words of participants to illustrate each to allow the reader to move through the process in a manner similar to the research team. Each protective factor in the model is translated into Yup'ik, the indigenous language of the group we plan to collaborate with on an intervention program. The complete Heuristic Model of Alaska Native Protective Pathways can be found in Figure 1. The mode represents a culture specific mapping of protective processes and as such, is presented in a format that allows for hypothesis testing using quantitative methods. The model is theoretical and heuristic in nature, and shows postulated relationships between factors consistent with Triadic Influence Theory, rather than empirically supported causal factors. We describe below each protective factor, along with its relationship to the model and function.
Figure 1 Heuristic Model of Alaska Native Protective Pathways Key. CC (community characteristics) Yuut cayarait includes the way the community organizes family, school, and community activity, and enforces alcohol policy and the drinking status of the community, CC includes role models, opportunities, limits, and safe places. FE (family environment) Ilakelriit cayarait includes family functioning in such areas as cohesion, conflict, recreation outlets, moral-spiritual focus, and home organization. Factors included parent-child relationship, affection and praise, transmission of expectations, safety and protection from harm and models of sobriety. IC (individual characteristics) Yuum Ayuqucia are belief in self (communal and self-mastery), wanting to contribute to others and Ellanqaq (Yup'ik mindfulness and awareness. SE (social environment) Yuuyaraq includes role models and social support from extended family, peers, and other adults outside of immediate, nuclear family. TR (trauma) Akngirneq includes sexual abuse, domestic violence, and death of loved ones. It includes being a victim and observing others being a victim. An individual's perception of trauma is critical, as is the meaning they attach to their experience and how they respond to it. ESU (experimental substance use) Meqerraaryaurtellemni are early experiences with substances, including alcohol, prior to the establishment of use patterns or abstinence. TO (thinking it over) Umyuangcallemni involves reflecting on one's experience and developing a personal life narrative. TP (turning point) Ayuqucinellemni comes out of this reflective process and leads to a decision about how the person will use alcohol.
Community Characteristics (CC)
Yuut cayarait. Participants described the context of the community that protected them during childhood and provided a sense of security. As one participant indicated, "I guess, my life as a child was pretty much sheltered...so, as the expression goes, the village was my oyster then." Protective communities possessed role models for the proactive caring of others that exemplified a sense of a collective responsibility for the care of children, or, as another participant described this, "That's also what I remember is people taking care of us even if we're not their children, they looked after us, and they corrected us." Participants described how protective communities provided both opportunities to learn and alternatives to drinking. One young man described how the community school gave opportunities to travel, engage in sports, debate, and engage in student leadership that gave him ideas about college and careers. Opportunities were also often contextualized in ways the community helped children through important culturally defined transitional rites in the development of adult roles: "They still do this community sponsored moose hunt. They go out and they go hunting for the moose and for a lot of young men that is the time that they have the rite of passage. This is their first moose. And in the beginning when it started out it was just the men, just boys were allowed to go. And it evolved into a community wide project and it does include girls. And the whole community is involved because they'll go and they'll come back in and they'll have a big potluck and it's the rite of passage for he who caught his first moose. Everybody gets to participate. He gets to provide for his community, you know for the first time and that is something that he can do."
One of the most important community protective factors related to how the community established limits. While some individuals discussed the local option laws that allow some communities to vote to regulate or ban alcohol, a larger number discussed how significant individuals in the community took a personal stand to protect children from alcohol-related harm. What was fascinating was in which community characteristics were frequently embedded within the context of the family, and occurred within the interface between the family and the community. A vivid instance of this is described by a middle-aged woman recalling her childhood: "When I first was aware of somebody drinking, I was already nine years old. And I never saw anybody drunk before. ...And my father stood up, and he said no; he just let him turn around and he walked out with him. And then I heard him out there, 'Don't you ever come in my house like that.' We asked my mom, what is wrong with that man? And she would never tell us; she would say in due time you will know. In your own time, you will know." Here we see the individual actions within a family as an important component within a community-wide expectation regarding the setting of limits upon alcoholic behavior, reciprocally mirroring and contributing to a community standard.
Participants reported how they were exposed in childhood to adults that abused alcohol. Protective communities had safe places children could go to that prevented them from becoming victims of alcohol-related violence. Most often the safe place was with a close relative, but it could include a friend, teacher, or member of the clergy. As one participant described: "I like the way my grandma took care of me when I was small. Her house was always clean, everything smelled good. It was always a safe place to go to. And I have realized after I got my own place and became an adult, that my home, to other people, was always a safe place to go to."
Family Characteristics (FC), Ilakelriit Cayarat. In the words of one participant: "In the Native community families are tied together in a certain way that they're close. And it doesn't matter who you are, we're tied together like a woven hat." This interdependence of family and community highlights both the kinship and collectivist [46] nature of Alaska Native communities. The most fundamental of the protective family factors described by participants was the nature of the caregiver relationship: An affection and praise that included important culture specific elements providing children a sense of being valued appears in the following narrative: "And I remember my grandparents bringing us to other elders' homes, just to introduce us to them, because our grandparents were proud of us, and they wanted to share us with the elders in the community. So they brought us to the elders and let us visit with them. I remember when we started hunting and fishing, we got a lot of praise, and even more praise than today, from our relatives and elders. You know, if an elder found out that you caught your first rabbit or your first moose, everybody praised you for that. And it helped to build up the esteem."
Another quality of the caregiver relationship was a sense of being treated as special, as very important to the family. One participant noted: "So I grew up to be pretty special, only because I was the only girl of my family. My older brothers took very good care of me. They treated me well." Others who avoided alcohol problems in their lives recalled being told they were to become healers or shamans, or would have similar important roles in the community, and were encouraged to live in a way that prepared them for this role.
Families also provided safety/protection from harm. In addition to simply providing a place of safety, caregivers also established limits and enforced them for the good of children. One narrative related the importance of modeling values through the power of both words and action: "I would put the kids to bed and make, you know, put them to bed and make sure those people that were there, some of them I would kick them out and other ones, a lot of times I would let them go, say 'Go drink somewhere else. This is not the place to drink."'
Participants who never developed a drinking problem also described models of sobriety in the family who taught them explicitly about how to deal with alcohol: "So my Dad was a non-drinker. And he said when I was eight year old he say, he sat me down, and he told me he said, my son being the oldest in the family, he said, there is something that I want you to do for me. And he said, I want you to carry a torch for me, a torch that you would say that all of my life I wouldn't drink and I wouldn't smoke. He say I took his word for it and he say, I want you to do the same for me. Carry that torch for me. And I guess that's the biggest thing you know that right there and then I thought okay."
Protective families also actively engaged in transmission of the expectations they had for their children: "We were a poor family as any village people. But things were happy when we were growing up, and our Mom very seldom went out to work so she was home with us a lot. And my dad would talk to the boys about what's expected of them when they grew up, and how to take their place in the community or in their tribal relatives, how everything worked together. So that's how we all grew up." Many of these protective factors mirror each as interdependent community/family systems that protected children from exposure to alcohol abuse and alcohol related violence.
Individual Characteristics (IC)
Yuum Ayuqucia. Protected individuals displayed a set of characteristics that included a preference towards a cognitive style of thinking through reflectively about what one will or will not do. This reflective style allowed self-control around alcohol use and decisions to immerse oneself in activities that avoid or are incompatible with alcohol use: "But, like I said, it hasn't bothered me – drinking hasn't bothered me. I don't know if it will. In my head – in my mind, it never will. I'm – I'm a positive person and that's the way I like to live my life, is live positively and things go smoother that way. But, living a Yup'ik life, just in general, doing all the traditional activities that we do on a daily – day-to-day basis here in the village, this keeps me away, makes me not think about it."
Participants describe this reflective process as part of a collectivist, other-centered orientation specific to Alaska Native cultures. One participant talked about wanting to be a role model: "And I had made a choice when I was ten or eleven to not drink alcohol, to remain sober and to show my brother, my sister that there is something different to do besides drinking and alcohol." The sense of responsibility within a kinship network led to a desire to give to others – contribute: "I think he [father] meant that I was going to help people sort out their lives, help them to understand, that you know, be a good listener for them, and counsel them when they need it, or at least let them know they have tools to help themselves.
In order to give and contribute one must have a fundamental sense of one's own capacity, a belief in self, as a competent individual. One participant describes: "Like I mentioned, my parents, from as far back as even both of us can remember, I have always been an adult to them. I have always talked to them. Even like when I was ten years old, I talked to them like I was an adult, meaning I listened to them, I didn't talk about silly things. But we were able to converse, and so they treated me like an adult...that gave me the choice to do what I wanted and also to make the decision not to drink."
Some participants described a sense of mastery as knowing and caring for oneself and one's capacity to endure. In the words of one Alaska Native person: "My mother taught me too much to love myself. I've always felt I was a very strong person. I have been able to put up with a lot of shit." However, important differences in mastery emerged between NPs and LAs. NPs often described a sense of efficacy and self-actualization focused more on self-confidence and independence than responsibility to the family and community. One traditional Yup'ik elder NP described how he took the initiative in his socio-cultural education. "Yes I learned on my own. Whenever I am going to construct something I would look at it from all sides and memorize it. When I was about to construct a large boat fashioned after one that is manufactured, I looked at a finished one from all sides and then I constructed it without anyone guiding me. I was not given a lot of advice by anyone." In contrast, for LAs, efficacy was described in more socially embedded terms better labeled as communal mastery [18,47], or a sense that one masters situations best by joining with others.
In this way, several of the life stories describe a socialization process within interconnected collectivist community and family structures that foster becoming aware of how one's actions affect others, described as an awareness of consequences: ellangneq. Ellangneq is a Yup'ik concept, but similar elements appeared throughout many of the narratives across all the Alaska Native cultural groups. The child learns that reciprocity exists between individual actions, and the good of the community and family: control over one's own actions can affect others positively. Ellangneq is this culturally valued awareness of the consequences of one's individual actions upon the whole. This special type of awareness is incompatible with intoxication; intoxication only reduces awareness and the ability to control oneself and one's own life, thereby engendering potentially negative reciprocal effects on family, community, and others. In the words of a Yup'ik LA, "But at that time I had already decided for myself that I wasn't going to drink. Part of that had to do with getting out into the woods. And that was part of my reason for refusal. Why would you want to go out and drink and kind of get out of your mind, loose mental control? You know I had so much fun doing the things I wanted to so I wanted to be aware of what I was doing."
Elaborating the Protective Process
Community and family protective characteristics lowered exposure to alcohol and alcohol-related trauma, or moderated the negative impact of traumatic experiences. They also fostered individual protective characteristics such as sense of mastery, awareness (Ellangneq), and a sense of responsibility to family and community.
Nearly half who never drank abusively describe directly experiencing or frequently observing significant trauma during childhood. Trauma and/or trauma exposure (TR), Akngirenq, included the death of loved ones or other unexpected and intense loss, witnessing domestic violence, or the experience of child abuse including sexual abuse. The pathway of participants who did not use alcohol as a coping response to trauma was facilitated by the protective community, family, and individual characteristics identified in the model, along with the youth's social environment, (SE) Yuuyaraq, including the presence of healthy, non-alcohol abusing role models and social support for lifestyles free of alcohol abuse from extended family, peers, and other adults outside of the immediate, nuclear family. Social environment is a subset of community characteristics specific to the time in youth when experimental substance use (ESU), Meqerraaryaurtellemni, begins, that functions as a support during periods of ESU or in times of crisis such as the experience of trauma. A male who had experienced significant family trauma described this:
"I have a Russian Orthodox priest who's going to wed us in a civil ceremony. And I asked him when I was 15, 'If I ever get married, will you marry me?' He is also somebody who was a mentor for me as a kid.... I think that he was there for me at the right time. Especially, I think, and I probably don't remember a lot of things that happened at that age, but I knew that there was somebody who I could look to."
A period of ESU was quite common in the narratives; a majority of NPs and several LAs engaged in ESU. This typically occurred in early or mid-adolescence, after which the decision to drink responsibly or not drink was made. Consistent with a worldview imbued with concepts allied with that of Ellangneq, NPs and in particular many LAs who tried alcohol decided in youth after ESU, or after the experience of significant alcohol-related trauma, that the consequences of alcohol did not fit with how they wanted to affect others. Though even in the presence of multiple family, community, and individual protective factors, children would often still engage in a period of ESU, the outcome among NPs and LAs who experienced these protective factors was a conscious decision, a turning point (TP) Ayuqucinellemn, that virtually all identified as a pivotal event in their narratives, to either not continue to use alcohol or not use it in a manner that led to abuse. This turning point typically occurred as part of a reflective process of thinking over (TO), Umyuangcallemni, one's personal experience with alcohol. As one NP described:
"Later on after I graduated from high school I still knew I didn't want to be a drunk or you know, get drunk or look all ugly and do stupid stuff. (...) I didn't want to not know what I was going through. I wanted to be totally aware of my every live moment and I wanted to be in control of everything that I was doing. And so I think that's when my responsible drinking started." Through this process of thinking over and turning point, LAs and NPs composed a personal life narrative in which they were in charge of their lives.
Figure 1 shows community, family, and individual characteristics reciprocally influencing each other. Strong, cohesive communities support the development of healthy families; together these institutions provide the networks of social support that develop a set of individual characteristics that enhance resilience. Strong and positive communities and familial relationships also decrease the likelihood of alcohol-related trauma exposure. They additionally are part of the development of a social environment from which individuals can seek support or resources if trauma is experienced. This occurs in part through development of individual characteristics that enhance the likelihood of a response to trauma or ESU experience that involves thinking over (TO) the experience and the broad and reciprocal consequences of one's actions. This reflective process (TO) facilitates a turning point (TP) in LA and NP outcomes, resulting in a decision to not abuse alcohol, in affirmation of a life goal of contribution to family and community.
Discussion
We present here a multifactorial and multilevel model for the understanding of the sobriety process of Alaska Natives that lead a life free of alcohol abuse. The model was generated through a participatory action research process, elements of which can be adapted for work with American Indian and other ethnic minority communities. Cultural factors emerged central to an understanding of the sobriety process of Alaska Natives demonstrating the importance of culture as proximal variable [48] in research that seeks to understand sobriety and alcohol abuse with American Indians and Alaska Natives.
The resulting heuristic model for Alaska Native protective pathways is an indigenous explanatory model [49] describing how culturally mediated protective factors interact in complex ways. However, it is also consistent with Triadic Theory of Influence [23] assertions that substance abuse in adolescents is best explained by the interaction of community, family, and individual level variables. The model suggests that community and family build a wider social environment that both supports the youth and interacts with individual factors in the decision to not abuse alcohol following a period of ESU. The mechanism that appears to facilitate the turning point of a sobriety decision is Ellangneq, a sense of awareness, mindfulness, and the reciprocity of action developed through the teaching of parents, extended family, and community. Ellangneq can be understood as a manifestation of an interdependent [50], constitutive [51], or expanded sense of self [52] found among many Alaska Native and other non-western people that links the individual to a collective, tribal context [46]. Individuals who are socialized within such a context are allocentric [46], with a heightened sensitivity to the effects of their behavior on the whole, and drawing strength from the whole.
Ellangneq becomes operative through the actions of family and community. Many researchers have found that a significant relationship with at least one parent is a critical variable in protective outcomes [53], though substitute caregivers can also be of great importance early in the child's life [9,11,54]. This emerged as an important factor in this Alaska Native sample as well; however, the mutual influences of a supportive extended family and community also contributed importantly to resilience. Also important were ways in which security, safety, pride, and affection were experienced through the parent-child interaction, and how the family related to other caregivers to enhance the community network of caregiving.
One important difference distinguished the NP and LA sobriety groups in this sample. For NPs, the resilience process drew from personal stores of self-confidence, self-efficacy, and self-mastery that derived from ability to successfully maneuver within stressful or potentially traumatizing environments [55]. In contrast, for many LAs, efficacy was described in more socially embedded terms of communal mastery [18,47]. One style of mastery is more associated with individualistic orientations, the other with more collectivistic. Future research is needed regarding the generalizability of the group difference in this finding. Nonetheless, the finding highlights important differences between Alaska Native individuals regarding the processes underlying the decision to not abuse alcohol. This finding is of importance both for future research, and in planning interventions for Alaska Native people. The fact that this important difference reflects culturally mediated processes also suggests the decision is itself mediated by variables such as acculturation and cultural identity.
Indeed, cultural factors surfaced repeatedly as important components in an understanding of how social influences within a community and family context functioned as salient protective factors in sobriety for Alaska Natives. As Triandis [46] remarked, "Culture is to society what memory is to the person" (p. 511). In our Alaska Native participants' narratives, cultural processes emerged as much more than immersion in activities, social grouping, or self-perception, imbuing structure and meaning to all aspects of their thoughts and behavior. Even basic components of cultural processes, such as a person's identification with their Alaska Native culture, emerged as complex, situational, and multidimensional, echoing previous critiques of cultural identity research with American Indians and Alaska Natives [56].
Conclusions
This study presents a heuristic model of Alaska Native pathways to sobriety. What is significant about the model is that it emerged from in-depth study of the experience of Alaska Natives, rather than that of other groups. The model moves current research in the direction of developing a culturally and contextually based explanatory model [49] or emic model [57] of Alaska Native sobriety, because it comes out of the life histories of Alaska Natives and a collaborative analysis process that included Native and non-Native researchers, the community of concern, and the participants themselves, as co-researchers [5]. Tests of hypotheses and path analytic models generated by the heuristic model, and design and investigation of the efficacy of prevention programs based upon the model are important future steps for research. In addition, the findings of this study offer perspectives on the resilience and the sobriety process of indigenous people and more precisely contextualize elements of the Triadic Theory of Influence within one indigenous group.
This initial analysis of the PA data set provides as many questions as answers for our understanding of the sobriety process of Alaska Natives. We hope that as the answers become more clearly defined, those pathways to recovery and resilience walked by the research participants become more known to those in need. The seeds of resilience form a sense of the family and community, a desire to make a difference as one Alutiq elder acknowledges:
"We're not here tomorrow. Got to leave a few tracks around, right? I want to. So my grandkids could say, well, I remember when grandma used to – you know. You know you feel like, hey, you want to be able to leave some kind of memory."
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
GVM is PI for the research. He led the analysis and interpretation, and completed many of the interviews. SMR completed much of the text analysis, wrote the first draft, and edited many drafts. LT interviewed most of the Tlingit participants, participated in all analysis and interpretation, and edited the paper. JA was a collaborating investigator involved in all aspects of data gathering, analysis, and interpretation. He edited each draft and significantly contributed to the final draft. KH was also a collaborating investigator and edited a number of the drafts. The People Awakening Team completed many of the interviews and assisted with the analysis and interpretation of the data. CH was research coordinator for PA and assisted in model development, analysis, interpretation of data, and editing.
Acknowledgements
Research reported in this paper was funded by National Institute of Alcohol Abuse and Alcoholism/NIH (1RO1 AA 11446-03) and the National Center for Minority Health Disparities (NCMHD). We also want to thank all of the participants, field interviewers, research assistants, and our Coordinating Council for their assistance in completing this research. The PA Coordinating Council members are Robert Charlie, Samuel Demientieff, Mary Miller, Don Mironov, Valerie Naquin, Elizabeth "Cookie" Rose, David Sam, Judy Simeonoff, Doreen Simmonds, Elvina Turner, Annie Wassilie. The PA Yup'ik Advisory Council and Translation Group are Eliza Orr, Anna Jacobson, Marty Hintz, Lorita Clough, Walkie Charles, and George Charles. The PA project staff are: Mary Stachelrodt, Chase Hensel, Alice Atuk, Dante Foster, Sharon Lindley. Additionally, our thanks go to the following individuals: Dolores Scoville, Carol Yakish, Caroline Brown, David Charles, Sue Charles, Kelly McGuire and Jamie Mohatt. Correspondence concerning this article should be addressed to Gerald V. Mohatt at P.O. Box 756480, University of Alaska Fairbanks, Department of Psychology, Fairbanks, AK 99775; email: [email protected].
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| 15548331 | PMC535345 | CC BY | 2021-01-04 16:36:44 | no | Harm Reduct J. 2004 Nov 17; 1:10 | utf-8 | Harm Reduct J | 2,004 | 10.1186/1477-7517-1-10 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-391553324310.1186/1475-2875-3-39OpinionMosquitoes and transmission of malaria parasites – not just vectors Paul Richard EL [email protected] Mawlouth [email protected] Paul T [email protected] Unité de Biochimie et Biologie Moléculaire des Insectes, Institut Pasteur, 28 rue du Dr. Roux, 75724 Paris cedex 15, France2 Laboratoire d'Entomologie Médicale, Institut Pasteur de Dakar, 36, Avenue Pasteur BP 220; Dakar, Sénégal2004 8 11 2004 3 39 39 10 8 2004 8 11 2004 Copyright © 2004 Paul et al; licensee BioMed Central Ltd.2004Paul 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.
The regional malaria epidemics of the early 1900s provided the basis for much of our current understanding of malaria epidemiology. Colonel Gill, an eminent malariologist of that time, suggested that the explosive nature of the regional epidemics was due to a sudden increased infectiousness of the adult population. His pertinent observations underlying this suggestion have, however, gone unheeded. Here, the literature on Plasmodium seasonal behaviour is reviewed and three historical data sets, concerning seasonal transmission of Plasmodium falciparum, are examined. It is proposed that the dramatic seasonal increase in the density of uninfected mosquito bites results in an increased infectiousness of the human reservoir of infection and, therefore, plays a key role in "kick-starting" malaria parasite transmission.
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Introduction
A strategic aim of the global commitment to Roll Back Malaria is the development of reliable and practicable forecasting methods to enable the containment of epidemics. Much of the basis of our knowledge about the causes and dynamics of malaria epidemics comes from the qualitative analyses of the large scale, regional epidemics occurring in the first third of the last century [1-3]. Such regional epidemics have been consistently associated with unusual seasonal climatic conditions, namely those favourable for malaria transmission, but which follow years of unfavourable conditions. Favourable conditions are those that promote both anopheline density and longevity, notably high atmospheric humidity [1]. Thus, epidemics occur when there are "unusually" favourable transmission conditions in an otherwise poorly immune population and that an epidemic reflects the loss of equilibrium between the degree of immunity and the force of infection [1,2]. The implementation of remote sensing technologies for use in early warning systems for predicting epidemics have been developed on the basis of these original observations [4] and the development of more precise measurement of key parameters, such as temperature and rainfall anomaly, is in progress. However, as pointed out, the application of such new technologies must be carried out within the context of the wealth of knowledge concerning the basic epidemiological processes of malaria [5]. In this spirit, recent advances in mathematical epidemiology have paid special interest to the formal analysis of the cyclical nature of epidemics and how the epidemiological system responds to demographic and environmental variability [4-8]. One conclusion from such analyses is that a deeper comprehension of longitudinal epidemiological patterns requires a more detailed appreciation of the intra-host parasite population dynamics, including the effects of co-infection with both multiple strains and species of Plasmodium [9].
Despite the extension by Macdonald of the basic models of malaria, pioneered by Ross and Lotka, to introduce some level of biological complexity, many points raised at the time of the great regional epidemics remain unexplored or attributed to the error inherent in estimating biological parameters [10]. In his reappraisal of epidemics in general [1] and of the 1934–5 Ceylon epidemic in particular [11], Gill not only emphasised the value of climatic features in predicting epidemics, but also made several observations that ran contrary to the opinion of malariologists of the day. Firstly, he noted that during the first five weeks of the epidemic, there was a complete absence of child morbidity. He subsequently suggested that the explosive nature of the Ceylon epidemic must have been due to a sudden increase in the infectivity of the adult population, and that, therefore, the beginning of the epidemic was characterised by relapses in the adult population that then generated the necessary human transmission population [11,12]. This was countered at the time by malariologists who believed, based on fever charts and the absence of gametocytes, that the initial wave of morbidity was due to novel infections [13]. However, to date there has been no satisfactory explanation to account for the age-structured nature of the morbidity rise, other than relapses (of both Plasmodium vivax and Plasmodium falciparum) in the adult population. If Gill was indeed correct, then what caused the sudden increase in infectiousness of the adult population?
Seasonal epidemic rises superimposed on endemic prevalence are probably characteristic of most regions with endemic malaria [2]; there is always a degree of seasonality in mosquito bionomics. If the unusually favourable conditions that generate epidemics are an extreme example of more common seasonal variations in transmission intensity, Gill's pertinent observations may throw light on a fundamental question in malaria epidemiology: How exactly does transmission restart upon return of the mosquitoes. Current wisdom suggests that the sheer number of mosquitoes under favourable conditions results in the rapid expansion of the parasite population from a few initial source infections – as formalized in the classic Ross-Macdonald model of malaria, Plasmodium (falciparum) has a very high R0 (reproductive rate) that is strongly dependent on the mosquito biting rate. But where do the initial source infections come from? Several authors have noted the absence of gametocytes during inter-epidemic periods, but commented that a geometric rise in gametocyte carriers could generate the observed rapid increase in gametocyte rates during the course of an epidemic [14-16]. In an endemic field situation, chronic infections were found to produce gametocytes throughout the dry season in Sudan, although the transmissibility of such gametocytes was not assessed [17,18]. However, induced infection studies in naive individuals have shown that a P. falciparum infection can produce infective gametocytes throughout its infection duration [19]. Even this, however, cannot explain the explosive nature of the Ceylon epidemic [11], which strongly suggests that there may be underlying seasonal changes in intra-host parasite population dynamics that could have an effect on parasite transmission to mosquitoes. Inherent seasonality in some Plasmodium spp. (notably P. vivax), is well known and yet rarely considered in models of malaria epidemiology. In this paper, the nature of seasonality in malaria parasites is addressed and by examination of three historical data sets from Africa and Asia, it is proposed that uninfected mosquito bites play a significant role in increasing the infectiousness of the human reservoir of infection to mosquitoes. Thus, the intense expansion of the mosquito population at the start of the seasonal transmission season (whether resulting in an epidemic or not) has a significant biological effect above and beyond the role of vector.
Seasonal chronology of infections: relapses, recrudescences and gametocyte production
(a) Temperate regions
The vernal (late winter – early spring) rise in the incidence of P. vivax in temperate regions is legendary, and was discussed in depth by Swellengrebel and De Buck (1938) [20]. Such vernal fevers occur in the apparent absence of Anopheles. Korteweg proposed that such fevers were the result of infections incubating from the previous year. Induced infection studies, implementing P. vivax and P. falciparum to treat neurosyphylitic patients, by Swellengrebel and others confirmed Korteweg's hypothesis that vernal fevers were due (at least in part) to latent vivax infections from the previous autumn [20-22]. A variety of strains were used in the course of such treatment, leading to the conclusion that there are inherent differences in the seasonal behaviour of strains, depending on their geographical (latitudinal) origin. James (1931) [21], using the Madagascar strain, found that only 1.6% of the infections had extended incubation periods (200–317 days); similarly Boyd & Kitchen (1949) [22], using the McCoy (southern USA) strain, found only 1.1% with incubation periods in excess of 75 days. In contrast, Swellengrebel and de Buck (1938) [20], using the Dutch strain, found 38% with protracted incubation periods. Thus, temperate strains were considered to exhibit inherent seasonal behaviour, emerging when the insect vector commences its seasonal activity.
Transmission to mosquitoes is achieved through the production of gametocytes. Ziemann (1914) [23] noted that a feature of the P. vivax spring relapses was a tendency for gametocyte production just before mosquitoes appeared. This is a feature of other haemosporidian parasites, most notably Leucocytozoan and Haemoproteus spp. that are widespread parasites of birds and lizards, the latter of which have recently been shown to be paraphyletic with Plasmodium species [24]. These parasites differ from Plasmodium spp. in maintaining the infection exclusively by exo-erythrocytic forms and only gametocytes are present in the circulating blood system; seasonal activity and the timing of gametocyte production are thus simultaneous in these genera. There is a rapid appearance of gametocytes in March and April, which is generally too early for novel infections by simulid black flies or ceratopogonid midges [25]; rather infection relapse appears to be related to altering hormone levels prior to the bird's reproductive season. Haemoproteus infections normally relapse during host breeding, peaking during egg laying and decreasing throughout the nestling rearing phase [26]. This reduction is attributable to an increase in immune function accompanied by a down-regulation of hormone levels [27]. An influence of sex hormones on gametocyte production is also suspected for Plasmodium infections in lizard malaria species. Gametocyte production is intensified during mid-late summer in Plasmodium mexicanum infections in lizards when similar seasonal fluctuations occur in testosterone levels. Although testosterone does not seem to affect overall parasite density and the course of infection, elevated testosterone significantly reduced variation in the timing of the onset of gametocyte production [28].
(b) Tropical regions and P. falciparum
In contrast to temperate and subtropical zones, seasonal patterns of parasite relapse in parasites such as P. vivax and Hepatocystis spp. (tropical relatives of Haemoproteus infecting baboons) in the tropics is less evident and thought not to occur [29]. Unlike temperate zones, seasons in the tropics are based largely on rainfall and are, therefore, intimately linked to mosquito bionomics. Given this intimate link between season, mosquitoes and transmission, how can seasonality in P. falciparum be detected? As stated previously, current wisdom holds that there is a continual production of low numbers of gametocytes that are the source of infection once mosquito numbers expand. However, contrary to accepted belief, mosquitoes can be found at all times of the year, even under very hostile climatic conditions [30] and seasonal mosquito activity is, therefore, primarily one of greatly increased numbers. Thus, as an initial test of transmission seasonality, the occurrence of infected "out of season" anophelines is examined. The traditional explanation for the absence of infections in anophelines is that climatic conditions reduce the lifespan of the adult mosquitoes such that the probability of their surviving long enough to allow completion of sporogonic development is negligible – that is, the vectorial capacity [31] is considerably reduced by the decreasing vector longevity. However, by examining the mosquito for oocyst (midgut) as well as sporozoite (salivary gland) infections, the extent of human infectiousness can be established irrespective of whether there is actual transmission. Bentley (1911) [32] summarizes several studies of this nature where anopheline stomachs and salivary glands were examined on a seasonal basis (Table 1). Although admittedly very limited, these data argue against a persistent level of human infectiousness to mosquitoes.
Table 1 Comparison of the oocyst and sporozoite rates in mosquitoes sampled during the non-transmission and transmission seasons. Data from Bentley (1911) [32].
Author Place Season N° anophelines dissected for oocyst/sporozoites Oocyst positive (%) Sporozoite positive (%)
Bentley 1911 Bombay Nov-June 178 / 123 0 0
July-Oct 659 / 703 12.7 4.1
Gosio 1905 Tuscany Apr-June 318 0 0
July-Oct 512 27 4
Daniels British Central Africa Dry season 1500 0 0
Such anopheline data are one side of the coin and gametocytes are the other. If tropical seasons are defined most precisely by mosquito bionomics, it is conceivable that Plasmodium spp. would have evolved to respond to the mosquitoes themselves. Thus, in a manner akin to temperate parasite species that utilize vertebrate host seasonal cues to produce gametocytes at the optimal time, tropical species may respond to tropical cues – the mosquito bites. Is there any evidence that P. falciparum, for example, produces gametocytes in response to mosquito bites? As with P. vivax, P. falciparum gametocyte production has been found to peak at a time when the anopheles abundance was at a maximum [33-35]. However, this peak gametocyte rate occurred notably after the peak in the number of clinical cases and, thus, after the peak in transmission. Such an increase in gametocyte rate would be expected as a result of novel infections and, thus, characteristic of an endemic region during the transmission season. However, what evidence is there that, at the beginning of the transmission season, there is an increase in infectiousness of humans to mosquitoes that is not due to novel infections, but due to relapses in existing chronic infections. Identifying the human reservoir of infection and correlating gametocyte density with transmission success to mosquitoes is far from clear [36]. The human reservoir of infection (during the non-transmission season) will depend on the rate of recovery from infection, which in turn depends on the extent of previous exposure and the immune reaction to infection. Both these factors will alter with age for a given intensity of transmission. Therefore, in the absence of novel infections, the human reservoir of infection will have an age-specific distribution. If mosquito bites are having a gametocyte-promoting effect on existing chronic infections, then age-specific patterns of gametocyte production might be expected. However, although increasing gametocyte density tends to result in greater infectiousness to mosquitoes [19,37,38], it has been repeatedly demonstrated that high gametocyte densities do not guarantee high mosquito infection rates [19,38-40]. Cryptic infectors with no or very few apparent gametocytes, are capable of infecting mosquitoes and may contribute to a very significant proportion of the human transmission reservoir [39-41]. Moreover, gametocyte density varies greatly according to the region of study, and also with age (i.e. history of exposure) of the individual and tends to reflect the overall asexual parasite density [42,43]; consequently, infections in the younger and therefore less immune individuals tend to produce higher densities of gametocytes [44-46] than adults. Therefore gametocyte density itself may not be a sufficiently sensitive indicator. Rather, age-specific seasonal changes in intra-host parasite prevalence rates are preferred. This measure encompasses changes in both sexual and asexual prevalence rates and the tendency for an infection in a particular age group to produce gametocytes; it is, thus, a measure of parasite behaviour within a particular age-group during a certain season. To examine the potential role of mosquito bites on the seasonal nature of P. falciparum transmission, three historical data sets, addressing age-specific longitudinal patterns of P. falciparum asexual and sexual prevalence rates in relation to mosquito abundance patterns, are analyzed. One of the major shortcomings of these studies is that they did not have recourse to PCR (quantitative, reverse transcriptase) technology and thus will have underestimated parasite prevalence rates and can not distinguish a relapsed chronic infection from a novel infection. However, despite such limitations, the data sets reveal much on the seasonality of P. falciparum transmission.
Study 1 : Barber & Olinger (Ref: 47.1931 – Urban, infant/mother, Lagos, Nigeria)
For a period of 18 months, records of vaccine visits to the Lagos local health office of 3–4 month old babies with their mothers provided P. falciparum seasonal prevalence data. During the same period, intensive surveys of anopheline activity and mosquito infection prevalence rate (both salivary gland sporozoite and midgut zygote (oocyst) infection rates) were carried out throughout Lagos by indoor resting catches in from 200 to more than 400 rooms. Infants (three to four months) are no longer expected to be protected by maternal antibodies and prevalence rates in this very young "naïve" age group provide a good indication of the force of infection and hence the current transmission intensity. Mothers will include a distribution of older age groups that are expected to have developed some immunity to infection and disease. It should be noted that although Plasmodium infection characteristics in pregnant and recently post-partum women differ from non-gravid women of similar ages [48,49], the mothers here are three to four months post-partum and so likely to be representative of the adult population. One confounding factor of the data set is that urban populations are mobile and individuals may thus have acquired infections elsewhere. Although it is likely that mothers and infants would have travelled together and thus be exposed to identical mosquito biting rates, the absence of paired mother-infant data weaken any comparison of parasite rates.
Using the mosquito data, the estimated monthly entomological inoculation rate (EIR) (mosquito density × mosquito biting rate × sporozoite rate) and the estimated human reservoir of infection (infectious gametocyte rate) are calculated, using the formula of Macdonald (1952) [50].
where s (sporozoite rate), (infective mosquito lifespan), a (human biting rate; here estimated as 0.33) and x is the proportion of human infections that are infectious to mosquitoes (hereon referred to as estimated gametocyte infectious rate. Note this is not necessarily equivalent to the gametocyte rate).
p can be calculated from the measured total mosquito infection rates: sporozoite rate, s, and zygote rate, z.
where n and m are the number of days required for the development of identifiable presence of sporozoites and oocysts respectively (i.e. the extrinsic incubation periods for sporozoites and oocysts). Here standard values of n = 12 and m = 3 [45,50] were taken using the Moshkovsky scale where and a mean annual temperature T fluctuating around 25°C.
In this way, values for x can be estimated, thereby enabling examination of how the infection rates in the two directions, mosquito man (EIR) and man mosquito (infectious gametocyte rates), relate to the age-specific (three to four month old infants versus mothers) seasonal changes in parasite prevalence rates and the mosquito activity patterns (Figs. 1a &1b).
Figure 1 Mother-infant seasonal P. falciparum prevalence rates during routine vaccination visits. Data adapted from Barber & Olinger (1931) [47], Lagos, Nigeria. (a) P. falciparum prevalence rates in three month old infants and their mothers. Anopheline mosquito inoculation parameters from selected houses throughout Lagos include the Entomological Inoculation Rate (EIR), which is the number of infected mosquito bites per person per month; the Uninfected biting rate, which is the number of uninfected mosquito bites per person per month; the sporozoite rate, which is the percentage of mosquitoes with sporozoites in their salivary glands. (b) Anopheline biting rates (as above) and the relative proportion of mosquitoes with zygote (oocyst stage infections) compared with sporozoite stage infections from which the estimated gametocyte infectious rates can be calculated (see text).
Interpretation
Start
At the beginning of the transmission season, there is a dramatic increase in anopheline biting density and, although the sporozoite rates do not increase, EIR increases from one to nine infectious bites per month (Fig. 1a). Paradoxically, however, although there is an increase in adult parasite prevalence rates, there is a decrease in infant prevalence rates (Fig. 1a). This latter, more sensitive marker of the force of infection, would suggest that any increase in transmission is offset by a greater infant recovery rate or that the increased EIR is not yet manifest in this age group. The estimated gametocyte infectious rate and the proportion of zygote vs. sporozoite infection prevalences decrease (Fig. 1b), further suggesting that the increase in EIR has little impact in generating novel or super infections that lead to gametocyte production.
Peak
By contrast, during the "Peak" of transmission, following the peak EIR (June), there is a parallel increase in prevalence of infection in both the young and older age groups (Fig. 1a), a large increase in both estimated gametocyte infectious rate and zygote/sporozoite proportions (Fig. 1b), prior to the decrease in mosquito numbers and longevity (August). Thus during this phase, the occurrence of novel/super infections is notable during the month after peak EIR (June) and is signalled not only by increases in prevalence of infection across all age groups (especially in the infant age class), but also by the increase in the estimated gametocyte infectious rate (Figs. 1a &1b). The EIR follows the sporozoite rate which increases in the absence of the emergence of new adult mosquitoes (note the drop in the mosquito activity).
End
In the final "End" phase, prevalence rates drop markedly and notably more rapidly in the younger age class (Fig. 1a), indicative of a greater rate of recovery from infection. The estimated gametocyte infectious rate drops, the zygote/sporozoite proportions return to an "equilibrium" value expected from the low and relatively stable mosquito densities. Transmission is, thus, minimal as this point, as indicated by the very low infant rates and the low estimated gametocyte infectious rate.
The data in the Peak and End phases follow the classic description of a malaria transmission season, most especially confirming the sensitivity of the infant versus the adult age groups in revealing changes in the transmission intensity and the relationship between transmission and the (estimated) patterns of gametocyte dynamics. However, the Start phase highlights a novel effect whereby there is little effective transmission (infant rates or estimated gametocyte rates) but an increase in adult prevalence rates, which notably parallels the dramatic increase in overall biting rate, predominantly by uninfected anophelines. This pattern suggests that the increase in mosquito activity per se may be having an effect on parasite prevalence rates in the adult population. In high endemicity areas, subpatent infections are common and likely to occur in older age groups with a previous history of exposure [51]. This contrasts with infants who respond to infections with strong fever and cytokine reactions, thus reducing the duration of infection. Such age-specific differences in immune response and duration of infections has been highlighted by molecular epidemiological studies of multiple clone infections by P. falciparum in highly endemic areas, which have demonstrated age dependence in both the multiplicity of infection and the relationships between this multiplicity and the risk of acute illness: in older children, a high multiplicity of infection is characteristic of low-level chronic parasitaemia [52,53]. In areas of high transmission, parasitaemias are likely to be determined mainly by the interaction of schizogony and anti-blood stage immunity, leading to periodic fluctuations in levels of parasitaemia [54]. Although the early increase in the parasite rates of the mothers in this reported study of Barber & Olinger (1931) [47] may be simply the result of stochastic variation in periodicity, the coherence of infant and mother parasite rates during the other phases does suggest that there may be a real biological cause underlying this apparently anomalous increase: that insect bites per se cause the re-emergence of existing infections in this adult population.
This hypothesis is to some extent corroborated by observed patterns of parasite prevalence versus parasite density in an adult population in Liberia [55]. Here, at the start of the transmission season, the prevalence of infection decreased but the mean parasite density increased. Rather than invoking superinfection, which would be at odds with the reduced prevalence rates, mosquito bites could actually be resulting in an increase in asexual (and thus sexual) parasite densities.
Such age-specific effects have also been noted by Muirhead-Thomson [56] during longitudinal studies in Jamaica (Fig. 2). Anopheles albimanus productivity from larval collections was at a maximum at the very time when sudden cold spells were producing a wave of relapses and a crop of gametocyte carriers. At that critical period, at the beginning of the malaria season, the increase in gametocyte rate was particularly marked in the group >7 years of age. In this age group, the gametocyte rate increases from 1.1% in the summer to 17.3% in the rainy season. Muirhead-Thomson [57] suggested that these age groups were more often bitten and that therefore age-specific differences were simply a result of differential exposure; that is the older age group were subject to a higher force of infection. Although such an explanation is possible, the differences in the seasonal prevalence rates are negligible. By contrast, the relative differences in gametocyte rates in the young and older age groups are marked: in the younger age groups the proportion of P. falciparum infections positive for gametocytes varied little with season (from 67 to 62%), whereas in the older age group the change was marked, increasing from 22 to 61%. Why should the two groups display very different changes in gametocyte production but similar increases in prevalence rates? Could mosquito biting be having an effect on infections in the older age group?
Figure 2 Age-specific (less than or greater than seven years old) seasonal P. falciparum all stage (Pf positive) prevalence rates; those with gametocytes and the proportion of P. falciparum positive individuals also with gametocytes. Data from Muirhead-Thomson, Jamaica (1952) [56].
Study 2 : Wilson (Ref. 44.1936 – Rural, active case detection, Gombero, Tanzania)
The second data set comes from a longitudinal study in a rural village in Tanzania characterised by seasonally intense transmission. In this study both gametocyte and asexual prevalence rates were measured intermittently (every two or three months) and classified into three age groups : <5 years, 6–20 years and 20+ years. In addition, anopheline activity and sporozoite rates were measured on a monthly basis. Wilson noted distinct seasonal fluctuations in gametocyte rates, whereas overall parasite prevalence rates varied little. The longitudinal age-specific parasite and gametocyte prevalence rates are shown in Fig. 3a. How the proportion of parasite positive individuals that also have gametocytes changes over time and by age group is shown in Fig. 3b. This is calculated simply as the proportion at time t+1 divided by the proportion at time t (1 is then subtracted from this figure such that a value of 0 indicates no change). The mosquito activity patterns and sporozoite rates are given in Fig. 3c.
Figure 3 Seasonal study of P. falciparum prevalence rates in a rural village in Tanzania. Data from Wilson (1936) [44]. (a) Age-specific parasite prevalence rates (all stages and those with gametocytes). Three age groups are considered: less than five, from six to 20 and greater than 20 years old. (b) Age-specific rate of change in the proportion of infected individuals that also have gametocytes (i.e. "(Proportion gametocyte positive at month 'm+1'/proportion at month 'm') - 1)". A value of 0, therefore, means no relative change in the proportion of infected individuals with gametocytes. (c) Entomological parameters measured in selected houses. Number of infected (EIR) and uninfected mosquito bites per person per month and the sporozoite rate.
Interpretation
The parasite prevalence rates change very little but do generally follow the monthly fluctuations in EIR; the absence of monthly data points, however, abnegates any rigorous comparison. The age-specific prevalence rates are characteristic of hyperendemic transmission intensity with peak rates in the very youngest age group. As discussed in the previous study [47], the occurrence of asymptomatic chronic infections would be expected to increase with age. Despite missing monthly data points for the human parasite prevalence data, comparison of the three graphs highlights several distinct points. Most notably, although overall gametocyte rates increase most significantly in the infant age group, the proportional changes are most significant in the older age groups (January) and notably in the adult age group (July). The January rise in gametocyte rates follows the October – December increase in EIR and thus could be indicative of novel/super infections. However, the prevalence rates scarcely change in any age group. Although prevalence rate is considered an insensitive marker of alterations in transmission intensity, here they do clearly decrease in March following the absence of transmission in February and markedly increase during the unusually sustained transmission season from March to October. Superimposed on this sustained seasonal transmission is a significant increase in the gametocyte production rate in the adult population (July). As shown in Figs. 3a,3b,3c, this coincides with large increases in mosquito abundance (June & July). Several features argue against this being due to novel/super infections : (i) the age-specific nature of this increase, (ii) the absence of significant EIR in June and (iii) the absence of any additional increase in adult prevalence rates over and above that following the general trend from March to October. By contrast, following this gametocyte rise in July, there is an increase in sporozoite rates and hence the EIR and subsequent increases in overall prevalence rates in October, i.e. the July gametocyte rise provides the source of infection. As observed in the previous study, there appears to be a distinct effect of mosquito biting per se on parasite prevalence rates in the adult population.
Study 3 : Rosenberg (Refs. 58–60 1990 – Rural, Active case detection, Thailand)
Rosenberg et al. (1990) [58-60] conducted a two year longitudinal study in a rural farming village with hyperendemic P. falciparum and P. vivax malaria in S.E. Thailand. During the two year study period, monthly human and mosquito data were collected. Despite the very different nature of malaria epidemiology in Thailand, when compared with the previous two African studies, Rosenberg et al. (1990) [60] noted very similar seasonal, mosquito associated fluctuations in gametocyte density to those observed in the Wilson study [44] – « Nonetheless, the pattern we observed in Thailand was strikingly similar to that which Wilson (1936) described for Tanzania 50 years earlier; the similarity is all the more remarkable as the 2 sites have virtually no features in common other than seasonally intense transmission. » Rosenberg et al. (1990) [60] discussed at length the seasonal fluctuations in gametocyte prevalence rates and noted that the perennial cycle of malaria incidence was more evident in the high trophozoite densities and the gametocyte prevalence rates than the gross prevalence rates. Such parasite fluctuations were interpreted as being the result of superinfections in 50% of the cases. The remaining 50% were considered to be the result of novel infections. However, they stated their uncertainty as to why such a considerable incidence of novel infections « ......did not inflate prevalence soon after transmission started » [60]. The pertinent data are displayed in Figs. 6a-c with the addition of the uninfected mosquito biting density [59]. In addition, from Rosenberg et al. [60] Table 1 and text, the following supplementary details are given: (i) 14+ age group harboured 49% of the gametocytes but 84.6% high gametocyte densities (<20/500 white blood cells) occurred in the group <14 years of age; (ii) dry season gametocyte prevalence was 2.6x higher than the wet season prevalence in <14 age group vs. 8x higher in the 14+ age group ; (iii) in year two, the EIR decreased by 67% and, whereas the gametocyte prevalence decreased in <14 age group, it increased in the 14+ group.
Interpretation
The younger age groups have higher gametocyte densities reflecting their generally higher parasite loads. Younger age groups also tend to have higher rates of acquisition and recovery (i.e. turnover) and, therefore, most gametocytes would be expected to be due to novel infections. This is supported by the finding that gametocyte prevalence decreased in the <14 group in year 2 when inoculation rate and force of infection was less. Gametocyte prevalence increased in the 14+ group despite a decrease in inoculation rate in year two; note, however, that despite the drop in EIR, the mosquito biting intensity was higher. Thus, the apparent paradox of there being considerable novel infections at the beginning of the transmission season, but with no influence on parasite prevalence, may be explained by the re-emergence of pre-existing chronic infections in the 14+ age group. This group is more likely to harbour such infections and is shown here to display the most significant seasonal changes in gametocyte rates [60].
The sequence of events in this study could thus be interpreted as follows : In year one, the mosquito biting rate increases dramatically in October, with a concomitant monthly rise in oocyst rates, but not sporozoites (data not shown here), and no rise in gametocyte rates. In November, the sporozoite rate and EIR increase as expected from the previous month's oocyst rates and the decrease in newly emerged mosquitoes (evident from the parous rate [59]). Despite the absence of high P. falciparum trophozoite density infections, suggesting an absence of novel infections, there is a small rise in gametocyte rates. The following month (December), this increased EIR leads to a dramatic increase in high P. falciparum trophozoite density infections and gametocyte prevalence rates. A similar pattern is seen in the second year, and notably, although the EIR and sporozoite rates were lower, resulting in reduced gametocyte prevalence rates in children, the peak mosquito biting density (which again preceded the increase in sporozoite rate) was higher, as was the gametocyte rate in adults. Whereas the large concomitant increases in high P. falciparum trophozoite density infections and gametocyte prevalence rates can be taken as evidence for novel/super infections, the increase in gametocyte rates in the absence of sporozoites at the start of transmission, coupled with the variation in age-specific gametocyte rates between the two years, suggests once more that mosquito biting itself may induce gametocyte production and subsequently augment mosquito infection rates.
Conclusions
Here, a case is presented for there being a role of uninfected mosquito bites in increasing the human reservoir of infection at the beginning of the transmission season. The available data is limited, but consistently suggest that the traditonal view that there is a rapid expansion of the parasite population from a few source infections following the seasonal increase in anophelines is overly simplistic. It must, however, be emphasised that in no way is it suggested that once transmission is under way, the epidemiology of malaria is not satisfactorily described by classic Ross-Macdonald models or variants thereof [10]. Rather, it is simply proposed that the image of intense parasite activity and dynamics during peak transmission detracts from a more subtle process occurring at the very beginning of the season. That is, Gill [11,12] may have been correct in suggesting that a subset of the human population becomes increasingly infectious to mosquitoes at the onset of mosquito activity [1] and that the factor responsible may be the dramatic increase in mosquito bites themselves. Moreover, it should be noted that mosquito bites do not need to be from anophelines. Nuisance culicine mosquitoes are often found to be well in excess of anophelines [61-63] and notably were so in the Rosenberg study [59]. Mosquito spp. differ in their seasonal bionomics and some culicines (Aedes spp.), for example, have a capacity to increase more rapidly in numbers than anophelines; this is because of the ability of their eggs to resist dessication. Thus, the seed population of culicines is probably greater than that of anophelines, which must expand from pockets of aestivating females. Clearly this view is very preliminary, but the available information is encouraging and this hypothesis warrants closer examination in regions of endemic seasonal transmission.
If, indeed, there is a role for mosquito bites, this could have far reaching possibilities, not only for novel intervention strategies, but also for strategies in combatting malaria in regions of seasonally intense transmission, including regions at risk of epidemics. If intervention measures can be taken to reduce the potential reservoir of infection, by targeted use of antimalarial drugs with gametocytocidal effects during the window of mosquito resurgence, for example, the increase in transmission intensity will be severely delayed. Moreover, reducing human-mosquito contact by the use of insecticide treated bednets (ITBNs) or repellents may have considerable impact at this crucial time. A mass action community level benefit of ITBNs has already been noted, where individuals not sleeping under nets in villages where bednets were implemented, had reduced mortality and morbidity rates [64,65]. ITBNs can clearly have several community level beneficial effects and it would be of considerable interest to see if ITBNs actually alter the transmissibility of the parasite as well as reducing the burden of disease.
Authors' contributions
RELP, MD and PTB were all involved in the conception of the article that arose out of lengthy discussions among the authors. The final draft was written by RELP with various sections added and/or removed by MD and PTB.
Figure 4 Longitudinal study of P. falciparum prevalence rates in a rural village in Thailand. Data from Rosenberg et al (1990) [58-60]. (a) Entomological parameters measured in sentinel houses. Number of infected (EIR) and uninfected mosquito bites per person per month and the sporozoite rate. (b) P. falciparum high trophozoite density and gametocyte prevalence rates. (c) Age-specific P. falciparum prevalence rates. Two age groups are considered: Greater than and less than 10 years old.
Acknowledgements
This work was supported by the Strategic Anopheles Horizontal Programme, Institut Pasteur. We thank the two anonymous reviewers for helpful suggestions and comments on the manuscript.
==== Refs
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-141553589110.1186/1743-7075-1-14Book ReviewReview on "Atkins Diabetes Revolution: The Groundbreaking Approach to Preventing and Controlling Type 2 Diabetes" by Mary C. Vernon and Jacqueline A. Eberstein Arora Surender [email protected] Samy I [email protected] State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, New York 11203, USA2 Kings County Hospital, Brooklyn, New York, USA2004 9 11 2004 1 14 14 Vernon MC, Eberstein JA: Atkins Diabetes Revolution. The Groundbreaking Approach to Preventing and controlling Type 2 Diabetes. William Morrow; 2004, 538. ISBN 0-06-054008-713 10 2004 9 11 2004 Copyright © 2004 Arora and McFarlane; licensee BioMed Central Ltd.2004Arora and McFarlane; 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.
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Before beginning the review of this book, we had no particular opinion about the role of low carbohydrate diets in diabetes. In order to write a fair and unbiased review, we have done a rather extensive search on the subject. One of the most disturbing findings of our search is the amount of hostility towards low carbohydrate diets that is on the web and in the scientific literature. We found several sites that present no scientific arguments but are, rather, full of ad hominem attacks. This was particularly disturbing in that we are in the midst of a growing epidemic of obesity and diabetes with very alarming figures and projections from all over the world. Any intervention that has the potential for helping curb this dangerous epidemic which claims thousands of lives every day should be looked at with a great deal of objectivity.
The low carbohydrate approach, in fact, is not new and was used in England more than a century ago, made popular by William Harvey [1], an ENT surgeon. He prescribed a low carbohydrate diet for William Banting, an obese carpenter who had been having a great difficulty losing weight. Banting was able to lose weight and as a service, he published in 1863 a small booklet entitled Letter on Corpulence Addressed to the Public [2], the first book to be published on obesity and one which popularized low carbohydrate diets. He has been called "Father of low carbohydrate diets" and was honored by his name being included in the dictionary as the verb "to bant" meaning "to diet". The low carbohydrate diet also been called a "Harvey-Banting diet" after the names of these pioneer. Since then, it has been in and out of fashion with different versions and names but with the same underlying concept, most recently popularized by the late Dr. Robert C. Atkins.
The Atkins Diabetes Revolution [3] plan is similar to the Atkins weight loss strategy: four levels of carbohydrate restriction are instituted. The induction phase restricts dieters to 20 g of carbohydrate. On the weight loss plan, this is recommended for about 2 weeks. In diabetes this is maintained until glycemic control is attained. In the latter stages, carbohydrates are added as long as weight loss or stability is maintained. For diabetes, carbohydrates are only reintroduced if glycemic control is acceptable. In the later phases, the Atkins Diabetes plan adds a Glycemic Ranking (AGR), derived from the glycemic index, glycemic load and net carbs. Preference is given to whole fruits and berries and juices and dried fruits are low on the list. As in weight loss, exercise is "mandatory."
The Atkins Diabetes Revolution book is an attempt by the authors to present the low carbohydrate diet as a preventive and treatment strategy for patients with type 2 diabetes and those with the metabolic syndrome, who are at high risk for developing diabetes and cardiovascular disease. In doing so, the book, which is very well written, and which clearly presents illustrative cases, explains very complex metabolic concept in a very easy to read and understandable format. The first nine chapters explain the different concepts involved in glucose and lipid metabolism and the interplay of the various cardiovascular risk factors that culminate in cardiovascular disease the number one killer of Americans today. Definitions of metabolic syndrome, pre-diabetes, body mass index, waist to hip ratio, central obesity and their relationship to diabetes, heart attacks and strokes, are eloquently presented with a great deal of accuracy yet in a simple format. Most impressive were the case presentations, especially that of reactive hypoglycemia and carbohydrate craving. This response is associated with hyperinsulinemia in the pre-diabetic phase and sometimes puzzles clinicians unless they know to look for it.
The second section of the book is devoted to an in-depth discussion of the various macro and micronutrients and their role in diabetes and obesity. Concepts such as the glycemic index and glycemic load are very well illustrated. The last section consists of meal plans and menus of low carbohydrate diet that the book is advocating.
The concept of low carbohydrate diet and glycemic control certainly has a pathophysiological merit. First, dietary carbohydrates are the principal source for the initial rise of glucose in the diabetic populations, who generally have a defect in the first phase insulin secretion that is responsible for handling the glucose load [4]. There is mounting evidence that postprandial hyperglycemia is in itself a risk factor for cardiovascular disease in the diabetic patients [5]. This evidence comes from large, well-conducted, randomized controlled trials [5,6]. Furthermore, control of postprandial hyperglycemia has been shown to provide cardiovascular benefits, and contribute to the overall decrease of hemoglobin A1c, something that has been clearly shown to reduce microvascular disease in both type 1 and type 2 diabetes [7,8]. Second, the initial blood glucose rise associated with high carbohydrate load, in the presence of absolute/relative insulin deficiency leads to significant rise in triglycerides and free fatty acids which perpetuate the cycle of insulin resistance [9,10]. So, from a metabolic stand point, low carbohydrate diet makes physiologic sense. However, in the science and practice of medicine, not everything that makes sense turns out to work the way it is supposed to. In looking at the low carbohydrate diet, we must examine the evidence from the studies that were conducted using such diets keeping in mind that weight loss by itself, is beneficial in terms of improving insulin sensitivity and correcting the abnormalities associated with the metabolic syndrome and insulin resistance [9,10]. Also, weight loss has much greater effect on the prevention of type 2 diabetes in pre-diabetic patients than pharmacological interventions [9]. This fact was well illustrated in the Diabetes Prevention Program, a large multicenter trial sponsored by the National Institute of Health, where pre-diabetic patients on diet and exercise program had a 58% reduction in the development of diabetes, compared to only 34% reduction with the use of metformin [11]. This landmark study had a population where women and minorities were very well represented [11]. The fact that weight loss was associated with reduction of type 2 diabetes in high risk populations was illustrated in several other studies including examples from Finland and from China, making it evident that weight loss works for a variety of ethnic populations [12-15].
In two recent randomized controlled trials published in the New England Journal of Medicine [16,17], the effects of low carbohydrate and low fat diets were compared in obese and diabetic patients. Both of these studies showed a substantial decrease of triglycerides in patients on low carbohydrate diet with simultaneous increase in high-density lipoprotein (HDL) over 6 month to 1 year period. The studies did not show a change in the low-density lipoprotein (LDL) values in the low carbohydrate group compared to their baseline, while those on traditional low fat diet had a reduction in LDL levels. Patients on low carbohydrate diet, however, had substantially significant weight loss, almost double that achieved with the traditional diet, in the first 3–6 months. At one year, there was no significant difference in weight loss between the two groups [16-18]. Although participants on the low carbohydrate diet initially tended to have higher rate of side effects such as nausea, muscle cramps and constipation, compliance with diet was similar in both groups. In fact, more participants adhered to the low carbohydrate diet. Although weight loss was similar after one year between groups, the effects on atherogenic dyslipidemia and glycemic control were still more favorable with a low-carbohydrate diet after adjustment for differences in weight loss.
Despite the evidence from these randomized controlled trials, published in the prestigious New England Journal of Medicine, there is a significant amount of reluctance in the scientific community to acknowledge the beneficial effects of low carbohydrate diets. These studies, in fact, provide a striking example of this resistance. A commentary in the same issue of the New England Journal of Medicine [20] states that "In both studies, the reduction in serum triglyceride levels in subjects randomly assigned to the low-carbohydrate diet might have been anticipated as a result of their greater weight loss, although it is true that reduced carbohydrate intake is generally associated with reduced triglyceride levels" [20]. In this statement, despite the fact that low carbohydrate diet is known to reduce serum triglyceride, the authors suggest otherwise. In another statement, the authors of the commentary state that "the rise in HDL cholesterol in the subjects following the low-carbohydrate diet (a change observed only by Foster et al.) may reflect a change in HDL subfractions that occurs with increased intake of saturated fats, and this change has not been shown to be beneficial. Thus, caution is urged about over-interpretation of this observation as a beneficial result of a low-carbohydrate, high-fat diet" [20]. Again this statement illustrates the difficulty in acknowledging what a randomized controlled trial has shown. The authors suggest, without any evidence that the rise in HDL cholesterol might have been in the non-beneficial HDL subfraction. In other words, when low carbohydrate diet is shown to decrease triglycerides, a suggestion is made that it might be just secondary to weight loss and when this diet increases HDL, it is also suggested that it could be the non-beneficial HDL. Now, let us examine the evidence provided by the one year follow-up study on the same group of patients where the investigators conclude that "Although weight loss was similar between groups, the effects on atherogenic dyslipidemia and glycemic control were still more favorable with a low-carbohydrate diet after adjustment for differences in weight loss" [18]. This indicates that the statements made in the commentary [20], in an attempt to dismiss or downplay the beneficial effects of low carbohydrate diet were simply wrong. Furthermore, the statement made in the commentary regarding the HDL cholesterol, not only lacks objective evidence, but also contradicts the current findings that lowering insulin level by controlled carbohydrates shift HDL production to a much more desirable, lighter HDL2subfractions [21,22].
On the other hand, the American Diabetes Association, despite recommending the traditional low fat diet, has recently reduced the recommended carbohydrate contents in the diet, perhaps reflecting a trend towards a reduced carbohydrate diet to follow [19].
Returning to the Atkins book, despite the fact that the book is very well referenced, certain statements such as "high carbohydrate diet leads to diabetes" are not well substantiated, unless of course such a diet leads to weight gain, which it may. Furthermore, the book does not devote a sufficient amount of space discussing the side effects associated with dieting in general and low carbohydrate diet in particular. This is of concern, since it leaves the reader with the impression that the low carbohydrate diet or dieting, in general, has no negative consequences. Nonetheless, the amount of information the book provides in a simple, yet accurate format will benefit patients with diabetes and their families as well as those who are at risk for developing diabetes and the metabolic syndrome. If, after reading this book, the reader is able to identify that he or she is at risk for diabetes and the metabolic syndrome and takes action that could potentially save his or her life the book will be a valuable contribution. Atkins Diabetes Revolution has a list price of $25.95 and is available at Amazon.com and presumably other sites for half that price. Possibly, a shorter and still more affordable version of the book would be helpful for diabetic patients, their families and for the general reader, to help identify their risk for the disease.
As clinicians, we would not be comfortable recommending any diet without first hand experience. The Atkins Diabetes Revolution, however, is sufficiently convincing to make us believe that some form of low carbohydrate intervention is worth investigating and should be considered by practitioners. The highly negative un-scientific response of critics, if anything, encourages us in this direction.
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Banting W Letter on Corpulence, Addressed to the Public 1863 London, Harrison
Vernon MC Eberstein JA Atkins Diabetes Revolution The Groundbreaking Approach to Preventing and Controlling Type 2 Diabetes 2004 New York, William Morrow
Polonsky KS Given BD Hirsch LJ Tillil H Shapiro ET Beebe C Frank BH Galloway JA Van Cauter E Abnormal patterns of insulin secretion in non-insulin-dependent diabetes mellitus N Engl J Med 1988 318 1231 1239 3283554
Hanefield M Fischer S Julius U Schulze J Schwanebeck U Schmechel H Ziegelasch HJ Lindner J the DIS Group Risk factors for myocardial infarction and death in newly detected NIDDM: the Diabetes Intervention Study, 11-year follow-up Diabetologica 1996 39 1577 1583 10.1007/s001250050617
DECODE Study Group European Diabetes Epidemiology Group: Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria Lancet 1999 354 617 621 10466661 10.1016/S0140-6736(98)12131-1
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McFarlane SI Banerji M Sowers JR Insulin resistance and cardiovascular disease J Clin Endocrinol Metab 2001 86 713 718 11158035 10.1210/jc.86.2.713
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McFarlane SI Shin JJ Rundek T Bigger JT Prevention of type 2 diabetes Curr Diab Rep 2003 3 235 241 12762972
Pan XR Li GW Hu YH Wang JX Yang WY An ZX Hu ZX Lin J Xiao JZ Cao HB Liu PA Jiang XG Jiang YY Wang JP Zheng H Zhang H Bennett PH Howard BV Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study Diabetes Care 1997 20 537 544 9096977
Long SD O'Brien K MacDonald KG JrLegget-Frazler N Swanson MS Pories WJ Caro JF Weight loss in severely obese subjects prevents the progression of impaired glucose tolerance to type II diabetes. A longitudinal interventional study Diabetes Care 1994 17 372 375 8062602
Knowler WC Barrett-Connor E Fowler SE Hamman RF Lachin JM Walker EA Nathan DM Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin N Engl J Med 2002 346 393 403 11832527 10.1056/NEJMoa012512
Foster GD Wyatt HR Hill JO McGuckin BG Brill C Mohammed BS Szapary PO Rader DJ Edman JS Klein S A randomized trial of a low-carbohydrate diet for obesity N Engl J Med 2003 348 2082 2090 12761365 10.1056/NEJMoa022207
Samaha FF Iqbal N Seshadri P Chicano KL Daily DA McGrory J Williams T Williams M Gracely EJ Stern L A low-carbohydrate as compared with a low-fat diet in severe obesity N Engl J Med 2003 348 2074 2081 12761364 10.1056/NEJMoa022637
Stern L Iqbal N Seshadri P Chicano KL Daily DA McGrory J Williams M Gracely EJ Samaha FF The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial Ann Intern Med 2004 140 778 785 15148064
Franz MJ Bantle JP Beebe CA Brunzell JD Chiasson JL Garg A Holzmeister LA Hoogwerf B Mayer-Davis E Mooradian AD Purnell JQ Wheeler M Nutrition principles and recommendations in diabetes Diabetes Care 2004 27 S36 46 14693924
Bonow RO Eckel RH Diet, obesity, and cardiovascular risk N Engl J Med 2003 348 2057 2058 12761363 10.1056/NEJMp030053
Westman EC Mavropoulos J Yancy WS Volek JS A review of low-carbohydrate ketogenic diets Curr Atheroscler Rep 2003 5 476 483 14525681
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| 15535891 | PMC535347 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Nov 9; 1:14 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-14 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-291553587910.1186/1471-2210-4-29Research ArticleEffect of dietary palm olein oil on oxidative stress associated with ischemic-reperfusion injury in isolated rat heart Narang Deepak [email protected] Subeena [email protected] Mathew Kadali [email protected] Amit Kumar [email protected] Subir Kumar [email protected] Department of Pharmacology, All India Institute of Medical Sciences, New Delhi-110029, India2 Department of Pathology, All India Institute of Medical Sciences, New Delhi-110029, India2004 9 11 2004 4 29 29 17 6 2004 9 11 2004 Copyright © 2004 Narang et al; licensee BioMed Central Ltd.2004Narang 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
Palm olein oil (PO), obtained from refining of palm oil is rich in monounsaturated fatty acid and antioxidant vitamins and is widely used as oil in diet in many parts of the world including India. Palm oil has been reported to have beneficial effects in oxidative stress associated with hypertension and arterial thrombosis. Oxidative stress plays a major role in the etiopathology of myocardial ischemic-reperfusion injury (IRI) which is a common sequel of ischemic heart disease. Antioxidants have potent therapeutic effects on both ischemic heart disease and ischemic-reperfusion injury. Information on the effect of PO on ischemic-reperfusion injury is, however, lacking. In the present study, the effect of dietary palm olein oil on oxidative stress associated with IRI was investigated in an isolated rat heart model. Wistar rats (150–200 gm) of either sex were divided into three different groups (n = 16). Rats were fed with palm olein oil supplemented commercial rat diet, in two different doses [5% v / w (PO 5) and 10% v / w (PO 10) of diet] for 30 days. Control rats (C) were fed with normal diet. After 30 days, half the rats from each group were subjected to in vitro myocardial IRI (20 min of global ischemia, followed by 40 min of reperfusion). Hearts from all the groups were then processed for biochemical and histopathological studies. One way ANOVA followed by Bonferroni test was applied to test for significance and values are expressed as mean ± SE (p < 0.05).
Results
There was a significant increase in myocardial catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities with no significant change in myocardial thiobarbituric acid reactive substances (TBARS) only in group PO 5 as compared to group C. There was no light microscopic evidence of tissue injury. A significant rise in myocardial TBARS and depletion of myocardial endogenous antioxidants (SOD, CAT and GPx) along with significant myocyte injury was observed in control rats subjected to ischemia-reperfusion (C IR). Hearts from palm olein oil fed rats subjected to ischemia-reperfusion (PO 5 IR and PO 10 IR) were protected from increase in TBARS and depletion of endogenous antioxidants as compared to C IR group. No significant myocyte injury was present in the treated groups.
Conclusions
The present study demonstrated for the first time that dietary palm olein oil protected rat heart from oxidative stress associated with ischemic-reperfusion injury.
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Background
Ischemic heart disease (IHD) is a major cause of death all over the world. Reduction in the blood flow to myocardium leads to IHD and its restitution (reperfusion), spontaneously or by drug / surgery, is essential for tissue/organ survival. However, reperfusion itself exacerbates myocardial injury, commonly known as myocardial ischemic-reperfusion injury (IRI) [1]. Therefore, IRI is considered as a common sequel of IHD. Oxidative stress has been largely implicated in the etiopathogenesis of IRI. Oxidative stress occurs due to increased production of reactive oxygen species (ROS) like, superoxide radical, hydrogen peroxide, hydroxyl radical at the time of reperfusion, which overwhelms the endogenous antioxidant defense [2]. Interaction of ROS with cell membrane and various other cellular components have deleterious effects on cellular functions and viability. Oxidative stress is evidenced by increased cellular accumulation of lipid peroxides and depletion of endogenous antioxidants [3-5].
Living organisms have developed antioxidant defense mechanisms against damage due to oxidative stress. These mechanisms in the heart have been extensively studied and the most active endogenous antioxidants involved in this process are superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) [2,6]. In addition to this, alpha-tocopherols or vit E, vitamin C and beta-carotene constitute important exogenous antioxidants present in diet [7,8].
The physiological actions of diet continue to be the focus of interest because of its major role in ischemic heart disease. Dietary antioxidants e.g., vitamin E, beta-carotene, vitamin C have beneficial effects in oxidative stress associated with various cardiovascular diseases, including ischemic heart disease [9-11]. Therefore, dietary antioxidants have potential therapeutic role in the prevention and treatment of ischemic heart disease.
Palm oil, obtained from the fruit of the tropical plant Elaeis guineensis, is the second major edible oil used worldwide [12]. Palm olein oil (PO), a liquid fraction obtained from the refining of palm oil, is rich in oleic acid (42.7–43.9%), beta-carotene and vitamin E (tocopherols and tocotrienols). PO is used as dietary oil in many parts of the world including India. In some previous studies, palm oil has been reported to have antioxidant effects in hypertension [13,14] and arterial thrombosis [15] in rats. In addition to this, palm oil has been shown to increase prostacyclin (PGI2) and reduce thromboxane A2 (TXA2) levels in tissues [16]. However scientific studies on antioxidant effects of palm olein oil on ischemic heart disease and ischemic-reperfusion injury are still lacking.
Therefore, the present study was designed to evaluate the effects of dietary palm olein oil on myocardial endogenous antioxidants and on oxidative stress associated with ischemic-reperfusion injury in isolated rat heart model.
Results
There was no mortality, changes in body weight as well as food and water intake pattern of rats in any group.
Biochemical parameters
I. Changes in the basal level of myocardial lipid peroxidation and endogenous antioxidants (Table 1)
Table 1 Effect of dietary palm olein oil on myocardial TBARS, catalase, SOD, and GPx levels in different groups
Group TBARS (nmol/mg protein) CATALASE (U/mg protein SOD (U/mg protein GPx (U/mg protein)
Control 7.8 ± 0.4 34.4 ± 2.1 3.5 ± 0.08 0.13 ± 0.01
Control IR 9.4 ± 0.3* 29.1 ± 0.8* 3.01 ± 0.15* 0.11 ± 0.002**
PO 5 8.5 ± 0.8 50.2 ± 3.5** 5.6 ± 0.5** 0.18 ± 0.01**
PO 10 8.2 ± 1.2 45.6 ± 4.0* 3.8 ± 0.3 0.16 ± 0.02
PO 5 IR 6.8 ± 0.4+++ 56.9 ± 4.4+++ 3.1 ± 0.2 0.12 ± 0.01
PO 10 IR 5.9 ± 0.6+++ 36.3 ± 5.6 3.4 ± 0.3 0.15 ± 0.01++
All values are expressed as Mean ± SE (n = 6)
p values: * < 0.05; ** < 0.01; vs Control; ++ < 0.01; +++ < 0.001 vs Control IR (one way ANOVA)
I.a. Basal myocardial TBARS levels
There were no significant changes in myocardial TBARS levels in both PO5 (8.5 ± 0.8 nmol / mg protein) and PO10 (8.2 ± 1.2 nmol / mg protein) groups when compared to that of control group (7.8 ± 0.4 nmol / mg protein).
I.b. Basal myocardial catalase (CAT) activity
There was a significant increase in myocardial CAT activity in both PO5 (50.2 ± 3.5 units / mg protein; p < 0.01) and PO10 (45.6 ± 4.0 units / mg protein; p < 0.05) groups as compared to that of control group (34.4 ± 2.1 units / mg protein).
I.c. Basal myocardial superoxide dismutase (SOD) activity
There were a significant increase in myocardial SOD activity in PO 5 (5.6 ± 0.5 units / mg protein; p < 0.01) group when compared to that of the control group (3.5 ± 0.08 units / mg protein). There was no significant change in myocardial SOD activity in PO10 (3.8 ± 0.3 units / mg protein) group.
I.d. Basal myocardial glutathione peroxidase (GPx) activity
There was a significant (p < 0.01) increase in myocardial GPx activity in PO5 (0.18 ± 0.01 units / mg protein) group when compared to that of the control group (0.13 ± 0.01 units / mg protein). There was no significant change in myocardial GPx activity in PO10 (0.16 ± 0.02 units / mg protein) group.
II. Changes in myocardial lipid peroxidation and endogenous antioxidants following ischemic-reperfusion injury (Table 1)
II.a. Myocardial TBARS levels after ischemic-reperfusion injury
There was a significant (p < 0.05) increase in myocardial TBARS level in the C IR group (9.4 ± 0.3 nmol / mg protein) when compared to that of the control group (7.8 ± 0.4 nmol / mg protein). There was a significant (p < 0.01) decrease in myocardial TBARS levels in both PO5 IR (6.8 ± 0.4 nmol / mg protein) and PO10 IR (5.9 ± 0.6 nmol / mg protein) groups when compared to that of the C IR group.
II.b. Myocardial CAT activity after ischemic-reperfusion injury
There was a significant (p < 0.05) decrease in myocardial CAT activity (29.1 ± 0.8 units / mg protein) in C IR group when compared to that of the control group (34.4 ± 2.1 units / mg protein). There was a significant rise in CAT activity in PO5 IR (56.9 ± 4.4 units / mg protein; p < 0.001) group with no significant change in PO10 IR (36.3 ± 5.6 units / mg protein) group when compared to C IR group.
II.c. Myocardial SOD activity after ischemic-reperfusion injury
There was a significant decrease in myocardial SOD activity in C IR group (3.01 ± 0.15 units / mg protein; p < 0.05) as compared to control group (3.5 ± 0.08 units / mg protein). No significant changes in SOD activities were observed in both PO5 IR (3.1 ± 0.2 units / mg protein) and PO10 IR (3.4 ± 0.3 units / mg protein) groups as compared to C IR group.
II.d. Myocardial GPx activity after ischemic-reperfusion injury
There was a significant (p < 0.01) decrease in myocardial GPx activity in C IR group (0.11 ± 0.002 units / mg protein) as compared to control group (0.13 ± 0.004 units / mg protein). There was no significant change in myocardial GPx activity in PO5 IR group (0.12 ± 0.01 units / mg protein) with a significant (p < 0.01) increase in myocardial GPx activity in PO10 IR group (0.15 ± 0.01 units / mg protein) as compared to C IR group.
Histopathological study
Fig. 1 shows the H&E micrograph of control heart with normal architecture. In PO5 and PO10 groups there was no evidence of cellular injury (not shown). Focal loss of myocardial fibres and marked edema was observed in C IR group (Fig. 2). Mild to moderate edema was observed in PO10 IR group (Fig. 4). Degree of edema was reduced in PO5 IR with no evidence of focal necrosis (Fig. 3)
Figure 1 Light micrograph of heart tissue. Control rat heart (C) showing normal architecture (H & E X 10).
Figure 2 Light micrograph of heart tissue. Control rat heart subjected to 20 min ischemia and 40 min reperfusion (C IR) showing marked edema and focal destruction of myocardial fibres (H & E X 10).
Figure 3 Light micrograph of heart tissue. Rat heart supplemented with 5% v/w of dietary palm olein oil subjected to 20 min ischemia and 40 min of reperfusion (PO5 IR) showing mild edema with occasional loss of myofibre (H & E X 10).
Figure 4 Light micrograph of heart tissue. Rat heart supplemented with 10% v/w of dietary palm olein oil subjected to 20 min ischemia and 40 min of reperfusion (PO10 IR) with mild to moderate edema and occasional loss of myofibre (H & E X 10).
Discussion
In the present study, a significant increase in myocardial SOD, catalase and GPx activity was observed in the lower dose of palm olein oil fed rats. However, their further augmentation was not observed in the higher dose, i.e., a dose dependent effect was not observed. The finding correlates with the previous studies in which an increase in response was not observed with the increase in the dose of supplemented vitamin E [26,27]. The possible reasons behind the lack of dose response relationship may be a decrease in intestinal absorption as a result of increase in dose [28] and newly absorbed vitamin E in part replacing the alpha-tocopherol in circulating lipoproteins [29].
Augmentation of endogenous antioxidants (SOD, CAT, GPx) has been recognized as an important pharmacological property, present in natural as well as many synthetic compounds [30-33]. This constitutes a major mechanism of protection against oxidative stress, offered by them [30,35,37]. The most abundant reactive oxygen species generated in living system is superoxide radical which is acted upon by SOD to produce hydrogen peroxide which in turn is inactivated by catalase and / or GPx into water and oxygen. Thus an increase in both SOD and catalase along with GPx activity is considered to be more beneficial in the event of oxidative stress [34].
Increase in myocardial TBARS and depletion of myocardial endogenous antioxidants support the occurrence of oxidative stress in the control hearts following ischemia-reperfusion in the present study. It was also accompanied by tissue injury with marked edema and focal loss of myocardial fibres. Similar changes have been reported earlier to occur following brief period of ischemia followed by reperfusion in rat heart [35-37]. Hearts from palm olein oil fed rats in both doses were protected against oxidative stress, as evidenced by inhibition of increase in TBARS, depletion of catalase, GPx and tissue injury following ischemia-reperfusion. In a previous study, palm oil has been reported to prevent oxidative stress induced hypertension in rats [13]. The mechanism of such protection can be attributed to the augmented endogenous antioxidant reserve of heart in the lower dose. However, the higher dose, which did not cause any significant augmentation of endogenous antioxidants, also inhibited depletion of antioxidants, rise in TBARS and tissue injury. It is possible that direct antioxidant effects of palm olein oil may be attributable to the presence of alpha tocopherols and tocotrienols, which are known to protect against oxidative stress.
Experimental as well as clinical studies with exogenous antioxidants supplementation have been shown to have protective effect in ischemic heart disease [38,39]. In this regard, the most commonly used exogenous antioxidants are vitamin E (tocopherols and tocotrienols), beta-carotene and vitamin C. Palm oil is also beneficial in conditions like hypertension [13,14,40], arterial thrombosis [15,41] and causes increase in PGI2 /TXA2 ratio [16]. Palm oil derived vitamin E rich in tocotrienols has shown beneficial effects against hypercholesterolemia [42,43] and is considered to be more potent than tocopherols [44].
The observations made in the present study have important nutritional significance for palm olein oil in relation to ischemic heart disease. However, further studies are required to establish the mechanism, underlying the augmentation of tissue antioxidants.
Conclusions
The present study, for the first time, demonstrated that long term oral supplementation of palm olein oil caused augmentation of endogenous antioxidants of heart, which were subsequently protected from developing oxidative stress following ischemia-reperfusion.
Methods
Preparation of diet
Palm olein oil (Ruchi Gold, India) was obtained from the local market. Commercial rat diet (Ashirwad, India) containing protein: 24%, fat: 5%, fiber: 4%, carbohydrates: 55%, calcium: 0.6%, phosphorus: 0.3% w / w was supplemented with palm olein oil in two different doses [5 % v / w and 10 %v / w of diet]. The doses were selected from the previous studies [13,17]. Diet and water were provided ad libitum.
Animals
The study was approved by Institute Animal Ethics Committee (245 / IAEC / 04) and all animal care and experimental protocols were in compliance with the NIH guidelines for the care and use of the Laboratory Animals (NIH Publication #85–23, 1985). Laboratory bred Wistar rats (150–200 gm) of either sex were maintained under standard laboratory conditions at temperature 25 ± 2°C, relative humidity 50 ± 15% and normal photo period (12 h dark / 12 h light) was used for the study.
Chemicals
All chemicals were of analytical grade and chemicals required for sensitive biochemical assays were obtained from Sigma Chemicals (St. Louis, USA). Double distilled water (DDW) was used in all biochemical assays.
Experimental protocol
After one week of acclimatization, rats were randomly divided into three groups, each group containing 16 rats. In control group (C), rats were fed with normal diet for 30 days. In groups (PO5 and PO10), rats were fed with palm olein oil supplemented commercial rat diet for 30 days in two different doses; 5% v / w and 10% v / w of diet. Changes in body weight, food and water intake patterns of rats in all the groups were noted throughout the experimental period. At the end of the 30 days, rats were fasted overnight and half the rats from each group were subjected either to protocol I or to protocol II as described below. Rats were heparinised (375 IU / 200 gms, i.p), and 0.5 h later rats were anesthetized with sodium pentobarbitone (60 mg / Kg, i.p) and euthanised.
Protocol I
Basal level of biochemical and histopathological studies
Immediately after euthanization, the hearts were rapidly harvested, washed in ice cold saline, frozen in liquid nitrogen and stored at -80°C until processed for estimations of biochemical parameters. For histopathological studies, heart was stored in 10% buffered formalin (pH 7.2).
Group C: Normal diet fed rats (n = 8)
Group PO 5: 5% v / w palm olein oil supplemented diet fed rats (n = 8)
Group PO 10:10% v / w palm olein oil supplemented diet fed rats (n = 8)
Protocol II
Production of in vitro ischemic reperfusion injury
Immediately after euthanization, hearts were rapidly harvested, washed in ice-cold saline, and perfused with the non-recirculating Langendorff's technique (Hufesco, Hungary), under constant pressure mode with modified Kreb Hensleit's buffer [18] containing [mM]: glucose 11.1; NaCl 118.5; NaHCO3 25; KCl 2.8; KH2PO4 1.2; CaCl2 1.2; MgSO4 0.6, with a pH of 7.4. The buffer solution equilibriated with 95% O2+ 5% CO2 was delivered to the aortic canula at 37°C and 65 mm Hg pressure. Following 10 min. of equilibration period, hearts were subjected to 20 min. of zero flow (global ischemia) and 40 min. of re-flow (reperfusion) [19,20].
Group C IR: Normal diet fed rats subjected to IR injury (n = 8)
Group PO5 IR: 5% v/w palm olein oil supplemented diet fed rats subjected to IR injury (n = 8)
Group PO10 IR: 10% v/w palm olein oil supplemented diet fed rats subjected to IR injury (n = 8)
At the end of each experiment, heart was frozen in liquid nitrogen and stored at -80°C until processed for estimations of biochemical parameters. For histopathological studies, heart was stored in 10% buffered formalin (pH 7.2).
Biochemical parameters
Myocardial TBARS [21]
Hearts were homogenized in 10% trichloroacetic acid (TCA) at 4°C. 0.2 ml homogenate was pipetted into a test tube, followed by addition of 0.2 ml of 8.1 % sodium dodecyl sulphate (SDS), 1.5 ml of 30% acetic acid (pH-3.5), 1.5 ml of 0.8% thiobarbituric acid (TBA) and volume was made upto 4.0 ml with DDW. Test tubes were boiled at 95°C for 60 min. and then cooled. 1.0 ml of DDW and 5.0 ml of n-butanol: pyridine (15:1 v / v) mixture was added to the test tubes and centrifuge at the 4,000 × g for 10 min. The absorbance of developed colour in organic layer was measured at 532 nm.
Commercially available 1, 1, 3, 3-tetraethoxypropane (Sigma Chemicals) was used as a standard for MDA. Data is expressed as nmol / mg protein.
Myocardial CAT [22]
Hearts were homogenized at 4°C (1:10) in 50 mmol/l potassium phosphate buffer (pH- 7.4) and centrifuged at 3,000 × g for 10 min. Supernatant (50 μl) was added to a 3.0 ml cubette that contained 1.95 ml of 50 mM phosphate buffer (pH-7.0). Then1.0 ml of 30 mM hydrogen peroxide was added and changes in absorbance were measured for 30 sec. at 240 nm at an interval of 15 sec. Catalase activity is expressed as units / mg protein as compared to the standard.
Myocardial SOD [23]
Hearts were homogenized in 0.25 M tris sucrose buffer and centrifuged at 10,000 × g for 15 min at 4°C. The supernatant was fractionated by 50% ammonium sulphate and dialysed overnight. Aliquots of the supernatant (100 μl) was added to sodium pyrophosphate buffer (pH-8.3) followed by addition of 0.1 ml of 186 μM phenazine methosulphate, 0.3 ml of 300 mM nitroblue tetrazolium and 0.2 ml of 780 μM NADH. Reaction mixture was incubated for 90 sec. at 30°C and stopped the reaction by adding 1.0 ml of glacial acetic acid. 4.0 ml of n-butanol was then added and centrifuged at 3,000 × g for 10 min. The absorbance of organic layer was measured at 560 nm. SOD activity is expressed as units / mg protein as compared to the standard.
Myocardial GPx [24]
Hearts were homogenized at 4°C in 0.25 M phosphate buffer saline (pH-7.0). Homogenate was centrifuged at 15,000 × g for 60 min. at 4°C and supernatant were assayed for the GPx activity. GPx activity was in a 1.0 ml cubette containing 400 μl of 0.25 M potassium phosphate buffer (pH-7.0), 200 μl of sample, 100 μl of 10 mM GSH, 100 μl of 2.5 mM NADPH and 100 μl of glutathione reductase (6 U / ml). Hydrogen peroxide (100 μl of 12 mM) was then added and change in absorbance was measured at an interval of 1 min for 5 min at 366 nm. GPx activity is expressed as units / mg protein as compared to the standard.
Protein concentration was measured by Bradford method [25].
Histopathological studies
Heart tissue was fixed in 10% buffered formalin, routinely processed and embedded in paraffin. Paraffin sections (3 μm) were cut on glass slides and stained with hematoxylin and eosin (H&E), periodic acid Schiff (PAS) reagent and examined under a light microscope (Nikon, Japan). Histopathological study was carried out by one of the authors (AKD), blinded to the groups.
Statistical analysis
All values are expressed as mean ± SE. One way ANOVA followed by Bonferroni test was applied to test for significance of biochemical data of the different groups. Significance is set at p < 0.05.
Abbreviations
IRI: ischemic-reperfusion injury; PO: palm olein oil; PO5: rats fed with 5% v/w palm olein oil supplemented diet; PO10: rats fed with 10% v/w palm olein oil supplemented diet; C: rats fed with normal diet; C IR: rats fed with normal diet subjected to ischemic-reperfusion injury; PO5 IR: rats fed with 5% v/w palm olein oil supplemented diet subjected to ischemic-reperfusion injury; PO10 IR: rats fed with 10% v/w palm olein oil supplemented diet subjected to ischemic-reperfusion injury; TBARS: thiobarbituric acid reactive substances; SOD: superoxide dismutase; CAT: catalase; GPX: glutathione peroxidase; IHD: ischemic heart disease; ROS: reactive oxygen species; PGI2: prostacyclin; TXA2: thromboxane A2 ; SE: standard error; ANOVA: analysis of variance; MDA: malondialdehyde; TCA: tricarboxylic acid; SDS: sodium dodecyl sulphate; DDW: double distilled water; NADH: nicotinamide adenine dinucleotide reduced; TBA: thiobarbituric acid; NADPH: nicotinamide adenine dinucleotide phosphate reduced; GSH: reduced glutathione; PAS; periodic acid Schiff reagent; H&E: hematoxylin and eosin.
Authors' contributions
DN carried out the animal experimentation, biochemical estimation and statistical analysis of results. SS and MT participated in the design of the study and statistical analysis. AKD carried out the light microscopic study. SKM conceived the study, participated in its design and coordination and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The study was supported by a financial grant from the Department of Science and Technology, Ministry of Science and Technology, Government of India (Grant no. SP/SO/B-15/99).
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| 15535879 | PMC535348 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Nov 9; 4:29 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-29 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-461555017510.1186/1471-2148-4-46Research ArticleAre both sympatric species Ilex perado and Ilex canariensis secretly hybridizing? Indication from nuclear markers collected in Tenerife Manen Jean-François [email protected] Université de Genève, Conservatoire et Jardin Botaniques, Impératrice 1, CH-1292 Chambésy/Genève, Switzerland2004 18 11 2004 4 46 46 17 8 2004 18 11 2004 Copyright © 2004 Manen; licensee BioMed Central Ltd.2004Manen; 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
Intra-specific and intra-individual polymorphism is frequently observed in nuclear markers of Ilex (Aquifoliaceae) and discrepancy between plastid and nuclear phylogenies is the rule in this genus. These observations suggest that inter-specific plastid or/and nuclear introgression played an important role in the process of evolution of Ilex. With the aim of a precise understanding of the evolution of this genus, two distantly related sympatric species collected in Tenerife (Canary Islands), I. perado and I. canariensis, were studied in detail. Introgression between these two species was previously never reported. One plastid marker (the atpB-rbcL spacer) and two nuclear markers, the ribosomal internal transcribed spacer (ITS) and the nuclear encoded plastid glutamine synthetase (nepGS) were analyzed for 13 and 27 individuals of I. perado and I. canariensis, respectively.
Results
The plastid marker is intra-specifically constant and correlated with species identity. On the other hand, whereas the nuclear markers are conserved in I. perado, they are highly polymorphic in I. canariensis. The presence of pseudogenes and recombination in ITS sequences of I. canariensis explain this polymorphism. Ancestral sequence polymorphism with incomplete lineage sorting, or past or recent hybridization with an unknown species could explain this polymorphism, not resolved by concerted evolution. However, as already reported for many other plants, past or recent introgression of an alien genotype seem the most probable explanation for such a tremendous polymorphism.
Conclusions
Data do not allow the determination with certitude of the putative species introgressing I. canariensis, but I. perado is suspected. The introgression would be unilateral, with I. perado as the male donor, and the paternal sequences would be rapidly converted in highly divergent and consequently unidentifiable pseudogenes. At least, this study allows the establishment of precautionary measures when nuclear markers are used in phylogenetic studies of genera having experienced introgression such as the genus Ilex.
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Background
Aquifoliaceae comprise one genus, Ilex [1] and approximately 400 species. The fossil record indicates that the genus was cosmopolitan during the Eocene. It is now largely extinct in Australia, Europe and Africa where only few species persist. Most diversity is currently found in South-America and in Southeast-Asia. They are evergreen or deciduous trees or bushes living in warm-moist-temperate, sub-tropical, tropical or montane-tropical areas.
The molecular phylogeny of the genus Ilex [2,3] shows that systematic relationships are still not well understood. The plastid phylogeny (inferred from the atpB-rbcL spacer, rbcL and trnL-F) is highly correlated with the geographic distribution of extant species. Four chloroplast clades are found: one exclusively Eurasian clade, one exclusively American clade and two different North-American/Asian clades (one of them comprising most of the deciduous species among other evergreen species). On the other hand, the nuclear phylogeny (inferred from ribosomal ITS and the 5S RNA spacer) is incongruent with the plastid phylogeny, suggesting frequent interlineage hybridizations. The nuclear phylogeny is not correlated with the geographic distribution of extant species.
Any of the plastid or the nuclear phylogeny corroborates previous morphological or biosystematic studies [3]. Using chloroplast RFLPs, trnL-trnF sequencing and nuclear ITS sequencing, a study of Asian Ilex of the Bonin Island and of the Ryukyu Island [4] confirmed that hybridization played a role in this region, leading to interspecific introgressions independently observed on both Islands. RAPD data indicate that the Japanese species Ilex leucoclada M. is highly polymorphic [5]. During its history, the genus Ilex probably experienced frequent incomplete lineage sorting and nuclear and/or cytoplasmic introgression, making the study of its history very complex.
Few data are reported on the chromosome number of Ilex [6]. The basic haploid number is 20, with deviation to 17, 18 and 19. From the 27 chromosome numbers available for the genus Ilex, three species are tetraploid (I. anomala, I. verticillata and I. argentina) and one species is hexaploid (I. pedunculosa), indicating probable hybridizations between species having divergent genomic background (alloploidy).
A previous study [3] showed that individuals of many species of Ilex contain polymorphic nuclear sequences (ITS and 5S rDNA spacer). Except for I. purpurea and I. guianensis, only one individual was studied per species. The sampling being too low for a correct evaluation of this intraspecific polymorphism, an exhaustive study of one plastid marker (the atpB-rbcL spacer) and two nuclear markers (the ribosomal internal transcribed spacers, ITS, and the nuclear encoded plastid glutamine synthetase, nepGS) was undertaken on several individuals of I. perado and I. canariensis collected in Tenerife (Canary Islands). These species were chosen because, based on DNA data, they are not closely related [2,3] and are growing sympatrically in Canary Islands. Both species are morphologically variable but few characters allow species identification. I. canariensis is endemic of Canary Islands, whereas I. perado has a wider distribution in Spain, Portugal, North-Africa and Canary Islands. Natural or artificial hybridization between both species was never reported. The data show that, contrarily to I. perado, I. canariensis has highly polymorphic ITS and nepGS sequences. The aim of this study was to (1) explain the polymorphism observed in ITS of I. canariensis by an investigation of its pattern of substitution and its functionality, (2) determine the evolutionary mechanisms responsible of this polymorphism and (3) focus on ITS evolution and consequences for phylogenetic reconstruction of the genus Ilex.
Results
ITS polymorphism
Figure 1 (inset) shows the unique plastid atpB-rbcL spacer phylogram obtained from the alignment of the sequences of I. perado and I. canariensis collected in Tenerife. All individuals of I. perado have the same atpB-rbcL spacer sequence. For I. canariensis, 26 individuals have the same atpB-rbcL spacer sequence and three substitutions are observed (in a T-rich variable region) for specimen 39. This plastid marker perfectly agrees with species determination.
Figure 1 Most parsimonious atpB-rbcL phylogram (in the inset) and one of the most parsimonious ITS phylogram. Branches conserved in the strict consensus ITS tree are thicker. Bootstrap values are indicated bellow the branches. ITS sequences of 43 Ilex species [3] are indicated by their species name followed by their GC content (in %) and by their DNA accession code in GenBank. ITS sequences of I. perado and I. canariensis collected in Tenerife are indicated by a species code ("per" and "can") and 3 numbers: "can 41_1_45" means I. canariensis specimen 41, clone 1, with a GC content of 45%. Clone 0 means that the sequence was read directly from the PCR product. Specimen "can 28B" represents the shorter PCR product found in specimen 28 of I. canariensis, cut out from agarose gel (see Figure 3). The average GC content is indicated for each clades of the ITS tree. Circled numbers refer to clades discussed in the text (see Figure 4). Bold characters indicate ITS sequences with no substitution at conserved 5.8S sites (see results). Black dots indicate ITS sequences studied in more details (see results). The asterisk indicates an alternative position of the GC 45% clade.
As the ITS sequences found in I. canariensis are extremely polymorphic, it was interesting to observe their relationships with available ITS sequences previously investigated species by Manen et al. [3]. Figure 1 shows one of the most parsimonious ITS tree of I. perado and I. canariensis sequences found in Tenerife altogether with 43 ITS sequences of other Ilex species. Thick bars indicate internal branches conserved in the consensus tree. The closest possible outgroups for the genus Ilex, are Helwingia and Phyllonoma. However these genera are so isolated systematically that their use to root Ilex should be taken with care. On 13 individuals of I. perado, only one substitution (a transition) is observed in ITS. On the other hand, ITS sequences of most individuals of I. canariensis are polymorphic and few sequences are identical. The divergence between all ITS sequences observed in I. canariensis is much higher than between available ITS sequences of all other species investigated. The GC content is 57% for I. perado and from 45 to 62% for I. canariensis. Regarding their GC content, three groups of ITS are found in I. canariensis: a clade with 45% GC, a clade with 61% GC in average and several clades with 53–54% GC in average. The GC content of other investigated species range from 55 to 61% (Figure 1).
The ITS sequences of I. canariensis are distributed in two groups in the phylogram represented in Figure 1. One group forms a large clade conserved in the consensus tree but not sustained by bootstrap statistics. Another group forms a small clade (GC 45%) which branches variably: as indicated in Figure 1, or at the position indicated by the asterisk.
The sequences of the GC 45% clade have a 110 bp deletion in ITS 1 and are suggested to represent pseudogenes. In many ITS PCR products of individuals of I. canariensis, a shorter PCR band is visible on ethidium bromide agarose gel electrophoresis altogether with the main ITS band (Figure 2). In specimen 28, this electrophoretic band has been cut out and directly sequenced (sample "can 28B"). It has a sequence very close to both the cloned sequences of the GC 45% clade found in specimens 35 and 41. Thus these putative pseudogenes seem rather common in I. canariensis. During selection of the clones to be sequenced, the longest PCR fragments were favored with the aim to select functional sequences. Thus, as the shorter pseudogene band seems frequent in I. canariensis, Figure 1 underscores this class of ITS sequences represented by the clade GC 45%.
Figure 2 Example of agarose gel electrophoresis of ITS PCR products of individuals of I. canariensis. The line numbers represent individuals of I. canariensis. The star indicates the position of the expected functional ITS band and the dot indicates the position of the GC 45% ITS pseudogene band.
Secondary structure of ITS 2
The secondary structure of both ITS regions is involved in the processing of the rRNA precursor and is thus constrained for this function. In angiosperms, an ITS 2 secondary structure has been proposed and comprises 6 conserved regions (C1 to C6) which are involved in common pairing relationships on the structure [7]. In order to determine which ITS sequences found in I. canariensis are functional, the secondary structure of ITS 2 was investigated from selected sequences representing a good sampling of ITS (sequences marked with a dot in Figure 1: per 1_0_57, can 28_B_45, can 36_1_53, can 44_4_54, can 90_2_62 and can 39 3 53). Figure 3 shows functional secondary structures found using Mfold [8]. Only the sequence of per 1_0_57, can 90_2_62 and can 39_3_53 provide an apparently functional ITS 2 secondary structure showing the common pairing relationships of conserved C1 to C6 regions according to Hershkovitz and Zimmer [7]. No such ITS 2 secondary structure was found for sequences can 28B_45, can 36_1_53 and can 44_4_54, suggesting pseudogenes.
Figure 3 Functional secondary structures of some ITS 2 sequences of I. perado and I. canariensis according to Hershkovitz and Zimmer [7]. The flanking coding regions (3'end of 5.8S and 5'end of 25S) are indicated in bold characters. Conserved regions (C1 to C6) are indicated.
Pattern of substitution
The GC content, indicated for all ITS sequences of Figure 1, suggests that the pattern of substitutions is biased towards A or T for sequences of the GC 45% clade and for sequences of the GC 53–54% clades, as expected for pseudogenes. A reconstructed ancestral sequence of Ilex was calculated by maximum likelihood from the ITS data of Manen et al. [3] and used to investigate the pattern of substitution on selected ITS sequences. Table 1 shows that a higher rate of substitution is observed for ITS sequences found in the GC 45% and GC 53–54% clades than for the I. perado sequences and for the I. canariensis sequences found in the GC 62% clade. This increased rate is statistically significant according to the Kruskal-Wallis rank test [9].
Table 1 Substitution patterns of I. perado and I. canariensis ITS sequences from a reconstructed maximum likelihood ancestral ITS sequence of Ilex.
Rate nmC mC Chi2
per 1_0_57 0.045 4/226 (1.77%) 11/174 (6.32%) *
can 28_B_45 0.188 24/226 (10.62%) 37/174 (21.26%) **
can 39_3_53 0.080 11/226 (4.87%) 19/174 (10.92%) *
can 44_4_54 0.061 12/226 (5.31%) 16/174 (9.20%) ns
can 36_1_53 0.090 10/226 (4.42%) 26/174 (14.94% ***
can 90_2_62 0.055 0/226 (0.00%) 2/174 (1.15%) ns
Rate: Kimura 2-parameter distance from the reconstructed ancestral sequence. nmC: Number of C>T substitutions / number of non-methylated cytosines on both DNA strands of the reconstructed ancestral ITS sequence of Ilex. mC: Number of C>T substitutions / number of methylated cytosines on both DNA strands of the reconstructed ancestral ITS sequence of Ilex. The corresponding ratios of C>T substitutions are indicated between brackets. Chi2: Chi-square homogeneity test between expected and observed C>T substitutions at methylated cytosines (ns: non significant; *, ** and ***: significant at 0.05, 0.01 and 0.001 levels, respectively).
As expected for pseudogenes [10,11], the observed rate of deamination-like substitutions at methylated cytosine sites (CpG and CpNpG sites) is higher than the expected rate of C -> T and G -> A substitutions at non-methylated sites for can 28B_45, can 39_3_53, can 44_4_54 and can 36_1_62 (Table 1). A chi-square homogeneity test [9] indicates that this is highly significant for can 28B_45 and can 36_1_53, which certainly represent pseudogenes.
Substitutions at conserved sites of the 5.8S rDNA
The alignment of fifty 5.8S sequences (modified from Muir et al. [11]) shows that 59 sites are conserved in vertebrates, invertebrates, fungi and plants and are expected to be functionally constrained. Substitutions observed at these sites would suggest non-functional pseudogenes. Contrarily to I. perado, many ITS sequences found in I. canariensis have substitutions at some of these conserved sites. Only the GC 61% clade of I. canariensis comprises non-substituted conserved 5.8S sites (sequences indicated in bold in Figure 1). Sequences of the GC 45% clade have 10–11 substitutions. Sequences of the GC 53–54% clades have 2 to 7 substitutions. Three sequences of the GC 61% clade have only one mutation, which may be PCR artifacts [11] and two sequences (can_20_1_58 and can_27_2_59) with a lower GC content (58 and 59%, respectively) have 2 mutations. Thus ITS sequences of I. canariensis having a GC content higher than 60% are expected to be functional genes, all other sequences with lower GC content are suspected to be pseudogenes.
Recombinations
Most ITS sequences of I. canariensis experienced frequent recombinations: in the entire ITS matrix of I perado and I. canariensis, the DnaSP program [12] detects 19 minimum possible recombination events (RM). From 0 to 8 minimum possible recombination events are calculated in the different ITS clusters (Figure 4A). No recombination was detected in I. perado and in clade 2 of I. canariensis. Clades 5 and 6 are highly recombined. An example of recombined ITS sequences of I. canariensis from clade 5 is shown in Figure 4B, where only informative sites are shown.
In order to exclude the possibility that the observed pattern of substitution is the result of homoplasy and to confirm that these sequences are actually recombined, maximum likelihood tests were carried out. Using PIST [13], the maximum-likelihood score of the sequences represented in Figure 4 is compared with the scores of 1000 simulated clonal sequences along the calculated maximum-likelihood tree and under the specified model of evolution (see methods). The observed score q (0.554) was greater than for all 1000 clonal replicates (mean value 0.381, higher value 0.505), indicating a history of recombination (significance 0.001).
Figure 4 Recombination evidence in ITS sequences. A: Minimum number of recombination events in ITS clades (numbered as in Figure 1) calculated using the DnaSP program [12]. "perado": I. perado clade. "canariensis": GC 61% clade of I. canariensis representing functional ITS sequences. B: An example of obvious recombined ITS sequences found in I. canariensis clade 5. Only informative nucleotides are represented. Homologous sequence fragments have the same color. Stars indicate the recombination points found by maximum likelihood (program LARD) for sequences can 90_6_54, can 25_2_54 and can 90_4_53 (see results).
The LARD maximum-likelihood method [14] was applied to find the breakpoints in the alignment, which gave the highest likelihood under an evolutionary model incorporating recombination. Only 3 sequences can be analyzed with this program. Three ITS sequences shown in Figure 4 were submitted to LARD: can 90_6_54, can 25_2_54 and can 90_4_53. Two recombination points were located by the program (at the left of positions 242 and at the left of position 582) in accordance with the delimitation indicated in Figure 4. There is no recombination point between positions 392 and 455 for these particular 3 sequences.
Nuclear encoded plastid glutamine synthetase (nepGS) data
There is no polymorphism in nepGS of I. perado. On the other hand, I. canariensis shows polymorphism for this gene. Thirty sites differentiate I. perado from I. canariensis, of which eight are polymorphic in I. canariensis, either heterozygous or homozygous (Figure 5). For all of these eight polymorphic sites, always one of the alleles is shared with I. perado.
Figure 5 Alignment of the nuclear encoded plastid glutamine synthetase (nepGS) of I. perado and I. canariensis. Only variable nucleotides are represented. Polymorphic sites of I. canariensis are boxed. R = A or G; Y = C or T; M = A or C; M = A or C; W = A or T; K = G or T.
Discussion
The high polymorphism of ITS sequences observed in I. canariensis is frequently reported for other plant groups [15,16]. It might have several origins: an incomplete lineage sorting from ancestral polymorphism or an horizontal transfer (introgression) through inter-specific hybridization (alloploidy), both of them not resolved by concerted evolution. Before the discussion on the origin of this polymorphism, the characterization and the fate of these different ITS sequences will be first examined.
The genome of Ilex canariensis contains ITS pseudogenes
High polymorphism of ITS has been explained by the presence of divergent pseudogenes in Gossypium, Nicotiana, Tripsacum, Exospermum, Zygogonum, Zea [10], Quercus [11], Leucaena [17], Adinauclea, Haldina, Mitragyna [18] and others. Thus, this could also be the case for I. canariensis. Individual criteria are not sufficient to identify pseudogenes unambiguously [17] and different criteria were chosen: GC content, secondary structure of ITS 2, rate of substitution, pattern of substitution at methylated cytosine sites and substitutions at highly conserved sites of the 5.8S rDNA. ITS sequences with a GC content of 45% are unambiguously pseudogenes and satisfy to all other criteria. Moreover they have a large deletion in the ITS 1 region, which make these sequences certainly non-functional. The deletion allows an easy detection of this pseudogene on agarose gels and it is observed in many individuals of I. canariensis (Figure 2).
Other classes of ITS sequences with a GC content of 53–54% are also suspected to be pseudogenes by one or the other criteria but not by all of them, as expected regarding their relatively high GC content. For instance, some ITS 2 sequences of the GC 53–54% class still have a typical angiosperm secondary structure (for instance can_39_3_53, Figure 3), but have (1) an increased rate of nucleotide substitution, (2) deamination-like substitutions or (3) mutations at normally highly conserved 5.8S rDNA sites. Only the GC 61 % clade contains functional ITS sequences. Thus, it can be considered that the functional ITS GC content is 57 % for I. perado and above 60 %. for I. canariensis.
It is interesting to note that most I. canariensis individuals of the GC 61 % clade never have ITS pseudogenes in the GC 45 % or GC 53–54 % clades. This is probably because these individuals do not contain pseudogenes. For other individuals, a PCR selection for pseudogenes occurred, as reported for Nicotiana [10], in which ITS sequences with a weak secondary structure (pseudogenes) are preferentially used as templates. The inclusion of dimethylsulfoxide (DMSO) in PCR reactions [10,19], but see [18], would allow amplification of functional ITS sequences in these individuals of I. canariensis.
In conclusion, the high divergence found in ITS sequences of I. canariensis with a GC content lower than 60% (clades 1, 2, 3, 4, 5 and 6) could be explained by a release of evolutionary constraint and a subsequent high rate of substitution. Indeed, ITS sequences have functional constraints in relation with the processing of the rRNA precursor producing the functional 18S, 26S and 5.8S subunits.
ITS sequences of Ilex canariensis are recombined
Evidence for recombination in divergent sequences is not obvious. It is difficult to recognize homoplasy generated by recombination from actual homoplasy (parallel history). Statistical methods (based on linkage desequilibrium, neutrality tests and substitution distribution along the locus) are still too rudimentary to precisely describe the recombination events in the set of ITS sequences found in I. canariensis. Moreover, recombinants could result from "jumping" PCR reaction [20-23], where prematurely terminated extension products can act as primer on paralogous templates. This has been shown on nepGS for Oxalis [24] and on four low-copy genes for Gossypium [25].
The minimum number of recombination events (RM) calculated with DnaSP [12] underestimates the total number of recombination events [26]. Thus, there is no doubt that I. canariensis ITS sequences experienced intra-molecular recombinations (Figure 4). The factor RM has been also calculated for PCR products of each individual in order to detect possible jumping PCR artifacts. In few of them (specimens 20, 22, 24, 27, 28 and 38) recombinants have been detected in ITS sequences resulting from a unique PCR reaction (data not shown). This could be the result of jumping PCR. However most of them are multiple recombinants and not simple recombinants as it is expected in jumping PCR [10]. As an example, the alignment represented in Figure 4 shows that specimen 90 comprises two different recombined ITS sequences resulting from the same PCR reaction, that could be the result of jumping PCR. DnaSP did not detect recombination between the four cloned ITS sequences of individual 90 because recombined fragments are paralogous sequences fragments found in other individuals. Moreover, the recombinants result from at least three crossover events and are suggested not PCR artifact. Thus, they represent true organismal intra-molecular recombinations.
The distribution of informative characters shown in Figure 4, as well as the use of programs PIST and LARD based on maximum-likelihood analyses, demonstrate unambiguously that sequences of clade 5 (Figure 4) experienced recombination events. This can not be generalized for other clades. Although DnaSP suggests recombination, an alignment demonstrating recombination, as for clade 5, was not possible for other clades, even with the help of PIST and LARD. This could be explained by the recent origin of the recombination events observed in clade 5 and by the fact that mutations did not yet obscured the recombined orthologous fragments. In this respect it is to be noticed that clade 5 shows much longer branches than other clades. This may indicate that, in clades with relatively shorter branches, mutations (or concerted evolution) did homogenize the recombined fragments, mimicking clonal divergence. Thus it can be considered that most clades also comprise recombined ITS sequences, as DnaSP suggests, but of more ancient origin than those of clade 5, and homogenized by mutation or concerted evolution.
Recombination in highly polymorphic ITS sequences seems a rule in plants. This is not surprising because the mechanisms of concerted evolution in rDNA arrays are based on crossing-over and gene conversion. It has been reported in Begonia [27], Microseris [28], Quercus [11], Amelanchier [29], Paeonia [30], Buddia, Gossypium, Nicotiana, Tripsacum [10], Armeria [31] and others.
In addition to the high rate of substitution of pseudogenes, at least some ITS sequences experienced recombination. This explains why the divergence between ITS sequences of I. canariensis is much higher than between ITS sequences of all other species investigated, knowing that, according to their GC content (see Figure 1), they all are potentially functional. This also explains the absence of a bootstrap support for a monophyletic clade of I. canariensis ITS sequences because of long branch problems due to accelerated rate of substitution and more certainly to recombination.
The origin of the ITS polymorphism in I. canariensis
Two evolutionary mechanisms could produce the observed ITS polymorphism: an ancestral polymorphism escaping lineage sorting or a past or recent introgression of an alien genotype escaping concerted evolution. Because of the influence of concerted evolution, ancestral polymorphism is not the most likely explanation of ITS polymorphism [31]. On the other hand, a growing number of reports shows that ITS polymorphism is attributable to interspecific hybridization, although the parents are not always identifiable [15,16].
Assuming that multiple ITS sequences found in I. canariensis are the result of experienced hybridization with another species, or an ancient polymorphism with incomplete sorting, the determination of the identity of the putative hybridizing species or the finding of genetic relationships of the putative polymorphism is not obvious. This is because ITS sequences enclosed in non-functional clusters have dramatically diverged from the putative functional sequences and are recombined. All available ITS sequences of 43 other species of Ilex, representing a good sampling of the genus [2,3] were incorporated in the phylogenetic analysis, altogether with all ITS sequences found in I. perado and I. canariensis of Tenerife (Figure 1). Most functional (above 60% GC) and non-functional (53–54% GC) ITS clades of I. canariensis group together but with no bootsrap support. They group with an American lineage (I. brevicuspis, I. anomala, I. microdunta, I. integerrima, I. theezans, I. guianensis, I. brasiliensis and I. cassine). Only the GC 45% clade does not group with the bulk of I. canariensis ITS sequences. Its position is not defined and varies in the vicinity of a Eurasian lineage (I. latifolia, I. leucoclada, I. maximocziana, I. rugosa and I. perado). Thus data do not support a particular relationship of most I. canariensis ITS pseudogenes with another Ilex species, except for the pseudogenes with a GC content of 45%, that are frequently observed in I. canariensis.
In the case of hybridization involving the island species I. canariensis, the most probable candidate would be the sympatric species I. perado. It can not be ruled out however that the distribution of I. canariensis was much wider in the past [32,33] and that this hybridization may have occurred with another unknown or extinct species of the Eurasian lineage represented here by I. latifolia, I. leucoclada, I. maximocziana, I. rugosa and I. perado. Pseudogene sequences (particularly the ITS sequences of clade GC 45%) being too divergent and of different nucleotide composition, the observed relationship of clade GC 45% with the group of species comprising I. perado is questionable because of possible spurious long branch attraction. However, the data of the nuclear encoded plastid glutamine synthetase (a nuclear single copy locus) are not conflicting with an introgression of I. perado in I. canariensis. All the eight polymorphic sites observed in I. canariensis always comprise one allele shared with I. perado. Another possibility is that these ITS pseudogenes represent a relictual ancestral polymorphism in the course of elimination by lineage sorting or concerted evolution. In fact ancestral polymorphism could also be the result of ancient introgressions. The data accumulated here do not allow a definitive conclusion.
If a putative cryptic hybridization between I. perado and I. canariensis is confirmed, the introgression would be unidirectional because ITS sequences of I. perado do not show any polymorphism. This situation is reminiscent of the unilateral hybridization observed between Begonia formosana and B. aptera, where on 60 ITS sequences analysed in natural or artificial hybrids, 58 sequences are clustering with the ovule donor B. formosana, and only 2 are found clustering with the pollen donor B. aptera [27]. Unidirectional interspecific hybridization linked to unilateral incompatibility is frequently described in plants. However, this is not the only mechanism that can explain unidirectional hybridization. The flowering time of I. perado precedes the one of I. canariensis, thus the loading of still living I. perado pollen grains on young effective I. canariensis stigmates is more favored than the contrary. Moreover, there are much more male than female I. perado plants in Tenerife [34,35]. These evidences could explain the proposed unidirectional introgression.
Conclusions
This study was undertaken with the aim to study and overcome the problem of ITS polymorphism found in many species of Ilex [3]. Introgression [3,4] and high polymorphism [5] have already been shown in several species of Ilex. Thus, precautionary measures should be taken when studying nuclear ITS sequences in the genus, particularly the search for recombinant and pseudogenes. Particular PCR conditions should be used [10,19,23]. Razafimandimbison and al. [18] were however unable to find PCR conditions to amplify a functional ITS sequence in 2 species of Rubiaceae. Amplified ITS sequences should be checked for function. A measure of the GC content (above 55% for a functional ITS sequence in Ilex is recommended. This study will probably make phylogenetic interpretations easier and will certainly help to the understanding of the complex evolutionary history of Ilex [3].
Methods
Material
Thirteen individuals of I. perado ssp. platyphylla Webb. & Berth. and 27 individuals of I. canariensis Poir. were collected in Tenerife (Las Mercedez, Aqua Garcia and Aqua Mansa) and genomic DNA was extracted from dry leaves as previously reported [2].
Sequencing
The plastid atpB-rbcL spacer was sequenced for the 40 specimens of Tenerife according to Cuénoud et al. [2]. In a first experiment all ITS sequences (ITS 1, 5.8S and ITS 2) were directly sequenced from the PCR fragment according to Manen et al. [3]. All individuals of I. perado and five individuals of I. canariensis produced a perfectly readable ITS sequence with no polymorphism. On the other hand, 22 individuals of I. canariensis produced an unreadable highly polymorphic ITS sequence. Cloning in E. coli was necessary and four clones per individual were sequenced. For all specimens, the nuclear encoded plastid glutamine synthetase (nepGS) was amplified and sequenced according to Emshwiller and Doyle [36]. Polymorphisms were also observed in most individuals of I. canariensis, but as indels are not involved in the polymorphism, sequences were readable and polymorphic sites were coded according to the international code for nucleotide polymorphism (see Figure 5). Sequences are deposited at GenBank (atpB-rbcL spacer: AJ786512-AJ786551; ITS: AJ786413-AJ786504; nepGS: AJ809595-AJ809628).
Phylogenetic analysis
The ITS sequences of I. canariensis and I. perado were aligned with ITS sequences of 43 species of Ilex previously studied in Manen et al. [3] and Phyllonoma and Helwingia, the closest relatives of the genus Ilex (for the ITS matrix see additional file 1).
Plastid atpB-rbcL spacer and nuclear nepGS matrices only comprise the sequences found in Tenerife for I. perado and I. canariensis.
Maximum parsimony trees were calculated from the atpB-rbcL spacer matrix, the ITS matrix and the nepGS matrix, using PAUP 4.0b10 [37] (heuristic search, TBR branch swapping with 10 random additions of sequences, only keeping the first 100 most parsimonious trees). Bootstrap statistics of the ITS tree were calculated from 1000 replications with the same method, except that the first 10 most parsimonious trees were kept.
Secondary structure of ITS 2
The secondary structure of ITS 2 was investigated using the minimum free-energy program Mfold [8], which has the advantage to provide sub-optimal folding. Sequences were constrained to force the pairing of the 5'-end of the 26S and the 3'-end of the 5.8S regions according to the results of Hershkovitz and Zimmer [7].
Pattern of substitution of ITS
In order to examine the pattern of substitution of ITS sequences found in I. perado and I. canariensis, these sequences were compared with a reconstructed Ilex ancestral sequence. This ancestral sequence was determined by maximum likelihood [PAUP 4.0b10 with base frequencies, ti/tv ratio, proportion of invariable sites and gamma shape parameter estimated under the HKY model (Hasegawa et al. 1985) allowing for different rate of transitions and transversions as well as unequal base frequencies] using the unique maximum parsimony tree obtained from the ITS matrix of Manen et al. [3] based on 45 species of Ilex and Helwingia and Phyllonoma as outgroup. For comparisons, the number of substitutions (Kimura 2-parameter) was calculated from this ancestral sequence for I. perado and I. canariensis ITS sequences using PAUP.
The high frequency of deamination-like substitutions (C -> T and G -> A at CpG and CpNpG sites) is typical for pseudogenes and was also calculated from the reconstructed Ilex ancestral sequence for I. perado and I. canariensis ITS sequences, according to Buckler et al. [10] and Muir et al. [11]. This frequency was compared with the frequency of C -> T and G -> A substitutions at non-methylated sites.
Substitutions at conserved sites of the ribosomal 5.8 S subunit
Based on the alignment of fifty 5.8S sequences including vertebrates, invertebrates, fungi and plants (modified from Muir et al. [11]), 59 totally conserved sites were determined. The number of substitutions observed at these invariant sites in all I. perado and I. canariensis 5.8S sequences was calculated.
Detection of recombinations
The minimum number of recombination events RM [26] was calculated using the DnaSP program [12] in the entire ITS matrix of I. perado and I. canariensis and in different ITS clusters observed in I. canariensis. This program is based on linkage desequilibrium, neutrality tests and substitution distribution along the locus. ITS sequences showing strong evidence of recombination, as detected by DnaSP, were submitted to PIST [13] to calculate the probability of recombination by maximum likelihood: the tree score of these sequences is compared with the tree scores of 1000 simulated clonal sequences along the specified tree under the specified model of evolution. The score of recombined sequences will tend to have larger score than the simulated clonal sequences because richer in conflicting phylogenetic information. A maximum likelihood tree of selected I. canariensis ITS sequences (see results) was constructed with base frequencies, ti/tv ratio, proportion of invariable sites and gamma shape parameter estimated under the HKY model [39] and these parameters were used to calculate the tree scores of simulated clonal sequences.
The recombination points of three selected sequences (see results) showing evidence of recombination were calculated by maximum likelihood using LARD [14] with the HKY model [38]. The program calculates a maximum likelihood unrooted tree of 3 sequences and searches for a tree with a better score assuming a recombination point in the input sequences. After the calculation of one recombination point, the sequence alignment was truncated at this point to search for other potential recombination points.
Supplementary Material
Additional File 1
ITS matrix in NEXUS
Click here for file
Acknowledgements
I wish to thank Yamama Naciri-Graven (CJB) for statistical evaluation of the pattern of substitutions, Gabrielle Barriera and Pierre-André Loizeau (CJB) for the confirmation of species determination and Gisèle Vuille-dit-Bille for technical assistance. This work was supported by the "Conservatoire et Jardin Botaniques" and the University of Geneva.
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| 15550175 | PMC535349 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 18; 4:46 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-46 | oa_comm |
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BMC ImmunolBMC Immunology1471-2172BioMed Central London 1471-2172-5-201536960010.1186/1471-2172-5-20Research ArticleDistinct gene expression profiles in different B-cell compartments in human peripheral lymphoid organs Shen Yulei [email protected] Javeed [email protected] Li [email protected] Ryan C [email protected] Andreas [email protected] Louis M [email protected] Simon [email protected] Karen [email protected] Guimei [email protected] James D [email protected] Jan [email protected] Timothy W [email protected] Wing C [email protected] Departments of Pathology and Microbiology, Eppley Cancer Institute, Department of Genetics Cell Biology and Anatomy, Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE, USA2 Department of Pathology, University of Würzburg, Würzburg, Germany3 Metabolism Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA4 Department of Pathology, Norwegian Radium Hospital, Oslo, Norway2004 15 9 2004 5 20 20 1 6 2004 15 9 2004 Copyright © 2004 Shen et al; licensee BioMed Central Ltd.2004Shen 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
There are three major B-cell compartments in peripheral lymphoid organs: the germinal center (GC), the mantle zone (MNZ) and the marginal zone (MGZ). Unique sets of B-cells reside in these compartments, and they have specific functional roles in humoral immune response. MNZ B cells are naïve cells in a quiescent state and may participate in GC reactions upon proper stimulation. The adult splenic MGZ contains mostly memory B cells and is also known to provide a rapid response to particulate antigens. The GC B-cells proliferate rapidly and undergo selection and affinity maturation. The B-cell maturational process is accompanied by changes in the expression of cell-surface and intracellular proteins and requires signals from the specialized microenvironments.
Results
We performed laser microdissection of the three compartments for gene expression profiling by cDNA microarray. The transcriptional program of the GC was dominated by upregulation of genes associated with proliferation and DNA repair or recombination. The MNZ and MGZ showed increased expression of genes promoting cellular quiescence. The three compartments also revealed distinct repertoires of apoptosis-associated genes, chemokines and chemokine receptors. The MNZ and GC showed upregulation of CCL20 and CCL18 respectively. The MGZ was characterized by high expression of many chemokines genes e.g. CXCL12, CCL3, CCL14 and IFN-associated genes, consistent with its role in rapid response to infections. A stromal signature was identified including genes associated with macrophages or with synthesis of extracellular matrix and genes that influenced lymphocyte migration and survival. Differentially expressed genes that did not belong to the above categories include the well characterized BCL6 and CD10 and many others whose function is not known.
Conclusions
Transcriptional profiling of B-cell compartments has identified groups of genes involved in critical molecular and cellular events that affect proliferation, survival migration, and differentiation of the cells. The gene expression study of normal B-cell compartments may additionally contribute to our understanding of the molecular abnormalities of the corresponding lymphoid tumors.
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Background
Appropriate T- and B-cell migration and timely interaction with antigen presenting cells (APC) are essential for the development of humoral immune responses [1,2]. Specialized compartments within lymphoid tissues facilitate these interactions [3]. Distinct populations of B-cells reside in these microenvironments, and, upon antigen stimulation, cells with appropriate antigen receptors differentiate and migrate among these compartments for a proper immunological reaction [4-7]. The initiation of a T-dependent B-cell response results from cognate interaction between a T-helper cell and a B-cell that primes the B-cell into two developmental pathways. An extrafollicular reaction takes place in the T zone and leads to the production of plasma cells with unmutated immunoglobulin (Ig) genes. The other pathway initiates a germinal center (GC) reaction, whereby activated B lymphocytes originating from extrafollicular foci enter the GC and undergo a stringent process of positive selection and affinity maturation. The selected cells differentiate into either memory B-cells or plasma cells with mutated Ig genes. The GC provides the important microenvironment for this crucial B-cell maturational process [8,9].
In follicles with developing GCs, the resting B-cells that are not the part of the GC response are pushed outward to form the mantle zone (MNZ) or corona around the GC B-cells. The mantle cell is a pre-GC, immunologically naïve B-cell that is also the putative cell of origin of mantle cell lymphoma [10]. These B-cells express unmutated immunoglobulin genes, sIgDhigh, CD27- [11] and are mostly restricted to the follicular mantle zone [12]. In the human spleen, there is a well defined zone between the follicular B-cells and the red pulp called the marginal zone (MGZ) containing marginal-zone macrophages, granulocytes and dendritic cells that are specialized to capture blood-borne antigens and present them to the resident marginal zone B-cells [13]. Unlike primary lymphoid follicles in spleen and lymph nodes, which contain mostly mature recirculating B-cells, non-recirculating B-cells are enriched in the splenic MGZ. These cells are specially adapted to respond rapidly to T-independent (TI) antigens and have a lower threshold than recirculating or immature B-cells for activation, proliferation and differentiation into antibody-secreting cells [7]. They may therefore provide the early rapid humoral response prior to the more refined but delayed response from the GC reaction. Most adult human MGZ B-cells have the IgMhigh, IgDlow and CD27+ phenotype, suggesting that this zone contains mainly memory B-cells [14].
Many previous studies [15,16] have provided important information concerning the biology of the GC. While morphology and immunophenotype are useful in defining the various B-cell compartments of peripheral lymphoid organs, the molecular signals that affect the life span, survival, retention, migration and functions of the cells in these compartments have not been widely investigated.
We used the Lymphochip cDNA microarray [17] to investigate the differences in gene expression profiles in the three different B-cell compartments, the MNZ with mostly naïve B-cells, the MGZ containing memory B-cells [18] and specialized non-recirculating B-cells and the GC with a mixture of highly proliferative centroblasts and more differentiated and non-dividing centrocytes. For this study, we used both tissue compartments isolated by laser capture microdissection (LCM) and naïve and memory B-cells isolated by fluorescence-activated cell sorting (FACS). The microdissected samples contained the dominant B-cell population in each compartment as well as other cell populations in the physiological microenvironment, whereas the FACS-sorted cells contained more uniform B-cell subsets. By comparing FACS-sorted cells with the corresponding compartment from LCM, we have identified a stromal cell gene expression signature that may provide insight into stromal/B-cell interaction.
Results and discussion
Isolation of naïve and memory B-cells and different anatomic B-cell compartments
GC and MNZ could be readily dissected from tonsillar frozen sections, but MGZ could only be reliably obtained from the spleen (Figure 1). Immunostaining was not applied on the sections to be microdissected because it was difficult to obtain cells from sections on charged slides and because immunostaining also led to a marked loss of amplifiable RNA from the sections, even when a rapid procedure was used [19]. Hence, immunostaining was performed on a consecutive section to guide the dissection. Immunostaining by us and others has shown that the MNZ contained over 90% B-cells, which are the IgD+ CD27-, similar to FACS-sorted naïve B-cells. The GC was easily recognizable and generally contained a higher percentage of non B-cells, including T-cells, macrophages and follicular dendritic cells (FDC). The MGZ was obtained from a spleen with a morphologically clearly defined MGZ containing mostly IgM+ CD27+ B-cells, corresponding to the phenotype of FACS-sorted memory B-cells [14]. The MGZ also contained scattered T-cells and has been shown by others to contain specialized macrophages and fibroblasts [20,21]. The FACS-sorted populations were over 90% pure, according to post-sort immunophenotyping (Figure 2).
Figure 1 Three different B-cell compartments isolated by LCM. Frozen sections of reactive tonsils or spleens were fixed, and a consecutive section was immunostained for CD3 to guide the dissection. The three B-cell compartments (GC, MNZ and MGZ) were isolated using LCM with the Arcturus PixCell II system (Arcturus Engineering, Mountain View, CA). Cells were captured at the 15-μm with laser set to pulse at 60 mW for 200 ms. The GC and MNZ were clearly recognizable, and the MGZ was obtained from a spleen with a morphologically clearly defined MGZ. Only well-defined GC, MNZ and MGZ were dissected to avoid contamination.
Figure 2 FACS-sorting of naïve and memory B-cells from splenocytes. A single B-cell suspension prepared from a fresh spleen was isolated using the Human B-cell Isolation Kit (See methods) and subjected to 3-color cell sorting. Memory B-cells from the splenic B-cells were gated on the IgMhighIgDlowCD27+ fraction, while naïve B-cells were gated at IgMlowIgDhighCD27-. FACS-sorted populations were over 90 % pure, judging from post sort immunophenotyping.
Gene expression profiling analytical approach
Fifteen data sets corresponding to the five sample groups were generated. Different hybridizations were correlated through a correlation matrix plot, and replicated hybridizations were shown to be closely related (R ≥ 0.85). The plots allowed us to check reproducibility of the microarray assay among different samples of each tissue (Figure 3). The number of genes showing differential expression between two compartments and the magnitude of difference calculated by t-statistics were further filtered by Significance Analysis of Microarrays (SAM) approach, as described previously [22]. On the Lymphochip, over 20% of the genes are represented by multiple clones, and, generally, several clones of same genes are selected by our analytical algorithm. The differentially expressed genes among the three compartments identified by SAM were grouped according to their major functional attributes and then viewed through Tree View.
Figure 3 Correlation Coefficient Mapping. Reproducibility of the different hybridization experiments was checked through correlation coefficient mapping programmed in MATLAB. High correlation is seen among samples from same compartment or FACS-sorted population.
Confirmation of the microarray analysis with semi-quantitative RT-PCR and with real time quantitative PCR
The differential expression of some of the transcripts that had no previously reported association with any of the compartments was further validated by a semi-quantitative RT-PCR. No discrepancies were found with any of the selected genes. By PCR analysis, some of the transcripts had almost exclusive expression in one compartment: ARK2 in GC, CCL20 in MNZ and CMRF-35H in MGZ. Other transcripts were expressed in all compartments with a relatively high differential expression in one, such as SET and FAIM in GC, Cyclin G2 in MNZ, and NM23-H1 and CARD11 in MGZ (Figure 4).
Figure 4 Confirmation of the Microarray analysis by semi quantitative RT-PCR. aRNA amplified from GC, MNZ and MGZ -cells was reversely transcribed and amplified by PCR. The human HPRT gene was used as the comparative standard. Five fold serially dilutions (4 dilutions) of each cDNA amplified with gene specific primers and analyzed by electrophoresis in 2% agarose gel. The transcripts had either an almost exclusive expression in one compartment (ARK2 in GC, CCL20 in MNZ and CMRF-35H in MGZ), or they were expressed in all compartments with a significant differential expression in one – for example, SET and FAIM in GC, Cyclin G2 in MNZ and NM23-H1, CARD11 and GAS2 in MGZ.
Some of the results of the semi-quantitative RT-PCR were further validated by the SYBR® Green real-time quantitative PCR (data not shown). The results corresponded well with both microarray and semi-quantitative RT-PCR.
Gene expression characteristics in anatomic B-cell compartments
Genes controlling cell proliferation and quiescence (Figure 5,6)
Comparing the gene expression profiles of LCM GC and FACS-sorted GC B-cells [17] revealed that the GC B-cell signature was largely represented in the microdissected GC profile. The GC gene expression profile was dominated by the increased expression of genes associated with proliferation (e.g., CCNB1, PCNA, Ki67), kinetochore association (e.g., CENPF, BUB1 and BUB3), functional components of mitotic checkpoints (e.g., CENPE and TTK) and regulators of cell-cycle related events, including centrosome separation/segregation and cytokinesis (e.g. KNSL5, ARK2), as expected from the known high proliferation rate of centroblasts. GC also highly expressed genes involved in DNA repair (e.g., RAD54, BRCA2, RAD51, ERCC5 and MSH2), as expected from the frequent physiological double-strand DNA breaks associated with somatic hypermutation and isotype switching. The GC profile also showed increased levels of transcripts involved in DNA replication (e.g., DNAJ, DNA2L, DNMT1, TOP, RFC4 and RPA1) and in transcription and translation (e.g., EIF2, TAF, TCF, UHRF1, UBD and UBE2).
The low expression of the cyclins CCNA, CCNB1and CCNF and of CDC2 (also known as CDK1) is consistent with the resting state of the MNZ and MGZ B-cells. Characteristically CCNA expression is very low in Go phase and begins to increase in early G1. For the cell to enter the G2/M phase, an association with CDC2 is required [23]. The transition requires CCNB1 to form a complex with CDC2 and relocate to the nucleus. This nuclear localization is mediated by CCNF [24]. However, MNZ and MGZ B-cells may also employ different mechanisms in maintaining quiescence. Cyclin G2 (CCNG2) was highly expressed in MNZ cells as compared to either GC or MGZ. The function of CCNG2 differs from the conventional cyclins in negatively regulating the cell cycle [25]. Studies on HeLa cells have showed that DNA damage induces the production of cyclin G2, which then arrests the cell cycle at the G1/S boundary, and this function is independent of p53. Cyclin G2 can directly interact with the catalytic subunit of protein phosphatase 2A (PP2A) and prevent cell cycle progression. The low expression of CARD11 in MNZ may also be part of the program in maintaining the quiescent state. CARD11 has been shown to be critical for immune receptor signaling of both T and B-cells through the activation of JNK and NF-κB [26]. The increased transcriptional level of CD72 may be involved in maintaining the quiescent state in MNZ B-cells. CD72 contains an immunoreceptor tyrosine-based inhibitory motif (ITIM) in its cytoplasmic domain and functions as a negative regulator of B-cell signaling [27]. Interestingly, many genes associated with proliferation were expressed at even lower levels in MGZ than in MNZ cells. These cells highly expressed growth inhibitory genes such as CMRF-35H [28], CBL-B [29], and GAS2 [30], which may contribute to the quiescent state in MGZ B-cells.
Figure 5 Cell proliferation and quiescence. Differential gene expression among B-cell compartments. Genes identified in SAM analyses were merged according to functional or operational categories and visualized in Tree View. Color changes within a row indicate expression levels relative to the median of the sample population. Only transcripts differing 2-fold or more in their magnitude than the median or mean of the other two compartments are shown.
Apoptosis (Figure 7A)
The markedly decreased BCL2 expression in GC B-cells makes them vulnerable to undergo apoptosis unless rescued by survival signals [31]. An increase in the expression of proapoptotic genes e.g. BIK, FASL and PDCD8 (AIF) suggests a further increase in susceptibility to apoptosis in the GC. However, FAIM is overexpressed in the GC and may represent a protective mechanism in GC B-cells that have appropriate BCR signaling and CD40L stimulation with resultant upregulation of FAIM and increased resistance to FASL-mediated apoptosis [32]. Presumably, GC B-cells with sIg having poor antigen affinity will be ineffective in activating FAIM. In addition, a TNF receptor family member (TNFRSF17/BCMA) which promotes the B-cell survival [33] showed increased transcription in the GC. Programmed Cell Death 4 (PDCD4), which functions mainly as an inhibitor of translation by inhibiting the activity of eIF4A helicase, which helps to unwind the 5' end of mRNAs, was markedly repressed in the GC B-cells [34]. This suggests that PDCD4 repression facilitates the rapid proliferation of centroblasts, which requires a high rate of protein synthesis. Both MNZ and MGZ had higher expression of BCL2, but have different profiles of other apoptosis/survival genes that may represent specific adaptation of these cells to their unique physiological states and microenvironment. The expression of BNIP3, encoding a proapoptotic protein of the BCL2 family[35], is markedly down regulated in MGZ cells, perhaps providing additional protection against apoptosis in memory B-cells. On the other hand, TCL1 was upregulated in MNZ only and may have an antiapoptotic role in that population.
There was increased expression of Suppressor of Death Domains (SODD) in MGZ cells, suggesting a complex regulation of signaling through the TNFR superfamily. SODD is associated with TNFR1 in vivo, maintaining the receptor in an inactive monomeric state. The release of SODD from TNFR1 permits the recruitment of proteins such as TRADD and TRAF2 to the activated TNFR1 signaling complex [36]. It has been demonstrated that TNF-induced activation of NF-κB is accelerated in SODD-deficient cells. The high expression of SODD may be a major mechanism to dampen TNFR1 signaling in MGZ B-cells in the resting state. The higher expression of CARD11 in MGZ may have a pro-survival function, but it may also have a role in MGZ organization. It has been shown that loss of CARD11 in mice resulted in the complete loss of CD5+ peritoneal cells and reduced number of IgD high IgM low mature splenic B-cells, indicating its role in B-cell development [37]. Two closely related genes, NM23-H1 and NM23-H2, which share an amino acid identity of 88%, were highly expressed in MGZ. NM23 H1 is a granzyme A-activated DNase (GAAD) that is inhibited by SET [38]. The high expression of NM23-H1 and the low expression of its inhibitor SET was opposite in their expression profile in the GC, suggesting that this expression may influence apoptosis in opposite directions in these two B-cell compartments.
Figure 6 DNA repair, replication and protein synthesis.
Figure 7 (A) Apoptosis and cell survival and (B) Cytokines and chemokines and their receptors.
Chemokines, cytokines and their receptors (Figure 7B)
Chemokines attract primary B-cells and play an important role in the homing and localization of B-cell subsets at different stages of antigen-independent and dependent reaction [39]. Our microarray data revealed that CCL18, encoding a chemokine secreted by immature dendritic cells (DC), is specifically upregulated in the GC compartment. Our finding was supported by a recent report showing that CCL18 is produced by GC DC and can attract MNZ B-cells towards GC [40]. The higher expression of CCL18 may be especially important during the onset of a GC reaction, the time point to recruit antigen primed pre-GC B-cells, which then interact with GC DC to initiate and maintain the GC reaction. The GC compartment showed increased expression of CXCL10 (IP-10). which has pleiotropic effects, including stimulation of monocytes and T cell migration [41].
The GC also showed increased transcriptional level of genes that may suppress or control inflammatory responses; e.g., SOCS1 limits cellular response to IFNγ, IL-2 and IL-6[42,43]. Macrophage inhibiting cytokine 1 (MIC1, also known as PLAB) [44], is only expressed by activated macrophages, but not by resting macrophages. Its higher expression in the GC may reflect the presence of moderate numbers of macrophages and its possible role in suppressing the inflammatory response in the GC.
Increased expression of the chemokine CCL20 was observed in MNZ cells. Human naïve and memory B-cells express the cognate receptor for CCL20, CC-chemokine receptor 6 (CCR6) [45]. The high expression of CCL20 may play a vital role in naïve B-cell migration and localization in the MNZ. The chemokine gene CXCL12 (also know as stromal cell-derived factor 1, SDF1) is highly expressed in MGZ cells. The receptor for CXCL12 is CXCR4, which is present on CD34+ cells, myeloid cells, CD4+ T cells, B-cells, epithelial cells, endothelial cells, and dendritic cells. In the bone marrow, stromal cells secrete CXCL12, which is involved in the emigration of hematopoietic precursors to the marrow during embryogenesis [46]. In peripheral lymphoid organs CXCL12 may be involved in the migration of B-cells and possibly other cells, such as T cells and plasma cells, to the MGZ. CCL14 (also known as HCC1) and CCL3 (also known as MIP-Iα) were more highly expressed in the MGZ. CCL14 can activate human monocytes via receptors that also recognize CCL3 [47]. CCL3 is a proinflammatory cytokine important in the clearance of viral infections and the stimulation of the innate immune response [48]. Thus, the expression of this gene may be important in innate immunity in the MGZ of the spleen. CXCL13 and CCL5 were upregulated in both microdissected MGZ and MNZ compared with the FACS-sorted B-cell populations. Previous studies have established an important role for CXCL13 (BLC) in the development of Peyer's patches (PP) and many peripheral lymph nodes. It also controls B-cell migration and thus the organization of B-cell areas [49]. CCL5 (RANTES), a stromal related chemokine, elaborated by activated T, NK and macrophages has been shown to interact with CD44 to activate the MAPK pathway [50]. It is possible that CCL5, under appropriate conditions, contributes to cellular activation that may be particularly relevant to MGZ B-cells, in which rapid response on recognition of antigen stress signal is important.
A number of IFN-induced genes (AIM2, IFNGR1, and IFNAR -1 and -2) were also preferentially expressed and may reflect the unique function of the MGZ to provide the first rapid response to particulate or T cell-independent antigens. The MGZ also showed increased expression of many members of G protein pathways consistent with more active chemotaxis, cell motility and secretory functions. In addition, MGZ cells showed higher expression of IL-7Rα, consistent with the role of the IL-7R in MGZ organization [51]. Aside from its role in B-cell differentiation and proliferation IL-7Rα expression is also required for the recruitment of precursor cells to develop in secondary lymphoid organs and for the proper structural organization of these organs [52].
Figure 8 Stromal signature.
Extracellular matrix and stromal signature (Figure 8)
Cells function within the context of a three-dimensional (3D) extracellular matrix (ECM) that participates in regulating cellular motility, proliferation and survival. In the GC, COL9A3, which encodes collagen IX [53], and COL2A1, which encodes collagen XI [54], were uniquely overexpressed. In the marginal zone COL14A1, COL16A1, COL3A1 and COL6A3 were expressed at higher level, which suggests a role for these genes in the synthesis of specific extracellular matrix. There was also a marked overexpression of macrophage metalloelastase 12 (MMP12), encoding a metalloproteinase that preferentially degrades elastin and takes part in the remodeling of extracellular matrix. No collagen-specific gene was up-regulated in the MNZ.
Microdissected compartments contained a minor component of stromal T cells, macrophages, dendritic cells and fibroblasts whereas FACS-sorted cells from lymphoid tissues comprise almost exclusively B-cells. Thus, an insight into the gene expression profile of the stromal elements can be obtained by comparing the expression profile of FACS-sorted and microdissected cells. We found a set of genes that likely represent the stromal signature. Osteonectin (SPARC), upregulated in LCM samples, encodes a matrix-associated protein that elicits changes in cell shape, inhibits cell-cycle progression, and influences the synthesis of extracellular matrix [55]. It regulates endothelial barrier function through F-actin-dependent changes in cell shape [56]. Two members of the Maf family (MafB, and c-Maf) were also part of the stromal signature. The Maf family of genes encode bZip nuclear transcription factors and play an important role in morphogenesis and cellular differentiation [57]. These genes are expressed in a variety of organs, including the spleen, in agreement with our finding. The MGZ expressed elevated levels of ICAM1 and VCAM1. MGZ B cells express the integrin LFA1 which binds to its ligands ICAM1 and VCAM1, and this interaction may control the localization of these B cells [58] in this compartment. Our results also showed elevated expression of VCAM1, ITGAL (LFA-1) and ITGA6 in the MNZ, suggesting a role for these adhesion molecules in mantle cell localization as well. The kruppel-like transcription factor BCL11a (also called Evi9), which is essential for normal B-cell lymphopoiesis, was upregulated in LCM cells only. Interestingly, bone marrow from BCL11a-/- mouse can induce thymic lymphoma in wild type mice. Thus, the increased expression of BCL11a in the MNZ and MGZ may be physiologically relevant to the function of lymphocytes in these regions [59].
Figure 9 Other unique compartment markers.
Other differentially expressed genes (Figure 9)
Many genes know to be specifically expressed in GC B-cells are found to be upregulated: e. g., BCL6, CD10, GCET1, GCET2, JAW1 and CD38. A number of genes were clearly upregulated in the MNZ or MGZ but their functional significance is largely unknown. Some of these would be interesting targets for further investigation. Among the genes encoding surface molecules, CD59 was highly expressed in the GC. CD59 antigen is a small protein that inhibits complement-mediated pore formation or lysis by preventing the formation of membrane-embedded C9 multimers [60]. It is likely that the over expression of CD59 in GC can prevent complement-mediated damage to FDCs with entrapped immune complex. CD10 and CD38 are well established markers of GC B-cell and over expression of the corresponding mRNA in the GC is expected [61-63]. Notably, CIITA was markedly down regulated in GC cells, associated with a general low expression of MHC transcripts.
Conclusions
The gene expression profiles of the three B-cell compartments reflect distinctive functional attributes of the resident B-cell populations. They also showed different molecular microenvironments that allow the different B-cell populations to differentiate and function properly. GC B-cells have a high proliferation gene signature, whereas MNZ and MGZ cells are characterized by signals that help to maintain the quiescent state. Genes involved in the apoptosis pathway are differentially expressed in the three B-cell compartments, reflecting different adaptations for survival in different B-cell populations. Expression of different chemokines, their receptors, and stromal molecules have been detected. Many of these have been implicated in the establishment of the normal lymphoid architecture in peripheral lymphoid organs and in attracting distinct immune-cell populations to specific lymphoid areas. The expression of unique sets of genes may also reflect the functional adaptation of cells in a specific location, such as genes involved in DNA repair in the GC and genes that are active in innate immune response to infection in the MGZ. Gene expression profiling of B-cell compartments has allowed us to obtain a global survey of the molecular signals that are functionally important in B-cell subpopulations as well as the respective microenvironments. One of the major challenges is to delineate the functions of the uncharacterized genes that are unique to each of the compartments. Another challenge is to exploit these normal transcriptional profiles to further our understanding of the normal immune response and the derangements resulting in the corresponding lymphoid tumors.
Methods
Laser capture microdissection (LCM)
Tissue blocks of tonsils and spleens were snap frozen in O.C.T immediately after surgery. Four micrometer thick frozen sections of reactive tonsils or spleens on plain glass slides were fixed with 70% ethanol for 30 seconds, rinsed in DEPC water and stained with hematoxylin for 30 seconds, followed by another water rinse. The sections were then dehydrated with 70%, 95% and 100% ethanol for 10 seconds each. Finally, the slides were passed through xylene twice, each for 30 seconds. A consecutive section was immunostained for CD3 to guide the dissection. The three B-cell compartments (GC, MNZ and MGZ) were isolated using LCM with the Arcturus PixCell II system (Arcturus Engineering, Mountain View, CA). To avoid contamination, only well-defined GC, MNZ and MGZ were dissected. Cells were captured at the 15-μm laser setting on CapSure LCM Caps (Arcturus). The laser was set to pulse at 60 mW for 200 ms. The Institutional Review Board of the University of Nebraska approved the usage of tissues for this study.
Cell preparation and FACS sorting
Tissue from fresh spleens or tonsils was cut into small pieces in cold RPMI-1640, and cells released by grinding with a glass tissue homogenizer. The crude cell suspension was passed through a nylon mesh (Spectrum Laboratories, Inc) to generate a single-cell suspension. B-cells were firstly isolated using the Human B-cell Isolation Kit (a cocktail of hapten-modified antibodies to CD2, IgE, CD4, CD11b, CD16 and CD36) and the Midi Macs system (Miltenyi Biotec, Auburn, CA). The highly enriched B-cell population (negative fraction) was subjected to 3-color cell sorting. Briefly, 1 × 107 B-cells were stained with IgM-Cy-chrome, IgD-FITC and CD27-PE (BD Pharmingen, SanDiego, CA) at 4°C for 30 min. MGZ B-cells were isolated from the splenic B-cells gated on the IgMhighIgDlowCD27+ fraction, whereas MNZ B-cells were selected based on IgMlowIgDhighCD27- using the BD FACSVantage™ SE high-speed cell sorter (Becton-Dickinson, SanJose, CA)
RNA extraction and T7 RNA amplification
Total RNA was extracted from each sample of microdissected cells with Trizol™ (Gibco BRL, Carlsbad, CA) and further purified with the RNeasy Mini Kit (Qiagen, Valencia, CA). RNA amplification was performed using a modified Eberwine protocol [64]. Briefly, first-strand cDNA was synthesized by reverse transcription using oligo dT(25)-T7 anchoring primer and superscript II at 42°C for 1 hour. Second-strand synthesis was performed with 40 units E. coli DNA polymerase I, 2 units RNase H, 10 units E. coli DNA ligase (Life Technologies, Carlsbad, CA) in 150 μl volume. Antisense RNA (aRNA) was generated by in vitro transcription (IVT) using the Ampliscribe™ High yield Transcription Kit (Epicentre Technologies, Madison, WI) containing 1000 units AmpliScribe T7 enzyme at 37°C for 8–12 hours, as per the manufacture's instruction. Second-round amplification and IVT were performed as described previously [65]. The quality and quantity of aRNA were monitored on agarose gel electrophoresis and by spectrophotometer. Typically, 30–50 μg of aRNA were generated from each 10 ng of total RNA by two rounds of amplification.
Gene expression profiling using the Lymphochip
Analysis of gene expression was performed using the Lymphochip cDNA microarray, which contained 15,132 cDNA clones representing 7399 known or uncharacterized genes [66]. Labeled cDNA from each compartment was first hybridized with a test cDNA microarray to assess the quality and quantity of the amplified aRNA before using them on the Lymphochip. In each experiment, reverse transcription was carried out on 8–9 μg of aRNA, and aminoallyl-dUTP was incorporated into the cDNA using a dUTP: dTTP ratio of 4:1 . The aminoallyl group on the dUTP reacts with the ester group on the cyanine dyes. Cy3 dye was used to label the standard cDNA and Cy5 dye the test probe, and hybridization was performed as previously described [17].
Data and statistical analysis procedure
Each tissue type was independently isolated, amplified and profiled in three separate experiments to enhance the reliability of the gene expression data. Images of hybridized microarrays were obtained and processed using GenePix 4000B microarray scanner (Axon Instruments, Union City, CA). Spots or areas of an array with obvious blemishes were flagged and excluded from subsequent analyses. Fluorescence ratios were normalized for each array by applying a single scaling factor to all fluorescent ratios from the array [17]. The correlation coefficients among 15 hybridized cDNA microarrays were calculated and expressed in Correlation Coefficients Mapping (CCM), programmed in MATLAB© (Mathworks, Inc, Natick, MA), which provided an overview of the similarity of expression profiles between multiple samples. Only genes with at least two values out of the triplicate experiments showing similar behavior were included for analysis. The expression data for each gene from an individual compartment was median/mean centered with weighted variance across the two or three replicates showing similar behavior. The initial data reduction was performed using the two-tailed student t-test to compare the differences in gene expression levels between individual compartments. Genes differentially expressed between the two compartments with a p-value of less than 0.05 were selected for further analysis using the Significance Analysis of Microarrays (SAM) approach, as described previously [22]. SAM assigns each gene a score based on its change in average expression between two groups, relative to the gene's standard deviation of permutated measurements. The scatter plots for observed relative difference vs expected relative difference between two compartments were used to find the potentially significant genes and plotted in the T-distance histogram correlating with the p-values. The genes selected from the common set of the analysis result from both t-statistics and SAM were grouped according to their functional characteristics after analyzing through OMIM or Gene Ontology database ( or ) and viewed by TreeView.
Semi-quantitative and real-time quantitative PCR
To confirm the differential mRNA expression of the genes identified by the Lymphochip in different B-cell compartments, a semi-quantitative RT-PCR was employed. In brief, 200 ng aRNA was reversely transcribed into cDNA with 200 ng random primer using MMLV-RNase H- reverse transcriptase as per the manufacturer's instructions (Invitrogen, Carlsbad, CA). Five-fold serially diluted cDNAs from GC, MNB and MZB were amplified with gene-specific primers for 30 cycles with the following cycling conditions: A denaturation step at 94°C for 2.5 minutes and then each PCR cycle at 94°C for15 sec, 52°C for 30 sec, and 72°C for 30 sec followed by a final extension at 72°C for 5 min. The human HPRT transcript was used as the comparative standard. The products were analyzed by electrophoresis in 2% agarose gel. The primers were designed to amplify the cDNA close to the 3' end of the transcript, and all the PCR products were less than 200 bp in length.
Some of the results from the semi-quantitative RT-PCR were also validated by the real-time quantitative PCR with DyNAmo™ HS SYBR® Green qPCR Kit (MJ Research, Reno, NV) on DNA Engine OPTICON2 (MJ Research, Reno, NV) as per the manufacturer's instructions. The PCR protocol used an initial denaturation of 95°C for 15 minutes followed by 35 cycles (95°C for 10 sec, 52°C for 15 sec and 70°C for 20 sec). The plate was read at 70°C according to the melting point of the amplicon. Serial dilutions of cDNA from the lymphoid standard [67] were used to construct standard curves for the target genes (FAIM, CCL3, SODD, NM23-H1, CARD11, Cyclin G2 and CIITA-8) and the endogenous reference genes (HPRT). For each unknown sample, the relative amounts of target cDNAs and reference cDNAs applied to the PCR reaction system were calculated using linear regression analysis from the corresponding standard curves [68]. Then the normalized expression level of the target gene in each sample was calculated by dividing the quantity of the target transcript with the quantity of corresponding reference transcript. The normalized values of the target transcript were used to compare its relative expression levels in different samples.
Authors' contributions
YS carried out the LCM, in vitro RNA amplification and semi-quantitative PCR. JI participated in the design of the study, microarray procedure, data analysis, and drafted the manuscript. LX, RL and SS participated in data analysis. JE provided the microarray facility and various technical assistance and advice. LS and AR provided the reference standard, the Lymphochip and helpful discussions. KD, GZ, TM participated in helpful discussions and interpretation of the data. WCC conceived, organized and supervised the study, and participated in the analysis and interpretation of the data. All authors have read and approved the final manuscript.
Acknowledgment
This work was supported in part by U.S. Public Health Service grants CA36727 and CA84967 awarded by the National Cancer Institute, Department of Health and Human Services. The UNMC Microarray Core Facility receives partial support from NIH Grant Number 1 P20 RR16469 and from the BRIN Program of the National Center for Research Resources. The authors wish to acknowledge Fan Zhou from the Creighton University, Omaha, NE for his helpful suggestion in FACS sorting and tissue collection.
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| 15369600 | PMC535350 | CC BY | 2021-01-04 16:28:17 | no | BMC Immunol. 2004 Sep 15; 5:20 | utf-8 | BMC Immunol | 2,004 | 10.1186/1471-2172-5-20 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-431554117110.1186/1471-2121-5-43Research ArticleFunctional interdependence between septin and actin cytoskeleton Schmidt Katja [email protected] Benjamin J [email protected] MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK2004 12 11 2004 5 43 43 1 9 2004 12 11 2004 Copyright © 2004 Schmidt and Nichols; licensee BioMed Central Ltd.2004Schmidt and Nichols; 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
Septin2 is a member of a highly conserved GTPase family found in fungi and animals. Septins have been implicated in a diversity of cellular processes including cytokinesis, formation of diffusion barriers and vesicle trafficking. Septin2 partially co-localises with actin bundles in mammalian interphase cells and Septin2-filamentmorphology depends upon an intact actin cytoskeleton. How this interaction is regulated is not known. Moreover, evidence that Septin2 is remodelled or redistributed in response to other changes in actin organisation is lacking.
Results
Septin2 filaments are associated with actin fibres, but Septin2 is not associated with actin at the leading edge of moving cells or in ruffles where actin is highly dynamic. Rather, Septin2 is spatially segregated from these active areas and forms O- and C-shaped structures, similar to those previously observed after latrunculin treatment. FRAP experiments showed that all assemblies formed by Septin2 are highly dynamic with a constant exchange of Septin2 in and out of these structures, and that this property is independent of actin. A combination of RNAi experiments and expression of truncated forms of Septin2 showed that Septin2 plays a significant role in stabilising or maintaining actin bundles.
Conclusion
We show that Septin2 can form dynamic structures with differing morphologies in living cells, and that these morphologies are dependent on the functional state of the actin cytoskeleton. Our data provide a link between the different morphological states of Septin2 and functions of Septin2 in actin-dynamics, and are consistent with the model proposed by Kinoshita and colleagues, that Septin2 filaments play a role in stabilisation of actin stress fibres thus preventing actin turnover.
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Background
Septins are a conserved family of GTPases implicated in various cellular processes. Septin-requiring processes include cytokinesis, polarity establishment, cell cycle checkpoints and formation of a diffusion barrier in yeast [1], as well as cytokinesis, vesicle trafficking and exocytosis in mammalian cells [2-4]. In humans, 12 septin genes have been found so far, many of which also undergo alternative splicing generating dozens of polypeptides [5]. Septins can be isolated from cytosol as hetero-polymeric complexes, which have the ability to polymerize and assemble into higher-order structures in vitro [6,7]. How the polymerisation is regulated and how such higher order assemblies contribute to septin function in vivo is far from clear.
Septin2 (formerly known as Nedd5) is the best-characterised member of the septin family so far. It is ubiquitously expressed and belongs to the acidic subgroup of the septin family consisting of a short N-terminus, a conserved GTPase domain and a coiled coil structure at the C-terminus [8]. Septin2 forms a complex together with Septin6 and Septin7 in vitro [9] and also co-localises with these septins in vivo [10]. Kinoshita and colleagues showed that Septin2 is required for cytokinesis [11]. Microinjection of an anti-Septin2 antibody interfered with cell division resulting in bi-nucleated cells. How Septin2 functions during cytokinesis is unclear. However, its localisation to the contractile ring and midbody structure in the cleavage furrow during late stages of mitosis is consistent with a functional role of Septin2 in limiting diffusion of membrane proteins across the cleavage furrow [12,13].
In interphase cells Septin2 co-localises with actin bundles, and disruption of the actin cytoskeleton by latrunculin or cytochalasin perturbs Septin2 distribution, inducing curved or circular Septin2-containing assemblies [9,14]. Reduction in Septin2 expression level in cells results in attenuation of actin fibres, implying a functional inter-relationship between actin and Septin2. An in vitro bundling assay showed that the interaction between actin bundles and recombinant Septin2/Septin6/Septin7 can be mediated by the bundling protein anillin [4]. Although these in vitro results suggest a mechanism to account for the recruitment of Septin2 to the actin contractile ring during cytokinesis, it is unlikely that anillin has the same function in interphase cells. Anillin is sequestered in the nucleus in interphase cells [15] and the functional significance of the co-localisation between actin and Septin2 in this phase of the cell cycle is still elusive. More generally, although artificial perturbation of actin cytoskeleton can cause re-arrangement of Septin2 in cells it is not clear that this phenomenon is ever replicated during normal cell function.
Here we characterise the mutual inter-dependence of the actin cytoskeleton and Septin2 using a range of in vivo approaches. Both knock down of Septin2 expression and a specific Septin2 truncation mutant resulted in loss of visible actin fibres or bundles. Expression of dominant-negative mutants of the actin-regulating Rho-GTPases RhoA, Rac1 and CDC42 caused re-organisation of both cortical actin and Septin2. Significantly, in ruffling and migrating cells we observed wholesale redistribution of Septin2 into ring-like structures with morphology and dynamics highly similar to those previously observed upon depolymerisation of actin filaments with latrunculin. We propose that Septin2 is required for actin bundling, and that global re-organisation of the actin cytoskeleton in migrating or ruffling cells triggers concomitant re-organisation of Septin2 into a distinct functional state.
Results
Septin2 and actin define interdependent systems
We sought to understand the functional relationship between actin cytoskeleton and Septin2 in vivo. As a first step, indirect immunofluorescence was used to characterise the distribution of these molecules in NRK cells. In interphase cells Septin2 had a filamentous and granular appearance, and co-localised with actin bundles [9]. The interaction with actin bundles, however, was not uniform along the entire length of the actin; Septin2 rather partially decorated the bundle [11]. Septin2 did not co-localise with vinculin, a marker for focal adhesions (Fig. 1B). As previously shown in 3T3 cells [11], disruption of actin filaments upon Latrunculin B treatment resulted in a striking loss of linear Septin2 staining and rearrangement of Septin2 into O- and C-shaped rings (Fig. 1C). These Septin2 rings were sometimes found together with actin-containing particles, but ring-like structures devoid of actin were also visible (arrows in 1C). Thus we could observe the dependence of Septin2 morphology on actin polymerisation described previously [9]. This dependence suggested that septin- and actin-cytoskeletal networks could be functionally inter-dependent. Some members of the septin family also interact with microtubules [16]. For Septin2, however, we were not able to establish such an association (Fig. 1D). In regions of the cell where Septin2 co-localised with tubulin was always actin present as well (Fig. 1E). Nocodazole treatment, which disrupts microtubules, slightly attenuated Septin2 filaments, but did not have a big effect on overall Septin2 organisation (Fig. 1F).
Figure 1 Septin2 interacts with actin, but not with tubulin. (A) Immunofluorescence of endogenous Septin2 (red) and actin (green, stained with phallacidin) in interphase NRK cells. Note the typical filamentous-granular organisation of Septin2 and its co-localisation with actin. (B) Septin2 (red) does not colocalise with endogenous vinculin (green), a marker for focal adhesions. (C) Staining of Septin2 (red) and actin (green) after disruption of the actin cytoskeleton. NRK cells were treated with Latrunculin B for 30 min. Septin2 now forms rings. (D) Septin2 (red) and tubulin (green) mainly have distinct distributions in NRK cells. (E) Enlarged image of the boxed region in D. Regions with overlapping Septin2 (red) and tubulin (blue) staining always contain actin (green) as well (arrow). (F) Cells were treated with nocodazole for 30 min. Microtubule organisation (green) is disturbed, but Septin2 (red) distribution is largely unaffected. All images show a single confocal section. Bars, 10 μm.
In order to test directly the requirement for Septin2 function in maintaining the actin cytoskeleton, we performed RNAi to knock down Septin2 expression in cells. NRK and Hela cells were transfected separately with two different siRNAs based on the Septin2 sequence. Both siRNAs produced equivalent effects, and control siRNAs produced none of the effects described below. 48 hrs after transfection cell morphology was severely compromised and the overall amount of cells was reduced to 43 ± 11.7% of control levels. 70–90% of the remaining cells showed a decreased Septin2 staining by immunofluorescence. There was no indication of selective arrest of the cell cycle at mitosis or cytokinesis, implying that Septin2 is required for correct cell morphology and adhesion or viability during interphase. Western blot analysis of whole cell homogenate revealed a 60% reduction of Septin2 level (expressed as a percentage of the Endoplasmic Reticulum Protein Erp72, so as to control for reduced cell number; Fig 2C). The most interesting observation was the effect on the actin cytoskeleton. Overall actin level (% of Erp72) was reduced to 45% of control levels (Fig. 2C), and in cells where Septin2 staining was diminished actin staining was markedly reduced as well (Fig. 2A). In fact, actin bundles were no longer present in these cells and the weak residual actin staining was restricted to the cell periphery. In contrast, microtubule organisation was not affected upon knocking down Septin2 levels by siRNA (Fig. 2B) and the amount of tubulin present in cells was unchanged (Fig. 2C).
Figure 2 Knock down of Septin2 expression abolishes actin organisation. NRK cells were transfected with siRNA targeting Septin2. (A) Immunofluorescence 48 hrs after transfection. Cells with reduced level of Septin2 (red) have no actin bundles (red) (0). (B) Tubulin distribution (green) is unaffected in cells showing a decreased Septin2 staining (red) (0). (C) 48 hrs after transfection with siRNA NRK cells were lysed and immunoblotted for Septin2, actin, tubulin and Erp72. The amounts of Septin2, actin and tubulin were quantified and normalized to the corresponding amount of Erp72 to account for reduction in cell number upon siRNA treatment. co, control; Erp72, Endoplasmic Reticulum Protein, member of the protein disulfide isomerase family. Similar results were obtained with Hela cells. A single confocal section is shown. Bars, 20 μm.
The loss of actin fibres in cells with reduced Septin2 expression could either be a consequence of reduced actin protein levels or could reflect a more direct role for Septin2 function in maintaining actin fibres. Given that Septin2 forms complexes with other septins and, potentially, further proteins, we reasoned that in the latter case over-expression of Septin2 might be sufficient to sequester or perturb factors required for actin bundling. Over-expression of full length YFP-Septin2 (YFP-Sept2-PB/G/CC) resulted in the formation of large polymeric structures (Fig. 3B). Untagged full length Septin2 also formed those structures when over-expressed, indicating that their formation was not dependent on the presence of YFP in the fusion protein (Fig. 3C). Induction of these anomalous structures correlated directly with a marked decrease in actin bundles (Fig. 3C). In order to ascertain which region of Septin2 is required for this effect various truncated YFP-Septin2 constructs were produced, containing different domains of the protein [17]. NRK cells transfected with YFP-Septin2-PB/G, a construct containing the N-terminus and the GTPase domain but lacking the coiled-coil domain (Fig. 3A), showed a more or less punctate distribution (Fig. 3D). In comparison to full length YFP-Septin2, no filaments aligned with actin stress fibres were visible, but actin organisation was not affected by over-expression of this construct. In marked contrast, a construct containing only the N-terminal polybasic domain (YFP-Septin2-PB) induced loss of actin bundles, and an apparent reduction in total actin staining to approximately 70% of control levels. YFP-Septin2-PB localized as a cytosolic haze. Staining with Septin2 antibodies revealed that endogenous Septin2 has a normal distribution in YFP-Septin2-PB-expressing cells. A third construct that lacks just the polybasic domain (YFP-Septin2-G/CC) did not affect actin bundles and had a more punctate distribution. Constructs fused to a myc-tag or untagged truncated versions of Septin2 showed the same distributions. Taken together, these experiments show that over-expression of Septin2 is sufficient to ablate actin bundles. However, the large polymeric structures induced by Septin2 over-expression are not themselves required for this effect as a construct containing only the polybasic region of Septin2 is sufficient to induce the same effect on actin bundles whilst having a diffuse cytoplasmic localisation. The finding that the polybasic region plus GTPase domain construct did not have the same effects hints at a regulatory role for the GTPase domain.
Figure 3 Characterisation of truncated YFP-Septin2 constructs. (A) Schematic presentation of full length YFP-Septin2 (YFP-Sept2-PB/G/CC) and truncated YFP-Septin2 constructs. (B) In some cells overexpression of full length YFP-Sept2-PB/G/CC induced the formation of higher-ordered YFP-Septin2 containing-structures with differing morphology (C) Untagged full length Sept2-PB/G/CC is also capable of forming these structures and actin filaments are attenuated in those cells. Full length Sept2-PB/G/CC was detected with anti-Septin2-antibody. (D) Distribution of YFP-Septin2 constructs in NRK cells and their effect on the actin cytoskeleton. Cells were transfected with either full length YFP-Sept2-PB/G/CC or the truncated YFP-Septin2 constructs and stained for actin with phallacidin. Only YFP-Sept2-PB, the construct lacking the GTPase domain and the coiled coil domain affects actin organisation. The distributions of YFP-Sept2-PB/G, the construct without the coiled-coil domain, and YFP-Sept2-G/CC, which lacks the polybasic region, are different to full length YFP-Sept2-PB/G/CC, but there is no effect on actin. Images show a single confocal section. Bars. 10 μm.
Another way of determining the extent of interdependence between actin and Septin2 is to perturb actin organisation by interfering with regulators of actin organization. Rho GTPases are such regulators and have diverse effects on the actin filament system [18,19]. RhoA, Rac1 and Cdc42 are the best-characterised family members, and each controls the formation of a distinct actin-containing structure. We studied the effects of the GDP-locked form of RhoA, Rac1 and Cdc 42 on Septin2 distribution. NRK cells were transfected with myc-tagged dominant-negative mutants of these GTPases and cells were stained for actin and Septin2. Although all mutants altered the appearance of the actin cytoskeleton and also affected Septin2 organisation, their effects on these two systems were quite diverse (Fig. 4). In cells expressing the GDP-locked form of Rac1 central actin bundles were attenuated or missing and the formation of thick actin bundles in the cell periphery was induced (Fig 4A). Septin2 was no longer associated with these bundles. The filamentous organisation of Septin2 was fragmented resulting in the formation of rings similar to those seen after latrunculin treatment (inset in 4A). The Cdc42-mutant, although increasing the prominence of actin bundles, had only a slight effect on Septin2. Septin2 still exhibited the filamentous-granular appearance but with more pronounced filaments (Fig. 4B). Cells transfected with the dominant-negative mutant of RhoA showed the most severe phenotype. The majority of the cells lost their ability to attach to the surface of the coverslip, which is explained by RhoA having a role in regulation the formation of stress fibres and focal adhesions, necessary prerequisites for proper cell attachment [20]. RhoA-mutant expressing cells that were still left on the coverslip had a disrupted actin and Septin2 organisation, with a reduction in both actin filaments and septin filaments (Fig. 4C). All these experiments again clearly show the mutual dependence and regulation of septin and actin filaments.
Figure 4 Effects of Rho-GTPases on actin and Septin2 organisation. NRK cells were transfected with GDP-locked forms of myc-Rac1 (A), myc-CDC42 (B) or myc-RhoA (C) and stained for Septin2 and actin. GTPases were detected with a monoclonal anti myc-antibody. (A) Cells expressing the myc-Rac1 mutant lack actin bundles and Septin2 organisation is disrupted. Instead of filaments, Septin2 forms rings (inset, inset was taken from another cell transfected with Rac1-mutant). (B) myc-CDC42-GDP induces the formation of thick actin bundles but has only little effect on Septin2 appearance. (C) Transfection with dominant-negativ myc-RhoA causes the detachment of most of the cells. Cells left on the coverslip have diminished actin filaments and disrupted Septin2 organisation. Images are single confocal sections. Bars, 10 μm.
Septin2 and actin have distinct distributions in moving and ruffling cells
Both actin depolymerisation and expression of Rho-family-GTPase mutants are non-physiological perturbations and functional significance of resultant changes in Septin2 distribution was unclear. We therefore sought a more physiological situation where parallel re-organisation of actin and Septin2 could be assayed. In NRK cells migrating into an experimentally wounded monolayer actin is concentrated in the leading-edge lamellipodia and ruffles characteristic of many moving cells (Fig. 5A) [21]. Septin2, however, was not detectable in these ruffling lamellipodia at the leading edge. It was segregated from actin and was localised in the cell body (Fig. 5A). The appearance of Septin2 in the cell body of ruffling cells was distinct to normal cells and very variable. Instead of filaments, Septin2 formed arc-shaped structures, O-and C-shaped rings and circles with diameters ranging from 0.6–1.4 μ (Fig. 5B, arrows), and punctae. The ring-like structures were not at all associated with actin, but were similar to rings observed after disrupting the actin cytoskeleton upon latrunculin treatment (Fig. 1C). Lamellipodial extensions and ruffling can readily be induced in NRK cells by growing on lysine-coated coverslips (Fig. 5C). As one would predict, actin and Rac1, a GTPase known to be involved in formation of lamellipodia, are readily detectable in the ruffles [22]. Septin2 was exclusively localized in the cell body, and was found in O-and C-shaped rings and circles as in migrating cells (Fig. 5D,5E). These results imply that although Septin2 is associated with actin bundles and plays an important role in regulating these structures, it is not associated with actin in situations where actin organisation is more dynamic. Septin2 that does not interact with actin forms variable curled and ring-like structures morphologically highly similar to those induced by actin depolymerisation.
Figure 5 Septin2 is not associated with actin in moving or ruffling cells. (A) DIC image of moving NRK cells in an experimentally wounded monolayer. Note the leading edges of the cells contain actin (green), but Septin2 (red) is completely missing. (B) Septin2 forms ring-like structures in the body of moving cells comparable to rings observed after latrunculin treatment (compare Fig. 1C). (C) In ruffling cells growing on lysine-coated coverslips Septin2 (red) is detectable in the cell body, whereas actin (green) is concentrated in ruffles. (D) Ruffles are also positive for endogenous Rac1 (green), but Septin2 (red) is clearly missing. (E) In the cell body of ruffling cells Septin2 (red) forms O- and C-shaped ring-like structures, which are not associated with actin (green, stained with phallacidin). Bars, 5 μm.
The filaments and rings formed by Septin2 in vivo are highly dynamic
To gain more insight into Septin2 dynamics we performed fluorescence recovery after photobleaching (FRAP). In a FRAP experiment fluorescent molecules are irreversibly bleached in a small area of the cell (= region of interest, ROI). Subsequent diffusion of surrounding non-bleached molecules into the bleached area leads to recovery and is monitored over time. We used full length YFP-Septin2 for bleaching experiments and investigated the dynamics of GFP-actin in parallel (Clontech). YFP-Septin2 had the same distribution as endogenous Septin2 in interphase and dividing cells and it did not alter the organisation of endogenous Septin2 [12]. First we checked whether Septin2 filaments, which are associated with actin stress fibres in normal NRK cells, are dynamic structures. Photobleaching of a part of the filament showed a recovery of fluorescence over time (Fig. 6B). There was no difference in the dynamics of recovery in the middle or at the end of the Septin2 filament. Although maximal recovery was only about 63% ± 18.74 of the initial fluorescence, the original structure of the Septin2 filament was clearly reformed.
Figure 6 Septin2 forms highly dynamic filaments and rings. (A) YFP-Septin2 has the same distribution like endogenous Septin2 and it does not alter the organisation of endogenous Septin2. Bar, 20 μm (B) Photobleach experiment. NRK cells were transfected with either YFP-Septin2 or GFP-actin. Part of the filaments was bleached and recovery monitored over time. The ratio between mean fluorescence intensity of the prebleached box and the mean fluorescence of the whole cell was normalised to the prebleach ratio and expressed as a function of time (Materials and Methods). The graph shows representative recovery curves, which were fitted to a single exponential curve (solid lines) to calculate tDs and amount of recovery. n = 5. Images show a single confocal section of YFP-Septin2 filaments in NRK cells. Bar, 4 μm. (C) Images of a time-laps movie of YFP-Septin2 in the cell body of ruffling NRK-cells. Arrows indicate the formation cycle of a ring. Bar, 2 μm. (D,E) Photobleach experiments of YFP-Septin2 rings (also see additional file 1 for lysine/ruffling cells and additional file 2 for latrunculin). NRK cells growing on lysine-coated coverslips to induce ruffles were transfected with YFP-Septin2. Rings formed in the cell body were bleached and recovery monitored over time. For bleaching of Septin2 rings upon latrunculin treatment, normal NRK cells were transfected with YFP-Septin2 and treated with Latrunculin B for 20–25 Min before photobleaching. The data were analysed as in B to calculate tDs of recovery (D) and amount of recovery (E). n = 5. Images show a single confocal section of YFP-Septin2 rings in ruffling NRK cells. (F) Quantitative comparison of outer diameters of Septin2 rings in ruffling cells (lysine, empty circles) and upon latrunculin treatment (filled circles). Measurements were done on images of cells transfected with YFP-Septin2.
Next we looked at the dynamics of the Septin2 positive ring-like structures formed in the cell body of ruffling cells. Time-lapse movies of these cells revealed a very active behaviour of Septin2 and a relationship between Septin2 filaments and rings. For example, as shown in Fig. 6C starting with an S-shaped filament half of a ring was formed. After 110 s an O-shaped structure with a diameter of 1.45 μm was seen which then further condensed into a smaller ring (Fig 6C, 190 s). The diameter of this ring was 0.7 μm and the morphology was comparable to rings observed after latrunculin treatment (Fig. 1C) and perturbation of actin/Septin2 organisation by Rac1-mutant (Fig. 4A). Instead of remaining in this structure the ring opened again and formed two half rings which were 1.6 μm apart from each other (Fig. 6C, 250 s). The two halves then came together again, this time forming a C-shaped structure (Fig. 6C, 440 s). Many examples of this cycle of opening and closing were observed and sometimes a disappearing and appearing of a ring-like structure was also detected. FRAP experiments of these ring structures in ruffling cells (grown on lysine-coated coverslips) confirmed that constant exchange of YFP-Septin2 into and out of these structures occurs. Halftimes for recovery (ranging from 10 s to 73.8 s) and amount of recovery (45%-86%), however, were very variable (Fig. 6D,6E. Additional files 1 and 2). Sometimes the recovered structure was distinct to the initially bleached structure in terms of position and morphology (see also Additional File 2).
Next we investigated whether Septin2 rings induced by latrunculin have equivalent properties to those seen in ruffling or migrating cells. Septin2 rings formed upon latrunculin treatment also recovered after photobleaching, with comparable dynamics (half-time for recovery was 8.5 s ± 4.5, total amount of recovery 58.3% ± 3.6; Fig. 6D,6E, movie3). Careful morphological comparison of ring structures in ruffling cells and in latrunculin treated cells again revealed considerable similarity. Rings formed upon latrunculin treatment had a slightly more uniform outer diameter (0.74 ± 0.14 μm) but were similar in size to ring-like structures in ruffling cells (0.5 μm-1.4 μm; Fig. 6F). In summary, Septin2 can participate in large-scale macromolecular assemblies with differing morphologies in response to differing physiological situations and remodelling of the actin cytoskeleton. In all instances these assemblies are highly dynamic with constant exchange of Septin2 in and out.
Discussion
Previous experiments have shown that Septin2 partially co-localises with actin fibres, that actin can affect Septin2 polymerisation in vitro, that depletion of Septin2 perturbs the morphology of actin bundles, and that depolymerisation of actin fibres can cause changes in Septin2 morphology. In this study we provide additional direct evidence for the in vivo significance of actin and Septin2 interaction. Thus we show for the first time that Septin2 expression is required for maintenance of normal actin protein levels, that over-expression of a truncated version of Septin2 causes loss of visible actin bundling without perturbing the distribution of endogenous Septin2, and that the circular and ring-shaped Septin2 structures induced by actin de-polymerisation are also found in physiological situations where the actin cytoskeleton is radically remodeled.
Partial co-localisation between Septin2 and the tubulin network has also been reported [10]. Our immunofluorescence data, however, argue, that the interaction between Septin2 and microtubules is different from the Septin2-actin interplay and is not crucial for microtubule integrity. Although Septin2 distribution is slightly affected upon nocodazole treatment, this effect is less severe and not comparable with the disruption of Septin2 organisation upon latrunculin treatment (Fig. 1). Moreover, neither knock-down of Septin2 expression upon siRNA nor overexpression of truncated Septin2 constructs affected microtubule organisation (Fig. 2, data not shown). Since in regions of the cell where Septin2 co-localises with tubulin we always find actin as well, we do not think that there is a direct interaction between Septin2 and tubulin, as has been shown for other septins [16,23]. The distinct distribution of Septin9, which is associated with tubulin and is clearly affected upon nocodazole treatment, and Septin2 in dividing cells also suggests that the co-localisation seen between Septin2 and tubulin is not functionally significant [16].
Septin2 and actin distributions, however, are clearly highly interdependent. Overexpression of GDP-locked form of RhoA, Cdc42 and Rac1 highlights this interplay (Fig. 4). At this point we do not know how these GTPases act in this context, whether it is via modulating actin dynamics and/or controlling Septin2 dynamics. It is known that the Cdc42 effectors Borg1, 2 and 3 can bind to Septins in vitro and in vivo [7,10]. Endogenous Septin6 and Septin7, which form a complex with Septin2, can be immunoprecipitated by an anti-Borg3 antibody and expression of Borg interferes with normal septin distribution. Full-length myc-Borg3 induces the formation of long and thick septin fibres and Cdc42 negatively regulates this effect by inhibiting the binding of Borg3 to septins. These data suggest that the formation of thick Septin2 filaments and actin filaments we see upon expression of the dominant-negative Cdc42 mutant is controlled by a regulatory mechanism, which modulates Septin2 function directly and not only via regulating actin organisation.
In our experiments comparing truncated versions of Septin2 in cells we could show that the GTPase domain of Septin2 is sufficient to prevent the loss of actin bundles induced by expression of the polybasic region alone. The exact role of septin GTPase activity is still unclear [17]. Using recombinant septins, Sheffield and colleagues [7] demonstrated that although pre-assembled Septin6/Septin7/Septin2-filaments show only a slow GTPase activity, GTP-hydrolysis occurs during formation of heterodimers, a process before the assembling of filamentous complexes. Microinjection of non-hydrolyzable GTP (GTPγS) in cells disrupted fibrous distribution of Septin2 suggesting that the fibrous distribution of Septin2 requires GTP-hydrolysis [11]. In contrast, there is no evidence that GTPase-activity is necessary for assembly of septin filaments in curved bundles and ring-like structures in vitro [9]. So far we do not know how Septin2 interacts with actin bundling proteins and how it gets recruited to actin filaments in interphase cells. We do not know whether the reduction in actin expression levels induced by septin2 RNAi is a cause or an effect of the observed loss of actin bundles. Identification of binding partners for Septin2 will be necessary to elucidate the mechanisms underlying the property of Septin2 to stabilise actin bundles.
It has been shown in vitro that Septin2 is capable of forming ring-like structures and spirals in an actin-independent fashion [9]. Here, we provide in vivo characterisation of this intrinsic property of Septin2. Moving cells and ruffling cells are the first cell systems described so far where the actin-independent distribution of Septin2 in O- and C-shaped rings has been studied in a physiological context, without interfering with cell viability and function. These model systems allow us to draw two firm conclusions: 1. That Septin2 is not associated with actin in regions where highly dynamic actin is not organised in fibres and is being constantly remodeled, and indeed Septin2 is actually efficiently excluded from these regions (e.g. at the leading edge of moving cells and in ruffles): 2. That Septin2 when not associated with actin forms rings and ring-like structures instead of filaments (Figs 5, 6). This is in agreement with in vitro data obtained from recombinant septin complexes showing their tendency to self-assemble into rings and spirals [9]. In contrast, however, to these in vitro structures, which are highly stable, the in vivo assemblies are highly dynamic (movies 1–3). Bleaching experiments clearly showed the constant exchange of Septin2 in and out of the assemblies as well as in and out of actin-dependent Septin2 filaments. The function of actin-independent Septin2 assemblies, however, is still elusive. Since they seem to be freely localised in the cytosol and are not associated with membrane structures (unpublished observations), they might represent storage containers of Septin2. The dynamic behaviour of Septin2 explains how cells can adjust Septin2 function to different needs. The identification of further proteins involved in this process (e.g. GAPs, GEFs, bundling proteins) is necessary for molecular characterisation of the interplay between Septin2 and actin.
Conclusions
Our data provide a link between the different morphological states of Septin2 and functions of Septin2 in actin-dynamics, and confirm the physiological relevance of the model proposed by Kinoshita and colleagues [9], that Septin2 filaments play a role in stabilisation of actin stress fibres thus preventing actin turnover.
Methods
Antibodies and constructs
Anti-Septin2 polyclonal antibody was a gift from M. Kinoshita. Anti-human vinculin monoclonal antibody (clone hVIN-1), anti-c-myc monoclonal antibody (clone 9E10) and anti-α-Tubulin monoclonal antibody (clone DM1a) were all from Sigma. Anti-beta actin monoclonal antibody (AC-15) for immunoblotting was from abcam, BODIPY®FL phallacidin and Alexa Fluor ® 568 phalloidin for immunofluoresence were obtained from Molecular Probes. Anti-Endoplasmic Reticulum Protein 72 (Anti-Erp72) was from Calbiochem. Myc-constructs of GDP-locked RhoA, Rac1 and CDC42 and the anti-rac monoclonal antibody were kindly provided by H. Mellor. pEGFP-actin was obtained from Clontech. Secondary antibodies used were as follows: Donkey Anti-Rabbit IgG Cy™3 conjugated (Jackson Immuno Research Lab.), Donkey Anti-Mouse IgG Cy™5 conjugated (Jackson Immuno Research Lab.) and Alexa Fluor ® 488 goat anti-mouse IgG1 (Molecular Probes) for immunofluorescence. IRDye 800 donkey anti-rabbit IgG (Rockland) and Alexa Fluor ® 680 goat anti-mouse IgG (Molecular Probes) for immunoblotting.
Cloning of YFP-Septin2-constructs
To generate full length and truncated YFP-Septin2-constructs, PCRs of Septin2 image clone (Clone Id: 548005, from HGMP, Hinxton/UK) were performed using the following primers: 5'-GCGCTCGAGTGTCTAAGCAACAGCCAAC (sense) and 5'-ATCCCGG GTTACACGTGGTGCCCGAGAGC (antisense) for full length YFP-Septin2-PB/G/CC (nucleotides 1–1081); 5'-GCGCTCGAGTGTCTAAGCAACAGCCAAC (sense) and 5'-CGCCCCGGGTTAGCCTCTCTTGAGTCTCTC (antisense) for YFP-Septin2-PB/G (nucleotides 1–922); 5'-GCGCTCGAGTGTCTAA GCAACAGCCAAC (sense) and 5'-CGCCCCGGGTTACACCATCAGTGTGAACTC (antisense) for YFP-Septin2-PB (nucleotides 1–127); 5'-GCGCTCGAGAGTTCACACTGATGGTGGT (sense) and 5'-ATCCCGGGTTACACGTGGTGCCCGAGAGC (antisense) for YFP-Septin2-G/CC (nucleotides 113–1081). The fragments were cloned in pEYFP-C1 digested with Xho1 and Xma1. To generate untagged full length Septin2 5'-GCGCTCGAGATGTCTAAGCAACA GCCAAC (sense) and 5'-ATCCCGG GTTACACGTGGTGCCCGAGAGC (antisense) were used in the PCR reaction. The fragment was cloned in SNAG4M cut with Xho1 and Xma1. All clones were confirmed by DNA sequencing.
Cell culture, drug treatment, plasmid transfections, immunofluorescence
NRK cells were grown using standard techniques in DMEM, 10% FCS, 1% Penicillin at 10%CO2. To induce the formation of ruffles in NRK cells coverslips were coated with 0.01% poly-D-lysine for 5 min, washed with PBS and air dried before cells were seeded. A wound assay was used to investigate distribution of Septin2 in moving cells. Briefly, NRK cells were grown until confluency and a wound was scratched in the monolayer using a tip. Two hours later cells were fixed and processed for immunofluorescence. To disrupt the actin cytoskeleton or microtubules cells were incubated for 30 min in DMEM containing 150 nM Latrunculin B (Molecular Probes) or 10 μM nocodazole (Sigma), respectively. Plasmid transfections were carried out with Fugene6 (Roche).
For immunofluorescence cells were fixed with 2% Formaldehyd in either PBS or microtubule-stabilisation buffer (0.1 M Pipes, pH 6.9, 2 mM EGTA, 2 mM MgCl2, 4% PEG 8000) for 20 min, permeabilised with 0.2% saponin/10%FCS in either PBS or microtubule-stabilisation buffer for 10 min and subsequently incubated with primary and secondary antibodies.
RNA interference (RNAi) with small interfering RNA (siRNA) and immunoblotting
Two siRNAs with the following sequences: 5'-AAAGGACATGAATAAAGACCA (sense), and 5'-AAGTGAATATTGTGCCTGTCA (sense) were chosen to target nucleotides 939–957 and 521–539 of Septin2, respectively, and were supplied by Dharmacon Research Inc. siRNAs were annealed to make siRNA duplexes according to the manufacture's protocol and transfections were carried out using Oligofectamine (Invitrogen). To check for specificity of knock down we used siRNA duplexes targeting caveolin 1 [24] and rhomboid (gift from A. McQuibban). 48 hrs after transfection cells were processed for immunofluorescence or immunoblotting. For immunoblotting cells were washed with PBS, scratched of in sample buffer (2% SDS, 80 mM Tris/HCl pH 6.8, 10% Glycerin, 0.01% bromphenolblue, 5% β-Mercaptoethanol) and sonicated. The samples were boiled at 80°C for 3 min, centrifuged and the supernatant was loaded onto the gel followed by Western blotting. Immunoreactive bands were detected by the Infrared Imaging System Odyssey (Li-Cor Biosciences).
Microscopy and photobleaching
All images and movies were obtained using BioRad Radiance and Zeiss LSM510 confocal microscopes equipped with standard filter sets and laser lines for the detection of YFP, Cy2, Alexa Fluor 488, BODIPY, Cy3 and Cy5. Live cell microscopy was carried out at 35°C in imaging medium (DMEM without phenol red, 10%FCS, 50 mM HEPES pH 7.2). Photobleaching experiments were performed with the confocal zoom set to 3 and the confocal pinhole set to 2–4 Airy units. Bleaching of actin or Septin2 filaments was carried out with a 40X 1.3 NA objective lens. A box of interest was bleached using 35 scans with the 488 and 514 laser line at full laser power. For photobleaching of ring structures a 63X 1.4 objective lens was used and a ring of interest was bleached using 25 scans with the 514 laser line at full laser power. Pre- and post-bleach images were monitored at low laser intensity. Fluorescence recovery in the bleached region and the overall fluorescence in the whole cell during the time series were quantified using the Zeiss LSM software. After subtracting the background (= mean fluorescence intensity in the bleached region after bleach) the ratio between mean fluorescence intensity of the bleached region and the mean fluorescence of the whole cell was expressed as a percentage of the pre-bleach ratio of these values. These normalised data were fitted to a single exponential curve using the PRISM software (GraphPad Software Inc., San Diego) to derive amount of recovery and characteristic diffusion time tD, which indicates the time at which half of the fluorescence has recovered.
List of abbreviations used
GFP, green fluorescent protein; YFP, yellow fluorescent protein; siRNA, small interference RNA; RNAi, RNA interference; ERP72, endoplasmic reticulum protein 72; FRAP, fluorescence recovery after photobleaching; ROI, region of interest; GAP, GTPase activating protein; GEF, GTP exchange factor.
Authors' contributions
KS carried out all the experimental work and drafted the manuscript. BJN conceived of the study, participated in its design and edited the manuscript.
Supplementary Material
Additional File 1
Photobleaching of YFP-Septin2 ring-like structures formed in the cell body of ruffling cells. Ruffling NRK cells were transfected with YFP-Septin2. The YFP-Septin2 ring in the middle was photobleached and recovery monitored over time. Movie covers 200 s (also see Fig. 6D).
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Additional File 2
Photobleaching of YFP-Septin2 rings upon latrunculin treatment. NRK cells transfected with YFP-Septin2 were treated with Latrunculin B for 20–25 min before photobleaching. A ring in the upper right part of the cell was photobleached and recovery monitored over time. Note recovery seems to occur from a structure in the background. Movie covers 200 s.
Click here for file
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| 15541171 | PMC535351 | CC BY | 2021-01-04 16:31:36 | no | BMC Cell Biol. 2004 Nov 12; 5:43 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-43 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-451554118510.1186/1471-2148-4-45Research ArticleDensity-dependence and within-host competition in a semelparous parasite of leaf-cutting ants Hughes William OH [email protected] Klaus S [email protected] Line V [email protected] Dorthe [email protected] Lene [email protected] Michael [email protected] Jacobus J [email protected] Department of Population Biology, Institute of Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark2 Department of Ecology, The Royal Veterinary and Agricultural University, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark3 School of Biological Sciences, A12, University of Sydney, Sydney, N.S.W. 2006, Australia2004 14 11 2004 4 45 45 25 5 2004 14 11 2004 Copyright © 2004 Hughes et al; licensee BioMed Central Ltd.2004Hughes 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
Parasite heterogeneity and within-host competition are thought to be important factors influencing the dynamics of host-parasite relationships. Yet, while there have been many theoretical investigations of how these factors may act, empirical data is more limited. We investigated the effects of parasite density and heterogeneity on parasite virulence and fitness using four strains of the entomopathogenic fungus, Metarhizium anisopliae var. anisopliae, and its leaf-cutting ant host Acromyrmex echinatior as the model system.
Results
The relationship between parasite density and infection was sigmoidal, with there being an invasion threshold for an infection to occur (an Allee effect). Although spore production was positively density-dependent, parasite fitness decreased with increasing parasite density, indicating within-host scramble competition. The dynamics differed little between the four strains tested. In mixed infections of three strains the infection-growth dynamics were unaffected by parasite heterogeneity.
Conclusions
The strength of within-host competition makes dispersal the best strategy for the parasite. Parasite heterogeneity may not have effected virulence or the infection dynamics either because the most virulent strain outcompeted the others, or because the interaction involved scramble competition that was impervious to parasite heterogeneity. The dynamics observed may be common for virulent parasites, such as Metarhizium, that produce aggregated transmission stages. Such parasites make useful models for investigating infection dynamics and the impact of parasite competition.
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Background
In most models of host-parasite dynamics, the parasites are considered as part of discrete infections, involving only a single parasite. However, as the transmission stages of parasites tend to be clustered, the majority of host-parasite interactions will rather involve multiple parasite individuals. This is particularly the case for those parasites that exhibit a semelparous life-history, releasing all their transmission propagules in a single event that normally coincides with host death [1,2]. In the same models, transmission is assumed to follow the mass action principle, βSI, where S and I are the densities of susceptible and infected individuals respectively and β is a constant describing the probability of infection [3]. Thus the probability of a host becoming infected per unit time will be directly proportional to the number of parasites that it encounters. Host-parasite interactions will also often involve multiple genotypes of parasites, further complicating their dynamics [4,5].
The co-occurrence of multiple parasites within a single host makes within-host competition between the parasites inevitable. Hosts represent limited resources and there is a carrying capacity for the total biomass of parasites that the host resources can support. The within-host growth of the parasites will thus normally be density-dependent, decreasing as the carrying capacity is approached [6,7]. Interactions between different parasite genotypes are often considered to result in increased virulence, but have also been predicted to lead to decreased virulence depending on the dynamics [4,8-11]. The outcome of interactions will depend critically on both the scale of competition and the relatedness of the parasites involved, with higher relatedness selecting for reduced competition, or even cooperation, and more local competition offsetting this [12-14]. The nature of the within-host competition has further been characterized as a continuum between two extremes: superinfections, which exhibit contest competition with the most virulent parasites eliminating those less virulent, and coinfections, where the parasites differ little in virulence and resources end up being shared amongst individuals via scramble competition (or in its purest form by parasites exploiting different within-host niches and not competing at all) [8,15,16]. The mechanism of competition can be: (1) exploitation, with parasites competing for resources, (2) interference, with parasites, for example, producing antagonistic compounds, or (3) apparent, being mediated by the host immune system [5]. Exploitation competition is inevitable whenever parasites do not have completely separate niches within the host, while interference competition via antagonistic compounds drives, for example, the interaction between strains of entomopathogenic bacteria that produce bacteriocins [17]. Apparent competition has been argued to be the most important type [4], and can occur even in invertebrates with their less complex immune systems. For example, malaria parasites suppress the immune response of their mosquito vector [18], and various entomopathogenic fungi have been shown to produce immunodepressant compounds [19-23].
In spite of the fundamental importance of interactions between different strains of parasites, empirical studies of mixed infections are relatively rare and a number of them have produced results that conflict with the theoretical predictions [5,24]. Most studies have found either that mixed infections are more virulent than single infections [25-29], or that virulence equalled that of the most virulent strain [23,25,30-32]. Interactions can be more complex though, and may also depend on environmental conditions, host genotype, or on the parasite genotypes involved [25,32-35]. For example, the virulence of bacteriocin-producing bacteria matches the winning strain only when one strain can kill the other, but is reduced compared to single infections when both strains can kill each other [17]. Furthermore, consideration of virulence only does not provide a full picture of the complexity of the interaction. The production of transmission stages may be increased [26-28,30] or decreased [31] during mixed infections, and may also depend upon the order of infection [32]. Although the finding that virulence matched that of the most virulent strain might suggest that that strain has outcompeted the less virulent strain, in some studies the transmission stages produced were from the both strains [29,31,33], or even entirely from the less virulent strain [23].
Here we examine the infection dynamics of the parasite M. anisopliae var. anisopliae (Metschnikoff) (Deuteromycotina: Hyphomycetes) in the leaf-cutting ant Acromyrmex echinatior Forel (Hymenoptera: Formicidae: Attini). M. anisopliae var. anisopliae is a generalist entomopathogenic fungus that is known to infect leaf-cutting ants [36-41], as well as many other insects. Infection may be from sporulating cadavers or from spores dispersed in the soil. Metarhizium spores have a tendency to remain attached to one another, making even dispersed spores likely to be clustered, and soil has been estimated to contain as many as 1,000 to 50,000 spores g-1 [41]. Spores germinate and penetrate directly through the host cuticle without first growing over the surface of it as some other fungi do. Host individuals will thus often be infected by multiple parasite propagules. In addition, the group-living life-style of leaf-cutting ants, as with other social insects, makes them especially prone to being exposed to multiple infections under natural conditions [42]. Inside the host, the parasite produces blastospores and then hyphal bodies that release immunodepressant and antibiotic toxins [19,21,22]. After a period of time the host dies by some combination of the depletion of its resources due to the parasite infection, direct invasion of tissues by hyphae or the action of the parasite's toxins [21], and the parasite sporulates shortly after this. Metarhizium thus has a semelparous life-history and is an 'obligate killer' [1], producing transmission stages only after host death. Such parasites represent excellent models for studying infection-growth dynamics because the rate of successful infections equates exactly to host mortality, the time of host death will relate to the number of hyphal bodies within the host and thus acts as a gauge of within-host growth, and because the lifetime reproductive output of the parasite is represented entirely by the spores produced upon host death in contrast to other parasites that produce transmission stages continually [1]. Yet while applied studies using Metarhizium are common, fundamental studies of its infection dynamics are rare.
There is a diversity of M. anisopliae var. anisopliae strains near leaf-cutting ant nests in Panama [41], indicating the potential for within-host competition between multiple parasite genotypes. We established and compared the dynamics of within-host competition in this system at two levels. We first investigated competition between parasites of the same genotype by establishing whether the infection rate of M. anisopliae var. anisopliae adhered to the mass action principle and whether parasite growth and fitness was density-dependent. We then examined whether the infection dynamics were consistent between different strains of parasite, and how the dynamics were affected by intraspecific within-host competition involving multiple parasite genotypes. We used two Panamanian strains that had had the opportunity to coevolve with A. echinatior and one exotic strain that had never previously encountered the host. A number of studies suggest that exotic strains are less competitive during within-host interactions than are native strains that have coevolved with the host [43-48]. We define parasite virulence to be parasite-induced host mortality as measured by case mortality and time-of-death. This differs from the instantaneous mortality rate used in many models but has been argued to be a more suitable measure of virulence [49]. Note that because the ant hosts were adult individuals that do not grow any further, the potential advantage to an obligate killer parasite of delaying host death to allow further host growth before semelparous reproduction [1], will not apply in this system.
Results
Experiment 1: intra-strain competition
The dose of Metarhizium anisopliae var. anisopliae spores applied had a significant effect on ant mortality (Wald = 131.1, d.f. = 10, P < 0.0001) (Figure 1). The colony of origin did not affect either the dose-response relationship (Wald = 13.2, d.f. = 10, P = 0.213) or mortality overall (Wald = 1.56, d.f. = 1, P = 0.212). The mortality caused by the lowest two doses did not differ significantly from that in the controls (Figure 1). There was also no significant difference in mortality between the highest three doses because mortality was close to 100% at all these doses. The dose-mortality relationship consequently followed a sigmoidal pattern (F3,7 = 162.5, P < 0.0001; Figure 2a).
Figure 1 Survival of ants in Experiment 1 treated with serial doses (spores/ant) of M. anisopliae var. anisopliae isolate KVL 02–56 or a control solution of 0.05% Triton-X (n = 60). Different letters indicate doses whose survival distributions differed significantly.
Figure 2 Dose relationships for ants in Experiment 1 treated with serial doses of M. anisopliae var. anisopliae isolate KVL 02–56 or a control solution of 0.05% Triton-X. (a) Mortality at end of experiment (y = -0.021x3 + 0.144x2 - 0.054x + 0.171, r2 = 0.986). (b) Proportion of dead ants sporulating (y = 0.113x + 0.269, r2 = 0.700). (c) Mean number of spores (± SE) produced per sporulating ant (y = 0.3692x + 0.6692, r2 = 0.4081). (d) k-values for ants in Experiment 1 treated with serial doses of M. anisopliae var. anisopliae isolate KVL 02–56 (y = 0.809x + 0.468, r2 = 0.976). The dashed line has a slope of 1 and is included for comparison. (e) Per capita fitness (dashed line) and probability of infection (solid line; calculated by multiplying the probability of death and of sporulation if death occurs). (f) Per capita fitness after adjusting for the probability of infection.
There was no effect of the time between death and the assessment of spore production upon either the proportion of cadavers sporulating (Wald = 1.97, d.f. = 1, P = 0.16) nor the number of spores produced (F1,127 = 1.13, P = 0.289). This supports the assumption that sporulation was complete at the time of assessment and also indicates that parasites that took longer to kill their host did not produce more spores. The proportions of cadavers sporulating were significantly less than expected based upon the number of ants estimated (from the control data) to have died from other causes (F1,17 = 8.55, P = 0.0095). This indicates that certain ants killed by Metarhizium failed to sporulate. Both the proportion of ant cadavers sporulating (Wald = 19.4, d.f. = 9, P = 0.022) and the mean number of spores produced per sporulating cadaver (F9,127 = 1.97, P = 0.048) increased significantly with dose (Figures 2b and 2c). However the increase in spore production was relatively small, only doubling over the full range of doses examined. When the spore numbers produced were used to calculate k-values (which assess the density-dependence of growth, with a zero value indicating that spore production increases proportionally to dose and positive values indicating spore production is less than proportional to dose), it was found that k increased significantly with dose (F1,9 = 231.1, P < 0.0001; Figure 2d). The slope of the relationship (0.8) was significantly less than 1 (t = 2.42, d.f. = 8, P = 0.042). Correspondingly, the per capita fitness of the parasite decreased linearly with dose, while, in contrast, the probability of infection (calculated by multiplying the probability of death by the probability of a cadaver sporulating) increased sigmoidally (Figure 2e). By multiplying these two variables, the overall fitness of the parasite can be calculated, and can be seen to decrease more or less linearly with dose (Figure 2f).
Experiment 2: inter-strain competition
Ant survival was affected significantly by the concentration of M. anisopliae var. anisopliae spores (Wald = 186.0, d.f. = 5, P < 0.0001), the strain (Wald = 9.42, d.f. = 3, P = 0.024), and also the colony of origin (Wald = 13.5, d.f. = 4, P = 0.009). There were no significant interactions between strain and concentration (Wald = 11.8, d.f. = 15, P = 0.681). Mortality was generally positively correlated with dose, although the significance of pairwise differences between doses did vary somewhat between strains (Figure 3). In none of the strains was there any difference in survival between the lowest two doses, and, with the exception of strain 02–73, there was also no difference in survival between the highest two doses. Overall, ant survival was greatest when treated with the allopatric Ma275 strain, followed by the 02–73 strain (obtained from a soil sample at the collection site), from which it did not differ significantly (Breslow statistic = 2.45, d.f. = 1, P = 0.118). Survival of ants treated with Ma275 was significantly greater than of those treated with either the 02–72 strain (obtained from an Atta worker) (Breslow statistic = 8.36, d.f. = 1, P = 0.004) or the mixture of all three isolates (Breslow statistic = 5.00, d.f. = 1, P = 0.025) (Figure 3). The survival distributions of ants treated with the different strains did not otherwise differ significantly (02–72 vs. mixture: Breslow statistic = 0.84, d.f. = 1, P = 0.360; 02–73 vs. mixture: Breslow statistic = 0.26, d.f. = 1, P = 0.613; 02–72 vs. 02–73: Breslow statistic = 2.04, d.f. = 1, P = 0.153).
Figure 3 Survival of ants in Experiment 2 treated with either Metarhizium anisopliae var. anisopliae isolate (a) KVL 02–73, (b) KVL 02–72, (c) Ma275 or (d) a mixture of all three. Different letters indicate doses whose survival distributions differed significantly.
Although the proportion of ant cadavers sporulating was consistently high (>80%) in strain 02–73 and was positively correlated with dose in the other strains (Figure 4a), the interaction between strain and dose was nonsignificant (Wald = 8.92, d.f. = 15, P = 0.882), as were both the main effects (strain: Wald = 2.81, d.f. = 3, P = 0.422; dose: Wald = 5.49, d.f. = 5, P = 0.359). Spore production from the sporulating cadavers was estimated by the ranking method described. It was found to be unrelated to the lag-time between death and ranking (F1,270 = 0.489, P = 0.485) and to increase slightly with dose in all treatments (F5,270 = 2.42, P = 0.036; Figure 4b). The relationship between spore production and dose did not differ between strains (F14,270 = 1.12, P = 0.342) and the strains also did not differ overall (F3,270 = 0.19, P = 0.903). The k-values for all three strains were positively correlated with the number of spores applied (F1,15 = 3612.9, P < 0.0001) and had similar slopes (F3,15 = 0.78, P = 0.523; Figure 4c). The values for Ma275, though, were significantly lower than for the other strains (F3,15 = 13.71, P = 0.0001).
Figure 4 Dose relationships and lines of best fit for ants in Experiment 2 treated with either Metarhizium anisopliae var. anisopliae isolate KVL 02–72 (circles; grey, dashed line), KVL 02–73 (triangles; black, dashed line), Ma275 (squares; grey, solid line) or a mixture of all three (diamonds; black, solid line). (a) Proportion of dead ants sporulating. Lines of best-fit are: KVL02–72: y = -0.0686x2 + 0.5434x - 0.0014, r2 = 0.8983, P = 0.032; KVL 02–73: y = 0.0292x + 0.8339, r2 = 0.5182, P = 0.107; Ma275: y = 0.1623x + 0.0839, r2 = 0.7699, P = 0.030; mixture: y = -0.0532x2 + 0.4286x + 0.1476, r2 = 0.9291, P = 0.019. (b) Spore ranks of sporulating ants. Lines of best-fit are: KVL02–72: y = -0.358x2 + 2.5553x + 3.391, r2 = 0.9148, P = 0.025; KVL 02–73: y = 0.411x + 5.5392, r2 = 0.7212, P = 0.032; Ma275: y = 0.222x + 6.1677, r2 = 0.116, P = 0.575; mixture: y = 0.1261x3 - 1.2959x2 + 3.7405x + 4.265, r2 = 0.9646, P = 0.833. (c) k-values. Lines of best-fit are: KVL02–72: y = 0.93x - 1.18, r2 = 0.992, P < 0.0001; KVL 02–73: y = 0.953x - 0.982, r2 = 0.999, P < 0.0001; Ma275: y = 0.972x - 2.06, r2 = 0.995, P = 0.0002; mixture: y = 0.991x - 1.127, r2 = 0.998, P < 0.0001.
Discussion
Parasite virulence, as measured by host mortality, was found to show a clear density-dependent pattern. This has also been recorded previously in Metarhizium [e.g. [50-53]] and other parasites [6,7,54]. Rather than being a linear relationship though, the pattern was sigmoidal. Mortality was not increased significantly by further increases in dose beyond 1 × 107 spores per ml. In addition, the mortality caused by doses of 1 × 104 spores per ml or less did not differ from that of ants treated with the control solution (Figure 2a). All parasite-induced mortality was expected to have occurred by twenty days after treatment, even at the lower doses, and this assumption was supported by the levelling out of mortality seen in both experiments. It follows from this that the infection rate of Metarhizium is directly represented by the host mortality rate after twenty days. The lack of a difference in mortality between ants treated with the two lowest doses and the control ants suggests the occurrence of an Allee effect, with an invasion threshold for infection to be successful [54]. Such an effect has also been evidenced in some other studies of Metarhizium [e.g. [50,53]], and is a subtle effect that will only be detected when a sufficient range of doses is tested. Many models of host-parasite dynamics are based upon the mass-action principle, under which the infection rate is a linear function of the density of parasites a host individual encounters [3]. The sigmoidal pattern observed in the Metarhizium-Acromyrmex system indicates that, here at least, this principle applies only at intermediate doses.
The Allee effect most probably relates to the effectiveness of the host defences against parasites. Leaf-cutting ant defences, as with most insects, consist of 'first-line' defences involving grooming and the secretion of antibiotic compounds on to the cuticle, and 'second line' defences based upon the cellular and humoral immune responses [37,40,55-58]. Spores will interact independently with the first line defences, the immune response may be saturated if it has to defend against very high numbers of parasites. This effect will be exacerbated by the toxins that the hyphae of many Metarhizium strains produce to incapacitate the immune system [19,21,22,59]. Greater doses of infecting spores will more quickly produce a larger pool of mycelium, which will produce greater quantities of these toxins and thus make the immune system decreasingly capable of mounting an effective response.
Neither the probability of a cadaver sporulating nor the number of spores it produced were related to the time of host death, indicating both that parasites did not gain increased spore production by taking longer to kill the host, and that spore production was complete at the time of assessment. Both spore production and the proportion of cadavers sporulating were positively correlated with the within-host density of the parasite. However, the efficiency of the conversion of host biomass into parasite propagules was negatively density-dependent, as demonstrated by the k-values. The number of spores produced was less than proportional to increases in the dose of parasites applied, indicating the occurrence of density-dependent parasite growth and within-host competition. Although this competition was between genetically identical parasites in the first experiment, it still resulted in a reduction in parasite fitness because of the less efficient use of host resources. Were spore production to be unaffected by, or even inversely related to dose, as found in a study with Beauveria [60], then this negative effect on fitness would be even greater.
It is important to note that this is based only upon the cadavers that sporulated. As both the proportion of cadavers that sporulated and the number of ants that died increased with dose, there is a trade-off for the parasite (Figure 2e). Higher doses will result in more hosts dying and producing parasite spores, but where cadavers do sporulate, the per capita spore production will decrease as dose increases. Interestingly, this trade-off resolves itself such that increases in dose always result in a decrease in fitness (Figure 2f). Although low numbers of parasite spores have only a small chance of successfully infecting a host, the strong effect of within-host competition makes dispersal the best strategy. This is even without taking into account the probability of at least one spore encountering a host, which will also be substantially increased by spores dispersing (and thus having multiple chances to encounter a host per unit time), rather than staying in a single aggregation (and having only a single chance per unit time to encounter a host). In other studies of obligate killer parasites, even stronger effects of within-host competition between propagules of the same parasite clone have been found [7,60]. It therefore seems likely that these dynamics may be broadly similar for most semelparous, obligate killer parasites, and that maximum dispersal is the best strategy for these parasites, as well as any others that exhibit strong within-host competition.
When the different strains of M. anisopliae var. anisopliae were compared in the second experiment, they were found to differ in their virulence. Interestingly, it was the exotic strain (Ma275), originating from a different geographical location and from a different host order (Lepidoptera) that had the lowest virulence, and the strain isolated from a leaf-cutting ant worker (KVL 02–72) that had the highest. Many studies have found Metarhizium strains to vary in virulence [e.g. [53,61]]. Differences in strain virulence may in part be due to the variation in the production of destruxins that occurs between strains, but other virulence factors are undoubtedly also important [59]. As virulence is defined here as parasite-induced host mortality, the differences between strains could also be due to differences in the proportions of spores germinating and penetrating into the host rather than differences in within-host growth [62,63]. Although all strains had similarly high germination rates on artificial media, the interaction with host cuticle is more complex and involves various antibiotic compounds such as those produced by the metapleural gland [56,64,65]. Differences between strains in their susceptibility to such compounds would seem quite likely. Aside from the differences in virulence, however, the strains did not otherwise differ in their infection dynamics. They showed similar spore production and identical density-dependent growth patterns. The dynamics described above therefore appear to be consistent across strains, at least for those tested here.
A fundamental assumption of most models of host-parasite relationships is that within-host competition is more intense, and results in heightened virulence, when it involves more than one parasite genotype [8-10]. However, in experimental studies virulence has often been found to be unaffected by parasite heterogeneity [24]. This was also the case in the current study, in which the virulence of the mixed infection was the same as that of the most virulent strain in the infection. In addition, the parasite k-value vs. dose relationship was identical for the mixed and single infections, indicating that the density-dependent growth patterns were unaffected by host heterogeneity.
There are two possible explanations for the results. The most parsimonious is that the most virulent strain in the mixed infections simply outcompeted the other strains and drove them to extinction within the host. Such superinfection dynamics have previously been suggested for Metarhizium and other entomopathogenic fungi [32,66,67] and would be in accord with some models [6]. However other studies have found mixed infections to produce transmission stages from more than one strain of parasite in spite of the overall virulence matching that of the most virulent strain [29,31]. It remains possible that the mixed infections in the current study did not involve any of the strains being competitively excluded and that the spores produced came from all the strains. To distinguish between these possibilities it would be necessary to isolate and sequence monospore cultures from the sporulating cadavers, something would be an interesting objective for future work.
Importantly, the infection dynamics did not differ between the single and mixed infections. The occurrence of within-host competition, whether it involves the production of antagonistic compounds or is mediated by the host's immune system, might be expected to force parasites to divert some resources from growth and the production of transmission stages to producing or coping with competitive mechanisms. While there is some evidence for this [31], the production of transmission stages has been increased during mixed infections in many other studies [26-28,30]. Clearly the outcome will depend upon the particular genotypes involved. If different strains produce antagonistic compounds that are effective against one another, as in the study by Massey et al. [17], then reduced transmission stages can be expected. If they do not, and if they exploit different within-host niches, produce cooperative compounds (such as iron-binding agents [13,14]), or act synergistically to depress the host immune system, then increased transmission stages can be expected. The fact that spore production was the same in single and mixed infections in the current study therefore suggests either that the most virulent strain outcompeted the others without suffering any cost from the competitive interaction, or that the Metarhizium strains were engaged in scramble competition with dynamics that are impervious to interactions being inter- or intraclone.
Conclusions
The importance of within-host competition between parasites is well illustrated by the Metarhizium-Acromyrmex system studied here. Even though the probability of a successful infection was increased substantially by parasites occurring in aggregations, the effect of competition between parasite propagules of the same clone makes dispersal the best strategy. It seems likely that this is generally true for semelparous parasites. Further investigations of the impact of within-host competition on parasite fitness are needed and should endeavour to establish where on the superinfection-coinfection continuum the interaction lies by identifying which parasites produce transmission stages. The fact that the production of transmission stages of semelparous parasites, such as Metarhizium, is concentrated into a single bout, and thus that their lifetime fitness can be readily quantified, makes them excellent models for doing this.
Methods
General methodology
Colonies of A. echinatior were collected from Gamboa, Panama, and maintained in the lab under controlled conditions (ca. 24°C, 70% RH) on a diet of bramble leaves (Rubus fruticosus) and rice grains. For the experimental replicates, large workers (head width 2.1 to 2.4 mm) were removed from their colonies and placed individually in plastic pots (diameter: 2.5 cm, height: 4 cm) where they were maintained at 24°C with an ad libitum supply of water and sugar water. A number of isolates of M. anisopliae var. anisopliae were collected from the vicinity of leaf-cutting ant nests at the same location in Gamboa, Panama, and were cultured as monospore isolates on Sabouraud dextrose agar [41]. Spore (conidia) suspensions were made by flooding agar plates with mature spores with a sterile solution of 0.05% Triton-X and scraping off the spores with a glass rod. The spores were centrifuged and washed three times with sterile 0.05% Triton-X solution with intervening centrifugation steps. The concentration of spores was then quantified using a haemocytometer and diluted to the required concentration. The viability of the spore suspensions was checked by spreading 100 μl of them on to Sabouraud dextrose agar plates and counting the proportions of spores that had germinated after 12–16 hours at 24°C. Spore viability was >95% in all cases.
Ants were treated with M. anisopliae var. anisopliae by applying 0.5 μl of a spore suspension to their thorax using a micropipette. Spore suspensions were vortexed thoroughly immediately prior to application to ensure spores were fully dispersed. Control ants had 0.5 μl of a 0.05% Triton-X solution applied in the same way. Following application, ant mortality was assessed daily for a period of twenty days. Based on previous work [23,40,56], this time period was judged sufficient to ensure that all parasite-induced mortality had occurred by the end of the experiment. Dead ants were surface sterilised [68], and placed in a petri dish lined with damp filter paper. After the completion of the experiments, the cadavers were left for a further ten days in order to allow full sporulation of the parasite. The level of sporulation on the cadavers was then assessed by one of two methods. In Experiment 1, sporulation was quantified by directly counting the number of spores on the cadavers. The cadavers were placed in individual vials with 1 ml of 0.05% Triton-X solution and vortexed for 1 min to remove the spores into suspension. The concentration of spores in the suspension was then quantified with a haemocytometer. In Experiment 2, sporulation was estimated by examining the cadavers under a binocular microscope and giving each a rank of between 0 (no spores visible) and 10 (cadaver almost completely covered by spores) depending upon the level of sporulation. Based on data collected prior to these experiments, spore ranks estimated in this manner correlate well with the actual number of spores on the cadavers (y = 0.159x - 0.862, r2 = 0.746, Spearman's r = 0.820, N = 73, P < 0.01) and thus provide a reliable estimate of spore production.
Experiment 1: intra-strain competition
The experiment involved M. anisopliae var. anisopliae isolate KVL 02–56, which had been isolated from the dump pile of an Atta colombica nest in Gamboa, Panama [41]. A spore suspension was made up and serially diluted five-fold to give concentrations from 1 × 108 to 5 × 103 spores ml-1 (equivalent to an average of 50,000 and 2.5 spores per ant respectively). Given that soil at the site in Panama has been estimated to contain as many as 1,000 to 50,000 spores g-1 [41], it seems likely that this range encompasses the natural doses that the ants are exposed to. Thirty ants from each of two colonies of A. echinatior (Ae47 and Ae109) were treated with each of these doses, or with the control solution, and their survival monitored for twenty days after application. The effect of dose and colony of origin on ant mortality was analysed with a Cox proportional hazard regression model to examine the effect of parasite density (dose) on virulence and infection rate. This incorporates both case mortality and the time of death. Pairwise comparisons of the doses were done using Kaplan-Meir survival analyses and the Breslow statistic. The ant cadavers were left for ten days after the end of the experimental period, in order to allow ample time for all cadavers to sporulate fully. The numbers of cadavers sporulating and the numbers of spores produced by these cadavers were assessed with binary logistic and general linear models respectively. These data were used to calculate k-values to assess whether parasite growth was density dependent, as done previously by Ebert et al. [7]:
ki = log10 (M0 Di / Mi D0)
where ki is the k-value at dose i, M is the number of spores produced, D is the number of spores applied, and M0 and D0 represent the number of spores produced and applied respectively at the lowest dose tested. A zero value for k indicates that the number of spores produced at dose i are exactly proportional to the increased dose (so, for example, a doubling of dose results in a doubling of the number of spores produced). The equation gives a positive value for k when the number of spores produced at dose i are less than proportional to the increase in dose. Zero values for k thus indicate that spore production is independent of parasite density, while positive values of k indicate that spore production is negatively density dependent.
Experiment 2: inter-strain competition
To establish whether the dynamics recorded in the first experiment were consistent for different strains of the parasite, three isolates of M. anisopliae var. anisopliae were compared. These were KVL 02–73 (isolated from soil at the field site from which the ant colonies were collected), KVL 02–72 (isolated from an Atta colombica leaf-cutting ant worker), and the strain Ma275 (isolated from Cydia pomonella (Lepidoptera: Tortricidae) in Germany). The strains therefore represented a range in terms of their potential coevolution with A. echinatior, with the former two being likely to have had some interaction while Ma275 would not previously have encountered A. echinatior. Each strain was diluted tenfold and tested at six doses from 5 × 108 to 5 × 103 spores ml-1. In addition, a mixed spore suspension was made up to examine inter-strain competition. The suspension contained equal numbers of spores of each of the three strains. This was serially diluted and applied at the same doses as the individual isolates. Four ants from each of five colonies of A. echinatior (Ae48, 109, 143, 153 and 154) were treated with each dose of each isolate or with the control solution and their survival monitored for twenty days. The survival of the ants was analysed as in the preceding experiment to examine if strains differed in virulence and if the growth of each strain was density-dependent. Ten days after the end of the experimental period, spore production was assessed by ranking each cadaver for the amount of sporulation as described earlier. These ranks were used to estimate the actual number of spores produced and these values were then used to calculate k-values. A regression analysis was carried out on these data in order to assess if the growth of each strain was density-dependent and whether the density-growth dynamics differed between strains.
Authors' contributions
WOHH conceived the study, assisted with the experiments, analysed the results, and drafted and revised the manuscript. KSP, LVU and DP carried out Experiment 1 and KSP also participated in Experiment 2. MP assisted with both experiments. LT assisted with mycological aspects and JJB provided support throughout. All authors contributed to the writing of the manuscript.
Acknowledgements
We would like to thank Boris Baer, Mischa Dijkstra and Seirian Sumner for assistance in collecting the ant colonies, Sylvia Mathiasen for technical assistance and four anonymous reviewers for their exceptionally thoughtful and constructive comments that greatly improved the manuscript. We are also grateful to Allen Herre and the Smithsonian Tropical Research Institute for providing facilities in Gamboa for the collection of the ant colonies, and the Instituto Nacional de Recursos Naturales Renovables for permission to collect and export them from Panama to Denmark. This research has been supported by an EU Marie Curie Fellowship (contract number HPMF-CT-2000-00543) and grant from the Carlsberg Foundation, both to WOHH.
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| 15541185 | PMC535352 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 14; 4:45 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-45 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1691551129010.1186/1471-2105-5-169Methodology ArticleOligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites Meinicke Peter [email protected] Maike [email protected] Burkhard [email protected] Rainer [email protected] Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany2 lnstitut für Biophysik and physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, 93040 Regensburg, Germany2004 28 10 2004 5 169 169 10 8 2004 28 10 2004 Copyright © 2004 Meinicke 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
Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals.
Results
We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon.
Conclusions
We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems.
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Background
During the last years, a large number of machine learning approaches have been developed to analyze and annotate genomic sequence data. Different concepts of statistical pattern analysis and modelling are successfully used for this purpose. Sophisticated methods like Hidden Markov Models [1], neural networks [2] or support vector machines (SVM) [3] became indispensable concepts and are routinely utilized in computational biology. Kernel-based learning algorithms, like SVM, are among the most advanced machine learning methods. As compared with probabilistic (Markov) models and classical neural networks, SVM provide a well-understood regularization [4] mechanism which makes learning from few examples in high-dimensional feature spaces possible. In that way, SVM and related methods can effectively cope with the "curse of dimensionality", which has been difficult for the more traditional tools in machine learning. Together with the kernel trick which provides a technical basis for learning in arbitrarily high-dimensional spaces, the principled approach to regularization has been the foundation for a large variety of successful applications of SVMs to realworld pattern-classification tasks [3]. However, a certain drawback of kernel methods is that, from a user's point of view, they usually behave like black boxes. Once the training phase is done, it is not easy to identify the features which actually determine the quality of the classification. This fact complicates the design of such systems and an interpretation of the learnt representation. Therefore additional feature selection procedures have been proposed to cope with that inherent shortcoming of traditional kernel methods. In that way, Degroeve et al. [5] proposed to combine SVM based on traditional kernels with techniques for feature selection in order to localize mononucleotide occurrences relevant for the prediction of splice sites.
Herein, we introduce oligo kernels which provide a novel approach to datamining on biological sequences, based on the powerful concept of kernel feature spaces [6]. Our approach has two main advantages compared to previous applications of kernel methods to biological sequence analysis: (a) the existing methods provide either position-dependent representations based on mononucleotide occurrences [5,7] or they consider general K-mer occurrences restricted to a completely position-independent representation [8,9]. By contrast, with our method it is possible to adjust the required level of position-dependency to any degree for oligomers of any length. This convenient feature, in turn, facilitates the modelling of positional and compositional variability of complex biological signals. On the other hand, as we shall show in the next section, traditional monomer-based representations and position-independent K-mer representations may also be realized by the oligo kernel. (b) Furthermore, our method provides an intuitive visualization approach to present relevant oligomers and their positional variability to the user. For that purpose a suitable measure of discriminative power can be utilized to score different motifs according to their relevance for classification. Oligo kernels can therefore be applied to infer characteristic sequence features which, in turn, can be used to identify functionally important signals. This property qualifies the oligo kernel as a useful datamining tool for the analysis of biological sequences.
To demonstrate the usefulness of our approach, we applied oligo kernels to analyze translational initiation sites (TIS) for Escherichia coli K-12 because a reliable set of biochemically verified sites is available for that case. Prediction of TIS is not satisfactorily solved for prokaryotic genes [10]. For bacteria, there are a number of gene-finding tools that reliably predict the location of genes in a genome under study. Essentially, these methods work by looking for open reading frames of a certain statistically significant minimal length. But while it is obvious how to identify the end position of a predicted gene, it is by no means trivial to determine the corresponding start position as start codons are not unique; they are also used to code for amino acids inside genes. Systematic studies have shown that existing gene-prediction programs perform poorly when it comes to predicting the correct TIS [11,12]. Consequently, many start positions are incorrectly annotated in databases and, due to the concepts used for gene annotation, these errors tend to be propagated to newly annotated genomes.
Although our method is based on classification of sequences we do not intend to provide a TIS prediction tool in this paper. On one hand the method provides a general tool for identification and characterization of signals in biological sequences which is not restricted to TIS sequences. On the other hand our datamining approach may be viewed as a prior step for constructing TIS predictors. By means of an easy visual interpretation of the inferred model it can provide new insights which in turn can steer the construction of efficient predictors. We expect that this feature will in particular be useful for analysis of prokaryotic genomes for which only small sets of experimentally verified TIS exist.
Oligo kernels
Kernel-based learning [6] provides a powerful framework for many kinds of pattern analysis tasks usually encountered in statistical evaluation of experimental data. Given an input space a kernel is simply a function . It implicitly applies a usually nonlinear transformation to elements x, x' in input space and then computes the inner ("dot") product within a resulting feature space :
k(x, x') = Φ(x)·Φ(x'). (1)
A kernel can be viewed as a similarity measure for the input space objects x and x' which is defined as an inner product of the feature space objects Φ(x) and Φ(x'). The concept of kernel-induced feature spaces may first be motivated by the objective to provide an adequate representation for the data, which is more suitable for the classification task at hand than the original input space . Second, the so-called kernel trick suggests to perform learning in that feature space without any explicit computation of the mapping Φ, just using inner products of the feature space representatives. The inner products in turn may be computed by some realization of the above kernel function (1) in a hopefully efficient way. That trick may prove useful in situations where the feature space objects are rather high-dimensional and computation of Φ is costly. As will be shown below, these objects may even be functions, i.e. objects of infinite dimensionality.
For construction of a linear classifier in feature space the discriminant for the two-class problem requires a feature space weight vector w ∈ and an scalar offset b. With an explicit feature space mapping of the input elements the primal form of the discriminant is
f(x) = w·Φ(x) + b. (2)
The sign of that discriminant can be used to assign x to one of the two classes, i.e. to realize a binary classifier. By means of the kernel trick the above primal form can be replaced by its dual representation, which does not require access to any feature space elements. The basis for constructing a kernel classifier is a training set of n labelled input space examples x1, x2,...,xn. Using these examples, we can construct a linear discriminant in feature space without explicit computation of the feature space representatives. Instead we only compute inner products according to the above kernel function weighted by parameters αi in order to get the dual form of the discriminant:
where we assumed b = 0 for convenience. The weights αi determine how much individual training examples contribute to the discriminant. Optimal values have to be determined by some suitable learning algorithm which will be outlined below.
Unlike most SVM approaches which just propose a kernel and do not care about the primal form of the discriminant, i.e. about interpretability, we here first propose a suitable primal representation and then apply the kernel trick in order to provide a general learning scheme for that discriminant. By means of the primal/dual-concept of oligo functions and oligo kernels we achieve both: learnability and interpretability of the proposed model. For a suitable realization of the primal form, the idea is to represent oligomer (k-mer) occurrences in terms of smooth functions which preserve positional information about oligomer locations to an adjustable extent. In order to model positional variability of the underlying biological signals we introduce some measure of positional uncertainty. The degree of positional uncertainty is controlled by some smoothing parameter σ which allows the model to be selected from a continuous space of candidates between the two extreme cases of completely position-dependent and position invariant models, respectively. An important advantage of the oligo kernel over previous position-dependent kernels is that, besides the dual representation of the feature space discriminant which is suitable for learning, it also provides a primal representation which is suitable for interpretation of the learnt discriminant. Therefore, classifiers based on oligo kernels not only provide a method for predicting signals in sequences but they may also help to identify relevant K-mers of any length and to analyze their positional variability. The two kinds of representations are detailed in the following.
Primal representation: oligo functions
For the primal representation of the feature space discriminant (2) we introduce the concept of oligo functions which encode occurrences of oligomers in sequences with an adjustable degree of positional uncertainty. For that purpose, occurrences are not represented by exact ("hard") assignment of the oligomers to their observed positions, but rather by some kind of "fuzzy" assignment according to the assumed positional uncertainty. Therefore oligo functions can be viewed as fuzzy membership functions [13], up to an arbitrary scaling, which in the case of fuzzy membership functions restricts the function values to the range [0,1]. In that way oligo functions assign K-mer occurrences to positions in a "soft" manner. For a convenient realization we choose these functions to be mixtures of Gaussians with the variance σ2 of the Gaussians controlling the degree of positional uncertainty. Thus, for an alphabet and a sequence s which contains K-mer ω ∈ at positions Sω = {p1, p2,...} we obtain the oligo function
with continuous position variable t which does not need to be restricted to a discrete domain so far. The smoothing parameter σ adjusts the width of the Gaussians which are centered on the observed oligomer positions and therefore it determines the degree of position-dependency of the function-based feature space representation. While small values for σ imply peaky functions, large values imply flat functions with the limiting case σ → ∞ preserving no positional information at all. The effect of positional uncertainty is shown in figure 1 where the Gaussian bumps of two distinct oligomer occurrences result in a single bump of the corresponding oligo function. Consequently, for the sequence s the occurrences of all K-mers contained in = {ω1, ω2,...,ωm} can be represented by a vector of m oligo functions which yields the final feature space representation of that sequence:
Note, that the feature space objects are vector-valued functions, which can be stressed using the following notation:
φs(t) = [μ1(t), μ2(t),..., μm(t)]T (6)
where we used the shortcut . Thus, the feature space representatives are curves in the m-dimensional space of all K-mers.
If we discretize the oligo functions according to the actual number of sequence positions, we would be able to assemble feature vectors of finite dimensionality, just stacking the vectors φs(ti) of function values for all positions ti considered. Then, usual vector-based learning algorithms could be applied in order to infer relevant sequence characteristics. Unfortunately, this approach would only be feasible for short oligomers, i.e. for small K, because already for DNA sequences with l positions the number of required vector dimensions is l × 4k while for protein sequences it is l × 20K. In our application we used l = 200 positions and therefore we would have got nearly a million dimensions for representation of hexamer occurrences, which is prohibitive for usual vector-based learning schemes.
Visualization
While oligo functions in general cannot be used directly for learning of a feature space discriminant they are well-suited for the interpretation of a learnt discriminant. The latter fact is an important prerequisite for our datamining approach which provides an intuitive insight of the kernel-based sequence model. Considering the primal form of the feature space discriminant, the weight vector becomes a vector-valued function arising from a linear combination of the feature space representations of the sequences. With the learnt parameters αi we can construct the vector-valued weight function of the discriminant:
which is a curve in the m-dimensional space of oligomers. For each of the m components we have a linear combination of oligo functions where the weights αi determine the contribution from each of the n training sequences. Due to its primal representation based on oligo functions, an oligo kernel classifier provides an intuitive insight into its discriminant because high positive (negative) values of the weight function contribute to prediction of the positive (negative) class. So the user can easily inspect the learnt representation which in turn allows him to draw conclusions about the relevance of certain oligomers and their locations in terms of their discriminative power.
In order to facilitate the interpretation of the discriminant, one may restrict the analysis to the most discriminative oligomers. This can be done by ranking the component weight functions of w(t) = [w1(t), w2(t),..., wm(t)]T according to their L2-norm
Because higher norms indicate a more important role in discrimination, selection of the corresponding weight functions helps to keep the focus on the relevant oligomers. A similar criterion has also been suggested for the position-independent spectrum kernel in order to identify discriminative motifs which can be used to distinguish real exons from pseudo exons [14].
Because the feature space weight vector can be represented as a vector of functions, a suitable visualization of these discriminative weight functions provides complete access to the information which has been extracted from the data. For an overview, all discriminative weight functions wi may be discretized and stored in a matrix which may be visualized as a bitmap image using grey values or color to encode the function values. Using l discrete sequence positions ti, the m × l image matrix can easily be obtained as
W = [w(t1),w(t2),...,w(tl)]. (9)
For longer oligomers only a subset of weight functions may be used, which can be selected according to the above L2-norm. For a closer look on the role of single oligomers the corresponding weight functions can be visualized as usual 2D function plots. Examples for both kinds of visualization will be shown in the section on results.
Dual representation: oligo kernels
In the previous section we have derived a feature space representation for our biological sequences, where a certain sequence si from our dataset is represented by a vector of oligo functions, i.e. a curve in the m-dimensional space of all K-mers. While this feature space representation is well-suited for interpretation of the discriminant, it is impractical for learning the discriminant from sequence data. For that purpose we shall utilize the dual form of the discriminant, based on oligo kernels. By means of the kernel trick the dual representation is well-suited for training and any kernel-based learning algorithm [6] may be utilized. With the shortcut an inner product of two sequence representations φi, φj can be defined as
Note that the second integral is a function of the distance d = |p - q| between two oligo positions p and q. This function I(d) is equivalent to the convolution of two Gaussians with equal variance. Up to some constant factor, the convolution of two Gaussians is a Gaussian whose variance is the sum of the original variances. Therefore the integral can be calculated according to
Replacing the second integral in (10), the oligo kernel, i.e. the inner product of two feature space representatives, can be computed according to
From the above definition of the oligo kernel, it is easy to see the effect of the smoothing parameter σ. For the limiting case σ → 0 with no positional uncertainty, only oligomers which occur at the same positions in both sequences contribute to the sum. In general it would not be appropriate to represent oligomer occurrences without positional uncertainty, which would imply zero similarity between two sequences if no K-mer appears at exactly the same position in both sequences. Regarding the other extreme σ → ∞ with maximum positional uncertainty, position-dependency of the kernel completely vanishes: all terms of oligomers, occurring in both sequences, contribute equally to the sum, regardless of their distance. Together with the normalization which is introduced below, in the latter position-independent case the oligo kernel becomes identical to the so-called spectrum kernel [8] which has been proposed for position-independent representation of sequences.
Regarding computational complexity of the oligo kernel, for two sequences of length l1 and l2, respectively, the above oligo kernel (12) can be computed by evaluation of at most l1 × l2 exponential functions. For a speed-up of the computations these evaluations may be realized by fast table lookups. Fortunately, the maximum number of l1 × l2 evaluations is hardly reached in practice, because we only have to compute terms of (12) for oligomers occurring in both sequences. Only in cases where oligomers often occur at many positions in both sequences the complexity is rising towards O(l1 × l2). These cases become more unlikely for longer oligomers and therefore, with an efficient implementation, the computational cost rapidly decreases with increasing oligomer length K.
Normalization
According to the above derivation, the feature space representations may have different norms. Here, for the representation of biological sequences, the norm of a feature space object roughly corresponds to the absolute count of the oligomer occurrences. In order to improve comparability between sequences of different length, the feature vectors should be normalized to unit L2-norm. Therefore we compute the normalized oligo kernel according to
Training
The basis for learning a kernel classifier is a training set of n labelled example sequences = {(s1,y1), (s2,y2),...,(sn,yn)} with yi = 1 or yi = -1 for positive and negative examples, respectively. Because we want to propose a general learning scheme which is even applicable to problems with small data sets, a regularized learning scheme is essential in order to prevent overfitting. Many regularized learning schemes are available, among which soft-margin SVMs [15] are usually best-known. For convenience, we here use regularized least squares classifiers [16], which have been shown to provide equivalent performance on many classification problems, as compared with SVMs. Implementation of these kernel classifiers is very simple because training can be achieved by solving a system of n linear equations, as shall be outlined below. Given the set containing n labelled sequences, with labels yi ∈ {-1,1} contained as components in n-vector y, we shall now train a kernel classifier based on discriminant (3) using the proposed oligo kernel. With the kernel matrix which contains all possible inner products of the training set examples in feature space, we realize a classifier by minimizing the λ-penalized prediction error
with respect to parameter vector α = [α1,...,αn]T. By means of the regularization parameter λ > 0 the penalty controls the norm of the feature space discriminant in order to avoid overfitting. Bounding the norm of the discriminant restricts the learning algorithm to put higher weights only on "effective" features which are important for classification. In that way learning is forced to focus on that task-specific information which can actually be drawn from the data. Choosing the parameter vector α to yield a minimum of the above error, an optimal realization can be found by solving the following system of linear equations:
(K + λnI)α = y (15)
where I is the n × n identity matrix. Because solving the above system in general is of complexity O(n3) the computational cost of the learning algorithm is rapidly increasing for an increasing training set. In practice we observed that effective routines for matrix inversion enable training with a few thousands of examples. However, on one hand this behavior is a general shortcoming of kernel methods and recently several approximations have been suggested, which can effectively decrease the computational cost [17,18]. With these approximations training with a few 10000 of examples becomes feasible. On the other hand, for our case study on prokaryotic TIS we encountered data sets with at most 3500 exemplary TIS sequences which can even be handled with the above learning scheme.
Results
Datasets
In order to create a reliable dataset we utilized E. coli genes from the EcoGene database [19] and considered only those entries with biochemically verified N-terminus. For training, validation and testing of classifiers for both the positive and negative examples we chose the sequences according to large windows of 200 nucleotides (nt) length around the candidate start codon, in order to encounter the risk of missing relevant information. The positive examples were 722 sequences covering a range of 100 nt upstream to 99 nt downstream of the annotated TIS. For the negative examples we extracted sequences centered around a codon from the set {ATG, GTG, TTG} and accepted sequences, if the codon was in-frame with one of the appropriate start sites used as positive case, if its distance was < 60 nt and if no in-frame stop codon occurred in between. The rationale for this approach comes from our analysis of predictions for the E. coli genome of those tools integrated into YACOP [12]: We found that nearly two-thirds of the predictions inconsistent with the annotated TIS were located within a distance less than 50 nt to its respective start codon (data not shown). Therefore we concluded that these false sites are the most difficult candidates for TIS discrimination. We finally obtained a set of 854 negative examples with 576 of them being located downstream and 278 upstream of a TIS.
Prediction performance
To analyze the TIS sequences by means of the proposed oligo kernel, first of all we tested the predictive power of the feature space representation as it depends on different oligomer lengths. In order to compare their discrimination performance, we trained classifiers using oligo kernels according to K-mers of length K = 1,...,6. For a reliable estimate of the prediction error we performed 50 runs for each oligomer length where each run comprises training, validation for adjusting the hyperparameters σ and λ and final testing on data which have not been used to adjust the parameters of the classifier. The latter was done on a test set which contained one third of the data. From the remaining data two thirds were used for training and one third for validation of the classifiers with respect to hyperparameter values. Thus, for each run the data were randomly partitioned into training, validation and test sets of size 631, 378 and 567, respectively. For validation we varied the smoothing parameter σ ∈ {0.5, 0.75, 1, 1.5, 2} and the regularization parameter λ ∈ {0.1·0.9i|i = 0, 1,..., 100} in order to minimize classification error on the validation set. With the optimal hyperparameter values we then trained a classifier on the union of training and validation set. Finally that classifier based on optimal hyperparameters was evaluated on the test set to yield the final classification error. The mean test error over the 50 runs together with the corresponding standard deviation is shown in table 1. Additionally, table 1 also shows the mean optimal σ over the 50 runs. The table shows that the lowest error rate of 8.9 percent has been achieved for the 3-mer kernel with a mean value 1.25 of the hyperparameter σ which models the positional variability of the oligomers. This result indicates that the best representation does not necessarily require the lowest positional variability. Note that the mean error is monotonically increasing for decreasing oligomer lengths below and for increasing lengths above the best length 3. Therefore we did not try to model the occurrences of K-mers for K > 6. To investigate the effect of an enlarged data set, we chose additional TIS sequences according to GenBank annotations to yield a data set with about four times the number of examples of our original EcoGene-based set. We included all non-hypothetical coding sequences from the E. coli (U00096) dataset, including those from the EcoGene dataset, as positive examples. The negative examples were generated in the same way for the EcoGene-based set. So we obtained an enlarged set of 2980 positive and 3968 negative examples. The enlarged set was randomly partitioned, as described above, with the same proportions of training, validation and test sets. The average performance over 20 runs with different partitions is shown in table 2. Unfortunately, the increased data set size resulted in a worse performance for all oligomer lengths, although an improvement would have been expected. Therefore we decided not to use the enlarged data set for further analysis because we could not exclude the possibility of a large number of erroneous annotations for TIS which had not been verified experimentally.
Visualization
For interpretation of the learnt TIS models, we applied the visualization techniques which have been described above. To visualize the feature space discriminant we first generated an overview bitmap image of the matrix W in equation (9) which contains the discretized functions of the primal form weight curve (7) as rows. The image contains the discriminative functions as horizontal lines, with one line for each oligomer. The color of a pixel indicates the level of the corresponding function at each integer position within a window of length 200 nt. Figure 2 shows a 64 × 200 pixel image which was obtained from an average over all optimal W-matrices from the above 50 runs using the trimer kernel. The complete matrix of function values is scaled to yield a unit maximum which is attained by the ATG-function at position 0. In addition, for noise reduction all matrix elements with an absolute value below 0.1 are set to zero. In figure 3 four exemplary weight functions for trimers ATG, GGA, AAA and TTT are depicted as 2D-plots showing more detailed information. The complete set of discriminative functions for K = 3 can be found on the web page [20].
Oligomer ranking
In the following we use the terms monomer, dimer etc. instead of the more specific terms mononucletide dinucleotide etc. In order to identify the most important K-mers for the kernel-based TIS prediction, we computed the L2-norm in equation (8) for all oligomer-specific weight functions of the learnt discriminants. The ten most discriminative K-mers are identified using the average norm over the above 50 runs. The resulting rankings for K = 3,...,6 are depicted as bargraphs in figure 4. The height of the bars is proportional to the average norm of the corresponding K-mer weight function and has been scaled to yield a unit maximum height for each K. For the monomer kernel we found that A is most discriminative followed by G, T, C in decreasing order of the norm. For dimer occurrences the most discriminative oligomer is GG followed by GA, AG, AT, TT and AA, again in decreasing order. For the longer K-mers depicted in figure 4, from the bargraphs one can identify two major groups of motifs which are most prominent: on one hand the start codon itself is an important signal for TIS prediction. Therefore some motifs which contain ATG are associated with high norms and high positive values of the corresponding weight functions at position 0. This can be observed best for the discriminative weight function of trimer ATG itself (see figure 2 and 3).
From these motifs and the associated weight functions we can conclude that C or T at position -1 and A at position 3 seem to be characteristic for TIS as they show high positive peaks at the corresponding positions (see also the above web page). From the pentamer and hexamer occurrences one can deduce a preference for AAA or GCT as a second codon. Start codons GTG and TTG are not characteristic for TIS and therefore the discriminative weight functions of these trimers show high negative peaks at position 0 which can be seen from the overview matrix image of figure 2.
On the other hand oligomers contained in AAGGAGA or GAGGAGA have high rank. The corresponding discriminative functions usually put high positive weights on regions located about 10 nt upstream the start codon (see GGA in figure 2 and 3 or GAG and AGG in figure 2). Obviously, these weight functions utilize the presence of a Shine-Dalgarno sequence for discrimination.
Besides the two prominent groups of motifs related to start codon and Shine-Dalgarno sequence, interestingly, poly-A and poly-T motifs seem to be discriminative, too. As it can be seen from the discriminative functions for AAA and TTT in figure 3, poly-A motifs mainly occur downstream next to the start codon while poly-T occurrences seem to be characteristic in a region ≈ 20 nt upstream.
The corresponding discriminative functions for the ten most important oligomers for lengths 1,...,6 can be found on the before mentioned web page.
Performance comparison
In order to compare our approach with standard methods for modelling of sequence sites, we utilized inhomogeneous, i.e. position-dependent, Markov models which are widely used for the estimation of positional weight matrices. The probabilistic models can easily be estimated from the data and do not require any hyperparameter optimization. For evaluation we utilized one model for each of the two classes (positive/negative) and assigned a sequence to that class with highest probability of the corresponding model. In the case of zero probabilities for both models, as a tie-breaking rule we assigned the sequence to that model with the smallest number of zero probability positions. Classification was considered correct if the model with the smaller number was associated with the right class. For training we estimated the position-specific oligomer probabilities of the model using two thirds of the data while testing was performed on the remaining third. Prediction performance on the test set was again averaged over 50 runs with different random partitions of the data. For comparison with a position-independent kernel we utilized the spectrum kernel (SK) which can be obtained as a special case of the oligo kernel for σ → ∞. Therefore the spectrum kernel was evaluated in the same way as the above oligo kernels. The methods were compared with the best previous oligo kernel, i.e. the trimer kernel (OK3), and with a combined oligo kernel (OK1...6) which incorporates all K-mers for K = 1,...,6, simply by adding the six different kernels. With respect to the primal representation of the discriminant, adding the kernels means to stack the vectors of oligomer-specific weight functions for different K-mer lengths. Obviously this implies an augmented feature space which might combine the advantages of representations based on short and long oligomers. For each of the six added kernels we chose the smoothing parameter according to the median of the optimal values obtained from the 50 previous runs with single oligo kernels which resulted in values [0.5, 0.5, 1.0, 1.0, 1.0, 1.0] for the length-specific smoothing parameters [σ1,...,σ6].
As can be seen from table 3 the position-dependent oligo kernels yield the best performance, while the best position-independent spectrum kernel with K-mer length 2 failed to discriminate TIS correctly for nearly half of the data. With an average error rate of 44.6%, performance was not much better than classification by chance, indicating the importance of position information for TIS prediction. With the combined kernel OK1...6 our method could successfully exploit the combination of different K-mer representations and slightly improved the performance of the trimer kernel. In addition, table 3 shows that 0th and 1st order Markov models, based on monomer (MM1) and dimer (MM2) occurrences, respectively, performed equally well. We found that for the data at hand the 0th order Markov model could not be improved using higher order Markov models. Using Markov models above order 1 the performance even broke down, with a resulting error of ≈ 30%. Therefore in our case the Markov model based representations do not provide an adequate tool for analyzing occurrences of oligomers above length 2. As shown in table 3 even for dimers the oligo kernel provides better discrimination than the 1st order Markov model and therefore it may be preferred over the more simple model. Considering the occurrences of monomers the 0th order Markov model performs slightly better than the corresponding oligo kernel with K = 1. While theoretically the oligo kernel should not be worse, in practice it is difficult to find the optimal hyperparameters for best discrimination. Performing a two-dimensional grid search for smoothing parameter σ and regularization parameter λ, it cannot be expected to find the exact global optimum. On the other hand, for monomer occurrences there seems to be less positional uncertainty, so that the smoothing parameter is not likely to improve the representation anyway. Therefore the oligo kernel does not seem to be the best tool for pure analysis of monomer occurrences.
Discussion
We have introduced a novel concept of datamining on biological sequences and exemplified its application on the analysis of prokaryotic translational initiation sites.
The interpretation of our results makes clear that the most pronounced signal indicating a TIS besides the start codon is the Shine-Dalgarno region [21]. With our approach we found oligomers contained in AAGGAGA or GAGGAGA to be most discriminative. The corresponding discriminative functions indicate that for characteristic TIS these oligomers are located ≈ 10 nt upstream the start codon. For trimers this can be seen in figure 2 and 3. These results correspond to known findings both with regard to composition and to localization [21,22] and confirm the validity of our approach. The variation in spacing between the start codon and the Shine-Dalgarno region determined previously [21], correlates well with the wider peak of the discriminative functions (compare GGA with ATG peak in figure 3). In addition we observed some evidence for a downstream box, which was previously identified as an additional element modulating the expression level [23-25]. The evidence is a weak positive maximum of the discriminative function for AAA (see figure 3) downstream the TIS. The ranking of discriminative hexamer functions (see figure 4) also shows a preference for the codon AAA immediately following the start codon, as proposed in [25]. In addition also GCT seems to be a characteristic second codon as implied by pentamer and hexamer rankings in figure 4.
The analysis of individual oligo functions (see figure 3) makes it possible to identify subtle signals: The slight decay observed in a region ± 40 nt around position 0 in the plots of the discriminative weight functions for ATG and GGA is due to our selection of negative examples (see Datasets). The weak local minima of these functions seen upstream and downstream of position 0 are simply caused by shifted ATG and GGA sites of positive examples occurring also in our negative examples. The positive signal for TAT and CAT at position -1 (see figure 2) is due to the prominence of the oligomer ATG at position 0, but also indicates a preference for T and C at -1. This is also indicated by the tetramer ranking in figure 4 which identifies TATG and CATG as discriminative oligomers.
Comparing our approach with other methods we found that position-dependency is crucial for an appropriate TIS representation. The position-independent spectrum kernel showed a bad discrimination performance which does not qualify that method for prediction or for analysis of TIS sequences. On the other hand the 0th order Markov model showed a competitive performance as compared with the monomer-based oligo kernel. Thus, for the analysis of monomer occurrences the oligo kernel seems to be "oversized" and we cannot recommend its application for merely that purpose. However, we argue that most biological signals are not well-characterized by monomer occurrences. In the living cell, sequence patterns for realization of relevant signals have to be selective and recognition of the these patterns is usually robust with respect to small variations. Therefore the observed patterns are more complex and show some specific variability. For that reason, in general a collection of longer oligomers, i.e. certain motifs should be more suitable for the modelling of biological signals. The oligo kernel can be used to identify these motifs and provides a tool for analyzing their positional variability. Unlike Markov model based representations, it is not restricted to short oligomers in practice, but may be used for K-mers of any length. By means of an effective regularization, characteristic occurrences of longer oligomers may even be found with small data sets, usually encountered in prokaryotic TIS analysis. As already mentioned above, computational cost is even decreasing for longer oligomers.
We made clear that oligo functions are valuable to identify relevant signals in biological sequences. The presented dissection of TIS clearly identifies those areas bearing relevant information. These findings directly influence the design of TIS prediction tools e.g. with respect to the length of up- and downstream regions that have to be analyzed or the selection of discriminative oligomers whose occurrences have to be considered. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. Both parameters allow an easy adaptation to different biological problems. We conjecture an important role of the oligo kernel in computational biology: In addition to the application presented here, the kernel is well-suited to analyze and model splice sites, transcription factor binding sites or eukaryotic transcription initiation sites. Although we only presented its application to DNA sequences, also sites on protein sequences, like signal peptide cleavage sites are well suited for analysis by means of the oligo kernel. We are currently preparing a web-interface which will allow biologists to perform the analysis, as presented here, on their own sequence data.
Conclusions
We introduced the oligo kernel for datamining on biological sequences and we showed that it can provide a valuable tool for the identification and analysis of relevant signals. In the case of translation initiation sites (TIS) we could clearly deduce the most discriminative motifs and their positional variation from example sequences. These findings directly influence the design of tools for TIS prediction e.g. with respect to the length of up- and downstream regions that have to be analyzed. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. We showed that the position independent spectrum kernel can be viewed as a special case of the oligo kernel and that for the analysis of TIS sequences the incorporation of position information is crucial. In contrast to other position-dependent sequence kernels our approach not only provides learnability of a suitable model but also an easy interpretation of the learnt representation.
Authors' contributions
PM designed, implemented and tested the oligo kernel and the associated visualization method and he drafted most of the manuscript. RM accounted for biological expertise and substantial parts of the draft. MT contributed in biological expertise and prepared the datasets. BM assisted in coordination and manuscript writing. All authors read and approved the final manuscript.
Figures and Tables
Figure 1 Illustration of positional uncertainty. Figure 1 illustrates how positional uncertainty is represented by the proposed oligo functions. In the example, we have an occurrence of a certain oligomer at position -5 (green curve) and -9 (blue curve). The degree of uncertainty depends on the variance σ2 of the Gaussian bumps centered on these positions. Adding the two Gaussians, the smoothness of the resulting oligo function (red curve) increases with increasing σ and the assignment of the oligomer to certain positions becomes more fuzzy.
Figure 2 Image matrix of discriminative weight functions derived from trained classifiers based on the trimer kernel. Each of the 64 lines shows the values of one trimer-specific weight function obtained from an average over 50 runs (see text). Each of the 200 columns corresponds to a certain position with 0 indicating the position of the start codon. The function values are visualized by the color of the corresponding matrix elements. The complete matrix of function values has been scaled to yield a unit maximum which is located at the ATG line at position 0. For noise reduction all matrix elements with an absolute value below 0.1 have been zeroed. In general, maxima and minima indicate discriminative features which contribute to the prediction of positive (true) and negative (false) TIS, respectively. Note that the region of discriminative features is rather small and mainly concentrated around the start codon on the left (upstream) side of the image.
Figure 3 Exemplary weight functions derived from trained classifiers based on trimer kernel. Shown are discriminative weight functions for ATG (the most frequent start codon) GGA (having its highest peak in Shine-Dalgarno region), AAA (showing a weak maximum downstream of the start codon) and TTT (with higher values in a region ≈ 20 nt upstream the start codon); function values are plotted versus position. All values are normalized, i.e. they are relative values with respect to a unit global maximum over all functions. The complete set of weight functions for K = 3 can be found on our web page [20].
Figure 4 Oligomer Ranking. Figure 4 shows the ten most important oligomers for discrimination based on the trimer, tetramer, pentamer and hexamer kernels. The bars show the relative norm of the oligomer-specific weight functions (see text), i.e. their relevance for classification. All values have been scaled to a unit maximum norm of the most discriminative oligomer.
Table 1 Performance of oligo kernel classifiers with oligomer length K = 1,...,6. The first line shows the mean classification error, given in percent, on the test sets. The rates are averages over 50 runs on randomly partitioned data. The second line shows the standard deviation of the classification error. The last line shows the mean over the 50 optimal values of σ which had been chosen from the set {0.5,0.75,1,1.5,2} for each run to minimize the error on a validation set.
oligomer length 1 2 3 4 5 6
mean (median) error 11.8 (11.8) 9.7 (9.6) 8.9 (8.7) 9.6 (9.5) 12.7 (12.6) 15.0 (15.0)
standard deviation 1.3 1.3 1.4 1.2 1.3 1.2
mean optimal σ 0.8 0.8 1.25 1.34 1.24 1.27
Table 2 Performance of oligo kernel classifiers on an enlarged data set with four times the number of examples than utilized for the EcoGene-based analysis with results shown in table 1. The oligomer length again varies according to K = 1,...,6. The table shows the mean classification error, given in percent, on the test sets. The rates are averages over 20 runs on randomly partitioned data with the same proportions of training, validation and test sets as for the previous results shown in table 1. According to the main paradigm of machine learning we would expect the error to decrease for an increased data set. However, obviously this is not the case, as the error rates are rising up to 6.4 percent, as compared with table 1. Therefore the results indicate that the additional data which have not been experimentally verified, are distributed in a different way than the verified TIS sequences from EcoGene. For that reason we conclude that these additional data should not be used for analysis of TIS, because it cannot be excluded that the distinct distribution is due to erroneous annotation.
oligomer length 1 2 3 4 5 6
mean error 17.3 15.6 15.3 16.0 17.0 18.9
Table 3 Comparison of oligo kernels (OK) with inhomogeneous Markov models of order 0 (MM1) and order 1 (MM2) based on monomer and dimer occurrences, respectively. All higher order Markov models led to a severe breakdown of the performance with an error rising to ≈ 30 percent. The best spectrum kernel (SK) among the position-independent oligo kernels (σ → ∞) with K = 1,...,6 is incorporated into the comparison in order to stress the importance of position information. The table shows the mean classification error, given in percent, on the test sets. The rates are averages over 50 runs on randomly partitioned data. The lowest classification error is achieved by the combined oligo kernel OK1...6 with simple adding of length 1,...,6 kernels. The combined oligo kernel is closely followed by the best single length trimer kernel OK3 which still performs better than the two Markov model based methods. Obviously, the "best" position-independent kernel SP2, based on dimer occurrences is performing worst, only slightly better than classification by chance.
method OK3 OK1...6 MM1 MM2 SP2
mean (median) error 8.9 (8.7) 8.1 (7.8) 11.4 (11.4) 11.3 (11.4) 44.6 (44.9)
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| 15511290 | PMC535353 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 28; 5:169 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-169 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1691551129010.1186/1471-2105-5-169Methodology ArticleOligo kernels for datamining on biological sequences: a case study on prokaryotic translation initiation sites Meinicke Peter [email protected] Maike [email protected] Burkhard [email protected] Rainer [email protected] Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany2 lnstitut für Biophysik and physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, 93040 Regensburg, Germany2004 28 10 2004 5 169 169 10 8 2004 28 10 2004 Copyright © 2004 Meinicke 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
Kernel-based learning algorithms are among the most advanced machine learning methods and have been successfully applied to a variety of sequence classification tasks within the field of bioinformatics. Conventional kernels utilized so far do not provide an easy interpretation of the learnt representations in terms of positional and compositional variability of the underlying biological signals.
Results
We propose a kernel-based approach to datamining on biological sequences. With our method it is possible to model and analyze positional variability of oligomers of any length in a natural way. On one hand this is achieved by mapping the sequences to an intuitive but high-dimensional feature space, well-suited for interpretation of the learnt models. On the other hand, by means of the kernel trick we can provide a general learning algorithm for that high-dimensional representation because all required statistics can be computed without performing an explicit feature space mapping of the sequences. By introducing a kernel parameter that controls the degree of position-dependency, our feature space representation can be tailored to the characteristics of the biological problem at hand. A regularized learning scheme enables application even to biological problems for which only small sets of example sequences are available. Our approach includes a visualization method for transparent representation of characteristic sequence features. Thereby importance of features can be measured in terms of discriminative strength with respect to classification of the underlying sequences. To demonstrate and validate our concept on a biochemically well-defined case, we analyze E. coli translation initiation sites in order to show that we can find biologically relevant signals. For that case, our results clearly show that the Shine-Dalgarno sequence is the most important signal upstream a start codon. The variability in position and composition we found for that signal is in accordance with previous biological knowledge. We also find evidence for signals downstream of the start codon, previously introduced as transcriptional enhancers. These signals are mainly characterized by occurrences of adenine in a region of about 4 nucleotides next to the start codon.
Conclusions
We showed that the oligo kernel can provide a valuable tool for the analysis of relevant signals in biological sequences. In the case of translation initiation sites we could clearly deduce the most discriminative motifs and their positional variation from example sequences. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems.
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Background
During the last years, a large number of machine learning approaches have been developed to analyze and annotate genomic sequence data. Different concepts of statistical pattern analysis and modelling are successfully used for this purpose. Sophisticated methods like Hidden Markov Models [1], neural networks [2] or support vector machines (SVM) [3] became indispensable concepts and are routinely utilized in computational biology. Kernel-based learning algorithms, like SVM, are among the most advanced machine learning methods. As compared with probabilistic (Markov) models and classical neural networks, SVM provide a well-understood regularization [4] mechanism which makes learning from few examples in high-dimensional feature spaces possible. In that way, SVM and related methods can effectively cope with the "curse of dimensionality", which has been difficult for the more traditional tools in machine learning. Together with the kernel trick which provides a technical basis for learning in arbitrarily high-dimensional spaces, the principled approach to regularization has been the foundation for a large variety of successful applications of SVMs to realworld pattern-classification tasks [3]. However, a certain drawback of kernel methods is that, from a user's point of view, they usually behave like black boxes. Once the training phase is done, it is not easy to identify the features which actually determine the quality of the classification. This fact complicates the design of such systems and an interpretation of the learnt representation. Therefore additional feature selection procedures have been proposed to cope with that inherent shortcoming of traditional kernel methods. In that way, Degroeve et al. [5] proposed to combine SVM based on traditional kernels with techniques for feature selection in order to localize mononucleotide occurrences relevant for the prediction of splice sites.
Herein, we introduce oligo kernels which provide a novel approach to datamining on biological sequences, based on the powerful concept of kernel feature spaces [6]. Our approach has two main advantages compared to previous applications of kernel methods to biological sequence analysis: (a) the existing methods provide either position-dependent representations based on mononucleotide occurrences [5,7] or they consider general K-mer occurrences restricted to a completely position-independent representation [8,9]. By contrast, with our method it is possible to adjust the required level of position-dependency to any degree for oligomers of any length. This convenient feature, in turn, facilitates the modelling of positional and compositional variability of complex biological signals. On the other hand, as we shall show in the next section, traditional monomer-based representations and position-independent K-mer representations may also be realized by the oligo kernel. (b) Furthermore, our method provides an intuitive visualization approach to present relevant oligomers and their positional variability to the user. For that purpose a suitable measure of discriminative power can be utilized to score different motifs according to their relevance for classification. Oligo kernels can therefore be applied to infer characteristic sequence features which, in turn, can be used to identify functionally important signals. This property qualifies the oligo kernel as a useful datamining tool for the analysis of biological sequences.
To demonstrate the usefulness of our approach, we applied oligo kernels to analyze translational initiation sites (TIS) for Escherichia coli K-12 because a reliable set of biochemically verified sites is available for that case. Prediction of TIS is not satisfactorily solved for prokaryotic genes [10]. For bacteria, there are a number of gene-finding tools that reliably predict the location of genes in a genome under study. Essentially, these methods work by looking for open reading frames of a certain statistically significant minimal length. But while it is obvious how to identify the end position of a predicted gene, it is by no means trivial to determine the corresponding start position as start codons are not unique; they are also used to code for amino acids inside genes. Systematic studies have shown that existing gene-prediction programs perform poorly when it comes to predicting the correct TIS [11,12]. Consequently, many start positions are incorrectly annotated in databases and, due to the concepts used for gene annotation, these errors tend to be propagated to newly annotated genomes.
Although our method is based on classification of sequences we do not intend to provide a TIS prediction tool in this paper. On one hand the method provides a general tool for identification and characterization of signals in biological sequences which is not restricted to TIS sequences. On the other hand our datamining approach may be viewed as a prior step for constructing TIS predictors. By means of an easy visual interpretation of the inferred model it can provide new insights which in turn can steer the construction of efficient predictors. We expect that this feature will in particular be useful for analysis of prokaryotic genomes for which only small sets of experimentally verified TIS exist.
Oligo kernels
Kernel-based learning [6] provides a powerful framework for many kinds of pattern analysis tasks usually encountered in statistical evaluation of experimental data. Given an input space a kernel is simply a function . It implicitly applies a usually nonlinear transformation to elements x, x' in input space and then computes the inner ("dot") product within a resulting feature space :
k(x, x') = Φ(x)·Φ(x'). (1)
A kernel can be viewed as a similarity measure for the input space objects x and x' which is defined as an inner product of the feature space objects Φ(x) and Φ(x'). The concept of kernel-induced feature spaces may first be motivated by the objective to provide an adequate representation for the data, which is more suitable for the classification task at hand than the original input space . Second, the so-called kernel trick suggests to perform learning in that feature space without any explicit computation of the mapping Φ, just using inner products of the feature space representatives. The inner products in turn may be computed by some realization of the above kernel function (1) in a hopefully efficient way. That trick may prove useful in situations where the feature space objects are rather high-dimensional and computation of Φ is costly. As will be shown below, these objects may even be functions, i.e. objects of infinite dimensionality.
For construction of a linear classifier in feature space the discriminant for the two-class problem requires a feature space weight vector w ∈ and an scalar offset b. With an explicit feature space mapping of the input elements the primal form of the discriminant is
f(x) = w·Φ(x) + b. (2)
The sign of that discriminant can be used to assign x to one of the two classes, i.e. to realize a binary classifier. By means of the kernel trick the above primal form can be replaced by its dual representation, which does not require access to any feature space elements. The basis for constructing a kernel classifier is a training set of n labelled input space examples x1, x2,...,xn. Using these examples, we can construct a linear discriminant in feature space without explicit computation of the feature space representatives. Instead we only compute inner products according to the above kernel function weighted by parameters αi in order to get the dual form of the discriminant:
where we assumed b = 0 for convenience. The weights αi determine how much individual training examples contribute to the discriminant. Optimal values have to be determined by some suitable learning algorithm which will be outlined below.
Unlike most SVM approaches which just propose a kernel and do not care about the primal form of the discriminant, i.e. about interpretability, we here first propose a suitable primal representation and then apply the kernel trick in order to provide a general learning scheme for that discriminant. By means of the primal/dual-concept of oligo functions and oligo kernels we achieve both: learnability and interpretability of the proposed model. For a suitable realization of the primal form, the idea is to represent oligomer (k-mer) occurrences in terms of smooth functions which preserve positional information about oligomer locations to an adjustable extent. In order to model positional variability of the underlying biological signals we introduce some measure of positional uncertainty. The degree of positional uncertainty is controlled by some smoothing parameter σ which allows the model to be selected from a continuous space of candidates between the two extreme cases of completely position-dependent and position invariant models, respectively. An important advantage of the oligo kernel over previous position-dependent kernels is that, besides the dual representation of the feature space discriminant which is suitable for learning, it also provides a primal representation which is suitable for interpretation of the learnt discriminant. Therefore, classifiers based on oligo kernels not only provide a method for predicting signals in sequences but they may also help to identify relevant K-mers of any length and to analyze their positional variability. The two kinds of representations are detailed in the following.
Primal representation: oligo functions
For the primal representation of the feature space discriminant (2) we introduce the concept of oligo functions which encode occurrences of oligomers in sequences with an adjustable degree of positional uncertainty. For that purpose, occurrences are not represented by exact ("hard") assignment of the oligomers to their observed positions, but rather by some kind of "fuzzy" assignment according to the assumed positional uncertainty. Therefore oligo functions can be viewed as fuzzy membership functions [13], up to an arbitrary scaling, which in the case of fuzzy membership functions restricts the function values to the range [0,1]. In that way oligo functions assign K-mer occurrences to positions in a "soft" manner. For a convenient realization we choose these functions to be mixtures of Gaussians with the variance σ2 of the Gaussians controlling the degree of positional uncertainty. Thus, for an alphabet and a sequence s which contains K-mer ω ∈ at positions Sω = {p1, p2,...} we obtain the oligo function
with continuous position variable t which does not need to be restricted to a discrete domain so far. The smoothing parameter σ adjusts the width of the Gaussians which are centered on the observed oligomer positions and therefore it determines the degree of position-dependency of the function-based feature space representation. While small values for σ imply peaky functions, large values imply flat functions with the limiting case σ → ∞ preserving no positional information at all. The effect of positional uncertainty is shown in figure 1 where the Gaussian bumps of two distinct oligomer occurrences result in a single bump of the corresponding oligo function. Consequently, for the sequence s the occurrences of all K-mers contained in = {ω1, ω2,...,ωm} can be represented by a vector of m oligo functions which yields the final feature space representation of that sequence:
Note, that the feature space objects are vector-valued functions, which can be stressed using the following notation:
φs(t) = [μ1(t), μ2(t),..., μm(t)]T (6)
where we used the shortcut . Thus, the feature space representatives are curves in the m-dimensional space of all K-mers.
If we discretize the oligo functions according to the actual number of sequence positions, we would be able to assemble feature vectors of finite dimensionality, just stacking the vectors φs(ti) of function values for all positions ti considered. Then, usual vector-based learning algorithms could be applied in order to infer relevant sequence characteristics. Unfortunately, this approach would only be feasible for short oligomers, i.e. for small K, because already for DNA sequences with l positions the number of required vector dimensions is l × 4k while for protein sequences it is l × 20K. In our application we used l = 200 positions and therefore we would have got nearly a million dimensions for representation of hexamer occurrences, which is prohibitive for usual vector-based learning schemes.
Visualization
While oligo functions in general cannot be used directly for learning of a feature space discriminant they are well-suited for the interpretation of a learnt discriminant. The latter fact is an important prerequisite for our datamining approach which provides an intuitive insight of the kernel-based sequence model. Considering the primal form of the feature space discriminant, the weight vector becomes a vector-valued function arising from a linear combination of the feature space representations of the sequences. With the learnt parameters αi we can construct the vector-valued weight function of the discriminant:
which is a curve in the m-dimensional space of oligomers. For each of the m components we have a linear combination of oligo functions where the weights αi determine the contribution from each of the n training sequences. Due to its primal representation based on oligo functions, an oligo kernel classifier provides an intuitive insight into its discriminant because high positive (negative) values of the weight function contribute to prediction of the positive (negative) class. So the user can easily inspect the learnt representation which in turn allows him to draw conclusions about the relevance of certain oligomers and their locations in terms of their discriminative power.
In order to facilitate the interpretation of the discriminant, one may restrict the analysis to the most discriminative oligomers. This can be done by ranking the component weight functions of w(t) = [w1(t), w2(t),..., wm(t)]T according to their L2-norm
Because higher norms indicate a more important role in discrimination, selection of the corresponding weight functions helps to keep the focus on the relevant oligomers. A similar criterion has also been suggested for the position-independent spectrum kernel in order to identify discriminative motifs which can be used to distinguish real exons from pseudo exons [14].
Because the feature space weight vector can be represented as a vector of functions, a suitable visualization of these discriminative weight functions provides complete access to the information which has been extracted from the data. For an overview, all discriminative weight functions wi may be discretized and stored in a matrix which may be visualized as a bitmap image using grey values or color to encode the function values. Using l discrete sequence positions ti, the m × l image matrix can easily be obtained as
W = [w(t1),w(t2),...,w(tl)]. (9)
For longer oligomers only a subset of weight functions may be used, which can be selected according to the above L2-norm. For a closer look on the role of single oligomers the corresponding weight functions can be visualized as usual 2D function plots. Examples for both kinds of visualization will be shown in the section on results.
Dual representation: oligo kernels
In the previous section we have derived a feature space representation for our biological sequences, where a certain sequence si from our dataset is represented by a vector of oligo functions, i.e. a curve in the m-dimensional space of all K-mers. While this feature space representation is well-suited for interpretation of the discriminant, it is impractical for learning the discriminant from sequence data. For that purpose we shall utilize the dual form of the discriminant, based on oligo kernels. By means of the kernel trick the dual representation is well-suited for training and any kernel-based learning algorithm [6] may be utilized. With the shortcut an inner product of two sequence representations φi, φj can be defined as
Note that the second integral is a function of the distance d = |p - q| between two oligo positions p and q. This function I(d) is equivalent to the convolution of two Gaussians with equal variance. Up to some constant factor, the convolution of two Gaussians is a Gaussian whose variance is the sum of the original variances. Therefore the integral can be calculated according to
Replacing the second integral in (10), the oligo kernel, i.e. the inner product of two feature space representatives, can be computed according to
From the above definition of the oligo kernel, it is easy to see the effect of the smoothing parameter σ. For the limiting case σ → 0 with no positional uncertainty, only oligomers which occur at the same positions in both sequences contribute to the sum. In general it would not be appropriate to represent oligomer occurrences without positional uncertainty, which would imply zero similarity between two sequences if no K-mer appears at exactly the same position in both sequences. Regarding the other extreme σ → ∞ with maximum positional uncertainty, position-dependency of the kernel completely vanishes: all terms of oligomers, occurring in both sequences, contribute equally to the sum, regardless of their distance. Together with the normalization which is introduced below, in the latter position-independent case the oligo kernel becomes identical to the so-called spectrum kernel [8] which has been proposed for position-independent representation of sequences.
Regarding computational complexity of the oligo kernel, for two sequences of length l1 and l2, respectively, the above oligo kernel (12) can be computed by evaluation of at most l1 × l2 exponential functions. For a speed-up of the computations these evaluations may be realized by fast table lookups. Fortunately, the maximum number of l1 × l2 evaluations is hardly reached in practice, because we only have to compute terms of (12) for oligomers occurring in both sequences. Only in cases where oligomers often occur at many positions in both sequences the complexity is rising towards O(l1 × l2). These cases become more unlikely for longer oligomers and therefore, with an efficient implementation, the computational cost rapidly decreases with increasing oligomer length K.
Normalization
According to the above derivation, the feature space representations may have different norms. Here, for the representation of biological sequences, the norm of a feature space object roughly corresponds to the absolute count of the oligomer occurrences. In order to improve comparability between sequences of different length, the feature vectors should be normalized to unit L2-norm. Therefore we compute the normalized oligo kernel according to
Training
The basis for learning a kernel classifier is a training set of n labelled example sequences = {(s1,y1), (s2,y2),...,(sn,yn)} with yi = 1 or yi = -1 for positive and negative examples, respectively. Because we want to propose a general learning scheme which is even applicable to problems with small data sets, a regularized learning scheme is essential in order to prevent overfitting. Many regularized learning schemes are available, among which soft-margin SVMs [15] are usually best-known. For convenience, we here use regularized least squares classifiers [16], which have been shown to provide equivalent performance on many classification problems, as compared with SVMs. Implementation of these kernel classifiers is very simple because training can be achieved by solving a system of n linear equations, as shall be outlined below. Given the set containing n labelled sequences, with labels yi ∈ {-1,1} contained as components in n-vector y, we shall now train a kernel classifier based on discriminant (3) using the proposed oligo kernel. With the kernel matrix which contains all possible inner products of the training set examples in feature space, we realize a classifier by minimizing the λ-penalized prediction error
with respect to parameter vector α = [α1,...,αn]T. By means of the regularization parameter λ > 0 the penalty controls the norm of the feature space discriminant in order to avoid overfitting. Bounding the norm of the discriminant restricts the learning algorithm to put higher weights only on "effective" features which are important for classification. In that way learning is forced to focus on that task-specific information which can actually be drawn from the data. Choosing the parameter vector α to yield a minimum of the above error, an optimal realization can be found by solving the following system of linear equations:
(K + λnI)α = y (15)
where I is the n × n identity matrix. Because solving the above system in general is of complexity O(n3) the computational cost of the learning algorithm is rapidly increasing for an increasing training set. In practice we observed that effective routines for matrix inversion enable training with a few thousands of examples. However, on one hand this behavior is a general shortcoming of kernel methods and recently several approximations have been suggested, which can effectively decrease the computational cost [17,18]. With these approximations training with a few 10000 of examples becomes feasible. On the other hand, for our case study on prokaryotic TIS we encountered data sets with at most 3500 exemplary TIS sequences which can even be handled with the above learning scheme.
Results
Datasets
In order to create a reliable dataset we utilized E. coli genes from the EcoGene database [19] and considered only those entries with biochemically verified N-terminus. For training, validation and testing of classifiers for both the positive and negative examples we chose the sequences according to large windows of 200 nucleotides (nt) length around the candidate start codon, in order to encounter the risk of missing relevant information. The positive examples were 722 sequences covering a range of 100 nt upstream to 99 nt downstream of the annotated TIS. For the negative examples we extracted sequences centered around a codon from the set {ATG, GTG, TTG} and accepted sequences, if the codon was in-frame with one of the appropriate start sites used as positive case, if its distance was < 60 nt and if no in-frame stop codon occurred in between. The rationale for this approach comes from our analysis of predictions for the E. coli genome of those tools integrated into YACOP [12]: We found that nearly two-thirds of the predictions inconsistent with the annotated TIS were located within a distance less than 50 nt to its respective start codon (data not shown). Therefore we concluded that these false sites are the most difficult candidates for TIS discrimination. We finally obtained a set of 854 negative examples with 576 of them being located downstream and 278 upstream of a TIS.
Prediction performance
To analyze the TIS sequences by means of the proposed oligo kernel, first of all we tested the predictive power of the feature space representation as it depends on different oligomer lengths. In order to compare their discrimination performance, we trained classifiers using oligo kernels according to K-mers of length K = 1,...,6. For a reliable estimate of the prediction error we performed 50 runs for each oligomer length where each run comprises training, validation for adjusting the hyperparameters σ and λ and final testing on data which have not been used to adjust the parameters of the classifier. The latter was done on a test set which contained one third of the data. From the remaining data two thirds were used for training and one third for validation of the classifiers with respect to hyperparameter values. Thus, for each run the data were randomly partitioned into training, validation and test sets of size 631, 378 and 567, respectively. For validation we varied the smoothing parameter σ ∈ {0.5, 0.75, 1, 1.5, 2} and the regularization parameter λ ∈ {0.1·0.9i|i = 0, 1,..., 100} in order to minimize classification error on the validation set. With the optimal hyperparameter values we then trained a classifier on the union of training and validation set. Finally that classifier based on optimal hyperparameters was evaluated on the test set to yield the final classification error. The mean test error over the 50 runs together with the corresponding standard deviation is shown in table 1. Additionally, table 1 also shows the mean optimal σ over the 50 runs. The table shows that the lowest error rate of 8.9 percent has been achieved for the 3-mer kernel with a mean value 1.25 of the hyperparameter σ which models the positional variability of the oligomers. This result indicates that the best representation does not necessarily require the lowest positional variability. Note that the mean error is monotonically increasing for decreasing oligomer lengths below and for increasing lengths above the best length 3. Therefore we did not try to model the occurrences of K-mers for K > 6. To investigate the effect of an enlarged data set, we chose additional TIS sequences according to GenBank annotations to yield a data set with about four times the number of examples of our original EcoGene-based set. We included all non-hypothetical coding sequences from the E. coli (U00096) dataset, including those from the EcoGene dataset, as positive examples. The negative examples were generated in the same way for the EcoGene-based set. So we obtained an enlarged set of 2980 positive and 3968 negative examples. The enlarged set was randomly partitioned, as described above, with the same proportions of training, validation and test sets. The average performance over 20 runs with different partitions is shown in table 2. Unfortunately, the increased data set size resulted in a worse performance for all oligomer lengths, although an improvement would have been expected. Therefore we decided not to use the enlarged data set for further analysis because we could not exclude the possibility of a large number of erroneous annotations for TIS which had not been verified experimentally.
Visualization
For interpretation of the learnt TIS models, we applied the visualization techniques which have been described above. To visualize the feature space discriminant we first generated an overview bitmap image of the matrix W in equation (9) which contains the discretized functions of the primal form weight curve (7) as rows. The image contains the discriminative functions as horizontal lines, with one line for each oligomer. The color of a pixel indicates the level of the corresponding function at each integer position within a window of length 200 nt. Figure 2 shows a 64 × 200 pixel image which was obtained from an average over all optimal W-matrices from the above 50 runs using the trimer kernel. The complete matrix of function values is scaled to yield a unit maximum which is attained by the ATG-function at position 0. In addition, for noise reduction all matrix elements with an absolute value below 0.1 are set to zero. In figure 3 four exemplary weight functions for trimers ATG, GGA, AAA and TTT are depicted as 2D-plots showing more detailed information. The complete set of discriminative functions for K = 3 can be found on the web page [20].
Oligomer ranking
In the following we use the terms monomer, dimer etc. instead of the more specific terms mononucletide dinucleotide etc. In order to identify the most important K-mers for the kernel-based TIS prediction, we computed the L2-norm in equation (8) for all oligomer-specific weight functions of the learnt discriminants. The ten most discriminative K-mers are identified using the average norm over the above 50 runs. The resulting rankings for K = 3,...,6 are depicted as bargraphs in figure 4. The height of the bars is proportional to the average norm of the corresponding K-mer weight function and has been scaled to yield a unit maximum height for each K. For the monomer kernel we found that A is most discriminative followed by G, T, C in decreasing order of the norm. For dimer occurrences the most discriminative oligomer is GG followed by GA, AG, AT, TT and AA, again in decreasing order. For the longer K-mers depicted in figure 4, from the bargraphs one can identify two major groups of motifs which are most prominent: on one hand the start codon itself is an important signal for TIS prediction. Therefore some motifs which contain ATG are associated with high norms and high positive values of the corresponding weight functions at position 0. This can be observed best for the discriminative weight function of trimer ATG itself (see figure 2 and 3).
From these motifs and the associated weight functions we can conclude that C or T at position -1 and A at position 3 seem to be characteristic for TIS as they show high positive peaks at the corresponding positions (see also the above web page). From the pentamer and hexamer occurrences one can deduce a preference for AAA or GCT as a second codon. Start codons GTG and TTG are not characteristic for TIS and therefore the discriminative weight functions of these trimers show high negative peaks at position 0 which can be seen from the overview matrix image of figure 2.
On the other hand oligomers contained in AAGGAGA or GAGGAGA have high rank. The corresponding discriminative functions usually put high positive weights on regions located about 10 nt upstream the start codon (see GGA in figure 2 and 3 or GAG and AGG in figure 2). Obviously, these weight functions utilize the presence of a Shine-Dalgarno sequence for discrimination.
Besides the two prominent groups of motifs related to start codon and Shine-Dalgarno sequence, interestingly, poly-A and poly-T motifs seem to be discriminative, too. As it can be seen from the discriminative functions for AAA and TTT in figure 3, poly-A motifs mainly occur downstream next to the start codon while poly-T occurrences seem to be characteristic in a region ≈ 20 nt upstream.
The corresponding discriminative functions for the ten most important oligomers for lengths 1,...,6 can be found on the before mentioned web page.
Performance comparison
In order to compare our approach with standard methods for modelling of sequence sites, we utilized inhomogeneous, i.e. position-dependent, Markov models which are widely used for the estimation of positional weight matrices. The probabilistic models can easily be estimated from the data and do not require any hyperparameter optimization. For evaluation we utilized one model for each of the two classes (positive/negative) and assigned a sequence to that class with highest probability of the corresponding model. In the case of zero probabilities for both models, as a tie-breaking rule we assigned the sequence to that model with the smallest number of zero probability positions. Classification was considered correct if the model with the smaller number was associated with the right class. For training we estimated the position-specific oligomer probabilities of the model using two thirds of the data while testing was performed on the remaining third. Prediction performance on the test set was again averaged over 50 runs with different random partitions of the data. For comparison with a position-independent kernel we utilized the spectrum kernel (SK) which can be obtained as a special case of the oligo kernel for σ → ∞. Therefore the spectrum kernel was evaluated in the same way as the above oligo kernels. The methods were compared with the best previous oligo kernel, i.e. the trimer kernel (OK3), and with a combined oligo kernel (OK1...6) which incorporates all K-mers for K = 1,...,6, simply by adding the six different kernels. With respect to the primal representation of the discriminant, adding the kernels means to stack the vectors of oligomer-specific weight functions for different K-mer lengths. Obviously this implies an augmented feature space which might combine the advantages of representations based on short and long oligomers. For each of the six added kernels we chose the smoothing parameter according to the median of the optimal values obtained from the 50 previous runs with single oligo kernels which resulted in values [0.5, 0.5, 1.0, 1.0, 1.0, 1.0] for the length-specific smoothing parameters [σ1,...,σ6].
As can be seen from table 3 the position-dependent oligo kernels yield the best performance, while the best position-independent spectrum kernel with K-mer length 2 failed to discriminate TIS correctly for nearly half of the data. With an average error rate of 44.6%, performance was not much better than classification by chance, indicating the importance of position information for TIS prediction. With the combined kernel OK1...6 our method could successfully exploit the combination of different K-mer representations and slightly improved the performance of the trimer kernel. In addition, table 3 shows that 0th and 1st order Markov models, based on monomer (MM1) and dimer (MM2) occurrences, respectively, performed equally well. We found that for the data at hand the 0th order Markov model could not be improved using higher order Markov models. Using Markov models above order 1 the performance even broke down, with a resulting error of ≈ 30%. Therefore in our case the Markov model based representations do not provide an adequate tool for analyzing occurrences of oligomers above length 2. As shown in table 3 even for dimers the oligo kernel provides better discrimination than the 1st order Markov model and therefore it may be preferred over the more simple model. Considering the occurrences of monomers the 0th order Markov model performs slightly better than the corresponding oligo kernel with K = 1. While theoretically the oligo kernel should not be worse, in practice it is difficult to find the optimal hyperparameters for best discrimination. Performing a two-dimensional grid search for smoothing parameter σ and regularization parameter λ, it cannot be expected to find the exact global optimum. On the other hand, for monomer occurrences there seems to be less positional uncertainty, so that the smoothing parameter is not likely to improve the representation anyway. Therefore the oligo kernel does not seem to be the best tool for pure analysis of monomer occurrences.
Discussion
We have introduced a novel concept of datamining on biological sequences and exemplified its application on the analysis of prokaryotic translational initiation sites.
The interpretation of our results makes clear that the most pronounced signal indicating a TIS besides the start codon is the Shine-Dalgarno region [21]. With our approach we found oligomers contained in AAGGAGA or GAGGAGA to be most discriminative. The corresponding discriminative functions indicate that for characteristic TIS these oligomers are located ≈ 10 nt upstream the start codon. For trimers this can be seen in figure 2 and 3. These results correspond to known findings both with regard to composition and to localization [21,22] and confirm the validity of our approach. The variation in spacing between the start codon and the Shine-Dalgarno region determined previously [21], correlates well with the wider peak of the discriminative functions (compare GGA with ATG peak in figure 3). In addition we observed some evidence for a downstream box, which was previously identified as an additional element modulating the expression level [23-25]. The evidence is a weak positive maximum of the discriminative function for AAA (see figure 3) downstream the TIS. The ranking of discriminative hexamer functions (see figure 4) also shows a preference for the codon AAA immediately following the start codon, as proposed in [25]. In addition also GCT seems to be a characteristic second codon as implied by pentamer and hexamer rankings in figure 4.
The analysis of individual oligo functions (see figure 3) makes it possible to identify subtle signals: The slight decay observed in a region ± 40 nt around position 0 in the plots of the discriminative weight functions for ATG and GGA is due to our selection of negative examples (see Datasets). The weak local minima of these functions seen upstream and downstream of position 0 are simply caused by shifted ATG and GGA sites of positive examples occurring also in our negative examples. The positive signal for TAT and CAT at position -1 (see figure 2) is due to the prominence of the oligomer ATG at position 0, but also indicates a preference for T and C at -1. This is also indicated by the tetramer ranking in figure 4 which identifies TATG and CATG as discriminative oligomers.
Comparing our approach with other methods we found that position-dependency is crucial for an appropriate TIS representation. The position-independent spectrum kernel showed a bad discrimination performance which does not qualify that method for prediction or for analysis of TIS sequences. On the other hand the 0th order Markov model showed a competitive performance as compared with the monomer-based oligo kernel. Thus, for the analysis of monomer occurrences the oligo kernel seems to be "oversized" and we cannot recommend its application for merely that purpose. However, we argue that most biological signals are not well-characterized by monomer occurrences. In the living cell, sequence patterns for realization of relevant signals have to be selective and recognition of the these patterns is usually robust with respect to small variations. Therefore the observed patterns are more complex and show some specific variability. For that reason, in general a collection of longer oligomers, i.e. certain motifs should be more suitable for the modelling of biological signals. The oligo kernel can be used to identify these motifs and provides a tool for analyzing their positional variability. Unlike Markov model based representations, it is not restricted to short oligomers in practice, but may be used for K-mers of any length. By means of an effective regularization, characteristic occurrences of longer oligomers may even be found with small data sets, usually encountered in prokaryotic TIS analysis. As already mentioned above, computational cost is even decreasing for longer oligomers.
We made clear that oligo functions are valuable to identify relevant signals in biological sequences. The presented dissection of TIS clearly identifies those areas bearing relevant information. These findings directly influence the design of TIS prediction tools e.g. with respect to the length of up- and downstream regions that have to be analyzed or the selection of discriminative oligomers whose occurrences have to be considered. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. Both parameters allow an easy adaptation to different biological problems. We conjecture an important role of the oligo kernel in computational biology: In addition to the application presented here, the kernel is well-suited to analyze and model splice sites, transcription factor binding sites or eukaryotic transcription initiation sites. Although we only presented its application to DNA sequences, also sites on protein sequences, like signal peptide cleavage sites are well suited for analysis by means of the oligo kernel. We are currently preparing a web-interface which will allow biologists to perform the analysis, as presented here, on their own sequence data.
Conclusions
We introduced the oligo kernel for datamining on biological sequences and we showed that it can provide a valuable tool for the identification and analysis of relevant signals. In the case of translation initiation sites (TIS) we could clearly deduce the most discriminative motifs and their positional variation from example sequences. These findings directly influence the design of tools for TIS prediction e.g. with respect to the length of up- and downstream regions that have to be analyzed. Attractive features of our approach are its flexibility with respect to oligomer length and position conservation. By means of these two parameters oligo kernels can easily be adapted to different biological problems. We showed that the position independent spectrum kernel can be viewed as a special case of the oligo kernel and that for the analysis of TIS sequences the incorporation of position information is crucial. In contrast to other position-dependent sequence kernels our approach not only provides learnability of a suitable model but also an easy interpretation of the learnt representation.
Authors' contributions
PM designed, implemented and tested the oligo kernel and the associated visualization method and he drafted most of the manuscript. RM accounted for biological expertise and substantial parts of the draft. MT contributed in biological expertise and prepared the datasets. BM assisted in coordination and manuscript writing. All authors read and approved the final manuscript.
Figures and Tables
Figure 1 Illustration of positional uncertainty. Figure 1 illustrates how positional uncertainty is represented by the proposed oligo functions. In the example, we have an occurrence of a certain oligomer at position -5 (green curve) and -9 (blue curve). The degree of uncertainty depends on the variance σ2 of the Gaussian bumps centered on these positions. Adding the two Gaussians, the smoothness of the resulting oligo function (red curve) increases with increasing σ and the assignment of the oligomer to certain positions becomes more fuzzy.
Figure 2 Image matrix of discriminative weight functions derived from trained classifiers based on the trimer kernel. Each of the 64 lines shows the values of one trimer-specific weight function obtained from an average over 50 runs (see text). Each of the 200 columns corresponds to a certain position with 0 indicating the position of the start codon. The function values are visualized by the color of the corresponding matrix elements. The complete matrix of function values has been scaled to yield a unit maximum which is located at the ATG line at position 0. For noise reduction all matrix elements with an absolute value below 0.1 have been zeroed. In general, maxima and minima indicate discriminative features which contribute to the prediction of positive (true) and negative (false) TIS, respectively. Note that the region of discriminative features is rather small and mainly concentrated around the start codon on the left (upstream) side of the image.
Figure 3 Exemplary weight functions derived from trained classifiers based on trimer kernel. Shown are discriminative weight functions for ATG (the most frequent start codon) GGA (having its highest peak in Shine-Dalgarno region), AAA (showing a weak maximum downstream of the start codon) and TTT (with higher values in a region ≈ 20 nt upstream the start codon); function values are plotted versus position. All values are normalized, i.e. they are relative values with respect to a unit global maximum over all functions. The complete set of weight functions for K = 3 can be found on our web page [20].
Figure 4 Oligomer Ranking. Figure 4 shows the ten most important oligomers for discrimination based on the trimer, tetramer, pentamer and hexamer kernels. The bars show the relative norm of the oligomer-specific weight functions (see text), i.e. their relevance for classification. All values have been scaled to a unit maximum norm of the most discriminative oligomer.
Table 1 Performance of oligo kernel classifiers with oligomer length K = 1,...,6. The first line shows the mean classification error, given in percent, on the test sets. The rates are averages over 50 runs on randomly partitioned data. The second line shows the standard deviation of the classification error. The last line shows the mean over the 50 optimal values of σ which had been chosen from the set {0.5,0.75,1,1.5,2} for each run to minimize the error on a validation set.
oligomer length 1 2 3 4 5 6
mean (median) error 11.8 (11.8) 9.7 (9.6) 8.9 (8.7) 9.6 (9.5) 12.7 (12.6) 15.0 (15.0)
standard deviation 1.3 1.3 1.4 1.2 1.3 1.2
mean optimal σ 0.8 0.8 1.25 1.34 1.24 1.27
Table 2 Performance of oligo kernel classifiers on an enlarged data set with four times the number of examples than utilized for the EcoGene-based analysis with results shown in table 1. The oligomer length again varies according to K = 1,...,6. The table shows the mean classification error, given in percent, on the test sets. The rates are averages over 20 runs on randomly partitioned data with the same proportions of training, validation and test sets as for the previous results shown in table 1. According to the main paradigm of machine learning we would expect the error to decrease for an increased data set. However, obviously this is not the case, as the error rates are rising up to 6.4 percent, as compared with table 1. Therefore the results indicate that the additional data which have not been experimentally verified, are distributed in a different way than the verified TIS sequences from EcoGene. For that reason we conclude that these additional data should not be used for analysis of TIS, because it cannot be excluded that the distinct distribution is due to erroneous annotation.
oligomer length 1 2 3 4 5 6
mean error 17.3 15.6 15.3 16.0 17.0 18.9
Table 3 Comparison of oligo kernels (OK) with inhomogeneous Markov models of order 0 (MM1) and order 1 (MM2) based on monomer and dimer occurrences, respectively. All higher order Markov models led to a severe breakdown of the performance with an error rising to ≈ 30 percent. The best spectrum kernel (SK) among the position-independent oligo kernels (σ → ∞) with K = 1,...,6 is incorporated into the comparison in order to stress the importance of position information. The table shows the mean classification error, given in percent, on the test sets. The rates are averages over 50 runs on randomly partitioned data. The lowest classification error is achieved by the combined oligo kernel OK1...6 with simple adding of length 1,...,6 kernels. The combined oligo kernel is closely followed by the best single length trimer kernel OK3 which still performs better than the two Markov model based methods. Obviously, the "best" position-independent kernel SP2, based on dimer occurrences is performing worst, only slightly better than classification by chance.
method OK3 OK1...6 MM1 MM2 SP2
mean (median) error 8.9 (8.7) 8.1 (7.8) 11.4 (11.4) 11.3 (11.4) 44.6 (44.9)
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| 15555068 | PMC535354 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Nov 21; 4:19 | latin-1 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-19 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1559939010.1371/journal.pmed.0010061PerspectivesDiabetes/Endocrinology/MetabolismObesitySleep disordersNutrition and MetabolismEndocrinologySleep, Appetite, and Obesity—What Is the Link? PerspectivesPrinz Patricia Patricia Prinz is research professor, Biobehavioral Nursing and Health Systems, and adjunct professor, Department of Psychiatry and Behavioral Sciences, at the University of Washington, Seattle, Washington, United States of America. E-mail: [email protected]
Competing Interests: The author declares that she has no competing interests.
12 2004 7 12 2004 1 3 e61Copyright: © 2004 Patricia Prinz.2004This 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.
Short Sleep Duration Is Associated with Reduced Leptin, Elevated Ghrelin, and Increased Body Mass Index
There is a well known relationship between short sleep duration and high body mass index. A new study suggests that the missing link could be the appetite regulating hormones leptin and ghrelin.
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There is a well-documented relationship between short sleep duration and high body mass index (BMI). In the largest study, a survey on sleep duration and frequency of insomnia in more than 1.1 million participants, increasing BMI occurred for habitual sleep amounts below 7–8 hours [1]. A recent prospective study found an association between sleep curtailment and future weight gain [2]. The mechanism linking short sleep with weight gain is unknown, but Mignot and colleagues' study in this month's PLoS Medicine [3] adds to the growing evidence implicating leptin and ghrelin, the two key opposing hormones involved in appetite regulation.
Hormones That Regulate Appetite
Leptin, a peptide hormone secreted from white adipocytes, is implicated in the regulation of food intake and energy balance. The hormone acts on the central nervous system, in particular the hypothalamus, suppressing food intake and stimulating energy expenditure. Leptin production is primarily regulated by insulin-induced changes in adipocyte metabolism—its secretion levels correlate with adipocyte mass and lipid loads.
The Land of Cockaigne, by Pieter Brueghel the Elder
Leptin promotes inflammation. The hormone provides an interesting link between obesity and pathophysiological processes such as insulin resistance and atherosclerosis, and disorders such as autoimmune and cardiovascular diseases and the metabolic syndrome. Increased serum leptin levels in obesity and metabolic syndrome support the view that these disorders are in fact low-grade systemic inflammatory diseases, characterized by increased concentrations of proinflammatory cytokines like interleukin-6, tumor necrosis factor-α and leptin. Leptin's proinflammatory role suggests that it may link energy homeostasis to the immune system [4,5].Ghrelin is a peptide hormone that stimulates appetite, fat production, and body growth—leading to increased food intake and body weight. It is secreted into the circulation from the stomach, but is also synthesised in a number of other tissues, including the kidney, pituitary, and hypothalamus, suggesting that the hormone has both distant and local (endocrine and paracrine) effects. These effects include stimulating the secretion of growth hormone, prolactin, and adrenocorticotropic hormone, and a diabetogenic effect on carbohydrate metabolism [6].
The New Study
In this study of 1,024 participants in the population-based Wisconsin Sleep Cohort Study [7], Mignot and colleagues found that in persons sleeping less than 8 hours, increased BMI was proportional to decreased sleep [3]. The researchers also found that shorter sleep times were associated with increased circulating ghrelin and decreased leptin, a hormonal pattern that is consistent with decreased energy expenditure and increased appetite and obesity.
These findings confirm earlier clinical reports on the effects of sleep deprivation and extend them to include naturalistic sleep in a large, community-based population. The study provides an exciting addition to the growing literature showing relationships between sleep curtailment, metabolic hormones, and metabolic disorders (including obesity). The data have important implications for our understanding of obesity and related disorders in the general population, with one caveat: the study population was enriched with snorers, making the results less applicable to a general population.
Mignot and colleagues' data are in accord with human and animal studies that show that experimental curtailment of sleep leads to lower levels of leptin [8,9,10,11] and increased ghrelin [12]. The new study therefore lends some support to the interpretation that reduced sleep levels cause the hormonal changes.
But there is also evidence of opposite effects—that is, that administration of leptin [13] and ghrelin can alter sleep. Ghrelin administration has been found to increase non-REM sleep in humans and mice, possibly via its interactions with the sleep-inducing peptide growth hormone releasing hormone (GHRH). Ghrelin is an endogenous ligand of the growth hormone secretagogue receptor, making it a candidate for an endogenous sleep-promoting factor [14]. Mignot and colleagues' study is congruent with the idea that inadequate sleep enhances ghrelin secretion, which in turn acts as an endogenous sleep factor in humans. This is an important new area of research that could conceivably lead to more physiological sleep aids than are currently available, with profound implications for improved public health.
Overall, the available studies suggest the presence of reciprocal interactions between metabolic hormones and sleep, relationships that are poorly understood at present. Does sleep interact with metabolic hormones directly or via intervening factors such as sleep-related breathing disorders? Patients with obstructive sleep apnea have impaired sleep and higher ghrelin levels than BMI-matched controls, and treatment with continuous positive airway pressure reduces ghrelin to control levels [15]. Although sleep-disordered breathing (SDB) was measured in the present study, the SDB analyses were not shown, making it difficult to evaluate the influence of SDB on ghrelin and leptin in this population.
There is a clear need for well-controlled, population-based studies that allow us to examine multiple relevant factors simultaneously. The present study highlights the importance of shortened sleep in relation to obesity, leptin, and ghrelin, a good start toward this goal.
Sleep and Public Health
Many other important questions remain, such as the roles that other hormones, cytokines, and SDB play in obesity. Many of the unanswered questions have important implications for public health. For example, diabetes, visceral obesity, hypertension, and hyperinsulinemia commonly aggregate together in large populations, and are considered a “metabolic syndrome” that has been linked to SDB [16] and to inflammatory disorders [17]. To what extent does long-term sleep curtailment contribute to these and related public health issues?
The possible role of sleep restriction in autoimmune and inflammatory disorders is of particular interest in light of recent findings linking immune function with ghrelin and leptin. Ghrelin and its receptor are expressed in human T-lymphocytes, where they can inhibit cytokine activation, including interleukins, tumor necrosis factor-α and leptin [18]. Conversely, leptin stimulates cytokine activation and immune-cell proliferation, an effect that predisposes to inflammatory conditions [4]. Is it possible, then, that sleep-related changes in leptin and ghrelin influence the development of metabolic and immune disorders? Can biologically restorative sleep reverse disease progression? Can biologically restorative sleep be defined on the basis of metabolic hormone responses? Future research may answer some of these and other questions, further elucidating the role of sleep in public health.
Citation: Prinz P (2004) Sleep, appetite, and obesity—What is the link? PLoS Med 1(3): e61.
Abbreviations
BMIbody mass index
SDBsleep-disordered breathing
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| 15599390 | PMC535424 | CC BY | 2021-01-05 10:38:00 | no | PLoS Med. 2004 Dec 7; 1(3):e61 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010061 | oa_comm |
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Curr Control Trials Cardiovasc MedCurrent Controlled Trials in Cardiovascular Medicine1468-67081468-6694BioMed Central 1468-6708-5-121554116910.1186/1468-6708-5-12ReviewA trial design for evaluation of empiric programming of implantable cardioverter defibrillators to improve patient management Morgan John M [email protected] Laurence D [email protected] Jodi L [email protected] Kevin T [email protected] Mary F [email protected] Bruce L [email protected] Southampton General Hospital, Southampton, United Kingdom2 Victoria Heart Institute, Victoria, BC, Canada3 Medtronic, Inc., Minneapolis, MN, United States4 Cleveland Clinic Foundation, Cleveland, OH, United States2004 12 11 2004 5 1 12 12 8 9 2004 12 11 2004 Copyright © 2004 Morgan et al; licensee BioMed Central Ltd.2004Morgan 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.
The delivery of implantable cardioverter defibrillator (ICD) therapy is sophisticated and requires the programming of over 100 settings. Physicians tailor these settings with the intention of optimizing ICD therapeutic efficacy, but the usefulness of this approach has not been studied and is unknown. Empiric programming of settings such as anti-tachycardia pacing (ATP) has been demonstrated to be effective, but an empiric approach to programming all VT/VF detection and therapy settings has not been studied. A single standardized empiric programming regimen was developed based on key strategies with the intention of restricting shock delivery to circumstances when it is the only effective and appropriate therapy. The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming for VT/VF detection and therapy in a broad group of about 900 dual chamber ICD patients. The trial will provide a better understanding of how particular programming strategies impact the quantity of shocks delivered and facilitate optimization of complex ICD programming.
Implantable cardioverter defibrillatorICDshocksprogrammingempiricdetectiontherapy
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Background
Over the past decade ICD implantation has become increasingly straightforward, yet ICD programming and follow up has become more complex due to device feature and capability enhancements. While sophisticated algorithms provide high sensitivity and improved specificity of arrhythmia detection, allowing delivery of necessary effective therapy with minimization of inappropriate defibrillation shocks, detection and therapy of ventricular tachycardia (VT) / ventricular fibrillation (VF) still requires programming about 100 settings [1-3].
Good programming choices are crucial as they relate to patient acceptance of ICD therapy. It has been found that patients who receive multiple shocks have greater difficulty adjusting to the ICD implant. These patients may become anxious or depressed, especially if a prior history of these ailments exists [4]. Reducing shocks delivered to the patient would improve overall patient management.
To date, there is no proven consensus on how to use information about the patient's complex diseases to program the ICD, and usually little is known about the patient's spontaneous VT rates, their risk of syncope, or therapies to effectively terminate spontaneous ventricular arrhythmias. Furthermore, ICD indications have dramatically changed within the last five years. Physicians may retain old programming habits even with enhanced devices or expanding patient indications, which may result in sub-optimal detection and therapy, such as unnecessary shocks for faster VT, supraventricular tachycardia (SVT), and non-sustained VT. Physicians often adjust many programmable settings that may benefit the patient. For example, physicians may prescribe patient-specific regimens for anti-tachycardia pacing (ATP) or shock energies based on lab testing. While one would expect this tailoring of programming to improve outcomes, it has never been studied.
Empiric programming has been shown to be effective for subsets of ICD settings, including subsets of dual chamber detection and ATP therapies [3,5-10]. Whether this holds true for comprehensive programming of VT/VF detection and therapy for all ICD patients is unknown.
A proven optimal programming approach would be useful for simplifying therapy prescription, improving therapy outcomes, reducing inadvertent programming errors, and overall reducing shock-related morbidity. The EMPIRIC trial has been designed to evaluate a standardized empiric programming regimen by testing the hypothesis stated below. The EMPIRIC trial outcome will provide an understanding of how programming strategies impact defibrillation shock delivery in ICD therapy.
EMPIRIC Trial Hypothesis
This trial tests the hypothesis that the shock related morbidity of ICD therapy is similar whether patients are treated with a standardized empiric programming regimen for VT/VF detection and therapy or with a patient-specific physician tailored approach.
Indices of Shock Morbidity
Only sustained VT/VF that cannot be painlessly terminated should result in shock therapy and it is unusual for supraventricular arrhythmias (SVT) to require shock therapy. Shock morbidity is related to the number and frequency of shocks that patients receive and therefore morbidity is reduced if shocks are delivered only when necessary for effective arrhythmia termination. Thus, indices that address shock morbidity should reflect both the frequency and appropriateness of shocks for VT/VF and SVT.
Shock morbidity is quantifiable by determination of the following:
♦ proportion of true VT/VF episodes that are shocked
♦ proportion of true SVT episodes that are shocked
♦ time to first shock (VT/VF or SVT)
♦ time to first VT/VF shock
♦ time to first SVT shock
These parameters are used to define the Empiric Trial's main objectives.
Empiric Trial Objectives
The primary objective is to demonstrate that the proportion of shocked VT/VF episodes and the proportion of shocked SVT episodes in a population whose ICDs are programmed using a standardized regimen for VT/VF detection and therapy, is either similar to or less than the same proportion in a similar population whose ICDs are programmed using a physician-tailored approach. This primary objective was chosen to independently evaluate the effects of programming on both appropriate and inappropriate ICD shocks (which are likely to have different implications for patient management). The advantage of this approach is that it focuses on frequency of shock delivery while also allowing an assessment of their appropriateness. However, this assessment could be confounded by a disproportionate number of SVT events in the two study groups. For example, an abundance of non-shocked SVT events in the physician-tailored arm, despite a greater incidence of inappropriate SVT shock therapies in that arm, nevertheless would result in the proportion of SVT episodes shocked being similar in the two arms. The analysis is also heavily dependent on the electrogram data stored in the ICDs. Given the electrogram storage capability of ICDs, differing rates of electrogram storage might occur between study arms or between VT/VF and SVT episodes that may skew the amount of data available for analysis. Therefore, the key secondary endpoint in this study is considered to be the time to delivery of first shock therapy in any given patient. This endpoint offers the advantage that it enables patient cross over to occur between the study arms without endpoint compromise and it is a clinically robust indicator of patient shock-related morbidity. Furthermore, its analysis is not influenced by the appropriateness or otherwise of a shock therapy and therefore cannot be confounded by differential occurrence of non-shocked SVT events in the study arms.
Other secondary endpoints will further evaluate the impact of the standardized programming regimen on patients by an assessment of detection performance, health care utilization, shock impact on device longevity, and "true VT/VF" episode durations.
EMPIRIC Trial Protocol Design
The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming. About 900 patients were enrolled worldwide at 52 centers from August 2002 to October 2003. Each patient will be followed for approximately one year.
The inclusion criteria require patients to meet all of the following conditions:
1. Indicated for an ICD according to internationally accepted criteria.
2. Willing to sign informed consent or offer a legal representative who can provide consent.
3. Achieved a 10 Joule safety margin at implant.
Patients are excluded if they:
1. Have permanent atrial fibrillation (AF).
2. Had a previous ICD.
3. Have a medical condition that precludes the testing required by the protocol or limited trial participation.
4. Have a life expectancy less than one year.
5. Are unable to complete follow-ups at the trial center.
6. Are enrolled or participating in another clinical trial.
Randomization
Patients receiving a Marquis DR ICD are randomized to one of the two programming approaches after meeting a 10 J safety margin. In order to control for physician practice between the two treatment arms, randomization is stratified by treatment center. Further, since the incidence and prevalence of spontaneous VT/VF and SVT among primary prevention patients is not well known, randomization is also stratified by ICD indication (secondary vs. primary). A secondary indication includes patients with a history of spontaneous sustained VT/VF or syncope with suspected VT. A primary prevention indication includes all other patients.
Programming Approaches
The physician tailored approach is based on the standard practice of each physician. All VT/VF programming may be tailored to the patient except that VT detection must be turned to 'On' or 'Monitor' to record episodes of slower VT.
The empiric standardized regimen is based on various programming strategies to reduce shocks. In this arm, initial device settings are fixed (see Table 1), with the exception of the VT detection interval, which can be set slower than 150 bpm when clinically necessary.
Table 1 Empiric Arm Programming
Detection Interval Beats To Detect Redetect Therapies
VF On 300 ms 18/24 9/12 9/12 30 J × 6
FVT via VF 240 ms NA Burst (1 sequence), 30 J × 5
VT On ≥ 400 ms* 16 12 Burst (2), Ramp (1), 20 J, 30 J × 3
SVT Criteria On: AF/Afl, Sinus Tach (1:1 VT-ST Boundary = 66%), SVT Limit = 300 ms
Burst ATP: 8 intervals, R-S1 = 88%, 20 ms decrement
Ramp ATP: 8 intervals, R-S1 = 81%, 10 ms decrement
VT/VF detection and therapy programming changes are permitted at follow-up in both arms only when medically justified. These changes must be documented, and are reviewed throughout the study.
Data Collection
Patients are followed for a 12-month period, with required clinic visits at 3, 6 and 12 months. Data collection includes: VT/VF and SVT episodes, device programming, medical justifications for VT/VF programming changes, cardiovascular medication, adverse device events, P and R wave measurements, and cardiovascular-related hospitalizations.
Study Design Challenges
A challenge of the study design is the possibility that physician practice could become biased by in-trial experience, causing physician practice to gravitate towards the empiric standardized regimen. This might occur if empiric programming is perceived to be efficacious, particularly with respect to management of rapid ventricular tachycardia by pace termination. Collection of pre-trial programming practices provides the capacity to evaluate potential "treatment drift". This result will be reported. Additionally, in an effort to prevent drifting or possible physician bias to programming in the physician tailored arm, a weekly comparison of programming status and initial implant programming will be assessed through device interrogation information. Any programming changes made must be supported by a medical justification with a basis of event-related occurrences (i.e. system- or procedure-related adverse events, spontaneous episodes, or inappropriate shocks). In order to protect protocol design integrity, reprogramming will be encouraged for non-justified programming deviations. In this manner the initial treatment strategies are tested using an intention-to-treat analysis with characterization of programming changes.
Empiric Arm Programming Strategies
The empiric arm standardized programming regimen is based on the following key strategies to reduce shocks.
1) Strategies to reduce shocks for VT/VF
• Multiple ATP attempts for VT≤ 200 bpm: Three sequences of ATP will be attempted for rhythms with ventricular rates ≤ 200 bpm. Empiric ATP has been shown to terminate ≥ 90% of VTs in the VT zone [5-10]. Furthermore, induced VTs do not predict spontaneous VT cycle length, morphology, or therapy efficacy [11]. Three sequences will be attempted for rates up to 200 bpm because the average rate of fast VTs was 199 bpm in the PainFREE Rx1 study, where the FVT zone was 188 – 250 bpm, and more ATP provided incremental shock reductions[6]. ATP will be used in all patients because even cardiac arrest patients have been shown to have VTs [5,12-14].
• ATP for VTs 201 – 250 bpm: One sequence of ATP will be delivered for fast VTs (FVT) using the FVT via VF zone, which maintains sensitivity to polymorphic VT (PVT) and VF and delivers ATP if the 8 beats prior to FVT detection are ≤ 250 bpm. Approximately 81% of ICD detected VF is monomorphic VT (MVT). MVT can be pace-terminated approximately 75% of the time with one sequence of ATP, without increased risk of syncope or acceleration [6,7,15].
• Longer detection duration: The VF initial beats to detect will be set to 18 of 24. Shorter beats to detect are often programmed by physicians, but may increase the unnecessary shocks for non-sustained VT and for SVTs. At least 25% of ICD-detected VF is non-sustained VT/VF [15-17].
• High Output 1st VF and FVT Shock: A 30 Joule energy will be used for the first VF and FVT shock. This will allow additional time for spontaneous conversions that frequently occur. A higher shock energy may also improve 1st shock success and therefore reduce the need for multiple shocks within an episode. The LESS study found no difference in 1st shock success with 31 J versus DFT++, however it analyzed all VT/VF faster than 200 bpm [18] ATP should terminate a majority of these rhythms and for that reason the benefit of empiric high-energy shocks for polymorphic VT (PVT)/VF or after a failed ATP is unknown. The primary reason some physicians program lower energy 1st shocks is due to concerns about syncope. Several recent studies have shown very low syncope rates [6,19] Furthermore, charge times are much faster and more stable over the life of the device than in older ICDs. For instance, the Medtronic Marquis DR 30 Joule charge time is 5.9 and 7.5 seconds at beginning and end of life, respectively [20].
2) Strategies to Reduce Shocks for SVTs and Sensing Issues
• Empiric SVT Criteria: The PR logic criteria of AF/A. Flutter and Sinus Tach will be programmed 'On' in all patients. These criteria have been shown to have a relative VT/VF sensitivity of 100% and a positive predictive value to 88.4% [3].
• SVT Criteria applied to faster rates: The SVT limit and VF rate cut-off will be increased to 200 bpm in all patients to provide SVT discrimination at faster rates. Two of the top five reasons for inappropriate detections in the GEM DR Study (933 patients) were a ventricular rate during AF in VF zone and a SVT cycle length faster than programmed SVT limit [3].
• Avoid detecting 1:1 SVTs with Long PRs as VT: 1:1 SVTs with long PR intervals accounted for 38% of inappropriate detections in the Gem DR (7271) Clinical Study [3]. A retrospective analysis found that changing the 1:1 VT-ST boundary programmable parameter from 50% to 66% might eliminate 32% of all inappropriate detections. The downside to this approach is that it may result in a 0.8% rate of VT/VF misclassification or delay [21].
• Longer detection duration: VF initial beats to detect will be set to 18 of 24. Shorter beats to detect may result in more unnecessary shocks for SVTs or ventricular over-sensing.
• ATP attempts: In addition to terminating ventricular arrhythmias without shocks, ATP should eliminate some inappropriate shocks when inappropriate detections occur by terminating SVTs or slowing conduction.
The VT rate cut-off is one of the most important ICD settings because it can result in untreated symptomatic VT if set too fast, however it can result in unnecessary therapies for non-sustained VT, SVTs, or sensing issues, if set too slow. Reports have shown that some secondary prevention patients have significant symptoms for VTs outside treated zones [22]. The VT cut-off in the empiric arm is set to ≤ 150 bpm to err on the side of treating VTs and to advance the understanding of the incidence of slower VTs in all patient populations. The optimal VT rate cut-off may need to be set according to the patient's presenting conditions at implant (e.g., faster cut-off in primary prevention patients).
Statistical Considerations
The primary endpoint is the proportion of true episodes that are shocked during the 12-month follow-up period. The standardized empiric programming regimen will be considered non-inferior to the physician tailored programming approach if both the proportion of shocked VT/VF episodes and the proportion of shocked SVT episodes are no more than 10 percentage points greater in the empiric arm than the physician tailored arm. The chosen margin 10 percent is considered clinically important.
It is assumed that 24% of patients will have at least one true VT/VF episode and 33% of patients will have at least one true SVT episode during the 12-month follow-up period. Based on unpublished data from other Medtronic trials, the within-patient correlation coefficient for multiple episodes is assumed to be 0.3. Assuming a similar distribution of episode counts per patient as observed in these previous trials and a shock rate of 30% and 14% for VT/VF and SVT episodes respectively, a total of 900 patients (450 in each arm) will give at least 80% power for the VT/VF hypothesis and 90% power for the SVT hypothesis, each tested at the significance level 0.05.
The critical secondary endpoint, time to first shock therapy, will be analyzed using the Cox proportional hazards model for 1) any VT/VF or SVT, 2) true VT/VF only and 3) true SVT only. The empiric programming approach will be considered non-inferior if the upper confidence limit for the hazard ratio is less than 1.5.
Other Planned Analyses
To better understand the changing ICD patient populations, we will investigate whether or not the proportion of appropriate and inappropriate shocks delivered is related to the following baseline characteristics: main indication for implant (especially spontaneous sustained monomorphic VT), left ventricular ejection fraction, CAD status, history of Atrial Tach/Atrial Fib/Atrial Flutter, NYHA classification, use of amiodarone, sotalol, or beta-blockers, and inducibility for VT/VF. In addition, to facilitate understanding of the optimal programmable settings for various patient sub-groups, we will consider the impact of programmable settings on outcomes. In particular, we will examine the "treated cut-off" (TC), which is the VT detection cut-off if VT detection is 'On' or the VF detection cut-off if VT detection is 'Off' or 'Monitor'. Outcomes in patients with a faster TC (physician tailored arm) will be compared to patients with slower TC (either physician tailored arm or empiric arm). Other programmable settings that will be investigated include the number of beats to detect VF and the number of ATP attempts based at various rates (e.g., <175 bpm, 175–200 bpm, >200 bpm). The types of arrhythmias, median ventricular cycle length, and therapies delivered will also be characterized relative to the patient's conditions and programming. Furthermore, the incidence of slower VTs in patients without a history of spontaneous, sustained monomorphic VT will be characterized.
Conclusions and Trial Impact
The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of shock- related morbidity in a population of about 900 ICD patients whose ICD therapy is determined either by a standardized programming regimen or by physician tailored programming of VT/VF detection and therapy. Shock-related morbidity is assessed by a primary objective that compares between study arms the proportion of VT/VF episodes that are shocked and the proportion of SVT episodes that are shocked, and by a key secondary endpoint that compares to time to first shock therapy.
ICD patient populations have rapidly changed within the last five years but little has been published on optimal programming for the emerging patient subsets (e.g., primary prevention). Therefore a standardized regimen of parameters is used in this trial for all patient populations. Today's patient population is quite diverse, so a slightly more sophisticated programming approach may be necessary (e.g. change VT cut-off based on main ICD indication) or perhaps complex physician tailoring is critical to reducing shocks.
The EMPIRIC trial will characterize the shock morbidity of a single empiric programming approach compared to patient-specific, physician tailored programming. Empiric programming may be an acceptable strategy if it achieves equivalence with physician tailored programming. The EMPIRIC trial results will also provide a better understanding of how particular programming strategies impact the frequency of shocks delivered and will facilitate a way to optimize complex ICD programming.
Competing Interests
1. Have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this paper in the past five years, or is such an organization financing the article-processing charge for this article?
Dr. Morgan: Yes, Medtronic has paid me honoraria.
Dr. Sterns: Yes, I am a paid investigator in several Medtronic clinical trials and key investigator in the present trial. I understand that Medtronic is paying for the processing fee for this article.
Dr. Wilkoff: Yes, Medtronic, Guidant, St. Jude Medical
Hanson, Ousdigian, and Otterness: Yes, Employees of Medtronic.
2. Have you held any stocks or shares in an organization that may in any way gain or lose financially from the publication of this paper?
Dr. Morgan and Dr. Sterns and Dr. Wilkoff: No
Hanson, Ousdigian, and Otterness: Yes, own Medtronic stock.
3. Do you have any other financial competing interests?
Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No.
4. Are there any non-financial competing interests you would like to declare in relation to this paper?
Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No.
Authors' Contributions
All 6 authors contributed to the study design and writing of this manuscript.
Acknowledgements
The authors wish to acknowledge Ryan Wilson and Zengri Wang for their critical review and feedback on the statistical considerations section.
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| 15541169 | PMC535530 | CC BY | 2021-01-04 16:47:31 | no | Curr Control Trials Cardiovasc Med. 2004 Nov 12; 5(1):12 | utf-8 | Curr Control Trials Cardiovasc Med | 2,004 | 10.1186/1468-6708-5-12 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-421554117810.1186/1475-2875-3-42ResearchKnowledge of malaria influences the use of insecticide treated nets but not intermittent presumptive treatment by pregnant women in Tanzania Nganda Rhoida Y [email protected] Chris [email protected] Hugh [email protected] Tanya [email protected] Kilimanjaro Christian Medical Centre, PO Box 3010, Moshi, Tanzania2 Joint Malaria Programme, PO Box 2228, Moshi, Tanzania3 London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E7HT, UK2004 12 11 2004 3 42 42 6 8 2004 12 11 2004 Copyright © 2004 Nganda et al; licensee BioMed Central Ltd.2004Nganda 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
To reduce the intolerable burden of malaria in pregnancy, the Ministry of Health in Tanzania has recently adopted a policy of intermittent presumptive treatment for pregnant women using sulphadoxine-pyrimethamine (IPTp-SP). In addition, there is strong national commitment to increase distribution of insecticide treated nets (ITNs) among pregnant women. This study explores the determinants of uptake for both ITNs and IPTp-SP by pregnant women and the role that individual knowledge and socio-economic status has to play for each.
Methods
293 women were recruited post-partum at Kibaha District Hospital on the East African coast. The haemoglobin level of each woman was measured and a questionnaire administered.
Results
Use of both interventions was associated with a reduced risk of severe anaemia (Hb<8 g/dL) compared to women who had used neither intervention (OR 0.31, 95% CI 0.14–0.67). In a logistic regression model it was found that attendance at MCH health education sessions was the only factor that predicted IPTp-SP use (OR 1.8, 95% CI 1.1–2.9) while high knowledge of malaria predicted use of ITNs (OR 2.3, 95% CI 1.1–4.9).
Conclusion
Individual knowledge of malaria was an important factor for ITN uptake, but not for IPTp-SP use, which was reliant on delivery of information by MCH systems. When both these interventions were used, severe anaemia postpartum was reduced by 69% compared to use of neither, thus providing evidence of effectiveness of these interventions when used in combination.
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Background
Plasmodium falciparum malaria in pregnancy poses a substantial risk to pregnant women and their offspring: it has been estimated that malaria in pregnancy is the primary cause of up to 10,000 maternal anaemia-related deaths in sub-Saharan Africa annually [1]. Further, malaria in pregnancy increases the risk of an infant being born with low birth weight (LBW) and is responsible for up to 35% of preventable LBW in malaria-endemic areas[2].
Over the last decade a body of evidence has accumulated which supports the use of both ITNs and IPTp-SP to reduce the adverse effects of malaria during pregnancy [1,3,4] and both these interventions are currently recommended by WHO [5]. In Tanzania it is now national policy to offer IPT-p with sulfadoxine-pyrimethamine (SP) for every pregnant woman attending Maternal and Child Health services (MCH). There is strong commitment in Tanzania to achieve the targets agreed on by African countries at the Abuja Conference of 60% coverage of ITNs for pregnant women by 2005 using social marketing strategies and a national voucher scheme [6] to improve access for pregnant women and their children. However, there are currently no reliable data in Tanzania on current levels of use of these interventions.
Evidence suggests that malaria treatment choices are affected by knowledge of the problem [7,8]. The success in implementing preventive interventions amongst pregnant women in Tanzania is thus likely to be determined in part by awareness of malaria and the strategies available to prevent it. This study set out to explore the determinants of uptake for both ITNs and IPTp-SP by pregnant women, with particular regard to knowledge of malaria and socio-economic status, and to estimate the impact that reported use of either of these interventions had on the prevalence of severe anaemia in the post-partum period.
Methods
Setting
This research was carried out at Kibaha District Hospital in the Coastal Region of Tanzania, an area with moderate to high malaria transmission. The area is predominantly rural, supported by a mixture of subsistence and cash crop farming, with a peri-urban belt along the main Tanzania/Zambia highway which crosses the district.
Study participants
All post-natal women who had delivered their baby in the hospital during April/May 2003, but had not been admitted to hospital during the pregnancy, were eligible for inclusion in the study.
Study tools
From studies elsewhere [9] it was estimated that a samples size of 293 women would give sufficient power to show a difference in uptake of malaria interventions by knowledge of malaria. On each working day of the study the first 13 women to come out of the labour ward and who gave informed consent to participate were registered and interviewed. Haemoglobin level was determined at the time of interview using a portable β-haemoglobin photometer (HemoCue©, HemoCue AB, Ängelholm, Sweden). Women with Hb<11 g/dL were referred within the hospital system.
Definitions
Knowledge of malaria score (KoM)
Participants were assigned a 'knowledge of malaria score' (KoM) according to their responses to a series of seven closed questions (Table 1).
Table 1 Components of the knowledge score
Malaria in pregnancy statement: Agreement
n/293 %
Risk of infection Increases in pregnancy 270 92
Consequences Low birth weight 81 28
Pregnancy Loss 112 38
Maternal anaemia 150 51
Best Interventions* ITN 266 91
SP 115 39
Transmission Mosquitoes alone 102 35
*Women asked to state up to two malaria interventions
ITN users
Women who reported that during this pregnancy they had normally (i.e. >75% of the time) slept under a bednet which had been impregnated within the previous six months.
IPTp-SP users
Use of intermittent presumptive treatment of malaria in pregnancy with sulfadoxine-pyrimethamine.
Severe anaemia
Severe anaemia post-partum was defined as Hb<8 g/dL.
Data analysis
Data were entered twice in Epi-Info version 6.04 and analysed in Stata version 7 (Stata Corporation, Texas USA). Evidence of an association was sought between the variables listed in Table 1 and the study outcomes (reported use of interventions or severe anaemia defined as Hb<8 g/dl). Variables found to have an association with each outcome (by χ2 P-value <0.10 or Mantel-Haenszel estimate of the rate ratio, P-value <0.10) were further analysed using multiple logistic regression. Significance in the multi-variate models was defined by a likelihood-ratio test (LRT) P-value <0.05.
Results
A completed questionnaire and measurement of haemoglobin level were available for 293 post-partum women whose characteristics are shown in Table 1. The group was predominantly made up of married women in the 20–29 years age-group, with primary level education only. Both urban and rural residents were represented. No women refused to participate.
Levels of knowledge
The KoM assessment consisted of four parts: knowledge of risk, consequences of risk, transmission and prevention, with seven questions in total (Table 1). Each question contributed one point to the overall KoM score (i.e. range of possible scores of 0–7) and overall the mean score was 3.7 (median 4). The KoM score was stratified into three groups: score 0–2 representing low (29% of respondents), score 3–4 representing median (38%) and score 5–7 representing high levels of knowledge (33% of respondents).
The lowest knowledge scores related to the impact of maternal malaria on the health of the foetus; only 28% (81/293) recognized low birth weight and 38% (112/293) pregnancy loss as a potential consequence of maternal malaria. There was also some confusion over the mode of transmission of malaria: 95% (279/293) agreed that mosquitoes could transmit malaria but only 35% thought that mosquitoes alone were responsible (excepting blood transfusion).
Having high KoM was strongly associated with education level (χ2 for linear trend 50.03, p < 0.0001). Because of colinearity between education level and high KoM, education was excluded from the logistic regression model used to identify determinants of high KoM. However, models for primary and secondary education respectively showed the same socio-economic effects as the data presented in Table 3. In the logistic regression model, only ages above teenage (LRT 9.8, p = 0.007), ownership of a radio (LRT 6.0, p = 0.01), ownership of a bicycle (LRT 4.8, p = 0.02) and citing the MCH rather than the community as the most important source of health information (LRT 7.5, p = 0.02) were significantly associated with a high KoM compared to low/median KoM.
Table 3 Unadjusted and adjusted OR's for characteristics of women with high knowledge of malaria1.
N Unadjusted OR Confidence Interval Adjusted OR Confidence Interval LRT P
Age 15–19 49 1.0 - 1.0 -
20–29 185 3.3 1.4–7.8 3.2 1.2–8.4
30+ 59 4.4 1.7–11.4 4.6 1.6–13.3 9.8 <0.01
Radio No 29 1.0 - 1.0 -
Yes 264 7.7 1.7–33.1 5.1 1.1–24.0 6.0 0.01
Bicycle No 69 1.0 - 1.0 -
Yes 224 2.3 1.2–4.5 2.1 1.0–4.3 4.8 0.02
Source of health information Community 36 1.0 - 1.0 -
MCH 83 5.8 11.8–18.0 4.3 1.3–14.0
Media 174 4.1 1.3–12.1 2.6 0.8–8.1 7.5 0.02
1All women with high malaria knowledge had formal schooling
Use of IPT-p or ITN
48% (141/293) of women were ITN users, 57% (166/293) had received IPTp-SP once and 12% (34/293) IPTp-SP twice. A multi-variate analysis of factors influencing the uptake of these two interventions is shown in Table 4. After adjustment, increasing age (LRT 10.3, P = < 0.01), owning a radio (LRT 4.0, P = 0.04) and having a high KoM, compared to a low/median KoM score (LRT 7.3, P = 0.02), were the only factors that independently predicted use of an ITN in pregnancy (table 4). By contrast, only a history of having received health education during the pregnancy significantly predicted uptake of any dose of IPTp-SP (LRT 5.6 p = 0.01).
Table 4 Factors associated with uptake of insecticide treated nets (ITNs) during pregnancy and at least one dose of sulphadoxine-pyrimethamine as part of intermittent presumptive treatment (IPTp-SP)
ITN users
N Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT P
Health Education
No 160
Yes 133
Age
15–19 49 1.0 - 1.0 -
20–29 185 3.2 1.6–6.1 2.5 1.3–5.0
30+ 59 4.2 1.8–10.3 3.8 1.5–9.4 10.3 <0.01
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.3 0.7–2.3 1.0 0.5–1.8
High 97 3.6 1.7–7.1 2.3 1.1–4.9 7.3 0.02
Radio
No 29 1.0 - 1.0 -
Yes 264 2.9 1.3–6.3 2.3 1.0–5.5 4.0 0.04
Used IPTp-SP
Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT p
Health Education
No 160 1.0 - 1.0 -
Yes 133 1.8 1.1–2.9 1.8 1.1–2.8 5.6 0.01
Age
15–19 49 1.0 - 1.0 -
20–29 185 0.8 0.4–1.6 0.8 0.4–1.6
30+ 59 0.9 0.4–2.0 0.7 0.3–1.7 0.4 0.8
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.7 1.0–3.0 1.7 1.0–3.1
High 97 1.6 0.9–2.9 1.6 0.8–2.9 3.8 0.14
Radio
No 29
Yes 264
41% (120/293) of women had used both interventions. Women with a median KoM were twice as likely (OR 2.1 (95% CI 1.1–4.0) and women with a high KoM three times more likely (OR 3.2 (95% CI 1.7–6.0) to have used both IPT-p and an ITN than women with low KoM scores.
Timing of first MCH attendance
26% (76/293) of women had first attended the MCH during the first trimester, 65% (192/293) during the second trimester and 9% (25/293) during the third trimester of pregnancy. Predictably the median number of visits to MCH by women was correlated with the trimester of first visit: first trimester – median of 7.5 visits overall (mean 6.9 (s.d.2.2), second trimester – median of 5 visits overall (mean 5.4 (s.d.1.8) & third trimester – median of 2 visits overall (mean 2.4 (s.d.0.8). Women attending MCH for the first time during their first trimester were more than twice as likely to have attended health education sessions as women attending for the first time in their third trimester (χ2 test for trend 5.1, p = 0.02).
Risk factors for severe anaemia
Overall, 27% (80/293) of study participants had Hb<8 g/dL immediately post-partum. In the regression model, three factors were found to independently predict the risk of having severe anaemia in the post-partum period (Table 5). Firstly, with women using either an ITN or IPTp-SP alone, there was a reduced risk of severe anaemia, but it was not statistically significant (OR 0.77 (CI 0.36–1.65 and OR 0.70 (CI 0.34–1.41 respectively). However, the use of an ITN in conjunction with IPTp-SP was associated with a significant reduction in risk of being severely anaemic post partum compared to women not using any intervention (OR 0.31 (CI 0.14–0.67) LRT 10.1 p = 0.01). Secondly, having had any level of education (as compared to none) and, thirdly, attendance at the MCH clinic during the first rather than third trimester of pregnancy were both associated with a reduced risk of severe anaemia (OR 0.41, 95% CI 0.17–0.98) LRT 4.5 p = 0.03 and OR 0.31, 95%CI 0.11–0.83, LRT 5.3 p = 0.05 respectively).
Table 5 Risk factors for severe anaemia post partum.
% with severe anaemia Unadjusted OR 95% confidence interval Adjusted OR 95% confidence interval LRT P
Formal education
No 29 1.00 - 1.00 -
Yes 16 0.45 0.19–1.07 0.41 0.17–0.98 4.5 0.03
Time of first ANC use
3rd trimester 48 1.00 - 1.00 -
2nd trimester 26 0.39 0.16–0.91 0.41 0.17–1.0
1st trimester 22 0.31 0.12–0.80 0.31 0.11–0.83 5.3 0.05
Use of malaria intervention
None 37 1.00 - 1.00 -
ITN only 32 0.82 0.39–1.71 0.77 0.36–1.65
IPTp-SP only 27 0.64 0.32–1.28 0.70 0.34–1.41
ITN + IPTp-SP 16 0.34 0.16–0.72 0.31 0.14–0.67 10.1 0.01
Discussion
The key finding was that while knowledge, wealth and age were all found to be independently predictive of ITN use, only participation in health education was associated with use of IPTp-SP. That predictors of uptake for each of the two interventions were different was an interesting and unexpected finding. The data indicate that, while ITN use is driven by both access (i.e. wealth) as well as knowledge, use of IPTp is determined by its being offered in an MCH clinic. This is a positive finding that suggests the need to prioritise strategies for maximising early attendance to boost IPTp-SP uptake.
This finding is in contrast to research from Kenya which showed that uptake of IPTp-SP increased with higher levels of formal education [9]. The National Malaria Control Programme of Tanzania does not currently have any data on IPTp-SP uptake nationally, although it is planned for integration into routine surveillance next year. It is likely that distribution systems for this, as other clinic based interventions, will need more attention. It is possible that the new MCH clinic based voucher system for ITN will have an added benefit by increasing early attendance and therefore uptake of IPTp-SP.
These findings on knowledge and awareness suggest that the increased risk posed to pregnant women by malaria was almost universally recognized, but that knowledge of the health impact of that risk – especially to the health of the foetus – was very low. Over 90% of women thought that ITNs were a good intervention against malaria in pregnancy, but less than half thought the same about IPTp-SP. Knowledge of malaria in pregnancy was strongly associated with use of a combination of both ITN and IPTp. Although this was a small observational study where it was not possible to control for all likely confounding variables, there is evidence that the use of a combination of ITN use and IPT-p provides additive protection against severe anaemia, and that this effect is not confined to trial conditions. Use of an ITN or IPT-p alone was associated with a 23% and 30% reduction in the risk of severe anaemia post partum respectively, similar to estimates from other controlled trials [10,11] The data suggest that MCH services are effective when used optimally. Women who accessed MCH services in their first trimester of pregnancy had a significantly lower risk of severe anaemia post-partum compared to women who first presented at MCH during their third trimester. Women attending MCH earlier were more likely to attend health education sessions, and women who attended health education sessions more likely to use IPTp-SP than women who did not attend. Women with high KoM were most likely to cite the MCH as their most important source of health information.
It must be noted that by recruiting only women who had uncomplicated pregnancies and who delivered in hospital this study had a selection bias towards the healthier and possibly wealthy members of the community. However, women from a considerable range of socio-economic situations are represented (no education vs. further education; mobile phone owners vs. not owning a radio) which probably reflects the rural/peri-urban catchment area of Kibaha and other newly urbanised areas. The data has shown that many of the factors indicative of relative wealth in a poor community were associated with increasing levels of knowledge – and that knowledge was positively associated with multiple intervention uptake and improved health outcome.
The importance of access to resources has been illustrated previously for both preventative interventions and treatment [12]. At the time of this study, IPTp-SP was offered free of charge to pregnant women via the antenatal clinics in Tanzania. There is social marketing of bednets (price approx $5) at the national level but not targeted at specific high risk groups. The newly initiated ITN voucher scheme for pregnant women is part of the Tanzanian commitment to improve access to ITNs for pregnant women as a whole. It is hoped that via mass health education and substantial price subsidy, some of these socio-economic inequities in access will also be addressed.
Conclusions
The findings highlight the importance of women's knowledge of malaria in pregnancy and of antenatal attendance for the uptake of preventative interventions. Now that effective malaria interventions are available and there is political will to implement them, to maximise the potential for health impact, it is essential to empower the intended recipients of interventions by providing the knowledge which can influence their health decisions.
Authors' contributions
RN carried out the fieldwork and performed preliminary analysis. CD & HR contributed to study design and logistics. TM performed statistical analysis and supervised the study. All authors read, edited and approved the final manuscript.
Table 2 Socio-economic breakdown of population.
n/293 %
Age 15–19 49 17
20–29 185 63
30+ 59 20
Gravidity Primigravidae 124 42
Mutigravidae 169 58
Residence Urban 124 42
Rural 169 58
Education None 44 15
Primary 217 74
Secondary + 32 11
Marital status Married 207 71
Unmarried 86 29
Travel time to MCH < 1 hour 252 86
1–2 hours 20 7
>2 hours 21 7
Household ownership Radio 264 90
Bicycle 224 76
TV 16 5
M/phone 13 4
Bednet 245 83
Religion Christian 104 35
Moslem 189 65
Main source of health information Community 36 12
Health workers 83 28
Media 174 59
Used IPTp-SP Once 166 57
Twice 34 12
Used bednet Yes 207 71
Used ITN Yes 141 48
Health education MCH Yes 133 45
Knowledge score Low 86 29
Medium 110 38
High 97 33
Acknowledgements
The authors thank the women who participated in the study and the staff of the maternity ward at Kibaha hospital for their cooperation. This research was conducted as partial fulfilment of the Degree of Master of Public Health of Tumaini University, Kilimanjaro Christian Medical Centre, and in collaboration with the Joint Malaria Programme, Tanzania. The Joint Malaria Programme is a collaboration between the National Institute for Medical Research (NIMR), Kilimanjaro Christian Medical College (KCMC), the London School of Hygiene and Tropical Medicine (LSHTM) and the Centre for Medical Parasitology, University of Copenhagen (CMP). During the period of study RN was jointly supported by United States Agency for International Development and the Ministry of Health, Tanzania.
==== Refs
Guyatt HL Snow RW The epidemiology and burden of Plasmodium falciparum-related anemia among pregnant women in sub-Saharan Africa Am J Trop Med Hyg 2001 64 36 44 11425176
Steketee RW Nahlen BL Parise ME Menendez C The burden of malaria in pregnancy in malaria-endemic areas Am J Trop Med Hyg 2001 64 28 35 11425175
Jones G Steketee RW Black RE Bhutta ZA Morris SS How many child deaths can we prevent this year? Lancet 2003 362 65 71 12853204 10.1016/S0140-6736(03)13811-1
Njagi JK Magnussen P Estambale B Ouma J Mugo B Prevention of anaemia in pregnancy using insecticide-treated bednets and sulfadoxine-pyrimethamine in a highly malarious area of Kenya: a randomized controlled trial Trans R Soc Trop Med Hyg 2003 97 277 282 15228241 10.1016/S0035-9203(03)90141-6
WHO Expert Committee on Malaria World Health Organ Tech Rep Ser 2000 892 i v, 1-74 10892307
Unwin A MoH T Malaria situation and strategies for its control in Tanzania Public Health Situation in Tanzania: Re-packaging Knowledge for Health Promotion 2001 Dar es Salaam, 17 19
Karanja J Wambari E Okumu D A study of awareness of malaria among Kibera population; implication for community based intervention National Institute of Public Health Journal 2002 51 51 55
Nyamongo IK Health care switching behaviour of malaria patients in a Kenyan rural community Soc Sci Med 2002 54 377 386 11824914 10.1016/S0277-9536(01)00036-3
Eijla AM Ayis GJ ter Kuile FO Implementation with IPT with SP for control of malaria in Kisumu, Kenya 2002 265 266
Shulman CE Dorman EK Cutts F Kawuondo K Bulmer JN Peshu N Marsh K Intermittent sulphadoxine-pyrimethamine to prevent severe anaemia secondary to malaria in pregnancy: a randomised placebo-controlled trial Lancet 1999 353 632 636 10030329 10.1016/S0140-6736(98)07318-8
ter Kuile FO Terlouw DJ Phillips-Howard PA Hawley WA Friedman JF Kariuki SK Shi YP Kolczak MS Lal AA Vulule JM Nahlen BL Reduction of malaria during pregnancy by permethrin-treated bed nets in an area of intense perennial malaria transmission in western Kenya Am J Trop Med Hyg 2003 68 50 60 12749486
Victora CG Wagstaff A Schellenberg JA Gwatkin D Claeson M Habicht JP Applying an equity lens to child health and mortality: more of the same is not enough Lancet 2003 362 233 241 12885488 10.1016/S0140-6736(03)13917-7
| 15541178 | PMC535531 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 12; 3:42 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-42 | oa_comm |
==== Front
Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-421554117810.1186/1475-2875-3-42ResearchKnowledge of malaria influences the use of insecticide treated nets but not intermittent presumptive treatment by pregnant women in Tanzania Nganda Rhoida Y [email protected] Chris [email protected] Hugh [email protected] Tanya [email protected] Kilimanjaro Christian Medical Centre, PO Box 3010, Moshi, Tanzania2 Joint Malaria Programme, PO Box 2228, Moshi, Tanzania3 London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E7HT, UK2004 12 11 2004 3 42 42 6 8 2004 12 11 2004 Copyright © 2004 Nganda et al; licensee BioMed Central Ltd.2004Nganda 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
To reduce the intolerable burden of malaria in pregnancy, the Ministry of Health in Tanzania has recently adopted a policy of intermittent presumptive treatment for pregnant women using sulphadoxine-pyrimethamine (IPTp-SP). In addition, there is strong national commitment to increase distribution of insecticide treated nets (ITNs) among pregnant women. This study explores the determinants of uptake for both ITNs and IPTp-SP by pregnant women and the role that individual knowledge and socio-economic status has to play for each.
Methods
293 women were recruited post-partum at Kibaha District Hospital on the East African coast. The haemoglobin level of each woman was measured and a questionnaire administered.
Results
Use of both interventions was associated with a reduced risk of severe anaemia (Hb<8 g/dL) compared to women who had used neither intervention (OR 0.31, 95% CI 0.14–0.67). In a logistic regression model it was found that attendance at MCH health education sessions was the only factor that predicted IPTp-SP use (OR 1.8, 95% CI 1.1–2.9) while high knowledge of malaria predicted use of ITNs (OR 2.3, 95% CI 1.1–4.9).
Conclusion
Individual knowledge of malaria was an important factor for ITN uptake, but not for IPTp-SP use, which was reliant on delivery of information by MCH systems. When both these interventions were used, severe anaemia postpartum was reduced by 69% compared to use of neither, thus providing evidence of effectiveness of these interventions when used in combination.
==== Body
Background
Plasmodium falciparum malaria in pregnancy poses a substantial risk to pregnant women and their offspring: it has been estimated that malaria in pregnancy is the primary cause of up to 10,000 maternal anaemia-related deaths in sub-Saharan Africa annually [1]. Further, malaria in pregnancy increases the risk of an infant being born with low birth weight (LBW) and is responsible for up to 35% of preventable LBW in malaria-endemic areas[2].
Over the last decade a body of evidence has accumulated which supports the use of both ITNs and IPTp-SP to reduce the adverse effects of malaria during pregnancy [1,3,4] and both these interventions are currently recommended by WHO [5]. In Tanzania it is now national policy to offer IPT-p with sulfadoxine-pyrimethamine (SP) for every pregnant woman attending Maternal and Child Health services (MCH). There is strong commitment in Tanzania to achieve the targets agreed on by African countries at the Abuja Conference of 60% coverage of ITNs for pregnant women by 2005 using social marketing strategies and a national voucher scheme [6] to improve access for pregnant women and their children. However, there are currently no reliable data in Tanzania on current levels of use of these interventions.
Evidence suggests that malaria treatment choices are affected by knowledge of the problem [7,8]. The success in implementing preventive interventions amongst pregnant women in Tanzania is thus likely to be determined in part by awareness of malaria and the strategies available to prevent it. This study set out to explore the determinants of uptake for both ITNs and IPTp-SP by pregnant women, with particular regard to knowledge of malaria and socio-economic status, and to estimate the impact that reported use of either of these interventions had on the prevalence of severe anaemia in the post-partum period.
Methods
Setting
This research was carried out at Kibaha District Hospital in the Coastal Region of Tanzania, an area with moderate to high malaria transmission. The area is predominantly rural, supported by a mixture of subsistence and cash crop farming, with a peri-urban belt along the main Tanzania/Zambia highway which crosses the district.
Study participants
All post-natal women who had delivered their baby in the hospital during April/May 2003, but had not been admitted to hospital during the pregnancy, were eligible for inclusion in the study.
Study tools
From studies elsewhere [9] it was estimated that a samples size of 293 women would give sufficient power to show a difference in uptake of malaria interventions by knowledge of malaria. On each working day of the study the first 13 women to come out of the labour ward and who gave informed consent to participate were registered and interviewed. Haemoglobin level was determined at the time of interview using a portable β-haemoglobin photometer (HemoCue©, HemoCue AB, Ängelholm, Sweden). Women with Hb<11 g/dL were referred within the hospital system.
Definitions
Knowledge of malaria score (KoM)
Participants were assigned a 'knowledge of malaria score' (KoM) according to their responses to a series of seven closed questions (Table 1).
Table 1 Components of the knowledge score
Malaria in pregnancy statement: Agreement
n/293 %
Risk of infection Increases in pregnancy 270 92
Consequences Low birth weight 81 28
Pregnancy Loss 112 38
Maternal anaemia 150 51
Best Interventions* ITN 266 91
SP 115 39
Transmission Mosquitoes alone 102 35
*Women asked to state up to two malaria interventions
ITN users
Women who reported that during this pregnancy they had normally (i.e. >75% of the time) slept under a bednet which had been impregnated within the previous six months.
IPTp-SP users
Use of intermittent presumptive treatment of malaria in pregnancy with sulfadoxine-pyrimethamine.
Severe anaemia
Severe anaemia post-partum was defined as Hb<8 g/dL.
Data analysis
Data were entered twice in Epi-Info version 6.04 and analysed in Stata version 7 (Stata Corporation, Texas USA). Evidence of an association was sought between the variables listed in Table 1 and the study outcomes (reported use of interventions or severe anaemia defined as Hb<8 g/dl). Variables found to have an association with each outcome (by χ2 P-value <0.10 or Mantel-Haenszel estimate of the rate ratio, P-value <0.10) were further analysed using multiple logistic regression. Significance in the multi-variate models was defined by a likelihood-ratio test (LRT) P-value <0.05.
Results
A completed questionnaire and measurement of haemoglobin level were available for 293 post-partum women whose characteristics are shown in Table 1. The group was predominantly made up of married women in the 20–29 years age-group, with primary level education only. Both urban and rural residents were represented. No women refused to participate.
Levels of knowledge
The KoM assessment consisted of four parts: knowledge of risk, consequences of risk, transmission and prevention, with seven questions in total (Table 1). Each question contributed one point to the overall KoM score (i.e. range of possible scores of 0–7) and overall the mean score was 3.7 (median 4). The KoM score was stratified into three groups: score 0–2 representing low (29% of respondents), score 3–4 representing median (38%) and score 5–7 representing high levels of knowledge (33% of respondents).
The lowest knowledge scores related to the impact of maternal malaria on the health of the foetus; only 28% (81/293) recognized low birth weight and 38% (112/293) pregnancy loss as a potential consequence of maternal malaria. There was also some confusion over the mode of transmission of malaria: 95% (279/293) agreed that mosquitoes could transmit malaria but only 35% thought that mosquitoes alone were responsible (excepting blood transfusion).
Having high KoM was strongly associated with education level (χ2 for linear trend 50.03, p < 0.0001). Because of colinearity between education level and high KoM, education was excluded from the logistic regression model used to identify determinants of high KoM. However, models for primary and secondary education respectively showed the same socio-economic effects as the data presented in Table 3. In the logistic regression model, only ages above teenage (LRT 9.8, p = 0.007), ownership of a radio (LRT 6.0, p = 0.01), ownership of a bicycle (LRT 4.8, p = 0.02) and citing the MCH rather than the community as the most important source of health information (LRT 7.5, p = 0.02) were significantly associated with a high KoM compared to low/median KoM.
Table 3 Unadjusted and adjusted OR's for characteristics of women with high knowledge of malaria1.
N Unadjusted OR Confidence Interval Adjusted OR Confidence Interval LRT P
Age 15–19 49 1.0 - 1.0 -
20–29 185 3.3 1.4–7.8 3.2 1.2–8.4
30+ 59 4.4 1.7–11.4 4.6 1.6–13.3 9.8 <0.01
Radio No 29 1.0 - 1.0 -
Yes 264 7.7 1.7–33.1 5.1 1.1–24.0 6.0 0.01
Bicycle No 69 1.0 - 1.0 -
Yes 224 2.3 1.2–4.5 2.1 1.0–4.3 4.8 0.02
Source of health information Community 36 1.0 - 1.0 -
MCH 83 5.8 11.8–18.0 4.3 1.3–14.0
Media 174 4.1 1.3–12.1 2.6 0.8–8.1 7.5 0.02
1All women with high malaria knowledge had formal schooling
Use of IPT-p or ITN
48% (141/293) of women were ITN users, 57% (166/293) had received IPTp-SP once and 12% (34/293) IPTp-SP twice. A multi-variate analysis of factors influencing the uptake of these two interventions is shown in Table 4. After adjustment, increasing age (LRT 10.3, P = < 0.01), owning a radio (LRT 4.0, P = 0.04) and having a high KoM, compared to a low/median KoM score (LRT 7.3, P = 0.02), were the only factors that independently predicted use of an ITN in pregnancy (table 4). By contrast, only a history of having received health education during the pregnancy significantly predicted uptake of any dose of IPTp-SP (LRT 5.6 p = 0.01).
Table 4 Factors associated with uptake of insecticide treated nets (ITNs) during pregnancy and at least one dose of sulphadoxine-pyrimethamine as part of intermittent presumptive treatment (IPTp-SP)
ITN users
N Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT P
Health Education
No 160
Yes 133
Age
15–19 49 1.0 - 1.0 -
20–29 185 3.2 1.6–6.1 2.5 1.3–5.0
30+ 59 4.2 1.8–10.3 3.8 1.5–9.4 10.3 <0.01
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.3 0.7–2.3 1.0 0.5–1.8
High 97 3.6 1.7–7.1 2.3 1.1–4.9 7.3 0.02
Radio
No 29 1.0 - 1.0 -
Yes 264 2.9 1.3–6.3 2.3 1.0–5.5 4.0 0.04
Used IPTp-SP
Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT p
Health Education
No 160 1.0 - 1.0 -
Yes 133 1.8 1.1–2.9 1.8 1.1–2.8 5.6 0.01
Age
15–19 49 1.0 - 1.0 -
20–29 185 0.8 0.4–1.6 0.8 0.4–1.6
30+ 59 0.9 0.4–2.0 0.7 0.3–1.7 0.4 0.8
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.7 1.0–3.0 1.7 1.0–3.1
High 97 1.6 0.9–2.9 1.6 0.8–2.9 3.8 0.14
Radio
No 29
Yes 264
41% (120/293) of women had used both interventions. Women with a median KoM were twice as likely (OR 2.1 (95% CI 1.1–4.0) and women with a high KoM three times more likely (OR 3.2 (95% CI 1.7–6.0) to have used both IPT-p and an ITN than women with low KoM scores.
Timing of first MCH attendance
26% (76/293) of women had first attended the MCH during the first trimester, 65% (192/293) during the second trimester and 9% (25/293) during the third trimester of pregnancy. Predictably the median number of visits to MCH by women was correlated with the trimester of first visit: first trimester – median of 7.5 visits overall (mean 6.9 (s.d.2.2), second trimester – median of 5 visits overall (mean 5.4 (s.d.1.8) & third trimester – median of 2 visits overall (mean 2.4 (s.d.0.8). Women attending MCH for the first time during their first trimester were more than twice as likely to have attended health education sessions as women attending for the first time in their third trimester (χ2 test for trend 5.1, p = 0.02).
Risk factors for severe anaemia
Overall, 27% (80/293) of study participants had Hb<8 g/dL immediately post-partum. In the regression model, three factors were found to independently predict the risk of having severe anaemia in the post-partum period (Table 5). Firstly, with women using either an ITN or IPTp-SP alone, there was a reduced risk of severe anaemia, but it was not statistically significant (OR 0.77 (CI 0.36–1.65 and OR 0.70 (CI 0.34–1.41 respectively). However, the use of an ITN in conjunction with IPTp-SP was associated with a significant reduction in risk of being severely anaemic post partum compared to women not using any intervention (OR 0.31 (CI 0.14–0.67) LRT 10.1 p = 0.01). Secondly, having had any level of education (as compared to none) and, thirdly, attendance at the MCH clinic during the first rather than third trimester of pregnancy were both associated with a reduced risk of severe anaemia (OR 0.41, 95% CI 0.17–0.98) LRT 4.5 p = 0.03 and OR 0.31, 95%CI 0.11–0.83, LRT 5.3 p = 0.05 respectively).
Table 5 Risk factors for severe anaemia post partum.
% with severe anaemia Unadjusted OR 95% confidence interval Adjusted OR 95% confidence interval LRT P
Formal education
No 29 1.00 - 1.00 -
Yes 16 0.45 0.19–1.07 0.41 0.17–0.98 4.5 0.03
Time of first ANC use
3rd trimester 48 1.00 - 1.00 -
2nd trimester 26 0.39 0.16–0.91 0.41 0.17–1.0
1st trimester 22 0.31 0.12–0.80 0.31 0.11–0.83 5.3 0.05
Use of malaria intervention
None 37 1.00 - 1.00 -
ITN only 32 0.82 0.39–1.71 0.77 0.36–1.65
IPTp-SP only 27 0.64 0.32–1.28 0.70 0.34–1.41
ITN + IPTp-SP 16 0.34 0.16–0.72 0.31 0.14–0.67 10.1 0.01
Discussion
The key finding was that while knowledge, wealth and age were all found to be independently predictive of ITN use, only participation in health education was associated with use of IPTp-SP. That predictors of uptake for each of the two interventions were different was an interesting and unexpected finding. The data indicate that, while ITN use is driven by both access (i.e. wealth) as well as knowledge, use of IPTp is determined by its being offered in an MCH clinic. This is a positive finding that suggests the need to prioritise strategies for maximising early attendance to boost IPTp-SP uptake.
This finding is in contrast to research from Kenya which showed that uptake of IPTp-SP increased with higher levels of formal education [9]. The National Malaria Control Programme of Tanzania does not currently have any data on IPTp-SP uptake nationally, although it is planned for integration into routine surveillance next year. It is likely that distribution systems for this, as other clinic based interventions, will need more attention. It is possible that the new MCH clinic based voucher system for ITN will have an added benefit by increasing early attendance and therefore uptake of IPTp-SP.
These findings on knowledge and awareness suggest that the increased risk posed to pregnant women by malaria was almost universally recognized, but that knowledge of the health impact of that risk – especially to the health of the foetus – was very low. Over 90% of women thought that ITNs were a good intervention against malaria in pregnancy, but less than half thought the same about IPTp-SP. Knowledge of malaria in pregnancy was strongly associated with use of a combination of both ITN and IPTp. Although this was a small observational study where it was not possible to control for all likely confounding variables, there is evidence that the use of a combination of ITN use and IPT-p provides additive protection against severe anaemia, and that this effect is not confined to trial conditions. Use of an ITN or IPT-p alone was associated with a 23% and 30% reduction in the risk of severe anaemia post partum respectively, similar to estimates from other controlled trials [10,11] The data suggest that MCH services are effective when used optimally. Women who accessed MCH services in their first trimester of pregnancy had a significantly lower risk of severe anaemia post-partum compared to women who first presented at MCH during their third trimester. Women attending MCH earlier were more likely to attend health education sessions, and women who attended health education sessions more likely to use IPTp-SP than women who did not attend. Women with high KoM were most likely to cite the MCH as their most important source of health information.
It must be noted that by recruiting only women who had uncomplicated pregnancies and who delivered in hospital this study had a selection bias towards the healthier and possibly wealthy members of the community. However, women from a considerable range of socio-economic situations are represented (no education vs. further education; mobile phone owners vs. not owning a radio) which probably reflects the rural/peri-urban catchment area of Kibaha and other newly urbanised areas. The data has shown that many of the factors indicative of relative wealth in a poor community were associated with increasing levels of knowledge – and that knowledge was positively associated with multiple intervention uptake and improved health outcome.
The importance of access to resources has been illustrated previously for both preventative interventions and treatment [12]. At the time of this study, IPTp-SP was offered free of charge to pregnant women via the antenatal clinics in Tanzania. There is social marketing of bednets (price approx $5) at the national level but not targeted at specific high risk groups. The newly initiated ITN voucher scheme for pregnant women is part of the Tanzanian commitment to improve access to ITNs for pregnant women as a whole. It is hoped that via mass health education and substantial price subsidy, some of these socio-economic inequities in access will also be addressed.
Conclusions
The findings highlight the importance of women's knowledge of malaria in pregnancy and of antenatal attendance for the uptake of preventative interventions. Now that effective malaria interventions are available and there is political will to implement them, to maximise the potential for health impact, it is essential to empower the intended recipients of interventions by providing the knowledge which can influence their health decisions.
Authors' contributions
RN carried out the fieldwork and performed preliminary analysis. CD & HR contributed to study design and logistics. TM performed statistical analysis and supervised the study. All authors read, edited and approved the final manuscript.
Table 2 Socio-economic breakdown of population.
n/293 %
Age 15–19 49 17
20–29 185 63
30+ 59 20
Gravidity Primigravidae 124 42
Mutigravidae 169 58
Residence Urban 124 42
Rural 169 58
Education None 44 15
Primary 217 74
Secondary + 32 11
Marital status Married 207 71
Unmarried 86 29
Travel time to MCH < 1 hour 252 86
1–2 hours 20 7
>2 hours 21 7
Household ownership Radio 264 90
Bicycle 224 76
TV 16 5
M/phone 13 4
Bednet 245 83
Religion Christian 104 35
Moslem 189 65
Main source of health information Community 36 12
Health workers 83 28
Media 174 59
Used IPTp-SP Once 166 57
Twice 34 12
Used bednet Yes 207 71
Used ITN Yes 141 48
Health education MCH Yes 133 45
Knowledge score Low 86 29
Medium 110 38
High 97 33
Acknowledgements
The authors thank the women who participated in the study and the staff of the maternity ward at Kibaha hospital for their cooperation. This research was conducted as partial fulfilment of the Degree of Master of Public Health of Tumaini University, Kilimanjaro Christian Medical Centre, and in collaboration with the Joint Malaria Programme, Tanzania. The Joint Malaria Programme is a collaboration between the National Institute for Medical Research (NIMR), Kilimanjaro Christian Medical College (KCMC), the London School of Hygiene and Tropical Medicine (LSHTM) and the Centre for Medical Parasitology, University of Copenhagen (CMP). During the period of study RN was jointly supported by United States Agency for International Development and the Ministry of Health, Tanzania.
==== Refs
Guyatt HL Snow RW The epidemiology and burden of Plasmodium falciparum-related anemia among pregnant women in sub-Saharan Africa Am J Trop Med Hyg 2001 64 36 44 11425176
Steketee RW Nahlen BL Parise ME Menendez C The burden of malaria in pregnancy in malaria-endemic areas Am J Trop Med Hyg 2001 64 28 35 11425175
Jones G Steketee RW Black RE Bhutta ZA Morris SS How many child deaths can we prevent this year? Lancet 2003 362 65 71 12853204 10.1016/S0140-6736(03)13811-1
Njagi JK Magnussen P Estambale B Ouma J Mugo B Prevention of anaemia in pregnancy using insecticide-treated bednets and sulfadoxine-pyrimethamine in a highly malarious area of Kenya: a randomized controlled trial Trans R Soc Trop Med Hyg 2003 97 277 282 15228241 10.1016/S0035-9203(03)90141-6
WHO Expert Committee on Malaria World Health Organ Tech Rep Ser 2000 892 i v, 1-74 10892307
Unwin A MoH T Malaria situation and strategies for its control in Tanzania Public Health Situation in Tanzania: Re-packaging Knowledge for Health Promotion 2001 Dar es Salaam, 17 19
Karanja J Wambari E Okumu D A study of awareness of malaria among Kibera population; implication for community based intervention National Institute of Public Health Journal 2002 51 51 55
Nyamongo IK Health care switching behaviour of malaria patients in a Kenyan rural community Soc Sci Med 2002 54 377 386 11824914 10.1016/S0277-9536(01)00036-3
Eijla AM Ayis GJ ter Kuile FO Implementation with IPT with SP for control of malaria in Kisumu, Kenya 2002 265 266
Shulman CE Dorman EK Cutts F Kawuondo K Bulmer JN Peshu N Marsh K Intermittent sulphadoxine-pyrimethamine to prevent severe anaemia secondary to malaria in pregnancy: a randomised placebo-controlled trial Lancet 1999 353 632 636 10030329 10.1016/S0140-6736(98)07318-8
ter Kuile FO Terlouw DJ Phillips-Howard PA Hawley WA Friedman JF Kariuki SK Shi YP Kolczak MS Lal AA Vulule JM Nahlen BL Reduction of malaria during pregnancy by permethrin-treated bed nets in an area of intense perennial malaria transmission in western Kenya Am J Trop Med Hyg 2003 68 50 60 12749486
Victora CG Wagstaff A Schellenberg JA Gwatkin D Claeson M Habicht JP Applying an equity lens to child health and mortality: more of the same is not enough Lancet 2003 362 233 241 12885488 10.1016/S0140-6736(03)13917-7
| 15555071 | PMC535532 | CC BY | 2021-01-04 16:37:48 | no | J Nanobiotechnology. 2004 Nov 22; 2:11 | latin-1 | J Nanobiotechnology | 2,004 | 10.1186/1477-3155-2-11 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-421554117810.1186/1475-2875-3-42ResearchKnowledge of malaria influences the use of insecticide treated nets but not intermittent presumptive treatment by pregnant women in Tanzania Nganda Rhoida Y [email protected] Chris [email protected] Hugh [email protected] Tanya [email protected] Kilimanjaro Christian Medical Centre, PO Box 3010, Moshi, Tanzania2 Joint Malaria Programme, PO Box 2228, Moshi, Tanzania3 London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E7HT, UK2004 12 11 2004 3 42 42 6 8 2004 12 11 2004 Copyright © 2004 Nganda et al; licensee BioMed Central Ltd.2004Nganda 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
To reduce the intolerable burden of malaria in pregnancy, the Ministry of Health in Tanzania has recently adopted a policy of intermittent presumptive treatment for pregnant women using sulphadoxine-pyrimethamine (IPTp-SP). In addition, there is strong national commitment to increase distribution of insecticide treated nets (ITNs) among pregnant women. This study explores the determinants of uptake for both ITNs and IPTp-SP by pregnant women and the role that individual knowledge and socio-economic status has to play for each.
Methods
293 women were recruited post-partum at Kibaha District Hospital on the East African coast. The haemoglobin level of each woman was measured and a questionnaire administered.
Results
Use of both interventions was associated with a reduced risk of severe anaemia (Hb<8 g/dL) compared to women who had used neither intervention (OR 0.31, 95% CI 0.14–0.67). In a logistic regression model it was found that attendance at MCH health education sessions was the only factor that predicted IPTp-SP use (OR 1.8, 95% CI 1.1–2.9) while high knowledge of malaria predicted use of ITNs (OR 2.3, 95% CI 1.1–4.9).
Conclusion
Individual knowledge of malaria was an important factor for ITN uptake, but not for IPTp-SP use, which was reliant on delivery of information by MCH systems. When both these interventions were used, severe anaemia postpartum was reduced by 69% compared to use of neither, thus providing evidence of effectiveness of these interventions when used in combination.
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Background
Plasmodium falciparum malaria in pregnancy poses a substantial risk to pregnant women and their offspring: it has been estimated that malaria in pregnancy is the primary cause of up to 10,000 maternal anaemia-related deaths in sub-Saharan Africa annually [1]. Further, malaria in pregnancy increases the risk of an infant being born with low birth weight (LBW) and is responsible for up to 35% of preventable LBW in malaria-endemic areas[2].
Over the last decade a body of evidence has accumulated which supports the use of both ITNs and IPTp-SP to reduce the adverse effects of malaria during pregnancy [1,3,4] and both these interventions are currently recommended by WHO [5]. In Tanzania it is now national policy to offer IPT-p with sulfadoxine-pyrimethamine (SP) for every pregnant woman attending Maternal and Child Health services (MCH). There is strong commitment in Tanzania to achieve the targets agreed on by African countries at the Abuja Conference of 60% coverage of ITNs for pregnant women by 2005 using social marketing strategies and a national voucher scheme [6] to improve access for pregnant women and their children. However, there are currently no reliable data in Tanzania on current levels of use of these interventions.
Evidence suggests that malaria treatment choices are affected by knowledge of the problem [7,8]. The success in implementing preventive interventions amongst pregnant women in Tanzania is thus likely to be determined in part by awareness of malaria and the strategies available to prevent it. This study set out to explore the determinants of uptake for both ITNs and IPTp-SP by pregnant women, with particular regard to knowledge of malaria and socio-economic status, and to estimate the impact that reported use of either of these interventions had on the prevalence of severe anaemia in the post-partum period.
Methods
Setting
This research was carried out at Kibaha District Hospital in the Coastal Region of Tanzania, an area with moderate to high malaria transmission. The area is predominantly rural, supported by a mixture of subsistence and cash crop farming, with a peri-urban belt along the main Tanzania/Zambia highway which crosses the district.
Study participants
All post-natal women who had delivered their baby in the hospital during April/May 2003, but had not been admitted to hospital during the pregnancy, were eligible for inclusion in the study.
Study tools
From studies elsewhere [9] it was estimated that a samples size of 293 women would give sufficient power to show a difference in uptake of malaria interventions by knowledge of malaria. On each working day of the study the first 13 women to come out of the labour ward and who gave informed consent to participate were registered and interviewed. Haemoglobin level was determined at the time of interview using a portable β-haemoglobin photometer (HemoCue©, HemoCue AB, Ängelholm, Sweden). Women with Hb<11 g/dL were referred within the hospital system.
Definitions
Knowledge of malaria score (KoM)
Participants were assigned a 'knowledge of malaria score' (KoM) according to their responses to a series of seven closed questions (Table 1).
Table 1 Components of the knowledge score
Malaria in pregnancy statement: Agreement
n/293 %
Risk of infection Increases in pregnancy 270 92
Consequences Low birth weight 81 28
Pregnancy Loss 112 38
Maternal anaemia 150 51
Best Interventions* ITN 266 91
SP 115 39
Transmission Mosquitoes alone 102 35
*Women asked to state up to two malaria interventions
ITN users
Women who reported that during this pregnancy they had normally (i.e. >75% of the time) slept under a bednet which had been impregnated within the previous six months.
IPTp-SP users
Use of intermittent presumptive treatment of malaria in pregnancy with sulfadoxine-pyrimethamine.
Severe anaemia
Severe anaemia post-partum was defined as Hb<8 g/dL.
Data analysis
Data were entered twice in Epi-Info version 6.04 and analysed in Stata version 7 (Stata Corporation, Texas USA). Evidence of an association was sought between the variables listed in Table 1 and the study outcomes (reported use of interventions or severe anaemia defined as Hb<8 g/dl). Variables found to have an association with each outcome (by χ2 P-value <0.10 or Mantel-Haenszel estimate of the rate ratio, P-value <0.10) were further analysed using multiple logistic regression. Significance in the multi-variate models was defined by a likelihood-ratio test (LRT) P-value <0.05.
Results
A completed questionnaire and measurement of haemoglobin level were available for 293 post-partum women whose characteristics are shown in Table 1. The group was predominantly made up of married women in the 20–29 years age-group, with primary level education only. Both urban and rural residents were represented. No women refused to participate.
Levels of knowledge
The KoM assessment consisted of four parts: knowledge of risk, consequences of risk, transmission and prevention, with seven questions in total (Table 1). Each question contributed one point to the overall KoM score (i.e. range of possible scores of 0–7) and overall the mean score was 3.7 (median 4). The KoM score was stratified into three groups: score 0–2 representing low (29% of respondents), score 3–4 representing median (38%) and score 5–7 representing high levels of knowledge (33% of respondents).
The lowest knowledge scores related to the impact of maternal malaria on the health of the foetus; only 28% (81/293) recognized low birth weight and 38% (112/293) pregnancy loss as a potential consequence of maternal malaria. There was also some confusion over the mode of transmission of malaria: 95% (279/293) agreed that mosquitoes could transmit malaria but only 35% thought that mosquitoes alone were responsible (excepting blood transfusion).
Having high KoM was strongly associated with education level (χ2 for linear trend 50.03, p < 0.0001). Because of colinearity between education level and high KoM, education was excluded from the logistic regression model used to identify determinants of high KoM. However, models for primary and secondary education respectively showed the same socio-economic effects as the data presented in Table 3. In the logistic regression model, only ages above teenage (LRT 9.8, p = 0.007), ownership of a radio (LRT 6.0, p = 0.01), ownership of a bicycle (LRT 4.8, p = 0.02) and citing the MCH rather than the community as the most important source of health information (LRT 7.5, p = 0.02) were significantly associated with a high KoM compared to low/median KoM.
Table 3 Unadjusted and adjusted OR's for characteristics of women with high knowledge of malaria1.
N Unadjusted OR Confidence Interval Adjusted OR Confidence Interval LRT P
Age 15–19 49 1.0 - 1.0 -
20–29 185 3.3 1.4–7.8 3.2 1.2–8.4
30+ 59 4.4 1.7–11.4 4.6 1.6–13.3 9.8 <0.01
Radio No 29 1.0 - 1.0 -
Yes 264 7.7 1.7–33.1 5.1 1.1–24.0 6.0 0.01
Bicycle No 69 1.0 - 1.0 -
Yes 224 2.3 1.2–4.5 2.1 1.0–4.3 4.8 0.02
Source of health information Community 36 1.0 - 1.0 -
MCH 83 5.8 11.8–18.0 4.3 1.3–14.0
Media 174 4.1 1.3–12.1 2.6 0.8–8.1 7.5 0.02
1All women with high malaria knowledge had formal schooling
Use of IPT-p or ITN
48% (141/293) of women were ITN users, 57% (166/293) had received IPTp-SP once and 12% (34/293) IPTp-SP twice. A multi-variate analysis of factors influencing the uptake of these two interventions is shown in Table 4. After adjustment, increasing age (LRT 10.3, P = < 0.01), owning a radio (LRT 4.0, P = 0.04) and having a high KoM, compared to a low/median KoM score (LRT 7.3, P = 0.02), were the only factors that independently predicted use of an ITN in pregnancy (table 4). By contrast, only a history of having received health education during the pregnancy significantly predicted uptake of any dose of IPTp-SP (LRT 5.6 p = 0.01).
Table 4 Factors associated with uptake of insecticide treated nets (ITNs) during pregnancy and at least one dose of sulphadoxine-pyrimethamine as part of intermittent presumptive treatment (IPTp-SP)
ITN users
N Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT P
Health Education
No 160
Yes 133
Age
15–19 49 1.0 - 1.0 -
20–29 185 3.2 1.6–6.1 2.5 1.3–5.0
30+ 59 4.2 1.8–10.3 3.8 1.5–9.4 10.3 <0.01
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.3 0.7–2.3 1.0 0.5–1.8
High 97 3.6 1.7–7.1 2.3 1.1–4.9 7.3 0.02
Radio
No 29 1.0 - 1.0 -
Yes 264 2.9 1.3–6.3 2.3 1.0–5.5 4.0 0.04
Used IPTp-SP
Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT p
Health Education
No 160 1.0 - 1.0 -
Yes 133 1.8 1.1–2.9 1.8 1.1–2.8 5.6 0.01
Age
15–19 49 1.0 - 1.0 -
20–29 185 0.8 0.4–1.6 0.8 0.4–1.6
30+ 59 0.9 0.4–2.0 0.7 0.3–1.7 0.4 0.8
Knowledge
Low 86 1.0 - 1.0 -
Medium 110 1.7 1.0–3.0 1.7 1.0–3.1
High 97 1.6 0.9–2.9 1.6 0.8–2.9 3.8 0.14
Radio
No 29
Yes 264
41% (120/293) of women had used both interventions. Women with a median KoM were twice as likely (OR 2.1 (95% CI 1.1–4.0) and women with a high KoM three times more likely (OR 3.2 (95% CI 1.7–6.0) to have used both IPT-p and an ITN than women with low KoM scores.
Timing of first MCH attendance
26% (76/293) of women had first attended the MCH during the first trimester, 65% (192/293) during the second trimester and 9% (25/293) during the third trimester of pregnancy. Predictably the median number of visits to MCH by women was correlated with the trimester of first visit: first trimester – median of 7.5 visits overall (mean 6.9 (s.d.2.2), second trimester – median of 5 visits overall (mean 5.4 (s.d.1.8) & third trimester – median of 2 visits overall (mean 2.4 (s.d.0.8). Women attending MCH for the first time during their first trimester were more than twice as likely to have attended health education sessions as women attending for the first time in their third trimester (χ2 test for trend 5.1, p = 0.02).
Risk factors for severe anaemia
Overall, 27% (80/293) of study participants had Hb<8 g/dL immediately post-partum. In the regression model, three factors were found to independently predict the risk of having severe anaemia in the post-partum period (Table 5). Firstly, with women using either an ITN or IPTp-SP alone, there was a reduced risk of severe anaemia, but it was not statistically significant (OR 0.77 (CI 0.36–1.65 and OR 0.70 (CI 0.34–1.41 respectively). However, the use of an ITN in conjunction with IPTp-SP was associated with a significant reduction in risk of being severely anaemic post partum compared to women not using any intervention (OR 0.31 (CI 0.14–0.67) LRT 10.1 p = 0.01). Secondly, having had any level of education (as compared to none) and, thirdly, attendance at the MCH clinic during the first rather than third trimester of pregnancy were both associated with a reduced risk of severe anaemia (OR 0.41, 95% CI 0.17–0.98) LRT 4.5 p = 0.03 and OR 0.31, 95%CI 0.11–0.83, LRT 5.3 p = 0.05 respectively).
Table 5 Risk factors for severe anaemia post partum.
% with severe anaemia Unadjusted OR 95% confidence interval Adjusted OR 95% confidence interval LRT P
Formal education
No 29 1.00 - 1.00 -
Yes 16 0.45 0.19–1.07 0.41 0.17–0.98 4.5 0.03
Time of first ANC use
3rd trimester 48 1.00 - 1.00 -
2nd trimester 26 0.39 0.16–0.91 0.41 0.17–1.0
1st trimester 22 0.31 0.12–0.80 0.31 0.11–0.83 5.3 0.05
Use of malaria intervention
None 37 1.00 - 1.00 -
ITN only 32 0.82 0.39–1.71 0.77 0.36–1.65
IPTp-SP only 27 0.64 0.32–1.28 0.70 0.34–1.41
ITN + IPTp-SP 16 0.34 0.16–0.72 0.31 0.14–0.67 10.1 0.01
Discussion
The key finding was that while knowledge, wealth and age were all found to be independently predictive of ITN use, only participation in health education was associated with use of IPTp-SP. That predictors of uptake for each of the two interventions were different was an interesting and unexpected finding. The data indicate that, while ITN use is driven by both access (i.e. wealth) as well as knowledge, use of IPTp is determined by its being offered in an MCH clinic. This is a positive finding that suggests the need to prioritise strategies for maximising early attendance to boost IPTp-SP uptake.
This finding is in contrast to research from Kenya which showed that uptake of IPTp-SP increased with higher levels of formal education [9]. The National Malaria Control Programme of Tanzania does not currently have any data on IPTp-SP uptake nationally, although it is planned for integration into routine surveillance next year. It is likely that distribution systems for this, as other clinic based interventions, will need more attention. It is possible that the new MCH clinic based voucher system for ITN will have an added benefit by increasing early attendance and therefore uptake of IPTp-SP.
These findings on knowledge and awareness suggest that the increased risk posed to pregnant women by malaria was almost universally recognized, but that knowledge of the health impact of that risk – especially to the health of the foetus – was very low. Over 90% of women thought that ITNs were a good intervention against malaria in pregnancy, but less than half thought the same about IPTp-SP. Knowledge of malaria in pregnancy was strongly associated with use of a combination of both ITN and IPTp. Although this was a small observational study where it was not possible to control for all likely confounding variables, there is evidence that the use of a combination of ITN use and IPT-p provides additive protection against severe anaemia, and that this effect is not confined to trial conditions. Use of an ITN or IPT-p alone was associated with a 23% and 30% reduction in the risk of severe anaemia post partum respectively, similar to estimates from other controlled trials [10,11] The data suggest that MCH services are effective when used optimally. Women who accessed MCH services in their first trimester of pregnancy had a significantly lower risk of severe anaemia post-partum compared to women who first presented at MCH during their third trimester. Women attending MCH earlier were more likely to attend health education sessions, and women who attended health education sessions more likely to use IPTp-SP than women who did not attend. Women with high KoM were most likely to cite the MCH as their most important source of health information.
It must be noted that by recruiting only women who had uncomplicated pregnancies and who delivered in hospital this study had a selection bias towards the healthier and possibly wealthy members of the community. However, women from a considerable range of socio-economic situations are represented (no education vs. further education; mobile phone owners vs. not owning a radio) which probably reflects the rural/peri-urban catchment area of Kibaha and other newly urbanised areas. The data has shown that many of the factors indicative of relative wealth in a poor community were associated with increasing levels of knowledge – and that knowledge was positively associated with multiple intervention uptake and improved health outcome.
The importance of access to resources has been illustrated previously for both preventative interventions and treatment [12]. At the time of this study, IPTp-SP was offered free of charge to pregnant women via the antenatal clinics in Tanzania. There is social marketing of bednets (price approx $5) at the national level but not targeted at specific high risk groups. The newly initiated ITN voucher scheme for pregnant women is part of the Tanzanian commitment to improve access to ITNs for pregnant women as a whole. It is hoped that via mass health education and substantial price subsidy, some of these socio-economic inequities in access will also be addressed.
Conclusions
The findings highlight the importance of women's knowledge of malaria in pregnancy and of antenatal attendance for the uptake of preventative interventions. Now that effective malaria interventions are available and there is political will to implement them, to maximise the potential for health impact, it is essential to empower the intended recipients of interventions by providing the knowledge which can influence their health decisions.
Authors' contributions
RN carried out the fieldwork and performed preliminary analysis. CD & HR contributed to study design and logistics. TM performed statistical analysis and supervised the study. All authors read, edited and approved the final manuscript.
Table 2 Socio-economic breakdown of population.
n/293 %
Age 15–19 49 17
20–29 185 63
30+ 59 20
Gravidity Primigravidae 124 42
Mutigravidae 169 58
Residence Urban 124 42
Rural 169 58
Education None 44 15
Primary 217 74
Secondary + 32 11
Marital status Married 207 71
Unmarried 86 29
Travel time to MCH < 1 hour 252 86
1–2 hours 20 7
>2 hours 21 7
Household ownership Radio 264 90
Bicycle 224 76
TV 16 5
M/phone 13 4
Bednet 245 83
Religion Christian 104 35
Moslem 189 65
Main source of health information Community 36 12
Health workers 83 28
Media 174 59
Used IPTp-SP Once 166 57
Twice 34 12
Used bednet Yes 207 71
Used ITN Yes 141 48
Health education MCH Yes 133 45
Knowledge score Low 86 29
Medium 110 38
High 97 33
Acknowledgements
The authors thank the women who participated in the study and the staff of the maternity ward at Kibaha hospital for their cooperation. This research was conducted as partial fulfilment of the Degree of Master of Public Health of Tumaini University, Kilimanjaro Christian Medical Centre, and in collaboration with the Joint Malaria Programme, Tanzania. The Joint Malaria Programme is a collaboration between the National Institute for Medical Research (NIMR), Kilimanjaro Christian Medical College (KCMC), the London School of Hygiene and Tropical Medicine (LSHTM) and the Centre for Medical Parasitology, University of Copenhagen (CMP). During the period of study RN was jointly supported by United States Agency for International Development and the Ministry of Health, Tanzania.
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Victora CG Wagstaff A Schellenberg JA Gwatkin D Claeson M Habicht JP Applying an equity lens to child health and mortality: more of the same is not enough Lancet 2003 362 233 241 12885488 10.1016/S0140-6736(03)13917-7
| 15535885 | PMC535533 | CC BY | 2021-01-04 16:36:44 | no | Harm Reduct J. 2004 Nov 9; 1:9 | latin-1 | Harm Reduct J | 2,004 | 10.1186/1477-7517-1-9 | oa_comm |
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-2-91553016710.1186/1477-9560-2-9Case ReportRecombinant activated factor VII (rFVIIa) as salvage treatment for intractable hemorrhage Aggarwal Anita [email protected] Vera [email protected] Joseph P [email protected] Kirsten [email protected] Washington Cancer Institute, Washington Hospital Center, 110 Irving Street, NW, Washington, District of Columbia, 20010, United States of America2 Dept of Pathology and Laboratory Medicine, Washington Hospital Center, 110 Irving Street, NW, Washington, District of Columbia, 20010, United States of America2004 5 11 2004 2 9 9 20 8 2004 5 11 2004 Copyright © 2004 Aggarwal et al; licensee BioMed Central Ltd.2004Aggarwal 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
Recently, there has been an increased use of recombinant activated factor VII (rFVIIa) to promote hemostasis in various hemorrhagic conditions. The objective of this study was to determine the outcome of patients treated with rFVIIa who had intractable bleeding associated with cardiac surgery (CSP) or as a result of other causes (OBP).
Methods
The medical records of 40 consecutive patients treated with rFVIIa were retrospectively reviewed for blood product use before and after treatment. In all patients, rFVIIa was given only after all other measures to stop bleeding had failed. The number of transfused units of red cells (R), platelets (P), fresh frozen plasma (F), and cryoprecipitate (C) were determined both before and after administration of rFVIIa, and the results compared. Mortality at 4 hours and 30 days was assessed. Patients dying within 4 hours of rFVIIa administration were not evaluable for response. Patient characteristics were also assessed as risk factors for mortality.
Results
Twelve of 24 CSP survived for more than 4 hours. These 12 patients required an average of 17 units (U) of R, 18 U of P, 18 U of F and 15 U of C pre-treatment compared to an average of 6 U, 10 U, 9 U and 4 U of R, P, F and C respectively, post-treatment. These differences were statistically significant. For the OBP, 11 of 16 survived more than four hours. These 11 patients required an average of 10 U of R, 11 U of P, 14 U of F and 10 U of C pretreatment compared to an average of 1 U, 2 U, 2 U and 0 U of R, P, F, and C respectively, post-treatment. With the exception of C, there was a statistically significant decrease in blood product use following treatment with rFVIIa. Of the survivors in each group, 6 of 12 CSP and 2 of 11 OBP died between 3 and 30 days post-treatment from causes other than bleeding. Mortality at 30 days for CSP and OBP survivors was 50% and 18% respectively, whereas overall 30 day mortality was 75% for CSP and 44% for OBP.
Conclusions
rFVIIa is effective in decreasing blood product use and promoting hemostasis in patients with intractable bleeding associated with cardiac surgery and a variety of other causes.
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Introduction
Recombinant factor VIIa (rFVIIa), originally developed for the treatment of acquired inhibitors associated with hemophilia [1,2], has been successfully used for bleeding due to acquired or congenital thrombocytopathy [3,4], extensive trauma and a variety of surgical procedures, including anecdotal reports of use in cardiac surgery patients [5-9]. It reverses the effect of warfarin in healthy volunteers [10], and corrects the prothrombin time in patients with hepatic failure [11].
Most critically ill patients with trauma suffer profound coagulopathy. Coagulation abnormalities in these patients have been attributed to multiple factors including disseminated intravascular coagulation (DIC), excessive fibrinolysis due to release of tissue plasminogen activator (tPA), dilutional coagulopathy from fluid replacement and massive blood product transfusion, dysfunctional platelets, and metabolic abnormalities including acidosis and hypothermia [12-18]. In addition, the use of cardiopulmonary bypass in patients undergoing cardiac surgical procedure may exacerbate the coagulopathy [19].
Although the mechanism of action of rFVIIa remains unclear, many investigators have suggested that it binds to the surface of activated platelets and directly activates Factor X, thus bypassing the early steps of the coagulation cascade. Activated Factor X (Xa) then combines with activated Factor V (Va) on the platelet surface, leading to rapid conversion of prothrombin to thrombin [20,21]. Hemostasis is promoted through high concentrations of thrombin generated near activated platelets at the site of vascular injury. Based on its mechanism of action, rFVIIa may be effective in controlling hemorrhage due to trauma, surgery and other causes.
The present report is a retrospective review of 40 consecutive patients treated with rFVIIa (NovoSeven, Novo Nordisk, Denmark) for intractable hemorrhage. Twenty-four of these patients had bleeding associated with cardiac surgery, and the remaining 16 suffered with bleeding from other causes. The need for transfusion of blood products both before and after treatment and the mortality at 4 hour and 30 day were assessed. Patient characteristics were also assessed as risk factors for mortality.
Methods
Patients
The study was approved by the Institutional Board Review of the Washington Hospital Center, Washington DC, USA. The medical records of 40 consecutive patients treated with rFVIIa between July 2001 and June 2003 at the Washington Hospital Center, a tertiary care teaching hospital and level 1 trauma center, were reviewed. The patients were divided into two groups: 24 patients who had undergone cardiac surgical procedures (CSP), and 16 patients who had bleeding from other causes (OBP). There were more male patients in both groups (19 males and 5 females in CSP, 10 males and 6 females in OBP). The median age in the CSP was higher at 65 years (range 26–85 years) compared to that of OBP (median age 42; range 23–82 years). The type of surgery in the CSP group included 15 coronary artery bypass grafts (CABG), 3 aortic arch repairs either elective or for dissection, 2 aorta rupture repair (one also underwent CABG), 3 valve replacements and 1 ventricular rupture. In the OBP group, 5 patients had suffered either stab or gunshot wounds, 2 had traumatic liver lacerations, 2 had undergone joint replacement, 3 had clotting factor abnormalities (acquired Factor VIII inhibitor, congenital Factor VII deficiency, acquired VWF deficiency), and one each had post partum hemorrhage, warfarin overdose, bowel resection, and trauma from a motor vehicle accident.
Administration of rFVIIa
Commercially available rFVIIa, (Novo-Seven, Novo Nordisk, Denmark) was used in all patients. There were no predetermined criteria for administration. In most patients, treatment was given after extensive blood product support had failed to control hemorrhage, and the decision to treat was made jointly by the surgeon, hematologist and blood bank pathologist. rFVIIa was administered as a bolus dose of 90 mcg/kg. The first two patients received a second dose of 90 mcg/kg 6 hours after the first.
Endpoints and Assessment of Risk
The total number of products given before and up to twenty-four hours after rFVIIa administration, or until death, whichever came first, was quantified. Patients who died within 4 hours of treatment were not evaluable because blood product support was suspended shortly after rFVIIa administration in these hemodynamically unstable patients deemed non-salvageable. Also, given the short half-life of rFVIIa, this time frame did not allow for an accurate determination of efficacy. The number of patients surviving at 4 hours and 30 days after the administration of rFVIIa was compared. Thirty days was chosen as an end point since this is the operative mortality cut off point, as defined by the Society of Thoracic Surgeons, for inpatient and out of hospital death. For patients surviving more than 4 hours, charts were reviewed for thromboembolic complications during hospitalization.
In an attempt to determine risk factors for poor outcome, patients were assessed for age, gender, comorbid conditions, left ventricular ejection fraction (LVEF), use of cardiopulmonary bypass (CPB), hemoglobin, platelet count, arterial blood gas results and coagulation parameters including prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, and D-Dimers. Laboratory parameters were not available for some patients.
Statistical Considerations
The Paired Student's t-Test was used to compare the number of blood product transfusions before and after treatment with rFVIIa, and, since the number of pair groups was less than 50, this result was confirmed with the Wilcoxon Matched-Pairs Signed-Ranks Test.
Results
Cardiac Surgery Patients (CSP)
Outcome
Twelve of the 24 patients survived for more than 4 hours after the administration of rFVIIa, and the results of their pre- and post-treatment blood product use are listed in Table 1. From the start of surgery until the administration of rFVIIa, patients received on average 17 U of R (range 5–39), 18 U of P (range 6–37), 18 U of F (range 6–33), and 15 U of C (range 0–50). Post rFVIIa treatment, an average of 6 U of R (range 0–28), 10 U of P (range 0–19), 9 U of F (range 0–28) and 4 U of C (0–20) were transfused. The difference between the pre- and post-transfusion requirement was significantly lower for all blood products: R (p = 0.007), P (p = 0.038), F (p = 0.009) and C (p = 0.03). Eight of the 12 patients showed a rapid hemostatic response to rFVIIa, as measured by a decrease in R transfusion to an average of 2 U (range 0–6). The other 4 patients (Patient # 24, 27, 32 and 35) continued to require blood product support beyond 24 hours after rFVIIa administration.
Table 1 Summary of rFVIIa treated cardiac surgery patients (CSP) who survived >4 hours
P# Age sex Procedure Co-morbidities Response to rFVIIa Outcome
CPB EF% Pre rFVIIa Post rFVIIa
R P F C R P F C
1 75M CABG - >45 18 18 8 10 0 4 0 0 Survived
13 36M Aortic arch repair + >45 28 18 16 10 6 19 20 10 Died
14 64M Aortic dissection - >45 39 37 32 50 2 6 4 0 Survived
17 63M AVR/MVR repair - <25 7 18 16 15 1 12 6 0 Survived
18 70M CABG + >25 6 20 20 20 0 0 0 0 Survived
20 72M CABG - >25 21 12 10 0 0 0 0 0 Survived
24* 79M CABG + >25 10 12 12 10 5 4 4 0 Died
27* 64M CABG, IVAD + <25 30 30 33 20 28 16 28 20 Died
30 85M CABG, ruptured aorta + >45 13 12 6 10 3 6 8 0 Died
32* 83M CABG + >25 5 18 18 10 6 18 20 0 Died
33 78F Ruptured aorta + <25 14 18 20 20 3 12 6 0 Died
35* 54M Aortic arch repair + >45 9 6 18 10 15 24 12 20 Survived
Average 17 18 18 15 6 10 9 4
P value .009 .038 .009 .03
P: Patient
R: Red Blood Cell; P: Platelet; F: Fresh Frozen Plasma; C: Cryoprecipitate
M: Male; F: Female
CPB: Cardiopulmonary Bypass
EF: Left Ventricular Ejection Fraction
CABG: Coronary Artery Bypass Graft
AVR: Aortic Valve Replacement
MVR: Mitral Valve Replacement
IVAD: Intraventricular Assisted Device
* Patient continued to require blood transfusion past 24 hours post rFVIIa treatment.
Six of the 12 patients who survived more than four hours were eventually discharged to home (Table 1). The remaining 6 patients (Patient # 13, 24, 27, 30, 32, and 33) died between 3 and 30 days after rFVIIa treatment from a variety of causes including multi-organ failure and infections. Amongst survivors beyond 4 hours, mortality at 30 days was 50% and overall mortality for the entire group was 75%.
Of the 12 patients who died from multiorgan failure and hemodynamic instability within 4 hours of receiving rFVIIa, 11 died within 2 hours and 1 died within 4 hours (8 during surgery and 4 in the recovery room). Due to the limited time frame, it was not possible to determine if bleeding in these patients diminished after rFVIIa treatment. In the urgent setting in which these patients were treated, determining the amount and timing of blood loss and blood transfusion was difficult. Moreover, for patients deemed non-salvageable, blood product transfusion was suspended before response to rFVIIa could be determined.
Risk factors and outcome in CSP
Overall, 17 of 24 CSP were over the age of 60. Twenty underwent CPB, and 15 had a LVEF below 25%. Fourteen underwent mediastinal re-exploration for bleeding. Twenty-three patients had a significant coagulopathy defined as thrombocytopenia below 100,000/mm3, prolonged PT and PTT or both (data not given due to wide variations in the results). Seven of the 24 patients did not have a documented arterial blood gas (ABG) around the time of rFVIIa administration; however, among the other 17 patients, pH's of 7.0 in 1, 7.2 in 5, and 7. 4 in 11 patients were noted. With regards to rFVIIa administration, there was no correlation between pH, PT, PTT, thrombocytopenia, anemia and outcome.
Of the 12 patients who died within 4 hours, all underwent CPB for over 30 minutes (time on CPB for 8 patients was more than 2.5 hours). All 12 had an LVEF of less than 25% and 7 had mediastinal re-exploration for bleeding. In contrast, only 3 of the 12 surviving patients had EF <25%, 8 had surgery with CPB and 7 underwent mediastinal re-exploration. The median age and coagulation parameters were not different between the two groups.
Other Bleeding Patients (OBP)
Outcome
Eleven of the 16 patients with bleeding due to other causes survived for more than 4 hours after rFVIIa administration, and the results of their blood product use before and after rFVIIa are summarized in Table 2. From the beginning of surgery or time of trauma until the administration of rFVIIa, patients received an average of 10 U of R (range 2–28), 11 U of P (range 0–36), 14 U F (range 0–34) and 10 U of C (range 0–70). Post administration of rFVIIa, an average of 1 U of R (range 0–6), 2 U of P (range 0–6), 2 U of F (0–10) and 0 U of C were transfused over 24 hours. The difference between the pre- and post-rFVIIa treatment blood product requirement was significantly lower for R (p = 0.004), P (p = 0.017), and F (p = 0.017), but not for C (p = 0.122). Nine of the 11 showed a rapid response to rFVIIa, as measured by a decrease in R transfusion to an average of <0.5 U (range 0–3). The other 2 patients (Patient # 2 and 15) continued to require blood product support beyond 24 hours post rFVIIa treatment.
Table 2 Summary of rFVIIa treated other bleeding patients (OBP) who survived >4 hours
P# Age
Sex Procedure Co-morbidities Response to rFVIIa Outcome
Pre rFVIIa Post rFVIIa
R P F C R P F C
2* 48F Colectomy Colon cancer 4 6 24 70 2 0 2 0 Died
15* 26F Cystectomy, nephrectomy, pelvic fracture, termination of Pregnancy MVA, 20 weeks gestation 18 12 8 0 6 6 10 0 Survived
16 53M Left femur fixation Multiple myeloma 12 12 6 0 0 0 0 0 Survived
19 82M Shoulder replacement HTN, CAD, Renal failure 7 10 20 10 2 6 4 0 Survived
21 54F Liver laceration Trauma 23 17 34 20 3 6 10 0 Survived
22 24M Neck soft tissue trauma FVII deficiency 2 0 4 0 0 0 0 0 Survived
23 64F Liver laceration Dialysis 28 36 33 0 0 0 0 0 Died
28 30F Postpartum hemorrhage None 12 18 6 10 0 0 0 0 Survived
29 64M Gastrointestinal bleed FVIII inhibitor 2 0 0 0 0 0 0 0 Survived
38 60M Thyroidectomy VWF deficiency 4 12 12 4 0 0 0 0 Survived
40 40F Splenic subcapsular bleed Amyloidosis, coumadin overdose 4 0 8 0 0 0 0 0 Survived
Average 10 11 14 10 1 2 2 0
P value .004 .017 .017 .122
P: Patient
R: Red Blood Cell;
P: Platelet; F: Fresh Frozen Plasma; C: Cryoprecipitate
M: Male; F: Female
EF: Left Ventricular Ejection Fraction
VWF: von Willebrand Factor
* Patient continued to require blood transfusion past 24 hours post rFVIIa treatment.
Nine of the 11 surviving patients were discharged to home and 2 (Patient # 2 and 23) died between 3 and 30 days from a combination of infection, cardiac arrest and multiorgan failure. Amongst survivors beyond 4 hours, mortality at 30 days was 18% and overall mortality for the entire group was 44%.
All five of the 16 patients who died within four hours of rFVIIa treatment had massive injuries either from gunshot or stab wounds. Upon arrival to the emergency room, these patients were hemodynamically unstable, with hemoglobin values ranging between 3–6 g/dL. rFVIIa was given as a last resort, but these patients were not evaluable since transfusions were suspended shortly after infusion of rFVIIa.
Risk factors and outcome in OBP
The median age of this group was 42 years. None underwent CPB, and two of the 7 patients tested had an EF <25%. Only 1 patient had a second exploratory laparotomy for bleeding. The patient suffering with a warfarin overdose had a splenic subcapsular bleed complicating systemic amyloidosis.
At the time of rFVIIa administration, a wide variation in laboratory parameters was observed in these patients (data not shown). There was no difference in coagulation parameters between patients who died and those who survived. Eight of the 16 patients did not have an ABG documented. In the 5 patients who died within four hours, the pH was lower (6.9–7.0) than for those who survived (7.3–7.4).
Thromboembolism after rFVIIa
Only one of the 23 patients in both the CSP and OBP groups who survived more than 4 hours had a documented thromboembolic complication after receiving rFVIIa. Deep vein thrombosis (DVT) of the left subclavian vein was confirmed by a Doppler ultrasound 2 days after rFVIIa administration. The thrombus was related to a central line placed before rFVIIa treatment. No other clinical or laboratory evidence of thromboembolism was documented in the medical records of the other patients throughout their hospital stays. None of the other 17 patients in both the groups who died within 4 hours were evaluable for thromboembolic complications.
Discussion
This retrospective case series suggests that rFVIIa is effective in promoting hemostasis even when given as a last life-saving measure in poor prognosis patients with massive, transfusion-refractory hemorrhage. A statistically significant decrease in blood product transfusion was evident in 12 CSP and 11 OBP who survived more than 4 hours after rFVIIa infusion. Six of 12 CSP and 2 of 11 OBP died later between 3 and 30 days after rFVIIa infusion, but these deaths were due to causes other than hemorrhage.
Despite the obvious limitations of a retrospective analysis, this is the largest series of such patients reported in the literature to date. Al Douri et al [8], described 5 patients who underwent open-heart surgery for valvular disease and experienced uncontrollable intraoperative or postoperative bleeding unresponsive to a blood product support protocol. The study was prospective, had well-defined criteria for "excessive bleeding" and excluded patients with a history of ischemic heart disease, stroke or venous thromboembolism. Hemostasis was achieved in all patients following a single dose of 30 ug/kg of rFVIIa, and there was no mortality from bleeding, although 1 patient died 3 days later of an unrelated cause. Overall, the 4-hour and 30 day mortality in the present series was high compared to that of Al Douri et al [8], but it is difficult to compare these two very different patient populations.
Factors predictive of inferior outcome after cardiac surgery include age over 60, low LVEF, significant co-morbidity, the use of CPB and re-exploration after initial surgery [22-24]. The use of CPB increases the rate of fibrinolysis and induces an inflammatory response [19,25]. Moreover, prolonged time on CPB, defined as 2.5 hours or more, is associated with increased bleeding and need for re-exploration. Two-6% of patients undergoing CABG require re-exploration for bleeding and is associated with a mortality rate of up to 22% [19,26,27]. In this series, the CSP group had multiple poor prognostic factors. The median age was 65 years and in addition, all 12 of the CSP who died within 4 hours and 3 of 6 CSP who died later had preoperative LVEF of less than 25%. All of the 12 CSP who died within 4 hours in this series, required CPB and 8 of these patients were on CPB for more than 2.5 hours. More than half of our CSP (14 of 24) required re-exploration, which undoubtedly influenced the mortality rate.
Both, the CSP and OBP groups had a significant decrease in blood product use after rFVIIa treatment but the short and long-term survival appeared to be worse in CSP probably due to multiple poor prognostic factors as discussed above. Poor outcome has also been independently associated with peri-operative transfusion of more than 7 units of R [28]. The CSP and OBP who survived received an average of 18 and 11 units of R in the peri-operative period, respectively. The R requirement of patients who died within 4 hours in both groups was probably higher but could not be reliably estimated. All 5 of the patients in the OBP group who died within 4 hours suffered from major blood vessel injury as a result of stabbing or gun shot wounds. These patients were unstable upon arrival to the emergency room with hemoglobins in the range of 3–6 g/dL.
Excessive hemorrhage requiring massive transfusion can lead to hypothermia, DIC, excessive fibrinolysis, dilutional coagulopathy, and metabolic acidosis, which may further exacerbate bleeding and morbidity. [14-17] A reduction in the pH to 7.0 nearly abolishes rFVIIa activity as reported by Meng et al [18]. In this present series, there was no obvious correlation between the coagulopathy and mortality in both the CSP and OBP groups. Five of the OBP who died within 4 hours had a pH of 7 or less. However, acidosis did not appear to play an important role in CSP.
The optimal dose and timing of administration of rFVIIa for these patients is unknown. The standard dose (90 mcg/kg) was first used in hemophiliacs and was based on the in vitro dose-dependent reduction in aPTT in plasma from these patients [1,29]. The 90 mcg/kg dose of rFVIIa corresponds to concentration of 1.25 mcg/ml FVIIa in plasma. Martinowitz et al [7], used a median dose of 120 mcg/kg (range 120–210 mcg/kg) to achieve hemostasis in 7 cases of trauma. In 2 patients, 120 mcg/kg was insufficient, and 2 additional doses of 60 mcg/kg were required to achieve hemostasis. Kenet et al. [30], have shown better efficacy with a 300 mcg/kg bolus dose followed by continuous infusion. The standard therapeutic dose of 90 mcg/kg of rFVIIa was used in this series of patients although a lower dose of 30 mcg/kg dose has also been shown to be effective [8]. However, in considering the data from the aforementioned studies, it would appear that higher initial doses, or additional doses, might improve outcome. It is also tempting to speculate that earlier treatment with rFVIIa may have prevented rapid clinical deterioration and complications associated with massive blood product transfusion in this series of patients. This question would best be addressed in controlled studies using standard dosing protocols and well defined criteria for intractable hemorrhage. Although rFVIIa is expensive, it would appear to be cost effective when compared with the combined cost of large amount of blood products.
Previous clinical experience with rFVIIa supports a good safety profile in patients with hemophilia and trauma. Less than 1% of patients receiving rFVIIa had thrombosis and thrombosis related complications [21,31]. In our series, only one patient had a deep vein thrombosis of the left arm associated with an indwelling line and not considered to be due to rFVIIa. There were no other documented cases of thrombosis or microemboli.
Conclusions
This retrospective study suggests that rFVIIa can play a beneficial role as an adjunctive hemostatic agent in patients after cardiac surgery or extensive trauma who experience bleeding that cannot be controlled by conventional therapies. Prospective studies are necessary to determine optimal patient selection, and dose and timing of rFVIIa administration.
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| 15530167 | PMC535534 | CC BY | 2021-01-04 16:36:20 | no | Thromb J. 2004 Nov 5; 2:9 | utf-8 | Thromb J | 2,004 | 10.1186/1477-9560-2-9 | oa_comm |
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Genet Vaccines TherGenetic Vaccines and Therapy1479-0556BioMed Central London 1479-0556-2-161550930310.1186/1479-0556-2-16ResearchIn vivo gene targeting of IL-3 into immature hematopoietic cells through CD117 receptor mediated antibody gene delivery Chapel Alain [email protected] Olivier [email protected] Morad [email protected]çois Sabine [email protected] Moubarak [email protected] Pascal [email protected]ürrbach Antoine [email protected] Jocelyne [email protected] Patrick [email protected] Norbert C [email protected] François [email protected] Dominique [email protected] Institut de Radioprotection et de Sûreté Nucléaire, Département de Protection et de santé de l'Homme et de Dosimétrie, Section Autonome de Radiobiologie Appliquée à la Médecine, Fontenay aux roses, France2 Laboratoire de Thérapie Cellulaire et de Radioprotection Accidentelle, LTCRA, UPRES 1632, CHU Saint Antoine, Paris, France3 Inserm U542 and Paris XI University, Villejuif, France4 Institut Pasteur, Paris, France2004 27 10 2004 2 16 16 7 6 2004 27 10 2004 Copyright © 2004 Chapel et al; licensee BioMed Central Ltd.2004Chapel 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
Targeted gene transfection remains a crucial issue to permit the real development of genetic therapy. As such, in vivo targeted transfection of specific subsets of hematopoietic stem cells might help to sustain hematopoietic recovery from bone marrow aplasia by providing local production of growth factors.
Methods
Balb/C mice were injected intravenously, with an anti-mouse c-kit (CD117) monoclonal antibody chemically coupled to a human IL-3 gene-containing plasmid DNA. Mice were sacrificed for tissue analyses at various days after injection of the conjugates.
Results
By ELISA, the production of human IL-3 was evidenced in the sera of animals 5 days after treatment. Cytofluorometric analysis after in vivo transfection of a reporter gene eGFP demonstrated transfection of CD117+/Sca1+ hematopoietic immature cells. By PCR analysis of genomic DNA and RNA using primer specific pIL3 sequences, presence and expression of the human IL-3-transgene were detected in the bone marrow up to 10 days in transfected mice but not in control animals.
Conclusions
These data clearly indicate that antibody-mediated endocytosis gene transfer allows the expression of the IL-3 transgene into hematopoietic immature cells, in vivo. While availability of marketed recombinant growth factors is restricted, this targeting strategy should permit delivery of therapeutic genes to tissues of interest through systemic delivery. In particular, the ability to specifically target growth factor expression into repopulating hematopoietic stem cells may create new opportunities for the treatment of primary or radiation-induced marrow failures.
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Introduction
In vivo gene targeting of highly specific cell subsets remains the main challenge for gene therapy of a broad range of conditions associated with acquired diseases, including infectious disorders, cancer and failure of the hematopoietic system [1,2]. In vivo gene transfection is more appealing than in vitro transfection of an aliquot of cells or tissue that would be then reinfused to the patients, because it potentially concerns the total population of targeted cells disseminated in the whole body; this is particularly relevant to patients with primary or secondary failures of the hematopoietic system, since, in most instances, residual foci of hematopoiesis exist that cannot be easily located and cannot be collected by a marrow harvest procedure. In vivo targeted transfection of specific subsets of hematopoietic stem cells (HSC) might help to sustain hematopoietic recovery from bone marrow aplasia by providing local production of growth factors.
Systemic gene delivery systems are needed for therapeutic applications in which the target cells are not directly accessible [3]. However, for several reasons including lack of cell specificity and safety, in vivo targeted gene transfer cannot use current viral vectors. Although cationic liposomes have been promising systems in transfecting cells in tissue culture, it has been recognised that their in vitro efficiency does not correlate with their ability to deliver DNA after in vivo administration [4-10].
Tissue-specific targeting can be achieved through ligand receptor interactions [11,12]. We have already described a technique of antibody-mediated targeted gene transfection termed antibody delivery system [11,12]: a ligand (capable of binding to the surface of the targeted cells) conjugated with plasmid DNA retains its ability to specifically interact with cognate receptors on the cell surface.
In previous studies, antibodies directed against internalised cell surface antigens such as the T lymphocyte-related CD3 molecule or the B lymphocyte-related surface IgD were chemically coupled to purified plasmid DNA encoding various reporter genes. This approach was validated both in vitro by the transfer of G418 resistance (neor) into human T-cell lines [13] or human hematopoietic immature cells [14] and in vivo by the transfer of β-galactosidase activity into mouse splenocytes [13]. We have reported that this strategy can be applied to targeted gene delivery to human renal carcinoma cells [15]. More recently, in vivo, we have shown a specific tumor targeting after a single intravenous injection in mice bearing tumour expressing the renal carcinoma – related G250 tumor associated antigen [16].
We have previously reported that the method is suitable for the production of a functional growth factor in specifically CD117+ targeted cells, mediating an in vitro biological effect on hematopoiesis [14]. As our previous report evidenced interaction of the conjugate with hematopoietic cells in vitro, this study was focused on specific in vivo targeting of hematopoietic tissues.
In the present study, we used anti-CD117 (c-kit) mAb covalently coupled to human IL-3-encoding plasmid DNA. CD117 antigen is expressed on a CD34+ hematopoietic subpopulation and is readily internalised upon binding to its ligand [17]. Thus, targeted-gene transfer through CD117 may be achieved in this cell subset. We indeed demonstrated an in vivo targeting of hematopoietic immature cells via a systemic route, mediating an efficient in vivo transgene expression.
Methods
Ab-DNA conjugation
The human IL-3 coding sequence (R&D Systems, Minneapolis, Minnesota) was ligated to synthetic fragments containing the natural leader sequence of human IL-3 and was subcloned into pCEP4 vector (Invitrogen Corporation). Transgene expression was controlled by the cytomegalovirus (CMV) enhancer-promoter sequence. The Epstein-Barr Virus replication (oriP) and nuclear antigen (encoded by the EBNA-1 gene) were carried by this plasmid to permit extrachromosomal replication in human, primate and canine cells [18]. pCEP4 also carries the hygromycin B resistance gene for stable selection of transfected cells. The resulting vector was named pIL3.
IgG mAbs were chemically coupled to plasmid DNA as previously described [13]. Briefly, purified IgG (3 mg/ml) in borate buffer (pH 8.2) (100 mM boric acid, 25 mM sodium tetraborate, and 75 mM NaCl) were activated using 3 mg/ml (final concentration) of benzoquinone (Sigma-Aldrich, St Louis, Missouri, USA). After gel filtration through a G25 column (Roche Diagnostics, Mannheim Germany) activated IgG were then covalently linked to pIL3 24 hours, in 0.1 M carbonate buffer (pH 8.7), in a ratio of 100 μg of plasmid DNA for 10 μg of IgG antibody. IgG-plasmid conjugates were then purified by HPLC. Antibodies used was clone 2B8 a monoclonal rat anti mouse IgG reacting with the mouse p145 c-kit protein (CD117) (BD Biosciences Pharmingen Tullastrasse, Heidelberg, Germany). The negative control was the mouse G250 IgG1 mAb reacting with human renal cell carcinoma (kindly provided by Dr A. Gorter, The Netherlands) [19]. The quantities of conjugates were expressed as the quantities of plasmid initially used for reaction.
In vivo transfection assessment
We have previously shown that in vitro transfection of HSC may be observed in a dose-dependent effect for up to 100 μg of conjugate [14].
BalbC mice (6 weeks) were intravenously injected with a dose of up to 400μg of monoclonal 2B8 (BD Biosciences Pharmingen) covalently coupled to the pIL3 plasmid (named conjugate) and as negative control the monoclonal 2B8 and plasmid DNA uncoupled (named unconjugate) or irrelevant human monoclonal antibody (G250) covalently coupled to the pIL3 plasmid (named control conjugate) or physiological serum (named control serum).
In a set of experiments, two intraperitoneal injections of chloroquine (32.5 mg/kg) were performed 2 hours and just a few minutes before intravenous injection of conjugates. The tolerance of chloroquine (used to prevent the degradation of the plasmid for transfection assays, 20) was in the range reported in mice for the study of malaria treatment [21]. Monoclonal antibody (mAb) 2B8 (BD Biosciences Pharmingen) was covalently coupled to 100 μg of the enhanced green fluorescent protein encoding plasmid pEGFP-1 provided from Clontech and was named eGFP conjugate.
Mice were intravenously injected twice (day 0 and day 2) and euthanasied 5, 7 or 10 days after the first injection of the conjugate, after proper anaesthesia.
Human IL-3 production in serum was assayed by High Sensitivity ELISA (R & D Systems). Controls were sera or cell culture supernatants of control mice (unconjugate, control conjugate, control serum).
After euthanasia, the presence of the transgene was investigated in blood, brain, lungs, liver, spleen, kidneys, adrenal glands and bone marrow. In order, to observe toxicity the weight of mice and their organs were measured (brain, lungs, liver, spleen, kidneys).
In mice injected with eGFP conjugate, a MACS magnetic cell separation systems (Miltenyi Biotec, Sunnyvale, CA) was used to enrich cells expressing CD117 and Sca1 from mononuclear bone marrow cells. Negative and positive cells were collected for experimental use. To achieve a purity greater than 50%, it was necessary to perform two sequential passes through magnetic columns. The overall recovery of CD117 was about 30% and enrichment 40 fold, as assessed by the fraction of CD117/Sca1 positive population before and after separation. Cells were analysed by flow cytometry to determine the purity of cell fractions. Then the presence of eGFP positive cells was investigated by flow cytometry into negative fraction (CD117/Sca1 negative populations) and positive cell fractions (CD117/Sca1 positive populations). All experiments were conducted according to French regulation for animal experimentation (Ministry of agriculture Act No.87848, 1987).
Long-term cultures
Long-term cultures of bone marrow cells were performed, as previously described [22]. At one week, 50 μg/ml of hygromycin were added to the long-term culture, in order to select for stably transfected cells (plasmid conferred hygromycin resistance to stably transfected cells). After 1-week selection, these cells were cultured 2 weeks in long-term culture medium. Viable cells were numbered using trypan blue exclusion assay.
Clonogenic hematopoietic progenitor assay
5 × 105 cells from bone marrow were assayed for clonogenic hematopoietic immature cells [23]. Briefly, cells were plated in triplicate in 35-mm dishes at a concentration of 5 × 105 cells/ml in complete methylcellulose M3434 from Stem Cell Technologies (West Broadway, Vancouver, Canada). Cultures were incubated at 37°C in 5% CO2 and removed at 14 days. Colonies were defined as containing more than 40 cells using an inverted microscope. Cells were then harvested and studied for IL-3 gene expression. Two weeks post-transfection, semi-solid colonies were removed from methylcellulose culture for PCR analysis of the presence of the pIL3 plasmid.
DNA and RNA analyses
The simultaneous isolation of total cellular RNA and DNA from tissues or cells was performed using TriPure Isolation Reagent Kit (Roche Diagnostics) [24]. Total cellular RNA was incubated 30 min in the presence of RNAse-free DNAse (Invitrogen), heated at 90°C for 5 min and promptly cooled at 4°C. The RT-PCR was then carried out as previously described [25]. Briefly, total cellular RNA was first annealed with 1 mM of oligo-dT15 (Sigma-Aldrich) and then incubated at 42°C for 1 hour in the presence of 100 units of Moloney murine leukemia virus reverse transcriptase (Invitrogen) in a final volume of 20 μl. DNA or the reverse transcriptase reaction mixtures were then subjected to PCR amplification using sense primer (GTGGTTTGTCCAAACTCATC) and anti-sense primer (AGAGCTCGTTTAGTGAACCG) located on both sides of the IL-3 gene (into the multiple cloning site of pCEP4), which resulted in a PCR product specific of the gene inserted in the pCEP4. Nested PCR was performed using sense (CCAAACTCAATGTATCTTATCATGTCT) and anti-sense (TCAGATTCTAGAAGCTTGGGT) primers localized in the multiple site of clonage of pCEP4 plasmid. These pairs of primers allow for detection of a 542 bp fragment when electrophoresed on a 2% agarose gel and visualization with ethidium bromide. Specificity of PCR products was controlled using an internal 33P-5'-end labeled oligo-probe specific of human IL3 coding sequence (ACGGCCGCACCCACGCGACA), in Southern blot analysis as previously described [26]. To detect a false positive due to plasmid contamination, we have tested RNA samples by direct amplification of RNA (without the reverse transcription step). Indeed in the absence of plasmid, Taq pol will be unable to amplified RNA whereas a PCR product would be observed if the RNA sample was contaminated with plasmid DNA. No DNA plasmid contamination was observed for all the assayed RNA samples. As internal control a 590 bp region of the endogenous mouse RAP-SYN gene was also amplified using a second set of unique 30 bp primers (sense: AGGACTGGGTGGCTTCCAACTCCCAGACAC, anti-sense: AGCTTCTCATTGCTGCGCGCCAGGTTCAGG), which allows the detection of a 590 bp fragment [27].
Results
Assessment of transgene product secretion
Balb/C mice were intravenously injected twice (day 0 and day 3), with the anti-mouse CD117 (c-kit) 2B8 mAb conjugated to pIL3 expression vector. Control animals received unconjugated pIL3 expression vector and 2B8 mAb (named control unconjugate) or irrelevant G250 mouse mAb covalently coupled to the pIL3 plasmid (named control conjugate) or physiological serum (named control serum). To increase the transgene processing into cells, mice were injected with the conjugate up to a dose of 400μg in the presence or not of chloroquine known to diminish endosomal DNA degradation [20]. Mice were euthanasied 5, 7 or 10 days after the first injection of the conjugate. The presence of human IL-3 in serum was measured by a human IL3 specific ELISA, from 5 to 10 days. Using 400μg of conjugate in the presence of chloroquine, we detected human IL-3 in the serum of mice at 50 pg/ml at day 5 (table 1). No human IL-3 was observed in the serum of mice sacrificed at days 7 and 10 nor in mice injected with lower dose of conjugate, with control unconjugate or control conjugate (data not shown).
Table 1 Detection of circulating human IL-3 in mouse serum at day 5 post injection of pIL3 conjugate
Treatment (IP injection) Quantity of conjugate pg/ml of human IL-3 in mice
Chloroquine unconjugate conjugate
mean sd mean Sd
0 100μg 0 0 0 0
0 400μg 0 0 0 0
2 × 32.5 mg/kg 100μg 0 0 0 0
2 × 32.5 mg/kg 400μg 0 0 50* 17
The presence of human IL-3 in serum was investigated by ELISA. The data are representative of three independent experiments and are the mean of triplicate determinations ± S.D. * indicates statistically significant differences by Student's t-test analysis; p < 0.007 as compared to 400μg of unconjugate.
Assessment of transfection cell specificity
Gene targeting was then evaluated by injecting mice with eGFP conjugated or unconjugated to either 2B8 mAb or to G250 control mAb. At day 5, the presence of transfected cells into bone marrow mononucleated cells was analysed into the purified CD117- and CD117+ subpopulations, by flow cytometry using anti-CD117 and anti-Sca1 Abs. As shown in Table 2, 4.7% cells from the CD117+/Sca1- and 2.8% cells from the CD117+/Sca1+ subpopulations collected from mice injected with the eGFP-2B8 conjugate were positive. All controls were negative.
Table 2 Detection of transfected cells in bone marrow mononucleated cells at 5 day postinjection of eGFP conjugate
plasmid eGFP
Cell population Control serum Unconjugate Control conjugate Conjugate
MNC 0 0 0 0
CD117- 0 0 0 0
CD117-/Sca1- 0 0 0 0
CD117+/Sca1- 0 0 0 4.7%
CD117+/Sca1+ 0 0 0 2.8%
The presence of transfected cells (eGFP positives) in bone marrow was investigated 5 days postinjection among mononucleated cells (MNC): CD117 negative cell population (CD117-), CD117/Sca1 negative cell population (CD117-/Sca1-), CD117 positive/Sca1 negative (CD117+/Sca1-) and CD117/Sca1 positive cell population (CD117+/Sca1+). In all cases no transfected cells were observed in the controls.
Assessment of transfection tissue specificity
To assess the tissue specificity of the targeting, presence of pIL3 plasmid was investigated in bone marrow, blood cells, liver, spleen, lungs, kidneys, adrenal glands, and brain. PCR analysis of genomic DNA and RNA isolated from bone marrow and blood (or serum) was performed using primer specific pIL3 sequences. Specificity of the PCR and RT-PCR products was assessed by a Southern blot hybridised with a specific radiolabelled human IL3 probe. The expected 542 bp band of the PCR product corresponding to the IL3-transgene presence (both DNA and RNA) were was specifically detected in the bone marrow of transfected mice up to 7 days for RNA and 10 days for DNA, post transfection (figure 1). Nested PCR also was positive for the IL3 transgene DNA in the spleen of transfected animals up to day 7 (not shown). In control animals (control serum, unconjugate, control conjugate), pIL3 DNA but no RNA was detected in peripheral blood but not in serum until day 5 after the first injection and then disappeared (figure 2); there was no detection of DNA or RNA in bone marrow (figure 1). Aside from this, all other tissues were negative when assayed by nested PCR on day 5, 7, 10 in transfected animals. IL3 transgene DNA was only found in the kidney of control animals receiving an unconjugated mixture of Ab and DNA or the control conjugate, on day 5 only (not shown).
The measurement of the weight of the mice and their organs (liver, kidneys spleen, brain, adrenal glands, lungs), did not reveal any change, suggesting the lack of toxicity detected in mice receiving the conjugate (data not shown). Furthermore, since no IL3 transgene was evidenced in these organs, further investigation of potential toxicity of conjugate might not be relevant.
Figure 1 Nested PCR detection of pIL3 plasmid in bone marrow 5, 7, and 10 days after injection of the conjugate. Mice were intravenously injected twice with 100μg of anti-CD117-pIL3 conjugate (at day 0 and at day 2). Control groups corresponded to bone marrow of mice treated with unconjugated pIL3 and anti-CD117 Abs or control conjugate (G250-pIL3). IL3 DNA and RNA were detected in the bone marrow of animals receiving the pIL3-anti CD117 conjugate up to day 10. The data are representative of three independent experiments.
Figure 2 Nested PCR detection of pIL3 plasmid in mononuclear peripheral blood cells 5, 7, and 10 days after injection of the conjugate. Mice were intravenously injected twice with 100μg of anti-CD117-pIL3 conjugate (at day 0 and at day 2). Control group corresponded to mononuclear peripheral blood cells or serum of mice treated with unconjugated pIL3 and anti-CD117 Abs. pIL3 DNA was only detected in peripheral blood of control animals until day 5 after the first injection. The data are representative of three independent experiments.
Finally, clonogenic assay hematopoietic immature cells were performed on cells removed from sacrificed animals. As shown in Table 3, their was no differences in mice receiving the conjugate, control unconjugate, control conjugate and mice receiving physiological control serum. These data clearly demonstrated that our approach did not alter the hematopoiesis.
Table 3 Frequencies of colonies in bone marrow following transfection anti-CD117-pIL3 conjugate
Days Control serum Unconjugate Control conjugate Conjugate
mean sd mean sd mean sd mean sd
5 191 10 183 8 192 10 185 25
7 157 55 152 50 192 12 197 16
10 187 23 173 11 182 22 187 26
Number of colonies was measured 5, 7 and 10 days following in vivo transfection with 100μg of anti-CD117-pIL3 conjugate. Control groups corresponded to mice injected with unconjugated pIL3 and anti-CD117 mAb or with the control conjugate (G250-pIL3). 5 × 105 cells from bone marrow were cultured in complete methylcellulose. Colony (aggregates of more than 40 cells) numbers were evaluated under inverted light microscope. The data are representative of three independent experiments and are the mean of triplicate determinations ± S.D.
Lack of transgene integration
Long-term cultures of bone marrow cells from mice receiving the conjugate or the controls were performed. After 1 week of selection in hygromycin-containing medium (plasmid conferred hygromycin resistance), cells were cultured for another 2 weeks and then viable cells were quantified using trypan blue exclusion assay. As illustrated on Figure 3, upon hygromycin selection, no viable cell was found in mice transfected with anti-CD117-pIL3 conjugate, suggesting that there was no integration of pIL3 into host DNA.
Figure 3 Morphology of survival long-term bone marrow cells. (a) Long-term bone marrow cells were cultured 7 days. (b) After a 1-week culture, 50μg/ml of hygromycin was added in order to select for stably transfected cells. After 1 week of selection, these cells were cultured 2 weeks in long-term culture medium. Cells observed in controls or in long-term culture in mice injected with the conjugate were viable (original magnification ×400).
Discussion
Although much progress has been accomplished in the field of gene therapy over the last years, there is still a need to develop more effective vectors and new strategies [28]. Using a non-viral gene delivery system, targeting primary hematopoietic stem/progenitor cells in vitro can be especially useful for studying the biological effects of various growth factors [29]. Our conjugate linking an anti-CD117 mAb to a pIL3 plasmid should be a good candidate to target specifically hematopoietic stem cells. We have previously reported that the method is suitable for the production of a functional growth factor in specifically CD117+ targeted cells, mediating an in vitro biological effect on hematopoiesis [14]. Since our previous report evidenced interaction of the conjugate with hematopoietic cells in vitro, the present study focus on specific targeting of hematopoietic tissues, in vivo.
We first demonstrated the efficacy of our approach since the transgene and its product (RNA and circulating human IL3) were found in mice injected with anti-CD117/pIL3 conjugate. It is of note that although human IL3 was only detected in plasma of chloroquine-treated mice injected with high quantity of conjugate (400μg); human IL3 encoding RNA were evidenced in treated mice injected with lower quantity of conjugate (100μg). These results were in accordance with the design of these experiments aiming at observing even a transitory and local effect (within the bone marrow).
PCR analyses of tissues evidenced the specific targeting of the hematopoietic system since brain, liver and lungs were negative. Only the spleen of mice transfected with the conjugate and kidneys of control animals (transfected with unconjugate mixture of Ab and DNA or with the control conjugate) displayed a positive PCR signal. Observed shortly after the last plasmid injection in blood, the presence of plasmid might be due to the intravenous administration route used and in kidney, to a progressive elimination of the plasmid in this organ of refinement. These results correspond to kinetic of plasmid availability when not using the specific vector (conjugate) to carry plasmid into progenitor cells. In the latter case, CD117+ cells were specifically transfected, and among them, Sca1+ cells were positive, suggesting a targeting of hematopietic progenitor cells via the systemic route.
Several parameters contribute to the efficiency and specificity of our system such as the internalisation of the antigen targeted, the choice of the transgene used, the tissues targeted, the conformation of the conjugate. Bone marrow was a good candidate for gene targeting as it is a highly proliferative tissue, as opposed to tissues which possess terminally differentiated cells such as hepatocytes or adipocytes, which are more resistant to transfection [30].
Factors affecting the bioavailabilty of the administered conjugates strongly determine their in vivo performance. These include avid interaction with serum components, resulting in colloidal instability, including both aggregation and dissociation of the conjugates and rapid elimination from blood circulation [31,32]. Therefore, the gene delivery carrier should function as a protector of DNA during in vivo administration. Protamine has been shown to cause condensation of DNA, which promotes cellular entry [33,34]. Our complex of plasmid and antibody may have been sufficiently compacted to resist nuclease degradation and non-specific interaction with plasma proteins. Furthermore the reduced dimensions of the conjugate may have been sufficient to allow its diffusibility through the extracellular space to reach bone marrow cells.
Conclusions
Our gene delivery system is specific and leads to transient gene delivery and expression. It may prove useful and safe for numerous clinical applications of gene transfer in hemato-oncology and radiopathology, whereby a stable genetic modification is not required, in contrast to the gene therapy approaches for genetic diseases. For example, it may be of interest to facilitate the long-term reconstitution of hematopoiesis through transient gene delivery into progenitor cells of patients after therapeutic and /or accidental exposure to chemo/radiotherapy. Whether our approach could be used to potentate hematopoietic reconstitution following irradiation remains to be studied.
List of Non-Standard Abbreviations Used
HSC Hematopoietic Stem Cells
Competing Interests
The author(s) declare that they have no competing interests.
Authors' contributions
AC, OD, AD, MB, SF, MM, PP carried out the studies. FH, DT participated to the designed of the study and its coordination. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by Electricité De France EDF-Comité de Radioprotection, Morad Bensidhoum was supported by a grant from Association Combattre la Leucémie. François Sabine was supported by a grant from Région Ile De France. F.H. and A.D. received support from the GDR 2352 "immunotargeting of tumors".
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| 15509303 | PMC535535 | CC BY | 2021-01-04 16:39:08 | no | Genet Vaccines Ther. 2004 Oct 27; 2:16 | utf-8 | Genet Vaccines Ther | 2,004 | 10.1186/1479-0556-2-16 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-381553588910.1186/1479-5876-2-38ResearchThe role of d-dimer as first marker of thrombophilia in women affected by sterility: implications in pathophysiology and diagnosis of thrombophilia induced sterility Di Micco Pierpaolo [email protected]'Uva Maristella [email protected] Ida [email protected] Antonio [email protected] Valeria [email protected] Alferio [email protected] Placido Giuseppe [email protected] IV Divisione di Medicina Interna e Patologie Epato-Bilio-Metaboliche, Seconda Università di Napoli, Naples, Italy2 Dipartimento Universitario di Scienze Ostetriche Ginecologiche e Medicina della Riproduzione, Area Funzionale di Medicina della Riproduzione ed Endoscopia Ginecologica, Università degli Studi di Napoli Federico II, Naples, Italy2004 9 11 2004 2 38 38 2 8 2004 9 11 2004 Copyright © 2004 Di Micco et al; licensee BioMed Central Ltd.2004Di Micco 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
D-dimer is considered a marker of hypercoagulable state and of endogenous fibrinolysis, so increased d-dimer is detectable in patients affected by thrombosis. Yet, several studies showed that also infertility, in particular secondary infertility due to recurrent fetal losses, has been often related to thrombotic events, in particular in women carrying thrombotic risk factors such as inherited thrombophilia (MTHFRC677T, PTHRA20210G, Factor V Leiden polimorphisms and/or inhAfter this screening we selected 39erited protein C, protein S, AT III deficiency) or acquired thrombophilia (primary antiphospholipid syndrome, acquired protein C, protein S, AT III deficiency, drugs induced thrombophilia). However, because its high predictive negative value in case of suspected thrombosis, increased d-dimer has been often associated to subclinical thrombophilia. The aim of this study is to investigate the role of d-dimer as first marker of thrombophilia in women affected by unexplained infertility and subsequently to search the cause of increased d-dimer, such as inherited and/or acquired thrombophilia.
Patients and Methods
We selected 79 patients with unexplained primary or secondary infertility. We excluded 40 patients affected by hydrosalpinx, uterine fibroids, uterine malformations, endocrinological and immunological diseases, luteal insufficiency, cytogenetical alterations. All remaining 39 patients were tested for d-dimer and divided in two groups: the patients of group A (25 patients) showed increased plasma d-dimer, in group B were included 14 patients with normal plasma level of d-dimer. After this step all 39 patients were screened for MTHFRC677T, PTHRA20210G, Factor V Leiden polimorphisms, protein C, protein S, AT III, anticardiolipin IgM and IgG, lupus anticoagulant. In the control group were included 15 age matched women without sterility problems referred to our outpatient's section of vascular medicine for suspected deep venous thrombosis.
Statistical analysis was based on χ2 test, differences were considered to be significant if p < 0.05.
Results
D-dimer was increased in 25/39 and 20/25 showed inherited/acquired thrombophilia while patients with normal d-dimer showed inherited/acquired thrombophilia in 7/14 (p: < 0.05, s).
Discussion
D-dimer is a well known marker of hypercoagulable state, in particular its high predictive negative value in case of suspected thrombosis has been recognised by several reports. Yet, increased d-dimer has been identified also for subclinical thrombophilia besides for vascular thrombosis. Our data, in fact, for the first time suggest an interesting role of d-dimer to identify women affected by unexplained primary or secondary infertility and thrombophilia. So, probably there is a role for d-dimer in these subjects for its predictive positive value. Of course, further data on large based population are needed to confirm our results, because these findings may speed up a diagnostic screening in these patients also for a good cost/effectiveness of this test.
d-dimerthrombophiliaalteration of haemostasissterilityrecurrent fetal loss
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Introduction
D-dimer is considered a marker of hypercoagulable state besides of endogenous fibrinolysis, so increased d-dimer is detectable in patients affected by arterial and/or venous thrombosis [1]. Yet, several studies showed increased d-dimer also in patients affected by subclinical thrombophilia without ongoing thrombosis [2]. Moreover, also in other clinical conditions, such as chronic inflammation as infectious disease (also as marker of disseminated intravascular coagulation if sepsis is associated) as cancer as necrosis as eldership and pregnancy we may observe an increase of plasma d-dimer [3-8]. So, for this reason d-dimer test is usually used in clinical management for its high predictive negative value in suspected thrombosis, particularly in deep vein thrombosis (DVT) [9-11]. However, several studies showed that frequently women affected by sterility, in particular secondary sterility for recurrent foetal losses, may be affected by an underlying inherited and/or acquired thrombophilia [12-20]. Besides, common thrombotic risk factors which include also a bad lifestyle (e.g. obesity, non regard to Mediterranean diet, sedentary life), a lot of molecular thrombotic risk factors such as inherited or acquired clotting inhibitor deficiency (i.e. protein C, protein S, antithrombin III), inherited thrombophilia (factor V Leiden, prothrombin A20210G mutation), primary or secondary hyperhomcysteinemia, primary or secondary antiphospholipid syndrome and increased plasma factor VIII levels have been identified [21]. Furthermore, these molecular alterations may be also associated in some subjects so inducing gene-gene interactions and/or gene-enviromental interactions [22-24]. So, because the high incidence of clotting abnormalities in these patients, according to the data of Brenner et al. [24,25], we investigated the role of d-dimer as first marker of thrombophilia in women affected by sterility in order to identify causes of increased d-dimer and probably of the induced sterility.
Patients and Methods
We selected 79 women affected by primary or secondary sterility (due to three or more fetal losses) referred to our sterility center. We excluded 40 patients affected by hydrosalpinx, uterine fibroids, uterine malformations, luteal insufficiency, anovulation, cytogenetical alterations, infectious diseases, endocrinological diseases (ie diabetes, subpituitarism), and by immunological diseases (inherited and/or acquired immunodeficiency, rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, vasculitis).
After this screening we selected 39 patients (12 affected by primary sterility and 27 by secondary sterility due to recurrent foetal loss). These 39 patients were tested for d-dimer. D-dimer was measured by several methods [26]; d-dimer were tested randomly in various periods of the ovarian menstrual cycle in 31 patients, in one patient during menstrual bleeding and in seven patients during hormonal therapy in order to obtain controlled ovarian hyperstimulation (COH). Following d-dimer examination patients were divided in two different groups: group A including 25 patients with increased d-dimer levels and group B including 14 patients with normal d-dimer levels. As control group we selected 15 age-matched women, without sterility problem in their anamnesis, referred to our outpatient's section of vascular medicine for suspected deep venous thrombosis.
Subsequently d-dimer evaluation, in order to identify a possible inherited and/or acquired thrombophilia, all patients were screened for methilene-tetra-hydro-folate-reductase C677T gene polimorphism (MTHFRC677T), Factor V Leiden gene polimorphism (FVL), prothrombin A20210G gene polimorphism (PTHRA20210G), protein S deficiency, protein C deficiency, antithrombin III deficiency (AT III), lupus anticoagulant, IgM and/or IgG anticardiolipin autoantibodies [22,27]. Moreover, all patients showing increased d-dimer were tested also for β-human-corionic-gonadotropin (β-HCG) to exclude early pregnancy, and lower limb ultrasound vascular examination associated to compression ultrasonography (CUS) to exclude a lower limb deep venous thrombosis (DVT); both conditions, in fact, are well known conditions associated to increased d-dimer [4,9-11].
Furthermore, patients with increased d-dimer (group A, 25 patients) and patients with normal d-dimer (group B, 14 patients) were compared also for possible differences in molecular markers of inherited and/or acquired thrombophilia.
Statistical analysis was based on χ2 test, differences were considered to be significant if p < 0.05.
Results
We found thrombophilia in group A, 80%, and in group B, 50%, so thrombophilia rate in all 39 selected was 65% if we consider together group A (i.e. women affected by sterility and showing increased d-dimer) and group B (i.e. women affected by sterility with normal d-dimer levels) (table 1, 'see additional file 1').
Twenty patients of group A (80%) affected by sterility with increased d-dimer levels, showed inherited and/or acquired thrombophilia [(six MTHFRC677T homozygosity, four FVL heterozygosity, five PTHRA20210G heterozygosity, three inherited Protein S deficiency, two showing combined defects (one MTHFRC677T homozygosity associated to protein S deficiency and one MTHFRC677T homozygosity associated to FVL heterozygosity), none protein C deficiency or AT III deficiency, none positive for the presence of lupus anticoagulant, none with increased anticardiolipin autoantibodies IgM and/or increased anticardiolipin autoantibodies IgG)] (table 2, 'see additional file 2'). Remaining five women of the group A did not show molecular thrombophilia, but in their anamnesis we found some possible correlation with an acquired thrombophilia: controlled ovarian hyperstimulation in one patient, monthlies in one patient, early pregnancy in one patient, miscarriage in one patient, none apparent cause in 1 patient; among them two of these five patients were heterozygous for MTHFRC677T. Furthermore, two patients of group A carrying inherited thrombophilia for the presence of heterozygous FVL and increased d-dimer revealed previous DVT with following pulmonary embolism in their anamnesis. Data of patients of group A are summarised in table 2 ('see additional file 2').
Seven patients of group B (50%) showed inherited and/or acquired thrombophilia (one MTHFRC677T homozygosity, one FVL heterozygosity, five PTHRA20210G heterozygosity, none inherited Protein S deficiency, protein C deficiency, AT III deficiency, none presence of lupus anticoagulant, none with increased anticardiolipin autoantibodies IgM and/or IgG), as reported in table 2 ('see additional file 2'). All remaining seven patients of group B showed all heterozygosity for MTHFRC677T. Moreover, none patients of group B revealed previous DVT and/or pulmonary embolism.
Five patients of group C (i.e. control group) (33.3%) showed increased d-dimer as molecular markers of ongoing proximal DVT confirmed by ultrasound vascular examination associated to CUS; moreover, all five patients revealed an underlying inherited and/or acquired thrombophilia (three MTHFRC677T homozygosity, one protein S deficiency, one combined thrombophilia: FVL heterozygosity associated to protein S deficiency). Data of patients of group C are summarised in table 2 ('see additional file 2').
In all groups positivity for anticardiolipin antibodies or lupus anticoagulant mimicking a primary antiphospholipid syndrome (APS) was not discovered.
So, as showed in table 3 ('see additional file 3'), increased d-dimer is frequently associated with thrombophilia in women affected by sterility, while this association is less present in patients with normal d-dimer, and this difference reaches statistical significance (p < 0.05); furthermore thrombophilia is more frequent in group A than in control group (i.e. group C) and also this difference reaches statistical significance (p < 0.05); finally, thrombophilia in group B is more frequent than in control group (i.e. group C), but this difference does not reach statistical significance (p: 0.08, ns).
Discussion
In this report for the first time the role of d-dimer was investigated in diagnostic screening of patients affected by sterility and this is a really innovative data available in this clinical setting.
D-dimer is a fibrin degradation product which usually is extensively screened in patients with suspected thrombosis and/or pulmonary embolism [9]. An increased plasma d-dimer might have a predictive positive value for DVT and/or pulmonary embolism, but because increased d-dimer has been observed also in several conditions not associated with ongoing thrombosis (malignancy, chronic inflammation, infections, acute coronary syndromes, necrosis, eldership) [3-9] the really interesting role of d-dimer in this clinical setting is for its high negative predictive value as reported by Bounameaux et al. in a series of patients with suspected pulmonary embolism [9]. However, increased d-dimer has been observed also in subjects affected by thrombophilia (i.e. inherited thrombophilia and/or acquired thrombophilia) showing hypercoagulable state without ongoing thrombosis as reported by Arkel et al. and Humphries et al. [2,23].
So, our data showed that patients of group A, carrying increased d-dimer, has been extensively screened for inherited and/or acquired thrombophilia and 80% of them revealed a well known molecular condition associated to hypercoagulable state which may explain increased d-dimer levels (table 2, 'see additional file 2'). Moreover, this our clinical and laboratory screening reaches statistical significance compared to group B and group C (table 3, 'see additional file 3'). Furthermore, five patients with increased d-dimer did not reveal inherited and/or acquired thrombophilia, but a thorough anamnesis and a clinical evaluation permitted to identify other causes of increased d-dimer in four of these patients: one patient showed early pregnancy (confirmed by β-HCG measurements and following ultrasound scan), a known condition associated to hypercoagulability and increased d-dimer [4,28,29], one patient revealed an early abortion, confirmed by following decrease of β-HCG, and increased dimer levels might be related to uteroplacental thrombosis and/or necrosis [30], one patient was ongoing to controlled ovarian stimulation and this condition may be associated to alteration of haemostasis with a trend toward thrombophilia [31,32] and one patient showed ongoing monthlies, a condition associated to wound healing which involves also clotting factors and might explain increased d-dimer [33]; remaining one patient showed increased d-dimer for unknown causes probably related to not well studied thrombophilia [34] or idiopathic thrombophilia and/or other conditions although we excluded in our selection criteria several other diseases associated to increased d-dimer.
So for the first time we showed an interesting and relevant role of d-dimer in the screening of sterility causes, particular an underlying thrombophilia may be suspected in pathophysiology of sterility if plasma d-dimer is increased. However, also an evaluation of other conditions associated to increased d-dimer (e.g. chronic inflammation, immunopathological diseases, infectious diseases, cancer, necrosis, eldership, pregnancy, controlled ovarian stimulation, monthlies) should be performed in order to avoid a misinterpretation.
Also group B, with normal d-dimer levels, showed an increased rate of thrombophilia (50%, table 1, 'see additional file 1'), so confirming one more time the clear relationship between thrombophilia and sterility, even if these data did not reach statistical significance compared to group C (table 3, 'see additional file 3'). Yet, patients of group B, although affected by thrombophilia and sterility did not show increased d-dimer. This finding might be explained by several causes and a laboratory mistake cannot be excluded; furthermore, these patients of group B may show also transient and/or silent thrombophilia which may trigger a hypercoagulable state if associated to other causes (i.e. acquired conditions associated to thrombophilia) during their natural history and our evaluation of d-dimer might be done during a not-hypercoagulable transient state.
An extensive screening of causes of increased d-dimer in our population was also performed. The association between thrombophilia and sterility due to recurrent foetal loss is well known as reported by several reports [12-20] and also by our data. However, recently an association between primary sterility and thrombophilia has been underlined such as also between thrombophilia and repeated in vitro fertilisation failures [35,36].
A clear relationship between thrombophilia and recurrent foetal loss has been reported for inherited deficiency of clotting inhibitors (i.e. protein C deficiency, protein S deficiency, AT III deficiency) [20,36], but we did not find in our population this strong association (only four cases of protein S deficiency, one of these associated to MTHFRC677T homozygosity, and none case of protein C deficiency and/or AT III deficiency). However, this aspect seems to be in agreement with other reports in which other thrombophilic conditions were more frequent than clotting inhibitor deficiencies (e.g. FVL, MTHFRC677T homozygosity, antiphospholipid syndrome and so on) [12-20].
FVL gene polymorphism has been frequently found in women affected by recurrent fetal loss, although the frequency of FVL differs in each study [15,24]. These differences could be related, besides to ethnic background, also to different inclusion criteria of investigated patients. However, FVL is associated to sterility also in our study (four cases in group A and one case in group B; table 2, 'see additional file 2').
An increased MTHFRC677T homozygosity has been found in our study population (six cases in group A and one case in group B), so confirming a clear role of homocysteine metabolism and of the related hypercoagulable state in sterility pathophysiology [38-40]. Of course, MTHFR gene polymorphism and related homocysteine metabolism may influence sterility also through folic acid and vitamin B12 deficiency due to uncorrected diet and/or lifestyle [41].
We found also an increased frequency of PTHRA20210G in women affected by sterility (five cases in group A and five cases in group B), and these data seem to be different from data reported by Pickering et al [42] and Deitcher et al [43] and in agreement with data reported by Brenner et al [24,25]. As we previously underlined, these differences could be related to inclusion criteria established by Investigators of each study and also to an ethnic background; this gene polymorphism, in fact, is more frequent in Southern Europe than in Northern Europe [44,45].
A really interesting data is the absence of APS from our study population and this data differs from data of the Literature. A possible explanation could be offered by different selection criteria: we exclude, in fact, women with immunopathological diseases (e.g. rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, vasculitis), so excluding the most common causes of secondary APS and so searching only primary APS that is more rare than primary [46].
In conclusion, in this investigation both groups of women affected by sterility, group A and B, showed increased incidence of thrombophilia compared to control group (group A vs group C, p: < 0.05, s; group B vs group C, p: 0.08, ns; table 3, 'see additional file 3'), so confirming, one more time the relevant role of thrombophilia in pathophysiology of sterility. So, the first relevant data we offer in this study is the role of d-dimer in the screening of sterility causes in order to early suspect an underlying thrombophilia; this screening, as also showed by our data, is in agreement with an elevated frequency of thrombophilia in women affected by sterility (80 % in group A, 50% in group B, 65% if we consider together group A and B). Of course, although several Authors already reported the association between thrombophilia and recurrent foetal loss we may testify that probably the role of thrombophilia is an underestimated problem if we consider all sterility conditions because usually thrombophilia is screened only for repeated foetal loss and not screened in any case of unexplained sterility as in this study.
So, based on our data further studies on large population are needed not only to confirm our results but also to focus a possible different prognosis of these groups, in particular to sterility prognosis.
Conclusion
Our data demonstrated a clear role of thrombophilia in patients affected by sterility, but suggesting a clear diagnostic role of increased d-dimer in a lot of these patients. This diagnostic screening of thrombophilia in women affected by sterility, based on the d-dimer levels, may also represent a really speed method to suspect thrombophilia in these subject and has also a good cost/benefit ratio, although other causes of increased d-dimer should be always considered. In a second step, if increased d-dimer levels are present causes of hypercoagulable state may be investigated (i.e. inherited thrombophilia and/or acquired thrombophilia). This approach may play a role not only in differential diagnosis of sterility but also in the early diagnosis of sterility due to thrombophilia. After the first step in which d-dimer may be evaluated, causes of increased d-dimer should be subsequently identified in order to start a possible antithrombotic treatment soon.
Nevertheless thrombophilia may be present in few cases also in subjects with normal d-dimer, it should be investigated always if other causes of sterility are not present.
So, we strongly suggest to test d-dimer in patients affected by sterility as first step of a possible underlying thrombophilia in order to early identify the cause of thrombophilia and its prompt treatment but other data should be confirmed by further investigations on large based population.
Supplementary Material
Additional File 1
Thrombophilia frequency in studied groups
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Additional File 2
Thrombophilia in studied groups
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Additional File 3
statistical analysis according with χ2 method
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| 15535889 | PMC535536 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Nov 9; 2:38 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-38 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-121553016810.1186/1743-7075-1-12ReviewRole of a critical visceral adipose tissue threshold (CVATT) in metabolic syndrome: implications for controlling dietary carbohydrates: a review Freedland Eric S [email protected] Boston University School of Medicine, 5 Bessom Street, No. 318, Marblehead, MA 01945, USA2004 5 11 2004 1 12 12 20 9 2004 5 11 2004 Copyright © 2004 Freedland; licensee BioMed Central Ltd.2004Freedland; 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.
There are likely many scenarios and pathways that can lead to metabolic syndrome. This paper reviews mechanisms by which the accumulation of visceral adipose tissue (VAT) may contribute to the metabolic syndrome, and explores the paradigm of a critical VAT threshold (CVATT). Exceeding the CVATT may result in a number of metabolic disturbances such as insulin resistance to glucose uptake by cells. Metabolic profiles of patients with visceral obesity may substantially improve after only modest weight loss. This could reflect a significant reduction in the amount of VAT relative to peripheral or subcutaneous fat depots, thereby maintaining VAT below the CVATT. The CVATT may be unique for each individual. This may help explain the phenomena of apparently lean individuals with metabolic syndrome, the so-called metabolically normal weight (MONW), as well as the obese with normal metabolic profiles, i.e., metabolically normal obese (MNO), and those who are "fit and fat." The concept of CVATT may have implications for prevention and treatment of metabolic syndrome, which may include controlling dietary carbohydrates. The identification of the CVATT is admittedly difficult and its anatomical boundaries are not well-defined. Thus, the CVATT will continue to be a work in progress.
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Introduction
Arguably, the major pathogenic factor in the metabolic syndrome is central obesity [1,2]. While abdominal obesity is determined by the accumulation of both subcutaneous adipose tissue (SCAT) and visceral adipose tissue (VAT), the excess accumulation of VAT appears to play a more significant pathogenic role. VAT depots, located in the body cavity beneath the abdominal muscles, are composed of the greater and lesser omentum (peritoneum that is attached to the stomach and links it with other abdominal organs) and the mesenteric fat. A lesser amount of VAT is located retroperitoneally. In general, VAT accounts for up to 20 percent of total fat in men and 5–8 percent in women. The abdominal SCAT is located immediately beneath the skin and on top of the abdominal musculature. The predominance of lower body fat is SCAT, most of which is stored in the femoral and gluteal regions [3-5]. Abdominal obesity can reflect a predominance of flabby SCAT; a firm, only modestly enlarged waist line resulting from deep VAT pushing the abdominal musculature outward; or a combination of enlarged SCAT and VAT depots. With the advent of more precise imaging techniques, e.g., magnetic resonance imaging (MRI) [6], computed tomography (CT) [7], and ultrasound [8], it has become evident that the accumulation of VAT not only accompanies but antedates the onset of the components of the metabolic syndrome and related disorders, e.g., insulin resistance, hypertension [9], glucose intolerance [10], type 2 diabetes, and coronary heart disease [11].
To date, it has not yet been established that insulin resistance, i.e., resistance of cells to insulin's effects, is responsible for the onset of the multiple risk factors associated with insulin resistance syndrome and subsequent development of atherosclerosis and cardiac events [12]. In fact, National Cholesterol Education Program Adult Treatment Panel (ATP III) criteria for Metabolic Syndrome have been found to have a low sensitivity for predicting insulin resistance [13-15] and may be better thought of as predictors for cardiovascular risk [16]. In a recent study of a large number of apparently healthy men and women of varying age, VAT area was significantly associated with all of the metabolic syndrome criteria as defined by the NCEP ATP III. This was independent of insulin sensitivity and SCAT area. Insulin sensitivity was found to be independently associated with the criteria for HDL cholesterol, triglycerides (TGs), and fasting plasma glucose (FPG). SCAT area was independently correlated with only waist circumference after adjusting for VAT area and insulin sensitivity [11]. In addition, the study results showed that clinical assessments of increased waist size and TG levels are strongly associated with decreased insulin sensitivity and increased VAT in individuals with fasting FPG <6.4 mmol/L [11].
The term "metabolic syndrome" is now preferable to "insulin resistance syndrome," and has a prevalence of 25 percent in U.S. individuals age >20, rising to >40 percent by age 60 [17]. The importance of central obesity is well-recognized in the definitions of metabolic syndrome [18] per the American College of Endocrinology, [19,20] National Cholesterol Education Program Adult Treatment Panel (ATP III), [21] European Group for the Study of Insulin Resistance, [22] and World Health Organization (WHO) [23]. However, even apparently lean individuals with normal BMIs can have a significant accumulation of VAT with increased risk factors for cardiovascular disease and diabetes (metabolically obese normal weight; MONW) [24-26]. Meanwhile, obese individuals with large BMIs but relatively little VAT can present with normal metabolic profiles and a paucity of risk factors for metabolic syndrome, cardiovascular disease, and diabetes, i.e., the metabolically normal obese; MNO) [5,27].
Ectopic Fat Storage Syndrome
The ectopic fat storage syndrome hypothesis suggests that as adipocytes hypertrophy and reach their capacity for storing more fat, then additional fat from excess dietary lipids or calories is deferred to non-adipose tissues intracellularly, e.g. liver, skeletal muscle, heart, and the beta cells of the pancreas where they can exert toxic effects and dysfunction [7]. This "lipotoxicity" may also be exacerbated by impaired oxidation of fat within tissues [7,28-30]. Furthermore, adipose tissue is a major endocrine organ that secretes numerous polypeptide hormones and cytokines that are proinflammatory and proatherogenic. These play a major role in affecting insulin action in skeletal muscle and creating a low-grade state of inflammation and endothelial dysfunction [31]. Compared to SCAT, VAT has been correlated more with endothelial dysfunction [32,33].
CVATT – A Working Hypothesis
It must be emphasized that the current proposal is a working hypothesis. Figure 1 describes a critical VAT threshold (CVATT) which is unique for a given individual and represents a range for the accumulation of a critical mass of VAT (CVATT) that when achieved, leads to the development of metabolic syndrome. Note that insulin sensitivity is important for weight gain [34] and accumulation of VAT, and investigators have proposed that insulin resistance may actually, to a certain extent, be beneficial by protecting cells with already impaired fatty acid oxidation. Once the CVATT is reached, insulin resistance (IR) occurs, which may be protective initially [29,35-37]. In addition to protecting against further weight and fat gain [34,38-41], insulin resistance prevents glucose and more fat from entering the cell and becoming preferentially oxidized. Hence, insulin resistance also allows intracellular fat already present within the cell to become oxidized rather than cause further damage through "lipotoxicity [29,30,40,42,43]."
Figure 1 Critical Visceral Adipose Tissue Threshold (CVATT). According to the hypothesis, there is an individual range for accumulating a critical amount of visceral adipose tissue (VAT). Insulin sensitivity is important for weight gain and accumulation of VAT. Once the critical VAT threshold (CVATT) is reached, insulin resistance occurs, which may be protective initially and impair further weight and fat gain. Continuation of VAT accumulation can lead to metabolic syndrome. However, only a modest weight loss (5–10 percent) with accompanying VAT loss can reverse the process.
Implications of VAT loss
It is encouraging that only a modest loss of 5–10 percent of body weight in obese patients is associated with preferential mobilization of VAT compared to SCAT, leading to simultaneous improvement in all metabolic markers of CHD risk. Such modest weight loss can prevent and reverse type 2 diabetes [44-48], and sustained weight loss in obese women results in a reduction in elevated inflammatory cytokine levels and an amelioration of endothelial dysfunction [49,50]. Surgical removal of VAT may reduce insulin resistance and plasma insulin levels [51,52], while liposuction of SCAT does not confer metabolic benefits [53]. Weight loss usually leads to VAT reduction as well as reduction of depots of fat in non-adipose organs, thereby improving insulin sensitivity [48]. However, once individuals improve insulin sensitivity by losing weight and crossing beneath their CVATT [48], they may now be more susceptible to weight gain and struggle to maintain this new state.
With total weight loss, those with greater amounts of VAT initially lose more VAT, and VAT is more sensitive to weight reduction because the VAT adipocyte is more metabolically active and sensitive to lipolysis [5,54]. After the initial weight loss, further dietary restriction may lead to an overall reduction in body fat, rather than specific loss from a particular site. The metabolic improvements observed with only modest reductions in total weight underscore the importance of VAT in insulin resistance and metabolic abnormalities [48,55]. Once the individual has lost a significant amount of VAT and is now below his CVATT, improvement in insulin sensitivity does not bear a linear relationship to the magnitude of weight loss [48].
The identification of the CVATT is admittedly difficult and its anatomical boundaries are not well-defined. Thus, the CVATT will continue to be a work in progress. While there are numerous studies linking VAT quantity to insulin resistance and metabolic syndrome, this does not necessarily prove that VAT is the cause. However, there are a number of plausible mechanisms linking VAT to the metabolic syndrome.
Adipose tissue as an endocrine organ
Adipokines
Once thought to be an inert energy storage depot, adipose tissue is now known to be a critical endocrine organ. The term "adipocytokines" or "adipokines" has been used to describe the numerous adipocyte secretory products which include: adiponectin, adipsin, estrogen, angiotensin II, angiotensinogen, leptin, plasminogen activator I (PAI-1), agouti protein, resistin [56], acylation stimulating protein (ASP), bone morphogenic protein (BMP), prostaglandins, IGF-1, and various IGF binding proteins, tumor necrosis factor alpha (TNFα), interleukins (ILs), transforming growth factor (TGF)-B [57], and fibroblasts, as well as FFAs themselves. Adipokines such as IL-6 and PAI-1 are more highly secreted by VAT than abdominal SCAT [58,59], while leptin is more highly secreted by SCAT [60]. Adipokines from VAT can be delivered via the portal system directly to the liver where they can affect hepatic, and ultimately systemic, inflammation. In an ex vivo study, VAT released greater amount of IL-6 and PAI-1 compared with abdominal SCAT [58,61].
Adiponectin has many beneficial vascular and metabolic effects, e.g., it serves as an anti-inflammatory molecule for vascular walls as well as adipose tissue, inhibits vascular smooth muscle proliferation, protects endothelium from macrophage adhesion and macrophage-induced injury [62], may increase fatty acid oxidation in peripheral tissues [63], protects against ectopic fat storage and has been linked with insulin sensitivity [64,65].
Ironically, although produced by adipose tissue, adiponectin levels are lowered with greater degrees of obesity and with overfeeding. Decreased concentrations of adiponectin are associated with type 2 diabetes, hypertension, elevated glucose levels, insulin and TGs, and cardiovascular disease (CVD). It has been suggested that adiponectin is under feedback inhibition in obesity and reduced in patients with metabolic syndrome [66]. Adiponectin mRNA and protein levels have been found to be reduced in omental VAT compared with SCAT [67], and VAT may also produce an as-yet-identified factor that destabilizes adiponectin mRNA [66,68]. The strong inverse correlation between serum adiponectin levels and VAT mass may in part explain the link between VAT and metabolic syndrome [66]. Over 90 percent of the adipokines released by adipose tissue, except for adiponectin and leptin, could be attributed to non-fat cells, e.g., macrophages, retained in the adipose tissue matrix [61].
Implications of fat mass expansion
Fat mass can expand in one of two ways: individual adipocytes can increase in volume or they can increase in number as more are derived from preadipocytes. As adipocytes grow larger, they become dysfunctional. The total number of adipocytes is increased with increasing fat mass, but it is the increased number and percentage of large adipocytes, compared to the smaller ones, that may partially account for the inability of adipose tissue to function properly [69]. While the smaller adipose cells tend to be more insulin sensitive, large adipocytes become insulin resistant and contribute more to the metabolic problems associated with obesity [69].
Preadipocytes from the SCAT depots have a greater differentiation capacity than those from the VAT depots [70,71]. The differentiation of preadipocytes into lipid-storing adipocytes is regulated in part by the nuclear hormone receptor, peroxisome proliferators activated receptor (PPAR). Activation of this receptor by natural ligands, such as prostaglandin metabolites, or synthetic ligands, such as thiazolidinediones (TZDs), leads to stimulation of the differentiation pathway [71]. This increases the number of smaller adipocytes in SCAT with a high avidity for FA and TG uptake. These increased adipose stores made up of new, smaller, more insulin sensitive adipocytes act as a sink or powerful 'buffers,' avidly absorbing circulating fatty acids and triglycerides in the postprandial period. This prevents their diversion to non-adipose tissues, thereby protecting against ectopic fat syndrome and metabolic syndrome. It has been proposed that an inability to differentiate new adipocytes to accommodate and store excess energy, underlies the development of type 2 diabetes [72,73].
A thiazolidinedione (TZD) paradox
TZDs can increase the number of new fat cells, and because obesity is a major cause of insulin resistance, this represents an apparent paradox. Ex-vivo studies of human preadipocytes from SCAT and VAT depots have demonstrated that TZD-stimulated differentiation is much greater in SCAT than VAT preadipocytes [71]. Since TZDs selectively promote adipogenesis in SCAT and not VAT, this would encourage the redistribution of body fat away from "harmful" VAT sites and toward "safer" SCAT ones [74-76]. Thus, in this way, TZDs could allow for pushing the patient to below his CVATT. Paradoxically, the TZDs can lead to weight gain while improving insulin sensitivity as the new SCAT adipocytes continue to trap FA and as fat storage continues, eventually the new adipocytes will enlarge, become less insulin sensitive, and ultimately contribute to insulin resistance [77]. TZDs may also exert anti-inflammatory effects on adipocytes by reducing the production of serum amyloid A (SAA) and preventing the TNFα-mediated expression of adiponectin production [69].
Adipose macrophages
Macrophages increase their accumulation within fat depots in direct proportion to increases in adipose tissue and adipocyte size. The increased macrophage activity observed in the adipose tissue of the obese may reflect a combination of conversion of local preadipocytes to macrophages and activation and recruitment of resident macrophages and circulating monocytes. This seems to occur after the onset of adiposity but prior to insulin resistance, and supports the notion that pathophysiological consequences of obesity involve macrophages and inflammation that contribute to insulin resistance and metabolic syndrome [78,79]. Evidence suggests that macrophages and adipocytes not only express overlapping sets of genes and serve similar functions, but also commingle in the same part of the body – the fat tissue [80].
VAT Versus SCAT (See figure 2)
Figure 2 VAT versus SCAT
VAT
There are numerous inherent differences between VAT and SCAT. VAT is a major predictor for insulin resistance [81] and metabolic syndrome [11]. Compared to SCAT, VAT adipocytes have a higher rate of lipolysis, which is more readily stimulated by catecholamines and less readily suppressed by insulin [82]. VAT also produces more IL-6 and plasminogen activator inhibitor-1 (PAI-1) [81]. The "Portal Theory" suggests that insulin resistance and many of its related features could arise from VAT delivering free fatty acids (FFAs) at a high rate to the liver via the portal vein into which VAT directly drains [83,84]. This, in turn, would increase hepatic glucose production, reduce hepatic insulin clearance, and ultimately lead to insulin resistance, hyperinsulinemia, hyperglycemia as well as non-alcoholic fatty liver disease (NAFLD). FFA flux could also lead to enhanced production of triglycerides (TGs) and apolipoprotein B-rich lipoproteins, which are features of the insulin resistance syndrome [55,85]. Delivery of VAT derived pro-inflammatory cytokines may contribute to hepatic pathology such as non-alcoholic steatohepatitis (NASH). VAT also releases a large amount of glycerol which enters the liver where it can be converted to glucose, thereby contributing to hyperglycemia [86]. It is likely that the relationship observed between VAT and metabolic complications may not exclusively result from FFA flux from VAT into the portal vein and the portal theory does not adequately hold up as the sole explanation of the role of VAT in metabolic syndrome [7].
VAT has twice the glucose uptake rate as SCAT
Recently, omental VAT cells have been shown to have an approximately two-fold higher rate of insulin-stimulated glucose uptake compared with SCAT adipocytes, and this could be explained by a higher GLUT-4 expression [87]. Perhaps in situations with a high intake of dietary glycemic load, a higher rate of glucose uptake and subsequently lipogenesis might be one mechanism by which TGs are stored preferentially in the VAT depot. VAT is highly lipolytic and resistant to insulin's lipogenic effects yet apparently can remain insulin sensitive to glucose uptake. This efficiency in glucose uptake may reflect VAT's ability to accumulate and maintain its activity. Enhanced glucose utilization in VAT would be accompanied by less lipid oxidation, which would indirectly promote TG storage [87].
Testosterone
VAT has a high density of androgen receptors and testosterone which can amplify its own effect by up-regulation of androgen receptors, inhibiting the expression of lipoprotein lipase (LPL) and FA uptake [5,88]. In men, VAT is strongly negatively correlated with plasma total and free testosterone and sex-hormone binding globulin (SHBG) concentrations. Thus, in young men whose plasma total testosterone and free testosterone are high, the amount of VAT is low. As men age, exceed their 20s, and reach middle age, their total and free testosterone decline, more fat is deposited in VAT stores, they often develop the "pot belly," and their risk for CHD increases [5,89]. The effects of testosterone on insulin resistance and metabolic syndrome risk factors are opposite in men and women [5,88,90,91]. Testosterone production often declines in women as they age, but VAT obesity in women is associated with elevated levels of total testosterone, free testosterone., and SHBG [92]. Hyperandrogenicity can also occur in polycystic ovary syndrome, where hyperinsulinemia can stimulate ovarian androgen production and suppress serum SHBG [88,93]. While weight loss in both sexes has been consistently shown to reverse the abnormalities in testosterone levels [94-97], a number of placebo controlled studies have consistently demonstrated that administering testosterone to obese men resulted in a significant reduction in VAT. This occurred without significantly altering amounts of total body fat or lean body mass [89,98-100]. However, the use of testosterone for VAT obesity is left open to debate [90].
11-β-Hydroxydehydrogenase1 (11-β HSD1)
Patients with type 2 diabetes and metabolic syndrome often appear Cushingoid, yet they invariably do not have elevated plasma cortisol [101]. Compared to SCAT, VAT has more glucocorticoid receptors [88]. The enzyme 11-β hydroxysteroid dehydrogenase type 1 (11-β HSD1) converts inactive cortisone to the active compound cortisol, and, if overexpressed, may cause increases in local cortisol concentrations [101]. Local production of active cortisol from inactive cortisone driven by 11-β-HSD-1 activity is very high in VAT and barely detectable in SCAT. Therefore it is likely that the VAT depot actively contributes to the production of high local concentrations of cortisol, which might not be reflected by plasma levels. These, in turn, might contribute to an increase in VAT accumulation [102]. 11-βHSD1 inhibition holds promise as a therapeutic target for VAT-associated metabolic syndrome [103,104].
VAT and impaired skeletal muscle oxidation
The amount of fat deposited within skeletal muscle (intramyocellular lipid – IMCL) and the ability of muscle to oxidize fat are important determinants of weight gain,[105] weight regain following weight loss [106], and the development of insulin resistance syndrome [107]. IMCL and the VAT depot might not be independent from each other. Furthermore, the relationship between IMCL and insulin sensitivity is independent of percent total body fat and SCAT but not of VAT [108]. In individuals with type 2 diabetes, among the depots of regional and overall adiposity, VAT was the depot of adipose tissue that was most strongly related to skeletal muscle insulin resistance [109].
Colberg et al studied the fasting patterns of skeletal muscle fatty acid uptake and oxidation in healthy, lean and obese premenopausal women who had a cross-sectional VAT area over a range from 18 to 180 cm2 and BMIs from 19 to 39 kg/m2 [110]. The researchers found that insulin sensitivity as well as postabsorptive rates of FFA utilization or oxidation by muscle were diminished in relation to VAT. Women with increased VAT did not have lower plasma FFA levels or lower rates for appearance of FFA, yet they had an impaired or reduced uptake of plasma FFA by the skeletal muscle in the leg [111]. Together, this supports a role for VAT, IMCL lipid deposition, and perhaps impaired oxidation of nonadipose tissue lipid in insulin resistance and metabolic syndrome.
VAT may influence central SCAT
Mauriege et al found that adrenoreceptor sensitivity was increased in SCAT cells of individuals who have a higher VAT accumulation compared to those with a low VAT deposition [112]. SCAT adipocytes from women with visceral obesity exhibit higher lipolysis rates in vitro than those obtained from women with little VAT [113]. Mauriege et al also demonstrated that among men with high levels of VAT, SCAT adipocytes are more sensitive to β-adrenergic lipolysis which may further exacerbate an impaired insulin action, a potentially important factor in the etiology of metabolic syndrome associated with visceral obesity [112]. Moreover, an increased truncal SCAT mass and an increased amount of VAT mass can independently predict insulin resistance [114]. Together, these findings support that VAT may enhance central SCAT lipolysis and accelerate release of peripheral FFAs.
The PPARs are important transcription factors that play an important role in the induction of adipose-specific genes, the proliferation and differentiation of adipocytes, and the development of mature adipose tissue. A number of transcription factors are involved, including PPARγs. Giusti et al suggest that in VAT, the expression of PPARγ2 is controlled by local transcription factors (RXRα, αSREBP1, and SREBP1c) promoting fat storage in adipocytes. Given that the fat storage capacity is limited in VAT, RXRα induces the expression of PPARγ2 in SCAT to increase its overall capacity [115]. These data also suggest that the signal to promote fat storage may occur in VAT and that other metabolic and hormonal factors are involved in the control and modulation of adipogenesis in visceral fat [115].
Perhaps the above can be explained as follows. SCAT cells may act as a buffer or sink for circulating FAs and TGs but once they reach their capacity they lose their protective benefits. Initially, VAT may influence SCAT to expand and act as a buffer. However, once the critical VAT threshold (CVATT) is achieved and metabolic syndrome has begun to develop, then VAT may influence central SCAT to become more VAT-like, i.e., more lipolytic and less sensitive to insulin's adipogenic or lipid storing effects.
SCAT
Greater preadipocyte differentiation and protection
As discussed earlier, preadipocytes from SCAT depots have a greater capacity than VAT to differentiate into numerous, small, insulin-sensitive, adipocytes [70,71]. These lipid-storing cells act as a buffer or sink for circulating FAs and TGs, thereby preventing their deposition in non-adipose tissues, e.g., skeletal muscle, pancreas, and liver, where they could contribute to lipotoxicity, apoptosis, and insulin resistance [73,116].
Does SCAT replenish VAT?
In defending the role of VAT accumulation in individuals with metabolic syndrome, we must postulate a high rate of lipid turnover, with high rates of lipolysis at certain times matched by high rates of lipid deposition at other times. Otherwise, as Frayn points out, the hyperlipolytic VAT would ultimately disappear [117]. He also suggests that if SCAT were to become insulin resistant, and therefore resistant to fat storage, then fat might tend to be deposited in VAT depots. Another possibility is that the usually larger SCAT depot has a greater potential to contribute to insulin resistance through release of FFA into the systemic circulation. However, this would not adequately explain the subset of individuals who demonstrate metabolic profiles consistent with insulin resistance but are in fact lean, healthy-appearing with normal BMIs, excess VAT, little SCAT, and are referred to as "metabolically obese, normal weight (MONW) [26]. As described above, perhaps once VAT expands and SCAT depots reach their capacity for storing FAs, then do SCAT adipocytes become insulin resistant, release FFAs, and contribute to systemic insulin resistance and metabolic syndrome.
While some studies cast doubt on the portal theory and its implications for VAT's direct delivery of FFA to the liver [118,119], they leave open other mechanisms via which VAT could induce insulin resistance and other metabolic disturbances, e.g., by producing proinflammatory cytokines which could be directly delivered to the liver where they can potentially affect hepatic metabolism [117]. These will be discussed below.
Peripheral fat mass may protect against atherosclerosis and metabolic syndrome
If trunk fat is taken into account, accumulation of fat in the hips and legs is an independent predictor of lower cardiovascular and diabetes-related mortality, and it seems to protect against impaired glucose metabolism, especially in women [120-124]. In a study of 1,356 women ages 60–85, those with excessive peripheral fat had less atherosclerosis (determined by aortic calcification scores), and the quartile with both the highest amount of central fat and peripheral fat seemed to be partially protected by the high percentage of peripheral fat mass as reflected in a number of measured risk factors [121]. These findings corroborate similar findings by the same group who followed 316 postmenopausal women for 7.7 years and monitored progression of aortic calcifications [120]. In yet another study, Tanko et al demonstrated that peripheral fat mass (SCAT) in generally obese, post-menopausal women is associated with increased adiponectin and higher insulin sensitivity [125]. Together, these support protective roles for peripheral fat. In addition to fat trapping, these might include possible influences on adipokines, e.g., they might contribute to an increase in adiponectin, which could improve FA oxidation [126]. One must interpret these results with caution because the measuring technique of dual-energy X-ray absorptiometry (DXA) does not allow separate quantification of intermuscular and subcutaneous fat in the arms and legs as well as SCAT in the trunk [121]. While VAT is a major predictor of insulin sensitivity in overweight and lean individuals [114,127], others have found abdominal SCAT to contribute to insulin resistance independently of VAT [128,129].
An example of metabolically innocent obesity
When there is an inability to store fat, due to lipodystrophy, the adipocytes' storage capacity is exceeded and lipids accumulate and cause lipotoxicity in liver, muscle, and other organ tissues [7]. A counterpart of lipodystrophy may be illustrated by patients with multiple symmetric lipomatosis (MSL), a condition characterized by regional excess of subcutaneous adipose tissue. These patients have higher adiponectin levels, a high degree of insulin sensitivity and glucose tolerance, very low lipid levels in liver and muscle cells, and markedly little VAT [130]. In this case, SCAT may be protective and beneficial. This may be analogous to thiazolidinedione action, which also promotes SCAT deposition while improving insulin sensitivity and glucose tolerance [74,75].
Estrogen
Estrogen promotes the accumulation of peripheral gluteo-femoral SCAT, which may be protective [131]. The abundant presence of peripheral fat mass in generally obese women is associated with increased plasma adiponectin, and the loss of estrogen with menopause is associated with an increase in central fat [132]. This accounts for the progression in many overweight women after menopause from a predominantly pear-shape or "gynoid" habitus to the apple or "android" shape. Contrary to popular belief, menopause does not seem to independently cause a gain in total body weight; the increases in BMI that often accompany menopause are usually consistent with normal aging [133]. However, even without weight gain, body fat distribution changes; postmenopausal obese women tend to accumulate abdominal fat along with deterioration of risk factors, even if total body weight and BMI do not change during menopause transition.
After menopause, when ovarian function declines, adipocytes become the primary source of endogenous estrogens [134], and compared to "gynoid" or pear-shaped women, those with central obesity (apple- or "android-" shaped) have lower plasma SHBG and higher estradiol [125,135]. This suggests regional differences in the enzymatic conversion of steroid hormones in VAT versus SCAT [125,136-138]. In ovarian hormone-deficient women, SCAT adipocyte size, lipoprotein lipase (LPL) activity, and basal lipolysis were not found to be significantly greater compared to regularly cycling premenopausal women. However, in the ovarian hormone-deficient women, omental (VAT) adipocyte size was significantly higher, and the omental/SCAT LPL activity ratio and VAT lipolysis were also significantly higher [139]. For a given amount of total body fat, men have been found to have about twice the amount of VAT than what is found in premenopausal women but this may change after menopause when VAT storage becomes predominant [140,141].
Along with an increase in VAT, a decline in estrogen is also associated with reduced lean body mass as well as other features of the metabolic syndrome including: dyslipidemia with elevation in Lp(a), triglycerides, and an increase in small, dense, LDL particles. Estrogen deficiency also may influence cardiac risk by its effects on the insulin action, the arterial wall, and fibrinolysis. Park et al showed that postmenopausal women lost less VAT compared with the premenopausal women during a weight reduction program (10.5 percent vs. 25.7 percent respectively) [142]. The reasons behind this are presently unclear.
As mentioned above, in menopause, adipocytes are primary sources of endogenous estrogens in women [125,134], and estrogens are known inhibitors of IL-6 secretion [143]. It is worth noting that the relationship between BMI and serum IL-6 was observed only in postmenopausal women, and this relationship was lost among those women receiving hormone replacement [144]. Adipose tissue-derived estrogens in postmenopausal women would not be sufficient to reduce IL-6 in a similar way as endogenous estrogens do in premenopausal women [145]. Perhaps in premenopausal women, endogenous estrogen from the ovaries helps keep VAT volume relatively low and is thereby protective. Estrogen by itself seems to protect postmenopausal women receiving replacement therapy from VAT accumulation, and in women with type 2 diabetes, estrogen replacement may protect against the risk of cardiac events [146,147].
Compared to men of similar age, premenopausal women appear to be significantly protected from CHD. However, by age 70 the incidence of CHD is equal in men and women, suggesting that estrogen deficiency causes a rapid acceleration in CHD risk [133]. Yet, in elderly, postmenopausal women, Tanko et al showed that those women with higher amounts of central versus peripheral obesity had significantly higher levels of estradiol and lower adiponectin. This suggests that prolonged and increased exposure of SCAT cells to estradiol may eliminate the protective effect of SCAT by affecting SCAT's ability to release adiponectin thereby promoting the atherogenic effects of IL-6 [125]. Perhaps future research will help clarify whether central obesity has any implication for increased susceptibility to the adverse cardiovascular effects of hormone replacement therapy (HRT) in diabetic patients early after initiation of therapy [125].
Obesity, particularly visceral obesity, as well as insulin resistance and hyperinsulinemia are associated with breast cancer [148]. Insulin may increase estrogen action by increasing bioavailable estrogen due to a decrease in sex hormone-binding globulin, by influencing estrogen receptors, and by increasing aromatization of androgen to estrogen at the tissue level, a phenomenon which has been demonstrated in breast tissue. Estrogen upregulates the IGF-1 receptor and IGFBP-1 and -2 and may directly activate the IGF-1 receptor, thereby increasing insulin signaling [149].
Around 1900, most women died soon after menopause. The average lifespan of persons in the United States has since lengthened by greater than 30 years [150], which means that women, and men, too, are now spending 30 or more years with hormonal and physiological states that society and medicine has not had to deal with previously. These, combined with significant dietary and lifestyle changes since 1900, must be considered as critical contributing factors to the world's current epidemic of metabolic syndrome.
Lipotoxicity Model
Overnutrition, lipotoxicity, leptin, and the metabolic syndrome
When one consumes too many calories, especially in the form of excessive carbohydrates, the liver converts excess glucose to fatty acids. First, glucose that is not oxidized or stored as glycogen is metabolized to acetyl CoA, which then enters the lipogenic pathway. Acetyl CoA is catalyzed to form malonyl CoA, which in turn inhibits carnitine palmitoyl transferase 1 (CPT-1, the enzyme responsible for fatty acid transport into the mitochondria) [42]. The net effect is that malonyl CoA (from excess carbohydrates, glucose, and insulin) reduces the oxidation of FAs [151]. This results in increased accumulation of intracellular fat in the form of long chain fatty acids and their derivatives, e.g., TGs and ceramide [28,29]. Cellular TG accumulation is not initially toxic and may actually be protective by diverting excess FAs from pathways that lead to cytotoxicity [152]. While glucose is being preferentially utilized, the FAs are metabolized by pathways other than their preferred β oxidation, leading to toxic products, e.g., ceramide, which may cause apoptosis and lipotoxicity [28,30,153]. The subsequent development of the cell's resistance to insulin-mediated glucose uptake, which prevents further influx of glucose, may be viewed as being protective in that it limits the amount of intracellular glucose to be preferentially metabolized over the β oxidation of intracellular FAs [29,37,154]. The cell can be insulin resistant with respect to glucose uptake and metabolism but remain sensitive to insulin's lipogenic effects and the de novo synthesis of fat. Overconsumption of calories, especially in the form of carbohydrates, also stimulates hyperinsulinemia that can then upregulate SREBP-1c and increase de novo lipogenesis [43].
Leptin protects against lipotoxicity
Leptin
The first adipocyte-specific hormone to be characterized, leptin is produced predominantly by SCAT adipocytes compared to VAT. Females produce leptin at about twice the rate in males [155], and leptin secretion increases with enlarged adipocyte cell size. Circulating leptin rises by 40 percent after acute overfeeding and more than three-fold after chronic overfeeding, whereas fasting is associated with decreased leptin levels [156].
Dietary carbohydrates may influence leptin action
The increase in leptin concentration after meals is not simply a result of a caloric load, but is in response to a signal that is not present following a fat load without carbohydrate [157]. Leptin circulates in a free form and is also bound to a soluble leptin receptor – sOBR, which is positively associated with energy intake from carbohydrates and negatively associated with energy intake from dietary fat [158].
Excess caloric consumption and fat deposition results in newly synthesized FAs that are transported as VLDLs and stored as TG in adipocytes. Initially, these expanding adipocytes secrete leptin in proportion to their growing fat accumulation. Leptin also crosses the blood brain barrier, stimulates its receptor in the hypothalamus, and causes the release of neuropeptide-Y (NP-Y), which reduces feeding behavior [85]. This, in turn, suppresses appetite and stimulates thyroid function. Leptin affects peripheral tissues, and is a determinant of insulin sensitivity. The ensuing hyperleptinemia increases fat oxidation in skeletal muscle [159-161], and also keeps de novo lipogenesis in check by lowering the involved transcription factor, i.e., SREBP-1c mRNA (sterol regulatory element binding protein 1c mRNA) [43]. It promotes cholesterol ester synthesis in macrophages in a hyperglycemic environment, an important process in the formation of foam cells in atherosclerosis which may suggest a protective role of relative leptin resistance [162]. Leptin also possibly increases sympathetic nervous system (SNS) activity with subsequent decreased FFA oxidation and thermogenesis [163]. All of these effects of leptin tend to limit further weight gain.
Leptin resistance
As the process progresses, inefficient leptin action can lead to the opposite of leptin's protective effects, e.g., hyperphagia, decreased fat oxidation, increased tissue TG levels, insulin resistance, and overweight. Subsequently, plasma leptin levels rise. The majority of obese individuals with high leptin levels show a leptin insensitivity or "resistance [164]," which occurs at the leptin receptor level. In animal models, leptin-resistance and leptin-deficiency increases, and upregulates the hepatic expression of SREBP-1c mRNA, which may stimulate an increase in fat production via de novo lipogenesis. Together, all of these features suggest a state of "leptin resistance" which may ultimately lead to obesity and metabolic syndrome [29,165].
It is quite possible that hyperleptinemia in diet-induced obesity serves to protect nonadipose tissues (e.g. muscles, liver, pancreatic β cells, and myocardium) from the toxic effects resulting from the spillover of full adipose stores and subsequent ectopic deposition of FFAs. In defense of this paradigm, Unger points out that normally rats can tolerate a 60 percent fat diet because 96 percent of the surplus fat is stored in an enlarging adipose tissue mass, in which leptin gene expression increases proportionally [166]. However, when leptin is congenitally absent or inactive, or ineffective due to resistance, even on a normal or low-fat diet, excess dietary fat is deposited in nonadipose tissues. This causes dysfunction (lipotoxicity), and possible cell death (lipoaptosis) [29].
Acquired leptin resistance occurs in aging, obesity, Cushing's syndrome, and acquired lipodystrophy, a condition associated with protease inhibitor therapy of AIDS. Preliminary evidence suggests that patients with these conditions have increased ectopic fat, i.e., lipid deposition in non-adipose tissues [29].
Role of triglycerides in leptin resistance
The relation between cerebrospinal fluid and serum levels of leptin in obese humans suggests that defective blood brain barrier (BBB) transport accounts for a great deal of leptin resistance in the CNS. Banks et al showed in mice that serum TGs directly inhibit the transport of leptin across the BBB and so could be a major cause of leptin resistance across the central nervous system (CNS). Thus they suggest that serum TGs are likely a major cause of the leptin resistance seen in both obesity and starvation [167]. This hypothesis explains why lowering TGs may be therapeutically useful in enhancing the effects of leptin.
Implications for VAT in relative hypoleptinemia and metabolic syndrome
Compared to VAT, SCAT is the predominant source of leptin [60], yet patients with VAT obesity may tend to have higher leptin levels than normal, lean individuals but lower than those with predominantly SCAT or subcutaneous obesity [29]. This suggests that the hyperleptinemia of predominantly VAT obesity is not high enough to overcome a leptin resistance due to the accumulation of ectopic fat in nonadipose tissues, which leads to lipotoxicity and ultimately the metabolic syndrome [29].
Lipodystrophies – A paradigm for the roles of fat depots and insufficient leptin action in metabolic syndrome
A number of clinical states exhibit evidence of leptin insufficiency, either leptin deficiency or resistance, and they all have in common the metabolic syndrome. These include rare genetic diseases known as lipodystrophies, which are characterized by a redistribution of fat. Ironically, in the more severe cases, e.g., congenital generalized lipoatrophy, near-complete fat loss is associated with severe insulin resistance, fatty liver, and classic features of the metabolic syndrome. There is hyperleptinemia along with hyperphagia and a predominance of intra-muscular fat [168]. Dunnigan-type familial partial lipodystrophy is a rare autosomal dominant condition characterized by markedly reduced plasma leptin levels along with gradual loss of SCAT from the extremities, trunk, and gluteal region, commencing at the time of puberty, as well as hyperinsulinemia, glucose intolerance, dyslipidemia (high TGs with low HDL), and diabetes [169,170]. These individuals do maintain central obesity and VAT [169], which supports a relatively protective role for SCAT and implicates VAT as being more pathogenic.
The aforementioned potential role of TGs in leptin resistance may have implications for patients with lipodystrophy and lipoatrophy who have little or no fat mass, and as a result, have very little or no leptin. They also have severe hypertriglyceridemia that is reversed by treatment with leptin [168,171]. The elevated plasma level of TGs in these patients is likely inducing leptin resistance that is preventing the leptin from inducing TGs to be used as an energy source. Thus the TGs in these patients are not oxidized, and they are unable to settle into fat stores that would normally act as a TG sink and prevent their diversion to non-adipose tissues where they contribute to lipotoxicity and insulin resistance.
Fat depots can protect against lipotoxicity
Fat provides leptin and adiponectin
Transplantation of adipose tissue grafts in animal models of congenital lipoatrophy reverses the signs of the metabolic syndrome in a dose-dependent fashion [172]. Furthermore, leptin treatment in humans and animals with lipodystrophies also reverses fatty liver and insulin resistance. However, transplantation of ob/ob adipose tissue (which does not produce leptin) in lipodystrophic rats does not reverse diabetes [173] nor is it beneficial to inject leptin in obese humans with leptin resistance [18]. These support the notion that insufficient leptin action may be a cause of metabolic syndrome, and that adequate leptin derived from SCAT is protective.
Like leptin, adiponectin secretion increases early on in obesity and plays a role in reducing the expression of lipogenic enzymes and increases FA oxidation in peripheral tissues thus limiting ectopic fat accumulation [174]. The fact that adiponectin is secreted initially by fat but levels are reduced as fat depots increase, may help resolve the paradox of both lipodystrophy and obesity both being insulin-resistant states [73].
Critical Visceral Adipose Tissue Threshold (CVATT) – Individual Variation
The CVATT has tremendous individual variation; thus a relatively "thin" individual (with a normal BMI) and an excess of VAT for him, may be metabolically obese, normal weight (MONW) [26]. Meanwhile, another individual with a large "pot belly" may have a great capacity to store fat as SCAT with relatively little VAT or he may have a high threshold for VAT. This may explain the finding that while some individuals weighing even up to 200 kg do not show any signs of type 2 diabetes or dyslipidemia, while in others, diabetes or dyslipidemia either develop or deteriorate with an increase in body weight of only one kg [175] – perhaps just enough to exceed the CVATT.
A number of studies have looked at a possible CVATT [176-182]. Using CT scans to measure VAT volume, Williams et al found that a value of above 110 cm2 was associated with an increased risk of CHD in pre-and postmenopausal women [177]. Similarly, Despres and Lamarche observed a VAT cutoff of 100 cm2 was associated with increased CHD risk in young adult men and premenopausal women (mostly of French Canadian descent) [179], and a cutoff range of 100–110 cm2 has also been observed by others [176,181]. Other studies have suggested thresholds of > 130 cm2 for metabolic deterioration [183,184]. De Nino et al found that insulin resistance did not appear until women were older than 60 years and had accumulated levels of VAT that approximated the levels seen in men, suggesting a possible threshold effect of VAT on insulin resistance [185]. As discussed below with MONW, genetic and ethnic factors play a role. For example, in nonobese and obese Japanese males and females, fat areas at the umbilicus as determined by CT had threshold values for metabolic syndrome with only > 100 cm2 for men and > 90 cm2 for women [186].
Brochu et al were unable to demonstrate that obese postmenopausal women who reduced their weight and attained a level of VAT below 110 cm2 would show greater improvement in their metabolic profile compared to those who also lost weight but remained above the 110 cm2 VAT threshold [178]. However, there were only 25 total subjects and the women had relatively normal metabolic profiles at baseline. Perhaps due to the relatively small number of subjects, only five lost less than 20 percent of their baseline VAT value. Thus it is unclear whether even smaller losses of VAT than those observed improved metabolic outcomes. The researchers did find larger losses of VAT and a greater improvement in insulin sensitivity in those who attained a VAT level < 110 cm2 [178]. It should also be noted that in postmenopausal women, peripheral SCAT may be protective, even in the face of large amounts of VAT, and this needs to be accounted for [120,121,125]. While studying obese Japanese women, Tanaka et al recently validated the 100 cm2 CVATT but their longitudinal data from both pre- and posttreatment suggest that these women should reduce their VAT area to <60 cm2 through weight reduction to improve CHD risk factors [181].
Metabolically obese normal weight (MONW)
VAT accumulation contributes to metabolic risk factors in nonobese individuals [187,188]. Ruderman et al have shown that normal weight individuals may also have insulin resistance and the disorders of the metabolic syndrome [26]. They designated such individuals as "metabolically obese normal weight – MONW [189,190]." MONW subjects (BMI < 25 kg/m2) have been characterized by an excess of VAT area (> 100 cm2 by abdominal CT), insulin resistance, and hyperinsulinemia [24-26]. As pointed out earlier, the development of insulin resistance may limit further weight gain [34,38-41,191]. A rapid and early development of insulin resistance prior to significant weight gain would explain that a significant number of the normal-weight population have insulin resistance [26]. The prevalence of MONW could be as high as 13 – 18 percent [26,192,193].
MONW and low birthweight
Both low birthweight (LBW) [194] and lowest weight at one year of age have been linked to, VAT accumulation[195] insulin resistance and cardiovascular risk factors in middle-aged and elderly individuals, many of whom could be classified as MONW with metabolic syndrome. By middle age, many LBW subjects have BMIs less than 24–26 kg/m2 and would be classified as MONW. While some data suggest that LBW babies have central adiposity in middle age, definitive measurements of VAT in these individuals are still lacking [26].
Ethnicity and MONW
One should consider ethnic differences when attempting to identify MONW subjects. Lean appearing individuals, especially in certain ethnic groups such as the Japanese, may have significant amounts of VAT that surpass their CVATT but appear with what, for the general population, would be considered a normal BMI and waist circumference [196]. For example, nonobese Japanese (BMI<25) with increased VAT areas (100–110 cm2) fulfill the criteria for MONW [25,181]. In another study, relatively lean Japanese patients with newly diagnosed type 2 diabetes had increased VAT. Through diet and without medication for three months, the amount of VAT in these patients became comparable to that in normal-weight control subjects. Therefore, a three-month dietary treatment regimen with small to moderate weight loss was very effective in decreasing excess VAT in this population [197]. This illustrates the importance of early recognition of an individual's approaching or exceeding his CVATT. Park et al were among the first to demonstrate that healthy, non-obese Asian American women may have higher amounts of VAT, and that normative values or standards for VAT derived from Caucasians may not be applicable to Asians [196]. On the other side of the spectrum, a 10-year prospective study studied increased BMI in Micronesian Nauruans (an ethnic group from the central Pacific Ocean with rapidly increase in prevalence of obesity) and Melanesian- and Indian-Fijians. Overall, there was little evidence to suggest that obesity was a risk factor for total or cardiovascular mortality in these populations [198].
Metabolically normal obese (MNO)
McGarry found that one of his most obese patients in a series (BMI 32.8 kg/m2) was one of the most insulin-sensitive but had one of the lowest values for intramyocellular lipid (IMCL). Conversely, another subject, with a BMI of only 18.9 kg/m2, proved to be highly insulin-resistant but had a large amount of IMCL. This supports that insulin sensitivity appears to correlate more with where the fat is located rather than the total amount in the body [42]. This has implications for the phenomena of the metabolically obese normal weight (MONW) and the metabolically normal obese (MNO) individuals.
Like some of the Micronesian Nauruans and Indian-Fijians above, there are individuals who are obese and who nevertheless are metabolically normal – "metabolically normal obese; MNO." Unlike their MONW counterparts, MNO individuals have very little VAT accumulation. They often share an onset of obesity early in childhood, normal VAT, lower TGs, and increased HDL. The actively competitive Japanese wrestlers maintain their gross obesity by consuming a 5,000 to 6,000 calorie diet. They are MNO, and their VAT is normal in amount, i.e., they have excessive amounts of SCAT [91]. On retirement, when they discontinue their rigorous training regimen, they markedly develop increased insulin resistance and metabolic syndrome. It is likely that that their VAT increases concomitantly [26,27] and exceeds their CVATT. Data from the European Group for the Study of Insulin Resistance (1146 hyperinslinemic/euglycemic clamp studies from 20 clinical centers in Europe) showed that in "simple" obesity, insulin resistance is not as prevalent as previously thought [199]. MNO could account for as much as 20 percent of the obese population [193]. In another study using HOMA to determine insulin resistance, Bonora et al showed that 11 percent of the entire group of overweight individuals fit the criteria of MNO [200]. Brochu et al extensively studied 43 sedentary, obese, postmenopausal women and found that 17 were MNO, while 26 had reduced insulin sensitivity (estimated by clamp) [201]. The two groups were similar in total body fat mass, SCAT amount, as well as waist circumference, and total daily energy expenditure. However, lean body mass was significantly greater in the metabolically abnormal subjects. Unlike SCAT, VAT measured by CT was inversely related to the insulin sensitivity and to a classification of MNO. In fact, despite similar levels of total body fatness, MNO individuals showed 49 percent less VAT as measured by CT. However, the level of VAT was still significant. Furthermore, using doubly labeled water and indirect calorimetry, Brochu et al were unable to demonstrate a meaningful difference between resting metabolic rate and daily physical energy expenditure between MNO and obese individuals at risk [201].
MNO and childhood obesity
Several investigators have found that there has been a positive association between insulin sensitivity and duration of obesity, i.e., those who are obese since childhood are more likely to remain insulin sensitive. In one study 48 percent of the MNO women presented with a history of an earlier age-related onset of obesity (between 13 and 19 years of age) and less VAT compared with 29 percent of the metabolically abnormal obese [201].
Insulin sensitivity seems to be dependent upon adipose cell size; as adipocytes within tissue grow larger, they become more insulin resistant [202]. Normal-sized, more insulin-sensitive adipocytes have been associated with early onset of obesity [203]. Perhaps today we are beginning to see that with the marked increase in overfeeding and extent of obesity at younger ages, hypertrophy of fat cells may occur earlier and hence metabolic syndrome is now occurring with greater frequency in children.
Puberty and VAT
During puberty, a certain degree of insulin resistance is normal, and children who are more insulin resistant have decreased SCAT fat gain [204]. Early in the development of juvenile obesity, increased VAT, hyperinsulinemia, and insulin resistance are closely linked [205]. Adrenal androgens are elevated in obese children and have been associated with early pubertal development in these children[206,207] Sex differences in VAT begin to emerge during puberty, with boys having more VAT than girls. Some studies suggest that the rate of VAT accumulation can be slowed in children by using exercise interventions [208,209].
Fit and fat
VAT is strongly associated with fitness even within individuals of the same weight. This is illustrated by the earlier mentioned example of the active Sumo wrestler in his prime who has relatively little VAT [91]. Regular exercise can selectively reduce VAT with minimal change in weight [210-212]. This could especially add to the frustration level of the middle-aged or post-menopausal woman who regularly exercises moderately without inducing measurable reduction in body weight or fatness. She may still benefit from reducing her VAT or attenuating the gain of VAT "normally" experienced by sedentary women as they age. It should be emphasized that the lower VAT level associated with increased fitness is modest but nonetheless clinically important. Reduced morbidity is likely explained by factors in addition to a reduced VAT, and VAT likely explains morbidity independent of fitness [213]. Sumo wrestlers tend to have most of their central adiposity stored subcutaneously (as SCAT), and, perhaps a shift toward more VAT accompanies their contracting metabolic syndrome upon their retirement – with premature death to follow [26,91]. This may also explain the body of work showing that overweight or "fat" individuals who are fit (according to cardiorespiratory testing on a treadmill) are at less risk for a cardiac event or developing type 2 diabetes than a "leaner" individual who is unfit [214,215]. Thus, the former could be considered "fit and fat." High levels of cardiorespiratory fitness (CRF) reduce CRP and the rate of cardiovascular morbidity and mortality, independent of obesity [216]. CRF is also associated with lower abdominal fat independent of BMI, and for a given BMI or waist circumference (WC), individuals with moderate CRF had lower levels of total fat mass and abdominal SCAT and VAT than individuals with low CRF for a given BMI or WC value [213,217]. Low CRF is an independent risk factor for mortality in healthy-appearing and diseased populations, and is associated with elevated CRP and reduced fasting glucose control in women with type 2 diabetes [218]. It is likely that compared to the fit and fat, the unfit and lean-appearing individual may have greater amounts of "hidden" VAT.
Effects of exercise
In obese patients, increasing physical activity can enhance fat oxidation, reduce IMCL and improve insulin sensitivity [219]. Exercise training may reduce waist size, independent of changes in BMI, and exercise without weight loss is effective in reducing VAT and preventing further increases in obesity [213,220].
Ross et al showed that either modality, caloric restriction alone or daily exercise without calorie restriction, is an effective strategy for reducing obesity in moderately obese men.
Their findings also suggest that exercise without weight loss is a useful method for reducing VAT and preventing further increases in obesity [220]. Irwin et al studied 168 overweight, postmenopausal, previously sedentary women in a randomly controlled trial of exercise versus no exercise. While the body weight lost at 12 months among the exercisers was modest, the amount of intra-abdominal fat lost was considerable (8.5 g/ cm2) and was dose-dependent. The women who exercised for approximately 200 min/wk lost 4.2 percent of total body fat and 6.9 percent of VAT without reducing their energy intake [212]. Exercise may counteract the abnormal metabolic profiles associated with abdominal obesity by reducing VAT along with other independent mechanisms. It promotes adaptive responses including those causing muscles to increase their use of lipid stores rather than relying primarily on carbohydrate reserves. Even a single bout of exercise can reduce triglyceride levels, increase HDL levels, reduce resting blood pressure, increase glucose tolerance, and reduce insulin resistance [221].
While evidence supports that CRF may be associated with a lower VAT, this is certainly not proven. However, study results suggest that individuals with moderate to high CRF levels have lower WC than men with low CRF independent of BMI [213,222]. Data support that the substantial reductions in health risk often associated with modest weight loss (<10 percent) may be mediated in part by a preferential reduction in VAT [48,216,217,220,223]. This is reinforced by the finding that reductions in VAT alone were related to improvements in glucose tolerance and insulin sensitivity [220,224]. Therefore, it would seem reasonable to infer that the combination of high CRF and low abdominal fat, especially VAT, would be associated with reductions in metabolic risk compared with those with the same BMI, but low CRF and high VAT [213]. Adding resistance training to aerobic exercise may add to an improvement in insulin sensitivity related to a loss of VAT and an increase in muscle density [225,226].
Surgical Interventions Shed Light on Pathophysiology
Surgical removal of VAT in animals and humans dramatically improves insulin resistance and diabetes. In a Swedish, single-center, randomized and controlled pilot trial of 50 severely obese adults, Thorne et al compared 25 patients who underwent adjustable gastric banding (AGB) alone with AGB plus surgical removal of the total greater omentum. At two-year follow-up there were no statistical differences between groups with regard to weight loss, changes in WHR or sagittal diameter. However, the improvements in oral glucose tolerance insulin sensitivity and fasting plasma glucose and insulin were 2–3 times greater in omentectomized as compared to control subjects, which was statistically independent of the loss in BMI [52]. More recently, this has led to a study of another experimental procedure performed by surgeons at Boston's Beth Israel Deaconess Medical Center working in conjunction with Joslin Diabetes Center. Using a two-hour laparoscopic procedure that involves extracting strips of only the omentum through tiny incisions, this will be the first study to examine the possible health benefits of removing only the omentum [227].
Recently Klein et al. demonstrated that liposuction conferred no benefits with regard to metabolic profile [53]. Furthermore, Weber et al showed that after 3 months, animals that had lipectomy of > 50 percent of SCAT had more intra-abdominal VAT as percentage of total body fat, higher insulinemic index, a strong trend toward increased liver fat content, and markedly elevated serum TGs compared with animals that had undergone a sham operation and received either a high- or low-fat diet [228]. Together with the findings above, these support a pathologic role for VAT and a possible protective role for SCAT. Removing SCAT might actually increase risk as one removes a buffer or sink for peripheral TGs [228].
Environmental Considerations
Organochlorines, adipose tissue, and energy balance
Since our genes have not changed significantly in the past 10,000 years, the rise in obesity can be attributed to the environment, including what we are exposed to in the way of food as well as the level of physical activity. While the main focus has been on diet and activity, what may be overlooked is the tremendous increase in exposure to synthetic organic and inorganic chemicals, which can damage many of the mechanisms involved in weight control. Most of us have been exposed to organochlorines found in pesticides, dyes, solvents, etc., and we contain residues in our adipose tissue, where they are preferentially stored. Thus, the obese tend to have increased organochlorine concentrations compared to lean individuals [229]. During body weight loss, a decrease in fat mass results in lipid mobilization, and organochlorine concentrations increase both in plasma and remaining adipose tissue. Even after adjustment for weight loss, the related increase in organochlorine concentration has been correlated with decreases in triidothyronine (T3) concentration and resting metabolic rate [230]. This is also associated with a reduction in activity of the skeletal muscle oxidative enzymes that normally are involved in fat oxidation [231]. The net effect could prevent further weight gain and might even encourage weight regain beyond the initial baseline [232], which could contribute to VAT.
Implications of Controlling Dietary Carbohydrates
Reduced fat oxidation and carbohydrates
Frisancho points out that an important contributing factor for obesity in modern as well as developing nations is a reduced fat oxidation and increased metabolism of carbohydrate. This has been brought about by a shift toward the body's preference toward oxidizing carbohydrate rather than fat – resulting in an increased deposition of body fat. In developing nations, obesity can co-exist with developmental undernutrition, which can result in obesity with short stature [233].
A solution to reducing the ectopic fat, as well as VAT, burden would be to enhance its oxidation in nonadipose tissues, e.g., liver, pancreas, and skeletal muscle. This will push the system toward below the CVATT and improve insulin sensitivity. In their review, Westman et al cite many studies that have consistently shown that low-carbohydrate/high-fat diets consumed for more than seven days induce powerful metabolic adaptations to enhance fat oxidation [37]. Such diets will reduce muscle glycogen content and carbohydrate oxidation, even in well-trained athletes who already demonstrate increased oxidation [37,154]. The authors' paradigm suggests that, under these conditions, insulin resistance could improve by reducing glucose appearance and cellular influx, resulting in a preferential fat oxidation and protection against lipotoxicity. In an elegant study, Bisschop et al support this by showing that high-fat, low-carbohydrate diets do not affect the action of insulin on total glucose disposal but decrease basal endogenous glucose production and improve insulin-stimulated nonoxidative glucose disposal [234]. Sharman et al demonstrated short term improvements of a ketogenic diet on lipids in normal weight men. These benefits occurred without total weight loss but there was evidence of a change in body composition toward more lean body mass [235]. One would also expect a reduction in VAT as he moves to the left or below his CVATT (See figure 1). Weight loss does not appear to be necessary to reduce mortality rates in overweight or obese men who increase their aerobic fitness or level of physical activity [224]. Similarly, in overweight, postmenopausal women, exercise may lead to improved metabolic profiles and VAT loss without total weight loss [212].
Dietary carbohydrate and VAT
Optimizing macronutrients and food preparation can have beneficial effects in those with visceral obesity. A number of recent reviews support the metabolic benefits of controlling glycemic index (GI) [236] and glycemic load (GL) [237]. In a 12-month pilot study of teens, compared to a conventional diet, a lower GI diet led to greater total weight and fat loss without regain from months 6–12. While insulin resistance as measured by HOMA increased in the conventional group (possibly in part attributable to puberty), the lower GI group showed no change [238]. Recently, Silvestre et al showed that compared to an energy-restricted low-fat diet, a short-term, very low-carbohydrate diet was associated with greater weight and fat loss with an apparent preferential loss of central fat [239].
VAT cells have a two-fold higher glucose uptake rate compared with SCAT cells [87]. It may follow that reducing glucose exposure by reducing glycemic load may reduce the supply of glucose to the VAT depot and possibly impair its accumulation. Glucose raises insulin concentration, which can stimulate 11-β-HSD1, increase active cortisol in VAT, and enhance VAT accumulation [102]. Feeding rats a high-GI starch diet over five weeks resulted in higher VAT and larger adipocyte volume than did feeding low-GI starch ad libitum. Replacing this with a low-GI starch diet increased insulin -stimulated glucose oxidation, decreased glucose incorporation into total lipids and decreased VAT adipocyte diameter [240,241]. Together, these add to the evidence supporting the benefits of lowering GI to reduce and maintain lower volumes of VAT. Feeding rats a high sucrose diet increases both VAT and muscle insulin resistance [242]. Keno et al. demonstrated in rats that a high sucrose diet compared to a lab chow diet led to a significantly greater fat cell volume in VAT depots [243]. Although fat cell number did not change, the ratio of VAT weight to SCAT weight was also significantly increased in the rats fed a high sucrose diet, providing further evidence for controlling the dietary GI and GL.
A number of studies have demonstrated an association between glycemic load (GL) and levels of CRP [244,245], which is a powerful predictor for diabetes and CHD, and is positively associated with both insulin resistance and the prevalence of the metabolic syndrome [246]. O'Brien et al showed that compared to a high carbohydrate diet, a low carbohydrate diet reduced SAA and CRP, both markers of inflammation and risk factors for metabolic syndrome [247]. Relative to fat (cream) and protein (casein), a glucose challenge elicits the greatest production of radical oxygen species (ROS) by polymorphonuclear and mononuclear white cells [248,249]. Chronic carbohydrate ingestion with a high GL diet can lead to hyperinsulinemia, as well as hypertrophy, functional dysregulation, and overresponsiveness of the pancreatic β cell and hepatic production of newly synthesized fatty acids via de novo lipogenesis [43]. A Johns Hopkins study examined intra-operative liver biopsies obtained from 74 consecutive morbidly obese patients undergoing bariatric surgery. Compared with patients with the lowest carbohydrate intake [246], a high-carbohydrate diet was associated with an odds ratio of 7.0 for liver inflammation. A high fat diet appeared to be protective, with those in the highest fat intake group having an OR of 0.17 [250]. This is consistent with the findings of others who found that dietary fat explained only two percent of the variance in general adiposity and dietary fat appears to play only a minor role in determining general adiposity and is not related to VAT when measured in cross-sectional studies [251]. Apparently, GL may be more significant in this regard.
Compared to SCAT, VAT (both adipose and non-adipose cells within VAT) is associated more with PAI-1 – a powerful risk factor for CHD [58,252]. In patients with type 2 diabetes, a simple and modest lowering of the GI compared to an otherwise similar diet led to dramatic changes: a normalized PAI-1 activity (-54 percent, P < 0.001) as well as lowering of both blood glucose and plasma insulin concentrations by 30 percent, and a 29 percent decrease in LDL-C [253]. All subjects began with a BMI < 27, and there was only a slight but similar weight loss in both groups over the 24 days. The results support the potential benefit of lowering dietary GI in patients with metabolic syndrome, especially those with VAT and elevated PAI-1. This is also supported by the observation of hyperglycemia induces PAI-1 gene expression in adipose tissue of rats [254].
Esposito et al demonstrated in both diabetics and non-diabetics that after consuming a high carbohydrate high-fiber meal, IL-18 (a potent pro-inflammatory cytokine) concentrations increased [49]. Adiponectin concentrations decreased after the high-carbohydrate, low-fiber meal in diabetic patients. The fiber content of complex carbohydrates seemed to affect circulating IL-18 and adiponectin concentrations in response to the same carbohydrate load. The pizza that was made with whole flour and was rich in fiber was associated with reduce serum IL-18 concentrations and unchanged serum adiponectin concentrations. Meanwhile, the pizza prepared with refined flour and was low in fiber raised circulating IL-18 concentrations. Serum glucose and TG concentrations were not significantly different between the two types of pizza. The study did not completely resolve the mechanism by which the fiber content of meals influences IL-18 and adiponectin. However, it appears that while the GL of each pizza was the same, the GI of the whole wheat pizza would be much less and may be more beneficial.
Recently, dietary TGs have been demonstrated to contribute to CNS leptin resistance by impairing the transport of leptin across the blood brain barrier where it would usually stimulate the release of neuropeptide-Y and reduce feeding behavior [167]. Reducing dietary carbohydrates lowers serum TGs, which theoretically should protect against this form of leptin resistance [167].
Dietary influences on leptin action
Leptin may enhance fatty acid oxidation and protects against fat deposition and lipotoxicity. As mentioned earlier, normally, rats can tolerate a 60 percent fat diet because 96 percent of the surplus fat is stored in an enlarging adipose tissue mass, in which leptin gene expression increases proportionally [166]. However, when leptin is congenitally absent or inactive, or ineffective due to resistance, even on a normal or low-fat diet, unutilized dietary fat is deposited in nonadipose tissues, causing dysfunction (lipotoxicity), and possible cell death (lipoaptosis) [29].
Acute overfeeding can cause circulating leptin levels to rise by 40 percent and more than three-fold after chronic overfeeding, whereas fasting is associated with a decreased leptin levels. This may suggest that overfeeding leads to leptin resistance. Dietary carbohydrates may influence leptin action. Some investigators have suggested that the increase in plasma leptin concentration observed after meals is not simply a result of an energy load but is in response to a signal that is not present following a fat load without carbohydrate [157]. SCAT-derived leptin (which circulates in a free form and is bound to a soluble leptin receptor – sOB-R) plays a key role in regulating energy homeostasis and metabolism, sOB-R is positively associated with energy intake from carbohydrates and negatively associated with energy intake from dietary fat [158]. While this suggests that dietary fat and carbohydrates regulate free leptin levels, the implications of this are not yet completely clear.
Stress
There is an association with lifestyle, worry, cortisol levels, and abdominal girth. Those who were found to have the highest levels of chronic stress had the highest levels of cortisol and VAT [255-257]. This is supported by evidence that a number of medications, including prednisone, may cause an excess of cortisol and insulin resistance. Taken orally, cortisol raises blood pressure, and it has been shown to impair brachial artery blood flow in response to an acetylcholine challenge, i.e., an indicator of endothelial dysfunction [88,255,257-262]. Even brief episodes of mental stress, such as those encountered in daily life, may cause transient endothelial dysfunction even in young, healthy individuals ([263,264]. In turn, subsequent cytokine release may increase anxiety and have negative effects on emotional and memory functions [265]. Psychological stress has also been demonstrated to acutely reduce clearance of triglycerides [266], which could contribute to CNS leptin resistance [167].
There are many other ways in which psychological stress might increase the likelihood of developing metabolic syndrome and type 2 diabetes, for example, chronic psychological stress may also be related to central activation of the HPA (hypothalamo-pituitary-adrenal) axis and the sympathetic nervous system (SNS) [267]. Psychological stress also induces IL-6, TNFα, and other cytokine secretions from macrophages [267-271]. Repeated stress with the repeated induction of corticosteroids can damage the hippocampus, which is involved in the downregulation of corticosteroid production by corticosteroid feedback. Impairment of this feedback mechanism can lead to persisting elevated circulating cortisol levels [267], which might play a role in inducing VAT accumulation.
Stress decreases splanchnic blood flow, impairs the integrity of the GI tract, increases intestinal permeability, and results in increased absorption of lipopolysaccharide endotoxin (LPS) from the gut (the greatest source of LPS). Elevated portal bloodstream LPS levels stimulate Kupffer cell receptors and cytokine release and possibly other immune-challenging activators, e.g., AGEs in food [271].
Stress and dietary carbohydrates
Dietary carbohydrate has been known to stimulate SNS activity though a number of studies have emphasized the role of insulin. Recent studies in rats have demonstrated that adding glucose to the basic diet increased SNS activity in peripheral tissues and increased GLUT 4 activity in interscapular brown adipose tissue and retroperitoneal fat (but not in epididymal fat) [272]. Overfeeding results in high insulin levels. In the presence of glucose, insulin acts on the brain to increase the SNS tone, which, in turn enhances thermogenesis and dissipation of excess calories [163]. There is a close relationship between postprandial insulinemia, SNS activation, and adipose tissue blood flow (ATBF). ATBF increases in response to stress states such as exercise or mental stress, and also in response to nutrient intake [273]. High insulin levels and increased SNS tone are useful for the maintenance of caloric balance, but in the long term they are conducive to CHD, hypertension, sudden death, and obesity as the SNS receptors become down regulated [163].
Chronic stress leads to elevated cortisol levels, which may lead to accumulation of VAT and metabolic syndrome [274]. Stress-induced increased levels of glucocorticoids can also have a major effect on food intake [275]. A subset of stressed or depressed humans may overeat, especially comfort food (e.g., sugar and fat), in an attempt to reduce anxiety and activity in the chronic stress-response network. This is supported by the finding that these people have decreased cerebrospinal corticosteroid releasing factor, catecholamine concentrations, and HPA activity. While comfort foods may calm them down in the short term, they may lead to abdominal obesity if this becomes a long term "solution." The chronic elevation of systemic glucocorticoids may contribute to VAT deposition. By itself, being obese may be a stressful stimulus to overeating. A weight loss program can be stressful, which can sabotage its success by eliciting the release of stress hormones, which, in turn can make a person crave high energy foods [275]. Feeding rats a long-term high-sucrose diet along with supplemental dexamethasone has been shown to increase fat depots and induce liver steatosis [276]. In addition to dietary intervention, stress management may improve one's cognitive, behavioral, and physiologic responses to stress, including glycemia [277].
Summary
The role of visceral adipose tissue (VAT) obesity in metabolic syndrome is critical and complex. (See figure 3). The paradigm of an individual critical VAT threshold (CVATT) has been presented along with a review of potential mechanisms and contributing factors. This includes the potential role of dietary carbohydrates in VAT obesity. As this area continues to evolve, perhaps the reviewed material and proposed concepts may have relevance to clinical assessment, prevention, and treatment of metabolic syndrome.
Figure 3 This diagram provides an overview of how behavioral and environmental factors can lead to VAT obesity and, ultimately, metabolic syndrome and related disorders.
Declaration of Competing Interests
The author(s) declare that they have no competing interests.
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| 15530168 | PMC535537 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Nov 5; 1:12 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-12 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-401556337410.1186/1479-5876-2-40ResearchVaccination with EphA2-derived T cell-epitopes promotes immunity against both EphA2-expressing and EphA2-negative tumors Hatano Manabu [email protected] Naruo [email protected] Tomohide [email protected] Jill E [email protected] Fumihiko [email protected] Karlyne M [email protected] Walter J [email protected] Hideho [email protected] Department of Neurological Surgery, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA2 Department of Surgery, University of Pittsburgh School of Medicine, 200 Lothrop Street, Pittsburgh, PA, 15213, USA3 Brain Tumor Program, University of Pittsburgh Cancer Institute, 5117 Centre Avenue, Pittsburgh, PA, 15213, USA4 Genetic Modifiers of Tumorigenesis Section, Mouse Cancer Genetics Program, National Cancer Institute at Frederick, West 7th Street at Fort Detrick, PO Box B, Building 560, Room 32-31B, Frederick, MD 21702-1201, USA2004 24 11 2004 2 40 40 11 10 2004 24 11 2004 Copyright © 2004 Hatano et al; licensee BioMed Central Ltd.2004Hatano 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
A novel tyrosine kinase receptor EphA2 is expressed at high levels in advanced and metastatic cancers. We examined whether vaccinations with synthetic mouse EphA2 (mEphA2)-derived peptides that serve as T cell epitopes could induce protective and therapeutic anti-tumor immunity.
Methods
C57BL/6 mice received subcutaneous (s.c.) vaccinations with bone marrow-derived dendritic cells (DCs) pulsed with synthetic peptides recognized by CD8+ (mEphA2671–679, mEphA2682–689) and CD4+ (mEphA230–44) T cells. Splenocytes (SPCs) were harvested from primed mice to assess the induction of cytotoxic T lymphocyte (CTL) responses against syngeneic glioma, sarcoma and melanoma cell lines. The ability of these vaccines to prevent or treat tumor (s.c. injected MCA205 sarcoma or B16 melanoma; i.v. injected B16-BL6) establishment/progression was then assessed.
Results
Immunization of C57BL/6 mice with mEphA2-derived peptides induced specific CTL responses in SPCs. Vaccination with mEPhA2 peptides, but not control ovalbumin (OVA) peptides, prevented the establishment or prevented the growth of EphA2+ or EphA2-negative syngeneic tumors in both s.c. and lung metastasis models.
Conclusions
These data indicate that mEphA2 can serve as an attractive target against which to direct anti-tumor immunity. The ability of mEphA2 vaccines to impact EphA2-negative tumors such as the B16 melanoma may suggest that such beneficial immunity may be directed against alternative EphA2+ target cells, such as the tumor-associated vascular endothelial cells.
EphA2dendritic cellscancer vaccinesmelanoma
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Background
EphA2 is a member of Eph family of receptor tyrosine kinases comprised of two major classes (EphA and EphB), which are distinguished by their specificities for ligand (ephrin-A and ephrin-B, respectively [1-3]). Recent reports suggest that EphA2 is frequently overexpressed and often functionally dysregulated in advanced cancers, where it contributes to multiple aspects of malignant character. These changes in EphA2 have been observed in a wide array of solid tumors, including melanoma [4,5] and prostate [6], breast [7] and lung [8] carcinomas. Indeed, the highest degree of EphA2 expression among tumors is most commonly observed in metastatic lesions [6,9].
These data suggest that EphA2 may serve as an attractive target for cancer vaccines. In this regard, we have identified five human leukocyte antigen (HLA)-A2 binding and three HLA-DR4-binding peptides derived from EphA2 that are capable of inducing specific, tumor-reactive CD8+ or CD4+ T-cell responses, respectively [10]. A more recent report has identified two additional HLA-A2 restricted T-cell epitopes encoded by EphA2 [11].
These observations and findings support our rationale for near future implementation of EphA2-targeted vaccine clinical trials. For pre-clinical evaluation of EphA2-targeted vaccines, however, there is little information on immune responses against EphA2 in mouse models. Therefore, we hypothesized that identification of mouse T-cell epitopes in mEphA2 would allow us to determine the effect of EphA2-targeted vaccinations in mice bearing tumors. In this study, we examined whether novel T-cell epitope peptides identified in the mEphA2 protein sequence could elicit protective and therapeutic anti-tumor immune responses in murine models. Our results indicate that DC-based vaccines incorporating these peptides elicit effective CTL responses that can inhibit the growth both EphA2+ and EphA2-deficient tumors.
Materials and methods
Animals
Female 6–8-week-old C57BL/6 mice were purchased from The Jackson Laboratory (Bar Harbor, ME). Animals were handled under aseptic conditions in microisolator cages within the Central Animal Facility at the University of Pittsburgh per an Institutional Animal Care and Use Committee-approved protocol, and in accordance with recommendations for the proper care and use of laboratory animals.
Cell Lines and Culture
Glioma cell lines KR129, KR130, KR233 and KR158D were derived from spontaneously arising gliomas in F1 between B6 × CBA (KR129), B6 × SJL (KR130), and C57BL/6 (KR233 and KR158D)-background NPcis mice that express mutations in two tumor-suppressor genes, Nf1 and Trp53 [12]. B16 melanoma, NPcis-derived glioma cells, GL261 glioma and MCA205 sarcoma (H-2b) cell lines were cultured in complete medium (CM) [RPMI 1640 supplemented with 10% heat-inactivated fetal bovine serum, 100 units/ml penicillin, 100 μg/ml streptomycin, and 10 mM L-glutamine (all reagents from Life Technologies, Inc., Grand Island, NY)] in a humidified incubator in 5% CO2 at 37°C.
Generation of DCs in Vitro from Bone Marrow
The procedure used to generate DCs has been previously described [13]. Briefly, C57BL/6 bone marrow cells were cultured in CM supplemented with 1000 units/ml recombinant mouse granulocyte/macrophage colony-stimulating factor and recombinant mouse interleukin-4 (Schering-Plough, Kenilworth, NJ) at 37°C in a humidified, 5% CO2 incubator for 7 days. DCs were then isolated at the interface of 14.5% (w/v) metrizamide (Sigma, St. Louis, MO) in CM discontinuous gradients by centrifugation. DCs typically represented >90% of the harvested population of cells based on morphology and expression of the CD11b, CD11c, CD40, CD54, CD80, CD86, and class I and class II MHC antigens as assessed using flow cytometry (data not shown).
Peptides and immunization
The protein sequences of mEphA2 was obtained from GenBank and analyzed for H-2Kb-, H-2Db-, and I-Ab-binding binding motifs using BIMAS, and a proteosomal cleavage site prediction system [14], respectively. Peptide sequences that were given high binding scores and predicted proteosomal cleavage sites at the ends of the sequences were chosen [15]. The H-2Db-binding mEphA2671–679 (FSHHNIIRL), H-2Kb-binding mEphA2682–689 (VVSKYKPM), and I-Ab-binding mEphA230–44 (LLDFAAMKGELGWLT) epitopes were synthesized using an automated solid-phase peptide synthesizer (Applied Biosystems, Foster City, CA) by the protein synthesis facility at the University of Pittsburgh Cancer Institute, purified (to greater than 95%) by reverse phase HPLC, and characterized for amino acid sequences by mass spectrometry.
Day 7 DCs were pulsed with 10 μM each of the indicated peptides for 4 hours at 37°C, as previously described [13]. Cells were then washed twice with Hank's balanced salt solution (HBSS), with animals receiving injections of the indicated numbers of peptide-pulsed DCs in 0.1 ml HBSS s.c.
Tumor challenge
In the s.c. model, animals were injected with the indicated numbers of tumor cells in the right flank. Anti-tumor responses were assessed based on comparative longitudinal measurements of tumor area. In the lung metastasis model, animals were injected i.v. with 2 × 105 B16-BL6 tumor cells on day 0, and they subsequently received s.c. vaccinations of peptide-loaded DCs on days 3, 10 and 17. Animals were sacrificed on day 28 post-tumor injection and analyzed for the assessment of pulmonary metastases by enumerating the number of surface tumor-nodules.
Western Blotting
Protein lysates isolated from normal mouse brain, spleen, liver, lung, heart, skeletal muscle, and mouse tumor lines were separated by SDS-PAGE, blotted onto nitrocellulose and analyzed for expression of mEphA2 using EphA2 monoclonal antibody (C-20 Ab; Santa Cruz Biotechnology, Inc., Santa Cruz, CA). Blots were imaged on Kodak X-Omat Blue XB-1 film (NEN Life Science Products, Boston, MA) after using horseradish peroxidase (HRP)-conjugated goat anti-rabbit Ig (Biorad, Hercules, CA) and the Western Lighting™ chemiluminescence detection kit (Perkin Elmer, Boston, MA).
CTL activity assay
Single cell suspensions of SPCs were cultured at 2 × 106 cells/ml with 2 μg/ml mEphA2671–679 or mEphA2682–689 in presence of 10 U/ml human IL-2 (Chiron, Emeryville, CA), 50 μM 2-mercaptoethanol (Sigma), and 50 μM NG mono-methyl-L-arginine (Cyclopss, Salt Lake City, UT) in 24 wells plates (Corning, Corning, NY) for 5 days. Specific CTL activity was determined in 4 h 51Cr release assays against the indicated target cells, as previously described [16].
Matrigel plug assay
Eight-week-old mice were injected s.c. twice (days -14 and -7) with DCs loaded with either EphA2-derived peptides (EphA2 671–679/30–44) or OVA-derived peptides (OVA 257–264/265–280). Each animal then received 400 μl of Matrigel (BD Biosciences) supplemented with 400 ng/ml vascular endothelial growth factor (VEGF) in the dorsal area [17]. The animals were sacrificed 10 days later, then the plugs were removed and photographed.
Statistical analysis
Comparative numbers of lung metastasis, growth of s.c. tumors and T cell responses were compared by Student's t test for two samples with unequal variances. Statistical significance was determined at p value <0.05.
Results
Expression of mEphA2 in murine tumor cells
We evaluated the expression of mEphA2 in H-2b-syngeneic murine tumor cell lines and normal organs from C57BL/6 mice by Western Blotting (Figure 1). MCA205 sarcoma, the NPcis mice-derived spontaneously developed glioma cell lines KR129, KR130, KR233 and KR158D (Figure 1A), as well as, the GL261 glioma (Figure 1B) expressed detectable but variable levels of mEphA2 protein. In marked contrast, none of melanoma lines tested, including B16 melanoma and its more metastatic sub-clones B16V1 and B16BL6, expressed detectable levels of mEphA2 (Figure 1A). The control β-actin staining on the same blots demonstrated that equal amount of protein was loaded in each lane. With regard to the normal organs examined, moderate levels of mEphA2 expression were identified in the spleen, liver and lung, whereas no detectable expression was observed in brain, heart and skeletal muscle (Figure 1B).
Figure 1 Expression of EphA2 in murine tumor cell lines and tumor/normal tissues. (A) Aliquots of protein lysates (10 μg/lane) were analyzed by Western Blotting for expression of mEphA2 protein using a specific monoclonal antibody (C-20 Ab; Santa Cruz Biotechnology, Inc., Santa Cruz, CA). Lysate samples were obtained from mouse tumor lines including the B16, B16V1 and B16BL6 melanomas and the KR129, KR130, KR233 and KR158D glioma cell lines derived from spontaneously developed glioma tissues in NPcis mice. Control β-actin labeling demonstrated equal amount of protein loading in each lane. (B) Protein lysates isolated from normal mouse brain, spleen, liver, lung, heart, skeletal muscle, and the G261 glioma and MCA 205 sarcoma cell lines were separated by SDS-PAGE, blotted onto nitrocellulose and analyzed for expression of mEphA2 using C-20 anti-EphA2 monoclonal antibody.
Vaccination of mice with synthetic mEphA2 T cell epitopes promotes specific CTL responses
We have previously characterized the immunogenicity of the H-2Db-binding mEphA2671–679, H-2Kb-binding mEphA2682–689, and I-Ab-binding mEphA230–44 epitopes based on delayed-type hypersensitivity responses in B6 mice (Storkus et al., unpublished results). We immunized syngeneic C57BL/6 mice twice s.c. with syngenic DCs loaded with the Kb-binding and the I-Ab-binding epitopes, or DCs loaded with the Db-binding and the I-Ab-binding epitopes on a weekly basis. Control animals received DCs loaded with the cancer-irrelevant Kb-binding OVA257–264 and the I-Ab-binding OVA265–280 epitopes [18]. One week after the secondary immunization, the animals were sacrificed, and SPCs were harvested. The SPCs were then stimulated in vitro with the MHC class I peptide that was used for in vivo immunization, in the presence of 10 U/ml hIL-2, for 7 days. SPCs from control animals immunized with OVA peptides were stimulated in vitro with the mEphA2671–79 to provide an index for a control, "primary" level of specific CTL induction. CTL activity against mEphA2-expressing, and H-2b-syngeneic MCA205 sarcoma, GL261 glioma and KR158D glioma cells were assessed by standard 51Cr-release assays (Figure 2). All three mEphA2+ cell lines demonstrated susceptibility to CTLs generated by the immunizations with Kb- or Db-binding mEphA2 derived epitopes. With 20 h – incubation time, the percent lysis by EphA2-induced CTLs increased remarkably in comparison to 4 h-incubation regimen, whereas the lysis by OVA-induced CTLs do not, suggesting that the specific lysis can be more appreciated with the prolonged incubation time (up to 20 h) for the CTLs raised against EphA2-epitopes. B16 melanoma and YAC cells, which were used as negative control target cells, displayed constantly less than 10% of specific lyses in all groups (data not shown). These data support the conclusion that these mEphA2-derived peptides may serve as effective in vivo immunogens for anti-tumor T cell activation.
Figure 2 Immunizations with mEphA2-derived peptides elicit anti-tumor CTLs. C57BL/6 mice received two cycles of s.c. immunization with 5 × 105 DCs loaded with mEphA2-derived peptides or control OVA-derived peptides. One week after the second immunization, the animals were sacrificed, and SPCs were re-stimulated in vitro with the indicated peptides in the presence of 20 IU/ml hIL-2 for 7 days. Standard 4-hr (upper panel) and 20-hr (lower panel) 51Cr-release assays were used to assess cytotoxicity of the responder SPCs against the EphA2+ MCA205, GL261 and KR158D tumor cell lines. Each value represents the average of triplicate determinations for each group. YAC cells were evaluated as non-specific target cells, while EphA2-negative B16 melanoma cells were examined as negative control target cells in all groups, with the lysis of each of these targets constantly less than 10% (data not shown).
mEphA2 peptide-pulsed DC-based vaccines effectively protect/treat s.c MCA205 sarcoma and B16 melanoma
We evaluated the efficacy of protective and therapeutic immunizations with mEphA2-derived peptides in s.c. tumor models. These included the mEphA2-positive MCA205 sarcoma and the mEphA2-negative B16 melanoma models. First, animals bearing established s.c. MCA205 tumors received therapeutic vaccinations with mEphA2-derived CTL (H-2Kb or H-Db presented) + Th epitope peptides on days 3 and 10 following tumor inoculation (5 animals/group). Control animals received DCs loaded with the OVA-derived CTL + Th epitopes. As displayed in Figure 3A, vaccination with either combination of mEphA2-derived CTL (H-2Kb or H-Db presented) + Th epitope peptides significantly inhibited the growth of tumors in comparison to control vaccinations with irrelevant (OVA)-derived T cell epitopes.
Figure 3 Immunizations with DCs loaded with Eph-A2 derived peptides inhibit the growth of both EphA2-positive and EphA2-negative tumors. (A) C57BL/6 mice received 1 × 105 MCA205 cells s.c. on day 0. These animals were then immunized with 1 × 106 DCs loaded with the indicated peptides on days 3, 10. Tumor growth was monitored for 28 days following the tumor inoculation (N = 5/group). P < 0.05 for both mEphA2671–679/30–44 and mEphA2682–689/30–44 treatments in comparison to OVA control for the tumor based on a two-tailed Student-t test (*). (B) C57BL/6 mice received s.c. pre-immunizations with 1 × 106 DCs loaded with either mEphA2671–679, mEphA2682–689, mEphA230–44 or control OVA257–264 on days -14 and -7 (N = 5 /group), followed by s.c. challenge with 5 × 104 B16 melanoma cells on day 0. On day 14, the size of tumors and the number of animals that had measurable tumors were assessed. P < 0.01 for EphA2671–679, EphA2682–689 and EphA230–44 treatments in comparison to OVA257–264 treated group (*).
We next examined whether vaccination with mEphA2-derived peptides could induce anti-tumor effects against mEphA2-negative tumors, expecting that this would not be the case. Since s.c. B16 melanomas grow aggressively, we employed a pre-immunization model to assess the anti-tumor response. Syngeneic C57BL/6 mice were pre-immunized with either mEphA2671–679, mEphA2682–679, or mEphA230–44 peptides on days -14 and -7 before s.c. injection with 5 × 104 B16 cells on day 0 (5 animals/group). The control group received irrelevant OVA257–264 instead of mEphA2-derived peptides. Tumor size and the number of animals that had measurable tumors are assessed on day 21 (Figure 3B). All control animals receiving OVA-immunization developed large tumors, whereas, surprisingly, 3 of 5 animals in each of mEphA2-derived peptide-treated groups rejected the tumor growth. Indeed, the average tumor size in each of mEphA2-immunized groups was significantly smaller than the control groups (P < 0.01). As B16 melanoma cells did not express mEphA2 protein in vitro (Figure 1A), these data suggest that vaccination with mEphA2-derived peptides may induce in vivo CTL activity not only directly against mEphA2 expressed in tumors, but also against mEphA2 expressed by other critical components of tumor-structure, such as tumor vascular endothelial cells. With regard to EphA2 expression in vivo, we attempted immunohistochemistry on s.c. B16 tumors to directly validate EphA2 expression. Although deposition of endogenous melanin in B16 tissues posed interference for the substrate-development, we did not observe EphA2-specific staining in s.c. B16 tumor tissues (data not shown).
EphA2 peptide-pulsed DC-based vaccines protect against/treat B16-BL6 lung metastasis
We next evaluated a lung metastasis model using B16-BL6 melanoma cell line. Syngeneic C57BL/6 mice received 2 × 105 B16-BL6 melanoma cells via tail vein injection. The animals subsequently received s.c. immunizations with 1 × 106 DCs loaded with mEphA2 CTL (H-2Kb or H-Db presented) + Th on days 3, 10 and 17 (5 animals/group). Control animals received DCs loaded with the OVA CTL + Th epitopes. On day 28, the animals were sacrificed, and the lungs harvested from each animal. The number of surface pulmonary metastases and lung weight were measured and compared between the groups. As demonstrated in Figure 4A and 4B, both of mEphA2-immunization regimens resulted in remarkable suppression of B16-BL6 lung metastases in comparison to the OVA-immunized control group in this early-stage treatment model.
Figure 4 Inhibition of B16-BL6 lung metastasis and VEGF-induced angiogenesis by s.c. immunizations with DCs loaded with EphA2-derived peptides. (A and B), C57BL/6 mice received i.v. injections of 2 × 105 B16-BL6 cells followed by s.c. immunizations with 1 × 106 DCs loaded with the indicated peptides on days 3, 10 and 17 (N = 4/group). The animals were sacrificed on day 28, with representative macroscopic pictures of the harvested lungs from each group (A) and the numbers of pulmonary surface metastases counted (B) P < 0.001 for both mEphA2671–679/30–44 (*) and mEphA2682–689/30–44 (**) treatments in comparison to OVA control for the number of metastasis based on two-tailed Student-t test. (C), mEphA2-targeted immunizations inhibited the vascular formation in the Matrigel plug assay. Syngeneic mice pre-immunized with control OVA- (left) or EphA2- (right) peptides were injected s.c. with Matrigel containing VEGF. After 10 days, the plugs were removed and photographed. The picture shows representative VEGF-containing plugs excised from total 5 mice/group.
VEGF has been implicated to be a major mediator of tumor-angiogenesis [19,20], and EphA2-blockade has been shown to inhibit VEGF-induced angiogenesis [21]. To examine whether immunizations with mEphA2-derived peptides inhibit VEGF-induced angiogenesis, Matrigel plugs containing VEGF were implanted in pre-immunized mice and recovered 10 days later. Macroscopic analysis revealed that the plugs from mice pre-immunized with mEphA2-derived peptides were much paler than those from control mice (Figure 4C). These results suggest that vaccinations with mEphA2-derived T cell epitopes may induce potent anti-tumor responses in mEphA2-deficient tumor models through inhibition of tumor angiogenesis.
Discussion
In this report, we have demonstrated that mEphA2-derived peptide epitopes can elicit specific CTL responses and anti-tumor response in vivo. Of major note, the anti-tumor response was not restricted to mEphA2-postive models, but was also observed in the case of both s.c. and metastatic B16 melanoma models.
While the melanoma cell lines were negative for EphA2 expression in vitro as assessed by Western Blotting, it was conceivable that they had become EphA2+ after in vivo injection, which could more easily explain the efficacy of the mEphA2 peptide-based vaccinations. However, we were unable to detect mEphA2 expression even in the in vivo grown B16 tumors (data not shown), suggesting that the T-cell immunity against mEphA2-derived epitopes suppress the in vivo tumor-growth not only through the direct anti-tumor cell mechanisms but also through indirect mechanisms, such as inhibition of tumor-angiogenesis.
The Eph kinases and ephrins play a crucial role in vascular development during embryogenesis and tumor formation [1]. In two independent tumor types, the RIP-Tag transgenic model of angiogenesis-dependent pancreatic islet cell carcinoma and the 4T1 metastatic mammary adenocarcinoma, Ephrin-A1 ligand is expressed in both tumor and endothelial cells, and EphA2 receptor was localized primarily in tumor-associated vascular endothelial cells [3]. Soluble EphA2-Fc or EphA3-Fc receptors inhibit angiogenesis and tumor formation in multiple models [3,22]. Further molecular analyses revealed that the EphA-ephrinA interaction is necessary for VEGF-induced angiogenesis [21,22]. These published observations suggest that immunological targeting of EphA2 may also inhibit tumor growth by suppression of tumor-neoangiogenesis. Immunization against angiogenic-associated antigens selectively expressed on tumor vasculature may provide for a novel strategy to block tumor growth. The feasibility of this approach has been borne out in reports demonstrating that the protein or DNA encoding angiogenic molecules, such as VEGF-R2, can be used as a vaccine to generate effective CTL and antibody responses against tumor vessels, thereby limiting tumor growth and metastasis [19,20]. In this regard, our results with Matrigel assays suggest that immunization of C57BL/6 mice with mEphA2-derived peptides may inhibit VEGF-induced angiogenesis. We are currently confirming these observations with quantitative analyses of vessel formations in the Matrigel plugs.
As expected, based on our Western Blotting, several normal organs examined expressed EphA2 including the spleen, liver and lung. This could provide concern for the potential clinical translation of EphA2-based vaccines, given concerns for autoimmune pathology that might occur in these anatomic sites. However, despite the effective induction of antigen-specific CTLs that are capable of mediating DTH-type reactivity (unpublished data) and anti-tumor responses in situ, our careful observation of EphA2-vaccinated animals at autopsy did not reveal any evidence for tissue infiltration by inflammatory leukocytes or the destruction of EphA2+ tissues.
In our assessment of CTL and anti-tumor response, in most cases, we employed both MHC class I and II associated peptides in our treatment regimens based on our previous study demonstrating that the combination of MHC class I and II-restricted OVA-immuno-epitopes induced higher levels of anti-tumor efficacy than MHC class I-restricted OVA-epitopes solely [18]. With regard to the immunogenicity of each mEphA2-derived peptide, our data with preimmunizations against s.c. B16 challenge indicated that each of mEphA2 671–679, mEphA2 682–689 and mEphA2 30–44 could induce anti-tumor response. It was noteworthy that mEphA2 30–44, which was found to bind I-Ab and induce DTH and specific CD4+ T cell responses, also suppressed the growth of s.c. (MHC class II-deficient) B16 melanomas as a single agent. This may suggest that neovessels in the B16 microenvironment are MHC class II+ either constitutively or under inflammatory conditions under which IFN-γ is elaborated, that B16 melanomas become class II+ after in vivo transfer or upon provision of IFN-γ in situ, or that the mEphA2 30–44 Th peptide also contains an embedded CTL epitope. We are currently assessing each of these possibilities.
It is also intriguing to determine whether there is a correlation between expression levels of mEphA2 in the target cells and their susceptibility to CTLs raised against EphA2-derived peptides. Our preliminary data indicated EphA2 expression levels did not simply correlate with the CTL susceptibility, suggesting other factors, such as MHC class I expression and intracellular anti-apoptotic proteins, may contribute in the CTL-mediated cytotoxicity (data not shown). To address this issue, our ongoing studies with human glioma cells employ small inhibitory RNA for EphA2 to determine whether specific inhibition of EphA2-expression affects the CTL response.
Conclusions
Although the precise mechanisms that underlie the anti-tumor efficacy of EphA2-based vaccines remain to be elucidated, our results demonstrate that the mEphA2-derived epitopes may represent important vaccine candidates for the development of clinical trials for the treatment of (both EphA2+ and EphA2-negative) cancers.
List of abbreviations
s.c., subcutaneous; i.v., intravenous; CTL, cytotoxic T lymphocyte; Th, T helper; DCs, dendritic cells; APCs, antigen-presenting cells; OVA, ovalbumin; CM, complete medium; HBSS, Hank's balanced salt solution; PBS, phosphate buffered saline; SPCs, splenocytes; HLA, human leukocyte antigen; ELISPOT, enzyme-linked immuno-spot; DTH, delayed-type hypersensitivity; VEGF, vascular endothelial cell growth factor
Disclosure of competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NK carried out the Western Blotting and immuno-assays. NK, JED, TT, MH and FH carried out assessment of in vivo anti-tumor response and preparation of peptide-pulsed DCs. WJS participated in peptide identification, the design of the study and critical review of data and the manuscript. HO conceived of the study, and participated in its design and coordination. All authors have read and approved the final manuscript.
Acknowledgements
This work was supported by P01 NS40923 [H.O.] and a grant from the Copeland Fund of the Pittsburgh Foundation [H.O.].
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| 15563374 | PMC535538 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Nov 24; 2:40 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-40 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-91555017210.1186/1743-422X-1-9Short ReportThe Israeli strain IS-98-ST1 of West Nile virus as viral model for West Nile encephalitis in the Old World Lucas Marianne [email protected] Marie-Pascale [email protected] Tomoji [email protected]énet Jean-Louis [email protected] Vincent [email protected]ès Philippe [email protected] Pierre-Emmanuel [email protected] Unité des Interactions Moléculaires Flavivirus-Hôtes, Institut Pasteur, Paris, France2 Unité de Génétique des Mammifères, Institut Pasteur, Paris, France3 Institute of Laboratory Animals, Kyoto University Graduate School of Medicine, Kyoto, Japan4 Unité de Biologie des Infections Virales Emergentes, Institut Pasteur, Lyon, France5 Institut Pasteur of Shangai, Shangai, P.R. China6 Département de Virologie, Institut Pasteur, Paris, France7 Unité Epidémiologie et Physiopathologie des Virus Oncogènes, Institut Pasteur, Paris, France2004 18 11 2004 1 9 9 8 10 2004 18 11 2004 Copyright © 2004 Lucas et al; licensee BioMed Central Ltd.2004Lucas 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.
West Nile virus (WNV) recently became a major public health concern in North America, the Middle East, and Europe. In contrast with the investigations of the North-American isolates, the neurovirulence properties of Middle-Eastern strains of WNV have not been extensively characterized. Israeli WNV strain IS-98-ST1 that has been isolated from a white stork in 1998, was found to be highly neuroinvasive in adult C57BL/6 mice. Strain IS-98-ST1 infects primary neuronal cells from mouse cortex, causing neuronal death. These results demonstrate that Israeli strain IS-98-ST1 provides a suitable viral model for WNV-induced disease associated with recent WNV outbreaks in the Old World.
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West Nile virus (WNV) is a single-stranded RNA flavivirus (family Flaviviridae, genus flavivirus) with a worldwide distribution ranging Africa, Europe, the Middle East, and Asia. WNV was first recognized in the Western Hemisphere in 1999. The emergence of WNV has been associated with a dramatic increase in severity of disease in humans and other species[1,2]. Recent WNV epidemics which include meningitis, encephalitis and poliomyelitis-like syndrome in humans have been reported in Europe, the Middle-East and in North America. During the summers of 2002 and 2003, more of 13,000 human cases and 500 deaths were reported from the United States, drawing the attention of WNV illness as an important public health concern.
Comparison of WNV strains identified two major genetic subtypes: the lineage II (enzootic strains from tropical Africa and Madagascar island) and the lineage I (tropical african strains) that caused the outbreaks of WNV infection in North Africa, Europe, Israel, and in the United States. Nucleotide sequencing revealed that American strains of WNV isolated between 1999 and 2000 are nearly identical to Israeli strains of WNV isolated in 1998 and 2000 [3,4]. This close relationship could be explained by the fact that an Israeli WNV strain was introduced in New York City in 1999 [4].
The murine model of WNV-associated encephalitis has been widely used to address the viral pathogenesis[5]. Strains of WNV isolated in the United States were found to be highly neuroinvasive in adult mice following intraperitoneal (i.p.) inoculation[6]. In contrast of the investigations of the North-American WNV strains, the virulence phenotype of Israeli strains of WNV has not been extensively characterized. The WNV strain IS-98-ST1 has been isolated from cerebellum of a white stork during an outbreak in Israel in 1998[7]; its phenotypic characterization was performed after 3 passages in the mosquito cell line Aedes pseudoscutellaris AP61[8] and its complete genomic sequence determined (GenBank accession number AF481864). Virus titration was performed on AP61 cells by focus immunodetection assay as previously described [9]. Infectivity titers were expressed as focus forming units (FFU).
In this study, we demonstrated that IS-98-ST1 has a high neuroinvasive potential in adult C57Bl/6 mice, and that the virus is capable to replicate in primary neuronal cultures from mouse brain cortex.
Mouse experiments were performed according to the European Convention 2001–486. After anesthesia, six-week-old female C57BL/6 mice (Harlan, France) were inoculated with 1,000 FFU of WNV via different routes (15 animals per group): intraperitoneal (i.p.), intradermal (i.d.), intracerebral (i.c.), and intranasal (i.n.). At Days 5 and 7 of infection, three animals per group were euthanasied; brain and spinal cord were rapidly removed, processed for viral titration or sectioned on cryostat (Jung Frigocut; 14 μm thick sections). Sections were fixed with 3.7% formaldehyde or acetone for 30 min and processed for indirect immunofluorescence with mouse polyclonal anti-WNV antibodies[8]. Some sections were also processed for Glial Fibrillary Acidic Protein (GFAP) using a rabbit polyclonal antibody (Promega). Sections were further washed, mounted and observed with a fluorescence microscope (DMRB Leica).
When infected i.c., mice died at day 7.3 ± 1 post-infection (p.i.) ; 100% mortality was also reached after i.p., i.n., or i.d. inoculation but with delayed kinetics (day 9.5 ± 0.5, 10.7 ± 0.7 and 10.5 ± 0.5 p.i. respectively). In all cases, WNV-infected mice exhibited characteristic disease progression with hind limb paralysis, cachexia and tremors. By day 7 p.i., WNV was found in brain tissue in all mice, reaching virus titers from 3.105 (i.d. route) to 3.108 FFU/g (i.c. route).
To investigate WNV location within the CNS, cryostat brain sections from three WNV-infected mice were assessed for the presence of viral antigens by immunofluorescence at day 7 p.i. When inoculated i.c, virus was found widespread in most of the brain structures (whereas no signal was seen in mock-infected controls), including cortex (Fig. 1A), pyramidal neurons of the hippocampus (Fig. 1B), spinal cord and olfactory bulb. In contrast, a lower level of infection was observed after i.p., i.d. or i.n. inoculation (Fig. 1E), showing regional variations according to the route of inoculation (Fig. 1C and 1D). In all sections, WNV-infected cells were negative for GFAP (Fig 1F). This suggests that neurons are the principle targets of infection in the CNS.
Figure 1 A to F: WNV antigens in different regions of the mouse CNS. Mice were inoculated with 103 FFU of IS-98-ST1 WNV upon different routes (i.c., i.p., i.n., i.d.); at Day 7 of infection, mice were euthanazied, brains were cut in 14 μm thick cryostat sections, and processed for immunofluorescence using anti-WNV serum (obtained from i.p.-inoculated resistant mice) as primary antibody. A: hippocampus (pyramidal layer), i.c. inoculation. B: frontal cortex, i.c. inoculation. C: spinal cord, i.p. inoculation. D: olfactory bulb, i.n. inoculation. Magnification: × 350. E: Average levels of infection of the different brain structures was estimated on 10 different sections for each of the 3 animals per group (I.C.: intracerebral, I.P.: intraperitoneal, I.D.: intradermal; I.N.: intranasal) according to the scale: +++: more than 10 positive cells per microscopic field; ++: between 3 and 9 positive cells; +: 1 or 2 positive cells; -: no positive cell. F: Immunodetection of WNV antigens (green) and Glial Fibrillary Acidic Protein (red) in cryostat section of WNV-infected mouse brain, day 7 of infection, i.c Magnification: × 700. G, H: WNV infection in primary neural cultures from C57BL/6 mouse brain cortex. Primary cultures were performed as described in text and infected with IS-98-ST1 WNV. G: Detection of WNV antigens (using anti-WNV mouse immune serum and a FITC-conjugated secondary antibody, green staining) and neuronal specific enolase (using a rabbit polyclonal antiserum and an anti-rabbit polyclonal antibody made in goat conjugated with Texas Red, red staining) by immunofluorescence at 24 h p.i. (m.o.i. 12.5). Magnification: × 700. H: Kinetics of infection and variation of cell number at various times post-infection for different m.o.i; three cultures for each m.o.i. were fixed and processed for WNV antigen detection by immunofluorescence, whereas cell nuclei were visualized with DAPI. Cell nuclei of adherent cells were counted in 8 different different fields for the three cultures (histogram) whereas the percentage of infected cell was estimated by counting WNV antigen positive cells and cell nuclei; the percentage of infected cells is indicated as values (%) in white squares.
For ex-vivo experiments, primary neuronal cultures were prepared from the brain cortex of C57/BL6 mouse embryos (day E15) (Harlan, France)[10]. Briefly, after rapid removal of the embryos and dissection of brain cortex, mechanical dissociation and centrifugation were performed; the cells were seeded on slides and grown in NeuroBasal/B27 medium (Invitrogen Corporation) and, around 10 days after plating, were infected with WNV at different multiplicities of infection (m.o.i.). Cell cultures were constituted by more that 90% neurons, as assessed by immunocytochemistry. At different times post-infection, cell culture supernatants were processed for viral titration; cells were fixed and processed for immunofluorescence detection for viral antigens (see above) or neural cell typing, using either an anti-neuron specific enolase (NSE) (Zymed) or an anti-GFAP (Promega). After 24 h of infection at a m.o.i of 25, ~50% of cells were infected (Fig. 1H). By 40 h p.i., 90% of cells became infected and > 107 FFU of WNV per ml was detected in the culture supernatant. Time course studies showed that IS-98-ST1 infection induced cell death through neuronal necrosis within 48 h of infection, and ~90% of cells had detached by 96 h (Fig. 1H). Whatever the time of infection, only neuronal cells were permissive for IS-98-ST1 as judged by double immunofluorescence staining for WNV antigens and NSE (Fig. 1G). GFAP positive cells, i.e. astrocytes, that constitute less than 10% of cells appeared to be relatively resistant ot WNV infection. To confirm this, astrocyte-enriched primary cultures from the brain cortex of mouse embryos were infected with IS-98-ST1 at a m.o.i of 50. By 48 h p.i., only 5% of GFAP immunoreactive cells expressed viral antigens (data not shown).
Although our study was limited in its scope, the results indicate that WNV strain IS-98-ST1 is suitable as viral model for West Nile encephalitis in the Old World. The Israeli strain IS-98-ST1 that caused the epizootic in Israel in 1998, was found to be highly neuroinvasive in mice following peripheral inoculation. Consistent with this observation, we reported that IS-98-ST1 has an i.p. LD50 value as low as 10 FFU[8]. IS-98-ST1 infection has allowed us to determine the role of the type-I interferon (IFN) response in controlling WNV infection and that IFN-inducible OligoAdenylate Synthetase molecules may play an important role in the innate defense mechanism against WNV[8,11]. High viral titers could be recovered in mouse brains whatever the route of inoculation (i.c., i.p., i.d., i.n.). Viral antigens were detected in most brain structures at day 7 of infection, consistent with the notion that IS-98-ST1 is able to reach the CNS and then replicate in the brain. Infected C57Bl/6 mice showed neurological symptoms and lethality, confirming the high neurovirulent characteristics of IS-98-ST1, that were described in another susceptible mouse model of WNV (North-American strain) infection [12]. These features may be linked to the predominance of neurological symptoms that have been observed in hospitalized patients during Israeli outbreaks [13] or during natural infections of horses [14]. Our data are compatible with a previous report[15] indicating that WNV replicates locally in draining lymph nodes in mice inoculated subcutaneously, then in the spleen and in multiple sites in the CNS, although the sites of extraneural viral infection and the possible cells that could be involved in such a passage remain elusive. The dissemination of foci of infection within the brain that is observed in our study is compatible with virus passage through the blood-brain barrier. However, the fact that infected neural cells are detected in the olfactory bulb after intra-nasal inoculation suggests that an intraneural transport of WNV cannot be ruled out. Such neuroinvasive properties have also been reported for WNV variants from North America in experimental infection in rodents [16] and avian species as well as in natural infections in horses or birds[5,17]. Although some of these studies support the infection of neural cells by WNV within the CNS, none used double immunocytochemistry for WNV antigen and cell typing.
Our study confirms the neurotropism of WNV and the huge preferential infection of neurons in vivo. Because neurons are believed to be main target neural cells of WNV, we developed an ex-vivo model of infection, by culturing primary neural cells from the brain cortex of susceptible mice. More than 90% of the neurons are found to be infected by IS-98-ST1 and infected neurons undergo necrosis. In contrast, astrocytes were mainly resistant to WNV infection. This is consistent with in vivo data showing a massive infection of brain structures such as brain stem, hippocampus and cortex of WNV-infected animals [12] and human patients[5]. The high neuropathogenicity of IS-98-ST1 isolated from a stork in Israel in 1998, as well as WNV strains present in North America does contrast with the low pathogenicity of most ancestral strains of WNV[18,19]. In conclusion, the Israeli strain IS-98-ST1 of WNV provides a relevant model for assessing the identification of viral factors that may responsible for West Nile pathogenesis.
Authors' contribution
ML carried out ex-vivo studies, M-PF and TM participated in in vivo experiments, VD and J-LG revised critically the article, PD and P-EC have written, drafted the article, and participated to in vivo and ex-vivo experiments.
Competing Interests
The authors declare that they have no competing interests.
Acknowledgment
The authors thank Nathalie Arhel for improving the manuscript
This work was funded by the Transverse Research Programs (Institut Pasteur) and Programme de Recherche Fondamentale en Microbiologie et Maladies Infectieuses et Parasitaires, Ministère de l'Education Nationale, de la Recherche et de la Technologie, France. ML and TM are post-doctoral fellows of the Transverse Research Program (Institut Pasteur) and Centre national de la Recherche Scientifique, respectively.
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| 15550172 | PMC535539 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Nov 18; 1:9 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-9 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-411554117410.1186/1475-2875-3-41ResearchWeather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia I. Patterns of lagged weather effects reflect biological mechanisms Teklehaimanot Hailay D [email protected] Marc [email protected] Awash [email protected] Joel [email protected] Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston MA 02115, USA2 Mailman School of Public Health, Columbia University, New York, NY, USA3 Department of Environmental Health, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA2004 12 11 2004 3 41 41 19 7 2004 12 11 2004 Copyright © 2004 Teklehaimanot et al; licensee BioMed Central Ltd.2004Teklehaimanot 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
Malaria epidemics due to Plasmodium falciparum are reported frequently in the East African highlands with high case fatality rates. There have been formal attempts to predict epidemics by the use of climatic variables that are predictors of transmission potential. However, little consensus has emerged about the relative importance and predictive value of different factors. Understanding the reasons for variation is crucial to determining specific and important indicators for epidemic prediction. The impact of temperature on the duration of a mosquito's life cycle and the sporogonic phase of the parasite could explain the inconsistent findings.
Methods
Daily average number of cases was modeled using a robust Poisson regression with rainfall, minimum temperature and maximum temperatures as explanatory variables in a polynomial distributed lag model in 10 districts of Ethiopia. To improve reliability and generalizability within similar climatic conditions, we grouped the districts into two climatic zones, hot and cold.
Results
In cold districts, rainfall was associated with a delayed increase in malaria cases, while the association in the hot districts occurred at relatively shorter lags. In cold districts, minimum temperature was associated with malaria cases with a delayed effect. In hot districts, the effect of minimum temperature was non-significant at most lags, and much of its contribution was relatively immediate.
Conclusions
The interaction between climatic factors and their biological influence on mosquito and parasite life cycle is a key factor in the association between weather and malaria. These factors should be considered in the development of malaria early warning system.
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Background
Malaria epidemics due to Plasmodium falciparum are reported frequently in the East African highlands [1-6]. Immunity to malaria in the populations of these epidemic-prone regions is often incomplete, so that epidemics cause high case fatality rates among all age groups. In 1958, a malaria epidemic covering over 250,000 square kilometers resulted in an estimated three million cases and 150,000 deaths in Ethiopia [2]. Since then, large scale epidemics of malaria have been noted every five to eight years. Thus, there is an urgent need for the development of malaria early warning systems [7-9] to predict where and when malaria epidemics will occur, with adequate lead-time to target scarce resources for prevention activities. Unusual meteorological conditions, such as especially high rainfall or high temperature, are often cited retrospectively as the precipitating factors for epidemics [10,11]. There have also been formal attempts to predict epidemics by the use of local weather and/or global climatic variables that are predictors of vector abundance and, therefore, of transmission potential [12-20]. However, little consensus has emerged about the relative importance and predictive value of different factors [2,6,21-27]. Woube [27] showed that although one epidemic in Ethiopia was associated with higher rainfall, an epidemic in another year was preceded by very little rainfall. Lindsay et al. [23] found a reduction in malaria infection in the Usambara Mountains of Tanzania following 2.4 times more rainfall than normal, while excessive rainfall during the same period was associated with increased malaria in south-western highlands of Uganda [3]. Moreover, Mbogo et al. [24] found variation in the relationship between the mosquito population and rainfall in different districts of Kenya and attributed the variation to environmental heterogeneity. Similarly, Zhou et al. [28] showed that there was high spatial variation in the sensitivity of malaria outpatient numbers to climate fluctuations in East African highlands. Similarly, determination of the amount of lead time between weather factors and malaria cases is necessary to develop prediction models, but results from different studies have revealed a range of lead times [3,4,22,24,29]. Despite the varying results of these studies, there has not been critical examination of the sources of variation in the association and lag structure (the magnitude of association between weather and malaria at a later time) between weather and incidence of malaria.
The inconsistency of findings from different studies may be due in part to the interaction of weather factors affecting vector abundance and survival, and parasite maturation, key determinants of malaria transmission. Deposition of mosquito eggs, and their maturation into larvae and then into adults, requires aquatic breeding sites, and is, therefore, dependent on rainfall [30,31]. The time required for mosquito maturation shortens as temperature increases [30,32]. At 16°C, larval development may take more than 45 days (reducing the number of mosquito generations and putting the larvae at increased risk of predators), compared to only 10 days at 30°C (Table 1). By affecting the duration of the aquatic stage of the mosquito life cycle, temperature determines the timing and abundance of mosquitoes following adequate rainfall.
Table 1 The effect of mean temperature on the duration of mosquito's life cycle and sporogonic cycle and its effect on the amount of lead time from the availability of breeding sites to the occurrence of malaria cases.
Weather factors Stages and duration of mosquito's life cycle and sporogony cycle affected by weather factors
Mean temperature (Rainfall temperature) Availability of breeding sites ----------------→ Malaria
Mosquito's life cycle* Sporogony† Incubation period in human host
Larva ----→ Adult(days) Adult first bite ----→ Infectious bite (days)
16°C 47 111 (10–16 days)
17°C 37 56
18°C 31 28
20°C 23 19
22°C 18 7.9
30°C 10 5.8
35°C 7.9 4.8
39°C 6.7 4.8
40°C 6.5 4.8
* see references [32, 44]; †see references [35, 45]
Once female adult mosquitoes emerge, they look for a blood meal, and in the process they ingest malaria parasites (gametocytes) with the blood. The feeding frequency of mosquitoes increases with temperature, resulting in increased proportions of infective mosquitoes [33]. The duration of the extrinsic phase of the parasite (sporogony cycle), which is the development of the ookinete, the egg of the parasite, in the midgut of the anopheline mosquito, depends on temperature. The sporogony cycle on average lasts about 10 days, but shortens as temperature increases [34,35], becoming as short as five days when the temperature exceeds 30°C (Table 1).
These data on the timing of the mosquito life cycle suggest that malaria cases should follow, at defined intervals, periods of increased temperature and increased rainfall. Moreover, because temperature accelerates several steps in the process of mosquito and parasite development, the time lag between the appearance of suitable weather conditions and the appearance of new malaria cases should shorten as temperature rises. Although based largely on laboratory findings, these data suggest quantitative hypotheses about the precise time lag between increases in temperature and rainfall, and increases in malaria cases. At an average temperature of 20°C, the aquatic phase of the mosquito will be completed in about 28 days (five days for eggs to hatch and 23 days for the larva to develop into adult stage); and sporogony is completed in 28 days (Table 1). At this temperature, malaria cases should, therefore, appear 9–10 weeks following rainfall, assuming an average incubation period of about 10–16 days. Similarly, in this situation, the number of malaria cases should be positively related to increases in temperature three to seven weeks beforehand (during the aquatic and sporogony stages). When the mean temperature is higher (30°C), the aquatic stage of mosquito and the sporogony cycle are completed in about 12 and 8 days respectively (Table 1). In this relatively hot environment, malaria cases should appear 4–5 weeks following rainfall and the lag in the effect of temperature should also be shorter.
The purpose of the investigations reported here is to test these hypotheses using weekly data on weather and malaria cases. Specifically, we used Polynomial Distributed Lag Models (PDL) to determine the effect of weather factors and their lag distribution on malaria in relatively hot and cold environments using a data set consisting of weekly parasitologically confirmed malaria cases collected from health facilities and locally collected meteorological factors in 10 districts of Ethiopia for the years 1990 to 2000.
Methods
Data
Microscopically confirmed malaria cases were collected from a health facility in each of ten districts of Ethiopia over an average of 10 years. Each of these health facilities serve people living in the surrounding localities with few exceptions coming from other places. The data were extracted from records of outpatient consultations for the years 1990 through 2000. These data were compiled by residency (urban and rural) and Plasmodium species; the analysis was restricted to P. falciparum. The original data collected on the basis of Ethiopian weeks were normalized to obtain mean daily cases for each Ethiopian week [36].
Daily meteorological data (minimum and maximum temperature and rainfall) recorded at the local weather stations nearest to the health facility were obtained from the National Meteorological Services Agency (NMSA) for the same period. The assumption is that the meteorological data from the local weather stations represent the surrounding localities whose inhabitants are served by the respective health facilities. These daily data were collapsed into weekly data to correspond with the weekly malaria cases. The weekly mean for minimum and maximum temperature and the total weekly amount of rainfall were calculated from the daily records.
Missing data
The data set consists of records with missing values for some of the variables (4.4%, 10.2% and 9.9% for rainfall, minimum and maximum temperature respectively). Because of the multiple time lags considered in the analysis (see below), discarding estimates with a single missing value results in multiple records lost for each missing value. To avoid this substantial loss of data, missing values of the independent variables (weather variables) were interpolated by fitting a linear regression using the value from the previous week and a dummy variable for that week. A new variable was created with original values for non-missing data points and interpolated values for missing data points.
Model
The daily average number of cases (of the weekly microscopically confirmed malaria cases) was modeled using a robust Poisson regression (implemented in Splus), using weekly rainfall, minimum and maximum temperature as explanatory variables in a distributed lag model. The model was:
where E(Yst) denotes the expected value for the daily average number of malaria cases at site s on week t; , and Rt - i are the weekly minimum and maximum temperature and the rainfall i weeks previously; αs and βs represent the intercept and coefficient for time trend (t), which are specific to the site under consideration.
As described in the introduction, biological considerations suggest that the lag between rainfall and associated malaria cases will be different from that between temperature and associated cases. Therefore, lags of 4–12 weeks were considered for rainfall, and relatively shorter lags of 3–10 weeks for the minimum and maximum temperature. The assumption is that changes in temperature and rainfall at a particular time do not have an important influence after 10 and 12 weeks respectively. The overall effect of a unit increase in minimum temperature (for example) is the sum of the coefficients βi.
A large number of variables (a total of 25; eight lags each for minimum and maximum temperatures and nine lags for rainfall) were introduced for the three weather factors in this model. The efficiency of the coefficient estimates may be affected due to the large number of parameters to be estimated and the possibility of multicollinearity between the lagged weather factors. When lag terms are put in the same model, correlation between measurements of weather on weeks close together will cause a high degree of collinearity that may result in unstable estimates.
Polynomial Distributed Lag Model
A polynomial distributed lag (PDL) model [37] imposes constraints on the coefficients βi,γi,θi, forcing each of them to take the form of a (separate) 4th-degree polynomial in i. This reduces the number of degrees of freedom for each weather factor from the number of lags considered to five and circumvents some of the difficulties associated with estimation of coefficients for multiple lags, including instability of estimates due to collinearity of the different lags of the same variable. With this model the coefficients for lagged minimum temperature, for example, are assumed to take the form:
where φk is the parameters of the d-th degree (here d = 4) polynomial distributed lag. To estimate the parameters describing the polynomial lag φk equation (2) was substituted into the unconstrained distributed lag model (1) to obtain a constrained polynomial distributed lag model:
Grouping of districts
The effects of weather factors on the number of malaria cases were distributed over multiple weeks and the separate analysis for each district (not shown) indicated heterogeneity in magnitude and direction of the effects of the weather factors. Districts with similar climatic characteristics were grouped, in order to reduce the effect of random error and to produce more reliable and precise estimates of weather effects. Moreover, this approach will produce more generalizable results within similar climatic conditions. Thus, the districts were grouped into hot (altitude < 1700 mm above sea level) and cold on the basis of altitude and temperature. The hot districts included Diredawa, Nazareth, Wolayita and Zeway; and the cold districts included Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma. Two separate PDL models were fitted to estimate the effects of different weather factors.
A basic issue in epidemiological analysis is controlling for the effect of confounding factors. Malaria transmission is affected by different factors and shows a systematic variation over time. To control for other factors that may affect the long-term trend, a time variable was included in the model, which would remove the long-term wavelength patterns, leaving the deviations representing short fluctuations. Since the long-term trend and the numbers of weekly cases vary between districts, an interaction term between the time variable and district (dummy variable) was introduced.
Urban areas may have other sources of breeding sites for mosquito not driven by rainfall. To examine the influence of these and other unmeasured factors that vary between urban and rural environments, separate models were fitted for urban and rural cases.
Results
The data set consists of microscopically confirmed P. falciparum cases from a health facility in each of 10 districts over an average of 10 years and meteorological data from local stations in each of the districts. Table 2 presents the descriptive analysis of cases, meteorological variables and altitude of each district. The daily averages treated by each of the 10 health facilities ranged from 11–39 malaria cases and over 300 cases during the peak transmission season. Minimum temperature was positively correlated with rainfall, significantly (rho = 0.37) in the cold districts and nonsignificantly (rho = 0.06) in the hot. Maximum temperature, however, was negatively correlated with rainfall, significantly (rho = -0.33) in the cold districts and nonsignificantly (rho = -0.033) in the hot.
Table 2 Characteristics of the study districts (average daily malaria cases and meteorological variables)
District Altitude Malaria cases Maxt Mint Rain Group
(m) Mean Min Max (°C) (°C) (mm)
Alaba 1750 39 0 163 27.4 11.7 19.6 cold
Awasa 1750 11.3 0 17 27.4 12.6 19.3 cold
Bahirdar 1770 22.1 1 83 27.2 13 25.2 cold
Debrezeit 1900 25.2 1 147 26.5 12.1 16.5 cold
Hosana 2200 19.4 0 96 22.4 10.7 23.4 cold
Jima 1725 13.2 0 85 27.6 11.6 30 cold
Diredawa 1260 25.3 0 330 31.8 19 13.8 hot
Nazareth 1622 17.7 0 109 27.5 14.3 17.1 hot
Wolayita 13.9 0 113 25.3 14.4 24.4 hot
Zeway 1640 11.4 1 102 27.5 13.9 13.7 hot
maxt-maximum temperature, mint-minimum temperature
The effect of rainfall and temperature on daily average microscopically confirmed cases was estimated by lag in the 10 districts grouped into two climatic zones, hot and cold (Table 2). Figure 1a shows the estimates of the distributed lag between rainfall and cases in cold areas. Coefficients represent the multiplicative effect of one additional millimeter of rain at a given lag on the number of malaria cases at a site. Rainfall is significantly associated with the number of malaria cases in the cold districts. The magnitude and direction of the association varies with lags. At shorter lags of 4 and 5 weeks, rainfall is negatively and significantly associated with malaria cases. At lags of six, seven and eight, rainfall is not significantly associated with malaria cases. Lags of nine, 10, 11 and 12 are positively associated with malaria cases and the magnitude of effect increases almost linearly with maximum effect at lag 12. The conclusion is that rainfall in the cold districts is associated with a much delayed increased malaria cases and immediate decrease in malaria cases.
Figure 1 Distributed lag structure for the association between 1 mm increase in rainfall, 1°C increase in minimum and maximum temperature and average daily malaria cases. (a) & (b) for rainfall, (c) & (d) for minimum temperature, and (e) & (f) for maximum temperature in the cold and hot districts respectively. The shaded areas represent 95% confidence intervals.
Similarly, rainfall is significantly associated with malaria cases in the hot districts. Compared to the cold districts, a significant and positive effect of rainfall in the hot districts manifests at relatively shorter lags (six, seven, eight, nine and ten weeks) and remains positive afterwards but declines and becomes non-significant for the longer lags (Figure 1b). Much of the contribution of rainfall to the increase in malaria cases in the hot districts occurs at relatively shorter lags (compared to its effect in cold districts) and wanes slowly with increasing lags. Thus, the results for rainfall agree qualitatively with biological expectations.
Figure 1c shows the estimated distributed lag relationship between minimum temperature and malaria cases in the cold districts; coefficients represent the multiplicative effect of one degree Celsius increase in temperature at a given lag on the number of malaria cases at a site. Minimum temperature is positively associated with the number of malaria cases, with a significant increase extending from 7 to 10 weeks prior to cases and the size of the effect growing over that range.
In the hot districts, by contrast, the effect of minimum temperature on malaria cases is more complicated. At short lags, its effect is small and non-significantly positive (Figure 1d). A significant positive association at longer lags is also observed. In summary, minimum temperature contributed significantly to the estimated increase in malaria cases in the cold districts with a delayed effect. In the hot districts, while its effect is non-significant, much of the contribution is relatively immediate. Unexpectedly, the only significant contribution of minimum temperature in the hotter districts occurs at long lags.
Figures 1e and 1f show the relationship between maximum temperature and malaria cases in the cold and hot district groups respectively. Maximum temperature is not significantly associated with the estimate of malaria cases in either group of districts. However, the trend of the estimates along the lags shows that at shorter lags of three, four and five weeks, maximum temperature is negatively associated with the number of malaria cases in hot districts, while it is positively associated at lags of six, seven and eight weeks in the cold districts.
To test the linearity of the association between the weather factors and malaria cases, a three dimensional relationship between weather factors, lag and the magnitude of effect at each lag was explored (See Additional file 1 for the method and figure). In that figure, a positive effect of a factor at a given lag is seen as a positive slope of the surface cut at the given lag; the magnitude of that slope corresponds to the linear effect estimated by the PDL model. The effect of rainfall plateaus at higher rainfall levels; beyond a given quantity of rain, additional rain adds little to the malaria risk. Similarly, although the effect of minimum temperature in the cold districts is linear, it levels off at higher temperature (>16°C) in the hot districts.
The results of analysis stratified by rural versus urban sites are shown in Figure 2. The association between rainfall and cases varies by residency (a & b). Rainfall is significantly associated with cases originated from rural residents but not generally among urban residents, however, the magnitude of effect of rainfall looks similar in both rural and urban areas,. The effect of minimum temperature on malaria cases does not vary by residency (c & d).
Figure 2 Distributed lag structure of the effects of rainfall and minimum temperature on average daily malaria cases by residency. (a) & (b) for rainfall in rural and urban respectively, (c) & (d) for minimum temperature in rural and urban respectively. The shaded areas represent 95% confidence intervals.
Predicted case numbers for hot and cold districts were compared against actual values to assess how well the models predict the seasonal peaks and interannual variability. Plots of the actual data and predicted values showed that the models predicted the seasonal fluctuations very well (Figure 3). However, the models were not able to differentiate clearly between years with high and low peaks.
Figure 3 Plot of observed number of cases and predicted cases from the polynomial distributed lag models of 4 districts.
Discussion
The development of malaria early warning systems [7,8] to predict where and when malaria epidemics will occur, in order to target scarce resources for prevention activities [9,38], has motivated many studies [13-15,18,19]. However, little consensus has emerged as to which factors should be used as indicators, because multiple studies have yielded differing results on the main determinants of increased malaria transmission [2,6,21-23,25,27] and the lead time prior to observable effects [3,4,22,24,29]. In this study a polynomial distributed lag model was used to assess the lag distribution of the effects of weather factors on Plasmodium falciparum malaria in relatively hot (Diredawa, Nazareth, Wolayita and Zeway) and cold (Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma) environments in Ethiopia.
The findings are largely consistent with hypotheses based on the relationship between weather factors and mosquito and parasite development. Rainfall is associated with malaria cases in both hot and cold districts with a lagged effect, and as expected, this lag is shorter in hot districts. The effect of rainfall on malaria is linear with saturating effects at higher rainfall levels (See Additional file 1 for the method and figure). Interestingly, malaria in the urban areas is not associated with rainfall. Although the maximum temperature is not generally associated with malaria cases in either group of districts, the minimum temperature is significantly associated with malaria cases in the cold districts with delayed effect, and the lag for the minimum temperature is shorter than that for rainfall, reflecting the two factors' effects on different stages of the transmission cycle. The detection of a positive effect of the minimum temperature at long lags (9–10 weeks) in the warmer districts was not predicted by biological considerations.
One of the most striking uncertainties in the literature on weather and malaria is the variability in the reported relationship between rainfall and malaria, with several studies showing the importance of rainfall as a precipitating factor for malaria transmission [3,4,10,11,29], while other studies show negative or neutral effects [21,23,26,27]. For rainfall to have a positive effect on malaria cases, the temperature must be warm enough to support mosquito and parasite development [39], and, as the data confirm, the effect of rainfall on cases becomes more immediate in warmer temperatures. This is consistent with the laboratory findings that a mosquito population peaks early at higher temperatures, while a mosquito population at low temperatures experiences slow, steady growth with a delayed peak [40]. Increases in rainfall may also fail to produce additional malaria cases if aquatic breeding sites are not limiting for mosquitoes; this mechanism is consistent with the observed saturating effect of rainfall in our data.
Furthermore, malaria in the urban areas is not significantly associated with rainfall, which may have been one of the sources of inconsistent findings of such analysis. The weak association may be due to the presence of other sources of breeding sites that may persist during the dry season such as brick pits, puddles, blocked drains and cisterns [41]. Moreover, developmental activities, aggregation of migrant labor forces and overall population movement affect urban malaria. It is also interesting to note that the effect of rainfall in the cold districts is negative at shorter lags, which may be due to breeding sites being flushed away during the rainy season [23]. Another possible explanation for the negative effect could be that low temperature during the rainy season might suppress malaria transmission. Maximum temperature was lower during the rainy season (shown by negative correlation with rainfall), however, the effect of maximum temperature on malaria is non significant. Moreover, minimum temperature seems to be elevated during the rainy season (positive correlation). Although the effect of rainfall in the hot districts declines after longer lags (due to evaporation and drying up of breeding sites), making the main transmission season shorter, the overall effect of rainfall (sum of the lag coefficients) is bigger in the hot than cold districts. Taken together, the analyses suggest that temperature requirements, saturating effects of rainfall, and urban-rural differences in the effect of rain on malaria transmission are all plausible mechanisms that could explain the inconsistent relationship between excessive rainfall and malaria epidemics.
The minimum temperature contributes significantly to the estimated increase in malaria cases with a delayed effect in the cold districts, but not in the hot districts. At lower temperatures, the larval and pupal stages of mosquitoes take longer to complete (for example, 47 days at 16°C) and a small increase in temperature substantially shortens the duration of these phases (to 37 days at 17°C). Similarly, the duration of the sporogony cycle will be short with increasing temperatures (Table 1). In addition, raised temperature increases the frequency of mosquito feeding and, hence, the probability of transmitting infection [33]. Although all such effects of minimum temperature increase malaria transmission in the cold districts, the effect will be seen after a lag. The effect of minimum temperature in the hot districts, on the other hand, is immediate but non-significant. These findings are consistent with reports that small increases in temperature will have a greater effect on malaria transmission in areas with relatively lower average temperatures than areas with higher temperatures [42,43]. The significant effect of minimum temperature at relatively long (9–10 week) lags is not explainable, to our knowledge, on biological grounds.
The maximum temperature is not significantly associated with cases in either hot or cold districts. However, the negative (but non-significant) correlation between weekly malaria cases and maximum temperature at shorter lags seen in hot districts may be due to its inhibitory and lethal effect on the survival of the parasites in the mosquitoes [30]. The survival rate of Anopheles gambiae is also reduced at higher temperatures. Nonetheless, the maximum temperature is not very extreme even in the relatively hot districts, thus the negative effect is not significant.
As with all observational studies of malaria incidence and weather, a limitation of this study is the likely presence of some confounding factors that may have influenced the number of malaria cases and may have been associated with weather. Existing interventions such as insecticide residual spraying and other methods are routinely applied and were not included in this analysis. The results would have been biased by such confounding factors if interventions were undertaken on the basis of weather, or if they were undertaken on the basis of incidence and their effect was differential depending on the weather. Another minor problem is with the assumption of a finite length for the delayed effect of the different weather factors. However, the lag length was chosen based on the inter-relationship between weather, mosquitoes and parasites (Table 1) and it was assumed that this is biologically plausible.
Weather factors alone explain seasonal cycles but were not accurate in explaining the magnitude of unusually bad years (Figure 3). This study was a scientific not a predictive exercise and suggests that no other factor is required for explaining seasonal cycles. A good early warning system has not been created, but some principles have been suggested for one. The first principle is that the lag length from time of rain to the expectation of malaria cases varies with climatic zone (with a saturating effect at higher rainfall levels), and rainfall may not be a key factor in urban malaria transmission. Secondly, minimum temperature is only important in the cold climatic zones, but not in the hot. Finally, maximum temperature makes little difference in either climatic zone. These key points need to be considered in the development of an early warning system for malaria. Such an early warning system would also include autoregressive terms or other terms that could improve prediction, but would have complicated the interpretation of coefficients in a model of the sort used, which was designed to detect the effects of weather factors on cases, rather than to predict case numbers. Such an early warning system is now being evaluated.
Conclusions
The findings are largely consistent with hypotheses, based on experimental data on mosquito and parasite development, about the interactions of climatic factors in determining the strength and lag structure of weather effects on falciparum malaria incidence. In the examined Ethiopian districts, weather-based predictors of malaria incidence are more useful in rural than in urban settings. These key points should be considered in the development of an early warning system for malaria.
Authors' contributions
HDT and ML conceived the study, undertook statistical analysis and drafted the manuscript. AT initiated the study and made data available in collaboration with WHO and Ministry of Health of Ethiopia. JS made major contributions to the study design and statistical analysis. All authors contributed to the writing of the manuscript and approved the submitted version of the manuscript.
Supplementary Material
Additional File 1
Three dimensional relationships between weather, lags and magnitude of effect at each lag
Click here for file
Acknowledgments
HT was funded by the Fogarty International Center (FIC) of the National Institutes of Health (NIH) (grant number 5D43TW000918). Financial support for data collection was provided by World Health Organization/RBM. ML thanks the Ellison Medical Foundation for support of this research. We thank the Ministry of Health of Ethiopia for allowing us to access the information. We also thank Asnake Kebede and Afework Tekle for their work on data collection.
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| 15541174 | PMC535540 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 12; 3:41 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-41 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-441555506110.1186/1475-2875-3-44ResearchWeather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions Teklehaimanot Hailay D [email protected] Joel [email protected] Awash [email protected] Marc [email protected] Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston MA 02115, USA2 Department of Environmental Health, Harvard School of Public Health, 677 Huntington Avenue, Boston MA 02115, USA3 Mailman School of Public Health, Columbia University, New York, NY, USA2004 19 11 2004 3 44 44 19 7 2004 19 11 2004 Copyright © 2004 Teklehaimanot et al; licensee BioMed Central Ltd.2004Teklehaimanot 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
Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy.
Methods
Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts.
Results
The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10–25% more cases at a given sensitivity in cold districts than in hot ones.
Conclusions
The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.
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Background
Malaria epidemics are reported frequently and have caused high morbidity and mortality among all age groups in the African highlands [1-4]. Early detection and accurate forecasting of the time, place and intensity of these epidemics is important for emergency preparedness, planning and response [5,6]. Considerable efforts are being made to promote, develop and implement early warning systems for malaria epidemics in Africa [5,7]. Ideally, public health and vector control workers would have access to a system that alerts them when substantial numbers of excess cases are expected, and such alerts should be sensitive (so that alerts are reliably generated when excess cases are imminent), specific (so that there are few "false alarms") and timely (so that there is adequate lead time to act). Generally, each of these performance characteristics is enhanced at the expense of another. The value of interventions – such as larviciding, residual house spraying and mass drug administration – to control malaria epidemics has been documented [8]. However, due to the explosive nature of malaria epidemics, the usefulness of such interventions in epidemic settings depends on timely information about the onset of a severe epidemic.
Early detection systems, which are used to detect epidemics once they have begun, can correctly identify periods that are defined by expert observers as "epidemic," albeit with varying specificity. A number of such systems have been proposed or implemented. For example, WHO has advocated the use of alerts when weekly cases exceed the 75th percentile of cases from the same week in previous years [9] and other methods, based on smoothing or parametric assumptions, have also been considered [10-12]. However, an early detection system, which generates alerts once unusually high case numbers are already observable, will be useful for targeting interventions only if it identifies epidemics at an early phase, when there is still time to take effective action [13] and if the epidemics persist (and indeed grow) over time so that action taken after the warning can still have an effect. It was previously shown, using weekly case numbers from 10 districts in the Ethiopian highlands over approximately 10 years, that simple weekly percentile cutoffs used for early detection are capable of identifying periods with unusually high malaria incidence, and that interventions that take effect within two weeks of such alerts could have a substantial impact in reducing excess cases [14].
While early detection systems appear to provide timely information about the onset of severe epidemics, they intrinsically trigger alerts only when unusual transmission is already underway. Another approach, known as "early warning," attempts to predict epidemics before unusual transmission activity begins, usually by using weather variables that predict vector abundance and efficiency, and therefore, transmission potential [6,15-18]. The advance notice provided by an early warning system could allow action to be taken earlier in the course of the epidemic, or could increase the span of time available to undertake control measures before the predicted excess cases occur. A number of authors have used weather factors to attempt to predict malaria epidemics [19-24], and Teklehaimanot et al. [25] showed that polynomial distributed lag (PDL) models incorporating lagged effects of minimum temperature, maximum temperature and rainfall could mimic seasonal patterns of malaria incidence in the same ten sites for which early detection algorithms were evaluated. Because the significant weather predictors of malaria cases are lagged by four or more weeks, such prediction systems may, in principle, provide a means of anticipating unusual malaria incidence with more lead time than early detection methods.
Here, an attempt to combine these avenues of previous work is described, using modified versions of previously described models based on weather factors to provide predictions of Plasmodium falciparum cases in these 10 districts of Ethiopia, and evaluating thresholds that trigger warnings. The hypothesis tested here is that the use of predicted cases (rather than actual cases, as in our previous work on early detection) would reduce the precision of the alert thresholds (resulting in alerts whose timing was less well matched to periods of excess cases than those generated by early detection), as the price of obtaining the alerts with greater advance notice. In fact, the early warning system based on predicted cases performed slightly worse in most cases than the early detection system, but the performance was rarely much worse and occasionally slightly better. These comparisons are described and their implications for the choice of malaria prediction/detection systems in epidemic-prone areas of Africa are discussed.
Methods
Study area and data
Microscopically confirmed malaria cases were collected from a health facility in each of ten districts of Ethiopia over an average of 10 years; this data set has been previously described [14]. Each of these health facilities serves people living in the surrounding localities with few exceptions coming from other places. The data were extracted (by species) from records of outpatient consultations for the years 1990 through 2000. The analysis was restricted to P. falciparum. The original data collected on the basis of Ethiopian weeks (where the number of days in each week varies between 5 and 9) were normalized to obtain mean daily cases for each Ethiopian week [14].
Daily meteorological data (minimum and maximum temperatures and rainfall) recorded at the local weather stations nearest to the health facility were obtained from the National Meteorological Services Agency (NMSA) for the same period. These daily data were collapsed into weekly data to correspond with the weekly malaria cases. The weekly mean for minimum and maximum temperatures and the total weekly rainfall were calculated from the daily records.
1) Modeling the relationship between predictors and malaria cases
The expected case numbers for a given week were modeled using a Poisson regression with lagged weather factors, an autoregressive term, a time trend and indicator variables for week of the year. Biological considerations about the interrelationship between weather, mosquito and malaria parasite suggest that malaria cases should follow periods of increased temperature and increased rainfall, at defined intervals [26-28]. Thus, lags of 4 – 12 weeks for rainfall, and 4 – 10 weeks for minimum and maximum temperatures were considered [25]. In addition week and time trend, as well as an autoregressive term (based on a moving average of the number of cases four, five and six weeks before) were included, which is intended to improve the prediction. Because of the Poisson regression context the autoregressive term enters logarithmically. A 4th-degree polynomial distributed lag (PDL) model [29] was fitted to the data. This reduces the number of degrees of freedom for each weather factor from the number of lags considered and circumvents some of the difficulties associated with estimation of coefficients for multiple lags, including instability of estimates due to collinearity of the different lags of the same variable. The generalized form of the model is thus expressed as:
where E(Yst) denotes expected value for the daily average number of malaria cases at site s on week t; , , Rt-i, and Yst-i are the weekly minimum and maximum temperatures, rainfall and autoregressive term i weeks previously; ts and Ws designate time trend and week in a year at site s; αs represent the intercept, at site s.
2) Epidemic Prediction Strategies
For each week at each location in the data set, the number of cases was predicted using equation (1) and data available four weeks prior to the week for which the prediction is made. Coefficients of equation (1) were obtained using all years except that for which the prediction was being made, to avoid circularity. The prediction for week t was then made using this all-but-current-year model with weather and case data for the weeks up to week t-4. The predicted number of cases is thus estimated using the following model:
where represent the predicted cases for year j; , , , , , and are parameter estimates (for intercept, minimum and maximum temperature, rainfall, time, week and autoregressive term respectively) from all years except j; , , , tj, and are minimum and maximum temperatures, rainfall, time, week and autoregressive term respectively from year j.
3) Evaluation of the prediction system
Expected number of cases to be used as threshold levels
In early detection algorithms, actual cases in a given time period are typically compared against some threshold level of cases to determine whether excess cases have been observed. Often, the threshold level represents an upper bound on "normal" case numbers from previous years. If this threshold level is crossed (perhaps, depending on the system, for several consecutive weeks), an alert is generated [14]. Such systems for early detection have been evaluated previously [14].
In this study of the usefulness of prediction systems for generating alerts, historically based thresholds were similarly used – weekly percentile (defined as a given percentile of the case numbers obtained in the same week) or weekly mean with standard deviation (defined as the weekly mean plus a defined number of standard deviations) algorithms as threshold levels [14] – but generated alerts when predicted cases for a week exceeded the threshold. The thresholds for each year were calculated on the basis of all other years in the data set for a given health facility, excluding the year under consideration. In each case, an alert was triggered if the defined threshold was exceeded by the predicted number of cases for two consecutive weeks (this choice is intended to improve the specificity of the alert system for any given threshold). If another alert was triggered within six months, it was ignored, on the assumption that intervention following the first alert would prevent another epidemic within the next six months. Algebraic descriptions of the thresholds are given below:
1. Weekly percentile
Threshold is exceeded when , where Tsij = Qpsij, where Qpsij represents the pth (p = 70, 75, 80, 85, 90, or 95) percentile of observations from week i at facility s in years other than j.
2. Weekly mean with standard deviation.
Threshold is exceeded when , where Tsij = μsij + βσYsij, where β = 0.5, 1.0, 1.5, 2.0, 2.5 or 3.
Measure of performance of each alert
The effectiveness of alerts generated by our four-week-ahead prediction system was compared against that of alerts based on a detection system using actual cases. Since the prediction system generates predicted numbers of cases four weeks ahead of time, this permits implementation of control measures four weeks earlier than under a detection system. On the other hand, one would expect that the accuracy of prediction might be less than that of detection. The comparisons were designed to assess this trade-off between the ability to act earlier in possible epidemics and the possible loss of accuracy.
A method previously described [14] was used to compare different alert-generating procedures on a scale that reflects their operational uses. Briefly, this method quantifies the usefulness of a particular alert-generating system set to a given sensitivity by estimating how many malaria cases might be prevented by measures taken after each alert generated by the system, with defined assumptions about the lead time from alert to the effectiveness of such measures, and about the duration of effectiveness of these measures. Potentially prevented cases (PPC) for each alert are defined as a function of the number of cases in a window following the alert. To obtain the PPC, the following three assumptions were made. (a) It was assumed that four weeks elapse from the decision to make an intervention based on an alert until the interventions take effect. (b) From that time, the window of effectiveness is assumed to last either eight or 24 weeks (to account for control measures whose effects are of different durations). (c) Since no control measure would be expected to abrogate malaria cases completely, two possibilities were considered for the number of cases in each week of the window that could be prevented: 1) cases in excess of the seasonal mean (low effectiveness) and 2) cases in excess of the seasonal mean minus one standard deviation (high effectiveness). These different assumptions allowed testing the sensitivity of the performance of the prediction and detection systems to the length of the window of effectiveness and the choice of function to define potentially prevented cases. When the observed number of cases in a week is less than the seasonal mean or the seasonal mean minus the standard deviation, PPC is set to a minimum value of zero for that week.
Methods of comparison
For each value of each type of threshold (applied to either the predicted and observed number of cases) at each health facility, the number of PPC was transformed into a proportion (percentage), by adding the number of PPC for the alerts obtained and dividing this sum by the sum, over all weeks in the data set, of the number of potentially prevented cases. Proportion rather than actual cases were used because the numbers of malaria cases vary from district to district. To compare the performance of the predicted and observed cases on a single scale, a curve was plotted for each algorithm showing the mean percent of PPC (%PPC) over all districts versus the average number of alerts triggered per year, with each point representing a particular threshold value. Better methods of generating a warning were those that potentially prevent higher numbers of malaria cases using smaller numbers of alerts.
Random and optimally timed alerts
The performance of the alerts provided by both the predicted and observed cases was compared with random and optimally timed alerts. PPC was estimated for alerts chosen on random weeks during the sampling period. To estimate the performance of optimally-timed alerts (which could not have been implemented but is optimal in hindsight), the optimal timing of alerts were identified by retrospectively going through data if one had perfect predictive ability; the optimal week for one alert was chosen; then by going through the remaining weeks, the optimal week for a second alert was chosen, and so on. The optimal alert would serve as an upper bound curve for the best choice of alert times, given a defined alert frequency [14].
Cold versus hot districts
The relative importance of weather factors in determining malaria transmission significantly depends on the climate of the area. It has recently been shown that although rainfall was significantly associated in cold and hot districts, minimum temperature contributed only in the cold districts of Ethiopia [25]. Furthermore, Zhou et al. [30] showed that there was high spatial variation in the sensitivity of malaria outpatient numbers to climate fluctuations in East African highlands. To determine the effect of the differential contribution of weather factors on the accuracy of predictions, the performance of predictions in the hot and cold environments were compared. Thus, districts with similar climatic characteristics (on the basis of altitude and temperature) were grouped, in order to produce more generalizable results within similar climatic conditions. The hot districts (altitude < 1700 mm above sea level) included Diredawa, Nazareth, Wolayita and Zeway; and the cold districts included Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma. Mean %PPC and the average number of alerts for the cold and hot districts were obtained and the same method was used to compare the performance of the prediction system in the hot and cold districts.
Results
The prediction algorithm indicates the overall pattern of cases well, yet underestimates the height of the largest peaks. Comparisons of the predicted and observed malaria cases, for each week in six of the ten districts, are shown in Figure 1. The model predicted the actual cases well, although the agreement between the observed and predicted cases varied from district to district. However, the models were not able to differentiate clearly between years with very high and moderately high peaks. To explore whether the predicted number of malaria cases using weather factors can accurately identify time periods with increased number of malaria cases, the timing of alerts triggered, for example, by a mean plus 1.5 standard deviation threshold algorithm, is presented in the same figure. Despite the fact that the actual height of peaks in the highest-incidence periods is poorly predicted by the model, the model nonetheless often triggered alerts prior to these high-incidence periods.
Figure 1 Observed and predicted number of malaria cases with alerts triggered by mean plus 1.5 SD using predicted cases. The solid lines for observed cases and the dotted lines for predicted cases. The red marks are the timing of alerts triggered using predicted cases; their position along the y-axis does not have a meaning.
The prediction system generates alerts that could prevent nearly as many cases as alerts generated by a detection system. To obtain a quantitative estimate of the usefulness of the prediction algorithm as an early warning system, the %PPC obtained from alerts triggered by predicted cases were compared with %PPC obtained from alerts based on observed cases (Figure 2) under an early detection scheme similar to that previously analyzed [14]. Percentile and mean + standard deviation thresholds are shown, with each point representing a particular value of the threshold (e.g., 85th percentile or mean + 1.50 standard deviations). The horizontal axis gives the number of alerts per year triggered by the particular threshold value, while the vertical axis shows the %PPC associated with that threshold value. Each point represents the mean across all 10 districts. Two different choices of the function for determining PPC (reducing cases to weekly mean: low-effectiveness, a and c, or weekly mean minus one s.d.: high-effectiveness, b and d) and the choice of window of effectiveness (eight weeks, a and b; 24 weeks, c and d) were considered. The performance of the predicted number of malaria cases using the mean plus (0.5, 1, and 1.5) standard deviation algorithm (for an eight-week window of low-effectiveness) reveals that it prevented 29%/0.9 alerts, 27.3%/0.6 alerts and 24.2%/0.43 alerts per year, which compares with 31.4%/0.85 alerts, 29.8%/0.65 alerts and 27.5%/0.52 alerts per year respectively when the observed cases are used to trigger alerts (Figure 2a). In general, relative to alerts triggered by observed cases, the alerts triggered by the predicted number of malaria cases performed slightly worse, within 5% of the detection system. All alerts triggered by predicted and observed cases potentially prevented larger numbers of cases than random alerts. Relative to the optimally timed alerts, both systems performed well, within 10%–20% of the best achievable performance. On average, the number of alerts per year triggered by the prediction system is less than the number of alerts triggered by the observed cases for the corresponding level of alert threshold. Comparative performance of the detection and prediction methods was insensitive to the length of the window of effectiveness and the choice of function to define potentially prevented cases (Figure 2).
Figure 2 Comparing performance of prediction and detection systems. Percent of PPC by number of alerts per year for different algorithms. (a) and (c) were obtained from cases in excess of the weekly mean (low effectiveness) with window of effectiveness of 8 and 24 weeks respectively. (b) and (d) were obtained from cases in excess of the weekly mean minus one standard deviation (high effectiveness) for windows of eight & 24 weeks, respectively. The solid lines are for detection (Obs) and the dotted lines for prediction (Pred). MeanSD and Percentile represent threshold algorithms based on mean plus standard deviation and percentile, respectively.
Prediction-based systems perform much better in cold than in hot districts. To compare the relative importance of weather factors in cold and hot districts, the %PPC obtained from predicted cases in the cold and hot districts were evaluated separately. Figure 3 shows that alerts triggered by the predicted number of malaria cases in the cold districts perform much better than in the hot districts. Comparative performance in the cold and hot districts was insensitive to the length of the window of effectiveness and the choice of function to define potentially prevented cases. In all cases, the prediction-based alerts were able to prevent 10–25% more cases of malaria at a given sensitivity in cold districts than in hot ones. On the other hand, although, the performance of the detection algorithms in the cold and hot districts was similar with eight-week window of effectiveness, it performed better in the cold than in hot districts with 24-week effectiveness (not shown).
Figure 3 Comparison of performance of prediction systems in cold and hot districts. Percent of PPC by number of alerts per year. PPC was obtained from cases in excess of the weekly mean (low effectiveness) with windows of effectiveness of eight weeks (a) and 24 weeks (b). The solid lines represent cold and the dotted lines hot districts.
Discussion
Timely and accurate information about the onset of P. falciparum epidemics is essential for effective control activities in epidemic-prone regions, especially those in which limited resources must be deployed to the areas of greatest need. In the Ethiopian highland fringe region, one such epidemic-prone area, early detection of epidemics based on simple algorithms for detecting excess cases had been shown to generate alerts that are well timed to precede periods of high incidence [14]. Early warning methods that provide earlier alerts may allow the interruption of transmission earlier in the epidemic, but perhaps at the expense of some level of accuracy. In this study, we have shown that predictions four weeks ahead, based on weather factors and past case numbers, can provide alerts that are of comparable value to those provided by an equivalent early detection system, based simply on observed cases.
An interesting feature of the results was that the prediction system performed well in generating alerts for control measures, despite the fact that the model under-predicts high peaks. Correlation analyses (data not shown) indicate that for most (but not all) districts, the model performed well qualitatively, in the sense of predicting more cases than expected from the weekly mean when such excess cases occurred, and predicting fewer when in fact fewer cases than the weekly mean occurred. This finding focuses attention on the fact that a system can give timely and accurate alerts for epidemic control, even if it is unable to provide accurate predictions of case numbers (Figure 1). The initial hypothesis was that the improved timeliness of an early detection system comes at the expense of some accuracy. The overall results show that these two effects nearly balance each other, so that early warning systems based on our predictive model provide alerts whose value in terms of epidemic control is comparable to those provided by equivalent early detection systems. In a separate analysis (not shown), these two effects were separated out. If the alert system is based on prediction, but the alerts are timed such that their effects start eight weeks after the alert (i.e., four weeks after the week in which the predicted cases cross the alert threshold, equivalent to the timing for early detection), they identify 5% to 10% fewer PPC than the equivalent detection algorithm. The main analysis (Figure 2) showed that the additional four weeks of notice available by implementing control measures so that their effects begin by the week on which excess cases are predicted (four weeks earlier than if the detection algorithm were used) nearly makes up for this deficit.
Studies have shown that temperature affects transmission in cold environments more than it does in hot environments [31,32]. Thus the addition of minimum and maximum temperature into the prediction model contributes less to predictions in the hot districts than it does in the cold districts. The study revealed this differential effect of weather on malaria transmission. The weather-based prediction system performed much better in the cold than the hot districts. Two mechanisms could have been responsible for this difference: epidemic alert algorithms in general could be less useful in hot districts, or weather-based algorithms, specifically, may be less useful in hot districts. Since simple detection-based alerts performed similarly in hot and cold districts (at least with an eight-week window of effectiveness), it appears that the problem in hot districts is with prediction-based methods. However, when 24 weeks were used as the window of effectiveness, the early detection system, like the prediction system, performed better in the cold than the hot districts. This may be because of the shorter transmission season in the hot than cold districts, due to evaporation and drying up of breeding sites in hot districts, such that rainfall's effects on transmission last for fewer weeks in hot areas [25]. In conclusion, an early warning system using weather and other predictor variables is more reliable in relatively cold than hot districts.
Non-climatic factors such as population immunity, migration and drug resistance are believed to influence malaria transmission and have been cited as causes of malaria epidemics [33-36]. The variability in accuracy of prediction seen in the ten districts may have been due to such factors and others [37-41]. These findings are consistent with the findings of Zhou et al. which indicated that there was high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in East African highlands [30]. Determining the relative contribution of the non-climatic factors would be an important step in the development of an early warning system for malaria and a predictive model which incorporates such indicators would give more accurate predictions, but this is not feasible in practice at this moment due to the absence of quantitative data on these factors.
The model chosen for the prediction of malaria cases was based loosely on a model previously evaluated for its ability to explain seasonal variation in malaria incidence in the same data set [25]. The former model, in turn, used polynomial distributed lags of weather factors based on biological considerations about the effects of these weather factors on malaria cases. To that model, additional terms – an autoregressive term and an indicator variable for the week of the year (on the Ethiopian calendar) were added – to improve predictive power. The usefulness of this predictive model has been shown, but modifications of the model have not been systematically explored which might improve its predictive ability still further. Further work should consider a range of prediction models for their ability to generate accurate and timely alerts.
Conclusions
This study showed that short-term (four-week-ahead) predictions of P. falciparum cases using lagged weather and case incidence data performed well in identifying periods of increased malaria cases. Furthermore, the prediction system allowed recognition of epidemic periods at an early stage, thereby facilitating interventions making epidemics preventable with adequate lead time. However, this study indicated that early warning system using weather and other predictor variables are more reliable in relatively cold than hot districts. In conclusion, it has been demonstrated that weather derived predictions identified epidemics with reasonable accuracy and better timeliness compared to early detection systems. Therefore, warning systems based on predictions derived from lagged weather variables may be a useful alternative to early detection systems for targeting resources against incipient falciparum malaria epidemics.
Authors' contributions
HDT, ML and JS conceived the study. HT and ML undertook statistical analysis. HT drafted the manuscript, which was revised by ML. JS participated in designing of the study and statistical analysis. AT initiated the study and made data available in collaboration with WHO and Ministry of Health of Ethiopia. All authors contributed to the writing of the manuscript and approved the submitted version of the manuscript.
Acknowledgments
HT was funded by the Fogarty International Center (FIC) of the National Institutes of Health (NIH) (grant number 5D43TW000918). Financial support for data collection was provided by World Health Organization/RBM. ML thanks the Ellison Medical Foundation for support of this research. We thank the Ministry of Health of Ethiopia for allowing us to access the information. We also thank Afework Tekle and Asnake Kebede for their work on data collection.
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| 15555061 | PMC535541 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 19; 3:44 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-44 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-311554833010.1186/1476-4598-3-31ResearchSaposin C promotes survival and prevents apoptosis via PI3K/Akt-dependent pathway in prostate cancer cells Lee Tae-Jin [email protected] Oliver [email protected] Ronald B [email protected] Shahriar [email protected] Department of Microbiology, Immunology, and Parasitology, Louisiana State University Health Sciences Center, 533 Bolivar Street, CSRB 4-17, New Orleans, LA 70112, USA2 Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, 533 Bolivar Street, CSRB 4-17, New Orleans, LA 70112, USA3 Department of Medicine, Louisiana State University Health Sciences Center, 533 Bolivar Street, CSRB 4-17, New Orleans, LA 70112, USA2004 17 11 2004 3 31 31 30 9 2004 17 11 2004 Copyright © 2004 Lee et al; licensee BioMed Central Ltd.2004Lee 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
In addition to androgens, growth factors are also implicated in the development and neoplastic growth of the prostate gland. Prosaposin is a potent neurotrophic molecule. Homozygous inactivation of prosaposin in mice has led to the development of a number of abnormalities in the male reproductive system, including atrophy of the prostate gland and inactivation of mitogen-activated protein kinase (MAPK) and Akt in prostate epithelial cells.
We have recently reported that prosaposin is expressed at a higher level by androgen-independent (AI) prostate cancer cells as compared to androgen-sensitive prostate cancer cells or normal prostate epithelial and stromal cells. In addition, we have demonstrated that a synthetic peptide (prosaptide TX14A), derived from the trophic sequence of the saposin C domain of prosaposin, stimulated cell proliferation, migration and invasion and activated the MAPK signaling pathway in prostate cancer cells. The biological significances of saposin C and prosaposin in prostate cancer are not known.
Results
Here, we report that saposin C, in a cell type-specific and dose-dependent manner, acts as a survival factor, activates the Akt-signaling pathway, down-modulates caspase-3, -7, and -9 expression and/or activity, and decreases the cleaved nuclear substrate of caspase-3 in prostate cancer cells under serum-starvation stress. In addition, prosaptide TX14A, saposin C, or prosaposin decreased the growth-inhibitory effect, caspase-3/7 activity, and apoptotic cell death induced by etoposide. We also discovered that saposin C activates the p42/44 MAP kinase pathway in a pertussis toxin-sensitive and phosphatidylinositol 3-kinase (PI3K) /Akt-dependent manner in prostate cancer cells. Our data also show that the anti-apoptotic activity of saposin C is at least partially mediated via PI3K/Akt signaling pathway.
Conclusion
We postulate that as a mitogenic, survival, and anti-apoptotic factor for prostate cancer cells, saposin C or prosaposin may contribute to prostate carcinogenesis at its early androgen-dependent or metastatic AI state.
Saposin CProsaposinProstate CancerApoptosisSurvival
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Background
Androgens, growth factors, neuropeptides, and other trophic agents are involved in normal and neoplastic growth of the prostate. Prosaposin is the intracellular precursor of four lysosomal glycoproteins, saposins A-D, that are involved in lysosomal hydrolysis of sphingolipids. These saposins, through their interaction with glycosphingolipid hydrolases and their substrates, increase lysosomal hydrolytic activities. Saposins and prosaposin are expressed by various cell types and as a secretory protein in body fluids including blood, seminal plasma, seminiferous tubular fluid, and prostatic secretions [1-5]. Prosaposin and its active domain, saposin C, are known for their potent neurotrophic activities and are involved in neuro-embryological development [6,7]. The neurotrophic activity of prosaposin has been attributed to the NH2-terminal portion of the saposin C domain of the molecule which is the source for a number of biologically active synthetic peptides such as prosaptides TX14A [4-6]. Prosaptides (i.e., TX14A), saposin C, and prosaposin exert their biological effects by binding to a partially characterized single high-affinity G-protein coupled receptor (GPCR) [6-8]. It has been reported that mice with an inactivated prosaposin gene die at 35–40 days of age due to neurological disorders. These mice also develop several abnormalities in their reproductive organs, such as atrophy and involution of the prostate gland and inactivation of MAPK and Akt in the prostate epithelium [9,10]. The spectrum of biological activities of prosaposin or saposin C in cancer biology in general and in prostate cancer has not been specifically addressed.
We have recently reported a higher expression of prosaposin in androgen-independent (AI) prostate cancer cells (PC-3 and DU-145) than in androgen-sensitive (AS) LNCaP or in normal prostate epithelial and stromal cells. In addition, we have found that prosaptide TX14A stimulates prostate cancer cell proliferation, migration, and invasion, activates the Raf-MEK-ERK-Elk-1 signaling cascade of the mitogen-activated protein kinase (MAPK) pathway, and inhibits the growth-inhibitory effects of sodium selenite administered at apoptogenic concentrations [11].
In the present study, we show for the first time that saposin C also functions as a survival factor, activates PI3K/Akt-signaling pathway, and in a cell type-specific manner, modulates the expression of procaspase- and caspase-3, -7, and -9 in prostate cancer cells under serum-starvation stress. We demonstrated that prosaptide TX14A, saposin C, or prosaposin decreased the growth-inhibitory effects, caspase-3/7 enzymatic activity, and apoptotic cell death induced by etoposide. In addition, our data show that saposin C activation of a p42/44 MAPK in prostate cancer cells is not only pertussis toxin-sensitive, but also PI3K/Akt-dependent. Moreover, the PI3K-inhibitor, LY294002, restores the apoptogenic effect of etoposide in prostate cancer cells studied.
We propose that as a survival and anti-apoptotic factor, saposin C or prosaposin may contribute to prostate carcinogenesis or to the development of hormone-refractory prostate cancer.
Results
Saposin C acts as a survival factor for prostate cancer cells
The effect of saposin C as a survival factor was assessed under serum-starvation stress. Androgen-sensitive (AS) LNCaP cells did not maintain their viability when cultured in serum-free, 0.25% FBS-RPMI, or 0.5% FBS-RPMI media, for more than 36 h. These cells started to detach from tissue culture plates and cell viability was decreased to less than 40% as determined by the trypan blue dye-exclusion method. However, in the presence of 1% FBS, cells remained attached to the tissue culture plate and their growth increased 31% at day 4 and 20% on day 6 as compared with the control values at day 2 (Fig. 1). Saposin C stimulated proliferation of these cells by 13% at day 2, 35% at day 4, and 33% at day 6 compared to the controls. PC-3 cells appeared to be more sensitive to serum deprivation and the number of live cells decreased 30% by day 4 and 60% by day 6 compared to the control values at day 2. However, saposin C (at 1.0 nM) increased cell proliferation by 9% at day 2, 19% at day 4, and 88% at day 6, compared to control plates at the same time period. The growth-response of DU-145 cells was different from PC-3 or LNCaP cells. In the absence of saposin C, the number of live cells increased 10% at day 4 and 29% at day 6 compared to day 2. These cells also demonstrated the highest proliferative response to saposin C at day 4 by 93% (Fig. 1). Taken together, these data indicate that saposin C in a dose-dependent and cell type-specific manner, promotes the survival of the serum-deprived prostate cancer cells.
Figure 1 Saposin C acts as a survival factor for prostate cancer cells. Cells were cultured in their complete media for 3 days and shifted to their basal (serum-free) media or RPMI-1% FBS (only for LNCaP) in the presence or absence of the indicated concentrations of saposin C for 2, 4, or 6 days. Tissue culture media and saposin C were refreshed every 2 days. At the end of incubation periods, cells were trypsinized and cell number was determined using a hemocytometer and trypan blue exclusion method. PC-3 and DU-145 were used as androgen-independent and LNCaP cells were used as androgen-sensitive prostate cancer cell lines. Data represent the average of three independent experiments in triplicate samples; bars, ± SEM. * indicates P < 0.05, and ** indicates P < 0.01 compared to control. Statistical significance was determined by one-way ANOVA with Bonferroni's corrections.
Saposin C activates the PI3K/Akt signaling pathway in prostate cancer cells
Several studies have demonstrated that the serine/threonine kinase Akt is a pivotal survival effector for prostate cancer cells and protects them from apoptotic-cell death induction by various types of stresses. Hence, we next evaluated the effect of saposin C on the Akt signaling pathway in cells.
Direct immunoblotting of serum-starved cells for 24 h showed that saposin C upregulates phosphorylative activity of Akt at serine 473 in androgen-independent (AI) PC-3 and DU-145 cells (Fig. 2A). This response was biphasic. The response of LNCaP cells was distinct and started at 1.0 nM that subsequently returned to a basal level at higher treatment concentrations. Under our experimental conditions, we did not detect any changes in the phosphorylative activity of Akt at threonine 308. Since Akt is a downstream effector of PI3K, we tested the effect of the PI3K-specific inhibitor, LY294002. Pretreatment of cells with LY294002 (50 μM, 3 h) followed by saposin C treatment substantially reduced phosphorylation levels of both serine and threonine residues of Akt in AI- and AD-prostate cancer cells (Fig. 2A).
Figure 2 Saposin C activates Akt signaling pathway in prostate cancer cells. A, cells were cultured up to 70% confluency in their complete media, serum-deprived for 24 h, and treated with 10% FBS or saposin C at 0.1, 1, or 10 nM for 10 min. A representative culture plate was also treated with LY294002 (LY; 50 μM) before treating with saposin C (at 1 nM for LNCaP and 10 nM for PC-3 and DU-145). Fifteen μg protein per sample was subjected to SDS-PAGE under reducing conditions and immunoblotting was carried out using phospho-specific Akt antibodies against serine 473 or threonine 308. B, non-radioactive in vitro kinase assay was performed to determine the effect of saposin C on Akt kinase activity as described in details in Materials and Methods. Briefly, cells were grown as described above and Akt was selectively immunoprecipitated from 250 μg protein using 20 μl of immobilized Akt 1G1 monoclonal antibody. Immunocomplexes were pelletted and resuspended in kinase buffer in the presence of 200 μM ATP and 1 μg of Akt/PKB substrate-glycogen synthase kinase fusion protein (GSK-3α/β) and incubated for 30 min at 30°C, allowing immunoprecipitated Akt (if activated) to phosphorylate GSK-3. After terminating the kinase reaction, phosphorylated GSK-3 was detected by SDS-PAGE and immunoblotting using phospho-GSK-3α/β antibody. Control loading was evaluated with anti-Akt antibody to determine total Akt-level. Each experiment was performed in duplicate, and the assays were repeated three times.
To determine whether the upregulation of Akt-phosphorylation by saposin C is associated with its kinase activity, in vitro kinase assays were performed. After 5 or 10 min exposure of cells to saposin C, activated-Akt induced phosphorylation of glycogen synthetase kinase-3 (GSK-3; a well-characterized Akt substrate) in both AS and AI prostate cancer cells (up to 3-fold compared to basal levels) at concentrations as low as 0.1 nM, was followed by a slight increase at higher concentrations (Fig. 2B). Using purified human milk prosaposin, we observed similar responses (data not shown). The above results indicate that saposin C activates the Akt-signaling pathway in a PI3K-dependent manner in both AS and AI prostate cancer cells.
Saposin C differentially modulates the expression or activity of caspases and PARP in prostate cancer cells under serum-starvation stress
An essential component of the programmed death pathway in many cell types involves proteolytic cleavage of inactive caspases to catalytically active products. We investigated the expression of cleaved (active) and non-cleaved (inactive) forms of the initiator caspase-9, its active downstream effectors (caspases-3 and -7), and poly (ADP-ribose) polymerase (PARP, a nuclear substrate for caspase-3) in the cells after a 48 h serum deprivation period. Caspase-9 is closely coupled to proapoptotic signals and we found that the expression of procaspase-9 was not affected by saposin C; however, we were able to detect a reduction in its cleaved form at 10 nM saposin C in all cells investigated (Fig. 3).
Figure 3 Effect of saposin C on expression/activity of caspases and PARP under serum-starvation stress. Cells were cultured routinely up to 60% confluency, washed with PBS, and incubated in their respective serum-free media supplemented with or without saposin C for 48 h. Cell lysates were prepared as described in Materials and Methods and 75 μg of clarified protein samples was subjected to SDS-PAGE under reducing conditions. Western analysis was carried out using monoclonal antibodies against non-cleaved and cleaved caspases-3, -7, and -9 and PARP. For control loading, membranes were probed or reprobed with anti-actin antibody. Each experiment was performed in duplicate, and the assays were repeated three times.
With respect to the effector caspases, we noticed a dramatic dose-dependent increase in the expression of procaspase-3 in the AI PC-3 and DU-145 cell lines. However, we observed a reduction in expression of caspase-3 in both AS and AI cancer cells. Furthermore, our data showed that procaspase-7 expression in these cells was not affected by saposin C and under our experimental conditions we did not detect caspase-7 in AI prostate cancer cells. In LNCaP cells, we found a reduction in the level of caspase-7 at 1 or 10 nM of saposin C (Fig. 3). To further follow the mechanistic response of cells to saposin C in the death cascade, we examined the expression of one of the final death substrates, PARP, and its cleaved product. The intensity of PARP expression was considerably higher in PC-3 and DU-145 cells than in LNCaP cells. Saposin C, in a dose-dependent manner, increased PARP expression and this effect was associated with a parallel dose-dependent reduction of the cleaved (active) PARP levels (Fig. 3). Interestingly, the ratio of PARP: cleaved PARP expression in AI PC-3 and DU-145 cells, either at its basal level or after stimulation with saposin C, was higher than AS LNCaP cells. In general, saposin C induced a cell type-specific (AI versus AS) alteration in the expression level of initiator and effector caspases. This effect suggests a better survival and anti-apoptotic activity of saposin C in AI prostate cancer cells than in AS LNCaP cells.
Saposin C protects prostate cancer cells from etoposide-induced apoptotic cell death
Next, we decided to evaluate the effect of an apoptogenic agent, etoposide, on cell growth, apoptosis, and caspase activity in the presence or absence of various effectors. Cells were treated in complete culture media for three days, and then subjected to the MTS assay. Using these experimental conditions, we empirically determined the lowest concentration of etoposide that would lead to the highest growth inhibition. We found that the growth inhibitory effect of etoposide on prostate cancer cells is also cell type-specific. For example, a 20 μM etoposide concentration was sufficient to reduce the cell number to 53% in PC-3 and to 58% in LNCaP cells as compared to their control values. However, DU-145 cells were more sensitive and treating these cells with only 2 μM etoposide led to a 69% reduction in the cell number compared to control values (Fig. 4A). Compared to etoposide-treated cells, saposin C increased cell growth by 13% in PC-3, 24% in DU-145, and 27% in LNCaP cells. Like saposin C, prosaposin reduced etoposide-induced growth inhibition to relatively the same degree. The highest increase in cell number was achieved with synthetic peptide TX14A treatment; however treatment of cells with the mutant 769M peptide showed only a negligible effect (2–5% increase the cell number) (Fig. 4A). These results indicate that TX14A peptide, saposin C, or prosaposin can reduce etopside growth-inhibition on prostate cancer cells.
Figure 4 Saposin C reduces growth inhibitory effect of etoposide and acts as an anti-apoptotic factor for prostate cancer cells. A, cells were seeded at 2000 per well in 96-well plates in their complete culture media for 3 (for PC-3 and DU-145) or 4 days (for LNCaP), treated with vehicle (DMSO), saposin C (0.1, 1, or 10 nM), prosaptide TX14A (10 nM), inactive mutant peptide 769M (10 nM), or prosaposin (1 ng/ml) at the indicated concentrations in the presence or absence of etoposide at the indicated concentrations for 3 days. After this period cell number was determined using MTS assay and cell type-specific OD/cell number calibration curve as described in Materials and Methods. B, apoptosis was determined by TUNEL assay. Cells were cultured in multiwell chamber slide up to 40% confluency in their complete culture media, and treated with etoposide in the presence or absence of saposin C (0.1, 1, or 10 nM) for 3 days. Percentage of apoptosis was determined by random selection of 10 microscopic field (at × 200 magnification) and cell count with a hemocytometer. Data expressed at the average of three independent experiments and twelve replicate samples; bars, ± SEM. * indicates P < 0.05, and ** indicates P < 0.01 compared to control (etoposide). Statistical significance of the effect of saposin C on cell growth and apoptosis was evaluated by one-way ANOVA with Bonferroni's corrections. Differences of vehicle (or etoposide)-only treated cells and any other single experimental group of interest (TX14A or prosaposin) was evaluated by Student's t-test and statistical significance was set at P < 0.05.
Using the TUNEL assay and above experimental conditions, we next evaluated the effect of saposin C on the percentage of apoptotic cells after treating cells with etoposide for three days. Apoptotic cells were identified as dense, bright, and punctate, with brownish pigmentation of poly-fragmented nuclei. Among the three cell lines investigated, PC-3 proved to be the most resistant cell line to apoptosis induction by etoposide. Overall, there was a dose-dependent reduction (with a peak effect at 10 nM) of apoptotic cells in the three cell lines investigated. Saposin C decreased apoptotic cells by 47% in PC-3, 89% in DU-145, and 58% in LNCaP cells (Fig. 4B). This result demonstrates the counteracting influence of saposin C and etoposide on apoptosis in prostate cancer cells.
We also employed a sensitive fluorometric assay to measure caspase-3/7 (based on DEVDase) activity using the experimental conditions described above. Saposin C, at 1 nM concentration, demonstrated the highest reduction in caspase-3/7 activity in AS LNCaP (21%) and in AI PC-3 (35%) and DU-145 cells (30%) (Fig. 5A). Prosaposin-treated cells also demonstrated a similar effect. TX14A peptide not only decreased the growth-inhibitory effect of etoposide (data not shown), but also proved to be a potent anti-apoptotic peptide, reducing caspase-3/7 activity by 43% in PC-3, 36% in DU-145, and 30% in LNCaP cells. However, the control (inactive mutant) peptide's (769M) effect was minimal (with a 2–5% reduction) (Fig. 5A). These results clearly indicate that the anti-apoptotic activity of saposin C is at least partially associated with modulation of caspase-activity.
Figure 5 Effects of saposin C on caspase-3/7 activity (A) and the influence of PI3-kinase inhibitor (B) in etoposide-treated cells. Cell culture, treatment period, growth and caspase activity was described in Figure 4. Caspase-3/7 activity was determined using the Apo-ONE Homoheneous Caspase-3/7 assay kit based on the cleavage of a profluorescent caspase-3/7 substrate (Z-DEVD-R110) and fluorimetric quantitation was performed at an excitation and emission wavelength of 485+20 and 535+25 nm, respectively. After correction of the fluorimetric reading with the blank (vehicle control), final fluorescent intensity was depicted as an arbitrary endpoint relative fluorescent unit, RFLU. PI3-kinase inhibitor (LY294002; LY) was used at final 1.5 μM concentration and saposin C was added at optimal 1 nM (for PC-3 and LNCaP) or 10 nM (for DU-145) concenration. Etoposide (Et) was added at optimal 20 μM (for PC-3 and LNCaP) or 2 μM (for DU-145). Data expressed are the average of three independent experiments and twelve replicate samples; bars, ± SEM. * indicates P < 0.05, and ** indicates P < 0.01. Statistical significance of the effect of saposin C on cell growth and apoptosis was evaluated by one-way ANOVA with Bonferroni's corrections. Differences of vehicle (or etoposide)-only-treated cells and any other single experimental group of interest (TX14A or prosaposin) was evaluated by Student's t-test and statistical significance was set at P < 0.05.
The PI3K/Akt inhibitor restores apoptogenic activity of etoposide in saposin C treated cells
To determine whether or not saposin C anti-etoposide apoptotic activity is PI3-kinase dependent, the effect of PI3K/Akt inhibitor (LY294002) on caspase-3/7 activity in the cells was examined in the presence or absence of saposin C ± etoposide. Through our initial studies, using trypan blue exclusion and MTS assays, we found 1.5 μM of LY294002 was a non-toxic and tolerable dosage for experimental period (3 days).
Compared to DMSO-treated cells, LY294002 slightly increased (up to 10%) the caspase-3/7 enzymatic activity in PC-3 and DU-145 and showed almost no change in LNCaP (Fig. 5B). As described above, saposin C significantly decreased induction of caspase-3/7 activity by etoposide. Saposin C also reduced the induction of casapases activity by LY294002 under our experimental conditions. Addition of LY294002 to the cells treated with saposin C and etoposide increased caspase-3/7 enzymatic activity, but to a level below than etoposide-only treated cells (Fig. 5B). These results indicate that antiapoptotic activity of saposin C and its effect on caspase activity is at least partially mediated via the PI3K/Akt signaling pathway.
Saposin C activation of MAPK is pertussis toxin-sensitive and PI3K/Akt-dependent
In neuro-glial derived cells, neurotrophic activity, cell-death protection and the activation of MAPK by prosaptides (i.e., TX14A), saposin C, or prosaposin are mediated by their binding to a pertussis toxin-sensitive GPCR [6,7,12,13]. Our previous data demonstrated that prostate cancer cells were differentially responsive to the TX14A peptide in a number of biofunctional assays [11]. Our current results indicate the presence of a sensitive and /or responsive receptor-ligand interaction that could be accountable for the subsequent activation of downstream signaling effectors in MAPK- and Akt-signal transduction pathways. In addition, there is also emerging data indicating that signaling proteins such as PI3K and Akt can also activate MAPK pathways [14-16].
We have previously demonstrated MAPK-activation by the TX14A peptide derived from a trophic sequence of saposin C [11]. To examine the involvement of GPCR in saposin C-activation of the Akt-signaling pathway as well as the possibility of MAPK and Akt cross-signaling initiated by saposin C, we evaluated the effect of saposin C on p42/44 MAP kinase activation in prostate cancer cells in the presence or absence of various inhibitors. Treatment of cells with saposin C increased the phosphorylative activity of p42/44 MAPK, which was substantially inhibited by pretreating cells with the specific MEK inhibitor (U0126; Fig. 6). We used 10% FBS treatment as an external positive control for induction of p42/44 activity in the cells. Saposin C treatment of cells pretreated with PT showed a modest (in LNCaP cells) to strong reduction (in AI prostate cancer cells) in the level of phospho-p42/44 MAPK. This result clearly indicates a cell type-specific PT-sensitivity and/or the potential involvement of one or more G-proteins in saposin C-activation of MAPK pathway in the cells.
Figure 6 Saposin C activation of MAPK pathway is pertussis toxin-sensitive and PI3K/Akt-dependent. Cells were cultured up to 60% confluency in their maintenance media, washed with PBS, serum-deprived for 20 h, and then fresh basal culture media was added for an additional 4 h in the presence or absence of LY294002 (50 μM, 3 h), Wortmannin (10 μM, 15 min), U0126 (10 μM, 1.5 h), or pertussis toxin (200 ng/ml, 4 h). After pretreatment, saposin C (0.1 nM) was added directly to the cells and incubated for 5 min at 37°C. Cell lysates were prepared and 10 μg protein per sample was subjected to SDS-PAGE and immunoblotting using phospho-specific p42/44 MAPK antibody. For control loading, membranes were also probed or reprobed with p42/44 antibody to detect total p42/44 MAPK. Parallel tissue culture plates, treated in the same manner, were also tested for cell viability by trypan blue dye-exclusion assay. FBS at 10% final concentration was used as a positive control. Each experiment was performed in duplicate, and the assays were repeated three times.
The intensity of reduction of p42/44 activity and its cell type-specific pattern was very similar to PT plus saposin C- or PT-treated values. Interestingly, we observed a more profound reduction in p42/44 phosphorylative activity in cells pretreated with LY294002 and then treated with saposin C. To rule out the potential cytotoxic effect of the inhibitors, viability of cells was also determined by trypan blue dye-exclusion. We observed essentially similar results using the other structurally and mechanistically different PI3-kinase inhibitor (wortmannin). This experiment revealed that at the end of the pre-treatment incubation period, cell viability was equal to or more than 95%. These results indicate that MAPK activation by saposin C is at least partially mediated by saposin C-regulated PI3K/Akt pathways in prostate cancer cells. This result also provides additional proof for simultaneous activation of multiple (inter-related) signal transduction pathways by saposin C.
Discussion
Induction of apoptosis by androgen-ablation therapy significantly reduces androgen-dependent (AD) prostate cancer cells, but fails to cure the majority of patients due to the presence of apoptosis-resistant cancer cells that are androgen-independent [17]. It is likely that the development of these cells is an adaptive response to hormonal therapy rather the overgrowth of resistant cells. In-depth understanding of apoptotic phenomena, identification of its intracellular components, and characterization of its extracellular effectors as inducers or inhibitors may contribute to therapeutic approaches for prostate cancer.
The prosaposin knock-out mouse model has revealed a number of interesting findings specifically in the male reproductive organs. Among these are atrophy of the prostate gland, epididymis, and seminal vesicles. Microscopic evaluation of affected tissues also shows undifferentiated phenotypes in prostate ventral and dorsal lobules and atrophy of the tubuloalveolar glands and their epithelial cell lining [9]. These data are suggestive of a primary role for prosaposin in development of the prostate gland.
In a variety of neuro-glial derived cells, synthetic peptides encompassing a trophic sequence of saposin C and/or prosaposin have been found to induce growth, survival, and/or differentiation, or to prevent apoptotic cell-death in vitro and in vivo [11,18-20]. For example, prosaptide-TX14A and prosaposin, in a dose- and time-dependent manner reduced apoptotic cell-death induction of primary Schwann cells cultured in low serum concentrations, and PI3K inhibitors (wortmannin or LY294002) blocked their anti-apoptotic effects [21].
Here, we found that under prolonged serum starvation (2 to 6 days), although the responses of androgen-sensitive (AS) LNCaP and AI PC-3 and DU-145 prostate cancer cells were different from each other, saposin C in a dose- and time-dependent manner, proved to increase proliferation and survival of both cell types (Fig. 1). Normal prostatic epithelial cells neither tolerated the basal medium nor responded to saposin C (data not shown). Like many other cancer cells, withdrawal of mitogens, growth factors, and other trophic factors by serum-starvation, serves as a potent stimulus and driving force activating different survival mechanisms that eventually lead to apoptotic cell-death in AD- and Al-prostate cancer cells [22,23]. Since the PI3-Kinase/Akt signaling pathway is known as a central cell-survival mechanism and an important mediator of survival signals driven by growth or trophic factors, we were also interested in examining the level of PI3K/Akt activity during serum starvation. Interestingly, we found that saposin C upregulated Akt-p473Ser phosphorylative activity in the prostate cancer cells under investigation. This effect was substantially inhibited by LY294002, an inhibitor of the upstream Akt effector, PI3K (Fig. 2A). In addition, using an in vitro kinase assay, we proved that saposin C induction of PI3K/Akt activity in cells was associated with increased phosphorylation of GSK3α/β, as a downstream key target of the Akt kinase (Fig. 2B). Unlike Akt-p473Ser, our study showed a considerably higher level of constitutively activated Akt-p308Thr in all cells and remained unaffected by saposin C. Other studies have also supported a central role for the PI3K/Akt pathway as a dominant growth factor-induced survival pathway for prostate cancer cells [14,16]. Constitutive activation of the PI3K/Akt survival pathway has been described as a mechanism that enables endocrine-related breast, lung, and prostate cancer to become refractory to cytotoxic therapy [24-27]. Interestingly, immunohistochemical analyses have demonstrated a direct correlation between Akt phosphorylation and the Gleason's score in prostate cancer [28]. Our results provide evidence that implicate the involvement of PI3K/Akt activity in saposin C (growth factor)-induced survival of prostate cancer cells.
With respect to prosaposin, immunohistochemical staining of the involuted and atrophied prostate tissues from homozygous knock-out mice also showed inactivation of the MAPK and Akt signaling pathways [9,10]. Apoptotic-death signal transduction pathways are not limited to PI3K/Akt and may involve multiple redundant physiological pathways. Several studies have shown the involvement of caspases in the apoptosis of the prostate gland in normal development or malignant conditions [29,30]. For example, immunohistochemical evaluation in castrated mice and rats showed the presence of (activated) caspases in prostate and a correlation between caspase-3 expression and the Gleason's grade of tumors [29]. In vitro studies also revealed that caspase-inhibitory mechanisms might be involved in metastasis of prostate cancer cells [15]. We found that saposin C, in a dose-dependent manner, increased procaspase-3 and PARP levels and decreased the cleaved form of caspase-9 and -3 and PARP (a caspase-3 substrate) in both AS and AI prostate cancer cells. PARP cleavage has been recognized as a sensitive marker of caspase-mediated apoptosis and its cleavage paralyzes the enzyme's ability to repair DNA strand breaks. Therefore, reduction of the PARP cleavage is a strong indicator for anti-apoptotic activity of saposin C. Although procaspase-7 expression was not affected in any of the cells investigated, its active form was reduced only in LNCaP cells and was not detected in AI prostate cancer cells (Fig. 3). This special pattern for alteration in the level of procaspase-3, its cleaved form, and PARP was coincident with saposin C-induced cell survival under serum-deprivation culture condition (Fig. 1). Such divergent regulation of caspase-3 and PARP has rarely been reported in prostate cancer cells [31]. However, it has been demonstrated frequently in the nervous system and therefore might represent a unique characteristic of prosaposin or saposin C as a neurotrophic molecule [32].
Next, we exposed cells to a universal apoptogenic agent, etoposide, and found that prosaposin or its active derivatives (saposin C or TX14A peptide), were able to decrease the growth-inhibitory effect of etoposide-treated prostate cancer cells (Fig. 4). TUNEL assay, as a direct measure of apoptotic death showed a dose-dependent reduction in the percentage of apoptotic cells by saposin C (Fig. 4B). Under similar experimental conditions, we also showed that saposin C, prosaptide TX14A, or prosaposin reduce caspase-3/7 activity in cells treated with etoposide. This effect could be counteracted by administration of a PI3-kinase inhibitor (LY294002) (Fig. 5A and 5B). These data are a clear indication that saposin C-inhibition of the apoptogenic activity of etoposide is at least partially dependent on the upstream Akt effector, PI3K (Fig. 5B). Together, the above findings suggest that the two closely inter-connected cell survival/apoptotic pathways (PI3K/Akt and caspases) activated by saposin C or prosaposin might potentially synergize and provide a growth and survival advantage to both AD- and AI-prostate cancer cells.
Induction of mitogenic, survival, and anti-apoptotic signals in physiological and pathological conditions may begin from a wide array of extracellular stimuli and receptors, including receptor tyrosine kinases (RTKs) and G-protein coupled receptors (GPCRs). From a historical point of view, considerable attention has been given to the role of RTKs and their cognate polypeptide ligands in prostate cancer. However, accumulating evidence support the involvement of lysophosphatidic acids, neurotransmitters, and neuropeptides such as bombesin and neurotensin through GPCR signaling in the initiation or progression of prostate cancer [32,33]. GPCRs also use pathways that are very similar to those utilized by RTKs to activate survival and anti-apoptotic signaling pathways such as the prototypic Raf-MEK-MAPK and PI3K/Akt signal transduction pathways and caspase cascades [32,34-37].
Here, we showed that saposin C, in a pertussis toxin-sensitive manner, activated p42/44 MAPK (Fig. 6). Our study demonstrated the involvement of GPCR as a responsible receptor system interacting with saposin C and therefore activating the subsequent signaling pathways. Due to the considerable importance of activation of MAPK signal transduction activation by saposin C-GPCR and activation of the Akt signaling pathways, we tested whether inhibition of PI3K/Akt could affect saposin C-induced p42/44 MAPK activation. Pretreatment of cells with LY294002 inhibited saposin C activation of p42/44 MAPK (Fig. 6). These data not only indicate cross-communication between MAPK- and PI3K/Akt-signaling pathways, but also might suggest that simultaneous activation of the two important signal transduction pathways by saposin C provide a potent cell survival and apoptotic-death protection program for prostate cancer cells.
Conclusion
Our data for the first time show that by activation of multiple inter-related signaling pathways (PI3K/Akt and MAPK) and cell type-specific modulation of expression or activity of caspases, saposin C and/or its precursor (prosaposin) serve as a survival and anti-apoptotic factor for both AS- and AI-prostate cancer cells. Elucidation of such intricate mechanisms could potentially provide a therapeutic option that combines cytotoxic therapy and inhibition of survival/anti-apoptotic signals for AI metastatic prostate cancer. Finally, our observations provide novel insights into the diversity of biological activities of prosaposin in prostate cancer cells.
Methods
Cell lines
Androgen-independent (PC-3, DU-145) and -sensitive (LNCaP) prostate cancer cell lines were obtained from the American Type Culture Collection (Manassas, VA) and grown in defined media (PC-3 and DU-145 in DMEM-10% FBS and LNCaP in RPMI-1640-10% FBS supplemented with 1 mM sodium pyruvate, 10 mM HEPES). Purified recombinant human saposin C and prurified human milk-prosaposin were characterized and provided by Dr. K. Sandhoff (University of Bonn, Germany) and Dr. M. Hiraiwa (University of California, San Diego), respectively.
Cell survival assays
Cells were initially grown in 100 mm plates in their respective culture media for 3 days, and after washing with PBS were incubated in serum-free DMEM (PC-3 and DU-145) or RPMI-1% FBS (LNCaP) in the presence or absence of saposin C (at 0.1, 1, or 10 nM) for 2, 4, or 6 days. Saposin C and culture media were replaced every 2 days. At the end of the incubation periods, cells were trypsinized and cell number was determined using a hemocytometer and the trypan blue exclusion method.
Western analysis
Protein expression analysis was performed according to standard procedures [38]. Briefly, the cell extract was prepared by washing cell monolayers with cold-PBS, lysing the cells on ice for 15 min with lysis buffer (20 mM PIPES [pH 7.4], 150 mM NaCl, 1 mM EGTA, 1% Triton X-100, 1.5 mM MgC12) supplemented with a protease inhibitor cocktail (Roche Diagnostic, Inc., Indianapolis, IN) and 1 mM sodium orthovandate, plus sodium dodecyl sulfate (SDS) at a final concentration of 0.1%. The lysates were then centrifuged (15 min, 4°C, 16,000 × g), aliquoted, and stored at -70°C until use. Protein concentration was determined by BCA assay (PIERCE, Rockford, IL). Each experiment was repeated at least two times. For western analysis, membranes were blocked with 5% BSA in the rinse buffer (150 mM NaCl, 20 mM Tris, 0.1% Tween 20) for 1 h, washed in rinse buffer for 10 min, and then incubated with the respective primary antibody at the indicated concentrations (see below). The membranes were then washed and incubated with the appropriate horseradish peroxidase-conjugated secondary antibody (1:1000 dilution; Santa Cruz Biotechnology, Santa Cruz, CA) for 1 h at room temperature, washed for 10 min and four more cycles of 5 min, and treated with an enhanced chemiluminescence (ECL) detection system (Amersham, Piscataway, NJ). In some cases, when the signal was very weak or undetectable, we used ECL-plus (Amersham).
(i) Effect of PI3K/Akt and MEK-inhibitors, or Pertussis Toxin on Saposin C Activation of p42/44 MAPK
Cells were grown in their respective complete culture media for 2–3 days (up to 60% confluency), washed with PBS, incubated in their serum-free (basal) media for 20 h, and then fresh basal media was added to all plates for an additional 4 h. Various inhibitors [LY294002 (50 μM, 3 h), Wortmannin (10 μM, 15 min), U0126 (10 μM, 1.5 h), and Pertussis toxin (200 ng/ml, 4 h)] were added to the culture medium, just before treating cells with saposin C (at 0.1 nM, 5 min). We used 10% FBS as a positive control. Cells were lysed and 10 μg of clarified protein samples was subjected to SDS-PAGE under reducing conditions. Phospho-specific p42/44 antibody (1:1000; Cell signaling Technologies, Bedford, MA) was used as the primary antibody and as a loading control. Filters were also probed or reprobed with anti-p42/44 antibody. Additional tissue culture plates that had been treated with or without inhibitors were also tested for cell viability by trypan blue dye-exclusion assay.
(ii) Saposin C and PI3K/Akt signaling pathway
Cells were cultured up to 70% confluency in their complete media and after washing with PBS, they were serum-starved for 24 h, and then treated with 10% FBS or saposin C at 0.1, 1 or 10 nM for 10 min. A representative tissue culture plate was also pretreated with the PI3K-inhibitor (LY294002, 50 μM for 3 h) before treating cells with saposin C (at 1 nM for LNCaP and at 10 nM for PC-3 and DU-145 cells). After preparation of the cell lysate, 15 μg of protein per sample was subjected to SDS-PAGE under reducing conditions. Immunoblotting was performed using phospho-specific Akt antibodies against serine 473 or threonine 308 (Cell Signaling Technology). A loading control was evaluated with anti-Akt antibody. Each experiment was performed in duplicate, and the assays were repeated three times.
Immunoprecipitation and in vitro Akt kinase activity assay
A non-radioactive Akt kinase assay kit (Cell Signaling Technologies) was used to determine whether saposin C treatment of cells under serum-starvation stress would lead to Akt-activation. For Akt-kinase assays, cells were grown up to 70% confluency in their maintenance media, serum-starved for 24 h, and then treated for 5 or 10 min in the presence or absence of saposin C at 0.1, 1, or 10 nM. Cells were washed once with ice-cold PBS and harvested under nondenaturing conditions using 1X ice-cold cell lysis buffer (from the Kit) supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF) on ice for 5 (or 10) minutes. Akt was selectively immunoprecipitated from 250 μg protein (whole cell lysates) using 20 μl of immobilized Akt 1G1 monoclonal antibody, and then incubated with gentle rotation for 4 h at 4°C. Samples were then centrifuged briefly (30 sec, 2000 × g) and pellets were washed twice with 1X lysis buffer and once with 1X kinase buffer. Immunocomplexes (pellets) were resuspended in 40 μl 1X kinase buffer [composed of 25 mM Tris (pH 7.5), 5 mM β-glycerolphosphate, 2 mM DTT, 0.1 mM Na3VO4, and 10 mM MgC12 supplemented with 200 μM ATP and 1 μg glycogen synthase kinase-3 [GSK-3; a well characterized Akt/PKB substrate] of fusion protein (GSK-3α/β) and incubated 30 minutes at 30°C, allowing immunoprecipitated Akt (if activated) to phosphorylate GSK-3. The kinase reaction was terminated by adding 20 μl of 3 × SDS sample buffer. Phosphorylated GSK-3 was then detected by western analysis using phospho-GSK-3α/β (for Ser 21 of GSK-3α and ser 9 of GSK-3β) antibody. The above in vitro kinase assay is based on the fact that phosphorylated-Akt (active) regulates GSK-3α/β kinase activity via phosphorylation at ser 21/9. For control loading, 10 μg protein per sample from the same whole cell lysates were subjected to western analysis using monoclonal anti-Akt antibody or actin. Each experiment was performed in duplicate, and the assays were repeated three times.
Apoptosis assays
(i) Effect of saposin C on expression of caspases by western analysis
Cells were cultured up to 60% confluency in their complete culture media. After washing with PBS, they were incubated with their respective serum-free media in the presence or absence of saposin C at 0.1, 1, or 10 nM for 48 h; whole cell lysates were prepared as described above. Clarified protein samples (75 μg) were subjected to SDS-PAGE under reducing conditions. Western analyses were carried out using monoclonal antibodies against non-cleaved and cleaved caspases-3, -7, and -9 and poly (ADP-ribose) polymerase (PARP) provided in an Apoptosis Sampler Kit (Cell Signaling Technology). Each experiment was performed in duplicate, and the assays were repeated three times.
(ii) Fluorometric measurement of caspase-3/7 activity in cells treated with etoposide
We next examined the effect of an apoptogenic agent, etoposide, on cell growth and caspase activity in the presence or absence of saposin C, prosaposin, prosaptide TX14A, or an analogous inactive mutant peptide (769M; 4). Cells were seeded at 1,000 per well in 96-well plates in their complete culture medium for 3 to 4 days. After this period, cells were treated in complete culture media for 3 days with etoposide (2, 20, or 200 μM) to find the lowest concentration that led to the highest growth inhibition, as measured by MTS assay (described above). Using the optimal cell type-specific etoposide concentration (20 μM for PC-3 and LNCaP and 2 μM for DU-145), cells were treated with the vehicle (DMSO), saposin C (0.1, 1, 10 nM), TX14A (10 nM), mutant peptide (769M, 10 nM), or prosaposin (at 1 ng/ml). Using the cell type-specific OD/cell number calibration curve as obtained by MTS assay (Promega, WI), cell number per well was determined for the above treatment conditions.
Parallel tissue culture plates were also used to determine caspase-3/7 activity using the Apo-ONE™ Homogeneous Caspase-3/7 assay (Promega, WI). This assay provides a homogeneous Caspase-3/7 reagent (Promega, Technical Bulletin-TB295) which performs a dual function, by rapidly and efficiently permeabilizing the cultured cells and at the same time exposing the intracellular space to the profluorescent caspase-3/7 substrate, rhodamine 110 (Z-DEVD-R110). After cleavage and removal of the DEVD peptides by caspase-3/7 activity, the fluorescence in each well was quantitated at an excitation wavelength of 485 + 20 nM and an emission wavelength of 535 + 25 nM and after correction based on blank control (DMSO-treated cells at a concentration equal to what used for dissolving etoposide) or the homogeneous caspase-3/7 reagent. Final fluorescent intensity was depicted as endpoint relative fluorescent unit, RFLU.
Using similar experimental conditions but as an independent study, the effect of LY294002 (a PI3-kinase inhibitor) was also evaluated on growth and caspase-3/7 activity of cells treated with saposin C ± etoposide. After initial studies to find the optimal concentration, we used a non-toxic tolerable dosage of 1.5 μM for LY294002. In addition, we chose the most effective (optimal) concentration of etoposide (20 μM for PC-3 and LNCaP and 2 μM for DU-145) and saposin C (1.0 nM for PC-3 and LNCaP and 10 nM for DU-145) for this study.
(iii) Terminal deoxynucleotide transferase-mediated nick end-labeling (TUNEL)
Cells were cultured in multiwell chamber slides and treated with etoposide in the presence or absence of saposin C at 0.1, 1, or 10 nM as indicated above. In situ determination of apoptosis by Terminal dUTP nick-end labeling (TUNEL) was performed using an ApopTag Peroxidase In Situ kit as recommended by the manufacturer (Chemicon International, Temecula, CA). The ApopTag Kit detects single- and double-stranded DNA breaks associated with apoptosis. Drug-induced DNA damage is not identified by the TUNEL assay unless it is coupled with the apoptotic response. Briefly, at the end of the incubation period, cells were fixed in 1% paraformaldehyde in PBS, pH 7.4 for 10 min at room temperature, washed with PBS-twice, and permeabilized in pre-cooled ethanol: acetic acid (2:1) for 5 min at -20°C. After washing twice in PBS, 5 min each time, endogenous peroxidase activity in the cells was quenched in 3% H2O2 in PBS for 5 min at room temperature, incubated with terminal deoxynucleotidyl transferase (TdT enzyme) and then with peroxidase-conjugated anti-digoxigenin antibody. Nuclear staining of the apoptotic cells was detected by 3',3'-diaminobenzidine tetrahydrochloride dihydrate substrate, as recommended by the manufacturer. Cells were then counterstained in 0.5% (w/v) methyl green and slides were mounted under a glass coverslip in permount mounting medium.
For control staining, the enzyme incubation step was deleted. Microscopic examination of cells was carried out using a phase contrast microscope. Cells were counted by choosing ten random fields and the percentages of apoptotic cells were determined. Apoptosis was indicated by the presence of apoptotic bodies, exhibiting brightly labeled punctuated nuclei.
Statistical analyses
For cell survival and other quantitative data, a one-way analysis of variance (ANOVA) was employed to evaluate the influence of one variable on multiple independent groups. Bonferroni's corrections were also applied whenever a significant group effect was observed. To compare a control group with a single experimental group of interest, we used the Student's t-test. For cell survival studies, each treatment concentration was examined three times and in triplicate samples. The effect of saposin C or other effectors in the presence of etoposide on cell growth, apoptosis, or caspase-3/7 activity was studied in twelve replicates and repeated three times. Statistical significance was set at p < 0.05 or 0.01. Statistical analyses were performed using GraphPad Prism version 3.00 for Windows (GraphPad Software, San Diego, CA).
Authors' contributions
Author 1 (T-JL) carried out experiments described in figure 4 and 5B. Author 2 and 3 (OS and RL) reviewed the manuscript and provided valuable comments in different sections of the manuscript. Author 4 (SK) conceived the study, designed all the experiments, performed experiments described in figures 1, 2, 3, 5A, and 6, carried out the statistical analysis, and drafted the paper and wrote the final version of the manuscript. All authors read and approved the final manuscript.
Acknowledgments
We thank Ms. Nicole Barron for excellent editorial assistance. We are grateful to Professor Konrad Sandhoff for his unfailing supports and supplying the purified recombinant human-saposin C (University of Bonn, Germany). We thank Professor Masao Hiraiwa for providing the purified human milk-prosaposin (University of California, San Diego, USA). This work was supported by the Stanley S. Scott Cancer Center (to S.K.) at Louisiana State University Health Sciences Center, New Orleans, LA.
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| 15548330 | PMC535542 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Nov 17; 3:31 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-31 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-621553588710.1186/1477-7525-2-62ResearchPsychometric performance of an assessment scale for strain in nursing care: The M-NCAS Kleinman Leah [email protected] Lori [email protected] Gabrielle [email protected] Marcia [email protected] Henry [email protected] MEDTAP International, Inc., 2601 4th Ave, Seattle, WA 98121, USA2 MEDTAP International, Inc., 7101 Wisconsin Avenue, Suite 600, Bethesda, MD 20814, (GC at the time the study was conducted, now at Medimmune), USA3 Janssen Pharmaceutica Inc., 1125 Trenton-Harbourton Road, Titusville, NJ 08560, USA4 School of Psychiatry, University of New South Wales, Randwick, New South Wales 2031, Australia2004 9 11 2004 2 62 62 13 7 2004 9 11 2004 Copyright © 2004 Kleinman et al; licensee BioMed Central Ltd.2004Kleinman 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
Multiple instruments exist to measure dementia behaviors, but the nursing staff perspective on those behaviors and their level of burden has not been well measured. The goal of this study was to examine the psychometric performance of the Modified Nursing Care Assessment Scale (M-NCAS), a 28-item nurse rating of burden associated with care for institutionalized individuals with dementia. Nurses rate items in terms of extent to which the behavior or characteristic is present ("attitude" domain), and extent to which it is a burden ("strain" domain).
Methods
Data from 282 patients enrolled in a 12-week, double-blind, randomized clinical trial comparing risperidone treatment to placebo was used to evaluate M-NCAS item performance, internal consistency reliability, and construct validity. Empirical subscales were identified via exploratory factor analysis (EFA).
Results
Four poorly-performing items were deleted from further analyses. EFA identified 3 "attitude" subscales and 5 "strain" subscales. Cronbach's alphas were 0.65 and above. Correlation with the Cohen-Mansfield Agitation Inventory and the BEHAVE-AD, clinical ratings of dementia behaviors, were low to moderate.
Conclusion
The M-NCAS provides a valid and reliable means of obtaining care burden ratings from formal caregivers in long-term care, and provides a method for evaluating dementia interventions from the perspective of nursing staff.
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Background
Caring for the institutionalized dementia patient can be challenging for nursing staff [1,2]. Persons with dementia often exhibit such disturbing behaviors as pacing, use of inappropriate language, acting out, refusal of necessary care, hallucinations, and delusions. Nursing staff caring for persons with dementia may have difficulty forming relationships with their patients, and the stress associated with such care may contribute to staff turnover. Nursing staff turnover in nursing homes is high, ranging from 40%–96% annually in the United States [3]. The societal costs of nursing staff turnover (particularly in the current environment of nursing staff shortages) are great, and can be expected to increase as the number of institutionalized older adults increases. Studies of disturbing or distressing behaviors among institutionalized dementia patients demonstrate that these behaviors occur frequently, and are a source of great frustration to the nursing staff [4-9]. The prevalence of dementia in nursing homes, estimated to be close to 50% in the U.S. [10] and over 70% in Australia [11], coupled with the behaviors associated with dementia, combine to make caring for patients with dementia a particular challenge to nursing staff. Measuring this burden can provide another means of evaluating outcomes of dementia treatment in the nursing home setting.
There are measures relevant to the burden of paid/formal caregivers (i.e., nursing staff caring for dementia patients), but most focus on such overt behaviors as "forgetting what day it is" or "pull away" only (e.g., the Memory and Behaviors Problems Checklist [12] and the Resistiveness to Care Scale [2]). In their comprehensive examination of formal caregiver stress and cognitive impairment, Novak and Chappell [9] used an extensive individual face-to-face interview that lasted approximately one and a hours – a form of the Burden Interview [13] modified for institutional caregivers and Maslach's Burnout Inventory [14]. In their earlier work, they also asked 5 dementia-specific questions [15].
The Strain in Nursing Care Assessment Scale (NCAS) [9,10] was developed to address the need for a more comprehensive measure of nursing burden – specific to the long-term care setting – that includes sources of burden beyond overt behaviors. It is based on the conceptualization of burden as deriving from patient behaviors, as well as patient characteristics as perceived by the nurse carer. Unlike solely behavior-based measures completed by nurses, the NCAS addresses other aspects relevant to the provision of nursing care, such as nursing staff's perceptions regarding the meaningfulness of resident lives and the residents' level of gratefulness for care. The NCAS demonstrated the ability to capture changes in nurses' rating of difficulty regarding dementia patients' characteristics in a year-long study of a care intervention [16]. The Modified Strain in Nursing Care Assessment Scale (M-NCAS) was adapted from the NCAS developed and used in Swedish long-term care facilities [16,17]. The M-NCAS includes more items than the original instrument to capture the presence and impact of additional behaviors not represented in the original instrument, thus providing an even more comprehensive measure of nursing burden.
The M-NCAS was used in a clinical trial of an atypical antipsychotic, risperidone, for treatment of nursing home patients with behavioral and psychological signs and symptoms in dementia. This preliminary investigation describes the psychometric evaluation of the M-NCAS using data from the clinical trial; thus, it is a post-hoc assessment of reliability and validity. Assessment of psychometric properties included exploration of item performance, subscale assignment, and assessment of reliability and validity based on the available data from the clinical trial.
Methods
Design and Procedures
The psychometric evaluation used data from the baseline assessment of a multi-site, double-blind, placebo-controlled trial of risperidone in the treatment of behavioral and psychological symptoms among institutionalized patients with dementia. Fourteen sites throughout Australia and New Zealand recruited patients from the various nursing homes associated with each site. Patients were included in the clinical trial if they were 55 years of age or older, residing in a nursing home environment for one month, with a DSM-IV diagnosis of either dementia of the Alzheimer's type with behavioral disturbance, vascular dementia with behavioral disturbance, or mixed dementia, had scores less than or equal to 23 on the Mini-Mental Status Exam (MMSE) [18], and met aggressive item score criteria on the Cohen-Mansfield Agitation Inventory (CMAI) [19]. Aggressive item score criteria was defined as a score of at least 4 on one aggressive item, a frequency score of 3 on at least two aggressive items, or a frequency score of 2 on at least three aggressive items or two aggressive items occurring at a frequency of 2 and one at a frequency of 3 on the CMAI. These criteria were designed by the trial investigators to limit the sample to moderate to severe dementia patients having recognizable behavioral disturbances. Patients were required to have a carer who was able and committed to assisting the subject to comply with medication intake and the trial protocol, and who was willing to provide the information required at assessment interviews. Nursing home patients were excluded if they had medical or neurological conditions other than dementia which diminished cognitive function, dementia secondary to alcoholism, a diagnosis of major psychiatric comorbidity, substance abuse, tardive dyskinesia, clinically-uncontrolled medical conditions or laboratory abnormalities, a history of adult seizures, an administration of a depot injection or a long-acting neuroleptic within two treatment cycles of selection, hypersensitivity to neuroleptic treatment, or a history of failure to respond to risperidone of four weeks duration or participation in clinical trials with investigational drugs within the past four weeks. All patients or their appropriate representatives provided written informed consent, and the research protocol was approved by the appropriate institutional review board and state guardianship boards where required. Nurse carers were identified for each patient participant.
Nursing Care Assessments
The Behavioral Pathology in Alzheimer's Disease Rating Scale (BEHAVE-AD), the CMAI, and the M-NCAS were administered to the nurse carers of the individual patients by the investigator or site coordinator at baseline, week 4, week 8, and week 12 (trial endpoints). Nurse carers could have more than one patient participating in the trial, and they completed questionnaires for each individual patient.
Modified Nursing Cares Assessment Scale (M-NCAS)
The M-NCAS is was adapted from the original NCAS instrument developed in Sweden, and contains 32 items based in part on the 21 original items of the NCAS. Additional items were selected based on comments made by long-term care nurses regarding their experience caring for dementia patients with dementia in long-term care facilities, and added to make the instrument more comprehensive.
The M-NCAS contains 32 items, with two domains per item: one addresses the occurrence and intensity of the behavior, in which the staff members indicate the extent to which they agree that the patient exhibits the behavior (the "attitude" domain); and one which addresses staff rating of the difficulty of coping with the behavior (the "strain" domain). Responses are measured on a four-point Likert-type scale (ranging from Agree to Don't Agree for the Attitude domain, and Very Easy to Very Difficult for the Strain domain). Lower scores are better for both domains. Domain total and subscale scores are calculated separately; total and subscale scores are calculated as an average of the individual item scores. The M-NCAS was translated from the original Swedish to English using standard forward and backward translation techniques.
For subscale analyses, if < 50% of the scale items were missing, the scale was scored with the mean score of the non-missing items for that individual used to impute a score for the missing items. If > 50% of the items were missing, no scale score was calculated; the subscale score was considered missing. Total scores were calculated only if < 50% of the items were missing. Missing item criteria was based on that recommended by Ware et al. [20].
Cohen-Mansfield Agitation Inventory (CMAI)
The CMAI is a 29-item scale specifically developed to assess agitated and disruptive behaviors, as well as care-related problems, occurring in demented subjects residing in nursing homes. The scale measures the frequency of behaviors, and systematically assesses agitation. The scale's 29 activities are rated on a 7-point scale indicating the frequency of a particular activity (range; never to a few times per hour). The activities are organized into 4 subscales: physical/aggressive, physical/non-aggressive, verbal/aggressive, and verbal/non-aggressive. The total aggression score is the sum of the physical and verbal aggression subscales. The total non-aggression score is the sum of the verbal and physical non-aggression subscales. A recall period of one week was used. The CMAI was used to assess the construct validity of the M-NCAS.
Behavioral Pathology in Alzheimer's Disease Rating Scale (BEHAVE-AD)
The BEHAVE-AD is a 25-item instrument designed to assess the severity of behavioral disturbances in subjects with dementia, based upon the caregiver's observation and report of the subject's behavioral problems. It consists of 7 subscales: paranoid and delusion ideation, hallucinations, activity disturbances, aggressiveness, diurnal rhythm disturbances, affective disturbances, and anxieties and phobias. Symptoms are rated on a scale from 0 (absence of the symptom) to 3 (greatest severity). A global item is included, in which a single judgment is made as to how troubling or dangerous the subject's behavior has been to the caregiver. A recall period of one month was used. The BEHAVE-AD was used to assess the construct validity of the M-NCAS.
Data Analysis and Psychometric Evaluation
We evaluated the item performance, scaling characteristics, reliability, and construct validity of the M-NCAS using baseline assessment data.
Item Performance and Scaling Characteristics
Item performance was examined to identify the presence of items that may reduce the instrument's ability to detect changes over time, and to discriminate between groups. The characteristics of individual M-NCAS items examined were mean, minimum and maximum responses, percent missing, floor and ceiling effects, and item-total correlations. These data were used to determine if some items should be deleted for subsequent analyses (e.g., item-to-total correlations below 0.40 [21]). Exploratory factor analysis was performed to evaluate the underlying subscale structure of the M-NCAS.
Reliability
Internal consistency reliabilities of total and subscale scores were estimated using Cronbach's coefficient α. Data to examine test-retest reliability was not available.
Construct Validity
Correlations were determined for total and subscale scores for attitude and strain domains and the CMAI physical and verbal aggression and non-aggression subscales. Correlations were also calculated between M-NCAS scores and the total score, seven subscales, psychotic symptoms subtotal and global rating question of the BEHAVE-AD, and the CGI-S and CGI-C scores. Pearson correlations were used unless otherwise specified. Correlations were expected to be low to moderate (0.20 ≤ r < 0.40).
All statistical analyses were conducted using SAS statistical software version 8.0. A significance level of 0.05 and 2-tailed tests were used unless otherwise noted. No adjustments were made for multiple comparisons.
Results
Two hundred and eighty-two patients had evaluable baseline data for the psychometric evaluation. The majority of patients were female (72.4%) and Caucasian (98.4%); 11.5% were between the ages of 65 and 74, while 87.1% were over age 74. Most subjects had a diagnosis of Alzheimer's Disease (59.1%), while 28.3% had vascular dementia and 12.5% had mixed dementia.
Nurse carers were primarily Caucasian. Further demographic data and information about the number and type of carers were not available.
Item Performance
Items and their distributional characteristics were reviewed. See Table 1 for a listing of items, mean scores, and floor (% of responses at 1) and ceiling effects (% of responses at 4). The attitude domain contained several items with high floor and ceiling effects. Item 12 had 79% of responses at floor – the highest among all items. Items 19 and 28 had the highest percentages of responses at ceiling (76% and 77%, respectively). Only one person had any missing data at the baseline visit. Examination of item correlation to total and subscale scores on the attitude domain demonstrated low or inverse correlations for four items (Item 5: tries to influence others in order to maintain control of his/her life; Item 12: is submissive; Item 27: patterns of behaviors you can foresee; and Item 19: has little control over his/her difficult behavior) (data not shown).
Table 1 Items in the M-NCAS
Attitude Strain
Description Mean (SD) Floor (N, %) Ceiling (N, %) Mean (SD) Floor (N, %) Ceiling (N, %)
Item 1R Seems to behave in a completely aimless way 3.16 (1.08) 1 (45, 15.96%) 4 (144, 51.06%) 2.64 (0.93) 1 (39, 13.83%) 4 (50, 17.73%)
Item 2R Is anxious 3.3 (0.97) 1 (28, 9.93%) 4 (160, 56.74%) 2.79 (0.88) 1 (26, 13.83%) 4 (59, 20.92)
Item 3R Is unpredictable 3.2 (1.1) 1 (50, 50.0%) 4 (169, 59.9%) 2.86 (0.92) 1 (26, 9.22%) 4 (74, 26.24%)
Item 4 Does things for a reason 2.86 (1.19) 1 (56, 45.0%) 4 (127, 45.0%) 2.57 (0.96) 1 (49, 17.38%) 4 (47, 16.67%)
Item 51 Tries to influence others in order to maintain control of his/her own life 2.904 (1.3) 1 (74, 26.24%) 4 (154, 54.6%) 2.22 (1.06) 1 (92, 32.62%) 4 (41, 14.54%)
Item 6 Is calm 3.17 (1.04) 1 (13, 4.61%) 4 (165, 58.5%) 2.76 (0.92) 1 (28, 9.93%) 4 (64, 22.70%)
Item 7R Is apathetic/seems to have limited emotions 2.54 (1.32) 1 (109, 38.65%) 4 (101, 35.82%) 2.48 (0.92) 1 (47, 16.67%) 4 (37, 13.12%)
Item 8R Is selfish 2.40 (1.32) 1 (116, 41.41%) 4 (94, 33.33%) 2.32 (0.99) 1 (69, 24.47%) 4 (39, 13.83%)
Item 9 Is rewarding to work with 2.37 (1.20) 1 (87, 30.85%) 4 (82, 29.08%) 2.55 (0.97) 1 (46, 16.31%) 4 (51, 18.09%)
Item 10 Is grateful for what is done for him/her 2.49 (1.18) 1 (71, 25.18%) 4 (89, 31.56%) 2.30 (0.94) 1 (61, 21.63%) 4 (33, 11.70%)
Item 11R Is paranoid 2.40 (1.29) 1 (111, 39.36%) 4 (89, 31.56%) 2.43 (1.04) 1 (65, 23.05%) 4 (52, 18.44%)
Item 12R Submissive/gives in to everything done to him/her 1.47 (0.93) 1 (222, 78.72%) 4 (15, 5.32%) 2.97 (0.89) 1 (20, 7.09%) 4 (88, 31.21%)
Item 13R Is attention seeking 2.57 (1.31) 1 (101, 38.82%) 4 (105, 37.23) 2.48 (1.00) 1 (56, 19.86%) 4 49, 17.38%)
Item 14R Is manipulative 1.97 (1.21) 1 (158, 56.03%) 4 (51, 18.09) 2.16 (0.96) 1 (82, 29.08%) 4 (29, 10.28%)
Item 15R Is ungrateful for the care he/she receives 2.23 (1.18) 1 (113, 40.07%) 4 (57, 20.21%) 2.30 (0.96) 1 (66, 23.40%) 4 (35, 12.41%)
Item 16R Is frightened/vulnerable 2.77 (1.18) 1 (72, 25.53%) 4 (109, 38.65%) 2.56 (0.90) 1 (38, 13.48%) 4 (41, 14.54%)
Item 17R Is lonely 2.76 (1.20) 1 (65, 23.05%) 4 (112, 39.72%) 2.49 (0.87) 1 (40, 14.18%) 4 (32, 11.35%)
Item 18R Has to concentrate exclusively on his/her needs in order to survive 2.36 (1.30) 1 (115, 40.93%) 4 (90, 32.03%) 2.40 (0.96) 1 (58, 20.64%) 4 (38, 13.52%)
Item 19R Has little control over his/her difficult behavior 3.59 (1.30) 1 (14, 4.97%) 4 (214, 75.89%) 3.16 (0.77) 1 (10, 3.55%) 4 (100, 35.46%)
Item 20R Is deliberately difficult 2.06 (1.17) 1 (138, 48.94%) 4 (46, 16.31%) 2.58 (1.00) 1 (52, 18.44%) 4 (53, 18.79%)
Item 21 Tries to maintain some independence 2.33 (1.27) 1 (108, 38.30%) 4 (89, 31.56%) 2.56 (0.83) 1 (34, 12.06%) 4 (29, 10.28%)
Item 22 Knows what he/she wants and stands up for his/herself 2.30 (1.25) 1 (103, 36.53%) 4 (86, 30.50%) 2.79 (0.85) 1 (28, 9.93%) 4 (51, 18.09%)
Item 23 Seems to experience the normal range of emotions 2.83 (1.24) 1 (63, 22.34%) 4 (134, 47.52%) 2.59 (0.83) 1 (30, 10.64%) 4 (33, 11.70%)
Item 24 Friendly 2.05 (1.03) 1 (94, 33.33%) 4 (48, 17.02%) 2.21 (0.84) 1 (58, 20.57%) 4 (17, 6.03%)
Item 25R Needs someone close by all the time/is demanding 2.57 (1.31) 1 (104, 36.88%) 4 (101, 35.82%) 2.58 (0.96) 1 (43, 15.25%) 4 (53, 18.79%)
Item 26R Has an empty life 2.81 (1.17) 1 (60, 21.28%) 4 (110, 39.01%) 2.56 (0.92) 1 (41, 14.54%) 4 (42, 14.89%)
Item 27R Has patterns of behavior you can foresee 3.00 (1.12) 1 (53, 18.79%) 4 (125, 44.33%) 2.75 (0.85) 1 (24, 8.51%) 4 (50, 17.73%)
Item 28R Is stubborn/resistive 3.68 (0.71) 1 (12, 4.26%) 4 (218, 77.31%) 3.22 (0.78) 1 (12, 4.26%) 4 (113, 40.07%)
Item 29R Is aggressive/ hostile 3.44 (0.91) 1 (26, 9.22%) 4 (181, 64.18%) 3.18 (0.84) 1 (16, 5.67%) 4 (113, 40.07%)
Item 30 Has a meaningful life 3.17 (1.00) 1 (25, 8.87%) 4 (145, 53.19%) 2.59 (0.86) 1 (32, 11.35%) 4 (38, 13.48%)
Item 31 Compliant/voluntarily co-operative 3.10 (1.01) 1 (9, 3.19%) 4 (150, 53.19%) 2.87 (0.80) 1 (16, 5.67%) 4 (58, 20.57%)
Item 32R Gives no job satisfaction 2.49 (0.91) 1 (43, 15.25%) 4 (37, 13.12%) 2.49 (0.91) 1 (43, 15.25%) 4 (37, 13.12%)
1Italics indicate items deleted from instrument in subsequent analyses. RItem is reverse-coded for the attitude total and subscale scores. Attitude ranges from 1 (agree) to 4 (don't agree) and Strain ranges from 1 (very easy) to 4 (very difficult) Lower scores are considered better for both dimensions.
Examination of any floor or ceiling effects on the strain domain demonstrated no outliers. Item-to-item correlations appeared acceptable, as did item-to-total correlations, all generally above 0.30.
Factor Analysis
With the number of factors unspecified, no clear factor pattern was discernible. Next, a six-factor solution for both dimensions (strain and attitude) was examined, based on the suitability of six factors for the original instrument [16]. Examination of the oblique rotated factor loadings demonstrated substantial item overlap. Results of subsequent factor analyses indicated that the best solution was a three-factor solution for the attitude domain, and a five-factor solution for the strain domain. Examination of the three-factor solution for the attitude dimension indicated that three items (item 19 has little control over his/her difficult behavior, item 27 has patterns of behavior you can foresee, item 7 is apathetic/seems to have limited emotions) did not perform well on the factor analysis – either overlapping substantially with another factor, or producing factor loadings less than 0.30.
Based on overall item analysis results, including floor and ceiling effects, item-to-item and item-to-total correlation results, and a review of factor analysis results, four poorly-performing items were deleted from the attitude domain: item 5 tries to influence others in order to maintain control of his/her own life; item 12 submissive/gives in to everything done to him/her; item 19 has little control over his/her difficult behavior; and item 27 has patterns of behavior you can foresee. The removal of these items resulted in three distinct subscales for the attitude domain, demonstrating good approximation of simple structure. The three factors, all with variable loadings of 0.30 or above were "Attention-seeking" (e.g., "is manipulative", "needs someone close by all the time/is demanding"), "Autonomy" (e.g., "does things for a reason", "is apathetic/seems to have limited emotions"), and "Difficulty" (e.g. "is unpredictable" and "is friendly"). Items were reverse-coded as appropriate.
To ensure inter-domain consistency, we removed the same four items from the strain dimension, thus resulting in an acceptable five-factor solution for this domain. The five factors, all with variable loadings of 0.30 or above, are "Affect" (e.g., "is calm", 'is anxious"), "Job satisfaction" (e.g., "is grateful for what is done for him/her", "gives no job satisfaction"), "Neediness" (e.g., "is selfish", "is manipulative"), "Predictability" (e.g., "knows what he/she wants and stands up for his/herself", "is aggressive/resistive"), and "Self-direction" (e.g., "is frightened/vulnerable", "seems to experience the normal range of emotions"). See Tables 2 and 3 for a list of specific items in each domain.
Table 2 Item-to-subscale correlations for the attitude domain of the M-NCAS (28 items) at baseline
Item Attention Seeking Autonomy Difficulty
Item 2: Is anxious 0.573 0.073* 0.117
Item 6: Is calm 0.492 0.194 0.338
Item 11: Is paranoid 0.569* -0.059 0.288
Item 13: Is attention seeking 0.673 -0.203 0.174
Item 14: Is manipulative 0.588 -0.199 0.343
Item 16: Is frightened/vulnerable 0.425 0.124 -0.078*
Item 17: Is lonely 0.568* 0.074 -0.009*
Item 18: Has to concentrate exclusively on his/her own needs in order to survive 0.546 -0.167 0.256
Item 25: Needs someone close by/is demanding 0.706 -0.041* 0.129
Item 26: Has an empty life 0.462 0.305 0.286
Item 1: Seems to behave in a completely aimless way 0.089* 0.479 0.0750*
Item 4: Does things for a reason -0.084 0.615 0.0043
Item 7: Is apathetic/seems to have limited emotions 0.037 0.514* 0.222
Item 21: Tries to maintain some independence 0.020* 0.652 0.115*
Item 22: Knows what he/she wants and stands up for his/herself -0.141 0.696 -0.013*
Item 23: Seems to experience the normal range of emotions -0.044* 0.601 -0.194
Item 30: Has a meaningful life 0.170 0.397 0.187
Item 3: Is unpredictable 0.164 0.082* 0.387
Item 8: Is selfish 0.391* -0.073 0.523
Item 9: Is rewarding to work with 0.159 0.241 0.651
Item 10: Is grateful for what is done for him/her 0.010 0.326* 0.630
Item 15: Is ungrateful for the care he/she receives 0.279 0.127 0.656
Item 20: Is deliberately difficult 0.259 -0.071* 0.482
Item 24: Friendly 0.076* 0.251 0.628
Item 28: Is stubborn/resistive 0.090* -0.012* 0.482
Item 29: Is aggressive/hostile 0.114* 0.023* 0.533
Item 31: Compliant/voluntarily co-operative 0.172 0.225 0.595
Item 32: Gives no job satisfaction 0.196 0.066* 0.525
*All correlations significant at p < 0.05, except as indicated
Table 3 Item-to-subscale correlations for the strain domain of the M-NCAS (28 items) at baseline
Item Affect Job Satisfaction Neediness Predictability Self Direction
Item 1: Seems to behave in a completely aimless way 0.727 0.447 0.409 0.348 0.488
Item 2: Is anxious 0.705 0.393 0.472 0.397 0.567
Item 3: Is unpredictable 0.694 0.441 0.479 0.452 0.306
Item 4: Does things for a reason 0.675 0.378 0.407 0.462 0.347
Item 6: Is calm 0.715 0.541 0.445 0.538 0.393
Item 7: Is apathetic/seems to have limited emotions 0.702 0.533 0.444 0.444 0.539
Item 9: Is rewarding to work with 0.572 0.777 0.475 0.536 0.422
Item 10: Is grateful for what is done for him/her 0.523 0.840 0.560 0.490 0.516
Item 15: Is ungrateful for the care he/she receives 0.520 0.828 0.601 0.521 0.513
Item 24: Friendly 0.406 0.728 0.477 0.500 0.515
Item 32: Gives no job satisfaction 0.549 0.807 0.536 0.580 0.615
Item 8: Is selfish 0.485 0.560 0.764 0.467 0.386
Item 11: Is paranoid 0.428 0.447 0.627 0.419 0.387
Item 13: Is attention seeking 0.483 0.432 0.809 0.389 0.471
Item 14: Is manipulative 0.442 0.482 0.784 0.389 0.381
Item 18: Has to concentrate exclusively on his/her own needs in order to survive 0.504 0.497 0.753 0.465 0.592
Item 20: Is deliberately difficult 0.414 0.510 0.672 0.537 0.450
Item 25: Needs someone close by all the time/is demanding 0.469 0.483 0.717 0.450 0.597
Item 21: Tries to maintain some independence 0.476 0.477 0.495 0.738 0.494
Item 22: Knows what he/she wants and stands up for his/herself 0.433 0.485 0.540 0.773 0.455
Item 28: Is stubborn/resistive 0.515 0.498 0.452 0.815 0.383
Item 29: Is aggressive/hostile 0.487 0.510 0.436 0.823 0.358
Item 31: Compliant/voluntarily co-operative 0.557 0.623 0.464 0.780 0.525
Item 16: Is frightened/vulnerable 0.476 0.423 0.413 0.383 0.710
Item 17: Is lonely 0.518 0.489 0.550 0.456 0.803
Item 23: Seems to experience the normal range of emotions 0.532 0.536 0.457 0.537 0.725
Item 26: Has an empty life 0.445 0.557 0.581 0.396 0.806
Item 30: Has a meaningful life 0.429 0.476 0.435 0.403 0.793
Following the removal of these 4 items, item-to-subscale correlations were examined for all subscales. In all cases, items correlated moderately to highly with the subscales to which they were assigned via the factor analysis. See Tables 2 and 3.
Reliability
The internal consistency reliability (Cronbach's alpha) for the attitude and strain total scores was good – 0.79 and 0.95, respectively. Internal consistency reliability for subscales in both domains was excellent in general. See Table 4.
Table 4 Internal consistency reliability
Scale Cronbach's α
Attitude Domain
Total Score 0.790
Autonomy 0.646
Attention Seeking 0.759
Difficulty 0.776
Strain Domain
Total Score 0.945
Affect 0.795
Job Satisfaction 0.856
Neediness 0.856
Predictability 0.845
Self Direction 0.826
Construct Validity
Table 5 summarizes the correlations between the M-NCAS attitude total and subscale scores and the CMAI total aggression, total non-aggression, and subscale scores. In general, correlations were low to moderate [22]. The highest correlation was between attention-seeking and verbal non-aggression (r = 0.68; p < 0.01).
Table 5 Correlations1 between the CMAI and the M-NCAS attitude and strain domains
CMAI Scales
M-NCAS Scales Physical Aggression Physical Non-Aggression Verbal Aggression Verbal Non-Aggression Total Aggression Total Non-Aggression
Attitude Domain
Total Score 0.286* 0.079 0.325* 0.467* 0.336* 0.306*
Attention Seeking 0.060 0.142 0.202 * 0.675* 0.107 0.463*
Autonomy 0.145 0.011 0.016 -0.074 0.129 -0.031
Difficulty 0.370* -0.005 0.390* 0.242* 0.426* 0.125
Strain Domain
Total Score 0.290* 0.141 0.273* 0.344* 0.324* 0.286*
Affect 0.330* 0.211 0.193* 0.246* 0.337* 0.286*
Job Satisfaction 0.320* 0.0189 0.333* 0.210 0.367* 0.125
Neediness 0.150* 0.128 0.222 0.458* 0.190* 0.338*
Predictability 0.297* 0.166* 0.258* 0.154* 0.327* 0.204
Self Direction 0.151* 0.060 0.143 0.302* 0.170* 0.204*
*significant at p < 0.01; 1Pearson product-moment correlations
Table 5 also summarizes the correlations between the strain total and subscale scores and the CMAI total aggression, total non-aggression, and subscale scores. Correlations were generally low to moderate, with only one correlation exceeding 0.40 (verbal non-aggression and neediness; r = 0.46, p < 0.01).
Table 6 presents the correlations between the M-NCAS attitude total and subscale scores and the BEHAVE-AD. With the exception of the activity disturbance subscale, most correlations between the BEHAVE-AD subscales and the attitude total score were statistically significant. In general, the attention-seeking attitude subscale was related to BEHAVE-AD scores, but the autonomy subscale was not. Results were mixed for the difficulty subscale, with a correlation of 0.47 to the aggressiveness BEHAVE-AD subscale.
Table 6 Correlations1 between the BEHAVE-AD and the M-NCAS
BEHAVE-AD Scales
M-NCAS Scales Total BEHAVE-AD Paranoid and delusional ideation Hallucination Activity disturbance Aggressiveness Diurnal rhythm disturbance Affective disturbance Anxiety and phobias
Attitude Domain
Total Score 0.380* 0.272* 0.159* 0.078 0.386* 0.182* 0.225* 0.354*
Attention Seeking 0.478* 0.421* 0.158* 0.091* 0.269* 0.185* 0.373* 0.556*
Autonomy -0.059 -0.176* 0.044 0.021 -0.032 0.101 -0.015 -0.041
Difficulty 0.268* 0.207* 0.104 0.038 0.471* 0.079 0.053 0.128
Strain Domain
Total Score 0.397* 0.281* 0.134* 0.146* 0.425* 0.209* 0.156* 0.353*
Affect 0.360* 0.195* 0.137 0.243* 0.362* 0.191* 0.169 0.286*
Job Satisfaction 0.265* 0.161* 0.074 0.061 0.431* 0.121 0.061 0.210*
Neediness 0.383* 0.325* 0.140 0.084 0.327* 0.234* 0.165* 0.372*
Predictability 0.331* 0.246* 0.107 0.169* 0.437 0.144 0.028 0.217*
Self Direction 0.310* 0.224* 0.093 0.066 0.241* 0.164* 0.213* 0.369*
*p < 0.01; 1Pearson product-moment correlations
Table 6 also presents results for the M-NCAS strain domain and BEHAVE-AD score correlations. The total and most of the strain subscale scores were significantly related to BEHAVE-AD total and subscale scores.
Discussion
The M-NCAS demonstrated good psychometric properties based on an analysis of baseline data collected during a clinical trial of risperidone versus placebo, as reported in this preliminary investigation. Item performance, particularly floor and ceiling effects (together with item-total correlation data), suggested that four items on the attitude domain were performing poorly from a psychometric standpoint. Two items performed poorly on item correlations as well as factor analysis, while item 12 had a high floor effect as well as poor correlations. The remaining 28 items still capture sufficiently comprehensive data on the domains of interest, based on content review and on the empirical performance of subscales derived from them. The collection of additional data would enhance the confidence of conclusions regarding M-NCAS performance.
In general, the M-NCAS demonstrated excellent internal consistency reliability, with only the autonomy subscale producing a Cronbach's alpha below 0.70. Cronbach's alpha values of 0.70 or greater are considered suitable for use in the analyses of group comparisons. Supporting construct validity of the M-NCAS were the moderate and significant correlations to the CMAI and BEHAVE-AD. Correlation with these measures was not expected to be high, given the more expansive focus of the M-NCAS relative to the CMAI and BEHAVE-AD.
These results support the use of the M-NCAS for the collection of valid, reliable, and comprehensive information on the burden experienced by nurse carers when caring for dementia patients in long-term care settings. The instrument can assist in staff management by identifying nurse carers with the greatest levels of burden – ideally so that remedial action could be taken, either through extra support for staff members or through the shifting of caseloads from particularly difficult patients for nurses at risk for attrition.
There are several limitations to this study. Of major concern is the lack of information on the nurse carers themselves. No data was available as to the type of carer, the duration they had cared for the patient, and their sociodemographic characteristics. The inability to characterize the respondents to the M-NCAS is a limitation in interpreting generalizability of results. No control was attempted for nurse raters, so the possibility exists that measurement properties of the M-NCAS differed across nurse raters – therefore skewing the results. The extent to which rater effects limit the validity of results is likely to be minimal, however, given that multiple subjects were being rated by each nurse rater. Future examination of between-subject properties would be helpful. The sample size for the study is small, relative to the standard recommendations regarding the number of subjects per item for factor analysis. However, we set criteria for relatively moderate to high factor loadings. Of note is that the final recommendations for the measure length are closer to that guideline. Because this was a post-hoc analysis of clinical trial data, the instruments selected for use in assessing construct validity may not have been optimal, in that they did not assess nursing strain per se. However, they did assess the propensity of exhibiting certain behaviors – as does the M-NCAS. Additional data on stability across time (test-retest reliability) is desirable for a full psychometric description of the M-NCAS, and was not available from this study. Finally, this sample of nurse carers was almost exclusively Caucasian. Therefore, the generalizability of these results to nurse carers of different ethnicities, and in different locales is limited.
Conclusions
The M-NCAS enables the detection of the presence or absence of specific behaviors similar to checklists (the attitude scale), but extends that information by providing a rating of the degree of burden of each aspect rated. It possesses good psychometric properties for use with nurse carers working with Alzheimer's patients in long-term care facilities. The M-NCAS provides a unique approach to identifying both positive and negative behaviors, and to quantifying the amount of stress felt by carers as a result of these behaviors. The nursing staff perspective on residents with dementia is unique [23], and the M-NCAS exploits this perspective by capturing the aspects of residents beyond overt behaviors.
List of Abbreviations
Behavioral Pathology in Alzheimer's Disease Rating Scale (BEHAVE-AD)
Cohen-Mansfield Agitation Inventory (CMAI)
Exploratory Factor Analysis (EFA)
Mini-Mental Status Exam (MMSE)
Modified Strain in Nursing Care Assessment Scale (M-NCAS)
Strain in Nursing Care Assessment Scale (NCAS)
Authors' contributions
LK participated in the data analysis design, data interpretation, and drafting of the manuscript. LF participated in the design, data interpretation, and drafting of the manuscript. GC performed the statistical analyses. MR conceived of the study and participated in data interpretation. HB conceived of the study and participated in both data collection and interpretation. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to acknowledge the investigators of RIS-AUS-5 trial: D Ames, M Woodward, J Snowdon, J Kirwan, R Clarnette. In addition, the following investigators contributed to the trial: P Flynn, G Davison, C Davis, G Seidel, M Ewer, C Ritchie, K Hall, S Chawla, J Wade, W Hermans. The authors would also like to acknowledge I Hallberg and M Bird for their invaluable advice on the NCAS.
This research was supported by funding from Janssen Pharmaceutica Inc.
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| 15535887 | PMC535543 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Nov 9; 2:62 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-62 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-371554118410.1186/1477-7819-2-37Case ReportLarge bilateral adrenal metastases in non-small cell lung cancer Karanikiotis Charisios [email protected] Apostolos Anto [email protected] Sotirios [email protected] Konstantinos [email protected] Department of Medical Oncology, 424 Army General Hospital, Thessaloniki, Greece2 Surgical Department, Didimotichon General Hospital, Didimotichon, Greece3 Department of Radiology, Didimotichon General Hospital, Didimotichon, Greece2004 13 11 2004 2 37 37 27 9 2004 13 11 2004 Copyright © 2004 Karanikiotis et al; licensee BioMed Central Ltd.2004Karanikiotis 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
The adrenal gland is one of the common sites of metastasis from primary lung cancer. Adrenal metastases are usually unilateral however bilateral adrenal metastases are seen in 10% of all lung cancer patients; of these 2–3% occurs at the initial presentation of non-small cell lung cancer. Secondary tumors can disrupt the structure and function of the adrenal. This can lead to adrenal hemorrhage, which constitutes a life threatening hazard for the patient.
Case presentation
A 59-year-old male presented with persisting abdominal pain. His initial work-up revealed significant anemia, an invasive process in the right upper lobe of the lung and large masses of heterogeneous texture, with hemorrhagic and necrotic elements in both adrenal glands. A biopsy confirmed it to be a large-cell carcinoma of the lungs. The patient developed severe leukocytosis akin to the paraneoplastic syndrome and died suddenly five days after the administration of chemotherapy.
Conclusion
Intratumoral hemorrhage is a rare but life threatening complication of adrenal metastases and should be treated as soon as it has been diagnosed. If adrenalectomy is not feasible, combination chemotherapy should be applied as in metastatic disease. For choosing the appropriate chemotherapeutic regimen it is important to accurately achieve the diagnosis.
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Background
The adrenal gland is a common site for metastases in breast, lung and renal cell carcinomas, melanoma, and lymphoma [1]. Adrenal metastasis, at the initial diagnosis of non-small cell lung cancer, occurs in less than 10% of lung cancer patients [2]. Most cases involve solitary, unilateral, small asymptomatic lesions, incidentally discovered by CT-scan of the upper abdomen during a staging evaluation. Bilateral adrenal metastases are observed in less than 3% of patients with lung cancer. Again, most cases involve small, asymptomatic lesions. Intratumoral hemorrhage is a rare but serious complication of adrenal metastases. We report a case of non-small cell lung cancer with large bilateral adrenal metastases complicated with intratumoral hemorrhage and paraneoplastic leukemoid reaction.
Case presentation
A 59-year-old male, working in a quarry, was admitted to the hospital complaining of abdominal pain persisting for 45 days, afternoon fever, fatigue and 8 kg weight loss during the last month. Previous medical history was non contributory. He was a heavy smoker for 45 years.
At first assessment, the patient presented with disseminated dry rales and decrease of vesicular murmur in the right upper lung field. Following abdominal examination, palpable smooth surface masses were discovered in both renal areas. Laboratory tests demonstrated a significant anemia with normocytic features (hematocrit 24%, Hb 7.2 mg/dl).
Chest X-ray revealed a consolidated area in the right upper lobe of the lung. Abdomen ultrasound demonstrated large masses with foci of hemorrhagic necrosis in both adrenal glands (Figure 1).
Figure 1 Abdomen ultrasound showing large masses with foci of hemorrhagic necrosis in both adrenal glands.
Computed tomographic (CT) -scan revealed an invasive process in the right upper lobe of the lung (Figure 2) and large masses of heterogeneous texture, with hemorrhagic and necrotic elements within both adrenal glands (Figure 3).
Figure 2 CT-scan of the chest showing a 60 × 53 mm invasive process in the right upper lobe of the lung.
Figure 3 CT-scan of the abdomen showing large masses (98 × 85 mm right and 90 × 65 mm left) of heterogeneous texture, with hemorrhagic and necrotic elements within both adrenal glands.
Magnetic resonance imaging (MRI) was performed which showed the masses with disseminated foci of high, heterogeneous signal intensity and pathologic enhancement after intravenous (i.v.) administration of contrast agent in both T1 and T2 weighted sequences. Radiological features were suggestive of adrenal metastasis from lung primary (Figures 4, 5). Histological examination of the biopsy specimen confirmed the diagnosis of a large-cell carcinoma of the lung. The patient showed significant deterioration and was hospitalized again for altered level of consciousness, blurring of vision, tinnitus, lability and intense fatigue. Laboratory examination demonstrated severe leukocytosis with 98,000 polymorphonuclear leukocytes, compatible with the leukemoid reaction within the framework of a paraneoplastic syndrome. Hematocrit (Ht) levels dropped significantly a second time (Ht: 21%, Hb: 6.9 mg/dl), the biochemical parameters were normal, while the adrenocorticotropic hormone (ACTH) stimulation test excluded the possibility of cortical adrenal failure (serum cortisol basal value: 9μg/dl and 34μg/dl six hours following intramuscular (i.m.) administration of 1 mg of ACTH). A repeat abdominal ultrasound revealed numerous hemorrhagic elements inside the adrenal masses. The patient received urgent chemotherapy with paclitaxel and carboplatin, with simultaneous supportive treatment. Following chemotherapy, leukocytes decreased progressively, while symptoms related to the central nervous system (CNS) improved, however, the patient died suddenly on the fifth day following chemotherapy. Autopsy was not performed. The physician's on-call medical report documented a typical cardiac arrest, without evidence of acute hemorrhage in adrenal mass.
Figure 4 T2 weighted MR image showing disseminated foci of high heterogeneous signal intensity.
Figure 5 T1 weighted MR image showing enhancement after i.v. administration of contrast agent.
Discussion
Adrenal metastases in the natural history of a malignant neoplasm occur in 20 – 45% of cancer patients, depending on the localization of the primary site [3]. Up to 40% of patients with non-small cell lung cancer develop unilateral or bilateral adrenal metastases, as the carcinoma progresses [4].
The differential diagnosis of an adrenal mass incidentally discovered during imaging examination of the abdomen in patients with non-small cell lung cancer, usually comprises one or more of the characteristics detailed in Table 1.
Table 1 Differential diagnosis of incidentally discovered adrenal mass on Imaging
• Nonfunctional adenoma
• Adrenal metastasis
• Primary carcinoma in adrenal glands
• Adrenal cyst
• Nonfunctional pheochromocytoma
• Other causes (myelolipoma, lymphoma, aldosteronoma, neuroblastoma, pheochromocytoma)
More than 90% of the cases comprising this involve a nonfunctional adenoma or an adrenal metastasis.
Nonfunctional adenomas can be distinguished from adrenal metastasis with precision using modern imaging techniques, especially with MR imaging, where adenoma presents a low intensity signals in T2 sequences compared to hepatic parenchyma. In contrast, the adrenal metastasis presents higher intensity T2 sequence signals than that of the adenoma, relative to hepatic parenchyma. In CT-scan adenoma presents low intensity signals, below 10 Hounsfield units, compared to higher intensity signals in adrenal metastasis. Porte et al, observed that CT-scan technology has low specificity and sensitivity, which culminates in false positive and negative readings, given that 21% of true adenomas can be mistaken for metastases (density > 10 Hounsfield units) and 11% of true metastases may be mistaken for adenomas (density < 10 Hounsfield units) [5]. CT-scan has 100% sensitivity in detecting the metastatic nature of adrenal masses; however, 50% of adenomas may be mistaken as metastasis. Diagnostic accuracy of adrenal metastasis typically increased with detection of a primary tumor, as well as changes in the size and architecture of the parenchyma over time or during treatment. In equivocal cases, CT-scan-guided percutaneous biopsy of the adrenal mass is a safe and reliable method for the diagnosis of the lesion (sensitivity 81%, specificity 99%, potential complications 2.8%) [6].
In presence of adrenal metastasis the disease is staged as stage IV, which demonstrates the aggressive biological behavior and systemic dissemination of secondary lesions. These are treated as any other metastatic neoplasm and the appropriate chemotherapy is started. In select patients, where the primary lung site is surgically resectable (T1, T2 and maybe T3), with no involvement or the involvement of only the peribronchial and portal lymph nodes (N0, N1) and where the adrenal metastasis constitutes the unique indication of the disease, simultaneous adrenalectomy along with resection of the primary can increase overall survival [7]. Technically, this can be achieved with thoracoabdominal or transabdominal approaches, or laparoscopically in cases of small tumors (< 5 cm), [8]. A recent meta-analysis suggests that the latter technique, when it is feasible, is associated with fewer complications than open adrenalectomy [9]. However, adrenal metastases are usually larger than 5 cm, as in our case, which makes this approach inappropriate for general application. There is also a perceived increased risk of local tumor recurrence and intraperitoneal tumor dissemination occurring after laparoscopic resection of malignant adrenal tumors [10].
The most important complication of adrenal metastases is intratumoral hemorrhage and only nine cases have been reported previously [11]. When hemorrhage risk is high, as in the case of large metastasis, timely adrenalectomy is a necessary intervention. For resecting larger adrenal tumors in presence of bilateral involvement the transabdominal approach with extended subcostal incision (Kocher) is preferred. When this is considered technically impossible, radiation therapy can be an alternative palliative approach. During the course of the disease, our patient developed a leukemoid reaction and the performance status dropped remarkably, making it difficult to proceed with the planned surgical resection. Thus, the paclitaxel and carboplatin chemotherapeutic regimen was administered in an attempt to decrease the leucocytes count and improve the CNS symptoms. Even though the infusion of paclitaxel has been associated with cardiovascular events, there is no clear relation between the drug administration and the sudden death of our patient.
A less common complication of bilateral adrenal metastases is primary adrenal insufficiency, caused by the destruction of adrenal gland architecture and function due to the development of the tumor. The main symptoms resulting from the decreased production of steroid hormones often overlap with the general indicators caused by the primary site, hindering the proper treatment of these patients. Primary adrenal insufficiency is diagnosed when serum cortisol basal values are low (<5μg/dl), and particularly when the values fail to increase within six hours following the i.m. administration of 1 mg of tetracosactride, the synthetic analog of ACTH. The treatment comprises daily administration of glucocorticoid and mineralocorticoid for life.
Conclusion
Bilateral adrenal metastases can be found in a small number of patients with non-small cell lung cancer. Early identification of these lesions is necessary, since adrenalectomy may increase survival in select patients. Intratumoral hemorrhage is a rare but life threatening complication and should be treated as soon as it has been diagnosed. If adrenalectomy is not feasible, combination chemotherapy should be applied.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
CK – conceived the study, did the literature search and coordinated the preparation of the manuscript for submission
AT – helped in literature search, reviewed and edited the manuscript for final submission
SM and KV – helped in literature search and drafted the manuscript for submission
All authors read and approved the manuscript.
Acknowledgement
Since patient died suddenly consent was not obtained and his only relative has been lost from contact. The Scientific Council of Didimotichon General Hospital unanimously decided to give its assent for the publication of data to any medical journal.
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| 15541184 | PMC535544 | CC BY | 2021-01-04 16:38:39 | no | World J Surg Oncol. 2004 Nov 13; 2:37 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-37 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-761553743010.1186/1477-7827-2-76ResearchUterine extracellular matrix components are altered during defective decidualization in interleukin-11 receptor α deficient mice White Christine A [email protected] Lorraine [email protected] Lois A [email protected] Prince Henry's Institute of Medical Research, Clayton, Victoria 3168, Australia2 Dept of Obstetrics & Gynaecology, Monash University, Clayton, Victoria 3168, Australia3 Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria 3001, Australia2004 10 11 2004 2 76 76 22 9 2004 10 11 2004 Copyright © 2004 White et al; licensee BioMed Central Ltd.2004White 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
Implantation of the embryo and successful pregnancy are dependent on the differentiation of endometrial stromal cells into decidual cells. Female interleukin-11 receptor α (IL-11Rα) deficient mice are infertile due to disrupted decidualization, suggesting a critical role for IL-11 and its target genes in implantation. The molecular targets of IL-11 in the uterus are unknown, but it is likely that IL-11 signaling modifies the expression of other genes important in decidualization. This study aimed to identify genes regulated by IL-11 during decidualization in mouse uterus, and to examine their expression and localization as an indication of functional significance during early pregnancy.
Methods
Decidualization was artificially induced in pseudopregnant wild type (IL11Ra+/+) and IL-11Rα deficient (IL11Ra-/-) littermates by oil injection into the uterine lumen, and gene expression analyzed by NIA 15K cDNA microarray analysis at subsequent time points. Quantitative real-time RT-PCR was used as an alternative mRNA quantitation method and the expression and cellular localization of the protein products was examined by immunohistochemistry.
Results
Among 15,247 DNA probes, 13 showed increased and 4 decreased expression in IL11Ra-/- uterus at 48 h of decidualization. These included 4 genes encoding extracellular matrix proteins; collagen III α1, secreted acidic cysteine-rich glycoprotein (SPARC), biglycan and nidogen-1 (entactin). Immunohistochemistry confirmed increased collagen III and biglycan protein expression in IL11Ra-/- uterus at this time. In both IL11Ra-/- and wild type uterus, collagen III and biglycan were primarily localized to the outer connective tissue and smooth muscle cells of the myometrium, with diffuse staining in the cytoplasm of decidualized stromal cells.
Conclusion
These data suggest that IL-11 regulates changes in the uterine extracellular matrix that are necessary for decidualization.
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Background
Implantation of the embryo, formation of the placenta and successful pregnancy are dependent on the proliferation and differentiation of endometrial stromal cells into decidual cells. Decidualization occurs in response to endometrial and embryonic signals and is thought to involve complex interactions between ovarian steroid hormones, the uterine extracellular matrix (ECM), growth factors and cytokines (reviewed in [1]). When implantation is initiated at day 3.5 of pregnancy in the mouse (plug is day 0), decidualization begins adjacent to the embryo on the antimesometrial side of the uterus to form the primary decidual zone [2]. Decidual transformation then extends mesometrially to form the secondary decidual zone by day 6, accompanied by a dramatic increase in vascular permeability [3]. The endometrial response to implantation can be induced artificially by the application of oil into the lumen of the hormonally primed uterus, producing a deciduoma [4]. Natural and artificial decidualization share many of the same features, with distinct decidual zones [5], and the progression of each response can be monitored by an increase in uterine weight [6].
Female mice with a null mutation in the gene encoding interleukin-11 receptor α (IL-11Rα) are infertile due to disrupted decidualization [7], suggesting a critical role for IL-11 and its target genes in the decidual response. Despite normal estrous cycles and no detectable ovarian defects, female IL-11Rα deficient (IL11Ra-/-) mice are unable to support implantation of either IL11Ra-/- or wild type embryos. The failure of decidualization between days 4.5 and 10.5 in IL11Ra-/- females is characterized by greatly reduced vascular permeability at implantation sites, areas of hemorrhage, impaired secondary decidual zone formation, absence of mesometrial decidualization and aberrant infiltration of trophoblast giant cells [7]. Although morphologically similar to the decidua of pregnancy, a minority of artificially induced deciduomata in IL11Ra-/- mice show some mesometrial decidualization. Females homozygous for a hypomorphic IL-11Rα allele also show reduced decidualization, with decreased cell proliferation, progressive degeneration of the deciduae, infiltration of trophoblast giant cells and absence of placental formation [8]. Neither of these mutations have been found to cause hematopoietic defects [8,9].
Interleukin-11 is a multifunctional cytokine, initially described as a bone marrow stroma-derived hematopoietic growth factor [10]. IL-11 shares many functions with other members of the IL-6 family of cytokines, including the induction of acute phase proteins [11], inhibition of adipogenesis [12] and the regulation of bone ECM metabolism via induction of tissue inhibitor of metalloproteinases (TIMP)-1 [13]. Like IL-6, leukemia inhibitory factor (LIF), oncostatin M, ciliary neurotrophic factor and cardiotrophin-1, IL-11 exerts its biological effects via a multisubunit receptor complex involving the signal transducer gp130 [14]. Following the formation of its hexameric receptor composed of two molecules each of IL-11, the low-affinity ligand-binding IL-11Rα and gp130 [15], IL-11 is capable of activating a number of downstream signaling pathways. In most cell types, IL-11-activated gp130 mediates its effects through Janus tyrosine kinases (JAKs1-3 and Tyk2) and the signal transducers and activators of transcription (STATs1-6) (reviewed in [16]). The rate of transcription of target genes is then modified by binding of activated STAT dimers to a DNA element in the promoter region. IL-11 signaling can also control the initiation of translation via sequential activation of PI3-K, Pdk-1/Akt, p70 S6 kinase and ribosomal protein S6 [17].
Localization and expression of IL-11, IL-11Rα and gp130 in human endometrium across the menstrual cycle suggests a role for this cytokine in decidual transformation in preparation for pregnancy [18-20]. Levels of immunoreactive IL-11 are highest during the secretory phase of the cycle, when the endometrium is receptive to implantation, and IL-11 is produced by the decidualized stromal cells. Treatment of human endometrial stromal cells in culture with recombinant human IL-11 increases their secretion of the decidual markers prolactin and insulin-like growth factor binding protein (IGFBP)-1, and is associated with enhanced differentiation [21]. Plasma levels of IL-11 are decreased in women with first trimester spontaneous abortion [22], and there is decreased expression of IL-11 protein in chorionic villi and decidua from anembryonic compared to normal pregnancy [23].
The molecular targets of IL-11 in the uterus are unknown, but it is likely that IL-11 signaling modifies the expression of other genes important in decidualization. This study aimed to identify genes regulated by IL-11 during decidualization by cDNA microarray, and to examine their expression and localization by immunohistochemistry, as an indication of functional significance during early pregnancy.
Methods
Animals
Mice deficient in IL-11 receptor α (IL11Ra-/-) had been previously generated by gene targeting, and serially crossed more than 10 generations onto a C57BL/6 background [9]. Heterozygotes were interbred to produce wild-type (IL11Ra+/+) and IL-11Rα deficient (IL11Ra-/-) mice, which were identified by Southern blot analysis of genomic DNA obtained from tail biopsies [9]. All mice were housed in conventional conditions, fed and watered ad libitum and maintained in a 12-h light, 12-h dark cycle. All procedures were approved by the Monash Medical Centre (B) Animal Ethics Committee (AEC# MMCB 2001/04), and were carried out in compliance with the Helsinki Declaration.
Artificial decidualization
Surgery was performed under xylazine/ketamine-induced anesthesia, by intraperitoneal administration of 10 mg/kg xylazine hydrochloride (Ilium Xylazil-20, Troy Laboratories, Smithfield, NSW, Australia) and 80 mg/kg ketamine hydrochloride (Ketalar, Pfizer, West Ryde, NSW, Australia) in sterile phosphate-buffered saline (PBS). Anesthesia was reversed with 250 μg yohimbine hydrochloride and 400 μg 4-amino pyridine (Reverzine S.A., Parnell Laboratories, Alexandria, NSW, Australia) in sterile PBS.
Female IL11Ra+/+ and IL11Ra-/- littermates at 8–12 weeks of age (n = 4 per genotype per time point) were mated with wild type vasectomized males to induce pseudopregnancy, with the day of plug detection designated day 0. At approximately 1400 h on day 3, decidualization was induced throughout both uterine horns of each animal by injection of 20 μl of sesame oil (Sigma Chemical Co., St Louis, MO) into the lumen of each uterine horn via a 26-gauge needle inserted just distal to the utero-tubal junction. Mice were necropsied prior to surgery (0 h) or at 18 h, 24 h or 48 h following artificial decidualization. These time points prior to the onset of secondary decidualization were chosen to ensure similar cellular composition of the IL11Ra+/+ and IL11Ra-/- uteri. Whole uteri were cleaned of fat and weighed. A section of each uterus was either fixed in 10% phosphate-buffered formalin overnight or Carnoy's fixative for 2 h and processed to paraffin wax, or snap frozen in liquid nitrogen for subsequent RNA isolation.
Statistical analysis of uterine weight data
The wet weights of whole uterus from IL11Ra+/+ (n = 4/time point) and IL11Ra-/- (n = 4/time point) mice were statistically analyzed using GB-Stat 6.5 (Dynamic Microsystems, Inc., Silver Spring, MD). Following Bartlett's test for homogeneity of variance, uterine weight was used as the dependent variable and genotype and time as the two independent variables in a two factor analysis of variance. Bonferroni multiple comparison testing was used to compare uterine weight across time in each genotype and between genotypes at each time point. A two-tailed p value of less than 0.05 was considered a significant difference.
RNA preparation, cDNA microarray hybridization and data collection
Total RNA was extracted from whole uterus by acid guanidinium thiocyanate-phenol-chloroform extraction [24], incorporating an additional chloroform purification step to remove contaminating phenol. RNA was then treated with ribonuclease (RNase)-free deoxyribonuclease (DNase; Ambion, Austin, TX) to remove genomic DNA. The concentration of RNA in the final preparation was determined spectrophotometrically, and RNA quality evaluated by gel electrophoresis (1.2% agarose; Roche Applied Science, Penzberg, Germany) and by the ratio of optical density (OD260:OD280 = 1.8–2.0). Each artificially decidualized IL11Ra+/+ or IL11Ra-/- uterus was processed individually for microarray hybridization. A reference pool of RNA was prepared from wild type unstimulated uteri (n = 16). The experimental design used indirect comparisons between IL-11Ra+/+ or IL11Ra-/- and the reference pool.
Total RNA (10 μg) served as a template for the synthesis of aminoallyl-cDNA, which was then coupled to a fluorescent dye ester (Cy3 or Cy5) as described by the manufacturers of the CyScribe cDNA Post Labelling Kit (Amersham Biosciences, Buckinghamshire, England). Microcon-30 size exclusion columns (Millipore, Billerica, MA) were used to initially concentrate RNA samples, and to purify the cDNA probes prior to and following the dye-coupling reaction. Slides were printed with sequence-verified duplicate spots of the NIA 15K cDNA clone set [25] at the Australian Genome Research Facility and prehybridized for 30 min at 42 C in 10 mg/ml bovine serum albumin (BSA, ICN Biomedicals, Aurora, OH), 25% formamide (BDH Laboratory Supplies, Poole, England), 5 × SSC (750 mM sodium chloride, 75 mM sodium citrate; BDH Chemicals/AnalaR, Kilsyth, Victoria, Australia) and 0.1% sodium dodecyl sulfate (SDS; BDH Chemicals/AnalaR). IL11Ra+/+ or IL11Ra-/- cDNA (Cy3) and reference cDNA (Cy5) were competitively hybridized to the microarrays in the presence of 1 mg/ml Cot1 DNA (Gibco-BRL, Life Technologies, Mount Waverley, Australia), 10 mg/ml salmon sperm DNA (Gibco-BRL) and 10 mg/ml polyadenylic acid (Sigma). Hybridizations were repeated using the alternate dye combinations to account for differential fluorescent dye incorporation. After washing, slides were scanned using an Axon GenePix 4000B microarray reader (Axon Instruments, Union City, CA) and GenePix Pro 4.0 software (Axon Instruments) to generate pairs of 16-bit tagged image file format (TIFF) files. Following manual quality control for hybridization artefacts, red (Cy5) and green (Cy3) mean foreground and median background fluorescence intensity measurements for each spotted DNA sequence were extracted for export to the statistical programming environment R 1.5.1.
Microarray data analysis
Differential gene expression between IL11Ra+/+ (WT) and IL11Ra-/- (KO) uterus at time points following artificial decidualization was determined using the normalization and analysis functions of the statistical language R [26] and the add-on package Statistics for Microarray Analysis [27]. For each time point, the red and green background-corrected intensities (R and G) from 4 slides were read into R using the read.genepix and init.data commands. The stat.ma function was used to calculate the log-ratios of expression (M = log2R - log2G) and average log-intensity (A = (log2R + log2G)/2) for each spot, and to normalize the red and green channels relative to one another using print-tip loess normalization [28]. Diagnostic MA plots of each slide were used to determine the effectiveness of this normalization method in adjusting for sources of variation arising from dye bias and print-tip effects.
Given the indirect dye swap design [29] of these experiments, log-ratio values were reversed in the dye-swapped slides and the log-ratio for each gene was calculated as the difference of two independent log ratios from the equation log (KO/WT) = log (KO/reference) - log (WT/reference). Differentially expressed genes were identified by considering a univariate testing problem for each gene and then correcting for multiple testing using adjusted p-values [30]. The function stat.lm [31] was used to fit a linear model for each gene to the series of 4 arrays and estimate the average fold change and a standard deviation for each gene, taking into account the pattern of dye swaps and duplicate spots. The stat.bay.est command [31] then computed a moderated t-statistic and B-statistic for each gene, to give a log odds ratio of difference in mRNA expression between IL11Ra+/+ and IL11Ra-/-. Genes with a log odds score of greater than 3 (ie. adjusted p-value < 0.05) in both replicates were considered to be significantly up- or down-regulated in IL11Ra-/- uterus compared to wild type.
Real-time RT-PCR
Total RNA samples extracted from IL11Ra+/+ (n = 2) and IL11Ra-/- (n = 2) uterus at 48 h of decidualization and used for microarray analysis, and an additional two samples per genotype prepared in the same way were used for validation of the microarray data by real-time reverse transcription polymerase chain reaction (RT-PCR). All samples (n = 4/genotype) were further purified through an RNeasy Spin Column (Qiagen, Valencia, CA), and quantitated by RiboGreen Assay (Molecular Probes, Eugene, OR) according to the manufacturer's instructions, prior to triplicate reverse transcription reactions.
Total RNA (1 μg) was reverse transcribed at 46 C for 1.5 h in 20 μl reaction mixture using 100 ng random hexanucleotide primers and 6 IU AMV reverse transcriptase (Roche, Castle Hill, Australia) in the presence of cDNA synthesis buffer (Roche), 1 mM dNTPs (Roche), 10 mM dithiothreitol (Roche) and 10 IU ribonuclease inhibitor (RNasin; Promega, Annandale, Australia). The resulting cDNA mixtures were heated at 95 C for 5 min before storage at -20 C in small volumes to avoid freeze-thawing. Negative controls were performed by omission of reverse transcriptase.
For real-time quantification of selected mRNA transcript levels in IL11Ra+/+ and IL11Ra-/- uterus at 48 h of decidualization, PCR was carried out using a Roche LightCycler. Prior to LightCycler analysis, standard cDNA for each gene of interest was generated using a PCR Express block cycler (Thermo Hybaid Instruments, Franklin, MA). A 1 μl aliquot of RT product was amplified in a total volume of 40 μl using 4 μl of 10 × PCR Reaction Buffer (100 mM Tris-HCl, 15 mM MgCl2, 500 mM KCl, pH 8.3; Roche), 62.5 μM dNTPs (Gibco-BRL, Life Technologies), 10 pmol sense and antisense primers (Sigma Genosys, Castle Hill, NSW, Australia; Table 1) and 2.5 IU Taq DNA polymerase (Roche). The PCR amplification consisted of a hot start at 95 C for 5 min followed by 35 – 40 cycles of denaturation at 94 C for 50 sec, annealing at x C (see annealing temperature in Table 2) for 40 sec and extension at 72 C for 40 sec. The final extension was performed at 72 C for 10 min. Optimal annealing temperature (x) and cycle number were determined for each primer pair (Table 2). PCR products were electrophoresed on a 1.5% agarose gel containing 200 ng/ml ethidium bromide. Each single amplified product band was excised from the gel and purified using the UltraClean GelSpin DNA Extraction Kit (Mo Bio Laboratories, Solana Beach, CA). The cDNA concentration was measured using a spectrophotometer and each sequence identity confirmed at the Wellcome Trust Sequencing Centre, Monash Medical Centre. Standard curves for each transcript were generated using serial 1:10 dilutions of this standard cDNA using sterile water. Samples were diluted 1:10 – 1:40 prior to LightCycler analysis. Absolute concentrations of mRNA present in IL11Ra+/+ and IL11Ra-/- uterus at 48 h of decidualization were calculated relative to the standard curve, and adjusted for 18S rRNA expression levels.
Table 1 Oligonucleotide primers used in real-time RT-PCR. Primer pairs previously published – COL3A1 [82], BGN [83], SPARC [84] and NID1 [85].
Target Sense primer sequence Antisense primer sequence
COL3A1 5'-GGCTCCTGGTGAGCGAGGACG-3' 5'-CCCATTTGCACCAGGTTCTCC-3'
BGN 5'-CGGGACCTTGCTGTCTTCTC-3' 5'-CCCGGCAAGAACCTGAAAG-3'
SPARC 5'-AGAAGGCCTTTAGCCCTCTGC-3' 5'-ACTTTGCGGATACGGTTGTC-3'
NID1 5'-AGCTTCTATGATCGTACGGACATCAC-3' 5'-GTAAAGAACTGTAGACCATCTTCAGG-3'
18S 5'-CGGCTACCACATCCAAGGAA-3' 5'-GCTGGAATTACCGCGGCT-3'
Table 2 Real-time RT-PCR amplification conditions for each primer pair
Primer pair PCR product size (bp) Annealing temp (C) No. of cycles [MgCl2] (mmol/l) Melting temp of PCR product (C)
COL3A1 522 64 40 3 91.2
BGN 131 58 40 5 86.3
SPARC 130 60 40 3 85.6
NID1 425 63 40 4 88.8
18S 187 60 35 4 86.8
The cDNA template (triplicate RT reactions for each of 4 animals per genotype; 4 μl) was added to sterile capillaries to a total volume of 20 μl containing SYBR Green I, dNTPs, Taq DNA polymerase and reaction buffer (LightCycler FastStart DNA Master SYBR Green I Kit; Roche), supplemented with 5 pmol of specific sense and antisense primers (Table 1) and an optimal concentration of MgCl2 (Table 2). An initial denaturing step was performed for 10 min at 95 C, prior to 35 – 40 cycles of 95 C for 15 sec, x C for 5 sec and 72 C for 10 sec. Fluorescence was monitored continuously during cycling at the end of each elongation phase. At the end of each program, melting curve analysis confirmed the specificity of the reaction products.
Statistical analysis of real-time RT-PCR data
Triplicate RT reactions for each sample, the standard curve and a no RT negative control were analyzed in the same run, and each run was repeated once. A mean value for the initial target concentration of each sample was calculated using the fit points function of the LightCycler software. The mean concentration of 18S rRNA for each sample was used to control for RNA input, as it is considered a stable housekeeping gene and was not altered in expression between IL11Ra+/+ and IL11Ra-/- uterus. Following normalization, levels of RNA for COL3A1, BGN, SPARC and NID1 in IL11Ra-/- compared to wild type were statistically analysed using the paired t-test function of GraphPad Prism 3.0 (GraphPad Software, San Diego, CA). A two-tailed p value of less than 0.05 was considered a significant difference.
Immunohistochemistry
To confirm differential expression of collagen III, biglycan, SPARC and nidogen-1 at the protein level, immunohistochemistry was carried out on transverse sections of IL11Ra+/+ and IL11Ra-/- uterus collected at 48 h of decidualization, using specific antibodies (n = 5 mice per genotype, n = 3 sections per mouse). The morphology and cellular localization of artificial decidualization in IL11Ra+/+ and IL11Ra-/- uterus was also determined by immunostaining for the decidual marker desmin [32]. Primary antibodies used were rabbit anti-mouse collagen type III (Abcam #ab7778, Cambridge, UK) at 5 μg/ml, rabbit anti-mouse biglycan (LF-159 [33], gift from Dr. Larry Fisher, Matrix Biochemistry Unit, National Institutes of Health, Bethesda, MD) at 1:1000 dilution of whole serum, goat anti-mouse SPARC (Santa Cruz Biotechnology #sc13326, Santa Cruz, CA) at 5 μg/ml, rat anti-mouse entactin/nidogen-1 (Lab Vision-NeoMarkers #RT797P, Fremont, CA) at 15 μg/ml and goat anti-mouse desmin (Santa Cruz) at 200 μg/ml. Secondary antibodies were biotinylated swine anti-rabbit immunoglobulin G (IgG, DAKO, Glostrup, Denmark; 1:200), horse anti-goat IgG (Vector Laboratories, Burlingame, CA; 1:100) and rabbit anti-rat IgG (DAKO; 1:200). For collagen III, SPARC, nidogen-1 and desmin, negatives were performed using a matching concentration of non-immune IgG of the species in which the primary antibody was raised; rabbit IgG (DAKO), goat IgG (R&D Systems, Minneapolis, MN) or rat IgG (DAKO). For biglycan, negatives were performed using a matching dilution of normal rabbit serum (Sigma). Mouse lung (collagen III and biglycan) and kidney (nidogen-1 and SPARC) were used as positive control tissues, and a section from a single block was included in each staining run for quality control. For collagen III and SPARC immunolocalization, all dilutions and washes were carried out in high salt Tris-buffered saline (HS-TBS, 300 mM NaCl, 5 mM TrisHCl) with 0.6 % Tween 20. TBS (150 mM NaCl, 5 mM TrisHCl) with 0.6% Tween was used for nidogen-1, TBS/0.3% Tween for biglycan and HS-TBS/0.1% Tween for desmin.
Five micron sections of Carnoy's-fixed (for collagen III and biglycan immunolocalization) or formalin-fixed (for SPARC, nidogen-1 and desmin) uterus were mounted on poly-L-lysine-coated glass slides, deparaffinized and rehydrated through a series of graded ethanols. In an humidified chamber at 25 C, sections were incubated for 10 min in 3% hydrogen peroxide to block endogenous peroxidase activity, then for 30 min (SPARC and desmin) or 1 h (collagen III, biglycan and nidogen-1) in 10% serum of the species in which the secondary antibody was raised (swine, horse or rabbit; Sigma) and 2% mouse serum in one of the above buffers. Sections were then incubated with primary antibody or negative at 4 C overnight (collagen III, biglycan, nidogen-1 and SPARC) or 30 min at 25 C (desmin), then washed in TBS/Tween prior to secondary antibody incubation for 30 min (SPARC and desmin) or 1 h (collagen III, biglycan and nidogen-1) at 25 C. Sections were again washed in TBS/Tween, then the secondary antibody detected using the Vectastain ABC Elite/HRP Kit (collagen III and nidogen-1; Vector Laboratories) or the StreptABComplex/HRP Kit (biglycan, SPARC and desmin; DAKO) according to the manufacturer's instructions. Protein localization was visualized using the Liquid DAB-Plus Substrate Chromogen System (DAKO), with Harris hematoxylin (Sigma) counterstain.
Results
Artificial decidualization of IL-11Rα deficient and wild type uterus
Following the injection of oil into the wild type pseudopregnant uterus, a progressive increase in uterine weight was observed from 0 through to 48 h, reaching statistical significance (p < 0.01) at the final time point (Fig. 1). In contrast, the weight of the artificially decidualized IL11Ra-/- uterus did not change significantly across consecutive time points. There was therefore a statistically significant difference (p < 0.01) in uterine weight at 48 h of artificial decidualization between IL11Ra+/+ (96.9 +/- 9.8 mg) and IL11Ra-/- (30.4 +/- 7.7 mg).
Figure 1 Uterine weight following artificial decidualization. Weight (mg +/- SEM) of uterine horns at times following artificial decidualization of IL11Ra+/+ (open bars) and IL11Ra-/- (shaded bars) littermates. ** p < 0.01.
Differential gene expression following artificial decidualization
Total RNA extracted from IL11Ra+/+ and IL11Ra-/- uterus artificially decidualized for 0, 18, 24 or 48 h (n = 2/genotype/time point) was used as a template for the hybridization of NIA 15K cDNA microarrays. Figure 2 shows the volcano style plots of the normalized data for all genes at each time point. Each plot summarizes the data for a series of 4 microarrays (two KO/REF and two WT/REF), with differentially expressed genes in each replicate represented by open circles above the horizontal line (p = 0.05). At 0 h, prior to application of the decidualizing stimulus on day 3 of pseudopregnancy, there were no reproducible differentially expressed genes between IL11Ra+/+ and IL11Ra-/- (Fig. 2A,2B). Following 18 h of decidualization (Fig 2C,2D; Table 3), five expressed sequence tags (ESTs) were consistently upregulated 2 – 3-fold in IL11Ra-/- uterus compared to wild type. At 24 h of decidualization (Fig. 2E,2F; Table 4), there was one EST upregulated 2.7-fold. Sequence information for these ESTs is available online [34], using the AGRF ID as a unique identifier. None of these ESTs are currently recognized as sharing strong homology to any known genes. At 48 h of decidualization, 13 cDNAs showed upregulation and 4 downregulation in IL-11Rα deficient uterus (Fig. 2G,2H; Table 5). A number of these genes have previously described roles in the endometrium, but prior to this study, none have been shown to interact with IL-11. The ECM genes COL3A1, SPARC, BGN and NID1 were among those upregulated in IL11Ra-/- uterus compared to wild type. Transcripts representing COL3A1 and SPARC were present at two different locations on the array, and in each case, both sets of duplicate spots showed consistent upregulation in the absence of IL-11Rα. There were no genes or ESTs which were differentially expressed at more than one time point.
Figure 2 Gene expression in IL11Ra+/+ and IL11Ra-/- uterus following artificial decidualization. Expression profiling of 15K genes between IL11Ra+/+ and IL11Ra-/- at 0 h (A, B), 18 h (C, D), 24 h (E, F) and 48 h (G, H) following the artificial induction of decidualization. Each volcano style plot shows the normalized log ratio (effect estimate) of IL11Ra-/- compared to wild type for each gene from a series of 4 microarrays, plotted against the log odds of differential expression. A, C, E, G represent the first replicates, and B, D, F, H the second dye-swapped replicates. Genes with log odds of differential expression greater than 3 (ie. adjusted p-value < 0.05, above horizontal line) are represented by open circles, and COL3A1, BGN, SPARC and NID1 are labeled in G and H. Only those genes with log odds of differential expression greater than 3 in both replicates were considered differentially expressed, as described in Methods.
Table 3 Differentially expressed genes in IL11Ra-/- uterus compared to wild type at 18 h of decidualization
AGRF ID GenBank Accession UniGene Cluster Gene Fold Change P Value
H3084H06 BG070211 Mm.25335 EST: Cdc14b: CDC14 cell division cycle 14 homolog B (S. cerevisiae) + 3.1 0.044
H3137G08 BG074662 Mm.197274 EST: Moderately similar to GNMSLL retrovirus-related reverse transcriptase homolog – mouse retrotransposon (M. musculus) + 3.0 0.046
H3123E03 BG073512 Mm.197252 EST: Weakly similar to TLM MOUSE TLM PROTEIN (M. musculus) + 2.9 0.047
H3073G12 BG069200 Mm.328026 EST: Transcribed sequence with moderate similarity to protein ref:NP_083358.1 (M. musculus); RIKEN cDNA 5830411J07 + 2.5 0.038
H3082G05 AU014577 Not assigned EST + 2.1 0.032
Table 4 Differentially expressed genes in IL11Ra-/- uterus compared to wild type at 24 h of decidualization
AGRF ID GenBank Accession Unigene Cluster Gene Fold Change P Value
H3091F10 BG064439 Mm.182580 EST: Transcribed sequence with weak similarity to protein ref:NP_081764.1 (M. musculus); RIKEN cDNA 5730493B19 + 2.7 0.027
Table 5 Differentially expressed genes in IL11Ra-/- uterus compared to wild type at 48 h of decidualization
AGRF ID GenBank Accession Unigene Cluster Gene Fold Change P Value
H3012B10 BG063737 Mm.28870 45S pre rRNA gene + 4.2 0.002
H3133G03 BG074327 Mm.147387 Procollagen III alpha-1 (COL3A1) + 3.0 0.010
H3124H10 BG073709 Mm.147387 Procollagen III alpha-1 (COL3A1) + 2.7 0.011
H3116A04 BG072874 Mm.35439 Secreted acidic cysteine rich glycoprotein (SPARC/osteonectin/BM-40) + 2.4 0.010
H3152F04 BG075853 Mm.22699 Selenoprotein P plasma 1 (SEPP1) + 2.3 0.004
H3023A11 BG064718 Mm.21228 EST: RIKEN cDNA 2610101J03 gene; expressed sequence C79684 + 2.2 0.038
H3024A05 BG064802 Mm.35439 Secreted acidic cysteine rich glycoprotein (SPARC/osteonectin/BM-40) + 2.2 0.027
H3128D02 BG073888 Mm.77432 Thioredoxin interacting factor (VDUP1) + 2.2 0.003
Hs.ALAD BC000977 Hs.1227 Aminolevulinate delta dehydratase (ALAD) + 2.0 0.028
H3127D03 BG073809 Mm.2608 Biglycan (BGN/PGI) + 1.8 0.010
H3025E04 BG064933 Mm.4691 Nidogen-1 (NID1/entactin) + 1.8 0.007
H3129D02 BG073972 Mm.2137 Transcriptional regulator SIN3 yeast homolog B + 1.7 0.014
H3078E09 BG069642 Mm.27816 Hexosaminidase B (HEXB) + 1.7 0.013
H3101C10 BG071626 Mm.161419 Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) - 1.6 0.015
H3122C04 BG073406 Mm.68999 EST: RIKEN cDNA 9430015G10 gene - 1.6 0.036
H3116E07 BG072919 Mm.172198 EST: Weakly similar to GNMSLL retrovirus-related reverse transcriptase homolog – mouse retrotransposon (M. musculus) - 2.0 0.032
H3144C05 BG075183 Mm.337732 EST - 2.1 0.014
Validation of gene expression by real-time RT-PCR
To confirm the altered mRNA expression of the ECM genes COL3A1, SPARC, BGN and NID1 at 48 h of decidualization, quantitative real-time RT-PCR was carried out using the same RNA samples used in the microarray analysis, plus two additional RNA samples of each genotype, collected in the same way. At a significance level of p < 0.05, there was no statistical difference in the abundance of 18S rRNA (data not shown), COL3A1 (Fig. 3A), BGN (Fig. 3B), SPARC (Fig. 3C) or NID1 (Fig. 3D) mRNA between IL11Ra+/+ and IL11Ra-/- uterus. When only the samples used in the microarray analysis were considered, the difference in NID1 abundance between IL11Ra+/+ (1.13 +/- 0.12 fg/μl) and IL11Ra-/- (1.66 +/- 0.09 fg/μl) uterus approached statistical significance at p = 0.0708.
Figure 3 Quantitative real-time RT-PCR for extracellular matrix components. Quantitative real-time RT-PCR for (A) COL3A1, (B) BGN, (C) SPARC and (D) NID1. Circled data points indicate samples used in the cDNA microarray analysis, and horizontal lines the mean of each genotype. Absolute values for mRNA abundance were normalized to that of 18S rRNA.
Validation of gene expression by immunohistochemistry
Four genes found to be differentially expressed in IL11Ra-/- uterus compared to wild type at 48 h of decidualization were investigated at the protein level by immunohistochemistry using specific antibodies. Decidualizing and fully decidualized cells were identified in adjacent sections by immunostaining for the intermediate filament protein desmin, well characterized as a marker for decidual transformation [32].
Microarray data showing highly significant and reproducible increases in COL3A1 and BGN mRNA levels in IL11Ra-/- uterus were reflected in increased staining intensity for collagen III (Fig. 4A,4B,4C,4D) and biglycan (Fig. 4E,4F,4G,4H) in IL11Ra-/- uterus (Fig. 4B,4D,4F,4H) compared to wild type (Fig. 4A,4C,4E,4G). In both IL11Ra-/- and wild type uterus, collagen III and biglycan were primarily localized to the outer connective tissue and smooth muscle cells of the myometrium, with diffuse staining in the cytoplasm of decidualized stromal cells (Fig. 4D,4H inserts). Interstitial compartments underlying luminal and glandular epithelium and surrounding blood vessels also showed strong immunoreactivity for both proteins, while the epithelial cells were negative. In the absence of IL-11Rα, stronger staining for collagen III was particularly evident underlying luminal epithelium and in the ECM surrounding decidualizing stromal cells. There was a consistent absence of subluminal collagen III staining on the antimesometrial side of the uterus in wild type animals, an effect not seen in IL11Ra-/- littermates (Fig. 4C,4D). There was also a clear difference in the localization of biglycan staining underlying luminal epithelium, with strong staining at the mesometrial pole of the uterus in wild type animals and no preferential localization to either pole in IL11Ra-/- animals (Fig 4E,4F,4G). Biglycan staining surrounding glands was much more intense in IL11Ra-/- uterus (Fig. 4H) compared to wild type (Fig. 4G insert).
Figure 4 Immunohistochemistry for extracellular matrix components. Immunohistochemical staining of wild type (A, C, E, G, I, K, M, O, P, Q, S) and IL11Ra-/- (B, D, F, H, J, L, N, R, T) uterus at 48 h of decidualization using specific antibodies for collagen III (A, B, C, D), biglycan (E, F, G, H), nidogen-1 (I, J, K, L), SPARC (M, N, O, P) and desmin (Q, R, S, T). Negative controls using a matching concentration of non-immune IgG (collagen III, nidogen-1, SPARC and desmin) or normal serum (biglycan) in place of the primary antibody are inset in A, B, E, F, I, J, M, N, Q and R. Black squares on A and B indicate the antimesometrial pole magnified in C and D. Abbreviations: connective tissue (ct), myometrium (my), mesometrial pole (m), antimesometrial pole (am), luminal epithelium (le), glandular epithelium (ge), decidualized stromal cell (dsc), non-decidualized stromal cell (sc), blood vessel (bv), glycocalyx (gly). Scale bar = 50 μm (A, B, E, F, I, J, M, N, Q and R are at the same magnification; C, D, G and P are at the same magnification; H, K, L, O, S, T and inset in G are at the same magnification).
While no detectable differences were observed in the overall intensity of immunostaining for nidogen-1 (Fig. 4I,4J,4K,4L) or SPARC (Fig. 4M,4N,4O,4P) in IL11Ra-/- uterus compared to wild type, the localization of these proteins has not previously been described in the deciduoma of wild type or IL11Ra-/- mice. In both genotypes, nidogen-1 was localized to the cytoplasm of decidual cells (Fig. 4K) and glandular epithelial cells (Fig. 4L), the basement membrane underlying luminal and glandular epithelium and surrounding blood vessels (Fig. 4L). The cellular localization of SPARC was more similar to that of collagen III and biglycan, with strong staining in the outer connective tissue and myometrium (Fig. 4M,4N). Strong SPARC staining was also detected in the cytoplasm of decidualized and non-decidualized stromal cells (Fig. 4O), endothelial cells (Fig. 4P), and at the glycocalyx of luminal and glandular epithelium (Fig. 4P).
Desmin immunostaining revealed a reduction in the overall extent of decidualization in IL11Ra-/- uteri at 48 h following the induction of deciduomata (IL11Ra+/+: Fig. 4Q,4S; IL11Ra-/-: Fig. 4R,4T), with an absence of secondary decidualization. Desmin-positive decidual cells were detected in all artificially decidualized uteri, indicating that the surgical induction of decidualization was successful in all cases.
Discussion
Interleukin-11 is one of only a few molecules known to be critical for decidualization in mice. This study has demonstrated for the first time that IL-11 regulates changes in the uterine extracellular matrix that are necessary for decidualization. The application of cDNA microarray analysis has revealed that lack of IL-11 signalling in IL11Ra-/- mice results in differences in mRNA expression compared to wild type during artificial decidualization. Each of the ECM molecules investigated further in this study, collagen III, biglycan, nidogen-1 and SPARC, showed protein expression patterns consistent with a role in decidualization, with immunostaining in endometrial stromal cells and their surrounding matrices. Collagen III and biglycan were more abundant during defective decidualization in IL11Ra-/- uterus, both at the mRNA and protein levels. This indicates that the cellular processes of decidualization including proliferation, differentiation, signal transduction and apoptosis may be facilitated by decreased expression of these matrix molecules.
As well as providing a dynamic structural framework, the ECM of the endometrium interacts with its associated cells to mediate critical processes, including adhesion, migration and differentiation (reviewed in [35]). Growth factor availability can also be regulated by binding to ECM components [36]. Collagens, elastin, structural glycoproteins (eg. fibronectin, laminin, SPARC and nidogen), proteoglycans (eg. biglycan) and glycosaminoglycans are the major components of endometrial matrix, and can act as ligands for both cell-cell and cell-matrix interactions [37]. Decidualization of endometrial stromal cells is associated with dramatic changes in matrix composition, including the phagocytosis and digestion of collagen fibrils, an increase in collagen fibril diameter [38], deposition of basement membrane proteins [39], the synthesis and secretion of sulfated glycosaminoglycans into the extracellular space and a decrease in elastic fibrils surrounding mature decidual cells [1]. Disruptions to the composition of uterine ECM during decidualization may be responsible for the failure of implantation in IL11Ra-/- mice.
While the microarray data showed consistent, reproducible upregulation of COL3A1, BGN, SPARC and NID1 in IL11Ra-/- compared to wild type uterus, this effect was not statistically significant when real-time RT-PCR was used as an alternative quantitation method. Many factors may contribute to discrepancies between cDNA microarray and real-time RT-PCR data. There are major differences in the approach to mRNA quantitation used by the two techniques. Using cDNA microarray, the mean fluorescence intensity ratio for each gene in IL11Ra-/- or IL11Ra+/+ uterus was calculated relative to a reference pool, and the ratio of IL11Ra-/- to IL11Ra+/+ determined by the use of computational algorithms. When quantitating the same mRNA species by real-time RT-PCR, a standard curve of known concentration was used to infer the absolute abundances of mRNA in the IL11Ra-/- and IL11Ra+/+ samples, which were then normalized for RNA input.
Real-time RT-PCR was chosen for cDNA microarray validation in this study because it has higher sensitivity and lower RNA requirements than Northern blot, but the lack of agreement between the two methods is not unusual. It is well recognized that fold change values for a given gene may vary widely, even between two different microarray techniques [40-43]. In using real-time RT-PCR to evaluate microarray data, Rajeevan et al [44] found that the majority of the array data were qualitatively accurate, but it was not possible to consistently validate genes showing less than a 4-fold difference on the array. Each of the genes examined in this study showed less than a 3-fold difference. It is not known how well array data correlates overall with data from RT-PCR or any other mRNA quantitation method [45], further complicating the interpretation of conflicting results.
There are a number of compelling arguments both for and against conducting corroborative studies for microarray data, and there is good evidence that the data is highly reliable when the experimental design and statistical analysis is sound [46]. In assessing the validity of the microarray data in this study, it is important to note that immunostaining for both collagen III and biglycan protein confirmed the differential expression seen by microarray analysis. This is striking, given that changes in protein expression detected by tissue microarray have been found to correlate with the mRNA change less than 50% of the time [45]. Given the cellular heterogeneity of the uterus, the localization of cell-specific expression is essential in extending microarray data on whole uterus to the investigation of decidualization. Neither SPARC nor nidogen-1 proteins were altered in expression by the absence of IL-11 signaling, but there may well be a delay between the mRNA and corresponding protein changes. This would not have been detected by using samples collected at the same time point for both mRNA and protein analyses.
For each of the genes examined, upregulation during defective decidualization in IL11Ra-/- uterus is supported by existing data in the literature. Collagen III is a fibrillar collagen with known roles in differentiation and migration [47], and together with collagen I forms the structural support for the endometrium during the establishment of pregnancy [48]. Consistent with a role in decidualization, collagen III is both secreted and phagocytosed by mouse decidual cells [49], and secreted by first trimester decidual cells in the human [50]. The reduced number of collagen fibrils surrounding endometrial stromal cells in early pregnancy [51] may facilitate decidual transformation and blood vessel development [52]. This decrease has previously been detected primarily in the subepithelial endometrial stroma at day 4 [53]. In the rat, low levels of collagen III have been reported in the primary decidual zone, with much higher concentrations in the outer stroma and myometrium as decidualization progresses [54]. This is consistent with immunohistochemical data obtained for wild type mice in this study, showing very low intensity staining in the antimesometrial decidua, and higher intensity in the outer compartments of the uterus.
During the human menstrual cycle, collagen III immunostaining is higher in the proliferative compared to the secretory phase [55], indicating that downregulation and/or metabolism and redistribution of collagen III occurs with the onset of endometrial receptivity. Compared to proliferative phase endometrium, the ratio of collagen III to collagen I is decreased in decidual cells [56]. Aplin et al [55] observed changes in collagen III distribution from dense fibrils in the proliferative phase to matrix channels between decidual cells in the secretory phase. This may be involved in maintaining tissue integrity as the level of hydration increases, and in supporting movement of leukocytes through the tissue [57]. Defects in any of these processes in IL11Ra-/- mice could contribute to impaired decidualization.
Using microarray analysis, the mRNA encoding procollagen III α1 (COL3A1) has been previously shown to decrease in abundance in the mouse uterus at estrus [58], and between days 3.5 and 5.0 of gestation [59], and to increase following ovariectomy in the rat [60]. Together with data from this study showing increased COL3A1 mRNA and mature collagen III protein in IL11Ra-/- uterus at 48 h of decidualization, it appears that successful decidualization involves downregulation of COL3A1 transcription.
Biglycan is a small leucine-rich proteoglycan, which binds to type I [61] and type V collagen fibrils, transforming growth factor-β [62] and tumour necrosis factor-α [63] in vitro. Its function in ECM has not been well defined, but biglycan is thought to be involved in the control of cell migration [64]. In the wild type mouse uterus, there is low endometrial biglycan expression post-implantation [65]. Biglycan mRNA expression has been shown by oligonucleotide microarray to be downregulated in the secretory compared to the proliferative phase of the menstrual cycle in human endometrium [66,67], coincident with the window of implantation. As defective decidualization in IL11Ra-/- mouse uterus was associated with the upregulation of biglycan mRNA, the activity of this proteoglycan in the ECM may inhibit the decidual response.
Decidual cells are known to express nidogen-1 as part of the pericellular basement-membrane laid down during decidualization [39]. The main function of nidogen in the basement-membrane is to connect networks of collagen IV and laminin [68], but nidogen also binds perlecan [69], fibulins [70] and fibronectin [71]. Changes in nidogen mRNA levels have been reported during the establishment of the placenta in the mouse, with in situ hybridization revealing highly restricted expression in decidual and maternal endothelial cells [72]. This study has now shown much earlier nidogen-1 protein expression in the decidual cells, glandular epithelial cells and epithelial basement membrane of the artificially induced deciduoma, and indicated aberrant increased expression of the NID1 gene during defective decidualization.
SPARC (osteonectin/BM-40) is described as a matricellular glycoprotein, in that it binds to both cells and ECM to regulate cell-matrix interactions [73]. Like other matricellular proteins, SPARC can bind and alter the activity of cytokines and induce the expression of proteinases and their inhibitors [74]. SPARC is often expressed in tissues undergoing cell proliferation, migration and ECM remodeling [75], so it is not surprising that substantial expression of SPARC has been observed in human decidua [76]. Differences in immunostaining intensity have been associated with the degree of decidualization, with the strongest staining seen in the cytoplasm of decidualizing cells, decreasing as decidualization progresses [76]. Fully decidualized cells were found to express SPARC pericellularly, indicating a role in mediating interactions of decidual cells with their surrounding matrix. Binding of SPARC to a number of ECM components, including collagen III and nidogen, may contribute to the structural integrity of the tissue [77]. It could therefore be hypothesized that during normal decidualization, SPARC, collagen III and nidogen-1 are coordinately downregulated to allow loosening of the tissue in preparation for trophoblast invasion. In both models of IL-11Rα deficiency [7,8], implantation sites have increased rather than decreased numbers of invading trophoblast giant cells. This pathological invasion is thought to occur subsequent to failure of decidualization, highlighting the importance of tight regulation of ECM components in normal decidual function.
Using mRNA and protein expression studies alone, it is not possible to determine whether IL-11 affects ECM molecule expression directly or indirectly. The presence of STAT binding sites in the promoters for COL3A1, BGN, NID1 and SPARC would confirm that a direct interaction is possible, but would not establish that the interaction is occurring in the uterus during decidualization. Given that IL-11 is a secreted cytokine with autocrine and paracrine activity, a direct effect on ECM molecule transcription would be dependent on coincident expression patterns of the ECM molecules, IL-11 and its receptors. Maximal expression of IL-11 and IL-11Rα mRNA has been reported in the predecidual and decidual cells of the mouse implantation site by in situ hybridization [7]. Expression of gp130 mRNA is detectable in the glandular epithelium from days 3 – 7, and in decidual cells from day 5 [78]. Given that the ECM molecules investigated in this study are more widely expressed in the uterus, it is likely that regulation by IL-11 is indirect.
Indirect effects of IL-11 on ECM composition could be mediated by matrix metalloproteinases (MMPs) and/or their inhibitors. Despite their presence on the NIA 15K microarray, differential expression was not observed for MMP-2, MMP-9, TIMP-2, or TIMP-3 in the IL11Ra-/- uterus compared to wild type. Previous in vitro studies have indicated that IL-11 inhibits MMP-1 and -3 protein in human synovium [79], and enhances the ability of mouse osteoblasts to synthesize MMPs responsible for the degradation of collagen I [80]. IL-11 does not influence the activity of stromelysin in human chondrocytes [13], or induce MMP-2, -7 or -9 in human endometrial epithelial or stromal cells [81], but tissue inhibitor of metalloproteinases (TIMP)-1 is known to be induced by IL-11 in vitro [13]. While not directly supporting a role in ECM degradation, these interactions suggest that IL-11 is involved in regulating the balance between MMP and TIMP activity in the tissue.
Conclusions
This investigation of the downstream targets of IL-11 during mouse decidualization has uncovered previously unknown interactions between IL-11 and uterine ECM composition. Dysregulation of collagen III, biglycan, nidogen-1 and SPARC in the absence of IL-11 signaling at the time of decidualization may indicate essential functions for these molecules during the implantation process in mice. Functional studies using mouse and human endometrium may further clarify the mechanisms of IL-11 action on the ECM during this critical time in embryo implantation. By elucidating the role of IL-11 regulated genes in decidualization, future work may identify potential new targets for the manipulation of human fertility.
Authors' contributions
CAW carried out all the experimental work and statistical analysis and drafted the manuscript. LR provided the heterozygote IL11Ra+/- breeding pairs. LAS conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Acknowledgments
The authors would like to thank Dr Melissa Brasted for her assistance with the surgical procedures, and Prof Terry Speed, Dr Gordon Smyth and Dr Rory Wolfe for their contributions to the planning of the microarray experiments and subsequent data analysis using R.
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| 15537430 | PMC535545 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Nov 10; 2:76 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-76 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-381555506510.1186/1742-4690-1-38ResearchAssessment of FIV-C infection of cats as a function of treatment with the protease inhibitor, TL-3 de Rozières Sohela [email protected] Christina H [email protected] Dennis A [email protected] Karen J [email protected] Ying-Chuan [email protected] Salvador [email protected] Steven [email protected] Bruce E [email protected] John H [email protected] Department of Molecular Biology, The Scripps Research Institute, La Jolla, USA2 Department of Experimental Medicine, The Scripps Research Institute, La Jolla, USA3 Department of Animal Resources, The Scripps Research Institute, La Jolla, USA4 Department of Neuropharmacology, The Scripps Research Institute, La Jolla, CA, 92037, USA2004 19 11 2004 1 38 38 16 9 2004 19 11 2004 Copyright © 2004 de Rozières et al; licensee BioMed Central Ltd.2004de Rozières 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
The protease inhibitor, TL-3, demonstrated broad efficacy in vitro against FIV, HIV and SIV (simian immunodeficiency virus), and exhibited very strong protective effects on early neurologic alterations in the CNS of FIV-PPR infected cats. In this study, we analyzed TL-3 efficacy using a highly pathogenic FIV-C isolate, which causes a severe acute phase immunodeficiency syndrome, with high early mortality rates.
Results
Twenty cats were infected with uncloned FIV-C and half were treated with TL-3 while the other half were left untreated. Two uninfected cats were used as controls. The general health and the immunological and virological status of the animals was monitored for eight weeks following infection. All infected animals became viremic independent of TL-3 treatment and seven of 20 FIV-C infected animals developed severe immunodepletive disease in conjunction with significantly (p ≤ 0.05) higher viral RNA loads as compared to asymptomatic animals. A marked and progressive increase in CD8+ T lymphocytes in animals surviving acute phase infection was noted, which was not evident in symptomatic animals (p ≤ 0.05). Average viral loads were lower in TL-3 treated animals and of the 6 animals requiring euthanasia, four were from the untreated cohort. At eight weeks post infection, half of the TL-3 treated animals and only one of six untreated animals had viral loads below detection limits. Analysis of protease genes in TL-3 treated animals with higher than average viral loads revealed sequence variations relative to wild type protease. In particular, one mutant, D105G, imparted 5-fold resistance against TL-3 relative to wild type protease.
Conclusions
The findings indicate that the protease inhibitor, TL-3, when administered orally as a monotherapy, did not prevent viremia in cats infected with high dose FIV-C. However, the modest lowering of viral loads with TL-3 treatment, the greater survival rate in symptomatic animals of the treated cohort, and the lower average viral load in TL-3 treated animals at eight weeks post infection is indicative of a therapeutic effect of the compound on virus infection.
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Background
Feline immunodeficiency virus (FIV) is a lentivirus that infects domestic and feral cat populations worldwide. Infected cats exhibit similar disease patterns as human immunodeficiency virus (HIV) infected patients by developing multiple immuno-depletive symptoms collectively referred to as acquired immunodeficiency syndrome (AIDS). As with HIV, differences in virulence among the different FIV subgroups are evident [1-4]. Thus, the cat represents an amenable animal model for testing certain anti-HIV-1 drug modalities in vivo.
One of the major breakthroughs in HIV-1 treatment has been the use of specific inhibitors of the viral aspartic protease family as part of a drug cocktail, called highly active anti-retroviral therapy (HAART), with the ultimate goal of suppressing HIV-1 replication in patients to low or undetectable levels [5-8]. Effective HAART therapy continues to be dependent on the development of new drug modalities due to the rapid mutation rate of HIV-1, leading to drug resistance development [9]. Therefore, an effective small animal model for evaluating new drugs and treatments for HIV is of paramount importance. Experimental testing of new protease inhibitors in cats has been of limited success due to the ineffectiveness of HIV-1 specific protease inhibitors against FIV [10,11]. The promising development of the protease inhibitor TL-3, which inhibits FIV, HIV-1 and SIV (simian immunodeficiency virus) infections in vitro with similar effectiveness [12] led us to analyze its efficacy in the cat model. Initial in vivo studies using the predominantly neurotropic FIV-PPR strain, showed that TL-3 treatment lowered plasma viral loads and resulted in a significant protective effect against neurologic alterations in the CNS in FIV infected cats [13]. In the present study, we employed the highly pathogenic FIV-C isolate (CABCpady00C), which causes a fulminant acute phase disease in the periphery, with high death rates from acute phase immunodeficiency disease [1].
Results
In Vivo Infection
Twenty-two female specific pathogen-free (SPF) cats were randomly divided into five groups. Group 0 consisted of two cats, which received TL-3 treatment without viral infection and were considered controls. Group 1 (n = 5) received 0.1 ml (105 RNA copies/ml) of FIV-C-infected plasma I.V. with TL-3 drug treatment. Group 2 (n = 5) received 0.1 ml FIV-C-infected plasma without TL-3 treatment. Group 3 (n = 5) received 0.5 ml FIV-C-infected plasma with TL-3 and Group 4 (n = 5) received 0.5 ml FIV-C-infected plasma without TL-3 treatment. Blood (1 ml) was drawn from all cats prior to the start of the experiment, at weekly intervals for the first four weeks after infection, and at bi-monthly intervals from week 4 until the end of the study. Complete blood counts were assessed as a function of infection and TL-3 treatment. In addition, quantitative reverse transcription PCR (QRT-PCR) analyses were performed to assess plasma viral load. All animals were continuously observed for any changes in general health. No significant differences were noted between viral load or disease phenotype between the two plasma dosages used in infection and subsequent discussion will not distinguish between these two groups.
By week 6 post infection, seven animals (221, 222, 220, 234, 229, 219, 215) were showing clinical signs of debilitating acute phase disease. Four of the seven affected animals (215, 219, 220, 234) were from the untreated groups, and three animals (222, 221, 229) were from the TL-3 treated groups. Symptoms in all seven symptomatic cats varied from conjunctivitis, anorexia, corneal ulcerations, and gingivitis to increases in temperature, dermatitis and marked lethargy. Despite intensive antibiotic treatment, the general state of health of 6 of the cats did not improve (221, 220, 234, 229, 215, 219) and they received mandated euthanasia between six to seven weeks post-infection. Cat 222 (TL-3 treated) responded to antibiotic therapy and recovered from acute phase symptoms.
Control and infected cats gained weight at approximately the same rate during the first 4 weeks post infection, regardless of drug treatment status (Figure 1, data expressed as a ratio to starting weight for each animal). However, at 6 weeks post infection, three animals in the + TL-3 cohort (Figure 1, upper panel) and three animals in the -TL-3 cohort (lower panel) had lost weight. All three of the animals in the -TL-3 cohort (bottom panel, shown in red; 215, 220, and 234) required mandated euthanasia prior to the next weighing at week 8. Cat 229 in the TL-3 treated cohort also required mandated euthanasia prior to week eight. Cat 221 in the + TL-3 cohort was euthanized on the same day as cat 229, but a final weight was not recorded. Cat 219 of the -TL-3 cohort had a normal weight at week 6, but required euthanasia at week 7, with an approx. 10% weight loss relative to week 6 (data not shown in figure). Thus, weight loss at week 6 occurred with onset of severe acute phase disease. Cat 222 in the TL-3 treated group (upper panel) responded to aggressive rehydration and antibiotic therapy, gained weight, and survived the acute phase. Cat 223 was never noticeably symptomatic and it is unclear why this animal showed a dip in weight at week 6 which it recovered by week 8.
Figure 1 Cat weight ratios as a function of FIV infection. Cats infected with FIV-C in the presence and absence of TL-3 treatment were weighed regularly throughout the course of infection. Graphs depict weight of individual cats as a ratio to respective starting weight. Control cats were 213 and 214; green symbols. Weight ratios of cats that required euthanasia as a result of acute phase feline AIDS are shown in red.
Brainstem auditory evoked potential changes (BAEPs)
Previous studies using FIV-PPR showed that the isolate induces marked and consistent delays in BAEPs of infected cats and that TL-3 could reverse this effect [13]. We, therefore, analyzed the FIV-C infected animals for similar BAEP delays with or without TL-3 treatment. Animals were analyzed at two-week intervals for the first eight weeks of the experiment. Interestingly, no delays in BAEPs were noted with FIV-C infection (data not shown) in spite of high viral loads in the periphery (see below).
Viral load quantification
Plasma viral RNA loads were measured at regular intervals throughout the experimental period to evaluate viral load changes in cats treated with or without TL-3. Viral loads ranged from undetectable to approximately 9 × 109 RNA copies/ml plasma (Figure 2). Examination of plasma viremia at 2 weeks (i.e. before an active immune response) revealed little differences in viral loads between cats in the TL-3 treated and untreated cohorts (Figure 2A). In contrast, after 2 weeks of infection, the highest viremias were found in cats not receiving TL-3 (compare + TL-3 to -TL-3, Figure 2B). Moreover, of the 6 FIV symptomatic cats that were euthanized due to the severity of clinical disease, 4 of these animals had received no TL-3 and 3 of these cats presented with the highest viral load (compare open symbols to filled symbols, Figure 2C).
Figure 2 Plasma viral loads of FIV infected cats as a function of TL-3 treatment and disease. (A), normalized viral load (copies/ml × 106) at week 2; (B), and at peak viremia between weeks 0 – 6.5 in FIV infected cats treated with TL-3 (+TL-3, solid symbols) or untreated (-TL-3, open symbols). (C), peak viremia between weeks 0 to 6.5 post infection, in healthy (asymptomatic) and symptomatic (euthanized) cats. The average value (largest horizontal bar, –) is plotted for each group. Equal volumes of plasma were normalized using an external RNA spike and analyzed as detailed in Materials and Methods for the presence of FIV RNA by reverse-transcription real-time quantitative PCR. * indicate average values in (C) differ significantly (p ≤ 0.05).
Analysis of viremia patterns in individual cats during the initial 8 week evaluation period yielded additional differences in the TL-3 treated vs. untreated groups. Although infected cats showed an initial viral peak in the second week post-infection, by week 4, viral loads decreased for each cat and the average viral load for symptomatic and asymptomatic cats was similar (Figures 3A and 3B). However, by week 6 the symptomatic group had an average viral load of 2 × 108 copies/ml, 27 times more virus than the average viral load of asymptomatic cats (compare Figures 3A and 3B). Half of the 6 symptomatic cats were euthanized at week 6 due to severe illness. The remaining three symptomatic cats (all from the untreated group) had an average viral load of 3.2 × 108 copies/ml at 6.5 weeks (Figure 3A) and required euthanasia by week 7. Within the asymptomatic cohort, three (223, 224, 230) out of eight TL-3 treated cats had reduced viral loads by week 4. Their viral loads remained low during week 6 and at week 8, four of 8 surviving TL-3 treated animals had viral load levels below detection limits. Of the six surviving animals that had not received TL-3, only one cat (233) had a viral load below detection limits at week 8 (Figure 3B).
Figure 3 Individual plasma viral loads as a function of TL-3 treatment over an 8 week period. Normalized viral loads in (A) symptomatic (euthanized) and (B) asymptomatic cats. Each value corresponds to the volume normalized viral load (copies /ml) between weeks 2–8. Control cats, 213 and 214 (not shown) had viral levels below detection. Cats are grouped for the presence (■ solid bars) or absence (□ open bars) of TL-3 treatment, and then in numerical order from left to right. Specific bars are marked for the approximate week of euthanasia (†) and viral levels below detection are denoted as (*).
Leukocyte Changes
Peripheral blood mononuclear cells (PBMC) were isolated from whole EDTA-treated blood from each animal and CD4+ and CD8+ T cell quantitations were performed by flow cytometry and leukocyte counts. Absolute values for CD4+ T cells showed a general decreasing trend in both the TL-3-treated and untreated cohorts when all animals were averaged in each group and CD8+ T cell counts and neutrophils did not show significant variance from control values (data not shown). However, analysis of CD4+ and CD8+ T cells counts of symptomatic cats as compared to treatment-matched asymptomatic animals showed interesting differences (Figure 4A and 4B, resp.). The CD4+ T cell population of the symptomatic cats decreased progressively over the first 6-week period (Figure 4A). CD4+ T cell counts of identically treated cats that showed no signs of disease also decreased, but less precipitously than those of animals eventually requiring euthanasia due to severity of illness (p ≤ 0.05). Uninfected control animals maintained their T cell values during the same time frame. The CD8+ population of T cells showed an even more drastic decrease in the symptomatic animals compared to the asymptomatic infected animals (Figure 4B). Between weeks two and four, the CD8+ T cell population in the symptomatic animals showed a small rebound from week 2, then declined through week 6 post infection. In contrast, CD8+ T cell counts in treatment-matched asymptomatic animals increased significantly (p ≤ 0.05) from week 2 onward, consistent with a strong CD8+ T cell response in the protected cats.
Figure 4 Peripheral CD4+ and CD8+ lymphocyte levels as a function of clinical outcome over 8 weeks. A) Total CD4+ cells per ml average and standard error of the mean; B) Total CD8+ cells per ml average and standard error of the mean. Symbols: ●: uninfected cats (213 and 214); □ Symptomatic cats (215, 219–221, 229 and 234); ◇ asymptomatic cats (216–218, 222–228, 230–233). * indicate values between symptomatic and asymptomatic cats differ significantly (p ≤ 0.05).
Consistent with previous reports [14-16], we also observed changes in the total neutrophil counts in the symptomatic FIV-C infected cats. Within one week post-infection, neutrophil numbers increased markedly in cats 220 (10, 962 cells/μl) and 234 (11, 926 cells/μl) as compared to other cats with identical TL-3 treatment (average = 6880 ± 2847 cells/μl). By week 4, neutrophil values fell drastically in the same two cats (220: 558 cells/μl; 234: 416 cells/μl) as well as in cats 215 (420 cells/μl) and 221 (400 cells/μl). By week 6, four of the symptomatic cats had neutrophil counts near zero. Only cat 220 slightly recovered its neutrophil cell count (3675 cells/μl) prior to mandated euthanasia.
Protease escape variants
Drug pressure induced viral resistance to protease inhibitors represents one of the major hurdles of HIV HAART treatment regimens [17,18]. HIV and FIV proteases share only 23–28% overall identity at the protein level, yet the enzymatic active site residues are virtually identical [19], allowing a convenient side-by-side comparison. When analyzing the surviving cohort of animals that had received TL-3 treatment, we noted high viral loads in some cats (Figure 3B; 222, 228, 231, 232) that remained elevated (except 228) between weeks 6 and 8, relative to other cats (Figure 3B; 223, 224, 230) that had significantly lowered viral loads. We, therefore, analyzed plasma samples from symptomatic and asymptomatic TL-3 treated animals to look for potential drug-resistance. The protease gene was cloned from week 6 plasma of the two symptomatic TL-3 treated cats (221 and 229) and sequenced. Twenty individual protease clone sequences revealed a wild-type protease gene sequence (data not shown). We then chose cats 231 and 228, two TL-3-treated asymptomatic cats with relatively high viral loads in week 6 (Figure 3B), as well as cat 222, which overcame its disease syndrome with drug treatment. Cloning and sequencing of the protease gene revealed a number of interesting mutations (Table 1). The aspartic acid to glycine point mutation at FIV protease residue 105 (D105G) occurred in cats 231 and 222. The equivalent site in HIV protease is residue 88 (HIV88N), which is associated with development of resistance to some HIV protease inhibitors [20]. Other potentially relevant point mutants cloned from cat 231 were the H72R (HIV63L) and N55D (HIV46M) from week 6 plasma and M107R (HIV90L) from week 8 plasma. The equivalent HIV residue escape mutants exhibit various degrees of resistance against current protease inhibitors [20]. Additional multiple point mutations, lying outside of the FIV protease active site or having no apparent important HIV equivalents were also observed (Table 1). The protease genes were cloned into expression vectors, expressed and purified for enzymatic analysis. The findings revealed that most of the mutants could not be distinguished from wild-type FIV-C protease as to TL-3 susceptibility. However, mutant D105G exhibited a Ki of 47.3 nM as compared to 9.9 nM for wild-type, consistent with a 5-fold increase in resistance to TL-3.
Table 1 FIV Protease Escape Mutants (HIV equivalent residue)
PR mutations Ki vs TL-3 (nM)
Wildtype PR ---- 9.9
Cat 231 D105G (HIV88N) 47.3
D94G (*) N.D.
H72R (HIV63L) 10.9
N55D (HIV46M) 9.1
G52R (HIV43K) N.D.
1M107R (HIV90L) inactive
1N51Y (HIV42W) N.D.
Cat 228 C69R (HIV60D) N.D.
Cat 222 D105G (HIV88N) 47.3
1 isolated from 8th week plasma
* no HIV equivalent site
Bold type, FIV-C point mutant of interest
N.D. not determined
Discussion
The protease inhibitor, TL-3, demonstrated broad efficacy against FIV, SIV and HIV in tissue culture [12], as well as against drug-resistant HIV isolates [21]. Furthermore, TL-3 treatment had a very strong protective effect on early neurologic alterations in the CNS of FIV-PPR infected cats [13]. However, molecularly cloned FIV-PPR causes little acute phase disease in the periphery. We, therefore, sought to test TL-3 efficacy in vivo in the context of the highly pathogenic, uncloned CABCpady00C species (FIV-C) [1], which causes a severe acute phase immunodeficiency syndrome, with high early mortality rates. Although only partial protection was afforded by TL-3 in our studies, the results are promising in that average peak viral loads in some cats were lower in the presence of drug, even in the face of a highly aggressive infection (Figure 2B).
Of 20 cats infected with uncloned FIV-C, seven animals showed signs of immunodepletive disease early on (Figure 4) and developed full-fledged acute phase AIDS symptoms with anorexia, conjunctivitis, corneal ulcerations, gingivitis and marked lethargy by week 6, mandating euthanasia of six animals. The symptomatic cats had viral RNA loads significantly higher (>108 RNA copies/ml, p ≤ 0.05) than asymptomatic infected animals independent of drug treatment. This finding suggests that the intense viral infection severely compromised the immune system leading to immunodeficiency and the development of concomitant AIDS, as evidenced by the rapid loss of CD4+ T cells as well as neutrophils in the affected cats during the first few weeks (Figure 4). Cats receiving TL-3 treatment had lower peak viral loads compared to cats not receiving TL-3 at weeks 4 and 8, indicating that the protease inhibitor reduced systemic expansion of viral infection. Previous studies have correlated disease progression with high initial peak viral loads [22]. Of the five out of eight cats treated with TL-3 and having higher viral loads at weeks 4 and 8 compared to non-TL-3-treated cats, three (222, 228, 231) were evaluated for FIV resistance to TL-3. None of the protease genes recovered from cat 228, whose viral levels fell below detection at week 8, showed evidence of TL-3 resistance. However, cats 222 and 231 were found to harbor virus in the plasma that encoded TL-3 resistant protease. In particular, a D105G mutant demonstrated a 5-fold resistance to TL-3 relative to wild type protease, which may indicate the onset of drug resistance development and explain the higher viral levels in some of the TL-3 treated cats. Preparation of isogenic virus containing the D105G point mutation will allow the direct determination of potential drug resistance.
Interestingly, TL-3 treated cats with highest viral loads (221 and 229) developed severe disease syndromes, which suggests that TL-3 efficacy was limited to a specific viral threshold in this study. Once the threshold has been crossed, TL-3 may not be able to overcome the full-blown, acute, viral infection, resulting in rapid onset of immune suppression.
We were unable to show any correlation between humoral antibody responses and clinical outcome (data not shown). However, a consistent observation was a marked and progressive increase of CD8+ T cells in animals surviving the acute phase infection and a lack of such responses in animals that required euthanasia within the first 8 weeks following infection. Although not formally tested, the findings imply a strong cell-mediated response in the surviving animals contributed to controlling the viral infection.
The above analyses underscore the natural variation in the response to FIV challenge in outbred cats, similar to the observed variation in responses to HIV infection in humans. However, more consistent responses, both in peak viral loads (Figure 2) and in lymphocyte counts (Figure 4) were seen when animals were analyzed as a function of disease severity. Thus, the question arises as to whether there is a genotypic link to susceptibility. Upon close scrutiny of the parental heritage pattern, we observed that one male in particular (96AGQ1) had sired eight of the experimental animals with three different females (Table 2). Two of his offspring (213, 214) had been randomly placed into the uninfected control group, while of the remaining six offspring, four (215, 219, 220, 221) succumbed to FIV induced disease. One of the surviving siblings (222) was in the TL-3 treatment group and exhibited conjunctivitis and possible corneal ulceration and was mildly to moderately lethargic. Cat 222 received Baytril treatment and fluid replacement therapy and eventually recovered from her symptoms. The last sibling (216) never showed any detectable signs of disease throughout the experiment. Although complicating the analyses and statistical treatments, the variable responses with FIV infection of cats parallels those observed with HIV infection in humans and thus affords a relevant model for study of infection and treatment modalities. As with humans, cats are outbred, which complicates defining susceptibility markers. However, identifying the genetic basis for susceptibility in the cat may yield important clues to similar phenomena in humans.
Table 2 Feline Lineages
Dam Sire Siblings
99AIE3 96AGQ1‡ 213* 214* 215† 216◇
00AIB4 96AAK4 217 218
98AXS5 96AGQ1‡ 219† 220†
00ANU5 96AGQ1‡ 221† 222◇
00AJT2 96ACJ2 224
01TBK5 99IAS1 225 226 227
01QAL4 00XAZ4 228
01QEJ3 98ATY2 229†
01QBJ3 00IRX4 230
95PAA3 98IUU1 231
01IQP4 98IUG3 232 233
00XBA1 00XAG1 234†
* control animals
† sacrificed animals
Bold type, offspring from same sire (‡)
◇ survivor offspring of sire
Viral protease inhibitors are of paramount importance for HIV treatment and successful tempering of viral infection. However, drug resistant escape variants are an important consideration in treatment protocols [17,18]. Although we previously failed to isolate TL-3-resistant FIV in vitro, the findings here suggest that in vivo, drug resistance to the compound may develop. The results were not unexpected in that we had been able to develop TL-3 resistant HIV variants [21] and it seemed unlikely that FIV would prove an exception. The finding of drug resistant mutants, in fact, strongly indicates that the feline/FIV model is valuable in the assessment of the ability of other protease drugs and drug cocktails to suppress virus infection and limit drug resistance development.
Conclusions
The findings indicate that the protease inhibitor TL-3, when given orally as a monotherapy, did not prevent viremia in cats infected with a high dose challenge with FIV-C and substantial virus loads were evident in circulation throughout the acute phase (between 2–6 weeks post infection) in all infected animals, regardless of drug regimen. Average peak viral loads in the acute phase were lower in TL-3 treated animals, but variability was such that the numbers did not reach statistical significance. However, of six animals that required euthanasia, four were from the untreated cohort and two were from the TL-3 treated group. Additionally, at eight weeks post infection, half the surviving TL-3 treated animals had viral loads below the detection limits, whereas only one of six untreated animals had markedly reduced viral loads. Thus, therapeutic benefit was noted with TL-3 treatment, even in the face of an aggressive FIV infection.
The findings also show clear differences in the lymphocyte responses of animals that succumb to acute phase illness versus those that survive to the asymptomatic phase. The most pronounced difference was in the lack of an increase in CD8+ cell numbers starting around three weeks post infection in animals that eventually required humane euthanasia versus a pronounced and significant increase in CD8+ T cell numbers in animals that survived the acute phase.
Certain animals that received TL-3 had higher than average viral loads after the acute phase. Analyses of the protease genes of FIV quasi-species prevalent in these animals revealed sequence variations relative to protease of wild type FIV-C. One particular protease, cloned and expressed from two TL-3-treated animals, contained the mutation D105G, which imparted 5-fold resistance against TL-3 relative to wild type protease. This may represent the initial stages of drug resistance development and preparation of this mutation in the context of isogenic virus will address this issue. The findings suggest that the cat model will serve as a valuable animal model for study of resistance development against lentivirus infections.
Materials and methods
Animals
22 female purpose-bred 8–9 week old kittens purchased from Liberty Laboratories were inspected upon arrival for signs of illness, examined by a veterinarian and weighed. Animals were maintained in a 2-week quarantine and observed for any signs of illness prior to the beginning of the study. IACUC number ARC 61 JAN 3.
Viral Infection
Plasma samples (105 RNA copies/ml) from a cat, that had died from an acute infection with CABCpady00C (FIV-C), were kindly provided by Dr. E. Hoover, of Colorado State University. Cats were injected I.V. with either 0.1 ml (105 RNA copies/ml) or 0.5 ml of plasma.
Drug Dosing
All procedures for care of cats during dosing as well as dosing procedures were mandated by TSRI's IACUC. Oral TL-3 (L-Iditol,1,2,5,6-tetradeoxy-1,6-diphenyl-2,5-bis [N-[(phenylmethoxy)carbonyl]-L-alanyl-L-valyl]amino]) [12] treatment was initiated in 12 cats, three days prior to infection of ten of the twelve animals with FIV-C. All TL-3 treated animals received 20 mg TL-3 by capsules at eight hour intervals, for approx. the first 7.5 weeks of the experiment. Dosage was then doubled to 40 mg TL-3 per dose at eight hour intervals for an additional week for the two control animals and the eight surviving animals in the TL-3 treated, infected cohort. No adverse effects were noted in the uninfected, TL-3 treated controls.
Evoked Potentials
Uninfected and FIV-infected animals were intermittently scheduled for analyses of evoked potentials in conjunction with the blood sampling, including testing for both auditory and visual evoked potentials as previously described [23]. Once recordings were complete, a blood sample was collected and animals returned to the vivarium.
Clinical Evaluation
Animals were examined daily and in case of health concerns further therapy/diagnostics were initiated. Animals with abnormal weight, or on antibiotics were placed on supplemental feeding with moist food and Nutrical. Dehydrated animals received subcutaneous fluid therapy. More affected animals received supportive care and medications, consisting of BID administration of antibiotics (Baytril), BID subcutaneous fluid therapy, BID temperature evaluation, BID application of antibiotic ophthalmic ointment supportive care. Any animals that required extensive supportive care (TID fluid therapy, TID force feeding) were euthanized. Euthanasia of research animals was conducted with strict adherence to NIH Office of Laboratory Animal Welfare protocols.
Blood Collection and Peripheral Blood Separation
Blood samples (1 ml/animal) were collected weekly during the first month of the study and then every two weeks thereafter, as described [13]. Samples were placed in EDTA blood tubes (1 cc/tube) for further use.
Plasma was separated from blood by centrifugation at 3000 rpm for 5 minutes at room temperature. Blood cells were resuspended in 3 ml PBS and PBMC were separated from buffy coats by density gradient centrifugation using Ficoll-Hypaque Plus (Amersham Biosciences, Sweden). PBMC were washed once in PBS and twice in PBS/2% FBS for flow cytometry analyses.
Statistical Analysis
Statistical p values for the FIV-C viral load were determined by the Student's two-tailed t-test (paired, two-tailed distribution between the treated and non-treated group and the symptomatic vs asymptomatic group). Statistical p values for the weekly total CD4 and CD8 cell counts were also determined by the Student's two-tailed t-test (paired, two-tailed distribution compared to base line levels at week 0).
Flow Cytometry Analysis
Two-color flow cytometry analysis was performed on cells stained with mouse α-feline CD4 FITC and mouse α-feline CD8 PE (Southern Biotech, Birmingham, AL). Anti-mouse IgG1κ FITC and PE (BD PharMingen, San Diego, CA) were used as isotype controls. Cells were fixed with 2% PFA prior to analysis performed on a FACScan flow cytometer (Beckton Dickenson Immunocytometry Systems) using the Cell Quest Software program.
RNA Isolation and Reverse Transcription
Plasma for weeks 0, 2, 4, 6 (terminal points for cats 215, 221, and 229), 6.5 (terminal points for cats 219, 220, and 234), and 8, were isolated from whole blood by centrifugation and stored at -20°C. Viral RNA was extracted using the QiaAmp Viral RNA Isolation Kit (Qiagen, Valencia, CA) according to manufacturer's instructions with slight modifications: Plasma samples (280 μl) were lysed in buffer AVL (1,120 μl) (Qiagen) for 10 minutes at room temperature in the presence of carrier RNA (10 μg/ml) and an external Kanamycin (KAN) RNA spike. Equal amounts of the external RNA spike (109 copies RNA /280 μl plasma), corresponding to the 1.2 kb KAN gene (Promega, Madison, WI), was used to normalize plasma volumes between samples and to correct for sample loss from viral RNA extraction and cDNA synthesis. An on-column DNase/ RNase free (Qiagen) incubation step for 10 minutes at room temperature was added to remove residual cellular DNA. Complimentary DNA (cDNA) was generated in a 20 μl total reaction using 13 μl of sample RNA, 0.5 μl (2 μM stock) each of KAN and FIV specific reverse primers (sequences below) and StrataScript reverse transcriptase, following the manufacturer's protocol (Stratagene, La Jolla, CA). After incubation, each cDNA sample was diluted in water to 30 μl and stored at -80°C for use in real-time PCR.
Real-Time Quantitative PCR
25 μl real-time PCR reactions were set up containing 2X Platinum Quantitative PCR SuperMix-UDG (12.5 μl) (Invitrogen, Carlsbad, CA), forward, reverse primers and probe mix (7.5 μl), and cDNA target (5 μl). The mixture was incubated at 50°C for 2 minutes, 95°C for 10 minutes, then cycled at 95°C for 15 seconds and 60°C for 60 seconds 55 times, using the ABI Prism 7700 Sequence Detection System (Applied Biosystems, Foster City, CA). Data were analyzed using the ABI 7700 Sequence Detection Software. Forward and reverse primers (10 nM, 100 nM, final concentration respectively) used in real-time PCR were made at IDT, (Coralville, IA), while the fluorescein-dabsyl Amplifluor UniPrimer (100 nM final concentration) was purchased from Serologicals (Norcross, GA). The primer sequences used for real-time PCR are as follows:
FIV reverse-transcriptase forward: 5'-ACTGAACCTGACCGTACAGATAAATTACAGGAA GAACCCCCATA-3'
FIV reverse-transcriptase reverse: 5'-TGTTAATGGATGTAATTCA TAACCCATC-3'
KAN forward: 5'-ACTGAACCTGACCGTACACGCTCAGGCGCAATCAC-3'
KAN reverse: 5'-CCAGCCATTACGCTCGTCAT-3'
Standard Curves and Background Detection
To determine the relative copy numbers of KAN and FIV from plasma samples, a linear standard curve was generated by plotting 10-fold dilutions (5 × 108 to 5 × 102 copies per well) of dsDNA plasmids of known copy number (log scale), against the cycle threshold (Ct) determined for that value. The pET28 vector (Novagen) was used as the target plasmid for the KAN gene, while a plasmid containing the molecular clone of FIV-C was used for the FIV reverse-transcriptase gene. Calculated values for each plasma sample represent relative copy numbers for the purposes of evaluation between individual samples.
Cloning, Purification and Analysis of Protease
Complementary DNA (cDNA) was synthesized from isolated plasma viral RNA of infected cats. The cDNA pool was used as a template for PCR reactions using 5' primer MFIVCPL5' (5'-GATTTATAAATCATATG GCATATAATAAAGTGGGTACCACTACAACATTAG-3'), which adds an NdeI restriction site, methionine, and alkaline to the N-terminal of the protease and 3' primer MFIVCPL33' (5'-CTGAGATCTGAGCAAGCTTTTACATTACTAATCT AATATTAAATTTAACCATG TTATC-3'), which adds a stop codon and a Hind III restriction site to the C-terminus of the protease. The amplified PCR product was gel purified and cloned into pCR-TOPOII vector (Invitrogen, Carlsbad, CA) for sequencing. Selected DNA of mutants, N55D, H72R, D105G, and M107R were digested with Nde I/Hind III and ligated into pET21a expression vector (Novagen). The mutant FIV-C proteases were expressed in Rosetta pLysS cells (Novagen) and purified as previously described [24].
Enzyme kinetics of FIV-C protease were assayed on the flourogenic substrate Arg-Ala-Leu-Thr-Lys(Abz)-Val-Gln/Phe(NO2)-Val-Gln-Ser-Lys-Gly-Arg-NH2. The concentration was determined by active-site titration with inhibitor TL-3. Inhibitor constant (Ki) of TL-3 against mutant FIV-C protease was analyzed as described [25].
Competing Interests
None of the authors have commercial interests or direct association with a company that is marketing TL-3. J.H.E. is a co-author of a patent application regarding use of protease inhibitors like TL-3 reported here, containing small P3 residues, as inhibitors of HIV and FIV.
Authors' Contributions
S.R. and C.H.S contributed equally to this work and carried out all tissue culture analyses, purification and characterization of cell populations isolated from PBMC, statistical analyses, and preparation of results for publication. Quantitative PCR analyses was carried out by D.A.S. K.J.C. was responsible for all in vivo animal work, including TL-3 administration and oversight of veterinary care for the animals. S.H-R. carried out measurements of brainstem auditory evoked potential changes. S.H., B.E.T, and J.H.E. acted as mentors for the various facets of the project, oversaw the writing of the manuscript, and provided space and funding to carry out the work.
Acknowledgements
The authors wish to thank Dr. Edward Hoover of Colorado State University for providing uncloned FIV-C used in these studies, Michael Carlson for technical assistance and Jackie Wold for administrative assistance. We also wish to thank the AIDS Resource and Reference Reagent Program for contracting the synthesis of TL-3 and providing the amount of compound required for these studies. D.A.S. was supported by the NIH/NINDS training grant 5 T32 NS41219-02. This research was supported by grants R01 AI40882 and R01 AI48411 of the Allergy and Infectious Diseases Institute of the National Institutes of Health and P30 MH6221 from the Mental Health Institute of the Institutes of Health.
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| 15555065 | PMC535546 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Nov 19; 1:38 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-38 | oa_comm |
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Biomed Digit LibrBiomedical Digital Libraries1742-5581BioMed Central London 1742-5581-1-31555017110.1186/1742-5581-1-3MethodologyUsing GIS to establish a public library consumer health collection LaRue Elizabeth M [email protected] McCormick Educational Technology Center, Library of Rush University, Rush University, 600 S. Paulina, Chicago, IL 60622 USA2004 18 11 2004 1 3 3 5 10 2004 18 11 2004 Copyright © 2004 LaRue; licensee BioMed Central Ltd.2004LaRue; 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
Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous.
As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library.
Methods
In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.
Results
ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.
Conclusions
ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general.
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Background
Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps [1]. The US Geological Survey office defines GIS as "a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location [2]."
Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do [3-7], but little has been published describing how a library has used GIS.
Governments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more [8]. Businesses have used GIS for real estate analysis, marketing, and demographic analysis [8]. Based on this approach, a research project was developed to test the use of GIS in collection development for a large metropolitan public library system.
Building a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order [9]." Each step in the process of collection development produces a layer of difficulties. In order to get to these steps a collection development librarian must be aware of their audiences. To name a few necessary pieces of information, the librarian must know the predominate age of their clients, their reading genre, their reading level, their culture, and their preferred language [9]. After one has gathered community information then the decision of what to purchase for use by the community, can be initiated and carried out [10].
This paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.
Methods
After comparing large library systems in major metropolitan areas, (Chicago, Los Angeles, and New York City) Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries [11]. The city of Chicago has 77 public libraries with one central library.
With the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS) [12] was selected as the GIS application. The software permits data manipulation and mapping capabilities. There are four components of ArcGIS that work together to give a high level of functionality to the program: ArcReader, ArcView, ArcEditor and ArcInfo. By inserting data files into ArcEditor the datum may convert into a graphical representation. Once a base map has been created with geographic boundaries, locations may be established to show distances within the map.
In order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER (Topologically Integrated Geographic Encoding and Referencing) Files available for free to the public [13]. These files contain geographic structures, such as streets, highways, and addresses, for tabulation and dissemination. To set the boundary lines of metropolitan Chicago, Cook County (the county in which the city of Chicago resides) was selected as the source for the data file [14]. This TIGER file contained the street map for the city of Chicago.
The next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website [15] which distributes the 2000 Census statistics in TIGER File format. Downloading the files for Census blocks in Cook County provided the necessary data to build a visual representation of neighborhoods.
The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries, . The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago.
With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library (See Figure 1). Because the Census data was used to build the map, ArcView can be queried to show demographics of the city. This allows useful data manipulation in relation to points of interest, in this case, public libraries. Thus, ArcView was queried to show the distribution of all men and women within the city of Chicago as well as the breakdown of their age groups. The software application produced a graphic which used color schemes with a map legend. If a librarian wanted to compare the number of men in their 40s to women in their 40s, ArcView can map those fields with a well-structured query. ArcGIS was also capable of creating maps that display census ethnic groups. For example, Figure 2 shows a high percentage of African Americans in the southern neighborhoods of the city as compared to the northern part of the city. With maps such as these, a collection development librarian can graphically see the demographic breakdown of neighborhoods and build collections according to its clientele [16].
Figure 1 Location of public libraries in the city of Chicago
Figure 2 Density of African American population in southern part of Chicago
Results
Fifteen maps were created showing a breakdown of each demographic with the city's public libraries. Each map provided information regarding gender, age, and race of the clientele surrounding the libraries. These maps used a combination of color schemes to graphically represent differences between groups. The maps displayed different age groups divided by ranges of age (30–39, 40–49, 50–64, 65 and up). The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian (men/women, Latinos (men/women). The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure 2, the numbers, 0–992 in the legend are the African Americans residing within the census block. Hence the red blocks have a large population (403–992) of African Americans as compared to the dark green areas. Figure 3 represents a query for African American women in their 40s. The results produced a map showing that 26–59 African American women in their 40s live within the shaded red blocks.
Figure 3 African American women in their 40s in proximity to public libraries
In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library.
To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure 4, the map shows a very high percentage of women 40–49 close to the Uptown Branch Library. The collection development librarian can now assess the libraries existing collection for health related materials. If there are very few resources then the collection development librarian should begin to purchase consumer health materials. If the library has a strong consumer health collection already then building maps to show the racial breakdown of the neighborhood could lead the collection development librarian to purchase culturally sensitive health materials.
Figure 4 High density neighborhoods with all women in their 40s in proximity to public libraries
Discussion
By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports [17]. It is easier to define the characteristics of the library's service area clientele by making visual representations (maps) of neighborhoods surrounding a library. The neighborhood information may then be used to justify additional resource purchases, a change in collection policy and even a change in library services to fit the culture of the neighborhood. By using GIS, libraries will be capable of finding specific populations within the library's neighborhood to support or serve thus taking some of the guesswork out of collection development [17,18]. Collections may be tailored specifically to the neighborhood by using the subjective data provided with GIS.
Many studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men [19-24]. Using this information to assist in collection development, a librarian would need to know how large the population of women in their 40s is in the neighborhood the library serves. Using the map created by ArcGIS showing the distribution of women in their 40s in respect to library locations can assist the collection development librarian in purchasing appropriate materials. Using the maps showing the race of women in their 40s would also assist the librarian in purchasing appropriate materials. For instance, if most women are African American then the collection development librarian would purchase materials supporting race-related health needs such as Sickle Cell Anemia, Coronary Heart Disease, Diabetes and others (See Figure 2 and 3).
Recommendations
Using ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far (physical distance) their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.
Limitations
There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly.
While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development.
In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library [17,25]. When using GIS one must take into account geographic barriers to library access such as major highways, and railways. People are often reluctant to cross these barriers and may elect to visit another library further away with no geographic obstructions [17,25,26]. These barriers must be represented in the GIS map to fully display the areas libraries serve. The TIGER files contain some geographic structures but most will have to be hand coded. Thus, all neighborhoods should be examined in order to assure the marking of geographic barriers.
While GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library [25]. To build a map showing a library's service area, patron addresses (information now protected by the Patriot Act) would need to be coded into GIS. Using patron zip codes of residence encompasses too much or too little area and there is no correlation between US Postal Service ZIP Codes and US Census Bureau Geography [27]. Thus, mapping or defining areas through zip codes is extremely difficult. Without patron address information, there can be no clear realization of exactly how far people will travel to a library, and if people cross-neighborhoods to use a certain library [18,25].
Conclusions
Budget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood [25] and knowing what those demands are will make an effective library [17]. GIS can assist collection development librarians located in a central library buy for branch libraries, by saving them time, and answering demographic questions about the population a library serves. Knowing details about its communities will make a more functional library within a neighborhood. To verify the use of GIS for collection development, an analysis of a library's collection should be conducted and compared with information provided from a GIS application to see if the collection satisfies the library's neighborhood.
List of Abbreviations
GIS = geographical information systems
Acknowledgements
The author wishes to thank Marina Chilov, MLS Reference/Monograph Collection Development Librarian, Health Sciences Library, Columbia University, New York, New York for insight into collection development.
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Cline RJW Haynes KM Consumer Health Information Seeking on the Internet: the State of the Art Health Education Research 2001 16 671 692 11780707 10.1093/her/16.6.671
Pastore M Online Health Consumers More Proactive About Healthcare Cyberatlas 2001
von Knoop C Lovich D Silverstein MB Tutty M Vital Signs: E-Health In The United States In Boston: Boston Consulting Group 2003 44
These Days on the Internet, RX-rated Beats X-rated
Coughlin RE Taieb F Stevens BH Urban analysis for branch library system planning Westport, CT 1972 Greenwood Publishing Company
Totterdell B Bird J The Effective Library: report of the Hillingdon project on public library effectiveness 1976 London: The Library Association
Answers to Frequently Asked Questions about Census Bureau Geography, Maps and Mapping Engines
| 15550171 | PMC535547 | CC BY | 2021-01-04 16:38:25 | no | Biomed Digit Libr. 2004 Nov 18; 1:3 | utf-8 | Biomed Digit Libr | 2,004 | 10.1186/1742-5581-1-3 | oa_comm |
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Biomed Digit LibrBiomedical Digital Libraries1742-5581BioMed Central London 1742-5581-1-31555017110.1186/1742-5581-1-3MethodologyUsing GIS to establish a public library consumer health collection LaRue Elizabeth M [email protected] McCormick Educational Technology Center, Library of Rush University, Rush University, 600 S. Paulina, Chicago, IL 60622 USA2004 18 11 2004 1 3 3 5 10 2004 18 11 2004 Copyright © 2004 LaRue; licensee BioMed Central Ltd.2004LaRue; 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
Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous.
As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library.
Methods
In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.
Results
ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.
Conclusions
ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general.
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Background
Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps [1]. The US Geological Survey office defines GIS as "a computer system capable of capturing, storing, analyzing, and displaying geographically referenced information; that is, data identified according to location [2]."
Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do [3-7], but little has been published describing how a library has used GIS.
Governments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more [8]. Businesses have used GIS for real estate analysis, marketing, and demographic analysis [8]. Based on this approach, a research project was developed to test the use of GIS in collection development for a large metropolitan public library system.
Building a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order [9]." Each step in the process of collection development produces a layer of difficulties. In order to get to these steps a collection development librarian must be aware of their audiences. To name a few necessary pieces of information, the librarian must know the predominate age of their clients, their reading genre, their reading level, their culture, and their preferred language [9]. After one has gathered community information then the decision of what to purchase for use by the community, can be initiated and carried out [10].
This paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.
Methods
After comparing large library systems in major metropolitan areas, (Chicago, Los Angeles, and New York City) Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries [11]. The city of Chicago has 77 public libraries with one central library.
With the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS) [12] was selected as the GIS application. The software permits data manipulation and mapping capabilities. There are four components of ArcGIS that work together to give a high level of functionality to the program: ArcReader, ArcView, ArcEditor and ArcInfo. By inserting data files into ArcEditor the datum may convert into a graphical representation. Once a base map has been created with geographic boundaries, locations may be established to show distances within the map.
In order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER (Topologically Integrated Geographic Encoding and Referencing) Files available for free to the public [13]. These files contain geographic structures, such as streets, highways, and addresses, for tabulation and dissemination. To set the boundary lines of metropolitan Chicago, Cook County (the county in which the city of Chicago resides) was selected as the source for the data file [14]. This TIGER file contained the street map for the city of Chicago.
The next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website [15] which distributes the 2000 Census statistics in TIGER File format. Downloading the files for Census blocks in Cook County provided the necessary data to build a visual representation of neighborhoods.
The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries, . The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago.
With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library (See Figure 1). Because the Census data was used to build the map, ArcView can be queried to show demographics of the city. This allows useful data manipulation in relation to points of interest, in this case, public libraries. Thus, ArcView was queried to show the distribution of all men and women within the city of Chicago as well as the breakdown of their age groups. The software application produced a graphic which used color schemes with a map legend. If a librarian wanted to compare the number of men in their 40s to women in their 40s, ArcView can map those fields with a well-structured query. ArcGIS was also capable of creating maps that display census ethnic groups. For example, Figure 2 shows a high percentage of African Americans in the southern neighborhoods of the city as compared to the northern part of the city. With maps such as these, a collection development librarian can graphically see the demographic breakdown of neighborhoods and build collections according to its clientele [16].
Figure 1 Location of public libraries in the city of Chicago
Figure 2 Density of African American population in southern part of Chicago
Results
Fifteen maps were created showing a breakdown of each demographic with the city's public libraries. Each map provided information regarding gender, age, and race of the clientele surrounding the libraries. These maps used a combination of color schemes to graphically represent differences between groups. The maps displayed different age groups divided by ranges of age (30–39, 40–49, 50–64, 65 and up). The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian (men/women, Latinos (men/women). The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure 2, the numbers, 0–992 in the legend are the African Americans residing within the census block. Hence the red blocks have a large population (403–992) of African Americans as compared to the dark green areas. Figure 3 represents a query for African American women in their 40s. The results produced a map showing that 26–59 African American women in their 40s live within the shaded red blocks.
Figure 3 African American women in their 40s in proximity to public libraries
In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library.
To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure 4, the map shows a very high percentage of women 40–49 close to the Uptown Branch Library. The collection development librarian can now assess the libraries existing collection for health related materials. If there are very few resources then the collection development librarian should begin to purchase consumer health materials. If the library has a strong consumer health collection already then building maps to show the racial breakdown of the neighborhood could lead the collection development librarian to purchase culturally sensitive health materials.
Figure 4 High density neighborhoods with all women in their 40s in proximity to public libraries
Discussion
By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports [17]. It is easier to define the characteristics of the library's service area clientele by making visual representations (maps) of neighborhoods surrounding a library. The neighborhood information may then be used to justify additional resource purchases, a change in collection policy and even a change in library services to fit the culture of the neighborhood. By using GIS, libraries will be capable of finding specific populations within the library's neighborhood to support or serve thus taking some of the guesswork out of collection development [17,18]. Collections may be tailored specifically to the neighborhood by using the subjective data provided with GIS.
Many studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men [19-24]. Using this information to assist in collection development, a librarian would need to know how large the population of women in their 40s is in the neighborhood the library serves. Using the map created by ArcGIS showing the distribution of women in their 40s in respect to library locations can assist the collection development librarian in purchasing appropriate materials. Using the maps showing the race of women in their 40s would also assist the librarian in purchasing appropriate materials. For instance, if most women are African American then the collection development librarian would purchase materials supporting race-related health needs such as Sickle Cell Anemia, Coronary Heart Disease, Diabetes and others (See Figure 2 and 3).
Recommendations
Using ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far (physical distance) their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.
Limitations
There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly.
While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development.
In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library [17,25]. When using GIS one must take into account geographic barriers to library access such as major highways, and railways. People are often reluctant to cross these barriers and may elect to visit another library further away with no geographic obstructions [17,25,26]. These barriers must be represented in the GIS map to fully display the areas libraries serve. The TIGER files contain some geographic structures but most will have to be hand coded. Thus, all neighborhoods should be examined in order to assure the marking of geographic barriers.
While GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library [25]. To build a map showing a library's service area, patron addresses (information now protected by the Patriot Act) would need to be coded into GIS. Using patron zip codes of residence encompasses too much or too little area and there is no correlation between US Postal Service ZIP Codes and US Census Bureau Geography [27]. Thus, mapping or defining areas through zip codes is extremely difficult. Without patron address information, there can be no clear realization of exactly how far people will travel to a library, and if people cross-neighborhoods to use a certain library [18,25].
Conclusions
Budget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood [25] and knowing what those demands are will make an effective library [17]. GIS can assist collection development librarians located in a central library buy for branch libraries, by saving them time, and answering demographic questions about the population a library serves. Knowing details about its communities will make a more functional library within a neighborhood. To verify the use of GIS for collection development, an analysis of a library's collection should be conducted and compared with information provided from a GIS application to see if the collection satisfies the library's neighborhood.
List of Abbreviations
GIS = geographical information systems
Acknowledgements
The author wishes to thank Marina Chilov, MLS Reference/Monograph Collection Development Librarian, Health Sciences Library, Columbia University, New York, New York for insight into collection development.
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Cline RJW Haynes KM Consumer Health Information Seeking on the Internet: the State of the Art Health Education Research 2001 16 671 692 11780707 10.1093/her/16.6.671
Pastore M Online Health Consumers More Proactive About Healthcare Cyberatlas 2001
von Knoop C Lovich D Silverstein MB Tutty M Vital Signs: E-Health In The United States In Boston: Boston Consulting Group 2003 44
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Coughlin RE Taieb F Stevens BH Urban analysis for branch library system planning Westport, CT 1972 Greenwood Publishing Company
Totterdell B Bird J The Effective Library: report of the Hillingdon project on public library effectiveness 1976 London: The Library Association
Answers to Frequently Asked Questions about Census Bureau Geography, Maps and Mapping Engines
| 15550173 | PMC535548 | CC BY | 2021-01-04 16:36:21 | no | Cytojournal. 2004 Nov 18; 1:5 | latin-1 | Cytojournal | 2,004 | 10.1186/1742-6413-1-5 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-101555507610.1186/1743-422X-1-10ResearchSusceptibility of different leukocyte cell types to Vaccinia virus infection Sánchez-Puig Juana M [email protected]ánchez Laura [email protected] Garbiñe [email protected] Rafael [email protected] Departamento de Biotecnología-I.N.I.A. Ctra. La Coruña km 7.5 E-28040 Spain2 Servicio de Inmunología. Hospital Ramón y Cajal. 28034 Madrid, Spain2004 22 11 2004 1 10 10 11 10 2004 22 11 2004 Copyright © 2004 Sánchez-Puig et al; licensee BioMed Central Ltd.2004Sánchez-Puig 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
Vaccinia virus, the prototype member of the family Poxviridae, was used extensively in the past as the Smallpox vaccine, and is currently considered as a candidate vector for new recombinant vaccines. Vaccinia virus has a wide host range, and is known to infect cultures of a variety of cell lines of mammalian origin. However, little is known about the virus tropism in human leukocyte populations. We report here that various cell types within leukocyte populations have widely different susceptibility to infection with vaccinia virus.
Results
We have investigated the ability of vaccinia virus to infect human PBLs by using virus recombinants expressing green fluorescent protein (GFP), and monoclonal antibodies specific for PBL subpopulations. Flow cytometry allowed the identification of infected cells within the PBL mixture 1–5 hours after infection. Antibody labeling revealed that different cell populations had very different infection rates. Monocytes showed the highest percentage of infected cells, followed by B lymphocytes and NK cells. In contrast to those cell types, the rate of infection of T lymphocytes was low. Comparison of vaccinia virus strains WR and MVA showed that both strains infected efficiently the monocyte population, although producing different expression levels. Our results suggest that MVA was less efficient than WR in infecting NK cells and B lymphocytes. Overall, both WR and MVA consistently showed a strong preference for the infection of non-T cells.
Conclusions
When infecting fresh human PBL preparations, vaccinia virus showed a strong bias towards the infection of monocytes, followed by B lymphocytes and NK cells. In contrast, very poor infection of T lymphocytes was detected. These finding may have important implications both in our understanding of poxvirus pathogenesis and in the development of improved smallpox vaccines.
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Background
Vaccinia virus, the prototype of the Poxviridae, is a large DNA virus whose replication takes place in the cytoplasm of the infected cell [1]. Although well characterized in vitro, little is known about the ability of vaccinia virus to infect different cell types in vivo. Vaccinia virus host range in cell culture is known to be determined by several genes. The importance of host restriction has been highlighted in recent years by the growing use of the Modified Vaccinia Ankara (MVA) virus strain, whose replication is severely impaired in human cells [2-4]. Genes known to influence the ability of vaccinia virus to infect cells, termed host range genes, have been identified, and shown to block productive infection at different points in the replication cycle. Significantly, MVA replication of non-permissive cells proceeds through early and late gene expression, but is blocked at late times in a step of virion morphogenesis [5].
In addition to host range genes, there are a number of factors that might influence the infection rate of a given cell type, such as the accessibility and amount of receptors, the ability to internalize the virus, and the metabolic state of the cell. In addition to cellular factors, genetic differences in the virus might influence the efficiency and fate of the infection. For instance, cellular nucleotide pools can be one of the factors that, in conjunction with the expression of viral thymidine kinase (TK), may influence the rate of infection.
The above considerations led us to hypothesize that, although receptors for vaccinia seem to be ubiquitous, and virus replication is relatively independent from the host cell, virus tropism in vivo may be determined by many complex factors that may be dependent on the cell type and metabolic state.
We have focused here on the differences between two widely used strains of vaccinia virus (Western Reserve-WR and MVA), and also to their respective TK(-) mutants, in their ability to infect different cell types in fresh human PBLs.
Results
Infection of human PBLs by GFP-expressing vaccinia virus
Previously, we have shown that GFP expression from a vaccinia virus recombinant can be used to monitor infection by flow cytometry [6]. Where adequate marker molecules for different cell populations exist, this approach should facilitate the study of the susceptibility of cell types to vaccinia virus infection. With this aim, fresh human PBLs from healthy donors were infected with virus WR-GFP, and analyzed by flow cytometry at different times post-infection. The overall rate of infection, measured as GFP-positive cells, was 4.5%, 7.6% and 10.0% at 1, 3 and 5 h, respectively. Staining with antibodies to CD3, CD14, CD19 and CD56 was performed on infected cells at 5 h.p.i. (Fig. 1). A marked preference was noted for the infection of non-T cells, since GFP positive cells amounted to 19% of non-T lymphocytes, while only 1.9% of T cells were infected. Among the non-T lymphocytes, there was a strong bias towards the infection of CD14 positive cells (monocytes), of which up to 77% showed green fluorescence, followed by B lymphocytes (CD19+, up to 20%) and NK cells (CD56+, up to 9%).
Figure 1 Analysis of vaccinia infected human PBLs. Human PBLs infected with vaccinia WR-GPF for 5 h were subsequently stained with cell-type specific mAbs, and analyzed by flow cytometry. Plots show the level of GFP fluorescence (recorded in the FL1 channel) versus the amount of labeling with the indicated antibody markers (recorded in the FL2 channel). Numbers inside the plots indicate the percentage of cells within the respective regions.
Construction of MVA-GFP, WR-TK(-) and MVA-TK(-)
Vaccinia virus MVA and TK-deficient viruses have been proposed as improved recombinant vaccines. In particular, the highly attenuated MVA strain has elicited much interest as a safer vaccine vector. We studied the influence of the virus strain and the TK phenotype in the infection of human PBLs. We thus constructed GFP expressing viruses from vaccinia virus MVA strain, by inserting the GFP cassette downstream of the F13L gene, using an intergenic region for the insertion. Additionally, thymidine kinase-deficient virus recombinants WR-TK(-) and MVA-TK(-) were constructed by inserting the GFP cassette within the viral TK locus. Those viruses grew to high titers and produced, upon infection of cell lines, bright GFP fluorescence (not shown).
Infection of human PBLs with MVA-GFP
The four GFP-expressing viruses were used to infect fresh human PBLs from a different individual, and subjected to flow cytometry analysis at 7 h.p.i. (Fig. 2). The results confirmed the above findings with respect to the low infection rate of T cells in comparison with monocytes, B and NK cells. Both CD4+ and CD8+ cells were poorly infected, although there was indication of an increased infection of low-CD8 T lymphocytes in comparison with high-CD8 cells. Notably, this experiment confirmed that most of the monocytes (CD14+) was infected in our experimental conditions, and showed a high level of GFP fluorescence. It was of interest to directly compare the ability of vaccinia MVA to infect PBLs with that of the standard laboratory strain WR. A side-by-side comparison of WR-GFP and MVA-GFP showed that both viruses infected a high percentage of CD14+ monocytes (83 and 70%, respectively), and a low percentage of T lymphocytes (0.46 and 0.2%, respectively). No significant differences were noted in the percentage of CD4 cells infected with both viruses. Although both virus strains were able to infect the majority of monocytes, MVA-GFP produced a lower level of GFP fluorescence than WR-GFP in the infected monocytes. Those differences could be the result of a lower expression level, or a delay in the course of infection, by the MVA strain.
Figure 2 Analysis of cell populations infected with different vaccinia viruses. Human PBLs were infected with vaccinia virus strains WR and MVA, or their respective TK(-) mutants. PBL-subsets were identified by staining with the specific mAbs indicated under each plot. Numbers inside the plots indicate the percentage of GFP-expressing cells within each PBL-subset.
In addition to increased GFP expression levels, WR-GFP was also more efficient than MVA-GFP for the infection of CD19+ B lymphocytes (7.1% vs 3.5%) and CD56+/CD16+ NK cells(7.6% vs 4%).
Infection with thymidine kinase-deficient viruses
As stated above, we constructed recombinant viruses from both WR and MVA by insertion of GFP into the TK locus. Infection of different populations in human PBLs with these viruses was again monitored in paralell using specific antibodies (Fig. 2). Infection of PBLs with WR-TK(-) virus resulted in similar percentages of infected CD14 and CD56/CD16 positive cells, although a slight decrease of infection rates was noted in CD19 cells. The level of GFP fluorescence in CD14-positive cells (monocytes) (and to a lesser extent, in all the WR-TK(-) infected lymphoid subsets) was markedly reduced with respect to the WR-GFP virus.
Discussion
Detection of cells infected by GFP-expressing vaccinia viruses provide a fast and sensitive method to measure virus infection [6]. In this report, we have taken advantage of this approach to measure infection in freshly prepared human PBLs. In combination with cell-type specific fluorescent antibodies, we have been able to study the rate of infection in different cell subset within the PBL population.
It is to note that the approach used in this work only allows the detection of viral gene expression derived from the infection, but does not address whether the infection results in the production of progeny virus. Early reports indicated that vaccinia virus cause cythopathic effect in human leukocytes, although only replicated in mitogen-stimulated cell populations, indicating that active cell replication is required for virus replication [7,8]. In this respect, it has also been reported that vaccinia infection of dendritic cells and monocytes/macrophages is abortive [9-11], and that dendritic cells and macrophages die by apoptosis upon infection [9,12-14]. Less clear is the case of transformed B lymphocyte cell lines, where virus infection has been described to be productive [9] and abortive [15] in different cell lines.
Our results point to a significant preference of vaccinia virus for certain cell types. In particular, monocytes were the most susceptible cells, followed by B cells and NK cells. In contrast, T cells were infected at very low rates. These observations are in broad agreement with previous studies, where different infection rates have been noted between monocytes and lymphocytes [16] and between B and T lymphocytes [17]. In our analysis, we have detected different rates of virus infection of different cells but at this point we cannot relate the differences in infection to differential virus binding, internalization or gene expression in different PBL cell lineages. In any event, the consequences of virus tropism in the pathogenicity of poxviruses remains to be further investigated.
Comparison of the patterns obtained with the two virus strains and their TK(-) mutants indicate that both the virus strain and the TK phenotype may determine the amount of gene expression, as was revealed by the intensity of GFP fluorescence in infected monocytes. In addition, the ability of the virus to infect certain cell types (CD19) seems to be affected to a certain extent by disruption of the TK gene. While this may be derived from our inability to detect those infected cells because of decreased gene expression, we cannot rule out a more direct requirement of TK activity in those cells.
MVA vaccinia virus strain has elicited much interest recently because of its safety record. Because clinical complications and side effects of smallpox vaccination are a critical issue in the event of mass vaccination, understanding the basis of MVA attenuation may lead to the development of better vaccine vectors. In this study, a number of differences were noted between the rates of infection obtained with WR and MVA virus strains. While both viruses were able to infect the monocyte population, WR infected B cells and the NK population (CD56, CD16 positive cells) more efficiently than MVA. Whether these observations have implications on the pathogenicity or immunogenicity of MVA will require further studies.
The fact that both WR and MVA showed a strong preference for certain cell populations indicate that, in addition to host range genes, there are other factors that might influence the infection rate of PBL cells. Those might include a variety of such as the accessibility and amount of receptors, ability to internalize the virus, and the metabolic state of the cell.
Conclusions
Monocytes (CD14+ cells) were the cells in the PBL population that showed a greater susceptibility to vaccinia virus infection, as measured by viral gene expression. On the other hand, T lymphocytes (CD3+ cells) were infected with low efficiency. An intermediate susceptibility was detected in B lymphocytes (CD19+ cells) and NK (CD56+ cells). Both the use of a highly attenuated virus strain (MVA) or the disruption of the thymidine kinase gene lead to decreased gene expression in the infected cells. Those observations highlight the existence of a different degree of susceptibility to infection if PBL subpopulations, a fact that may have important implications in understanding virus pathogenicity and immunogenicity.
Methods
Cells, plasmids and virus
Vaccinia virus strain WR was grown and titrated in BSC-1 or CV-1 cells, grown in minimal essential medium (EMEM) supplemented with 5% fetal bovine serum (FBS) and 2 mM L-Glutamine. MVA virus and recombinants were grown in BHK-21 cells (ATCC CCL10) cultured in BHK medium containing 5% FBS, 3 g/ml tryptose phosphate broth and 0.01 M hepes. All cells were maintained in a 5% CO2 atmosphere at 37°C. Plasmid pRB21 [18] contains vaccinia virus gene F13L and flanking sequences, and a synthetic early/late promoter placed downstream of the P37 coding sequence.
Construction of recombinant viruses expressing GFP
Plasmid pRBrsGFP, designed to mediate the insertion of the gene coding an enhanced version of the green fluorescent protein gene, rsGFP, (Quantum Biotechnologies, Inc.) was constructed as follows. rsGFP gene in plasmid pQBI25 was amplified using oligonucleotides GFP 5' (AATATAAATGGCTAGCAAAGGAGAAGAA) and GFPH3 (TTTAAAGCTTTACTAGTGGATCCTCAG), that include NheI and HindIII restriction sites, respectively. After digestion with NheI and HindIII, the gene was inserted into the corresponding sites in plasmid pRB21 [18], downstream of a synthetic vaccinia early/late promoter.
Plasmid prsGTK, containing the above GFP expression cassette located between recombination flanks for the TK locus, was obtained by cloning the rsGFP cassette from plasmid pRBrsGFP in plasmid pGPTK (Sanchez-Puig and Blasco, unpublished) after digestion with XhoI and BamHI.
Viruses WR-GFP and MVA-GFP were obtained by transfection of plasmid pRBrsGFP in cells infected with WR mutant vRB12 [19] or MVA, respectively. After plaquing of the progeny virus, GFP-positive virus plaques were identified by inspection in a Nikon TE-300 inverted fluorescence microscope, plaque-purified three times and amplified.
Recombinant virus VVrsGFPTK, was isolated after transfection of plasmid prsGTK in cells infected with virus WR. Recombinant virus plaques were isolated by plaquing on 143B TK(-) cells in the presence of 25 μg/ml bromodoxyuridine. GFP positive plaques were identified under the microscope, and plaque-purified three times before amplification.
Recombinant virus MVA-GFPTK was isolated by transfection of plasmid prsGTK in MVA-infected BHK-21 cells. GFP-positive virus were identified under the microscope, isolated by three consecutive rounds of plaque purification in BHK-21 cells and amplified in BHK-21 cell cultures.
Finally, virus recombinants were analyzed by Southern Blot, using digoxigenin-labelled GFP gene sequence as the probe. The analysis demonstrated that the recombinants contained the GFP expression cassette in the desired genome position and that they were stable, double recombinants.
Isolation of human PBLs
Peripheral blood mononuclear cells from healthy subjects were obtained by density gradient centrifugation of heparinized blood on Ficoll-Paque (Pharmacia, Uppsala, Sweden). Cells obtained from the interface were washed three times in saline solution and then resuspended in complete medium (CM) consisting of RPMI 1640 (Gibco, Life Technologies, Germany) supplemented with 10% FBS (Gibco), 2 mM L-glutamine (ICN, USA), 100 U/ml each of penicillin and streptomycin (Laboratorios Normon, Spain). Viability of the isolated cells always exceeded 95% as determined by trypan blue exclusion.
Infection of human PBLs was performed as follows: 2 × 105 PBLs were infected with virus recombinants VV-rsGFP, VVrsGFPTK, MVA-GFP, and MVA-GFPTK, at 10 p.f.u./cell, in 0.7 ml of RPMI medium containing 2% FBS. After 1 h adsorption, cells were pelleted and resuspended in 2 ml of fresh RPMI medium containing 2% FBS. At different infection times, the cells were sedimented by low-speed centrifugation, resuspended in 100 μl FACS-FLOW, and labeled with the appropriate conjugated monoclonal antibodies (mAb) for flow cytometric analysis (FCM) (phycoerythrin, PE- peridinil chlorophyll protein, PerCP- and allophycocianin, APC-conjugated mAb directed against CD3, CD4, CD8, CD14 and CD16 were obtained from BD; mAb against CD19 and CD56 from Beckman Coulter).
Cells were incubated with the antibodies for 30 min at 4°C in the dark, washed twice with saline solution and finally resuspended in 200 μl Cytofix/Cytoperm (BD Pharmingen). Cells were analyzed in a FACSCalibur (BD Biosciences, San Diego, CA) and data were processed with Cell Quest software (BD).
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JMS carried out the isolation of virus recombinants and performed viral infections and participated in the drafting of the manuscript. LS and GR performed the preparation of PBLs, carried out the flow cytometry and elaborated the data. GR participated in the interpretation of the data and helped in the elaboration of the manuscript. RB conceived the study, designed the virus recombinant constructs, supervised the experimental work and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by contract QLK2-CT2002-01867 from the European Commission, and grant BMC2002-03047 from Dirección General de Investigación Científica y Técnica, Spain.
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| 15555076 | PMC535549 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Nov 22; 1:10 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-10 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-121556084610.1186/1743-422X-1-12ReviewBiochemical prevention and treatment of viral infections – A new paradigm in medicine for infectious diseases Le Calvez Hervé [email protected] Mang [email protected] Fang [email protected] Abgent, Inc. 6310 Nancy Ridge Drive, Suite 106, San Diego, CA 92121 USA2 NexBio, Inc. 6330 Nancy Ridge Drive, Suite 105, San Diego, CA 92121 USA2004 23 11 2004 1 12 12 10 11 2004 23 11 2004 Copyright © 2004 Le Calvez et al; licensee BioMed Central Ltd.2004Le Calvez 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.
For two centuries, vaccination has been the dominating approach to develop prophylaxis against viral infections through immunological prevention. However, vaccines are not always possible to make, are ineffective for many viral infections, and also carry certain risk for a small, yet significant portion of the population. In the recent years, FDA's approval and subsequent market acceptance of Synagis, a monoclonal antibody indicated for prevention and treatment of respiratory syncytial virus (RSV) has heralded a new era for viral infection prevention and treatment. This emerging paradigm, herein designated "Biochemical Prevention and Treatment", currently involves two aspects: (1) preventing viral entry via passive transfer of specific protein-based anti-viral molecules or host cell receptor blockers; (2) inhibiting viral amplification by targeting the viral mRNA with anti-sense DNA, ribozyme, or RNA interference (RNAi). This article summarizes the current status of this field.
viral mRNAanti-sense oligonucleotideribozymeRNA interferenceviral infectious diseaseblocking antibodysoluble receptorrhinovirus
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Introduction
A landmark in the battle against viral infectious diseases was made in 1798 when Jenner first inoculated humans against smallpox with the less virulent cowpox. For about two centuries since then, humans relied almost exclusively on vaccines for protection against viruses. Only in the recent years, new strategies for controlling viral infectious diseases have emerged, which have so far led to a couple of viral prophylaxis/therapeutics on the market. These strategies are fundamentally different from vaccines in that they attempt to directly interrupt viral infectious life cycle at molecular level by using proteins or oligonucleotides. To differentiate them from the conventional vaccines that prevent viral infection by boosting immune system, we refer the new antiviral approaches as "Biochemical Prevention and Treatment" (see figure 1). Biochemical Prevention and Treatment, as an alternative to vaccines and chemical compound based antiviral drugs, may prove to be particularly valuable in the areas where vaccines and/or chemical drugs can not be generated or have not been successful in human, including diseases caused by some common pathogenic viruses, such as HIV, hepatitis C virus (HCV), RSV and human rhinovirus (HRV). In this review, we will discuss various molecular intervention approaches.
Figure 1 Targets of different Biochemical Prevention and Treatment strategies. Antibodies (Ab) or soluble receptors (Rc) can inhibit the viral entry. Antisense oligonucleotides (AS-ONs), ribozymes (Rz) or siRNA (SI) pair with their complementary target genomic DNA, RNA or mRNA. AS-ONs can block recombination, transcription, translation of the mRNA or induce its degradation by RNaseH. Rz possess catalytic activity and cleave their targets. SiRNAs (SI) induce degradation of the target mRNA via RNA-induced silencing complex (RISC).
1. Biochemical Prevention and Treatment via Protein targeting
Among the biochemical therapeutics currently in clinical trials, the majority consists of monoclonal antibodies (MAbs). Soluble receptor drug candidates have gradually lost favor over the past several years due to issues relating to low potency and cost. Peptide-based drug candidates are limited by insufficient efficacy and unfavorable pharmacokinetics. MAbs have increasingly gained favor in large part because of the development of chimeric, humanized, and human antibodies have reduced the immunogenicity of antibody therapies. The MAbs that are currently in clinical trials for viral infection prophylaxis and treatment are listed in Table 1.
Table 1 Monoclonal Antibodies in Clinical Trials
Product Company Disease Status
MEDI-501 MedImmune Genital Warts HPV II
Nabi-HB Nabi Biopharmaceuticals Hepatitis B Market
Ostavir Protein Design Labs Hepatitis B II
XTL-002 XTL Biopharmaceuticals Ltd. Hepatitis C I
Civacir Nabi Biopharmaceuticals Hepatitis C I/II
1F7 Antibody Immune Network Ltd. Hepatitis C, HIV/AIDS Preclinical
PRO 140 Progenics Pharmaceuticals HIV/AIDS Preclinical
hNM01 AbNovo Inc., Immune Network Ltd. HIV/AIDS I
PRO 367 Roche Holding Progenics Pharmaceuticals HIV/AIDS I/II
TNX-355 Tanox, Inc., Biogen, Inc. (Massachusetts) HIV/AIDS I
OraQuick HIV-1 OraSure Technologies, Inc. HIV/AIDS Market
Cytolin CytoDyn Amerimmune Pharmaceuticals, Inc. HIV/AIDS I/II
Tipranavir TIPRANAVIR HIV/AIDS III
HXB AAI International, AnaaiPharma Company Herpes Simplex Virus type 2 Preclinical
MEDI-491 MedImmune Human B19 parvovirus I
Synagis™ (Palivizumab) MedImmune Respiratory Syncytial Virus Approved in 1998
Numax MedImmune Respiratory Syncytial Virus Preclinical
INS37217 Intranasal Inspire Pharmaceuticals Rhinovirus (common cold) II
Biochemical Prevention and Treatment of Respiratory Syncytial Virus Infection
The respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infection in infants and young children producing bronchiolitis and pneumonia worldwide. RSV infection leads to more than 90,000 hospitalizations and a 2% mortality rate among infants nationwide [2-5]. Approximately two-thirds of infants are infected with RSV during the first year of life and approximately 95% of children test seropositive for RSV by the age of two [6]. Unfortunately, even natural RSV infection produces limited immunity at best. In fact, an inactivated RSV vaccine paradoxically resulted in more severe disease instead of protection [7].
The most successful approach to date has been Biochemical Prevention and Treatment with anti-viral antibodies. In 1996, RespiGam™ (respiratory syncytial virus immune globulin or RSV-IG) became available for use in children less than two years of age with high-risk factors [8-10]. The use of RespiGam™ was largely supplanted with the approval of Synagis™ (Palivizumab) in 1998. Palivizumab is an IgG1 MAb administered IM monthly that selectively binds to the RSV surface glycoprotein F [1,51]. The drug specifically inhibits RSV replication by preventing the virus from fusing with the respiratory endothelial cell membrane. Palivizumab has been shown to reduce the rate of hospitalization of at-risk infants by about 55% in clinical studies and now serves as the primary medical means of RSV prevention [11-13].
Prevention of Human Rhinovirus infections
Human rhinovirus (HRV) causes over 80% of the common cold in the fall [14]. Developing vaccines against HRV is unfeasible because HRVs have at least 115 antigenically distinct serotypes [15,16]. One of the proven methods to prevent and inhibit viral infections is to block host cell receptors that are used by viruses to gain cell entry. Receptor blockage is commonly achieved via application of MAbs that bind to specific epitopes on the receptor molecules. A plethora of in vitro studies have reported effective viral inhibition by receptor-blocking MAbs. However, these works have not yielded yet any approved drug on the market.
In HRV infection, about 90% of HRV serotypes utilize a single cell surface receptor exclusively, which is the intercellular adhesion molecule-1 (ICAM-1), for viral attachment and subsequent viral entry [17,18]. As such, ICAM-1 has become a very promising target for biochemical prevention. A receptor blocking approach has shown that the soluble ICAM-1 and an anti-ICAM-1 monoclonal antibody, Mab 1A6, could prevent infections by a broad spectrum of rhinovirus serotypes in human cells in vitro [19-21]. Administration of soluble ICAM-1 and MAbs in human clinical trials had indeed achieved reduction in symptoms, but did not prevent the incidence of the disease [22-24]. For the MAbs, the limited efficacy is most likely due to its low functional affinity (or avidity) for ICAM-1 when compared to that of the multivalent HRV particles [25].
High avidity is achieved by multivalency. To improve avidity of HRV receptor blocking antibody, a novel tetravalent recombinant antibody, CFY196, has been generated against ICAM-1 [26]. CFY196 is composed of Fab fragment of a humanized version of MAb 1A6 fused with a linker derived from human immunoglobulin D (IgD) hinge and a tetramerization domain derived from the coiled-coil sequence of human transcription factor ATFα. CFY196 is expressed in bacteria and purified as a homogenous tetrameric molecular complex. CFY196 exhibited almost two-orders-of-magnitude improvement in functional affinity compared with its bivalent counterpart based on the kinetic parameters measured by BIAcore analysis. Such kinetic improvement also directly leads to functional superiorities of CFY196. In in vitro assays, CFY196 consistently and significantly outpaced the best commercial anti-ICAM-1 MAbs in preventing HRV infection as measured by reduction of cytopathic effects and HRV viral titers [26]. The preclinical findings of CFY196 bode well its efficacy in human since MAb 1A6, from which CFY196 is derived, has already exhibited positive effects in a human trial. Moreover, to prevent possible immunogenicity, CFY196 is humanized [27]. Further pre-clinical and clinical development of CFY196 is warranted to fully evaluate its potential as a prophylaxis and therapeutics for the HRV induced common colds.
2. Biochemical Prevention and Treatment via targeting on viral mRNA
Targeting viral mRNA is one of the most active areas of research and development. Several strategies have emerged over the years and are being tested pre-clinically and clinically. They include: antisense-oligonucleotides (AS-ONs), ribozymes, and recently, RNA interference (RNAi). All these strategies share the features of conceptual simplicity, straightforward drug design and quick route to identify drug leads. However, the challenges have been to improve potency, pharmacokinetics and, most importantly, intracellular delivery of the drug candidates. As the oldest strategy, AS-ON technology has produced to date one drug in the market place, Vitravene®. A number of clinical trials of drug candidates from these technologies are currently ongoing.
Antisense-oligonucleotides
Antisense-oligonucleotides (AS-ONs) are short synthetic oligonucleotides that form complementary pair with specific viral mRNA targets. AS-ONs inhibit viral protein production by both blocking viral mRNA translation and triggering its degradation. Since the discovery of viral inhibition effect of AS-ONs by Zamecnik and Stephenson in 1978 [28], antisense technology has been developed as a powerful tool for target validation and therapeutic purposes.
Vitravene is the first AS-ON based drug approved by FDA. Vitravene, or fomivirsen sodium, is a 21-base phosphorothioate oligodeoxynucleotide complementary to the messenger RNA of the major immediate-early region proteins of human cytomegalovirus, and is a potent and selective antiviral agent for cytomegalovirus retinitis, a herpes-like eye disease that afflicts the immune-suppressed [29,30]. A number of clinical trials as well as one approved therapy based on AS-ON technologies are summarized in Table 2.
Table 2 Clinical trials and an approved therapy based on AS-ON technologies [31-33].
Product Company Target Disease Chemistry Status
Vitravene (Fomivirsen) ISIS Pharmaceuticals CMV IE2 CMV retinitis PS DNA Approved in 1998
Affinitac (ISIS 3521) ISIS PKC-α Cancer PS DNA Phase III
Genasense Genta Bcl2 Cancer PS DNA Phase III
Alicaforsen (ISIS 2302) ISIS ICAM-1 Psoriasis, Crohn's disease, Ulcerative colitis PS DNA Phase II/III
ISIS 14803 ISIS Antiviral Hepatitis C PS DNA Phase II
ISIS 2503 ISIS H-ras Cancer PS DNA Phase II
MG98 Methylgene DNA methyl transferase Solid tumors PS DNA Phase II
EPI-2010 EpiGenesis Pharmaceuticals Adenosine A1 receptor Asthma PS DNA Phase II
GTI 2040 Lorus Therapeutics Ribonucleotide reductase (R2) Cancer PS DNA Phase II
ISIS 104838 ISIS TNFα Rheumatoid Arthritis, Psoriasis 2nd generation Phase II
Avi4126 AVI BioPharma c-myc Restenosis, cancer, Polycystic kidney disease 3rd generation Phase I/II
Gem231 Hybridon PKA RIα Solid tumors 2nd generation Phase I/II
Gem92 Hybridon HIV gag AIDS 2nd generation Phase I
GTI 2051 Lorus Therapeutics Ribonucleotide reductase (R1) Cancer PS DNA Phase I
Avi4557 AVI BioPharma CYP3A4 Metabolic redirection of approved drugs 3rd generation Phase I
Phosphorothioate (PS) oligodeoxynucleotides are the 'first generation' DNA analogs. The 'second generation' ONs contain nucleotides with alkyl modifications at the 2' position of the ribose. They are less toxic than PS-DNAs and have a slightly enhanced affinity. DNA and RNA analogs with modified phosphate linkages, or different sugar residues substituting the furanose ring have been referred as 'third generation' [34]. For instance, peptide nucleic acids and their analogs display superior sequence specificity and are resistant to nuclease degradation. These third generation AS-ON have limited non-specific interactions with other genes and, therefore, have shown great potentials in clinical trials.
Ribozymes
Ribozymes (Rz) are catalytically active ONs that both bind and cleave target RNAs. They were discovered after the AS-ON technology. Initial findings on ribozymes raised the hope that they may offer a more potent alternative to AS-ONs. Many cell based and animal tests have performed on anti-viral effects of ribozymes, including HIV, hepatitis B, hepatitis C, influenza, etc. Results from these tests have shown that ribozymes are promising viral inhibitors [35-38]. However, further progress in the field has been hampered by difficulties to achieve satisfactory potency and efficient intracellular delivery of ribozymes in vivo. HEPTAZYME is a modified ribozyme that cleaves the internal ribosome entry site of the Hepatitis C virus. The Rz was demonstrated to inhibit viral replication up to 90% in cell culture [39]. HEPTAZYME was tested in a Phase II clinical trial, but was later withdrawn from further clinical trials due to insufficient efficacy. So far, there is no anti-viral ribozymes that are being actively tested in advanced clinical trials.
RNA Interference (RNAi)
RNA interference, or RNAi, is the inhibition of expression of specific genes by double-stranded RNAs (dsRNAs). It is becoming the method of choice to knockdown gene expression rapidly and robustly in mammalian cells. Comparing to the traditional antisense method, RNAi technology has the advantage of significantly enhanced potency; therefore, only lower concentrations may be needed to achieve same level of gene knockdown. RNAi gained rapid acceptance by researchers after Tuschl and coworkers discovered that in vitro synthesized small interfering RNAs (siRNAs) of 21 to 23 nucleotides in length can effectively silence targeted genes in mammalian cells without triggering interferon production [40,41]. In mammalian cells, the level of gene inhibition mediated by siRNA routinely reaches an impressive 90% [42].
Several initial studies, which test the potential application of synthetic siRNAs as antiviral agents, have shown very promising results. To date, RNAi has been used effectively to inhibit the replication of several different pathogenic viruses in culture, including: RSV (respiratory syncytial virus) [43], influenza virus [44], poliovirus [45] and HIV-1 [46-48]. In the case of HIV-1, several specific mRNAs have been successfully targeted for siRNA-mediated silencing, including those that encode Gag, Pol, Vif and the small regulatory proteins Tat and Rev. These studies show that RNAi can effectively trigger the degradation of not only viral mRNAs, but also genomic RNAs at both the pre- and post-integration stages of the viral lifecycle. In addition to targeting viruses directly, alternative strategies have employed siRNAs that silence the expression of essential host factors including Tsg101, required for vacuolar sorting and efficient budding of HIV-1 progeny [49], and the chemokine receptor CCR5, required as a co-receptor for HIV-1 cell entry [50].
Conclusions
Currently, our understanding of the biological mechanisms underlying RNAi lags behind the movement to apply this technology to human diseases such as viral infections. Some major technical hurdles need to be overcome before siRNA-based anti-viral prophylaxis and treatments move into the clinics. Especially, intracellular delivery of siRNA needs to be greatly improved. The next few years of research will indicate whether RNAi technology will realize its potential as the next wave of Biochemical Prevention and Treatment.
Competing Interests
Dr. Hervé Le Calvez declares that he has no competing interest. Dr. Mang Yu and Dr. Fang Fang are the co-founders and current share holders of Perlan Therapeutics who has developed CFY196.
Figure 2 3D model of the tetrameric Fab anti-ICAM-1 molecule CFY196 [26]. Each identical subunit is represented by a different color.
Acknowledgements
The authors wish to thank Kosi Gramatikoff for graphic assistance and helpful discussions. They are grateful to Libby Weber for the critical assistance on the completion of this manuscript.
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| 15560846 | PMC535550 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Nov 23; 1:12 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-12 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-81554833410.1186/1743-422X-1-8Short ReportPermissive human cytomegalovirus infection of a first trimester extravillous cytotrophoblast cell line LaMarca Heather L [email protected] Bruno [email protected] Cindy A [email protected] Interdisciplinary Program in Molecular and Cellular Biology, Tulane University Health Sciences Center, New Orleans, LA, USA2 Department of Microbiology and Immunology, Tulane University Health Sciences Center, New Orleans, LA, USA2004 17 11 2004 1 8 8 2 9 2004 17 11 2004 Copyright © 2004 LaMarca et al; licensee BioMed Central Ltd.2004LaMarca 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.
Human cytomegalovirus (HCMV) is the leading cause of congenital viral infection in the United States and Europe. Despite the significant morbidity associated with prenatal HCMV infection, little is known about how the virus infects the fetus during pregnancy. To date, primary human cytotrophoblasts (CTBs) have been utilized to study placental HCMV infection and replication; however, the minimal mitotic potential of these cells restricts experimentation to a few days, which may be problematic for mechanistic studies of the slow-replicating virus. The aim of this study was to determine whether the human first trimester CTB cell line SGHPL-4 was permissive for HCMV infection and therefore could overcome such limitations. HCMV immediate early (IE) protein expression was detected as early as 3 hours post-infection in SGHPL-4 cells and progressively increased as a function of time. HCMV growth assays revealed the presence of infectious virus in both cell lysates and culture supernatants, indicating that viral replication and the release of progeny virus occurred. Compared to human fibroblasts, viral replication was delayed in CTBs, consistent with previous studies reporting delayed viral kinetics in HCMV-infected primary CTBs. These results indicate that SGHPL-4 cells are fully permissive for the complete HCMV replicative cycle. Our findings suggest that these cells may serve as useful tools for future mechanistic studies of HCMV pathogenesis during early pregnancy.
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Findings
Human cytomegalovirus (HCMV) is a ubiquitous beta-herpesvirus that is the leading cause of congenital viral infection in the United States and Europe. Intrauterine transmission of the virus occurs in approximately 40% of pregnant women with primary HCMV infection, and the incidence of congenital HCMV infection is an estimated 1% of newborns [1-3]. Although the pathogenesis of HCMV transmission to the fetus during pregnancy is unclear, the placenta has been implicated as an important determining factor [4-8]. Primary first trimester extravillous cytotrophoblasts (CTBs), which are specialized placental epithelial cells that invade and remodel the uterine wall during placentation, have been previously shown to be fully permissive for HCMV infection in vitro [7,9]. Additionally, using an in vitro coculture system, Maidji and colleagues demonstrated that infected uterine microvascular endothelial cells transmit HCMV to differentiating invading CTBs, suggesting that placental HCMV infection can occur in a retrograde fashion that initiates in the maternal endothelium [8]. Despite these reports, the minimal mitotic potential of primary CTBs restricts experimentation to a few days, which may be problematic for mechanistic studies of the slow-replicating virus. Alternatively, the utilization of trophoblast cell lines would provide an easily manipulative in vitro model for the study of HCMV infection of the placenta. In the present study, we used a first trimester human extravillous CTB cell line, termed SGHPL-4, to investigate HCMV replication. SGHPL-4 cells were derived from first trimester chorionic villous tissue and have been described previously. Importantly, these cells share many characteristics with isolated primary cells, including the expression of cytokeratin-7, HLA class I antigen, HLA-G, BC-1, CD9, human chorionic gonadotrophin, and human placental lactogen[10-12].
The lytic replication cycle of HCMV is a temporally regulated cascade of events that is initiated when the virus binds to host cell receptors. Upon entry into the cell, the viral DNA translocates to the nucleus where viral gene expression occurs in a stepwise fashion beginning with the expression of immediate early (IE) genes (reviewed in [13]). To initiate studies of HCMV infection in the SGHPL-4 cell line, placental CTBs and human foreskin fibroblasts (HFFs) were infected with HCMV and the nuclear HCMV IE proteins (IE 1/2; Chemicon, Temecula, CA) were examined by immunofluorescence at various intervals after viral infection. At 3 h p.i., IE 1/2 was present in SGHPL-4 cells in similar numbers to that of HFFs. In fact, the percentages of IE-positive cells initially did not differ between CTBs and HFFs, suggesting that viral entry into the cells and IE transcription occurred at similar rates between the cell types (Figure 1A,1B,1C). Characteristic cytopathic effects of HCMV infection including swollen cells with nuclear inclusions were observed in both SGHPL-4 and HFF cells by 48 h p.i. (data not shown), and throughout a 6 day culture period, the numbers of IE-positive cells increased continuously in both cell types (Figure 1A). Interestingly, the rate of IE 1/2 protein expression in SGHPL-4 cells as compared to HFFs appeared to differ beginning at 72 h p.i. By 72 h p.i., there was a 40% increase in the percentage of IE-positive HFFs over SGHPL-4 cells. While nearly 100% of HFFs stained positive for IE 1/2 120 h (5 days) p.i., the maximum fraction of IE-positive SGHPL-4 cells did not exceed 60% (Figure 1A,1D,1E), suggesting that subsequent viral gene expression and thus cell-to-cell viral spread may be kinetically delayed. These findings are consistent with other reports demonstrating delayed kinetics of viral gene expression in primary CTBs as compared to primary fibroblasts [14].
Figure 1 Productive HCMV infection in SGHPL-4 and HFF cells. (A-E) HCMV IE protein expression in human cytotrophoblasts. SGHPL-4 (□) or HFF (■) cells were infected with HCMV strain RVdlMwt-GFP [17] at a MOI of 2.5 PFU per cell and incubated at 37°C for 1, 4, 8, 12, 24, 48, 72, 96,120 or 144 h. At the indicated times, cells were fixed and stained for HCMV IE 1/2 and DAPI (Molecular Probes) and visualized on a Zeiss Axio Plan II microscope (Thornwood, NY). To determine the number of HCMV-infected cells, three fields of view were considered and the percent of IE-positive cells was calculated as: (average number of IE-stained cells/average number of DAPI-stained cells) × 100. The graph demonstrates an increase in the percentage of SGHPL-4 and HFF cells expressing IE 1/2 over a period of time. Representative images of HCMV IE 1/2 are depicted at 8 h p.i in (B) CTBs and (C) HFFs and at 120 h p.i. in (D) CTBs and (E) HFFs; IE 1/2-red, DAPI-blue, overlaid-purple. (F) Infected CTBs produce and release infectious virions. SGHPL-4 or HFF cells were inoculated with HCMV at a MOI of 0.1 PFU per cell. At the indicated times, cells or culture medium were harvested, freeze-thawed three times, and titers of infectious virus in SGHPL-4 cell lysates (○) and supernatants (△) and HFF cell lysates (●) and supernatants (▲) were determined by a microtiter plaque assay on HFFs [18]. Infectious progeny virus was detected in both cell lysates and culture supernatants of SGHPL-4 and HFF cells. The dashed line represents the lower limit of detection of the plaque assay used to measure viral titers.
Although several studies have shown that first trimester primary trophoblasts can be permissively infected with HCMV, some reports have demonstrated that progression through the replicative cycle was slow and progeny virus remained predominantly cell associated [9,15,16]. To determine whether SGHPL-4 cells support productive HCMV replication, 9 day viral growth assays were performed (Figure 1F). SGHPL-4 and HFF cells were inoculated with HCMV at a MOI of 0.1 PFU per cell, and both culture lysates and supernatants were titered for infectious virus at various days p.i. While viral titers in infected HFFs were detectable as early as 2 days p.i., viral replication was undetectable or below the lower limit of detection of the assay in SGHPL-4 lysates up to 3 days p.i. However, at days 5–9 p.i., HCMV replicated to titers of ≥ 5000 and 3600 PFU/ml in SGHPL-4 cell lysates and supernatants, respectively. Relative to HFF-infected control cultures, viral titers recovered from SGHPL-4 culture lysates and supernatants were reduced by ~20- and ~200-fold, respectively (Figure 1F). While viral titers were decreased in infected SGHPL-4 cells as compared to infected HFFs, placental CTBs effectively supported productive viral replication as measured by infectious intracellular and extracellular virions. Moreover, when SGHPL-4 cells were infected with another laboratory-derived strain of HCMV (strain AD169), similar results were obtained (data not shown) suggesting that viral replication was not virus-strain specific. Collectively, these data indicate that SGHPL-4 cells support productive HCMV replication.
In the present study, we demonstrate that the first trimester extravillous CTB cell line SGHPL-4 is fully permissive for HCMV replication. The utilization of a CTB cell line, rather than primary CTBs and explant cultures that are short-lived cultures, may provide an experimental advantage for in vitro studies of placental HCMV infection. We propose that the permissiveness for HCMV replication in SGHPL-4 cells may allow for future studies in elucidating the molecular mechanism(s) of HCMV infection and pathogenesis at the maternal-fetal interface during early pregnancy.
List of abbreviations
human cytomegalovirus (HCMV), cytotrophoblast (CTB), human foreskin fibroblasts (HFFs), immediate early (IE), hours (h), post-infection (p.i.), multiplicity of infection (MOI), plaque forming unit (PFU), 4', 6-diamidino-2-phenylindole, dihydrochloride(DAPI)
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HL participated in the experimental design, performed all experiments and drafted the manuscript. BS participated in the experimental design and assisted with viral propagation and viral replication assays. CM conceived of the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to thank Dr. Mark Stinski at the University of Iowa for kindly supplying the virus strain used in these studies and Dr. Guy Whitley at St. George's Hospital Medical School in London for kindly providing the SGHPL-4 cell line and for critical review of this manuscript. This work was supported by the National Institutes of Health (HD045768; C.A.M.).
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| 15548334 | PMC535551 | CC BY | 2021-01-04 16:38:33 | no | Virol J. 2004 Nov 17; 1:8 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-8 | oa_comm |
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BMC Plant BiolBMC Plant Biology1471-2229BioMed Central London 1471-2229-4-191555017610.1186/1471-2229-4-19Methodology ArticleUse of a highly sensitive two-dimensional luminescence imaging system to monitor endogenous bioluminescence in plant leaves Flor-Henry Michel [email protected] Tulene C [email protected] Bruxelles Guy L [email protected] Michael R [email protected] Biolumonics Ltd., Staveley Mill Yard, Staveley, nr. Kendal, Cumbria, LA8 9LS, UK2 Department of Biological Sciences, Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, LA1 4YQ, UK2004 18 11 2004 4 19 19 7 7 2004 18 11 2004 Copyright © 2004 Flor-Henry 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
All living organisms emit spontaneous low-level bioluminescence, which can be increased in response to stress. Methods for imaging this ultra-weak luminescence have previously been limited by the sensitivity of the detection systems used.
Results
We developed a novel configuration of a cooled charge-coupled device (CCD) for 2-dimensional imaging of light emission from biological material. In this study, we imaged photon emission from plant leaves. The equipment allowed short integration times for image acquisition, providing high resolution spatial and temporal information on bioluminescence. We were able to carry out time course imaging of both delayed chlorophyll fluorescence from whole leaves, and of low level wound-induced luminescence that we showed to be localised to sites of tissue damage. We found that wound-induced luminescence was chlorophyll-dependent and was enhanced at higher temperatures.
Conclusions
The data gathered on plant bioluminescence illustrate that the equipment described here represents an improvement in 2-dimensional luminescence imaging technology. Using this system, we identify chlorophyll as the origin of wound-induced luminescence from leaves.
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Background
It is well documented that essentially all living systems spontaneously generate and emit very low levels of light (reviewed in [1]). This ultra-weak bioluminescence is generally characterized by emission of photons (sometimes termed "biophotons") at an intensity less than 10-14 W.cm-2 (< 1000 photons.sec-1.cm-2). This is in contrast to the more widely known bioluminescence from animals that visibly glow, such as certain species of jellyfish, fireflies and beetles, which employ fluorescent proteins and/or luciferase enzymes to catalyse reactions that result in chemiluminescence. Ultra-weak bioluminescence is generally considered to result from oxidative chemistry occurring within cells [1,2], though in most cases, the source of observed emissions has not been identified. Nevertheless, changes in emission levels in response to stress – particularly oxidative stress – have been observed in many systems, including bacteria [3], plants (e.g. [2,4-6]) and animals [7,8]. For this reason, measurement of ultra-weak bioluminescence may be a useful non-invasive technique to monitor rapid perturbations to cellular activity and for early detection of diseased and damaged cells.
In plants, increases in spontaneous low-level luminescence have been observed in response to pathogen infection [5], salt stress [9], osmotic stress [10] and mechanical damage or wounding [2,4,6,11]. It is suggested that luminescence is produced by singlet oxygen and excited carbonyl species generated as a result of lipid peroxidation reactions [2,11]. Lipid peroxidation in wounded and pathogen-infected plant tissues is a common consequence of the generation of ROS, which also act as signals to induce plant defence responses [12].
Quantitative measurements of such ultra-weak photon emissions are normally obtained using sensitive photomultiplier tubes as photon counting devices. However, more recently, 2-dimensional photomultiplier tubes and cooled charge-coupled device (CCD) cameras have also been employed as a means of imaging the spatial distribution of light emission from diseased and damaged plant tissues [2-6,9]. The clearest images were obtained by Chen et al., [2], using a micro-channel plate coupled to a cooled CCD to image light captured through a lens. However, the acquisition time required for what is a relatively weak signal, was 1 hour, preventing a detailed temporal investigation of stress-induced bioluminescence.
Here, we present a novel configuration of a cooled CCD that enables high sensitivity, high resolution 2-dimensional imaging with short integration times. We demonstrate the utility of the system to image and quantify delayed chlorophyll fluorescence and wound-induced luminescence from plant leaves.
Results and Discussion
Construction of a luminescence imager
The experimental imaging set-up, shown in Figure 1, essentially consists of a glass sample stage coupled to a cooled CCD with fibre optics. This configuration enables maximal capture of light emitted from one side of a flat sample placed on the stage by avoiding the inevitable light dispersal associated with lens-based systems. The system is useful for imaging bioluminescence from flat samples such as plant leaves or cells grown transparent culture vessels or on solid supports such as microscope slides or membranes. By analysing the data captured from the CCD, it is possible to estimate the number of photons detected per unit area over a very wide dynamic range. We routinely measured emission levels in the region of 1000 photons/sec/mm2 from wounded leaves (see below). In addition to its high sensitivity, the imager is significantly less expensive than equipment based on photomultiplier tubes.
Imaging delayed chlorophyll fluorescence
Delayed chlorophyll fluorescence is a well known phenomenon that is the result of emission of photons from excited chlorophyll molecules following transfer of leaves to the dark. It is generally measured using devices such as photomultiplier tubes and in principle, can be detected by existing lens-based CCD imaging systems. We first tested the spatial and temporal resolution of our system by imaging delayed chlorophyll fluorescence from leaves of Arabidopsis thaliana and Tradescantia albiflora (Figure 2). The image of a leaf from a variegated T. albiflora plant clearly indicates a dependence on chlorophyll for the emission to occur, since white, non-pigmented regions of the leaf do not emit light (compare Figure 2B and 2C). Bright images could be obtained with relatively short exposure times of 1–5 minutes. Delayed chlorophyll fluorescence rapidly subsided between 5 and 10 minutes after transfer to dark. The absolute level of delayed chlorophyll fluorescence in Arabidopsis leaves varied greatly, generally appearing to be lower later in the day.
Spatial and temporal resolution of ultra-weak luminescence in wounded leaves
Delayed chlorophyll fluorescence produces relatively strong light emission compared to typical levels of ultra-weak bioluminescence. To test the sensitivity of our equipment, we wanted to try to image these lower level emissions. Several previous reports had indicated that mechanical wounding increases ultra-weak luminescence from plant leaves, but studies on the temporal and spatial nature of this phenomenon are limited. We used a haemostat to inflict crushing wounds on Arabidopsis leaves and imaged them over a time course by making successive 5-minute exposures. In many experiments, no wound-induced luminescence could be seen in the first 5 minutes after transfer to the imager, since it was masked by delayed chlorophyll fluorescence. After this initial period however, photon emission was much more intense around the wounded tissue. In other experiments, where delayed chlorophyll fluorescence was lower, signals could be seen even in the first exposure. Results from such an experiment are shown in Figure 3 and in an accompanying extended animation (Additional File 1). In the first image, which includes an unwounded control leaf and two leaves each wounded twice across the lamina, striking wound-induced luminescence can be seen above a general background of delayed chlorophyll fluorescence (Figure 3A). In the two subsequent 5 min exposures, chlorophyll fluorescence has subsided, whilst the wound-induced luminescence remains high (Figure 3B,3C). The serrated pattern of the haemostat surface is clearly reflected in the pattern of luminescence from the leaf, with areas of greater damage exhibiting maximal photon emission. In general, we observed that induced luminescence is strongest immediately after wounding and then gradually decays over a period of an hour or more.
Origin of wound-induced luminescence
In order to understand more about the origin of the luminescence emitted by wounded leaves, we used coloured filters to investigate its spectral characteristics. The results are shown in Figure 4. A red filter (LEE 182), which transmits visible light of wavelengths greater than 600 nm, was effectively transparent with respect to the light emitted from wound sites, whereas blue and green filters (LEE 183 and LEE 735), which absorb light in the region of 550 – 750 nm, caused a significant reduction in signal reaching the detector when placed between the wounded leaf and the sample stage. Interestingly, a different blue filter (LEE 118), which transmits light above 700 nm, did not significantly affect luminescence (Figure 4). These results suggest that light emitted from wounded Arabidopsis leaves has a wavelength between 700 and 750 nm, i.e. the red region of the visible spectrum. This is consistent with typical fluorescence from chlorophyll (730 nm). To test whether chlorophyll might be the source of wound-induced photon emission, we used the carotenoid biosynthesis inhibitor, norflurazon, to generate photobleached leaves lacking chlorophyll [13]. Compared with equivalent leaves from control plants, these white leaves failed to luminesce following wounding, except in remaining chlorophyll-containing areas (Figure 5). Together, these results demonstrate that light emission from wounded leaves is chlorophyll-dependent. Since luminescence occurs in the dark, and extends for times well beyond delayed chlorophyll fluorescence, we conclude that excitation energy produced by some wound-induced process is transferred to chlorophyll and emitted as light. One possible source of such energy might be products of oxidative damage, such as excited triplet carbonyls produced during lipid peroxidation [14].
Temperature-dependence of wound-induced luminescence
That delayed chlorophyll fluorescence exhibits temperature-dependence has been shown previously in several systems [15]. Since we determined that chlorophyll is the major source of light emission from wounded leaves, we investigated whether wound-induced luminescence might also be affected by temperature. Application of heat to the sample via a foil heater in the lid of the sample chamber significantly increased the intensity of luminescence over the tested temperature range. Without applied heat, the temperature of the sample stage was approximately 7°C, when only low levels of luminescence could be detected. Luminescence increased significantly upon activation of the heater, resulting in an increase in the temperature of the sample stage to something in the region of 20°C. This phenomenon is illustrated in Figure 6, and in an accompanying extended animation (see Additional file 2).
Furthermore, we found that heating could activate luminescence in leaves wounded up to 3 hours before imaging. For example, Figure 7 shows a series of images from an experiment in which leaves were wounded at different times before imaging. Without heating, only the leaf wounded immediately before imaging showed detectable wound-induced luminescence, and this emission decayed to very low levels within 20 min. After 45 mins, heating was applied to the sample chamber, and within 10 minutes, wounds applied immediately, 15 mins and 1 hour before the start of imaging were clearly visible, with a faint emission also detectable from a wound applied to the leaf 2 hours before the start of imaging (therefore the image shown here (t = 65) was captured over 3 hours after wounding this particular leaf). This phenomenon is reminiscent of the light emission measured during thermoluminometry [14]. Thermoluminescence is a chlorophyll-dependent light emission observed at high temperatures (maxima at 70–90°C and 120–140°C), which, like ultra-weak bioluminescence, is attributed to singlet oxygen and triplet carbonyls produced by lipid peroxidation during oxidative stress [14]. Coupled with the results above, these data suggest that temperature increases the rate of decay of excited triplet carbonyls, and that the energy released is transferred to chlorophyll and emitted as light. There may also be an additional temperature-dependent component in the rate of emission of light from excited chlorophyll, as observed for delayed fluorescence [15].
Conclusions
The novel configuration of cooled CCD that we have used in this study to capture 2-dimensional images of plant leaves provides excellent temporal and spatial resolution of bioluminescence/chemiluminescence processes in biological materials, which are important markers of various forms of stress and disease. Using this system, we are able to define the origin of wound-induced luminescence from plant leaves as chlorophyll, and suggest that this arises via the temperature-dependent release of energy from excited triplet carbonyls produced by oxidative stress.
Methods
Plant material
Plants of Arabidopsis thaliana, Columbia ecotype, were grown in soil in a glasshouse at 22°C with a 16-hour photoperiod. Wounding was performed by crushing leaves with a haemostat. To generate leaves lacking chlorophyll, Arabidopsis plants grown for 3 weeks under 16 h light/8 h dark, were watered with 0.1 mM norflurazon (Sigma Aldrich PS1044) and grown under constant illumination at 120 μmol/m2/sec. Under these conditions, newly emerging leaf tissue was white. Leaves with white regions were taken 15 days after the start of norflurazon treatment, and used for luminescence imaging.
Two dimensional luminescence imaging apparatus
The imager used is a two-dimensional imaging system based on a slow-scan 'scientific grade' Silicon Photo Area-Detector [SPAD] (Integrated SPAD Imaging System – PiXx*ell 1A, Biolumonics Ltd), which consists of a cooled ultra-sensitive area photo-detector within a stainless steel vacuum vessel and associated image acquisition electronics. The input image is coupled to the detector with optical fibres (numerical aperture = 1). The detector contains 222,336 pixels, and in the experiments presented here, ran at an operating temperature of -58°C. The combination of the proprietary low read-out noise electronics, cooling of the detector and the optical fibre coupling results in a sensitive imaging system capable of recording the faintest luminescences at high resolution, (40 μm to 200 μm depending on geometry of sample and integration time). The spectral absorption response of the SPAD ranges from the near UV to IR, thus including the entire visible spectrum up to the band-gap energy barrier of Silicon (1.1 eV) i.e. at approx 1100 nm; however, for ultimate sensitivity the detector output is monochrome, i.e. there is no wavelength selectivity in the present instrument. Further information is available on request from Michel Flor-Henry, Biolumonics Ltd.
Filters
Coloured filters were obtained from Lee Filters (Andover, UK). We used lighting effect filters LEE 182 – "light red," LEE 118 – "light blue," LEE 183 – "moonlight blue" and LEE 735 – "velvet green." Spectral characteristics of the filters can be viewed at , though readers should note that the transmittance spectra provided with the filters covers the range 300–800 nm, rather than the more limited 400–700 nm spectra shown on the web site. Filters were placed between the leaf and the sample stage prior to imaging.
Authors' contributions
MF-H designed the PiXx*ell 1A luminescence imager. MF-H and MRR conceived of the current study, participated in its design and coordination, carried out experiments and wrote the manuscript. MF-H performed data analysis. TCM performed the norflurazon experiment, and both TCM and GLdB participated in some experimental design and execution. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Animated gif file showing images from five sequential 5-minute exposures of one control leaf (bottom) and four wounded Arabidopsis leaves.
Click here for file
Additional File 2
Animated gif file showing images from seven sequential 5-minute exposures of two control leaves (bottom) and four wounded Arabidopsis leaves. Images were initially captured from samples on a cold imaging stage, but the heater in the lid of the sample stage was switched on when indicated.
Click here for file
Figures and Tables
Figure 1 Configuration of the PiXx*ell 1A imager
Figure 2 Delayed chlorophyll fluorescence images. Luminescence from leaves of Arabidopsis (A) and Tradescantia (B). Images are 5-minute exposures taken as soon as possible after transfer of leaves to the equipment. A conventional photograph of the Tradescantia leaf imaged in (B) is shown to illustrate the pattern of variegation (C).
Figure 3 Wound-induced bioluminescence in Arabidopsis. Sequential 5-minute exposures of wounded Arabidopsis leaves, showing strong luminescence from wound sites. "t = 5" represents an exposure from 0–5 mins after the start of the experiment; "t = 10," 5–10 mins and "t = 15," 10–15 mins. The image from t = 5 min also includes a control, unwounded leaf, which is omitted from subsequent images for brevity.
Figure 4 Spectral characteristics of wound-induced luminescence. Luminescence images of wounded Arabidopsis leaves in the presence of spectral filters. Pairs of wounded leaves are shown imaged with no filter, or with emitted light filtered through green (LEE 735), blue (LEE 183 and LEE 118) or red (LEE 182) filters.
Figure 5 Chlorophyll is required for wound-induced luminescence. Luminescence imaging of leaves containing or lacking chlorophyll. The figure shows luminescence images (5 minute exposures) of wounded control (top) and photobleached, norflurazon-treated (bottom), leaves of Arabidopsis. Conventional photographs of the leaves imaged are shown at left.
Figure 6 Temperature-dependence of luminescence. Images of the same wounded Arabidopsis leaf taken at different times following wounding, before and after the application of heat to the sample. "t = 5" represents an exposure from 0–5 mins after the start of the experiment; "t = 10," 5–10 mins and "t = 25," 20–25 mins. The heater in the lid of the sample stage was switched on after 15 mins.
Figure 7 Heating re-activates luminescence hours after wounding. 5-minute exposures taken at different times during an experiment showing a control Arabidopsis leaf (C) and leaves wounded immediately before (0) and 15, 60 and 120 minutes before imaging. The first frame shows the luminescence image using a different display palette from the rest of the images, since it more clearly shows the strong delayed chlorophyll fluorescence emitted by all leaves over the first 5 minutes of the time course, which is absent from the dehydrated areas of the old wounds (but not the freshest wound applied immediately before imaging). All other images are displayed using the same colour palette, which is included in the figure for reference. Times indicated in the upper left corner of each panel represent the time (in minutes) relative to the start of the experiment at which each 5-minute exposure was completed. Heating began at 45 minutes.
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| 15550176 | PMC535552 | CC BY | 2021-01-04 16:03:51 | no | BMC Plant Biol. 2004 Nov 18; 4:19 | utf-8 | BMC Plant Biol | 2,004 | 10.1186/1471-2229-4-19 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-471553895310.1186/1471-2334-4-47Research ArticleAIDS-defining illnesses among patients with HIV in Singapore, 1985 to 2001: results from the Singapore HIV Observational Cohort Study (SHOCS) Bellamy Richard [email protected] S [email protected] Nicholas I [email protected] Department of Infectious Diseases, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, Singapore2 Current address: Nutrition and Public Health Intervention Research Unit, London School of Hygiene and Tropical Medicine, London, UK2004 12 11 2004 4 47 47 10 8 2004 12 11 2004 Copyright © 2004 Bellamy 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
The objective was to describe the causes of initial and overall AIDS-defining disease episodes among HIV patients in Singapore.
Methods
A retrospective observational cohort study was performed of all adult patients seen at the national HIV referral center between 1985 and 2001. Data were extracted from the patients' records by ten trained healthcare workers. AIDS-defining conditions were established using predefined criteria.
Results
Among 1504 patients, 834 had experienced one or more AIDS-defining diseases. The most frequent causes of the initial AIDS-defining episode were Pneumocystis carinii pneumonia (35.7%), Mycobacterium tuberculosis (22.7%) and herpes simplex (7.4%). In total 1742 AIDS-defining episodes occurred. The most frequent causes were Pneumocystis carinii pneumonia (25.1%), Mycobacterium tuberculosis (16.2%) and cytomegalovirus retinitis (9.5%).
Conclusions
The most frequent causes of AIDS-defining illnesses in Singapore are similar to those reported in the West, prior to the introduction of anti-retroviral therapy. Opportunistic infections remain the most frequent AIDS-defining illnesses.
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Background
Cohort studies have provided valuable information on the clinical course of HIV infection in patients from Europe [1-16], North America [3,17-21], South America [22,23] and Africa [24-32]. The introduction of antiretroviral therapy has dramatically altered the incidence of AIDS-defining illnesses in the West. In the EuroSIDA study there have been substantial falls in the incidence and total number of AIDS-defining episodes between 1994 and 1998 [10]. During this period the proportions of reported AIDS-defining illnesses due to cytomegalovirus retinitis and Mycobacterium avium have decreased from 9% to 2% and 8% to 3% respectively [10]. The proportion due to non-Hodgkin's lymphoma has increased from 4% to 16% [10]. Substantial decreases in the incidence of disease caused by cytomegalovirus, Pneumocystis carinii, Mycobacterium avium and other opportunistic infections have also been observed in the United States [20,21].
Relatively few data are available on the course of HIV infection in Asian populations. In Bangkok between 1987 and 1993 the most frequent AIDS-defining diagnoses were extrapulmonary tuberculosis (22.8%), Pneumocystis carinii pneumonia (7.0%) and cryptococcal meningitis (10.9%) [33]. Little is known regarding whether the pattern of HIV-related conditions has changed following the introduction of antiretroviral therapy in Asia.
The Communicable Diseases Centre (CDC) in Singapore is the national reference centre for adult patients with HIV and AIDS and nationwide 94.9% of all Singaporean adult residents, who have ever been diagnosed, have been referred there. Underreporting of HIV does not occur as reporting occurs automatically from the reference laboratory. Records have been kept since the first case was identified in Singapore in 1985. Therefore the Singapore HIV Observational Cohort Study (SHOCS) contains almost the entire country's HIV experience. Nucleoside analogues have been available in Singapore since the early 1990s and protease inhibitors and non-nucleoside reverse transcriptase inhibitors since 1997. The usage of these drugs has been increasing annually and during 2000 and 2001 approximately 70–80% of patients regularly attending CDC were taking some form of anti-retroviral therapy.
Methods
The details of the SHOCS cohort have been described previously [34]. The study was approved by the Ethics Committee of Tan Tock Seng Hospital. Data were extracted from the case notes of all Singaporean residents who were seen at CDC on or before 31st December 2001, by ten trained healthcare workers. Probable and confirmed criteria were developed for the diagnosis of category C [34] and category B (AIDS-related complex) conditions, based on the 1993 guidelines of the United States' Centers for Disease Control and Prevention [35].
All disease episodes were initially determined by the data extractors and then checked by an infectious disease physician (RB). Diagnoses were not included if they did not fulfill the specified criteria even if there was a strong clinical suspicion of a particular condition. If there was insufficient evidence to satisfy the criteria for a specific diagnosis, a more general diagnosis was assigned, for example cerebral lesion (cause unknown) would be used if there was insufficient evidence to support a diagnosis of toxoplasmosis of the brain or primary cerebral lymphoma.
Median CD4 counts were calculated based on the sample taken nearest the time of diagnosis of the initial AIDS-defining condition. CD4 counts from 893 patients were used, as in 37 (4.0%) cases no suitable count was available. Data were entered into a computer database (Microsoft Access™) and checked for errors and inconsistencies. Statistical analyses were performed using Stata 7.0™. Each new or recurrent AIDS-defining condition counted as one event (i.e. a patient with three diagnoses would contribute three events). An AIDS-defining condition was counted as a separate additional event if it recurred six or more months after the initial diagnosis or in the case of tuberculosis, six or more months after treatment completion. To examine the effects of highly active anti-retroviral therapy (HAART) on the proportions of AIDS-defining illnesses due to each of the three most common infections (Pneumocystis carinii pneumonia, Mycobacterium tuberculosis and cytomegalovirus retinitis), comparisons were made between the pre-HAART era (1986 to 1995) and the established HAART era (2000 to 2001) using χ2 tests.
Results
Among the 1504 patients infected with HIV who were seen at CDC between 1985 and the end of 2001, 834 developed one or more AIDS-defining conditions. The most frequent initial AIDS-defining disease episodes were due to Pneumocystis carinii pneumonia (35.7% of total diagnoses), M. tuberculosis (22.7%), herpes simplex (chronic mucocutaneous) (7.4%) and candidiasis (esophageal or tracheobronchial) (6.9%) (table 1). The median CD4 count at which the initial AIDS-defining condition occurred was 27 (inter-quartile range 11 – 63). The median CD4 count was higher for M. tuberculosis infection (52, IQR = 18 – 110) and Kaposi's sarcoma (46.5, IQR = 23.5 – 227.5) than for Pneumocystis carinii pneumonia (16.5, IQR = 9 – 40), herpes simplex (25, IQR = 9 – 63) and extrapulmonary cryptococcosis (13, IQR = 6.5 – 39) (table 1).
1742 AIDS-defining disease episodes were recorded. The 10 most frequent AIDS-defining conditions were all of infectious etiology. There were 1658 first episodes of disease and 84 recurrences. The most frequent AIDS-defining diseases were Pneumocystis carinii pneumonia (25.1% of total diagnoses), M. tuberculosis (16.2%), cytomegalovirus retinitis (9.5%), candidiasis (7.8%), chronic mucocutaneous herpes simplex (8.2%) and disseminated M. avium (8.2%) (table 2). Several additional conditions could have included undiagnosed AIDS-defining diseases. The most common of these infections was pneumonia of unknown cause (117 cases), which is likely to include some patients with Pneumocystis carinii pneumonia (table 3). Weight loss of >10% body weight was a frequent problem and affected 383 (25.5%) patients (table 3).
Between 1996 and 2001 there was a continuous increase in the total number of AIDS-defining disease episodes occurring each year, due to the increase in the number of patients being followed up. There was no significant change in the overall proportion of AIDS-defining episodes caused by infections. Pneumocystis carinii and M. tuberculosis remained the two commonest causes. Between 1986 and 1995 they respectively accounted for 26.7% and 10.8% of all AIDS-defining disease episodes. During 2000 to 2001 the percentage of episodes caused by Pneumocystis carinii was not significantly different at 26.3% (Yates' χ2 = 0.01, 2df, P = 0.94), but that due to M. tuberculosis had increased to 18.0% (Yates' χ2 = 8.10, 2df, P = 0.004). There was no significant change in the percentage of episodes due to cytomegalovirus retinitis (9.7% between 1986 and 1995 and 8.5% during 2000 and 2001; Yates' χ2 = 0.25, 2df, P = 0.62) (table 4).
Discussion
Comparison of HIV-associated morbidity with other populations
In the SHOCS cohort the proportion of conditions which were of infectious etiology remained very high throughout the study period. The distribution of conditions was similar to that seen in Western countries prior to the introduction of anti-retroviral therapy [1]. However the proportion of conditions caused by mycobacteria was higher than in Europe [1,12]. In Bangkok the most common AIDS-defining conditions were infections. Between 1986 and 1993 the most frequent AIDS-defining illnesses in Bangkok were extra-pulmonary tuberculosis (22.8%), cryptococcal meningitis (10.9%) and Pneumocystis carinii pneumonia (7%) [33]. Between 1993 and 1996 they were extrapulmonary cryptococcosis (38.4%), tuberculosis (37.4%), wasting syndrome (8.1%) and Pneumocystis carinii pneumonia (4.8%) [36]. Pneumocystis carinii pneumonia caused a much higher proportion of AIDS-defining diseases in Singapore than in Bangkok and cryptococcal meningitis occurred less frequently in Singapore.
Potential sources of bias
The SHOCS cohort provides data on the experience of 94.9% of Singaporean residents who have been diagnosed with HIV since the epidemic began. There are good diagnostic facilities in Singapore and patients with HIV-related illness receive extensive investigations. Therefore Singapore offers an ideal location for collecting information on the causes of morbidity and mortality in persons with HIV in Asia. Medical record keeping is of a high standard in our hospital and this allowed accurate diagnoses to be assigned for AIDS-defining conditions which occurred throughout the AIDS era. To ensure consistency, data was extracted by carefully trained healthcare workers and diagnoses were assigned using predefined criteria. All AIDS-defining diagnoses and all causes of death were reviewed by the same infectious diseases physician (RB). Follow-up rates in our cohort are very high with only 5.8% of patients lost to follow-up at 12 months and 9.3% after 3 years. The SHOCS cohort therefore provides a unique source of information on HIV infection in an Asian country since the start of the epidemic.
In a retrospective study it is impossible to ensure that all patients received a full series of investigations in order to satisfy the study criteria for diagnosis of HIV-related conditions. As a consequence the criteria for making a probable diagnosis are unavoidably biased against certain conditions. For example if a patient undergoes a therapeutic trial of tuberculosis treatment he or she can be given the diagnosis of "probable Mycobacterium tuberculosis". However if a patient receives a successful trial of treatment for disseminated M. avium, the diagnosis is "probable mycobacteriosis (species unidentified)".
Differences in physicians' awareness of the link between HIV and opportunistic infections can influence diagnosis rates. For example, the increase in the proportion of AIDS-defining disease episodes caused by tuberculosis between 1986–1995 and 2000–2001 may have been due to greater awareness of the link between HIV and tuberculosis. Diagnosis rates for M. avium and M. tuberculosis may also have been affected by improvements in mycobacterial culture techniques.
Conclusions
Despite the availability of anti-retroviral medication, opportunistic infections remain a common problem among HIV patients in Singapore. This may partly be explained by the high proportion of Singaporean patients, who present with advanced disease and very low CD4 counts. These patients often already have one or more AIDS-defining opportunistic infections at first presentation. Opportunistic infections may also occur before adequate immune-reconstitution can occur when HAART is commenced when the patient has a very low CD4 count. Studies in the West have shown that AIDS-defining illnesses occur more frequently among patients who have a CD4 count below 50 when anti-retroviral therapy is commenced than among those with higher CD4 counts [37,38]. Immune-reconstitution may also be impaired by difficulties in maintaining high levels of adherence, sub-optimal treatment regimens and unplanned treatment interruptions (due to adverse effects, financial or logistical reasons).
The current reductions in the cost of anti-retroviral therapy (Eg. due to generic manufacture) may enable high levels of anti-retroviral use to be achieved in many Asian countries. The experiences of the SHOCS cohort are likely to be repeated throughout Asia. Our results suggest that avoidable opportunistic infections may continue to occur even when high levels of anti-retroviral use are achieved. To reduce the burden of morbidity and mortality caused by these infections, efforts must be made to diagnose HIV infection earlier and to make the treatment of HIV and its associated opportunistic infections more affordable.
List of abbreviations
SHOCS = Singapore HIV observational cohort study, CDC = Communicable Diseases Centre, AIDS = acquired immunodeficiency syndrome, HIV = human immunodeficiency virus, HAART = highly active anti-retroviral therapy.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RB participated in the design of the study, supervised data extraction, performed the statistical analysis and wrote the manuscript. SS designed the database and supervised data extraction. NP participated in the design of the study and contributed to the statistical analysis and production of the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The Singapore HIV Observational Cohort Study was funded by a grant from the National Healthcare Group, Singapore. We are grateful to Ms Marline Yap, Dr Rumiana Plugarova, Mrs Monica Kalra, Dr Ambihai Sivalingam, Dr Vidhya Gupta, Mrs Prithi Minhas, Dr Celin Chacko, Dr Mona Lisa Rodrigues, Dr Samon Husna and Mrs Manisha Kishore for their meticulous extraction of information from the patients' records. We also gratefully acknowledge the excellent administrative support provided by Dr Bernard Peperstraete, Ms Ravathi Subramaniam and the staff of the Infectious Diseases Research Centre.
Figures and Tables
Table 1 The most frequent initial AIDS-defining conditions in 834 Singaporean patients.
Condition Median CD4 (IQR1) Confirmed Probable Total (%)
Pneumocystis carinii pneumonia 16.5 (9 – 40) 203 139 342 (35.7)
Mycobacterium tuberculosis 52 (18 – 110) 148 70 218 (22.7)
Herpes simplex, chronic mucocutaneous 25 (9 – 63) 26 45 71 (7.4)
Candidiasis, esophageal or tracheobronchial 21 (8.5 – 86) 25 41 66 (6.9)
Cytomegalovirus disease 25 (10 – 51) 28 12 40 (4.2)
Cryptococcosis, extrapulmonary 13 (6.5 – 39) 41 NA 41 (4.3)
Cytomegalovirus retinitis 15 (8 – 38.5) 0 37 37 (3.9)
Mycobacterium avium, disseminated 13 (5 – 26) 35 NA 35 (3.6)
Toxoplasmosis of the brain 29 (15 – 44) 1 31 32 (3.3)
Mycobacterium, species unidentified 31 (12 – 80) 0 16 16 (1.7)
Kaposi's sarcoma 46.5 (23.5 – 227.5) 8 4 12 (1.3)
Cryptosporidiosis, chronic intestinal 31 (4 – 56) 8 NA 8 (0.8)
Burkitt's lymphoma 34 (15.5 – 64) 8 NA 8 (0.8)
Wasting syndrome of HIV 36 (25 – 65) NA 6 6 (0.6)
Progressive multifocal leukoencephalopathy 42.5 (24.5 – 56.5) 2 3 5 (0.5)
Histoplasmosis, disseminated 22 (4 – 138) 5 NA 5 (0.5)
Other2 ----- 11 6 17 (1.8)
Total 27 (11 – 63) 549 410 959 (100)
1IQR = inter-quartile range. 2The category designated "other" includes conditions for which four cases or fewer were diagnosed. The number of initial AIDS-defining conditions is greater than the number of patients with AIDS because many patients presented with more than one condition. NA = not applicable.
Table 2 The most frequent conditions among 1742 overall category C episodes
Condition Confirmed Probable New Recurrent Total (%)
Pneumocystis carinii pneumonia 251 196 399 28 437 (25.1%)
Mycobacterium tuberculosis 194 89 263 20 283 (16.2%)
Cytomegalovirus retinitis 0 166 166 NA1 166 (9.5%)
Candidiasis, esophageal or tracheobronchial 40 96 129 7 136 (7.8%)
Herpes simplex, chronic mucocutaneous 59 84 133 10 143 (8.2%)
Mycobacterium avium, disseminated 141 NA 138 3 141 (8.2%)
Cytomegalovirus disease 79 35 112 2 114 (6.5%)
Cryptococcosis, extrapulmonary 84 NA 78 6 84 (4.8%)
Toxoplasmosis of the brain 2 61 57 6 63 (3.6%)
Mycobacterium, species unidentified 1 33 34 0 34 (2.0%)
Kaposi's sarcoma 17 6 23 0 23 (1.3%)
Cryptosporidiosis, chronic intestinal 19 NA 18 1 19 (1.1%)
Lymphoma, primary cerebral 10 10 20 0 20 (1.1%)
Wasting syndrome of HIV NA 16 16 0 16 (0.9%)
Encephalopathy, HIV-related NA 10 10 0 10 (0.6%)
Pneumonia, recurrent bacterial 9 NA 8 1 9 (0.5%)
Burkitt's lymphoma 9 NA 9 0 9 (0.5%)
Progressive multifocal leukoencephalopathy 2 5 7 0 7 (0.4%)
Histoplasmosis, disseminated 6 NA 6 0 6 (0.3%)
Mycobacterium scrofulaceum, disseminated 5 NA 5 0 5 (0.3%)
Other2 16 1 17 0 17 (1.0%)
1Following an initial diagnosis of cytomegalovirus retinitis, recurrences were not recorded as additional episodes of category C disease. 2The category designated "other" includes conditions for which four cases or fewer were diagnosed.
Table 3 Conditions which may have been undiagnosed category C illnesses and important non-category C conditions
Possible category C conditions New Recurrent Total
Pneumonia, cause unknown1 114 3 117
Non-Hodgkin's lymphoma, extracerebral2 25 0 25
Meningitis, cause unknown3 21 0 21
Dementia4 20 0 20
Cerebral lesion, cause unknown5 18 0 18
Non-category C conditions
Weight loss, >10% bodyweight 379 46 383
Pneumonia, bacterial 102 47 106
Salmonella septicemia 86 08 86
Penicillium marneffei, disseminated 5 1 6
1Some of these patients died before a diagnosis could be established and others recovered following a mixture of antibacterial and Pneumocystis treatments. Therefore the majority of these cases are likely to be due to bacteria or Pneumocystis carinii infections. 2Histology in these cases was not sufficiently specific to confirm a diagnosis of Burkitt's or immunoblastic lyphoma. 3These patients represent a diverse mixture of cases without a confirmed diagnosis, but they are believed to include mycobacterial, bacterial, viral and fungal infections. 4This excludes cases which fulfilled the diagnosis of HIV-related encephalopathy. 5Many of these cases died soon after diagnosis and are likely to include a large number of patients with toxoplasmosis of the brain. 6Recurrent weight loss indicates that weight was regained and subsequently lost again and not that 20% of body weight was lost. 7These cases of recurrence of bacterial pneumonia were not category C conditions as they occurred more than 12 months after the preceding case. 8Four cases of recurrent Salmonella septicaemia are included in the "other" category of table 3.
Table 4 Number of episodes per year for the 12 most frequent overall AIDS-defining conditions
Condition 1986–1995 1996–1999 2000–2001 Total
Pneumocystis carinii pneumonia 94 (26.7) 197 (23.6) 146 (26.3) 437 (25.1)
Mycobacterium tuberculosis 38 (10.8) 145 (17.4) 100 (18.0) 283 (16.2)
Cytomegalovirus retinitis 34 (9.7) 85 (10.2) 47 (8.5) 166 (9.5)
Candidiasis, esophageal or tracheobronchial 16 (4.5) 70 (8.4) 50 (9.0) 136 (7.8)
Herpes simplex, chronic mucocutaneous 27 (7.7) 71 (8.5) 45 (8.1) 143 (8.2)
Mycobacterium avium, disseminated 22 (6.3) 86 (10.3) 33 (5.9) 141 (8.1)
Cytomegalovirus disease 29 (8.2) 46 (5.5) 39 (7.0) 114 (6.5)
Cryptococcosis, extrapulmonary 19 (5.4) 34 (4.1) 31 (5.6) 84 (4.8)
Toxoplasmosis of the brain 19 (5.4) 24 (2.9) 20 (3.6) 63 (3.6)
Mycobacterium, species unidentified 7 (2.0) 14 (1.7) 13 (2.3) 34 (2.0)
Kaposi's sarcoma 15 (4.3) 6 (0.7) 2 (0.4) 23 (1.3)
Cryptosporidiosis, chronic intestinal 3 (0.9) 9 (1.1) 7 (1.3) 19 (1.1)
Total1 352 (100) 834 (100) 556 (100) 1742 (100)
This table shows the new and recurrent episodes combined. Figure in brackets are the percentages for each time period. 1Total also includes AIDS-defining conditions other than the 12 most frequent.
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| 15538953 | PMC535553 | CC BY | 2021-01-04 16:03:31 | no | BMC Infect Dis. 2004 Nov 12; 4:47 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-47 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1741551858910.1186/1471-2105-5-174DatabaseThe Molecular Pages of the mesotelencephalic dopamine consortium (DopaNet) Le Novère Nicolas [email protected] Marco [email protected] Computational Neurobiology, EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton Cambridge, CB10 1SD UK2004 1 11 2004 5 174 174 6 7 2004 1 11 2004 Copyright © 2004 Le Novère and Donizelli; 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
DopaNet is a Systems Biology initiative that aims to investigate precisely and quantitatively all the aspects of neurotransmission in a specific neuronal system, the mesotelencephalic dopamine system. The project should lead to large-scale models of molecular and cellular processes involved in neuronal signaling. A prerequisite is the proper storage of knowledge coming from the literature.
Methods
DopaNet Molecular Pages are highly structured descriptions of quantitative parameters related to a specific molecular complex involved in neuronal signal processing. A Molecular Page is built by maintainers who are experts in the field, and responsible for the quality of the page content. Each piece of data is identified by a specific ontology code, annotated (method of acquisition, species, etc.) and linked to the relevant bibliography. The Molecular Pages are stored as XML files, and processed through the DopaNet Web Service, which provides functionalities to edit the Molecular Pages, to cross-link the Pages and generate the public display, and to search them.
Conclusions
DopaNet Molecular Pages are one of the core resources of the DopaNet project but should be of widespread utility in the field of Systems Neurobiology.
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Background
Although the use of Systems Biology to understand the function of neurons recently started to gain momentum, it is still in its infancy. A crucial step leading to meaningful simulations is the reconstruction of large neuronal systems based on the elementary building blocks. However, this approach suffers from the lack of truly quantitative values. Some projects of functional genomics have been recently launched to try to remedy the problem, e.g. the Genes to Cognition consortium ([1,2]). Another problem impairing the integration process resides in the large diversity of protocols and model systems used to gather the data. Since a large proportion of the data is potentially shared by numerous different systems, it is sensible to focus the effort on a restricted and well delineated system. This approach has been pioneered by the Alliance for Cellular Signaling, launched by Alfred Gilman ([3,4]), which originally focused on the B-lymphocyte and the cardiac myocyte.
DopaNet
Similarly, we started at the end of 2001 the mesotelencephalic dopamine consortium (DopaNet, [5]). The initiative aims to investigate precisely and quantitatively all the aspects of neurotransmission – at the levels of the molecule, the supra-molecular assembly, the neuronal cell and the neuronal network – in a specific neuronal system involved in many neuropathologies, such as Parkinson's disease, schizophrenia and drug addiction [6-8]. The resulting integrated knowledge will not only provide relevant, up-to-date information about such pathologies, but will also form a firm substrate to link the function of the neurobiological structures and the implementation of cognitive and mental abilities. As of June 2004, 35 European teams from 8 countries are part of the project. DopaNet became a network of the European Science Foundation (ESF) in January 2003.
A first step, prior to the design of large-scale dedicated experiments, consists in data mining the current literature in molecular and cellular neurobiology for existing quantitative knowledge. The resulting data has to be properly stored and annotated.
DopaNet Molecular Pages
A DopaNet Molecular Page is a collection of annotated numerical data relative to a "molecular complex" present in one or several DopaNet target cells. The "molecular complex" is taken here in the sense of the DopaNet Neuronal Ontology (see below), as a "stable assembly of molecules", a "molecule" being described as a "set of atoms linked together by covalent bounds". As a consequence, we can have a Molecular Page storing data relative to a molecular complex made up of components, that are themselves described in other Molecular Pages. An anticipated example is an heterotrimeric G protein and its α and βγ subunits. The information collected deals with the structure of the complex, its anatomical distribution within DopaNet target cells, and its functional properties. Each page is under the responsibility of its maintainer(s), who decide which data is to be included or not, and acknowledge the input of the various contributors. All the data included in a Molecular Page is annotated (Species, Methods, variability etc.), and linked to bibliographic references. In addition, each single data stored in the databases of DopaNet is attached to one or several terms of the DopaNet Neuronal Ontology. This ontology will therefore act as a glue, relating the various pieces of data one to the other.
DopaNet Neuronal Ontology
An ontology is defined here in its information science meaning, as a hierarchical structuring of knowledge. In our case, it is a relational vocabulary, that is a set of terms linked together, aiming to describe a neuron. Each term has a definition and a unique identifier. Terms are related by "is a" inheritances, which represent sub-classing, and "part of" inheritances which represent deepening knowledge. For instance, the nicotinic receptor subunit alpha6 "is a" nicotinic receptor subunit, and is "part of" the (alpha6)2(beta2)3 nAChR. Each term can be the child of several others. Therefore the complete picture is not a genealogical tree, but rather a network or relationships.
There are several biological ontologies, the most famous (and complete) being Gene Ontology ([9,10]). Numerous other projects can be found at repository of the Open Biological Ontologies ([11]). See the discussion below for the relation between Gene Ontology and DopaNet Neuronal Ontology. In DopaNet Neuronal Ontology, a "molecular complex" is defined as a "stable assembly of molecules". An obvious example of a molecular complex is a protein. A molecular complex contains one or several components. Those components are all "molecule", a molecule being defined as a "set of atoms linked together by covalent bounds". For instance, "(alpha4)_2(beta2)_3 nAChR" is a "nicotinic acetylcholine-gated receptor". It is made up of two components: "alpha4 nicotinic receptor subunit" and "beta2 nicotinic receptor subunit", that are present in the "molecule" subtree as "nicotinic receptor subunit". The "polypeptide" subtree of "molecule" is built following the sequence resemblances, and then the 3D structure, similarly to the Structural Classification Of Protein database ([12,13]) and InterPro ([14,15]). Contrary to the "molecular complex" branch, the "molecule" branch should not contain any group based on the function.
Construction and content
Molecular page structure
A Molecular Page is made up of a header followed by several lists, each list containing a sequence of identical elements. There are currently twelve main lists, described below. Several other lists of items are used to described the page data at a finer level. According to the molecular complex described in the page, some of the lists can be empty.
The Molecular Page header contains the name of the molecular complex described in the page, an abbreviation, the unique ontology code used to identify the page, the dates of creation and last modification of the page, and the page status. The possible status are:
stable
The Molecular Page has been submitted by the maintainers and is ready for public release.
unstable
A new version of the Molecular Page, not yet ready for public release.
forthcoming
A new Molecular Page in construction, that has never been submitted for public release.
For instance, the nicotinic acetylcholine receptor (α4)2(β2)3 is described in the Page describing the complex "(alpha4)_2(beta2)_3 nAChR" . The related Ontology term can be found at
<molecularPage DopaNetontology="DA:0000027"
abbreviation="nAChRa4_2b2_3"
creation="2003-01-05T00:00:00"
modification="2004-09-02T15:49:41"
name="(alpha4)_2(beta2)_3 nAChR"
status="stable">
The main lists that compose a Molecular Page are:
List of maintainers
Maintainers are the only people authorized to directly modify the Molecular Pages. They are responsible for the quality and the completeness of the data included in the Page. However, maintainers are not assumed to systematically gather the information all by themselves. They are encouraged to contact experts to help them. Helpful people should be acknowledged as contributors.
List of contributors
Contributors are all the people who bring new information about a Molecular Page, or correct an existing piece of information. Contributors can be seen as the equivalent of authors of an article. Except maintainers (who are contributors by definition), they cannot directly modify a Molecular Page. They have to contact a maintainer instead. Note that the database administration team can directly modify the Molecular Pages to comply with the guidelines.
List of components
A Molecular Page describes a molecular complex. This complex is made up of components (at least one). The listOfComponents describes those components, their stoichiometry, and lists useful related resources. Each component is annotated by its ontology code.
The complex "(alpha4)_2(beta2)_3 nAChR" [DA:0000027] is made up of two components, the subunit α4 and the subunit β2.
<listOfComponents>
<component DopaNetontology="DA:0000188"
name="alpha4 nicotinic receptor subunit"
stoichiometry="2">
<listOfResources>
<resource identifier="ACHa4hosa"
name="Ligand-Gated Ion Channel database"
references="1"
url="">
<taxon>Homo sapiens</taxon>
</resource>
</listOfResources>
</component>
</listOfComponents>
List of states
The function of a molecular complex is most often modulated by permutations between various states (conformational transitions, covalent modifications etc.). Accordingly, most of the quantitative data are actually relevant only for one state or a subset of states. Those states should therefore be listed, described and annotated. The quantitative data described in the "functional" lists (see below) will refer both to the states of the molecular complex itself, listed here, and the list of states of other relevant Molecular Pages.
The complex "(alpha4)_2(beta2)_3 nAChR" may exist under (at least) three different states: "basal", "active", and "desensitized".
<listOfStates>
<state identifier="basal" name="basal">
<description>
In the basal state, the ionic pore is closed. This state displays a weak affinity for agonists such as acetylcholine or nicotine.
</description>
</state>
</listOfStates>
List of generic properties
A list of properties that depends solely on the molecular complex itself, and not on its relationships with other entities, such as ligands or substrates. Example of such properties are molecular weight or Stoke radius.
<listOfGenericProperties>
<property name="MW" stateMolecule="basal">
<taxon>Homo sapiens</taxon>
<listOfValues>
<value mean="310971" unit="Dalton">
<comment>without covalent modifications.</comment>
</value>
</listOfValues>
</property>
</listOfGenericProperties>
List of cells
The distribution of the molecular complex and its components is described within the relevant DopaNet target cells: cortical glutamatergic pyramidal neuron, mesencephalic dopaminergic neuron, striatal cholinergic interneuron, striatal enkephalinergic/GABAergic medium spiny neuron, striatal substance p/GABAergic medium spiny neuron. It is likely that a listOfExtracellular shall be necessary at some point.
Each cell is divided into compartments, where the distribution of transcripts and molecules can be described. The approach used to explore the distribution is specified, since both the accuracy and the quantitativeness of the observations strongly depends on the method chosen. As for all the following data, the species where the study has been conducted is also mandatory.
One entry in the complex "(alpha4)_2(beta2)_3 nAChR" is the fact that in the cell soma of the rat mesencephalic dopaminergic neuron, single cell RT-PCR experiments showed that α4 is present in 100% of neurons and β2 is probably also present in 100% of neurons.
<listOfCells>
<cell cellName="mesencephalic dopaminergic neuron" DopaNetontology=" DA:0000702">
<listOfCompartments>
<compartment DopaNetontology="DA:0000137" name="cell soma">
<listOfTranscripts>
<transcript method="single cell RT-PCR" references="17">
<taxon>Rattus norvegicus</taxon>
<description>
a4 is present in 100% of neurons. b2 is probably also present in 100% of neurons.
</description>
</transcript>
</listOfTranscripts>
</compartment>
</listOfCompartments>
</cell>
</listOfCells>
List of ligands
The ligands of a molecular complex are molecules or ions that bind to it. The size of the ligand relative to the molecular complex is irrelevant. Within the Molecular Page of "transforming growth factor receptor type I", one ligand is "transforming growth factor betal". Conversely, in the Molecular Page of "transforming growth factor beta1", one ligand is "transforming growth factor receptor type I"! See table 1 for an example of receptor-ligand reversion. The endogenous ligands are identified by their ontology code.
Functional parameters such as kon, koff or Km can be stored in a controlled manner, in order to be easily retrieved later. Whenever possible, the quantitative values are related to the states of the molecular complexes involved, not only the state of the molecular complex subject of the Molecular Page but also the state of the ligand. This remark holds for the substrates, the translocators and the modulated substances as well (see below). See table 1 for an illustration of the use of state references.
The desensitized "(alpha4)_2(beta2)_3 nAChR" of the rat binds acetylcholine with a Ki versus the epibatidine of 8.6 ± 1.98 nM.
<listOfLigands>
<ligand DopaNetontology="DA:0000184"
name="acetylcholine"
origin="endogenous">
<listOfProperties>
<property name="Ki_epibatidine"
references="10 18"
stateMolecule="desensitized">
<taxon>Rattus norvegicus</taxon>
<listOfValues>
<value mean="8.6" sd="1.98" unit="nanomole per litre"/>
</listOfValues>
</property>
</listOfProperties>
</ligand>
</listOfLigands>
List of substrates
All substances modified as a result of an interaction with the molecular complex. The parameters stored here are for instance Km, kcat or Vmax.
List of translocators
The translocators are substances that go from one subcellular compartment to another, the translocation being mediated by the molecular complex. Typical parameters are conductance or relative permeability.
The active complex "(alpha4)_2(beta2)_3 nAChR" of the rat translocates cations with a conductance of 13.3 ± 1.5 pS.
<listOfTranslocators>
<translocator DopaNetontology="DA:0000264"
name="cation"
origin="endogenous">
<listOfProperties>
<property name="conductance" references="14" stateMolecule="active">
<taxon>Rattus norvegicus</taxon>
<listOfValues>
<value mean="13.3" sd="1.5" unit="picosiemens"/>
</listOfValues>
</property>
</listOfProperties>
</translocator>
</listOfTranslocators>
List of modulated
In many case, one knows about the effect of a molecular complex on a substance, without knowing the detailed mechanism of action. The modulated entries are to be avoided as much as possible, since they generally reflect a set of binding and/or enzymatic events.
List of transitions
Possible conversions between the states described in the listOfStates, such as a conformational transition, or a covalent modification.
The complex "(alpha4)_2(beta2)_3 nAChR" undergoes conformational transitions between the basal and active states.
<listOfTransitions>
<transition state 1="basal" state2="active">
<comment>
In the absence of ligand, the equilibrium is strongly displaced toward the basal state. Agonists, such as acetylcholine and nicotine, stabilise the active state and shift the equilibrium. The transition from basal to active corresponds to an opening of the ionic pore.
</comment>
</transition>
</listOfTransitions>
List of bibitems
The list of bibliographic resources used to gather and annotate the data. Each piece of data included in the Molecular Page should be linked to those bibliographic items by internal references.
Molecule Page storage
Molecular Pages are saved as XML files [16], and their structure is described by an XML schema [17] available at
Molecular Pages XML files are stored within two different repositories, depending on the status of the Page. One repository contains only the stable Pages ready for public release (22 pages as of October 21, 2004), while the other repository contains also the unstable and the forthcoming Pages (39 pages as of October 21, 2004).
In addition to the two XML repositories, there is also a third HTML repository, containing the human-readable HTML versions of the stable Pages, automatically generated from their XML counterparts using XSL Transformations [18] with the Xalan processor [19]. Based on the identifier attributes, this processing generates the links within the Molecular Pages, but also between different, although related, Molecular Pages.
Processing
As described above, Molecular Pages are continuously modified and updated by the maintainers, with the help of the contributors. In order to automatize, safe-guard and simplify as much as possible the work required by a maintainer to create and edit a Molecular Page, an application called the DopaNet Web Service has been designed and implemented, which provide functionalities to:
1. authenticate Page maintainers
2. browse pages by maintainers
3. grant exclusive Page editing rights to a maintainer
4. create and edit a Page via a rich user interface
5. save or submit the edited Page, setting the "stable/unstable" status
The DopaNet Web Service is made of both server-side and client-side components, all written in Java, and communicating via either the SOAP [20] or the HTTP protocol. The server is deployed into an Apache Tomcat server [21], while the clients are both a Java Applet and a collection of dynamic (Java Server Pages) and static (HTML) pages. The Applet provides a very rich interface to edit a Molecular Page, but due to the Applet technology limitations (security sandbox, download time, etc.), a form-based HTML Page editor is current under development. Most of the Applet and the HTML editor components are derived directly from the DopaNet Molecular Page XML schema, using a mapping between XML schema types and Java GUI or HTML form widgets. Both server- and client-side, Molecular Page XML data is handled using Apache tools such as the Xerces parser [22] and the Xalan processor.
In addition to support the remote creation and editing of Molecular Pages, the DopaNet Web Service provide also functionalities to:
1. register new DopaNet contributors
2. update existing DopaNet contributor information
3. browse Molecular Pages by status
4. search Molecular Pages
Searching of Molecular Pages is implemented by using the API provided by the Apache Xindice [23] native XML database, which has proved to be adequate in terms of speed for the amount of data we currently have in DopaNet.
Utility and discussion
Although in their early stage of development, DopaNet Molecular Pages provide a unique source of structured, annotated quantitative data about the molecules involved in neuronal signaling. They will feed both the experimental biologist and the theoretician with the best available estimates for all kind of knowledge, whether biochemical, anatomical or functional. This will allow them to design better experiments or formal models, and to benchmark their results. As a side-effect triggered by the mandatory annotations, DopaNet Molecular Pages will also a bibliographic resource, each page being the equivalent of a small review of the literature.
DopaNet Neuronal Ontology
Gene Ontology is now a fully grown project, and is being widely used in several biological domains. Nevertheless, in its present form, Gene Ontology was not found suitable to be directly used by the DopaNet project. We hope to collaborate with Gene Ontology maintainers in the future. In particular, effort will be made to complete Gene Ontology in the area of Neurobiology. However, DopaNet Neuronal Ontology will never actually be a subset of Gene Ontology. Indeed, the purpose of the latter is to classify the gene products – and one of its most useful application so far has been the annotation of sequence database entries. The purpose of DopaNet Ontology is broader in term of knowledge, and not limited to the classification of gene products. At the same time it is focused onto a specific system, and therefore of interest for a narrower audience.
The Gene Ontology consortium defined three different vocabularies molecular function, biological process, and cellular component. Only the latter is at the moment relevant to DopaNet purposes, that is the Molecular Pages. However, it is anticipated that the biological process vocabulary will be needed in the near future, for instance to annotate electrophysiological data. DopaNet cellular component vocabulary is larger than Gene Ontology one, since it contains the different kinds of neuronal cells (see the Cell Type ontology [24]) In addition, one can foresee the need of other types of vocabularies to handle more integrated information such as mutant phenotypes, for instance Molecular function or vocabularies dealing with behaviors (other efforts have already started in that direction, see for instance the Mammalian Phenotype Ontology, ([25]).
A cellular component may be for instance an anatomical structure, e.g. "dendrite" or "synaptic vesicle" but also a cell or a protein. Note that a "molecule" is defined in the Neuronal Ontology as a set of atoms covalently linked. A molecule cannot contains other molecules. Hence, a protein made up of several subunits, or a polypeptide and a co-enzyme are not "molecules", but "molecular complexes". Although our ontology is built for DopaNet purposes, it can be viewed as a more general "Neuronal Ontology". Therefore, we incorporate terms related to components present (or events taking place) in any neuron, not necessarily DopaNet target cells. In particular, such additions are advised if they clarify some hierarchical relationship.
As described above, a "molecular complex" in DopaNet Neuronal Ontology contains one or several components, also present in the "molecule" branch. It could be considered redundant that all monomeric proteins are represented by two terms, as a "molecule" part of a "molecular complex". However, the meanings of the two branches are different. The "molecule" can be seen as an ideal entity, while the latter would rather represent an actual physical object of the cell. Moreover, the hierarchical structures of the two branches are different. In addition, a lot of proteins have only recently been discovered as functional complexes (e.g. the polymeric G-protein coupled receptors), and more are to be discovered. Finally, the systematic dissociation between the functional molecular complex and its components is handy when it comes to write the Molecular Pages.
Molecular Pages
The Alliance for Cellular Signaling was a pioneer in designing Molecule Page. Contrary to DopaNet Molecular Pages, their focus is truly a "molecule" rather than a "molecular complex". For instance, an heteropolymeric receptor will not be represented by a Molecule Page, but rather by a collection of Pages, one per subunit.
DopaNet Molecular Pages are highly structured. While this could appear as an obvious choice, it actually comes with a double burden. First, the edition interface has to be sufficiently complex to reflect the underlying structure. This complexity certainly acts as a repellent for the biologist who wish to build a Molecular Page. Second, the high quality required, in particular concerning the annotations, leads to the rejection of a significant portion of the published knowledge. However, we think that a piece of data that cannot be properly annotated is of limited use for the community. For instance, a large amount of pharmacological properties is published without the species. Since those properties vary from one species to the other, one cannot easily re-use the value provided. Similarly, a numerical piece of knowledge cannot be used without caution if one does not know the method used to collect it, and the variability attached to it. Currently, the access to the data is only possible through the web interface. Moreover, although the user is able to search the content of the Molecular Pages using various criteria, the result is always presented as one or several Molecular Pages. However, the DopaNet Web Service should be enriched on a steady pace, and specific pieces of data should be served soon. One can envision interfaces providing precise and meaningful responses to queries like "All Kd for the ligand X of all molecules that bind it", under the form of a list of Kd In addition, pieces of quantitative knowledge, like binding or enzymatic reactions, should be provided in standardized format such as the Systems Biology Markup Language [26].
The Molecular Pages are maintained in a distributed fashion, with one or several experts in charge of each complex. Such an approach is mandatory for two reasons. Firstly, the knowledge accumulated by the project will soon become much too large to be handled by one individual, or even one team. Secondly, the level of detail and accuracy sought by the resource is such that only experts can fruitfully mine the adequate literature for relevant information. To extract the simple affinity of a receptor for a ligand can be a daunting prospect. Not only that affinity can be expressed by various parameters with different meaning, Ka, Kd, Ki, Kp, IC50, but all those quantities can only be analyzed in regards of the knowledge about the various states of the complex, and its conformational transitions. The distributed annotation can cause concerns related to peer-validation and quality control. With the help of Nature Publishing Group, a peer review process has been set-up by the Alliance for Cellular Signaling to survey the edition of its Molecule Pages. Such an infrastructure is currently out of reach of DopaNet. However, we ensure that the maintainers are always recognized experts in the fields, or, for members of the EBI group, work in close relation. In addition, we included as much as possible guidance through the constraints imposed by the Page editing environment. That way, any Molecular Page complies with at least a minimal set of quality rules. Such an approach has already been successful in other areas. One of the most striking example is the Debian operating system project ([27]), that maintains around 9000 software packages for 11 computer architectures, with the help of about 1000 developers. The project has been running since 1993, and it is recognized as one of the most robust operating systems.
On the contrary of the Molecular Pages, the Neuronal Ontology is currently developed only by the EBI team. Everyone can contribute by sending their suggestions, but for the sake of coherence the final building is centralized.
Conclusions
DopaNet Molecular Pages allow to store annotated numerical data about molecular complexes involved in neuronal signaling. Although the Pages are one of the core resources of the DopaNet project, and therefore their focus on the mesotelencephalic dopamine system, the repository should be of widespread utility in the field of Systems Neurobiology. This is also the case of The DopaNet Neuronal Ontology. The resource is in its early stage of development and will benefit much from the feedback of users.
Availability and requirements
All data contained in the DopaNet Molecular Pages may be copied and redistributed freely, under terms derived from the MIT license [28].
More information about the DopaNet project can be found at the URL .
DopaNet ontology is available at the URL
DopaNet Molecular Pages are available at the URL
Authors' contributions
NLN designed the database DTDs and schemas, wrote the XSL and acted as the final editorial authority on Molecule Pages. MD implemented all the edition and validation software, as well as the user interface, including the servers.
Acknowledgements
This work was supported by the European Molecular Biology Laboratory , the European Science Foundation , the Centre National de la Recherche Scientifique and the Institut Pasteur . Special thanks to the initial contributors to the Molecule Pages database, Véronique Bernard, Bruno Giros, Denis Hervé, Amelia Sanchez-Capelo and Serge Schiffmann. Authors also thank Éric Fernandez for reading the manuscript.
Figures and Tables
Table 1 Use of internal and external state references This example (in pseudocode for sake of compactness) shows the how the notions of receptor and ligand are relative to the current Molecular Page. It also makes use of internal and external links to the various states of the interacting complexes. Note that each interaction is coded twice, although the stateMolecule and stateLigand attributes are reversed. Internal links are in italic, while external links are underlined.
molecularPage name="mol1"
listOfStates
state identifier="greedy"
state identifier="neutral"
ligand name="mol2" stateLigand="active"
property name="Kd" stateMolecule="greedy"
value mean="10" unit="nanomole per litre"
property name="Kd" stateMolecule="neutral"
value mean="1" unit="micromole per litre"
molecularPage name="mol2"
listOfStates
state identifier="active"
state identifier="inactive"
ligand name="mol1" stateLigand="greedy"
property name="Kd" stateMolecule="active"
value mean="10" unit="nanomole per litre"
ligand name="mol1" stateLigand="neutral"
property name="Kd" stateMolecule="active"
value mean="1" unit="micromole per litre"
==== Refs
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Mulder NJ R A Attwood TK Bairoch A Barrell D Bateman A Binns D Biswas M Bradley P Bork P Bucher P Copley RR Courcelle E Das U Durbin R Falquet L Fleischmann W Griffiths-Jones S Haft D Harte N Hulo N Kahn D Kanapin A Krestyaninova M Lopez R Letunic I Lonsdale D Silventoinen V Orchard SE Pagni M Peyruc D Ponting CP Selengut JD Servant F Sigrist CJA Vaughan R Zdobnov EM Systems biology in neuroscience: bridging genes to cognition Nucleic Acids Res 2003 31 315 318 12520011 10.1093/nar/gkg046
Extensible Markup Language (XML) 1.0 Third
XML Schema Part 0: Primer
XSL Transformations (XSLT) Version 1.0
Apache Xalan Java
SOAP Version 1.2 Part 0: Primer
Apache Tomcat
Apache Xerces Java
Apache Tomcat
Cell Type Ontology
Mammalian Phenotype Ontology
Hucka M Finney A Sauro HM Bolouri H Doyle JC Kitano H Arkin AP Bornstein BJ Bray D Cornish-Bowden A Cuellar AA Dronov S Gilles ED Ginkel M Gor V Goryanin II Hedley WJ Hodgman TC Hofmeyr JH Hunter PJ Juty NS Kasberger JL Kremling A Kummer U Le Novère N Loew LM Lucio D Mendes P Minch E Mjolsness ED Nakayama Y Nelson MR Nielsen PF Sakurada T Schaff JC Shapiro BE Shimizu TS Spence HD Stelling J Takahashi K Tomita M Wagner J Wang J The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models Bioinformatics 2003 19 524 531 12611808 10.1093/bioinformatics/btg015
Debian free operating system
MIT License
| 15518589 | PMC535554 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Nov 1; 5:174 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-174 | oa_comm |
==== Front
BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-421551129110.1186/1471-2148-4-42Research ArticleAdaptive evolution of transcription factor binding sites Berg Johannes [email protected] Stana [email protected]ässig Michael [email protected] Institute for Theoretical Physics, University of Cologne, 50937 Cologne, Germany2004 28 10 2004 4 42 42 20 7 2004 28 10 2004 Copyright © 2004 Berg et al; licensee BioMed Central Ltd.2004Berg 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
The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks in the evolution of complex regulatory networks. We study a theoretical model for the sequence evolution of binding sites by point mutations. The approach is based on biophysical models for the binding of transcription factors to DNA. Hence we derive empirically grounded fitness landscapes, which enter a population genetics model including mutations, genetic drift, and selection.
Results
We show that the selection for factor binding generically leads to specific correlations between nucleotide frequencies at different positions of a binding site. We demonstrate the possibility of rapid adaptive evolution generating a new binding site for a given transcription factor by point mutations. The evolutionary time required is estimated in terms of the neutral (background) mutation rate, the selection coefficient, and the effective population size.
Conclusions
The efficiency of binding site formation is seen to depend on two joint conditions: the binding site motif must be short enough and the promoter region must be long enough. These constraints on promoter architecture are indeed seen in eukaryotic systems. Furthermore, we analyse the adaptive evolution of genetic switches and of signal integration through binding cooperativity between different sites. Experimental tests of this picture involving the statistics of polymorphisms and phylogenies of sites are discussed.
==== Body
Background
The expression of a gene is controlled by other genes expressed at the same time and by external signals, a process called gene regulation [1]. Due to the combinatorial complexity of regulation, a large number of functional tasks can be performed by a limited number of genes. Differences in gene regulation are believed to be a major source of diversity in higher eukaryotes.
To a large extent, gene regulation is the control of transcription. It is accomplished by a number of regulatory proteins called transcription factors that bind to specific sites on DNA. These binding sites contain about 10 – 15 base pairs relevant for binding and are mostly located in the cis-regulatory promoter region of a gene. A cis-regulatory region in E. coli is about 300 base pairs long and contains a few transcription factor binding sites [2]. There may be two or more sites for the same factor in one promoter region. At the same time, the sequences of binding sites are fuzzy, that is, different sites for the same factor differ by about 20 – 30 percent of the bases relevant for binding [2]. This makes the identification of sites a difficult bioinformatics problem [3-5]. Frequently, the simultaneous binding at two nearby sites is energetically favoured. This so-called binding cooperativity can be related to various functions. In a genetic switch such as the famous phage lambda switch in Escherichia coli [6], it produces a sharp increase of the expression level at a certain threshold concentration of a transcription factor. A pair of sites for two different kinds of factors with cooperative binding can be a simple module for signal integration, leading to the expression of the downstream gene only when both kinds of factors are present simultaneously [1]. These examples are discussed in more detail below. Regulation in higher eukaryotes shares these features but is vastly more complicated [7]. A promoter region is typically a few thousand base pairs long and contains many different binding sites with often complex interactions. At the same time, individual sites are shorter, with about 5–8 relevant base pairs. The sites are sometimes organized in modules interspersed between regions containing no sites. In many known cases, the expression of a gene depends on the simultaneous presence of several factors. Well-studied examples of regulatory networks in eukaryotes include the sea urchin Strongylocentrotus purpuratussea [8] and the early developmental genes in Drosophila [9].
The sequence statistics of binding sites has been addressed in two recent theoretical studies [10,11]. Based on a model incorporating the biophysics of sequence-factor interaction [12,13], a fitness landscape for binding site sequences is constructed (see the discussion in the next section). The resulting mutation-selection equilibrium is analysed using a mean-field quasispecies approach [14]. This approach, which neglects the effects of genetic drift, is applicable in very large populations. In both studies [10,11], fuzziness is attributed to mutational entropy as a possible reason: the single or few sequence states with optimal binding of the transcription factor can be outweighed by the vastly higher number of sub-optimal states at some mutational distance from the optimal binding sequence. This effect is similar to the fuzziness of amino acid sequences in proteins discussed in [15].
From an evolutionary perspective, explaining the molecular programming of regulatory networks presents a striking problem. The diversification of higher eukaryotes, in particular, requires the efficient generation and alteration of regulatory binding interactions. One likely mode of evolution is gene duplications with subsequent complementary losses of function in both copies [16,17]. However, the differentiation of regulation should also require complementary processes that generate new functions of genes as a response to specific demands. This task must be accomplished mainly by sequence evolution of regulatory DNA. There are examples of highly conserved regulatory sequences with a conserved function but binding sites can also appear, disappear, or alter their sequence even between relatively closely related species; see, e.g., refs. [18-22]. This turnover of binding sites has been argued to follow an approximate molecular clock in Drosophila [23]. The transcription factors themselves are known to remain more conserved, especially if they are involved in the regulation of more than one gene.
The modes of regulatory sequence evolution and their relative importance remain largely to be explored. Contributions may arise from point mutations, slippage processes [24], and larger rearrangements of promoter regions [25]. The latter processes may lead to the shuffling of entire modules of binding sites between different genes. In this paper, we are more interested in the local sequence evolution within a module, which has been argued to contribute most of the promoter sequence difference between species [26]. It is also the most promising starting point for a quantitative analysis of binding site evolution. We study a theoretical model that takes into account point mutations, selection, and genetic drift. The form of selection is inferred from the biophysics of the binding interactions between transcription factors and DNA. We derive the stationary distribution of binding sites under selection, which shows specific correlations between nucleotide frequencies at different positions in a binding site. The non-stationary solutions of the model describe efficient adaptive pathways for the molecular evolution of regulatory networks by point mutations. This efficiency can be quantified in terms of the length of the binding motif, and the length of the promoter region, and the fitness landscape for factor binding, which is amenable to quite explicit modeling.
With the parameters found in natural systems, our model predicts that a new binding site for a given transcription factor can be generated by a fast series of adaptive substitutions, even if the expression of the corresponding gene bears even a modest fitness advantage. The evolutionary time required for site formation in response to a newly arising selection pressure is estimated in terms of the characteristic time scales of mutation, selection, and drift. For Drosophila, it may be as short as 105 years even for moderate selection pressures. However, this pathway is found to depend crucially on the presence of selection. It would be too slow under neutral evolution, in contrast to the results of [7], see also the recent discussion in [27]. Cooperative interactions between binding sites can evolve adaptively on similar time scales, as we show for the two simple examples alluded to above, the genetic switch and the signal integration module. These results are discussed at the end of the paper with particular emphasis on possible experimental tests.
Factor binding and selection
The binding energy (measured in units of kBT) between a transcription factor and its binding site is, to a good approximation, the sum of independent contributions from a small number of important positions of the binding site sequence, , with ≈ 10 - 15 [28-30]. The individual contributions εi depend on the position i and on the nucleotide ai at that position. There is typically one particular nucleotide preferred for binding; the sequence is called the target sequence. The target sequence can be inferred as the consensus sequence of a sufficiently large number of equivalent sites. The so-called energy matrix εi(a) has been determined experimentally for some factors from in vitro measurements of the binding affinity for each single-nucleotide mutant of the target sequence. Typical values for the loss in binding energy are 1–3 kBT per single-nucleotide mismatch away from the target sequence. In this paper, we use the further approximation εi = ε if ai = and ε = 0 otherwise, the so-called two-state model [12]. The binding energy of any sequence is then, up to an irrelevant constant, simply given by its Hamming distance r to the target sequence: E/kBT = εr. (The Hamming distance is defined as the number of positions with a mismatch ai ≠ .)
It is important to note the status of this "minimal model" of binding energies for the discussion in this paper. Both approximations underlying the model can be violated. Even though typical mismatch energies are of the same order of magnitude, there can be considerable differences between different substitutions at one position and between different nucleotide positions. Moreover, deviations from the approximate additivity of binding energies for the single nucleotide positions have also been observed. However, these complications do not affect the order-of-magnitude estimates for adaptive sequence evolution. As it will become clear, the efficiency of binding site formation depends only on the qualitative shape of the fitness landscapes derived below. In these landscapes, the regime of weakly-binding sequences and of strongly-binding sequences are separated by only a few single nucleotide substitutions. The relative magnitude of the fitness increase of these substitutions does not matter in first approximation. Indeed, inhomogeneities in the values of the εi(a) tend to reduce the number of crucial steps in the adaptive process and thereby to further increase its speed.
Within the two-state model, the binding probability of the factor in thermodynamic equilibrium is
Here ε is the binding energy per nucleotide mismatch and ερ is the chemical potential measuring the factor concentration. Both parameters are expressed in units of kBT and hence dimensionless. Appropriate values for typical binding sites have been discussed extensively in refs. [10,13]. It is found that ε should take values around 2, which is consistent with the measurements for known transcription factors mentioned above [28-30]. The chemical potential depends on the number of transcription factors present in the cell, on the binding probability to background sites elsewhere in the genome (which have a sequence similar to the target sequence by chance), and on the functional sites in the in the genome other than the binding site in question that may compete for the same protein. Binding to background sites does not significantly reduce the binding to a specific functional site [13]. This leads to values ρ ≈ (log nf)/ε ≈ 2 - 4, given observed factor numbers nf of about 50 – 5000 [13]. Binding to other copies of the same functional sequence becomes only relevant at low factor concentrations and high number of copies, when sites compete for factors.
A fitness landscape quantifies the fitness F of each sequence state at the binding site. Fitness differences arise due to different expression levels of the regulated gene, and these in turn depend on the binding of the transcription factors. It is only these fitness differences that enter the population dynamics of binding site sequences in the next section. Following the conceptual framework of ref. [10], we assume that the environment of the regulated gene can be described by a number of cellular states (labelled by the index α) with different transcription factor concentrations, i.e., with different chemical potentials ρα. These cellular states can be thought of as different stages within a cell cycle. In each state, the fitness depends on the expression level of the regulated gene in a specific way. This expression level is determined by the binding probability pα of the transcription factor. Assuming that both dependencies are linear (this is not crucial) and that the cellular states contribute additively to the overall fitness F, we obtain
Here the selection coefficient sα is defined as the fitness difference (due to different expression of the downstream gene) between the cases of complete factor binding and no binding in the state α. Such fitness differences can now be measured directly in viral systems [31]. Inserting (1), the fitness becomes a function of the Hamming distance r only. We note that the fitness F is measured relative to that of a phenotype with zero binding probability in any state α.
In a simple case, there are just two relevant cellular states. The on state favours expression of the gene, the off state disfavours it. It is then natural to assume selection coefficients of similar magnitude; here we take for simplicity s = son = -soff > 0. We then obtain a crater landscape,
with a high-fitness rim between ρoff and ρon flanked by two sigmoid thresholds; see fig. 1(a). The generic features of this fitness landscape are easy to interpret: the two-state selection assumed here favors intermediate binding strength (i.e., intermediate Hamming distances r) where binding occurs and the gene is expressed in the on state but not in the off state. Sequences with large Hamming distance r >ρon can bind the factor neither in the on nor in the off state, while sequences with r <ρoff lead to binding in the on and the off state. Both cases lead to misregulation of the downstream gene, and hence to a lower fitness. We note that the key feature of these fitness landscapes, the sigmoid thresholds, is independent of the particular choices of son and soff.
Figure 1 Fitness landscapes and adaptive evolution for a single binding site (a) Crater landscape (3) and (b) Mesa landscape (4), as a function of the Hamming distance r from the target sequence (within the approximation of the two-state model). rm gives the point where the binding probability reaches a maximum (crater landscape), or else values close to 1 (mesa landscape). rs approximately indicates the onset of selection, i.e. a binding probability appreciably different from zero. (c) Adaptive dynamics as a function of time t measured in units of 1/(2sμN) in the crater landscape at strong selection (sN = 100). Single history r(t) (dashed lines), ensemble average (thick solid lines) and width given by the standard deviation curves ± δr(t) (thin solid lines), (d) Same as (c) in the mesa landscape at moderate (sN = 6.8) selection, (e) Stationary ensembles Pstat(r) of binding site sequences with in the crater landscape at strong selection (filled bars) and for neutral evolution (empty bars). (f) Same as (e) in the mesa landscape at moderate selection, together with the histogram of Hamming distances of CRP site sequences in E. coli from their consensus sequence (diamonds, from [10]).
An even simpler fitness landscape is obtained if only the on state contributes significantly to selection, i.e., if s = son > 0 and soff = 0. The crater landscape then reduces to the mesa landscape discussed in [10,32],
which has a high-fitness plateau of radius ρ and one sigmoid threshold; see fig. 1(b). In this case, all sequences with sufficiently small Hamming distance to the target sequence (r <ρon) have a high fitness. In both cases, the parameters of the binding model have a simple geometric interpretation: ε gives the slope and the ρα give the positions of the sigmoid thresholds in the fitness landscape. Eqs. (3) and (4) are again to be understood as minimal models of fitness landscapes for binding sites, representing target sequence selection for a given level of binding (ρoff <r <ρon) and for sufficiently strong binding (r <ρon), respectively. Despite its simplicity, this type of selection model based on biophysical binding affinities is nontrivial from a population-genetic viewpoint since it leads to generic correlations between frequencies of nucleotides ai and aj within a site, see the Results section below. We will also study generalized models with correlations between two sites generated by cooperative binding. On the other hand, these models neglect the context dependence of the binding process through cofactors and chromatin structure. However, they are a good starting point for order-of magnitude estimates of the adaptive evolution of binding sites.
Mutation, selection, and genetic drift
The rates of nucleotide point mutation show a great variation, ranging from μ ~ 10-4 per site and generation for RNA viruses to values several orders of magnitude lower in eukaryotes, e.g. μ ≈ 2 × 10-9 in Drosophila [33]. (Here we model mutation as a single-parameter Markov process; we do not distinguish between transitions and transversions.) The evolution of a sufficiently large population under mutation and selection can be described in terms of the average fraction of the population with a given binding sequence. This so-called mean-field approach neglects the fluctuations due to finite population size (genetic drift). It leads to the so-called quasispecies theory [14]. For a population of sequences at a single binding site, the quasispecies population equation can be written for the fraction n(r,t) of individuals at Hamming distance r from the target sequence at time t. Along with a generalisation for two binding sites, it has been analysed in detail in ref. [10]. For the mesa landscape, the stationary solution nstat(r) has been found exactly [32]. It depends only on the ratio s/μ and describes a stable polymorphic population, i.e., several sequence states coexist. The mean-field approach is valid as long as the stochastic reproductive fluctuations are leveled out by mutations. This requires absolute population numbers Nnstat(r) ≫ 1/μ for all relevant r, a stringent condition on the total population size N.
This paper is concerned with a different regime of population dynamics, as described by the Kimura-Ohta theory for finite populations evolving by stochastic fluctuations (genetic drift) and selection [34-36]. According to this theory, a new mutant with a fitness difference ΔF relative to the pre-existing allele could spread to fixation in the population. This is a stochastic process, whose rate constant is given by
in a diffusion approximation valid for ΔF ≪ 1 [37]. Here N is the effective population size (with an additional factor 2 for diploid populations). Eq. (5) has three well-known regimes. For substantially deleterious mutations (NΔF ≲ - 1), substitutions are exponentially suppressed. Nearly neutral substitutions (N|ΔF| ≪ 1) occur at a rate u ≈ μ approximately equal to the rate of mutations in an individual. For substantially beneficial mutations (NΔF ≳ 1), the substitution rate is enhanced, with u ≃ 2μNΔF for NΔF ≫ 1.
In this picture, a population has a monomorphic majority for most of the time and occasional coexistence of two sequence states while a substitution is going on. The time of coexistence is T ~ N for nearly neutral and T ~ 1/ΔF for strongly beneficial substitutions. The picture is thus self-consistent for Tu ≪ 1, i.e., for μN ≪ 1. Asymptotically, it describes monomorphic populations moving through sequence space with hopping rates u.
Introducing an ensemble of independent populations, this stochastic evolution takes the form of a Master equation. For a single binding site, we obtain
Here P(r,t) denotes the probability of finding a population at Hamming distance r from the target sequence, and ur,r' is given by (5) with ΔF = F(r') - F(r). The combinatorial coefficients arise since a sequence at Hamming distance r can mutate in different ways that increase r, and in r ways that decrease r, where c = 4 is the number of different nucleotides. The stationary distribution is
Pstat(r) ~ exp[S(r) + 2NF(r)]. (7)
Here is the mutational entropy (the log fraction of sequence states with Hamming distance r) [32] and we have used the exact result ur+1,r/ur,r+1 = e2(N - 1)ΔF. To derive (7), we then simply approximated N - 1 by N. The form of Pstat(r) reflects the selection pressure, i.e., the scale s of fitness differences in the landscape F(r). For neutral evolution (2sN = 0), the stationary distribution
is obtained from a flat distribution over all sequence states. For moderate selection (2sN ~ 1), Pstat(r) results from a nontrivial balance of stochasticity and selection. For strong selection (2sN ≫ 1), Pstat(r) takes appreciable values only at points of near-maximal fitness, where F(r) ≳ Fm - 1/2sN. In this regime, the dynamics of a population consists of beneficial mutations only, i.e., the system moves uphill on its fitness landscape.
The Master equation (6) and the mean-field quasispecies equation thus describe opposite asymptotic regimes, μN ≪ 1 and μN ≫ 1, of the evolutionary dynamics. Effective population sizes show a large variation, from values of order 109 in viral systems to N ~ 106 in Drosophila and N ~ 104 - 105 in vertebrates. (These numbers bear some uncertainty; one reason is that TV varies across the genome [38].) We conclude that the mean-field quasispecies is well suited for viral systems, while eukaryotes clearly show a stochastic dynamics of substitutions.
Results and discussion
Stationary distributions and nucleotide frequency correlations
In the previous sections, we have expressed the fitness landscape and the resulting population distributions as a function of the Hamming distance r because it is a convenient parameterization of the binding energy in the two-state model. In order to compare this approach to standard population genetics, it is useful to recast eq. (7) for the elementary sequence states (a1,...,al),
where the sum runs over all sequence states at fixed r. At neutrality, the distribution over sequence states factorizes in the single nucleotide positions,
In the specific case of the two-state model, ν0(ai) is simply a flat distribution over nucleotides but it is obvious how this form can be generalized to arbitrary nucleotide frequencies.
According to eq. (7), the stationary distribution under selection takes the form
The salient point is that F(r) is generically a strongly nonlinear function of r due to the sigmoid dependence of the binding probability on r. An analogous statement holds beyond the two-state approximation for the dependence of F on the binding energy E. Hence, even if (a1,...,al) factorizes in the single nucleotide positions, (a1,...,al) does not. The selection introduces specific correlations between the nucleotides: the fitness differences and, hence, the nucleotide frequencies at one position i depend on all other it l - 1 positions in the motif.
Adaptive generation of a binding site
We now apply the dynamics (6) to the problem of adaptively generating a binding site in response to a newly arising selection pressure. We study a case of strong selection (sN = 100) in the crater fitness landscape (3) with parameters = 10, ε = 2, ρon = 3, ρoff = 1 (implying that the factor concentrations differ by a factor of 50), and a case of moderate selection (sN = 7) in the mesa landscape with parameters = 10, ε = 1, ρ = 3.6. (The mesa type may be most appropriate for factors with multiple binding sites such as the CRP repressor in E. coli, where binding to an individual site is negligible in the off state.) The fitness landscapes for both cases are shown in fig. 1(a),1(b) in units of the selection pressure s. Substantially beneficial mutations occur only on their sigmoid slopes, i.e., in narrow ranges of r. The upper boundary of this region is given by rs = ρon + log[sN(eε - 1)]/ε which takes typical values rs = 5 - 7. In fig. 1(c),1(d), we show a sample history of adaptive substitutions from r = 5 to lower values of r, which are close to the point rm of maximal fitness. The statistics of this adaptation is governed by the ensemble P(r, t); the average and the standard deviation δr(t) appear also in fig. 1(c),1(d). In the case of strong selection, the expected time of the adaptive process is readily estimated in terms of the uphill rates in (6),
and takes values of a few times 1/sμN. We emphasize again that this simple form depends only on the qualitative form of the fitness landscape, namely, that weakly and strongly binding sequence states are separated only by few point mutations. The conclusions are thus largely independent of the details of the fitness landscape, which justifies using the two-state approximation.
Can such a selective process actually happen? This depends on the initial state of the promoter region in question before the selection pressure for a new site sets in. The region is approximated as an ensemble of L1 = L - + 1 candidate sites undergoing independent neutral evolution, i.e., the simultaneous updating of sites by one mutation is replaced by independent mutations. The length of the promoter region is denoted by L. At stationarity, the Hamming distance at a random site then follows the distribution Pstat(r) ~ exp[S(r)] shown as empty bars in fig. 1(e),1(f). The minimal distance rmin in the entire region is given by the distribution , where is the cumulative distribution for a single site. is found to be strongly peaked, taking appreciable values only in the range around its average. We assume selective evolution sets in as soon as at least one site has a Hamming distance r ≤ rs. This is likely to happen spontaneously if , leading to a joint condition on , L, and rs. For , there is a neutral waiting time before the onset of adaptation. Its expectation value
is calculated in the appendix. It is generically much larger than the adaptation time Ts, rendering the effective generation of a new site less feasible.
The stationary distribution Pstat(r) under selection is given by (7) and shown as filled bars in fig. 1(e),1(f). For strong selection, it is peaked at the point rm of maximal fitness. For moderate selection, it takes appreciable values for r = 0 - 4: the binding site sequences are fuzzy. Assuming that the CRP sites at different positions in the genome of E. coli have to a certain extent evolved independently, we can fit Pstat(r) with their distance distribution (data taken from [10]). At the values of ε and ρon chosen, the two distributions fit well, see fig. 1(f). This finding is discussed in more detail below.
Adaptation of binding cooperativity
The cooperative binding of transcription factors involves protein-protein interactions which may be specific to the DNA substrate. These interactions often do not require conformational changes of either protein involved and depend only on few specific contact points. They result in a modest energy gain of order 3 – 4kBT [1]. Hence, it is a reasonable simplification to study the adaptive adjustment of binding affinities using a simple generalisation of the two-state binding model. We define the energies E1/kBT = εr1 and E2/kBT = εr2 for the binding of a single factor and for the simultaneous binding of both factors. The cooperativity gain is assumed to result from mutations at positions in the DNA sequences of the factors, which encode the amino acids at the protein-protein contact points. These mutations define a Hamming distance from the target sequence for optimal protein-protein binding, and 2γε/ is the binding energy per nucleotide. Here we use the values ε = 2, = 6 and γ = 1 but the qualitative patterns shown below are rather robust.
The resulting equilibrium probabilities for the four thermodynamic states (--) (both factors unbound), (+-) and (-+) (one factor bound), and (++) (both factors bound) are
q--,
q+- = q-- exp[-ε(r1 - ρ1)],
q+- = q-- exp[-ε(r2 - ρ2)], (14)
q++ = q-- exp[-ε(r1 + r2 - ρ1 - ρ2 - 2γ)],
with the normalisation q-- + q+- + q-+ + q++ = 1. The scaled chemical potentials ρ1 and ρ2 are independent variables if the two sites bind to different kinds of factors and are equal if they bind to the same kind. As before, the binding probabilities determine expression levels and, therefore, the fitness. Here we study only pairs of sites contributing additively to the expression level in each cellular state, where we have
Other important cases include activator-repressor site pairs such as the famous lac operon [39], where the transcription-factor induced expression level is proportional to q+-. The stochastic dynamics of substitutions is straightforward to generalise; it leads to a Master equation like (6) for the joint distribution P(r1, r2, , t). This higher-dimensional equation can again be solved exactly for its steady state
Pstat(r1, r2, ) ~ exp[S(r1) + S(r2) + S() + 2NF(r1, r2, )]. (16)
Here we discuss two simple examples of fitness landscapes where binding cooperativity evolves by adaptation to specific functional demands. A genetic switch with a sharp expression threshold is favoured in a system with a single transcription factor having similar concentrations in its on and off cellular state. As can be seen from eq. (14), cooperative binding can sharpen the response of the binding probability to variations in factor concentration, q++ ~ 1/[1 + exp(-2ερ + ...)] versus p ~ 1/[1 + exp(-ερ + ...)] as given by (1) for individual binding. Figs. 2(a),2(c) show the fitness landscape F(r1, r2, γ) obtained from (14) and (15) for ρon = 2.5, ρoff = 1.5, and s = son = -soff. A simple signal integration module responds to two different factors in four different cellular states, (on, on), (on, off), (off, on), (off, off). Individually weak but cooperative binding leads to expression of the gene only if both factors are present simultaneously. This case is favoured by a fitness function of the form (15) with selection coefficients s = -soff,off = -son,off = -soff,on = son,on/2. The resulting fitness landscape F(r1, r2, γ) is shown in figs. 2(b),2(d) for chemical potentials ρon = 3, ρoff = 1 (for each factor).
Figure 2 Fitness landscapes and adaptive evolution for a pair of sites with cooperative binding. Genetic switch (left column), signal integration module (right column). (a,b) Fitness landscape F(r1, r2) without cooperativity (γ = 0). (c,d) Fitness landscape. F(r1, r2) with cooperativity (γ = 1). Next-nearest neighbour states (r1, r2) and of similar fitness are linked by compensatory mutations if the intermediate states (r1, ) and (, r2) have lower fitness. (e,f) Adaptive dynamics: ensemble averages and (thick lines), ensemble width given by (same for r2 and (thin lines); cf. fig. 1(e,f).
In both cases, a pair of sites with weaker individual binding (r1, r2 = 3 - 4) and cooperativity (γ = 1) is seen to have a higher fitness than an optimal pair (r1 = r2 = 2) without cooperativity, as expected. Adaptive pathways and for strong selection (sN = 100) are shown in fig. 2(e),2(f). Typical adaptation times Ts are again a few times 1/(sμN). A closer look reveals that this fast adaptation sometimes leads to a metastable local fitness maximum with some degree of cooperativity. Compensatory mutations (see below) are then required to reach the global maximum, a process that may be considerably slower. The fuzziness δr1,2(t) and δγ(t) observed in fig. 2(e),2(f) decays on the larger time scale of compensatory mutations, reflecting the presence of such metastable states.
Conclusions
Transcription factors and their binding sites emerge as a suitable starting point for quantitative studies of gene regulation. Binding site sequences are short and their sequence space is simple. Moreover, the link between sequence, binding affinity, and fitness is experimentally accessible. For a single site, the simplest examples are of the mesa [10] or of the crater type, see fig. 1(a),1(b). Landscapes for a pair of sites with cooperative binding interactions are of a similar kind as shown in fig. 2(a),2(b),2(c),2(d). They can be used to predict the outcome of specific single-site mutation experiments to a certain extent.
Fast adaptation may generate or eliminate a new binding site
Despite this simplicity, the evolutionary dynamics of binding sites is far from trivial, since it is governed, in the generic case, by the interplay of three evolutionary forces: selection, mutation, and genetic drift. Here we have focused on the dynamical regime appropriate for eukaryotes, where the evolution can be approximated as a stochastic process of substitutions. We find the possibility of selective pathways generating a new site in response to a newly arising selection pressure, starting from a neutrally evolved initial state and progressing by point substitutions. Such a selective formation takes roughly Ts ≈ Δr/(2sμN) generations, where Δr is the number of adaptive substitutions required. This number is given by the Hamming distance between the onset of selection and the point of optimal fitness, Δr = rs - rm, and takes values 2 – 3 for typical fitness landscapes; see fig. 1(a),1(b). For Drosophila melanogaster, with μ ≈ 2 × 10-9 [33] and N ≈ 106, the resulting Ts is of the order of 106 generations or 105 years even for sites with a relatively small selection coefficient s = 10-3. Such selective processes are faster than neutral evolution by a factor of about 1000 and would allow for independent generation of sites even after the split from its closest relative Drosophila simulans about 2.5 × 106 years ago. Notice that new sites are more readily generated in large populations. As discussed above, generating a new site may also require a neutral waiting time to T0 until at least one candidate site in the promoter region of the gene in question reaches the threshold distance rs from the target sequence, where selection sets in. For site formation to be efficient, however, selection must be able to set in spontaneously, i.e., T0 must not greatly exceed the adaptive time Ts. This places a bound on the relevant length of the binding motif that can readily form in a promoter region of length L. Given L ≈ 300, for example, a motif with = 8 and rs = 3 could still allow for spontaneous adaptive site formation. (For longer motifs, corresponding to groups of sites with fixed relative distance, this pathway would require promoter regions of much larger L.) A more general case has recently been treated numerically in [27], where the dependence of the neutral waiting time on the G/C ratio of the initial sequence has been investigated. One may speculate that this adaptive dynamics is indeed one of the factors influencing the length of regulatory modules in higher eukaryotes. Clearly, the present model also allows for pathways of negative selection leading to the elimination of spurious binding sites in regulatory or non-regulatory DNA where the binding has an adverse fitness effect. This is important since under neutral evolution, candidate sites with a distance of at most rs from the target sequence occur frequently on a genome-wide scale. A recent study has indeed found evidence for such negative selection from the underrepresentation of binding site motifs over the entire genome [40].
Binding sites under selection have nucleotide frequency correlations
We have shown that under stationary selection the frequencies of nucleotides at any two positions of the binding sequence are correlated. For the two-state model, the correlations are the same for any pair of positions i ≠ j and can be computed exactly from the joint distribution (11). We emphasize that these correlations refer to an ensemble of independently evolving (monomorphic) populations and are not to be confused with linkage disequilibria within one population. This finding limits the accuracy of bioinformatic weight matrices, which are often assumed to factorize in the nucleotide positions even in the presence of selection.
Experimental tests: binding site polymorphisms and phylogenies
The predictions of our model lend themselves to a number of experimental tests. In the dynamical regime appropriate for eukaryotes (μN ≪ 1), populations should be monomorphic at most positions of their binding site sequences and polymorphic at a few. On the other hand, the quasispecies model discussed in refs. [10,11] (which assumes μN ≫ 1) may be most appropriate in viral systems. The intermediate regime μN ~ 1 with frequent polymorphisms and genetic drift could be realized in some bacterial systems and presents a challenge for theory. Thus it would be very interesting to compare the statistics of single-nucleotide polymorphisms at binding sites in eukaryotes, bacteria, and viruses. Polymorphism data are expected to contain evidence for adaptive evolution. However, statistical tests of selection must be modified for promoter sequences [40,41]. A recent study uses data on binding sites in three yeast species and deduces the rates of sequence evolution [42].
A complementary source of information are phylogenies of binding sites. Trees with functional differences between branches contain information on the generation of new sites or of interactions between sites and on the time scales involved. In a tree for a conserved site or group of sites with sufficiently long branches, the fuzziness of the sequences observed on different branches is given by the ensemble Pstat introduced above. For strong selection, Pstat lives on the quasi-neutral network of sequence states with maximal fitness, where two neighbouring sequence states are linked by neutral mutations or by pairs of compensatory mutations at two different positions. In the crater landscape for a single site, this quasi-neutral network consists of all sequences with a fixed distance r = rmax from the target sequence; see fig. 1(a). Beyond the two-state approximation for binding energies, it will be smaller since only some of the positions are energetically equivalent. For a group of sites, however, quasi-neutral networks can be larger since compensatory mutations can also take place at positions on different sites as shown in fig. 2(d) for the example of a signal integration module. This is consistent with experimental evidence that the sequence divergence between Drosophila melanogaster and Drosophila pseudoobscura involves compensatory mutations and stabilising selection between different binding sites [43].
For weaker selection, site fuzziness increases further since Pstat extends beyond the sequence states of maximal fitness and is influenced by mutational entropy. As shown in fig. 1(f), one can explain in this way the observed fuzziness in CRP sites of E. coli. It would then reflect different evolutionary histories of independent populations, rather than sampling in one polymorphic population as in the quasispecies picture of refs. [10,11]. (In a mean-field quasispecies, appreciable fuzziness occurs only for selection coefficients s ~ μ, minute in other than viral systems.) However, the data are also compatible with strong selection if the selection coefficients sα, and hence the value of rm, vary between different genes. Clearly, comparing Pstat with the distribution of sites in a single genome requires the assumption that the evolutionary histories of sites at different positions are at least to some extent independent. Future data of orthologous sites in a sufficient number of species will be more informative. Thus, further experimental evidence is needed to clarify the role of mutational entropy in the observed fuzziness.
Evolvability of binding sites
The present work was aimed at obtaining some insight into the molecular mechanisms and constraints underlying the dynamics of complex regulatory networks, thereby quantifying the notion of their evolvability. The programming of binding sites and of cooperative interactions between them is found to provide efficient modes of adaptive evolution whose tempo can be quantified for the case of point mutations. The formation of complicated signal integration patterns and of multi-factor interactions in higher eukaryotes, however, requires generalizing our arguments in two ways. There are further modes of sequence evolution such as slippage events, insertions and deletions, large scale relocation of promoter regions, and recombination. Our ongoing work is aimed at quantifying their relative importance in terms of substitution rates. Moreover, there are also more general fitness landscapes describing, e.g., binding sites interacting via the expression level of the regulated gene (such as activator-repressor site pairs) and the coupled evolution of binding sites in different genes.
The rapid evolution of networks hinges upon the existence of adaptive pathways for these formative steps with a characteristic time scale Ts ~ 1/(sμN) much smaller than T0 ~ 1/μ, the time scale of neutral evolution. The presence of these two time scales has a further interesting consequence. If the selection pressure on an existing site ceases, that site will disappear on the larger time scale T0. It is possible, therefore, that large existing networks have accumulated a considerable number of redundant regulatory interactions acquired by selection in their past. This may be one factor contributing to their robustness against perturbations.
Methods – neutral evolution of binding sites
To estimate the average neutral waiting time T0, we study the mutation dynamics in the restricted range r = rs + 1,...,, allowing mutations from rs + 1 to rs but suppressing mutations from rs back to rs + 1. We evaluate the time-dependent solution P(r, t) of the Master equation (6) with the initial condition P(r, 0) = Pstat(r), and the resulting cumulative probability . The current across the lower boundary, J(t) = μ(rs + 1)P(rs + 1,t) = -dQ/dt, determines the waiting time for a single site,
This is formally solved by expanding in eigenfunctions of the mutation operator. In the case relevant here, the system remains close to equilibrium since the boundary current is much smaller than typical currents for r ≥ rs. Hence, P(r,t) ≈ Pstat(r) exp(-λt) with λ = J(0)/Q(0) = μ(rs + 1)Pstat(rs + 1)/Qstat(rs + 1). We conclude that the waiting time for a single site is positive with probability Qstat(rs + 1), following a distribution ~exp(-λt), and 0 otherwise. The resulting expectation value is T0 = Qstat(rs + 1)/λ. For L1 independent sites, the distribution of positive waiting times is still exponential, and to is given by an expression of the form (17) with a total boundary current . This yields as given by (13). The average waiting time (in units of 1/μ) becomes large for values of rs in the tail of the distribution , where . This is the case for .
Authors' contributions
JB carried out analytical and numerical work, SW performed numerical work and data processing. ML conceived of the study, carried out analytical work, and coordinated the project. All authors read and approved the final manuscript.
Acknowledgements
We are indebted to N. Rajewsky and D. Tautz for interesting discussions, and to P. Arndt and U. Gerland for a critical reading of the manuscript. This work has been supported by DFG grant LA 1337/1-1.
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| 15511291 | PMC535555 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Oct 28; 4:42 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-42 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-441555507510.1186/1471-2121-5-44Research ArticleThe architecture of chicken chromosome territories changes during differentiation Stadler Sonja [email protected] Verena [email protected] Robert [email protected] Stefan [email protected] Christoph [email protected] Constanze [email protected] Thomas [email protected] Steffen [email protected] Department Biologie II, Biozentrum der Ludwig-Maximilians-Universität München, GroßhadernerStraße 2, 82152 Planegg-Martinsried, Germany2 Kirchhoff Institut für Physik, Universität Heidelberg, 69120 Heidelberg, Germany3 Molecular Haemopoiesis and Epigenetics Group, St. James's University Hospital, University of Leeds, Leeds LS9 7TF, UK2004 22 11 2004 5 44 44 14 7 2004 22 11 2004 Copyright © 2004 Stadler et al; licensee BioMed Central Ltd.2004Stadler 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
Between cell divisions the chromatin fiber of each chromosome is restricted to a subvolume of the interphase cell nucleus called chromosome territory. The internal organization of these chromosome territories is still largely unknown.
Results
We compared the large-scale chromatin structure of chromosome territories between several hematopoietic chicken cell types at various differentiation stages. Chromosome territories were labeled by fluorescence in situ hybridization in structurally preserved nuclei, recorded by confocal microscopy and evaluated visually and by quantitative image analysis. Chromosome territories in multipotent myeloid precursor cells appeared homogeneously stained and compact. The inactive lysozyme gene as well as the centromere of the lysozyme gene harboring chromosome located to the interior of the chromosome territory. In further differentiated cell types such as myeloblasts, macrophages and erythroblasts chromosome territories appeared increasingly diffuse, disaggregating to separable substructures. The lysozyme gene, which is gradually activated during the differentiation to activated macrophages, as well as the centromere were relocated increasingly to more external positions.
Conclusions
Our results reveal a cell type specific constitution of chromosome territories. The data suggest that a repositioning of chromosomal loci during differentiation may be a consequence of general changes in chromosome territory morphology, not necessarily related to transcriptional changes.
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Background
It is a longstanding observation that chromatin distribution in the interphase cell nucleus varies with the cell type. Flemming described differences in nuclear appearance in 1882 [[1], p.100]. Since then methodological advancements have made it possible to study nuclear chromatin architecture in much more detail. The spatial restriction of each chromosome to a limited area of the interphase nucleus, the chromosome territory, has been unequivocally demonstrated by fluorescence in situ hybridization (FISH) [2,3]. However, although progress has been made over the last decade [for reviews see [4-7]], the internal organization of chromosome territories is still largely unknown. Here we asked whether chromosome territories display differences between cell types in their internal chromatin organization.
We amended our experimental approach with the determination of the position of a gene locus relative to its chromosome territory. In several previous studies it was observed that a number of active genes located preferentially at the surface of their chromosome territories or even outside [8-12], while others noted that active genes could also be positioned in the chromosome territory interior [13]. A general labeling of transcription sites resulted in signals throughout chromosome territories [14,15] demonstrating that the periphery of chromosome territories is not the only region where transcription occurs. Fluorescence in situ hybridization (FISH) studies investigating the major histocompatibility complex MHC [10] or the epidermal differentiation complex EDC [11] showed looping beyond the surface of the chromosome territory upon activation in up to 25% of the cases. Both loci are in the megabase size range with multiple co-regulated genes. Even higher frequencies of a location outside the territory were described for a gene rich human region without coordinate gene expression on chromosome 11p15.5 [16] and for genes of the Hoxb cluster in mouse embryonic stem cells entering differentiation [12]. It has thus been suggested that strongly expressed genes may be on chromatin loops that loop to the periphery of the territory, while genes expressed at low levels may occupy either a more interior or a random position [13]. Difficult to interpret were data concerning looping out of the β-globin gene locus from its chromosome territory in mouse erythroleukemia cells. In unstimulated cells where the locus shows DNase-hypersensitive sites but is not yet expressed, nearly half of the loci looped out. In stimulated cells where expression occurs, however, this was found only in about a third of the cases [17]. Consistent with the looping out of endogenous loci found in FISH studies, an opening of GFP-labeled artificial chromosomal regions was observed upon transcriptional activation or binding of transcription factors [18-24].
So far, a correlation of gene activation with increasing looping-out from the chromosome territory has only been shown for gene clusters but not for single genes. In the present study we have chosen the chicken lysozyme gene (cLys), which is highly active in macrophages, as a model system to explore the possibility of positional changes during activation of a single gene. cLys does not have co-regulated neighbors. Recently the gene cGas41 was found only 200 bp downstream of the polyA-site of cLys. cGas41 is expressed on a low level in all chicken tissues and cell lines tested, including all cell lines used here [25]. Macrophage differentiation is an interesting model system for studies of cell fate decisions. As all blood cells, macrophages originate from pluripotent hematopoietic stem cells and develop via defined multipotent and then progressively restricted precursor types. The developmental regulation of lysozyme in this differentiation system is well characterized. Expression is not detectable in multipotent myeloid precursors, which are able to differentiate to either the erythroid, granulocytic or the macrophage lineage (Figure 1). The gene is also not expressed in the erythroid lineage. Expression is first detected at a low level in granulocyte-macrophage precursors (myeloblasts) and is further upregulated in macrophages. By the addition of bacterial lipopolysaccharide (LPS) to macrophages, another tenfold increase in lysozyme expression is caused [26]. Studies of cLys regulation were greatly facilitated by cell lines representing these differentiation stages [27,28].
Figure 1 Cell lines used in this study. Pluses and minuses indicate the expression state of the lysozyme gene. Colors are the same as used in Figures 4, 5 and 7.
The chicken karyotype consists of several pairs of so called macrochromosomes with sizes comparable to that of mammalian chromosomes and many much smaller microchromosomes [for review see [29,30]]. cLys is located on the short arm of chromosome 1 which is with about 190 Mbp comparable in length to human chromosome 4 [31].
In the present study, we investigated the large-scale chromatin organization of chromosome territories in well characterized cell lines representing the five chicken cell types described above (Figure 1). Multi-color 3D FISH was applied to cells with structurally preserved nuclei, followed by confocal microscopy and three-dimensional image analysis. We assessed the morphology of chromosome territories 1 and 8 by visual inspection and measured the dispersion of the painted territories in each cell type. We demonstrate that chromosome territory dispersal increases in the more differentiated cell types. Further, we determined the 3D positions of the chicken lysozyme gene domain (including cGas41) and the chromosome 1 centromere relative to their chromosome 1 territory. We found that not only the lysozyme gene domains but also the centromeres were mostly in the chromosome territory interior in multipotent myeloid precursor cells and relocated to the territory periphery in further differentiated cell types. In addition, we determined the radial positioning of the chromosome territories 1 and 8 within the nuclei of each cell type and measured nuclear volumes.
Results
The morphology of chromosome territories changes during differentiation
3D FISH was performed on formaldehyde fixed, structurally preserved nuclei. Visual examination of painted chromosome 1 and chromosome 8 territories revealed differences between the cell types (Figure 2, Figure 3). Territories in multipotent myeloid precursor cells were relatively compact and homogeneously stained (Figure 2b, Figure 3a). In proerythroblasts, territories were more diffuse and borders became less definable (Figure 2f,2g, Figure 3d). These changes cannot be explained with an increase in nuclear volume since nuclei of proerythroblasts were smaller than those of myeloblasts (see below and Figure 4). In a minority of proerythroblasts, in addition to the labeled territories our paint probe labeled DNA-clusters in the center of the nucleus. These clusters had low DNA counterstain but were often associated with strongly counterstained regions (see arrow in Figure 2g). The clusters may play a role in forming heterochromatin as observed during differentiation of human erythroid cells [4]. Chromosome territories in myeloblasts (Figure 2c, Figure 3b) appeared less compact than in precursors but more compact than in proerythroblasts. Territories in unstimulated macrophages (Figure 2d) had even more diffuse borders. In their interior we observed agglomerations of labeled DNA and lacunas in some cases. A maximum of dispersal was noted in stimulated macrophages (Figure 2e, Figure 3d). Here, territories had grooved, fuzzy surfaces and a heterogeneous label throughout. Lacunas were frequent in the larger chromosome 1 territories.
Figure 2 FISH with a chromosome 1 paint probe (red) and the lysozyme gene domain probe (green/yellow). (a) FISH on metaphase chromosomes. The chicken lysozyme gene domain is located on the short arm of chromosome 1. Note that the library probe mix used gives particularly strong signals at the centromeres (arrows). (b-g) 3D-FISH on structurally preserved nuclei. For each cell type, single confocal sections of one nucleus are shown. In b-f, nuclear outlines were drawn after the DNA counterstain which was omitted from the figure to avoid obstruction of the territory signals. In addition to the cLys domain signals, centromeres are in focus in some of the sections (arrows). (b) Multipotent myeloid precursor cell. (c) Myeloblast. (d) Macrophage without LPS-activation. (e) Macrophage with LPS-activation. On the right hand side, a threshold of 80 was applied to the territory signal of the central image to visualize disaggregation into several objects. While usually only few objects are present in any given focal plane, in this particular example the breakup is well recognizable. The algorithm applied in the calculations works on 3D-stacks, however. The macrophage cell line is aneuploid (see Methods), the cells shown in d and e have three territories with chromosome 1 material, each containing a cLys signal. (f) Erythroblast. (g) An additional section of the erythroblast shown in f visualizes a cluster of chromosome 1 material (red in left image) in a central nuclear area (arrow) with low DNA-counterstain but associated with a brightly stained region (see main text). Such clusters were less pronounced when only paint probes from early DOP-PCR-amplification rounds were used (see Methods for details). DNA counterstain is blue in left, gray in right image. Scalebar 5 μm for b-g. Whereas in precursor cells the lysozyme gene domain signal was found nearly always inside the territory, in differentiated cells more external positions were frequent. Note that the multipotent myeloid precursor cell has a relatively small nucleus and nuclear volume is increased in the further differentiated cell types.
Figure 3 3D-FISH on structurally preserved cell nuclei with paints for chromosome 1 (red) and chromosome 8 (green). For each cell type, two confocal sections of one nucleus are shown. Nuclear outlines were drawn after the DNA counterstain which was omitted to avoid obstruction of the territory signals. (a) Multipotent myeloid precursor. (b) Myeloblast. (c) Macrophage activated with LPS. (d) Proerythroblast. Scalebar 5 μm.
Figure 4 Nuclear volumes. Each dot indicates the volume of one nucleus. Nuclei from experiments with hybridization of chromosome 1 and cLys probes (left) and those with chromosome 1 and 8 probes (right) were sorted by size to allow an easier comparison by eye. See main text for mean values.
A comparison of chromosome territory surface and chromatin texture by visual inspection is bound to subjective influences. To allow an unbiased, quantitative evaluation of chromosome territory morphology, we counted the number of objects to which chromosome territories disaggregate at increasing threshold levels. With a computer program newly developed for this purpose (for details see Methods and Figure 2e) we could confirm the visual impression of relatively compact chromosome 1 territories in multipotent precursors by showing that at higher thresholds they disaggregate to smaller numbers of objects than the fuzzier territories of more differentiated proerythroblasts (Figure 5a). Statistical inferences about the means of the maximal number of objects in these three cell types were highly significant as determined by Analysis of Variance (one-way ANOVA; F(2,119) = 58.5, p < 0.001; see Methods for details). Post-hoc Sidak tests revealed a significant difference between precursor cells and myeloblasts (p = 0.013) and highly significant differences between proerythroblasts and the two other cell types (p < 0.001). Chromosome 1 territories in macrophages also yielded high numbers of objects. However, macrophages of the utilized cell line contain an additional fragment of the short arm of chromosome 1 and sometimes complete additional chromosomes 1. Due to this aneuploidy object numbers are biased towards higher numbers. Results are thus not directly comparable with other cell lines. Notably, in stimulated macrophages territories disaggregate into more objects than in unstimulated ones, suggesting that stimulation triggered a change to a more dispersed chromatin texture (p < 0.001). The stronger dispersion of chromosome 1 territories in more differentiated cells was confirmed in a second experimental series with painted chromosome 1 and chromosome 8 territories (Figure 3) in multipotent precursors, proerythroblasts, myeloblasts and activated macrophages (data not shown). The DNA content of chicken chromosome 8 is about 30 Mbp [31]. This is roughly one sixth of the DNA content of chicken chromosome 1 and about two thirds of the smallest human chromosome, 21. Chromosome 8 was diploid in all utilized cell lines. As expected due to its smaller size, it disaggregated in all cell types to a much smaller number of objects (Figure 5b). ANOVA analysis of all groups showed a highly significant aberration from the assumption of similar distributions in all cell types (F(3,116) = 38.2, p < 0.001). A difference between multipotent precursors, proerythroblasts or myeloblasts was not detectable (p > 0.9 in post-hoc Sidak tests) but in activated macrophages chromosome 8 territories did break up to a larger number of objects than in other cell types over a wide threshold range (p < 0.001 with all other cell lines).
Figure 5 Disaggregation of chromosome territories in objects. (a,b) mean number of objects at increasing thresholds for chicken chromosome 1 (a) and chromosome 8 (b) territories. When the starting threshold of 20 is gradually increased, the nuclear background produces at first few and then many objects (around threshold 40). Suppression of nuclear background occurs at thresholds between 60–70, leaving chromosome territories only. The range above these thresholds is thus the most interesting since it is here where the territories start to break up in several objects (compare Fig. 2). These objects are gradually lost at further increasing thresholds. Values for macrophages are not directly comparable to other cell types since they contain additional chromosomes (see Methods). (c, d) Chromatin content per surface. Signal intensity of objects was divided by object surface area and averaged (see Methods for details). Since additional chromosomal parts add intensity as well as surface, this parameter is unsusceptible to aneuploidy.
To allow a comparison of chromosome 1 territories in the aneuploid macrophages with those of other cell types, we analyzed the intensity of objects, i.e. their chromatin content, per surface area (Figure 5c,5d, see Figure legend and Methods for details). The chromatin content per object surface area was measured in multipotent precursor cells for both, chromosomes 1 and 8, again confirming their more compact structure in this cell type as compared to more differentiated cells. As a third parameter, we measured the average amount of labeled chromatin (signal intensity) per voxel (volume pixel) of the segmented objects. This parameter did not show differences between the cell types. Thus in all cell types a given amount of chromatin within the segmented objects was distributed over a similar volume over a wide threshold range (data not shown).
The positions of the lysozyme gene domain and of the chromosome 1 centromere change during myeloid differentiation
To determine the positioning of the lysozyme gene domain relative to the chromosome 1 territory, we performed dual color FISH with a chromosome 1 paint probe and a 20 kb plasmid probe for the lysozyme gene domain (Figure 2). By using a particular probe mix (see Methods) we were able to obtain an especially bright signal at the centromere in the same color channel as the paint probe.
The positions of both, the lysozyme gene domain and the centromere, differed largely between the cell lines representing the various differentiation stages. To classify the positions of the signals, we used the scheme shown in Figure 6a. In multipotent precursor cells (Figure 2b) we found the cLys gene domain inside the harboring chromosome territory, away from the territory border (categories A and B) in 48% of the cases (Figure 6b). In additional 46% the gene signal was inside the territory touching the border (cat. C). It was previously shown that these cells do not express lysozyme but do show low level expression of the neighboring cGas41 [25]. We conclude that a location inside the territory is compatible with low-level expression. In further differentiated cells, the lysozyme gene locus was found more often in the periphery of chromosome 1 territories (Figure 2, Figure 6b). This is true for myeloblast/macrophage lineage cells with lysozyme expression as well as for proerythroblasts in which the expression of cLys and cGas41 does not differ from the precursor cells. The most peripheral localization, sometimes outside of the painted territory, was found in activated macrophages (Figure 2e) where the cLys expression level is highest. Here 82% of the gene signals were on the surface or further outside (cat. D-F). The difference in distribution between precursor cells and all other cell types was highly significant (p < 0.001) as was the difference between activated macrophages and all other cell types (p < 0.001). The difference between unstimulated macrophages and proerythroblasts (p = 0.003) or myeloblasts (p = 0.024) was also significant whereas the difference between myeloblasts and proerythropblasts was not (p = 0.5). To test the robustness of our results, we repeated statistical analysis after reducing the number of applied categories of localization to only three: internal (A+B), peripheral (C-E) and external (F+G). We confirmed highly significant differences when precursor cells or activated macrophages were compared to any other cell type (p = 0.003 or smaller). In summary, for the lysozyme gene we found a change in position from interior when not expressed in myeloid precursor cells to peripheral when strongly expressed in activated macrophages. This would fit the hypothesis that highly expressed genes are preferentially located in the territory periphery, as it was found previously for large gene clusters [10,11]. However, this hypothesis does not explain the difference in positioning between the precursors and the proerythroblasts.
Figure 6 Classification of cLys gene domain and centromere signals. (a) Scheme used to classify the localization of gene and centromere signals relative to their chromosome 1 territory [adopted from 11]. The red ellipsoid represents the territory, the yellow dots the signals of genes or centromeres. Categories are: A, inside the territory delineated by the paint probe, away from the surface. B, inside, closer to the territory surface but not touching it. C, inside and touching the surface. D, on the surface. E, outside and touching the surface. F, without contact to the territory but in immediate neighborhood. G, away from the territory. (b, c) Distribution of the lysozyme gene domain (b) and the centromere (c) relative to the chromosome 1 territory in 5 different cell types. Between 79 and 95 cLys gene domain and centromere signals were evaluated for each cell line and assigned to the categories A-G.
Surprisingly, the centromeres of chromosome 1 showed a change in distribution very similar to the cLys gene domain (Figure 6c). In no cell type we found a significant difference between the two (p = 0.255 or larger). For example, in multipotent precursors all detected centromeres were inside the territory, either without (cat. A, B) or with contact to the surface (cat. C). In contrast, in activated macrophages 93% of the centromeres were on the surface (cat. D) or outside with contact to the surface. Again, the difference in distribution between precursor cells and all other cell types was highly significant (p < 0.001) as was the difference between activated macrophages and all other cell types (p < 0.001). Myeloblasts and unstimulated macrophages showed a moderately significant difference (p = 0.044) whereas the differences between proerythroblasts and myeloblasts (p = 0.654) or unstimulated macrophages (p = 0.084) were not significant. When applying only three categories of localization as described above, differences between precursor cells or activated macrophages and any other cell type again where highly significant (p = 0.001 or smaller) with the exception of precursor cells compared to myeloblasts showing a moderate significant difference (p = 0.035).
Silent lysozyme genes do not colocalize with centromeric heterochromatin
Brown et al. [32,33] showed examples of genes in hematopoietic cell types, which were tethered to centromeric heterochromatin when silent, but located remote from heterochromatin when active [4,32,34]. We asked whether the same nuclear location could be found for silent and active lysozyme genes. A probe that would label all centromeres of chicken chromosomes is not available. We reasoned that if the inactive lysozyme gene would be tethered to centromeric heterochromatin, at least in a number of cases this centromeric heterochromatin should include the centromere of its own chromosome. High precision 3D-distance measurements [35,36] from the lysozyme gene domain to the corresponding chromosome 1 centromere in the data sets described above showed that there is no such colocalization (Table 1). In those cell types where the lysozyme gene is completely shut off, the smallest distances found were 0.6 μm in proerythroblasts and 0.5 μm in multipotent precursor cells. This finding rules out a colocalization of the two loci. Distances in multipotent precursors are on average somewhat smaller than in the other cell types (Table 1). This can be attributed to a more compact chromosomal shape and to a smaller nuclear volume in this cell type (see below).
Table 1 3D-Distance measurements between the lysozyme gene domain and the centromere of the corresponding chromosome 1 in interphase nuclei of different cell lines.
multipotent precursor cells proerythroblasts myeloblasts macrophages LPS induced macrophages
evaluated territories 70 85 80 89 78
mean value 1,5 μm 2,1 μm 2,5 μm 2,2 μm 2,2 μm
median 1,4 μm 2,0 μm 2,2 μm 2,2 μm 2,1 μm
Standard-deviation 0,8 0,8 1,1 0,8 0,9
Smallest value 0,5 μm 0,6 μm 0,7 μm 0,7 μm 0,5 μm
Largest value 4,8 μm 5,8 μm 5,3 μm 4,4 μm 5,1 μm
Radial positioning of chromosome territories 1 and 8 within the nucleus
Habermann et al. [37] showed that in embryonic chicken neuronal and fibroblast nuclei the gene poor macrochromosomes 1–5 are located at the nuclear periphery. Intermediate chromosomes 6–10 were found further inside but not as central as the gene rich microchromosomes. Respective results were also found in human and other primate cells [38-43]. According to the current release of the chicken genome sequence [31] chromosome 1 has a length of 188 Mbp with ~11 genes/Mbp and chromosome 8 has 30 Mbp with ~19 genes/Mbp. These numbers are likely to change somewhat with further releases of the sequence. They do suggest however that the relative gene content is higher for the smaller chromosome 8. To test for a difference in the radial distribution of individual chicken chromosome territories, we measured 3D radial distributions in the nuclei painted with chromosomes 1 and 8 from experiments described above (Figure 7).
Figure 7 Radial distribution of chromosomes 1 (red) and 8 (green) in nuclei of (a) multipotent precursor cells (n = 37), (b) myeloblasts (n = 27), (c) activated macrophages (n = 23) and (d) proerythroblasts (n = 40). Unlike in the median distribution used for determination of significance levels, in the graphs shown here all voxels of a segmented signal are represented. Chromosome 8 has only about one sixth of the size of chromosome 1. Accordingly, its interphase territories are much smaller, leading to a smaller sample of voxels and thus accounting for less smooth curves than for chromosome 1 territories, e.g. in myeloblasts. All curves for each chromosome are shown in one graph in a supplemental figure in additional file 1.
In multipotent myeloid precursor cell nuclei, chromosome 1 territories were located more peripheral than chromosome 8 territories (p < 0.005). The same was true for proerythroblasts (p < 0.001) but no significant difference was present in myeloblasts (p > 0.1). These three cell types grow in suspension and have round nuclei. In flat nuclei of LPS-stimulated macrophages we again found chromosome 1 territories more peripheral than chromosome 8 territories (p < 0.005). The radial distribution is also reflected by the signal median values. It indicates at which nuclear radius half of the signal voxels are more internal and half are more external. In 73% of the precursor cells the chromosome 1 signal median is more external than the chromosome 8 signal median. The respective values are 52% for myeloblasts, 82% for proerythroblasts and 91% for activated macrophages.
When comparisons between cell types were made, chromosome territories 8 showed a rather similar radial distribution in all cell types (p > 0.1 or >0.05 for all combinations with a frequency maximum of ~10% at or near 80% of the nuclear radius, Figure 7, supplemental figure in additional file 1). Chromosome 1 territory distribution did show significant differences between cell types (Figure 7, supplemental figure). Chromosome 1 territory radial distribution was compared between nuclei co-hybridized either with the cLys-probe (series 1, Figure 2, not shown as graph) or the chromosome 8 paint probe (series 2, Figure 3 and Figure 7). In LPS-stimulated macrophages chromosome 1 territories were further outside than in proerythroblasts (p < 0.001 in series 1 and series 2), in myeloblasts (p < 0.001 in series 1 and p < 0.01 in series 2) and in precursors (p < 0.01 in series 1 and p < 0.05 in series 2). No significant difference was found between stimulated and unstimulated macrophages (p > 0.1, series 1 only). A significant difference in radial distribution of chromosome 1 territories between precursors and myeloblasts was found in series 1 (p < 0.01) but not in series 2 (p > 0.1). The same was true for the comparison of precursors and proerythroblasts (series 1: p < 0.001; series 2: p > 0.1). Proerythroblasts and myeloblasts did not show a significant difference (p > 0.1 in both series).
The radial distribution of the lysozyme gene domain did not differ significantly between the cell lines (p > 0.1). The mean value of the signal medians was between 71 and 76% of the nuclear radius for all cell lines. In erythroblasts, chromosome 1 territory signal medians were more internal than cLys signal medians (66% vs. 72%, p < 0.005). In unstimulated macrophages the opposite was true (76% vs. 73%, p < 0.05). In the other cell types the differences between the signal medians of cLys and chromosome 1 territories were between 0–2% and not significant.
Our results are compatible with previous data [37] in describing an external location for chromosome 1 and a somewhat more internal location for chromosome 8. In addition we find differences in the radial distribution of chromosome territories between the chicken cell types that have not been observed previously.
Nuclear volumes
The volume of nuclei was measured in confocal stacks of DNA counterstain of data sets described above by the same program that was used for the calculation of the radial distributions (Figure 4). The mean value for nuclear volumes for multipotent myeloid precursors was 212 μm3 (± 75 standard deviation, n = 76) and for myeloblasts 327 ± 101 μm3 (n = 50). The difference between the two cell types was highly significant (p < 0.001). The mean nuclear volume of proerythroblast was 296 ± 109 μm3 (n = 78). Proerythroblasts did not consistently show significant differences when compared to precursor cells or myeloblasts. Since the macrophage cell line carries additional chromosomes, its nuclear volume cannot be directly compared to the other cell lines. For unstimulated macrophages we determined nuclear volumes of 459 ± 91 μm3 (n = 42) and for LPS stimulated macrophages 554 ± 139 μm3 (n = 78). This difference was not significant.
The measured nuclear volume depends on the chosen signal threshold. Since the volume increases by the power of 3 with the nuclear radius, small differences in the segmentation can lead to large volume differences. A cautious interpretation of such measurements is thus advised. The difference between multipotent myeloid precursors and myeloblasts is so large however that we are confident that it is real and not a thresholding artifact.
Discussion
Cell type specific chromatin distributions on the nuclear level have been described for over a century [[1], p.100]. Differences between cell types have also been described for the distribution of heterochromatin detected with antibodies against methylated histones [44], for the radial distribution of gene rich and gene poor chromosomes [[37,40,45] this study] and the occurrence of clustering between specific chromosome territories [45]. Here we show an example were large-scale chromatin organization of chromosome territories changes during differentiation, and thus add a new feature to the list of nuclear architectural properties that can differ between cell types.
To quantify chromatin dispersal of labeled chromosomes in cells of various differentiation stages, we counted the number of separate, labeled chromatin objects to which the chromosome territories disintegrated at increasing thresholds. In the investigated chicken cell types, chromosome territories of further differentiated cell types disaggregated into more objects. An increase in object number during differentiation may indicate that a significant number of compact chromatin domains with silent genes separate from each other into several, more decondensed, "open" chromatin domains. This would increase the accessibility to transcription factor complexes from the interchromatin compartment by increasing the available chromatin surface area of the chromosome territory. In human lymphoblasts gene rich chromosome 19 territories were found more decondensed than gene poor chromosome 18 territories [38] and electron microscopic evidence suggests that active genes are exposed at chromatin domain surfaces in a zone called the perichromatin region, a transitional zone that marks the transition form the chromatin domain periphery to the interchromatin compartment [46]. A caveat of this interpretation is that so far no unequivocal proof for a profound influence of higher order chromatin compaction on gene activation and gene silencing has been presented. A further possibility is that inactive loci in the more differentiated cells do not require a tight spatial silencing by chromatin compaction anymore because the set of available molecular activators and repressors has changed. At present we can only speculate whether the correlation of increased dispersal of chromosome territories with differentiation state is a widespread feature or restricted to a few chicken blood cell types. At the highest, nuclear level of chromatin organization it was described for mammalian nuclei that heterochromatin shows distinct patterns of large blocks in terminally differentiated cells but not in blood stem cells and tumor cells [47,48]. This indicates a compaction of chromatin in differentiated cells rather than in their precursors, unlike in our current data on the chromosome territory level. It is possible, that heterochromatin (consisting mainly of repetitive sequences) and the bulk of labeled chromosome territories behave differently in these aspects. Due to their suppression with unlabeled repetitive DNA, repetitive sequences are underrepresented in chromosome territories detected by FISH as in the present study. Also, the rather small amount of repetitive sequences and heterochromatin in the chicken genome (genome size ~1.2 Gbp according to [29], 1.1 Gbp according to [31]) as compared to mouse and human genomes (~3.2 Gbp each, [31]) may lead to differences in nuclear organization.
Multipotent myeloid precursor cells have the smallest nuclei of the cell types investigated here. Myeloblasts have on average larger nuclei than proerythroblasts. If the observed disaggregation of chromosome territories were based on a nuclear volume increase, the larger myeloblast nuclei should have a stronger dispersion of chromosome territories than proerythroblasts. However, the opposite is true (Figure 5). We thus conclude that chromosomal dispersion is not related to nuclear size. In general, we observed larger nuclear volumes for further differentiated cell types. Increasing nuclear size was also observed during maturation of nerve ganglia cells [49] while a volume decrease was described during the maturation of lymphocytes [47]. Accordingly, unlike recently suggested [50], a decrease of nuclear size does not appear to be a phenomenon generally associated with terminal differentiation events.
The lysozyme gene domain is positioned inside the chromosome 1 territory in multipotent myeloid precursor cells where the lysozyme gene is inactive, but on the surface or outside in most of the territories in activated macrophages where the gene is strongly expressed. We thus did find a tendency to more exterior regions of the chromosome territory for the highly expressed gene from in activated macrophages although actual looping-out (without visible contact to the territory) was observed in only about 6%. Interestingly, while the radial distribution of the lysozyme gene domain within the nucleus is about the same in all cell types, the harboring chromosome 1 territories show differences. The finding that in erythroblasts the cLys signal is more exterior than the chromosome 1 territory signal median but in unstimulated macrophages the opposite is true also argues for a cell type specific organization of chromosome territories. A similar observation has been described for a IL-3 induced differentiation of human leukemic K562 cells where the β-globin gene cluster does not change nuclear position but the harboring chromosome 11 territory does [51]. However, in human hematopoietic cells a relocation of a gene to a different preferential radial position [52] or to or away from heterochromatic nuclear compartments has been observed for some genes, correlated with transcriptional regulation at different developmental stages [e.g. [33,53]]. Unfortunately, the harboring chromosome territories were not labeled in these studies.
While we can exclude a tethering of the inactive lysozyme gene to the centromere, at first glance this result seems compatible with the hypothesis that inactive genes are stored away in internal regions of the chromosome territory and active genes are on their surface or even looped out. However, several aspects suggest an alternative explanation. (i) Embedded in the chicken lysozyme gene domain is a second gene, cGas41, which, albeit on a low level, is expressed in all cell types used in this study, including multipotent myeloid precursor cells [28]. Thus we found an example of an active gene with a location inside the territory as it was described previously for some mammalian genes [13]. (ii) Although the position of the lysozyme gene domain is most peripheral in activated macrophages where the expression is highest, we also found a shift towards more external positions from multipotent myeloid precursor cells to further differentiated proerythroblasts, both non-expressing cell types. (iii) In addition to the lysozyme gene domain, we investigated the chromosome 1 centromere. Surprisingly, both loci showed a very similar distribution in all cell types investigated. Transcription from centromeres has been observed in yeast [reviewed in [54]] and from a human neocentromere [55]. Formally, we thus cannot fully exclude that centromeric transcription may occur in chicken. We regard it as extremely unlikely, however, that tissue dependent differences in centromeric transcription play a role in the cell type specific spatial positioning found here. The observed modification in the morphology of chromosome territories during differentiation rather invites to hypothesize that the positional changes observed for the lysozyme gene domain are not restricted to this particular chromatin loop or only to those chromatin loops which harbor genes that become activated during cell differentiation. Instead, these positional changes may reflect a more general, differentiation dependent change in large-scale chromatin structure. Differentiation processes may thus have a more global impact on chromatin structure than previously suspected.
Conclusions
We describe several features of chromosome territory organization that differ between various hematopoietic chicken cell lines. While multipotent myeloid precursor cells had compact chromosome territories, the more differentiated cell types investigated here displayed somewhat disaggregated, diffuse territories. Although nuclear volumes generally are larger in the more differentiated cell types, they do not correlate with the changes in chromosome territory morphology. The chicken lysozyme locus as well as the chromosome 1 centromere is located preferentially in the interior of the chromosome territory in precursor cells and more external in more differentiated cells. Our data suggest that such a repositioning of chromosomal loci during differentiation may be a consequence of general changes in chromosome territory morphology, not necessarily related to transcriptional changes. The radial distribution of chromosomes 1 and 8 also differed between cell types. In summary our data argue for a cell type specific chromosome territory organization in the investigated cell lines.
Methods
Cells
All cells are from retrovirally transformed chicken cell lines [56,57]. HD50MEPs represent multipotent myeloid precursor cells before the separation of erythroid and monoblast/macrophage lineages. Proerythroblast-like HD37 were used as an example for a differentiated cell type that is negative for lysozyme expression. Myeloblasts (HD50 myl, a cell line derived from the same precursors as HD50MEP) are an intermediate stage in the differentiation to macrophages (HD11). While macrophages grow adherent, all others grow in suspension. Cytogenetic analysis showed that all lines were diploid for chromosome 1 except HD11. The latter had 2 to 4 normal chromosomes 1 plus a translocation chromosome with the short arm of #1 including cLys but not the #1 centromere and also other karyotype aberrations, e.g. a trisomy of chromosome 2. All lines were diploid for chromosome 8. Cells were cultured in IMDM supplemented with 2% chicken serum, 8% fetal calf serum and 2 mmol L-Glutamin and maintained at 37°C with 5% CO2. For 3D-FISH experiments cells were cultured on #1.5 glass coverslips (170 μm thick). Except for macrophages, coverslips were pretreated for 35 min with poly-L-lysine (0.1 mg/ml; MW 300000, Sigma, Deisenhofen, Germany, P5899). Activation of HD11 was achieved by the addition of 5 μg LPS (Sigma, L-8275) per ml medium and subsequent cultivation over night [58]. Activated macrophages become postmitotic. Coverslips with attached cells were washed with PBS, fixed in 1,2% formaldehyde freshly made from paraformaldehyde [59] for 15 min, washed in PBS 3 × 3 min, incubated in 0,5% Triton X-100 in PBS for 20 min and equilibrated in 20% glycerol in PBS for 60 min. Dipping in liquid nitrogen and thawing at room temperature in 20% glycerol/PBS was performed five times. After washing 3 × 3 min in PBS and incubation in 0,1 M HCl for 10 min, coverslips were washed 2 × 3 min in 2 × SSC and stored in 50% formamid/ 2 × SSC for at least 1 h but usually overnight or longer at 4°C.
FISH
Chromosome-specific paint probes for chicken chromosomes 1 and 8 were kindly provided by Dr. Felix Habermann and Dr. Johannes Wienberg, Munich. They were generated by flow sorting of metaphase chromosomes and subsequent degenerated oligonucleotide primed (DOP)-PCR [60]. They produce a uniform labeling of metaphase chromosomes. When the amplification products were repeatedly reamplified using the same PCR conditions, however, we noted after about a dozen rounds that the label on metaphase chromosomes became non-uniform in appearance indicating a reduction in complexity by the loss of sequences. The higher likelihood of retaining repetitive sequences was reflected by the finding that the by far brightest spot was found at the centromere as identified by the primary constriction in the DNA counterstain. To identify chromosome 1 centromeres together with completely delineated chromosome 1 territories, we used a mixture of an early amplification product with repeatedly reamplified probe. We thus obtained intense painting of the entire chromosome and a particular bright signal at the centromere (Figure 2a). In experiments where chromosomes 1 and 8 were cohybridized, detection of centromeres was not necessary and thus only early amplification products were used. DOP-PCR for amplification and labeling (biotin-16-dUTP for #1 and digoxigenin-16-dUTP for #8, both from Roche Applied Science, Mannheim, Germany) was performed as described [61]. The lysozyme gene domain is contained on the pPoly-III-i Lys-plasmid [62]. It was labeled by nick-translation with digoxigenin-dUTP. Chicken cot 1 DNA was prepared from liver using standard procedures. The same result was obtained for the chicken chromosome 8 paint probe (data not shown).
All DNA for a given assay was mixed, precipitated and solved in deionized formamide. The same volume of 20% dextransulfat in 2 × SSC was added. In experiments with cLys the following amounts of DNA were precipitated for each μl hybridization mix: 2 μl label-DOP-PCR product of an early amplification round of the #1 paint plus 2 μl of a highly amplified paint probe for centromere detection, 50 ng pPoly-III-i Lys, 2.5 μg cot 1 DNA. When paint probes for #1 and #8 were cohybridized, 2 μl label-DOP-PCR product of an early amplification round was used for each chromosome and supplemented with cot1 DNA as above. Denaturation was 5 min at 85°C. Preannealing with the cot 1 DNA was performed for 25 min at 37°C. For 3D FISH, coverslips with cells were denatured in 70% formamide for 3 min at 70°C, and placed immediately in ice-cold 50% formamide/2 × SSC. They were then incubated with 5 μl hybridization mix under a sealed 18 × 18 mm2 coverslip at 37°C for 24 h-72 h. Air-drying was carefully avoided at all steps. Metaphase chromosomes were hybridized as described [37].
Detection was performed as described [63], using rabbit anti-digoxigenin (1:500) and goat anti-rabbit-Alexa488 (both from Sigma) and for biotin detection Avidin-Cy3 (Dianova, Hamburg, Germany). Slides were counterstained with DAPI and TOPRO-3 (Molecular Probes, Eugene, Oregon) and mounted in Vectashield (Vector Laboratories, Burlingame, CA).
Confocal laser scanning microscopy
3D image stacks (8 bit) were recorded with a Leica TCS SP confocal laser scanning microscope equipped with an argon (488, 514 nm) and a HeNe laser (633 nm) (Leica Mikrosysteme, Bensheim, Germany). A 100 × N.A. 1,4 oil objective was used to obtain stacks with a voxel size of 0.08 × 0.08 × 0.24 μm. Nuclei with separated homologous chromosome territories were preferably selected for recording. To measure the chromatic aberration, 0.5 μm multi-color latex beads (Polysciences Europe, Eppelheim, Germany) were fed to activated macrophages. After phagocytosis the cells were fixed and embedded like 3D-FISH preparations. The beads were in the cytoplasma and thus their optical environment was closer to the situation of FISH signals than beads mounted directly on a glass cover slip. The chromatic aberration in x, y and z was corrected before the assignment of signals to categories or distance measurements were performed.
Image analysis
The program used for object counting was first applied by Cremer et al. [44]. The original 8-bit gray level image stack is first subjected to Gaussian filtering and then normalized, i.e. the lowest existing gray value is set to zero, the highest to 255 and the values in-between are recomputed accordingly. A starting threshold of 20 was chosen and voxels above the threshold were determined. Of those, all touching voxels (26 voxel neighborhood) were combined to objects. Only structures with at least 10 voxels were regarded as 'objects' and included in the further analysis. After counting the objects and calculating the other parameters, the threshold was raised for 5 gray levels, object determination and calculation was repeated and so on until the highest applied threshold of 250 was reached. For statistical calculations, from each nucleus the maximum number of objects occurring at any threshold of 80 or higher was used. The restriction to thresholds of 80 or higher was made to confidently exclude background objects. Statistical inferences about the means of the maximum values of objects were based on one-way analyses of variance (ANOVA). Post-hoc comparisons generating p-values relied on Sidak tests. These test were performed with SPSS version 12 (SPSS Inc., Chicago, IL).
For the second parameter, for each nucleus at each threshold the ratio (object intensity)/(object surface voxels) was measured for all objects and averaged. Object surface voxels are defined as voxels belonging to an object and having at least one of the 26 neighbors not belonging to the object. The unit is 1/μm2. This value reflects the amount of intensity (chromatin) that is enclosed in a given surface area. Chromosome territories with a richly folded surface thus have a rather low value whereas compact, homogeneous territories have a higher value. Most nuclei had zero or few objects at very high threshold levels (Figure 5a,5b). Therefore, the calculation of meanvalues for the intensity/surface parameter was stopped when less than five nuclei with at least one object where left. For the third parameter, average amount of labeled chromatin per volume, the intensity of all voxels belonging to an object was summed up and divided by the number of object voxels and the average over the objects was computed.
Localization of cLys and centromere signals with regard to chromosome 1 territories: Image stacks were imported in ImageJ (freely available on the internet at [64]) and each fluorochrome was assigned to one channel of an RGB-Stack. A Gaussian Blur filter was applied before using the 'brightness & contrast' function to enhance signals and decrease background. The 'make montage' function was then used to show all planes of the RGB-stack side by side. For each gene or centromere signal the z-plane was selected where it was brightest. This plane was then used for the categorization (Figure 6a) [11]. The Mann-Whitney-U test from SPSS was used for statistical analysis. For the aneuploid cell line HD11 an evaluation was performed only if normal chromosomes were unequivocally distinguishable from the translocated p-arm (without centromere). The latter was excluded from further analysis.
High precision 3D-distance measurements [35,36]: Gravity centers of the signals were determined with Showpos, a program written by Kurt Sätzler, Heidelberg, for Silicon Graphics Workstations running under Irix. The 3D coordinates of cLys and the respective centromere were corrected for the chromatic aberration and the distance was calculated.
The quantitative assessment of 3D radial distributions of painted chromosome territories and the measurement of nuclear volumes was performed using a program developed by Dr. Johann von Hase, Heidelberg which is described in detail elsewhere [42]. To determine the statistical significance of radial distribution differences, we used the medians of each signal in each nucleus and applied the two-sided Kolmogorov-Smirnov test [65]. The same test was applied to nuclear volumes.
Authors contributions
The study was designed by SD together with CB and TC. First experiments and development of techniques were performed by RM. S Stadler and VS performed 3D-FISH, microscopy, determination of cLys and centromere 1 localization and high precision distance measurements of all evaluated cells. RM carried out radial distribution statistics and volume measurements. All three were supervised by SD and TC. S Stein programmed and adapted the object counting program, supervised by CC. Object counting and statistical analysis was performed by SD. The manuscript was written by SD with substantial contributions by CB, TC and VS. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Radial distribution of chromosomes 1 and 8 in nuclei of different cell types. These graphs show the same curves as presented in Figure 7, but now all curves for chromosome 1 are combined in (a) and those for chromosome 8 are combined in (b).
Click here for file
Acknowledgement
We thank Felix Habermann and Johannes Wienberg for chicken chromosome paint probes Johann von Hase for providing the software for the determination of the radial distribution of territories and Alexander Crispin for essential hints towards the application of ANOVA and Mann-Whitney-U tests. This work was supported in part by a common grant of the Deutsche Forschungsgemeinschaft to SD and TC (Cr59/21-1).
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| 15555075 | PMC535556 | CC BY | 2021-01-04 16:31:36 | no | BMC Cell Biol. 2004 Nov 22; 5:44 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-44 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1791555017710.1186/1471-2105-5-179Methodology ArticleCLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species Pellegrino Maurizio [email protected] Paolo [email protected] Lorenzo [email protected] Cunto Ferdinando [email protected] Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy2 Fondazione per le Biotecnologie, Viale Settimio Severo, Torino, Italy2004 19 11 2004 5 179 179 11 8 2004 19 11 2004 Copyright © 2004 Pellegrino 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
Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation.
Results
We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest.
Conclusions
CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.
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Background
The availability of genome sequences from several model organisms, including humans, and of high-throughput technologies to study gene function is dramatically changing the approach to biological problems. In particular, the consolidated reductionist gene-by-gene strategy is being replaced by a 'modular approach', in which several genes are studied simultaneously to gather a more comprehensive picture of the many different cellular processes [1]: in living organisms, the majority of gene products are part of intricate molecular circuits, composed of physical, functional and regulatory interactions. In higher eukaryotes, the study of gene function is further complicated by the alternative use of transcriptional units, frequently resulting in the production of proteins with different or even antagonistic activities from the same genes [2,3].
It is well recognized that one of the most important and widespread mechanisms used by cells to regulate functional modules is the coordinate transcriptional and/or post-transcriptional modulation of mRNA levels of the interacting genes. Therefore, DNA microarrays represent a fundamental tool to unravel biological complexity on a genome-wide scale. Information concerning the expression of thousands of genes, and also of different transcripts from the same gene, can be obtained in a single experiment, and the relationships among gene expression patterns can be studied systematically [4]. The extensive use of this technology by hundreds laboratories has resulted in the production of an enormous amount of data, many of which have been deposited in public databases [5,6].
Besides being useful to other researchers to confirm the published results, the deposited datasets can be used as a substrate for new analysis, aimed at discovering functional modules by searching for related expression profiles. Recent studies have shown that, if the expression of two or more genes is consistently related throughout many independent microarray datasets, the genes display a significant degree of functional similarity [7,8]. However, if this approach were applied to predict physical and functional relationships, a very high number of false positives would still be expected. A first method that can be used to reduce the number of false positives is to consider only co-expression links that are consistent among many different experimental datasets [7]. Nevertheless, even when the co-expression of two genes is reproducibly observed under a certain number of experimental conditions, this does not imply necessarily that they are functionally related. For instance, extensive meta analysis of microarray data across different species has revealed that neighboring genes are likely to be co-expressed, even though they are not functionally related in any obvious manner [9,10].
Phylogenetic conservation has been recently proposed as a very strong criterion to identify functionally relevant co-expression links among genes [11]. Significant co-expression of two or more orthologous genes across many species is very likely due to selective advantages, strongly suggesting a functional relation. In fact, the comparison of data across species as distant as Homo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans was very effective in identifying new genes involved in core biological functions [11]. Although extremely specific, this multi-species approach would be unable to identify the relationships among genes involved in more specialized biological processes.
Since regulatory regions diverge much more rapidly than coding sequences [12,13], a similar approach would be predicted to succeed even when comparing expression patterns in more closely related species, such as mice and humans. In this case, the possible loss of specificity would be strongly compensated by the increased sensitivity in the identification of functional links related to mammalian-specific gene modules. This possibility has not been so far explored.
Additionally, when using microarray data to establish significant correlations among gene expression profiles, almost invariably the information obtained with probes covering different gene portions is averaged [14]. Though useful in many cases to reduce the experimental noise, this procedure could result in a significant loss of information in the case of genes expressing different isoforms with distinct expression patterns [15]: on one hand the isoform-specific expression profiles would not be detected; on the other hand, the average expression profile would be artificial and non-informative.
In this study we describe CLOE (Coexpression-based Linking of Orthologous ESTs) a new data mining method for the identification of transcripts showing evolutionary conserved co-expression in cDNA microarray datasets. This approach is based on the pairwise comparison of data from two species. The predictive capability of the method was proved by comparing human with mouse data. Our results show that CLOE is a valuable tool for biologists that can be used to identify putative partners for genes of interest and/or to predict some of their functional properties.
Results and discussion
The top percentiles of expression similarity ranked lists obtained with human-mouse orthologous ESTs pairs are strongly enriched of orthologous ESTs
The aim of our method is to use the available microarray expression data to identify high- confidence putative partners for genes of interest.
The basic assumption is that, if two or more genes are part of a functional module, conserved between two species, they will be likely co-expressed in both species. In contrast, if the co-expression of two genes in one species has no functional meaning, it should not be conserved in the other.
A flow chart of the method is given in Figure 1. In summary, after finding representative EST clones for the gene of interest in cDNA microarray datasets of both species, we order all the ESTs in each dataset according to similarity of their expression pattern with that of the chosen ESTs. We then extract the orthologous pairs found in a given top percentage of the ranked lists. Moreover, to obtain a functional characterization of the identified putative partners, we analyze the co-expressed orthologous pairs for overrepresentation of Gene Ontology (GO) keywords [16].
Although in principle our method could be applied to every pair of organisms for which cDNA microarray data are available, we decided to compare the human and mouse datasets contained in the Stanford Microarray Database (SMD) [6] (2803 experiments for 74588 EST probes and 145 experiments for 37521 ESTs, respectively, data downloaded in Jan. 2004). The first reason for doing so is that this comparison is particularly relevant in the perspective of identifying mammalian-specific gene modules. The second is that, considering the widely different number of experiments and the relatively short phylogenetic distance between the two species, this represents a particularly severe test.
As a first proof of the method's effectiveness, and in order to empirically determine a reasonable default cutoff for obtaining the final list of candidates, we analyzed whether genes with the highest ranks in the single organism lists are actually enriched of orthologous sequences. To this aim, we randomly chose 100 orthologous gene pairs represented in both the human and mouse datasets and selected, for each one, the most representative EST (i.e. the probes with the highest number of experiments in each dataset). We then generated the respective ranked lists, subdivided them in 1% rank intervals and analyzed the number of orthologous pairs in corresponding rank intervals. As a control, we performed the same analysis on an equal number of randomly chosen (and hence non-orthologous) human-mouse EST pairs. The analysis was repeated three times with essentially identical results.
As shown in Figure 2, compared to the control, a strong average enrichment of orthologous pairs was observed in the top 1% rank interval (p = 1.6·10-94, chi square test). The difference was still very significant in the 2% rank interval, even though with a much higher p value (p = 1.3·10-10), but was not detectable below that threshold. Interestingly, a slight enrichment was also observed in the last rank interval (average number of orthologous pairs equal to 7.8 for CLOE and 5.7 for random lists, p = 2.2·10-7). The latter observation is consistent with the previously noted fact that negative correlations tend to be less common and significant than positive correlations [7]. Based on these results, we chose a top 1% cutoff for all the following analysis.
Predictive value of CLOE compared to single organisms and multiple species co-expression analysis
We next investigated the effectiveness of our approach, by comparing it to single and multiple organisms co-expression analysis. To address this point, we analyzed the ability of the three methods to predict known physical and functional interactions among mammalian proteins. Protein-protein complexes have begun to be determined on a genome-wide scale only for Saccharomyces cerevisiae [17], Drosophila melanogaster [18] and Caenorhabditis elegans [19], but no comparable datasets have been so far published for mammalians, making it impossible to perform a systematic comparison. Therefore, we focused on three supramolecular structures, which have been analyzed by different proteomic strategies at a high level of detail: the centrosome [20] (110 proteins), the post-synaptic density [21] (105 proteins) and the TNF-alpha/NFkB signalosome [22] (128 proteins). For the single organism and CLOE approaches, the analysis was restricted to proteins represented in the SMD by at least one human and one murine EST probe. These corresponded to 62, 67 and 97 ESTs pairs, respectively, covering on average 66% of the proteins found in these complexes. In contrast, only 37% of these genes were represented in the multiple species network, thus confirming that the previous two methods can be applied to a number of genes much higher than the latter.
The average number of candidates produced by CLOE for each analyzed protein was approximately 17, which represents a strong reduction if compared to the single organism approach (746 and 375 for the human and mouse datasets, respectively). On the other hand, the average percentage of CLOE links that correspond to a documented protein-protein interaction was 6.6 %, i.e. approximately 5 times higher than that obtained with the single organism method (Table 1). Significantly, the predictive value of human-mouse CLOE was very similar to that obtained by the multiple species co-expression network (Table 1).
Since considering only the proteomically-identified interactions could lead to a strong underestimation of the positive results, as low affinity and purely functional interactions would be completely excluded, we decided to evaluate the predictive power of the three different methods respect to a less stringent functional index. To this aim, we first determined which GO keywords represent the best annotation of the three complexes, by identifying the ones that are significantly overrepresented in the annotation of the respective proteins. Then, every predicted candidate partner obtained with the three methods for all analyzed proteins was considered as a true positive if it is annotated to at least one of the overrepresented keywords of the corresponding complex. The results of this analysis are summarized in Table 2.
Interestingly, even though also in this case our approach and the multiple species comparison gave, on average, a higher percentage of compatible predictions, this was not dramatically different from the single-organism method. These results strongly suggest that, compared to the single organism approach, the highly reduced number of candidate partners produced by multiple organism co-expression analysis and CLOE is strongly enriched of genes characterized by more stringent functional relationships.
Conclusions
We have shown that CLOE represents a very flexible and effective data mining approach to infer a list of putative partners and the potential functions for genes of interest. It can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage, producing significant results even when data from two species as close as mouse and human are analyzed. Compared to single organisms co-expression analysis, it strongly reduces the number of potential partners for genes of interest, producing a list of targets that is highly enriched in physically interacting proteins. On the other hand, compared to multiple species co-expression analysis, it retains a comparable specificity, but can find candidate partners for a greater number of genes. Since the number of candidate partners obtained by this analysis is, on average, in the range of standard experimental validation techniques, we believe CLOE represents a useful tool for the exploration of gene function.
Methods
Definition of orthologous ESTs
The first step of our procedure is the identification of orthologous ESTs in the two datasets. Although many different methods could be used to this purpose, we relied on the InParanoid algorithm [23], which is ideally suited for the identification of orthologous sequences between two species. The results shown were obtained using the pre-computed release 2.6 of the InParanoid database [24]. ESTs were linked to InParanoid clusters through their UniProt codes [25,26], and their association to UniGene identifiers [27,28].
Choice of representative probes for the gene of interest
The procedure has been implemented for the analysis of cDNA microarray datasets, such as those contained in the SMD [6,29]. However, it could be adapted, with minor modifications, to the analysis of Affymetrix datasets.
Ratiometric data for the different organisms are not subjected to any further normalization, and downloaded as log-transformed (base 2) ratios.
Within these datasets, the number of ESTs representing a given transcription unit, as well as the number of valid experiments for each EST, are highly variable. While this feature would pose serious problems, if one should attempt to average the data of all the probes belonging to the same gene, it may offer extremely valuable information when every EST is considered independently, since each clone explores the expression properties of a particular group of exons, in a particular combination of experimental situations.
Non-correlated or anti-correlated expression between two well-represented EST probes belonging to the same gene would strongly suggest that they correspond to alternatively expressed transcripts. For this reason, we decided not to merge the data of probes belonging to the same gene, but to treat separately every EST probe in the datasets.
The choice of the most representative EST for the genes of interest represents a particularly critical aspect of our procedure. If interested to a single gene, the more exhaustive solution to this problem would be to generate and evaluate a list of candidate partners for every possible pairwise combination of ESTs probes. However, this would greatly complicate the analysis should one be interested in analyzing the potential partners of many genes. An alternative possibility is to focus, for every Unigene cluster, on the most representative ESTs, i.e. the probes with the highest number of experiments. For these reasons, the decision about what ESTs to analyze is left to the end user.
Our implementation of the method can accept as input both a UniGene cluster ID or the results of a BLAST search performed with the sequence of interest against the EST database. In both cases, it retrieves a list of all probes found in the two datasets for the orthologous UniGene clusters.
To help the user decide which ESTs to analyze, the program provides basic information about all the EST probes representing the gene of interest in the two datasets. Moreover, to help the user identify the most representative EST probe in each dataset, i.e. the probe with the highest number of experiments, it also provides the number of valid data points for every probe. Finally, to assess the redundancy of the information provided by the different probes, i.e. whether they represent different experiments and display similar/different expression profiles, the program calculates, for every pair of probes belonging to the same UniGene cluster, the number of common experiments and the Pearson correlation coefficient between their expression profiles.
Identification of orthologous sequences coexpressed in both species
After finding representative EST clones for the gene of interest in both species, we calculate, for each one, the Pearson correlation coefficient (r) with every other EST in the respective dataset. The raw r is the normalized for the number of common data points (n) obtained for the analyzed ESTs. This is done by multiplying r for , since the statistical significance of r is a function of the product ·r. Such normalization is particularly important when, as in our case, n has a very wide range of variation. The ESTs are then ranked by decreasing normalized score. Finally, a user-defined top percentage of the two ranked lists is compared to identify those ESTs that are associated to the same InParanoid cluster ID. A non-redundant list of the positive InParanoid clusters, sorted by the average highest rank obtained in the two organisms, represents the main output of the program (see Table 3 for an example). Clearly, the choice of the cutoff top rank percentage represents a critical parameter, which may strongly influence the number of identified candidates. The empirical determination of an average best cutoff is reported in the results.
Functional characterization of the co-expressed orthologous clusters
After obtaining a list of putative partners for the genes under study, we analyze their functional characterization according to the GO vocabulary [16,30]. This is very useful to obtain new insight about the putative functional properties of the gene of interest. GO terms are associated to ESTs through the corresponding UniProt identifiers. For each list of candidates, we compute the prevalence of all GO terms among the annotated ESTs, and the probability that such prevalence would occur in a randomly chosen set of ESTs of the same size. We always consider a gene annotated to a GO term if it is directly annotated to it or to any of its descendants in the GO graph. For a given GO term t let K(t) be the total number of ESTs annotated to it in the first organism dataset that have an orthologous sequence in the second organism dataset, and k(m, t) the number of ESTs annotated to it in the final list S(m). If J and j(m) denote the number of orthologous ESTs in the dataset and in S(m) respectively, such probability is given by the right tail of the appropriate hypergeometric distribution:
where
As an example, the results of the analysis performed on the output shown in Table 4. A similar strategy was used in all other cases where GO keywords overrepresentation test were performed.
Availability
The programs used for this work are publicly available at the URL:
. This page contains the following files:
programs.zip (the program files and the corresponding General Public License);
readme.txt (detailed instructions for using our program).
Authors' contributions
MP: development of most of the software, execution of the described analysis.
PP: development of the routines used to calculate the normalized Pearson, supervision of the statistical analysis.
LS: assessment of the biological significance of results.
FD: supervision of the project, manuscript writing.
Acknowledgements
We are grateful to Michele Caselle, Davide Corà and Mara Brancaccio for helpful discussion. This work was supported by the '60% local research fund' of the Italian Ministry for Education and Scientific Research.
Figures and Tables
Figure 1 Schematic representation of the CLOE approach
Figure 2 The top percentiles of single organisms ranked lists obtained with orthologous probes are strongly enriched of orthologous sequences. 100 orthologous (CLOE) and 100 randomly chosen (Random) EST pairs were used to rank the ESTs in the human and mouse datasets on the basis of expression similarity. The ranked lists were divided in 1% rank intervals, and the average number of human ESTs in a given rank interval with at least one orthologous EST in the corresponding mouse rank interval was determined. The average number of these ESTs in the first top 10 rank intervals was plotted. Error bars = standard error.
Table 1 Percentage of known protein-protein interactions in the lists of candidate partners generated by the different co-expression-based approaches. For every protein found in the three analyzed complexes, and represented by at least one EST probe in both datasets, we selected the most representative human and mouse probes. A CLOE analysis with a top 1% cutoff was performed on these sequences. In parallel, the human dataset was ranked for each human EST, and lists of candidates corresponding to the top 1% ranks where obtained (Single organism). The prevalence of ESTs corresponding to other proteins of the same complexes was then determined for both approaches. Finally, to determine the prevalence of correct predictions by the Multiple Species approach, we determined the ratio between the number of links with other proteins of the same complex and the total number of links for all the complex components (data from [11]).
Single organism Multiple organisms Human/mouse CLOE
Centrosome 1.4 4.2 6.2
PSD 0.9 5.5 6.5
TNFα/NF-kB 1.6 6.1 6.8
Average 1.3 5.7 6.6
Table 2 Prevalence of functionally compatible predictions obtained with the three different methods. The percentage of compatible predictions was determined as in the previous table using the functional index described in the text.
Single organism Multiple organisms Human/Mouse CLOE
Centrosome 19.5 36 26.3
PSD 33.8 47.8 41.3
TNFα/NF-kB 47.2 47.4 44.8
Average 33.5 43.7 37.4
Table 3 List of candidate partners generated by CLOE analysis on the most representative ESTs corresponding to the protein of unknown function FAD104.
InParanoid Human EST with highest rank Human UniGene name Rank Mouse EST with highest rank Mouse UniGene name Rank Average rank
523 IMAGE:240295 FAD104 1 H3020H08 1600019O04Rik 1 1
1668 IMAGE:343072 ITGB1 2 IMAGE:1051975 Itgb1 45 23.5
9060 IMAGE:786680 ANXA5 21 H3016C05 Anxa5 103 62
8045 IMAGE:486787 CNN3 102 H3056D03 Cnn3 29 65.5
9769 IMAGE:488479 TPM1 107 3110002E24 Tpm1 57 82
11683 IMAGE:487437 PPIC 87 H3028H10 Ppic 93 90
6369 IMAGE:142788 SERPINH1 70 H3125A07 Serpinh1 129 99.5
899 IMAGE:469969 ITGAV 18 1110004F14 Itgav 182 100
9579 IMAGE:345538 CTSL 140 2600002C17 Ctsl 95 117.5
8192 IMAGE:613056 RCN1 52 H3027B09 Rcn 195 23.5
11615 IMAGE:230261 RALA 78 H3121E01 Rala 198 138
221 IMAGE:897760 LAMC1 43 H3113E11 Lamc1 239 141
1306 IMAGE:897164 CTNNA1 258 2210403L09 Catna1 48 153
4123 IMAGE:840697 FKBP9 83 H3147A05 Fkbp9 236 159.5
12331 IMAGE:841664 CAV1 24 H3089D06 Cav 301 162.5
5726 IMAGE:377384 NR2F2 308 H3124H07 Nr2f2 26 167
13914 IMAGE:810485 ID1 5 H3003F10 Idb1 365 185
Table 4 Gene Ontology keywords overrepresented in the list shown in the previous supplementary table. The results strongly suggest that this protein could be involved in some aspects of the functional interaction between the cytoskeleton and the extracellular matrix.
Keyword Organizing principle p-value
Endoplasmic reticulum Cellular Component 9.3·10-3
Protein binding Molecular Function 6.5·10-3
Peptidyl-prolyl cis-trans isomerase Molecular Function 6.5·10-3
Structural constituent of muscle Molecular Function 3.4·10-3
Collagen binding Molecular Function 3.1·10-3
Structural molecule Molecular Function 1.7·10-3
Tropomyosin binding Molecular Function 8.9·10-4
Basement membrane Cellular Component 5.7·10-4
Cytoskeleton Cellular Component 5.6·10-4
Cell adhesion Biological Process 6.4·10-5
Actin binding Molecular Function 4.6·10-8
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| 15550177 | PMC535557 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Nov 19; 5:179 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-179 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-331555016910.1186/1472-6963-4-33Research ArticleUse of the bootstrap in analysing cost data from cluster randomised trials: some simulation results Flynn Terry N [email protected] Tim J [email protected] MRC Health Services Research Collaboration, Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Bristol BS8 2PR, UK2 Academic Unit of Primary Health Care, Department of Community Based Medicine, University of Bristol, Cotham House, Cotham Hill, Bristol BS6 6JL, UK2004 18 11 2004 4 33 33 1 6 2004 18 11 2004 Copyright © 2004 Flynn and Peters; 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
This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date.
Methods
The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data.
Results
The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%.
Conclusion
In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately.
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Background
The greater complexity of cluster randomised controlled trials (RCTs) compared with their individually randomised counterparts has led to much methodological work concerning their design and analysis[1]. However, the analysis of cost data from these trials has received little attention to date. The conceptual issues arising in this context have been explored [2] but, briefly, there are two problems.
The first is that many trials randomise only a small number of clusters. This can sometimes produce inadequate randomisations where, for example, all clusters with a characteristic related to outcome are allocated to one treatment arm [3]. It may also be difficult to make inferences on cluster level covariates and between-cluster variability. Although in theory this problem is just as relevant to clinical data, in practice methods of analysis based on the t-test are fairly robust to moderate violations of the assumptions of normality and homogeneity of variances [4]. The situation with highly skewed continuous economic data, however, may be more serious. Indeed, at either the individual or cluster level, skewed data are potentially very problematic, particularly with small numbers of clusters. Appeals to normality of data may not be reasonable for the distribution of cluster means, given variation in medical practice, social and geographical factors. In individually randomised trials, problems of skewness and small sample sizes have sometimes resulted in confidence intervals with poor coverage properties (such as negative lower limits for mean costs). In such circumstances economic data have been analysed using methods such as the nonparametric bootstrap [5], first proposed by Efron [6]. This relies on computer-intensive resampling methods rather than a formula and commensurate appeals to the central limit theorem. In essence, by treating the sample at hand as the population, repeated resampling with replacement from this 'population' and calculation of a parameter of interest builds up a picture, the 'empirical distribution' of this parameter, based on so-called 'B bootstrap estimates' of the parameter of interest. This can be used to construct directly the required confidence interval by, for instance, reading off the 2.5% and 97.5% percentiles of the distribution. Bootstrapped confidence intervals may, therefore, be asymmetric and be better able to deal with skewed data.
The bootstrap approach is flexible but does assume that the data are independently and identically distributed [7,8]. When stratification, cluster sampling or probability weights are introduced into sampling this assumption is violated and the bootstrap as described above will give incorrect inferences. Work has been carried out in the 1980s and 1990s to generalise the bootstrap to survey sampling and regression analysis [7,9]. The bootstrap is included in some standard statistical packages, but it is often overlooked that confidence intervals from this may have poor coverage properties when there is a small number of clusters – a phenomenon that is common in cluster RCTs.
This paper details the results of simulation studies to evaluate how clustered cost data might be analysed. Small numbers of clusters together with skewed data were utilised to ascertain how the bootstrap performed against a method of analysis commonly used in clustered clinical data. Thus it details the generation and analysis of a single outcome model. This single outcome model was primarily conceptualised as a model for cost data and the term cost is therefore used as short-hand. However, some of the scenarios presented may be equally applicable to clinical data.
Methods
The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted non-parametric bootstrap procedures were compared in terms of confidence interval coverage. Specifically, the comparison was of the percentage of 20,000 simulations for which the estimated 95% confidence interval contained the true value of the treatment effect. If the observed coverage were to be 95% on average across the simulations, then 20,000 simulations would from simple binomial theory give a margin of error of approximately , which was considered acceptable. Over 20,000 simulations per parameter set, then, the following were noted:
1. The mean value (over all simulations) of the estimated treatment effect and the confidence limits under each of the three procedures,
2. The percentages of simulations for which the estimated confidence interval did not contain the true value of the treatment effect (zero) and whose lower/upper limit was greater/less than this true value,
3. The percentage of simulations for which the estimated confidence interval contained the true value for the treatment effect. This figure was simply 100 minus the sum of the two percentages in 2 above.
Thus ideally the two figures in 2 should each be 2.5% whilst that for 3 should be 95% (the nominal level). It was decided to split observed non-coverage rates according to whether there was spurious positive treatment effect or a spurious negative one because skewed distributions were expected to have different implications for each of these.
Data generation process
The data were constructed by assuming there were n individuals in each of 2k clusters. Half of these were randomised to a hypothesised intervention group, whilst the other half were randomised to a control group. A random effects model incorporating a treatment dummy variable was used:
Ehij = αhi + βTh + εhij h = 0,1; i = 1,...k;j = 1,...n;
E(αhi) = 0; E(εhij) = 0
Thus the jth individual in the ith cluster was randomised to receive treatment h. Each individual's outcome, Ehij, comprised three elements: the effect of treatment, βTh, the cluster-specific effect, αhi, and the individual-specific effect, εhij. In order to inform the values of these parameters, it was necessary to undertake some exposition of how cost data might be distributed in a cluster RCT.
Conceptualising costs in a cluster RCT
In attempting to construct realistic scenarios for cost data from a cluster RCT, three main factors were considered:
1. What is the nature of the distribution of individual patient costs expected to be in the population of patients normally eligible for treatment?
2. How representative of this population distribution are the cost distributions within clusters likely to be? This has implications for the ICC and the assumptions regarding the distributions.
3. How might the introduction of an intervention affect 2 above?
As detailed previously, the existence of one unrepresentative cluster (such as a London teaching hospital) in one arm may affect the ICC, independently of treatment, or the treatment could directly change the ICC and the distribution [2].
Parameters varied in the simulation model
This section formally sets out the parameters which, when varied in the simulation model, attempted to capture two potential extreme scenarios as well as situations in between. Under the first scenario, all clusters are equally representative of the population, leading to a high degree of skewness within most, if not all, clusters. Under such a scenario, the ICC is likely to be small and results would be expected to be robust to any incorrect assumptions made regarding the between-cluster distribution. Under the second scenario, within-cluster costs are expected to be more homogenous and much of the skewness in the cost data at the population level is attributable to differences in the cluster mean costs. As a result, the ICC would be expected to be much larger, and the between-cluster distribution might be expected to exhibit considerable skewness. Six factors were varied:
1. The between-cluster distributions,
2. The within-cluster distributions,
3. The ICC in the control group,
4. The ICC in the intervention group,
5. The number of clusters in each intervention arm,
6. The number of individuals in each cluster.
The between-cluster distributions
There were two distributional assumptions used for the cluster means. The first was the normal distribution and the second was the lognormal distribution, which, for a given mean and variance, exhibits higher kurtosis and skewness than the gamma distribution, the main alternative skewed distribution.
For each of the ICC combinations given below, there were three possible combinations of the between-cluster distributions:
1. The cluster means in both the control and intervention groups were normally distributed
2. The cluster means in both the control and intervention groups were lognormally distributed
3. The cluster means in the (nominal) control group were normally distributed, whilst in the (nominal) intervention group they were lognormally distributed.
The within-cluster distributions
The same distribution was used for individual level data in both treatment arms in order not to further increase the number of parameter combinations possible. Lognormal data were used in order to provide a scenario applicable to individual level cost data.
The ICC in the control group
Given that the likely range of cost ICCs is largely unknown, values between zero and 0.5 were used. While high, the upper value is consistent with the one published source of cost ICCs [10]. The ICCs used were 0.01, 0.1 and 0.25 for one of the two treatment groups. The value 0.5 was not used for the control group but was achieved in the intervention group as a result of changes in the ICC (see below). The total variance, equal to the between-cluster variance plus the within-cluster variance, was arbitrarily fixed at 100.
The ICC in the intervention group
For a given value of the ICC in the control group, the intervention ICC can remain the same or it can change in a number of ways. In particular, the intervention ICC:
1. Remained the same as the control ICC,
2. Doubled, as a result of an appropriate increase in the between-cluster variance,
3. Doubled, as a result of an appropriate decrease in the within-cluster variance,
4. Halved, as a result of an appropriate decrease in the between-cluster variance,
5. Halved, as a result of an appropriate increase in the within-cluster variance.
Although it is only the between-cluster variance or the within-cluster variance that changes at any one time (not both), the changes involved are large ones. Hence these extreme scenarios should cover a range of findings.
The number of clusters in each group
The number of clusters in each group was 6, 12 or 24. These figures reflect the small numbers of clusters recruited in many cluster RCTs and, coupled with the cluster sizes given below, they allowed alternative combinations of cluster size and number of clusters to be investigated for a given total trial size.
The number of individuals in each cluster
The cluster size was 25, 50 or 100. Mean cluster sizes between 50 and 100 are not unusual in health services research trials, but there is enormous variation in cluster size, depending upon treatment area and type of cluster[1].
Comparison of methods that allow for clustering
The first method utilises a standard procedure, where 'standard' has been taken to mean a 95% confidence interval quoted for continuous data in packages such as Stata [11], utilising a point estimate and a Huber-White (robust) cluster-adjusted standard error [12-14]. Bootstrapping was performed for the two other methods, as described in Davison and Hinkley [7]. Under both methods the sampling structure was maintained in a bootstrap replication by selecting k clusters with replacement from the treatment group and selecting k clusters with replacement from the control group – in other words, resampling of clusters was stratified by intervention group. Under method 1 all individuals within a resampled cluster were then selected. Under method 2 a second level of bootstrap was performed on individuals within clusters selected at level one. The difference between the two randomisation group means was then calculated for each method. This was repeated to give 1000 bootstrap estimates (estimates performed on the resampled data) of the treatment effect. A bias-corrected and accelerated (BCa) confidence interval was then estimated at the same nominal 95% level as for the robust method [8]. Given the nature of the BCa method, the resulting confidence interval need not be symmetric. The bootstrap methods are described in more detail below.
Method 1
Under this procedure (BS1), clusters are bootstrapped and each resampled cluster is kept intact. This method is utilised by Stata [11] when the cluster() option is added to the bootstrap command. Suppose that within a randomisation group, for each of k clusters, n responses are obtained, yij, such that
yij = αi + εij i = 1,...k;j = 1,...n.
The αis are sampled randomly from the distribution Fα and the εijs are independently sampled randomly from the distribution Fε.
E(αi) = 0; (1)
It can be shown that for the bootstrap estimates (with superscript asterisks):
When compared with (2) and (3) it should be noted that the expected variance and covariance of the resampled outcome data are slightly biased downwards. However, an estimator such as the sample mean is strongly consistent (in that its bias is zero and its variance tends to zero as the total sample size approaches infinity); the level of bias is small unless the number of clusters becomes very small.
Method 2
An alternative method (BS2) involving resampling individuals as well as resampling whole clusters was also considered. This uses a first stage bootstrap applied to the estimated cluster means (sampling with replacement). The second stage, in which individuals are bootstrapped, involves resampling the deviations from the estimated cluster means. However, the estimated cluster means incorporate both within and between-cluster variability and any analysis that restricts itself to the cluster means will over-estimate the variance in these means [7]. By incorporating the deviations from the estimated cluster means we have, in effect, double-counted the within-cluster variance. Therefore, the cluster means were shrunk using Davison and Hinkley's shrinkage estimates, (see page 102 of their book):
where c is given by
; if the right hand side is negative, it is reset to zero.
The variance of the adjusted cluster means, , is then .
The deviations from the estimated cluster means were also standardised to
Finally, for all resampled clusters, the 'shrunken' mean is added to the standardised deviation for each resampled individual:
This method, with the rescaling procedures will in future be referred to as the BS2 method, or double bootstrap.
Results
The primary focus of this work was on confidence interval coverage and rejection rates; the estimated confidence limits are, therefore, not presented but general inferences regarding the relative width of various confidence intervals can be made easily from the coverage and rejection rates presented below.
Coverage rates for cost confidence intervals
As a summary, Tables 1 to 3 show coverage rates for each of the three methods of analysis for each sample size combination when averaged over the 15 parameter/distribution combinations for a given control group ICC – that is, three distributional assumptions for each of five ICC combinations.
Issues specific to variance or distribution combinations are presented below. In the three tables, within each box the first number represents the coverage of the robust confidence interval, the second represents the coverage of the BS1 method whilst the third figure represents that of the BS2 method. A number of results are immediately apparent:
• All three methods produce coverage of less than 95%, the nominal level,
• All three methods appear to be consistent with respect to the impact of the number of clusters per arm. In other words, as the number of clusters per arm increases, the observed coverage approaches 95% for each method,
• The coverage of the robust method is within approximately 1.5% of 95%,
• The BS1 method is always outperformed by the other two methods. In other words the other two methods always achieve coverage that is closer to 95%,
• The BS2 method performs much better than the BS1 method but never as well as the robust method,
• As the ICC in the control group increases (that is, across tables) the robust method performs slightly worse whilst the performances of the bootstrap methods are noticeably poorer,
• When examining numbers along the diagonal in each figure (that is, for equivalent total sample sizes), the bootstrap methods perform much better for a large number of clusters and small cluster size compared with vice-versa. This is probably due to the slight downward bias in the second moments; the degree of bias is an inverse function of the number of clusters.
From the results in these tables there does not appear to be much to commend the bootstrap, since both bootstrap methods are always outperformed by the robust method. Moreover, when examining the confidence interval coverage results split by distribution and variance combination (results not shown), there was only one parameter combination for which the robust method was outperformed and this was by an amount that was consistent with Monte Carlo sampling error. However, the rejection rates for confidence intervals were split to ascertain if any method was noticeably better at taking account of the skewness in the data.
Rejection rates for confidence intervals
When examining the rejection rates for six clusters of size 25 (results not shown), those of both bootstrap methods virtually always exceeded those under the robust method. The exceptions were for a control ICC of 0.01 when the within-cluster variance increased as a result of the intervention for two of the distributional combinations. For the third distributional combination the rejection rates were the same. Since the individual level data were always lognormally distributed (reflecting typical cost data at this level), an increase in the within-cluster variance for lognormally distributed data is likely to have a large effect on skewness, provided the ICC is not too large (which would dilute the effect of within-cluster factors).
Thus, if the desired criterion is ability to match the nominal 2.5% rejection rate in any given direction, there are occasions when one or both bootstrap methods outperform the robust method in terms of the proportion of lower rejections. In particular, this appeared to happen when the ICC was moderate to large, together with an extremely skewed distribution of the treatment effect, typically achieved by a large change in the ICC and something other than a normal, normal (N, N) distribution combination. Under these circumstances the distribution of the treatment effect is most skewed, since the cluster means in the intervention group are exhibiting large skewness by way of a lognormal distribution whose (already moderate to large) variance has doubled. However, this must be balanced against the poor performance of the bootstrap methods in terms of the proportion of upper rejections, which tended to be particularly high compared with those from the robust method.
Larger cluster size
With a cluster size of 50 or 100 (results not shown), similar results were seen to those above, in that a large difference in the absolute value of the ICC together with extremely skewed distributions were required for the double bootstrap to achieve a rejection rate closer to the nominal level than the robust method. In particular, increases in the between-cluster variance accompanied by skewed distributions at the between-cluster level typically caused the double bootstrap to give better lower rejection rates.
Larger number of clusters
For 12 clusters of size 25 per arm (results not shown), all three methods performed better than for an equivalent sample size with fewer clusters per arm (6 clusters of size 50). There were very few instances in which the rejection rate exceeded that for 6 clusters of size 50. Even the BS1 method appeared to perform more consistently than when there were only six clusters per arm. Although it was still always outperformed by the BS2 and robust methods, its maximum rejection rate was 10.26%, compared with 14.24%. As before, when the ICC was very small (that is, most of the variability was within clusters), the double bootstrap method typically only outperformed the robust method when the within-cluster variance increased. In addition, for larger values of the ICC, changes in the between-cluster variance or the within-cluster variance could result in the double bootstrap outperforming the robust method, confirming the results obtained for six clusters of size 100. Lastly, normal distributions at the between-cluster level in both arms were sufficient to ensure that the robust method always performed better than the bootstrap.
Larger number of clusters and larger cluster size
All the trends identified in previous sections were replicated for 24 clusters of size 25 (see Tables 4, 5, 6). Interestingly, for ICC = 0.25, this sample size combination produced the largest number of occasions on which the BS2 method outperformed the robust method, namely eight. However, this should be balanced against the continued better coverage of the robust method overall.
Discussion
This work constitutes early stages in the further research that has been advocated to identify appropriate approaches to the analysis of cost outcomes from cluster RCTs [10]. The ICCs used in the present simulations were comparable to those estimated for costs in this previous study, but highly variable ICCs for costs at different levels and the magnitude of patient costs relative to total costs have both been emphasised as important issues [10]. Thus, decisions as to whether any of the scenarios investigated here are relevant to future trials will depend, in part, upon the issue of which cost component is most important.
Limitations
The main limitation of the work presented here is the lack of empirical data to inform the modelling. Empirical data on costs from cluster RCTs are required to investigate whether the bootstrap method should be evaluated in the presence of even more highly skewed data. The bootstrap methods of Davison and Hinkley also require testing in trials with non-constant cluster size. For the single bootstrap, in the presence of a non-constant cluster size, each bootstrap sample of clusters will have a different composition. The sample mean will exhibit a different degree of variability depending upon, for example, whether the bootstrap sample has happened to select many large or many small clusters. The result may be incorrect inferences about the variability in the sample mean. Moderate variability in cluster size or a large number of clusters might not be expected to have a large effect upon the estimated confidence limits, but again the researcher would have to be cautious. Whilst the double bootstrap can address this issue, this version resamples only the deviations from the cluster mean of an individual's 'own' cluster, potentially omitting valuable statistical information [7]. However, more complex bootstrap methods such as those of Rao and Wu and Carpenter et al do not suffer from this restriction and future work should allow the cluster size to vary [9,15,16].
Future research
Despite these limitations, the results from the simulations present a coherent picture of the relative strengths of the methods of analysis that were compared. They also show that methods of analysis that can deal adequately with trial data incorporating a small number of clusters must be developed and investigated. For cost data the robust method gave confidence intervals with broadly correct coverage when the cluster size was constant. The Huber-White estimator can take account of non-constant cluster size, so future work should address its ability to give acceptable confidence intervals when data are skewed and the cluster size varies within the trial.
Conclusions
There are a number of general points that can be drawn from these results. First, when the between-cluster distribution is normal in both treatment arms, there is virtually no evidence in favour of using a bootstrap method. Second, when the ICC takes values of about 0.1 or greater, the double bootstrap can give a lower rejection rate which is closer to the nominal level than that achieved by the robust method, particularly when the between-cluster distributions are skewed. However, this is only common when the ICC changes as a result of the intervention. Third, the downward bias in the second moments of the bootstrap methods is particularly problematic. In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of cluster and extremely skewed data would be necessary for the bootstrap to approximate the results from the robust method with any consistency. The likelihood of such a scenario will clearly vary, but in any case these simulations have brought out a very important issue: a normal distribution for the cluster means is usually sufficient to eliminate the bootstrap from any consideration, regardless of the skewness of the individual data.
This work has related to skewed cost (or potentially clinical) data. Whilst this is of interest, policymakers are more often interested in cost-effectiveness data. These have usually involved ratio statistics, which often cause major problems for traditional estimators. Future work should, therefore, investigate how well the cluster bootstrap deals with cost-effectiveness data and hence address the question of whether the disadvantages of the cluster bootstrap identified here are outweighed by its potential advantages in dealing with ratio statistics.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
TNF conceived of the study, wrote and conducted the simulation programs and drafted the manuscript.
TJP participated in the design and coordination of the study, read, critically reviewed and contributed to drafts of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Funding: The work presented here formed part of TNF's PhD thesis, funded by the UK Medical Research Council (MRC Studentship Ref G78/5775) and supervised by TJP and EW. TNF is currently funded by the MRC Health Services Research Collaboration, for which Bristol is the lead centre.
We acknowledge the assistance and helpful comments of Dr Paul Mulheran, Dr Elise Whitley (EW) and Professor Allan Donner.
Figures and Tables
Table 1 Observed coverage (%) for robust, BS1 and BS2 methods of analysis (Control ICC = 0.01)
Cluster Size
25 50 100
Number of clusters per arm 6 94.1 93.8 94.0
88.2 88.2 88.3
93.2 93.1 92.7
12 94.6 94.5
91.1 91.3
93.4 93.3
24 94.8
92.7
93.7
Table 2 Observed coverage (%) for robust, BS1 and BS2 methods of analysis (Control ICC = 0.1)
Cluster Size
25 50 100
Number of clusters per arm 6 93.9 93.9 93.8
87.6 87.3 86.9
90.9 90.6 90.3
12 94.5 94.4
90.5 90.2
91.8 91.6
24 94.8
92.1
92.6
Table 3 Observed coverage (%) for robust, BS1 and BS2 methods of analysis (Control ICC = 0.25)
Cluster Size
25 50 100
Number of clusters per arm 6 93.6 93.5 93.5
86.9 86.6 86.4
90.1 89.9 89.6
12 94.3 94.2
89.9 89.6
91.3 91.1
24 94.6
91.5
92.1
Table 4 Control ICC = 0.01, 24 clusters of size 25 per arm.
How does the ICC change as a result of the intervention? Between-cluster distribution combination1 Rejection Rate (%)2
Huber-White Single Bootstrap Double Bootstrap
Lower Upper Lower Upper Lower Upper
No change - N, N 2.65 2.45 3.55 3.46 3.11 3.08
LN, LN 2.57 2.64 3.80 3.68 3.25 3.27
N, LN 2.42 2.76 3.64 3.67 3.21 3.39
Double N, N 3.08 2.35 3.65 3.57 3.13 3.37
LN, LN 2.88 2.37 4.00 3.75 3.36 3.40
N, LN 2.84 2.46 3.93 3.45 3.29 3.27
N, N 2.55 2.59 3.38 3.40 3.26 2.99
LN, LN 2.44 2.82 3.91 4.05 3.53 3.55
N, LN 2.07 3.00 3.50 3.77 3.04 3.41
Halve N, N 1.97 3.27 3.23 3.97 2.79 3.30
LN, LN 2.10 3.03 3.49 3.83 2.93 3.04
N, LN 1.92 3.20 3.24 3.77 2.87 2.95
N, N 2.66 2.59 3.66 3.58 3.06 3.06
LN, LN 2.54 2.46 3.63 3.71 3.00 3.14
N, LN 2.38 2.57 3.43 3.51 2.87 3.05
1 Control arm is first, followed by intervention arm. N, N denotes normal distribution in both arms; LN, LN denotes lognormal distribution in both arms; N, LN denotes normal distribution in control arm and lognormal distribution in intervention arm
2 Entries in bold indicate where double bootstrap method achieved rejection rate closer to nominal 2.5% than Huber-White method.
Table 5 Control ICC = 0.1, 24 clusters of size 25 per arm.
How does the ICC change as a result of the intervention? Between-cluster distribution combination1 Rejection Rate (%)2
Huber-White Single Bootstrap Double Bootstrap
Lower Upper Lower Upper Lower Upper
No change - N, N 2.70 2.72 3.25 3.34 3.00 3.16
LN, LN 2.47 2.38 4.95 4.92 4.53 4.47
N, LN 1.48 3.64 3.23 4.04 2.85 4.05
Double N, N 2.66 2.53 3.12 3.19 2.85 3.13
LN, LN 2.28 2.36 4.99 5.07 4.60 4.63
N, LN 1.59 3.70 3.49 4.25 3.04 4.25
N, N 2.43 2.77 2.94 3.38 2.71 3.05
LN, LN 1.42 3.90 4.45 5.94 3.99 5.49
N, LN 1.10 4.97 3.38 5.00 2.91 4.95
Halve N, N 2.38 2.72 3.17 3.30 3.18 3.03
LN, LN 2.25 2.50 4.48 4.61 4.15 4.14
N, LN 1.65 3.77 3.47 4.02 3.10 3.89
N, N 2.73 2.48 3.33 3.16 3.10 3.09
LN, LN 3.34 1.69 5.26 4.06 4.91 3.64
N, LN 2.20 2.95 3.61 3.56 3.17 3.49
1 Control arm is first, followed by intervention arm. N, N denotes normal distribution in both arms; LN, LN denotes lognormal distribution in both arms; N, LN denotes normal distribution in control arm and lognormal distribution in intervention arm
2 Entries in bold indicate where double bootstrap method achieved rejection rate closer to nominal 2.5% than Huber-White method.
Table 6 Control ICC = 0.25, 24 clusters of size 25 per arm.
How does the ICC change as a result of the intervention? Between-cluster distribution combination1 Rejection Rate (%)2
Huber-White Single Bootstrap Double Bootstrap
Lower Upper Lower Upper Lower Upper
No change - N, N 2.76 2.73 3.31 3.27 3.12 3.06
LN, LN 2.28 2.32 5.77 5.55 5.30 5.16
N, LN 1.38 4.08 3.42 4.43 3.00 4.30
Double N, N 2.84 2.66 3.30 3.20 2.91 3.09
LN, LN 2.03 2.17 5.55 5.81 5.07 5.25
N, LN 1.41 4.13 3.60 4.45 3.18 4.21
N, N 2.63 2.74 3.12 3.22 2.88 2.98
LN, LN 0.84 5.38 3.84 7.90 3.43 7.39
N, LN 0.71 6.53 3.25 6.20 2.94 5.89
Halve N, N 2.56 2.65 3.16 3.19 2.99 2.84
LN, LN 1.98 2.38 4.96 5.32 4.57 4.70
N, LN 1.47 4.21 3.45 4.41 3.12 4.11
N, N 2.56 2.79 3.09 3.25 2.91 3.10
LN, LN 4.26 1.39 6.70 4.54 6.16 4.15
N, LN 1.79 3.36 3.24 3.73 2.95 3.57
1 Control arm is first, followed by intervention arm. N, N denotes normal distribution in both arms; LN, LN denotes lognormal distribution in both arms; N, LN denotes normal distribution in control arm and lognormal distribution in intervention arm
2 Entries in bold indicate where double bootstrap method achieved rejection rate closer to nominal 2.5% than Huber-White method.
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| 15550169 | PMC535558 | CC BY | 2021-01-04 16:03:29 | no | BMC Health Serv Res. 2004 Nov 18; 4:33 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-33 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-831556084410.1186/1471-2407-4-83Research ArticleThe in vitro effect of gefitinib ('Iressa') alone and in combination with cytotoxic chemotherapy on human solid tumours Knight Louise A [email protected] Nicolantonio Federica [email protected] Pauline [email protected] Stuart [email protected] Sanjay [email protected] Sharon [email protected] Penny [email protected] Ian A [email protected] Translational Oncology Research Centre, Department of Histopathology, Queen Alexandra Hospital, Portsmouth, UK2 Mayday University Hospital, Croydon, UK3 Richards Hospital, Chichester, UK2004 23 11 2004 4 83 83 16 7 2004 23 11 2004 Copyright © 2004 Knight 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
Activation of the epidermal growth factor receptor (EGFR) triggers downstream signaling pathways that regulate many cellular processes involved in tumour survival and growth. Gefitinib ('Iressa') is an orally active tyrosine kinase inhibitor (TKI) targeted to the ATP-binding domain of EGFR (HER1; erbB1).
Methods
In this study we have used a standardised ATP-based tumour chemosensitivity assay (ATP-TCA) to measure the activity of gefitinib alone or in combination with different cytotoxic drugs (cisplatin, gemcitabine, oxaliplatin and treosulfan) against a variety of solid tumours (n = 86), including breast, colorectal, oesophageal and ovarian cancer, carcinoma of unknown primary site, cutaneous and uveal melanoma, non-small cell lung cancer (NSCLC) and sarcoma. The IC50 and IC90 were calculated for each single agent or combination. To allow comparison between samples the IndexSUM was calculated based on the percentage tumour growth inhibition (TGI) at each test drug concentration (TDC). Gefitinib was tested at concentrations ranging from 0.0625–2 microM (TDC = 0.446 microg/ml). This study represents the first use of a TKI in the assay.
Results
There was heterogeneity in the degree of TGI observed when tumours were tested against single agent gefitinib. 7% (6/86) of tumours exhibited considerable inhibition, but most showed a more modest response resulting in a low TGI. The median IC50 value for single agent gefitinib in all tumours tested was 3.98 microM. Interestingly, gefitinib had both positive and negative effects when used in combination with different cytotoxics. In 59% (45/76) of tumours tested, the addition of gefitinib appeared to potentiate the effect of the cytotoxic agent or combination (of these, 11% (5/45) had a >50% decrease in their IndexSUM). In 38% of tumours (29/76), the TGI was decreased when the combination of gefitinib + cytotoxic was used in comparison to the cytotoxic alone. In the remaining 3% (2/76) there was no change observed.
Conclusion
The in vitro model suggests that gefitinib may have differential effects in response to concomitant cytotoxic chemotherapy with the agents tested during this study. The mechanism involved may relate to the effect of TKIs on growth rate versus their effect on the ability of the cell to survive the stimulus to apoptosis produced by chemotherapy.
==== Body
Background
The epidermal growth factor receptor (EGFR) is involved in many cellular processes including cell proliferation, motility, adhesion and angiogenesis via the activation of three pathways: phosphatidylinositol-3 kinase (PI3)/Akt pathway, the Jak/STAT pathway and the ras/raf pathway. EGFR is expressed or highly expressed in a variety of human tumours including non-small-cell lung cancer (NSCLC), breast, bladder, ovarian and head and neck [1] and is therefore a promising target for cancer therapy.
Gefitinib ('Iressa') is an EGFR-tyrosine kinase inhibitor (EGFR-TKI) that competitively inhibits binding of ATP at the ATP site on EGFR. It also displays remarkable selectivity for EGFR (IC50 = 0.033 microM) compared with other receptor tyrosine kinases (RTKs) that share sequence homology in the ATP binding domain [2]. In pre-clinical studies, gefitinib has demonstrated in vitro growth inhibition against a variety of human cell lines including NSCLC, ovarian, breast, colon and head and neck and is active in a range of xenograft models, including breast, colon and prostate [3]. Phase II trials with gefitinib monotherapy have produced encouraging results with clinically significant benefits observed, such as disease control rates at 250 mg/day gefitinib of 54% and 42% in IDEAL 1 and IDEAL 2, respectively [4,5]. Results from Phase III trials investigating gefitinib in combination with cisplatin and gemcitabine (INTACT 1) [6] and gefitinib in combination with paclitaxel and carboplatin (INTACT 2) [7] in NSCLC concluded there was no added benefit in patients receiving chemotherapy plus gefitinib; however the tolerability of gefitinib was confirmed.
At present, there is conflicting evidence relating the activity of gefitinib directly to the levels of EGFR expression. One group found that the concentration of gefitinib required to inhibit ligand-independent growth by 50% (IC50) in four bladder cancer cell lines ranged from 1.8–9.7 microM and correlated with EGFR protein and transcript level [8]. However, another study using human tumour xenografts found that gefitinib caused growth inhibition of tumours and enhancement of the activity of a number of cytotoxic drugs, but neither was dependent on high levels of EGFR expression [9]. Moreover, no consistent association was demonstrated between EGFR expression and clinical outcome in IDEAL 1 and 2 [10]. Alternative explanations for the activity of gefitinib in systems where EGFR is not over expressed include inhibition of EGFR pathway activation mediated by increased levels of receptor ligands e.g. epidermal growth factor (EGF) and transforming growth factor-alpha (TGF-alpha); heterodimerization with HER2 and cross talk with heterologous receptors; and EGFR mutations yielding a constitutively active receptor that is not down-regulated by endocytosis [11]. There is evidence that the ras/raf pathway mediates proliferation [12], whereas the PI3/Akt pathway is essential for cell survival and may be constitutively activated in many tumours by loss of PTEN [13].
We have previously shown that the ATP-based tumour chemosensitivity assay (ATP-TCA) can be used to measure the effects of cytotoxic agents and antibodies against human tumour-derived cells, and that this matches clinical outcome in a number of tumour types [14,15]. Use of the assay to direct choice of chemotherapy has been shown to improve response rate and progression-free survival in ovarian cancer [16,17] and a fully randomized trial of assay-directed versus physician's choice of chemotherapy for platinum-resistant ovarian cancer is in progress [18]. The assay system has been used to assist the development of a number of new agents and combinations [19,20], but this represents the first use of a TKI in the assay.
EGF and TGF-alpha, ligands of EGFR, act as survival factors for many cells as well as growth factors. As many cytotoxic agents induce apoptosis, gefitinib may be able to potentiate their effects by reducing survival stimuli. The current pilot study was undertaken to assess the effect of gefitinib in combination with existing chemotherapeutic agents (cisplatin, gemcitabine, oxaliplatin, treosulfan) against a wide range of tumour types.
Methods
Tumours
A total of 86 tumours (57 females:29 males) were tested in this study, with a median age of 59 years (range 21–90). The samples tested consisted of the following tumour types; breast adenocarcinoma (n = 8), colorectal carcinoma (n = 18), cutaneous melanoma (n = 7), NSCLC (n = 1), oesophageal adenocarcinoma (n = 4), ovarian carcinoma (n = 26), sarcoma (n = 2), squamous cell carcinoma (n = 2), sweat gland carcinoma (n = 1), uveal melanoma (n = 12) and carcinoma of unknown primary site (n = 5). The 26 ovarian carcinomas were all recurrent stage 3/4 cancers and 25/26 were pre-treated (11 with carboplatin and 14 with carboplatin + paclitaxel). Of the remaining samples, 10/86 had been treated with a variety of chemotherapy regimens and some patients had more than one treatment; epirubicin + cisplatin + 5-Fluorouracil (5-FU) (n = 3), epirubicin + cyclophosphamide (4-HC) (n = 1), 4-HC+methotrexate+5-FU (CMF) (n = 2), cisplatin + vinorelbine (n = 1), mitomycin C + 5-FU (n = 1), mitoxantrone + paclitaxel (n = 1), chlorambucil (n = 1), 4-HC (n = 1) and irinotecan (n = 1). The remaining 51 patients had no previous treatment. In each case only tumour material not required for diagnosis was sent for ATP-TCA, and in all cases consent had been obtained and permission had been granted by the local ethics committee.
ATP-TCA
The ATP-TCA was performed as previously published [14,21]. Solid tumour or ascites samples were transported to the laboratory in transport medium, consisting of Dulbecco's Eagles Media (DMEM) (Sigma, UK; D6171). Solid samples were dissected under sterile conditions in a BioQ Microfuge Class II Hood and placed into a 0.75 mg/ml collagenase solution (Sigma, UK; C8051) for enzymatic dissociation overnight. Following dissociation, the single celled suspension or ascites sample was washed using DMEM supplemented with 1 M HEPES, (Sigma, UK; H0887), 100 IU/mL penicillin, 10 mg/mL streptomycin (Sigma, UK; P0781) and 10 mg/mL gentamicin (Sigma, UK; G1272). The final cell suspension was then plated in 96-well polypropylene plates (Corning Life Sciences, High Wycombe, UK) at 20,000 (solid sample) or 10,000 (ascites sample) cells/well in a serum-free complete assay medium (CAM, DCS Innovative Diagnostik Systeme, Hamburg, Germany). Drugs were added to triplicate wells at serial dilutions corresponding to 200–6.25% of a test drug concentration (TDC) estimated from pharmacokinetic data, which included the degree of protein binding. Two controls were included in each plate: one with no drug and consisting of media only (MO), and a maximum inhibitor (MI) control which killed all cells present. The plates were incubated for 6 days at 37°C with 5% CO2. At the end of the incubation period, remaining cells were lysed by addition of an ATP extraction reagent (DCS Innovative Diagnostik Systeme). An aliquot of the lysate from each well was added to the corresponding wells of a white 96 well microplate (Thermo Life Sciences, Basingstoke, UK), followed by addition of luciferin-luciferase reagent. The light output corresponding to the level of ATP present was measured in a luminometer (MPLX, Berthold Diagnostic Systems, Hamburg, Germany). These data were transferred automatically to an Excel spreadsheet where the % inhibition achieved at each concentration tested was calculated using the equation; 1-(test-MI)/(MO-MI) × 100. Several parameters of efficacy can be calculated e.g. IC50 and IC90, however previous ATP-TCA studies have found that a natural logarithmic sum index (IndexSUM) calculated by direct addition of the percentage survival at each concentration tested (Index = 600-Σb3;%Inhibition6.25...200) provides a better indication of sensitivity or resistance to different drugs in different tumour types [22]. The total inhibition of growth resulted in an index of 0, and no inhibition of growth at any concentrations produces an index of 600 [23]. Area under the concentration-inhibition curve (IndexAUC) was calculated from the data using the trapezoidal rule.
Data Analysis
The results were entered into an Access 2000 database for further analysis. Statistical tests were performed using non-parametric methods.
Drugs
The cytotoxic drugs used in the assay were obtained as vials for injection and made up according to manufacturers' instructions. Gemcitabine, oxaliplatin and treosulfan were stored in aliquots at -20°C, while cisplatin was stored at room temperature. Table 1 shows the 100% TDC for each of the drugs used. Drug combinations were tested by combining single agents. The EGFR-TKI, gefitinib (kindly provided by AstraZeneca) was tested at concentrations ranging from 0.06–2 microM (100% TDC = 0.99 microM).
Immunohistochemistry
Tissue was available for EGFR immunohistochemical staining in 31/86 (36%) cases comprising of 4 breast carcinomas, 12 colon carcinomas, 2 oesophageal carcinomas, 2 ovarian carcinomas, 1 sarcoma, 4 skin melanomas, 5 uveal melanomas and 1 carcinoma of unknown primary site. Paraffin embedded sections of 4 μm thick were dewaxed and rehydrated in preparation for immunohistochemical staining. Endogenous peroxidase was blocked using 3% hydrogen peroxide in methanol. The sections were pretreated with 0.1% Trypsin (CaCl2/Tris buffer pH8.0) for 10 minutes at 37°C for antigen retrieval. Immunohistochemical studies were performed according to manufacturer's instructions of the Vectastain Universal ABC-AP kit (Vector Laboratories, Burlingame, California, U.S.A), which uses an avidin-biotin complex method and Vector red as the chromogen. Monoclonal antibody for EGFR, Clone E30 (Dakocytomation, Cambridgeshire, U.K) was used at a dilution of 1:20 and incubated with sections for 18 hours at 4°C. Positive (squamous cell carcinoma tissue) and negative controls were included in each staining procedure. Samples were assessed by a pathologist using the H-score. Intensity was graded on a scale ranging between 0, 1+, 2+ or 3+, (where 1+ equals weak staining, 2+ equals moderate and 3+ equals intense) and the proportion of cells stained at the highest intensity. The two values were then multiplied together to give the final value.
The same tissue available for EGFR staining was also available for pAkt staining. Paraffin embedded sections of 4 μm thick were dewaxed and rehydrated in preparation for immunohistochemical staining. Endogenous peroxidase was blocked using 3% hydrogen peroxide in methanol. The sections were pretreated with 0.1 M citrate buffer in a pressure cooker for 2.5 minutes for antigen retrieval. Immunohistochemical studies were performed according to manufacturer's instructions of the Vectastain Universal ABC-AP kit (Vector Laboratories, Burlingame, California, U.S.A), which uses an avidin-biotin complex method and Fuchsin as the chromogen. Phospho-Akt, Ser473 (#9277 L, Cell Signalling, MA. USA) was used at a dilution of 1:50 and incubated with sections for 18 hours at 4°C. Positive (prostate cancer tissue) and negative controls were included in each staining procedure. Samples were assessed as described previously.
Results
Gefitinib showed low inhibition (IndexSUM >300) across the range of concentrations tested in the ATP-TCA, with little evidence of increasing inhibition with increasing drug concentration. 7% (6/86) of tumours exhibited considerable inhibition (>50% inhibition at 100% TDC), but most showed a more modest response resulting in a low maximum percentage inhibition (Figure 1). The estimated median IC50 and IC90 value for single agent gefitinib in all tumours tested was 3.98 microM (<0.1–69.9 microM) and 6.45 microM (2.4–125.9 microM) respectively. The median IC50 for individual tumour types tested is shown in Table 2.
There was heterogeneity in the degree of inhibition observed when tumours were tested against single agent gefitinib (Figure 2). To compare between tumours, an IndexSUM of <300 corresponding to 50% inhibition across the range of concentrations tested was used to compare results. On this basis, single agent gefitinib was effective against 5% (4/86) of samples, comprising 1 colorectal tumour, 1 ovarian tumour, 1 uveal melanoma and 1 unknown primary carcinoma. In 88% (76/86) of samples there was sufficient material to test gefitinib in combination with different cytotoxics.
Table 3 shows the median results for single-agent cytotoxics tested compared to results when tested in combination with gefitinib. In samples tested with gefitinib in combination with cisplatin (n = 6) only 33% (2/6) showed increased sensitivity (i.e. a decrease in their IndexSUM), compared to when cisplatin was used alone. The remaining 67% (4/6) showed increased resistance (i.e. an increase in their IndexSUM). This compares with gefitinib in combination with oxaliplatin (n = 10) where 90% (9/10) of samples showed an increase in sensitivity with the combination, with 1 sample showing a >50% decrease in the IndexSUM. When gefitinib was combined with gemcitabine (n = 2), both samples showed an increase in their sensitivity.
Of the tumours tested with treosulfan + gefitinib, 38% (13/34) were of ovarian origin. Of these, 62% (8/13) showed potentiation, with 1 sample showing a >50% decrease in IndexSUM. 31% (4/13) showed increased resistance with the combination in comparison with treosulfan alone and 1 sample showed no change (Figure 3). Of the remaining samples tested with gefitinib + treosulfan, 57% (12/21) showed an increase in sensitivity, 38% (8/21) showed an increase in their resistance and one sample showed no change. Figures 4a and 4b show differential effects of gefitinib in combination with treosulfan in cells derived from a skin melanoma sample (Figure 4a) and an ovarian carcinoma sample (Figure 4b). When gefitinib was tested in combination with treosulfan + gemcitabine (n = 24), 54% (13/24) showed an increase in sensitivity, with 3 samples showing a >50% decrease in their IndexSUM and 46% (11/24) showed an increase in resistance.
In summary, the addition of gefitinib appeared to potentiate the effect of the cytotoxic agent or combination in 59% (45/76) of tumours tested; of these 11% (5/45) had a >50% decrease in their IndexSUM. In 38% of tumours (29/76), the combination of gefitinib + cytotoxic caused the IndexSUM to increase thereby increasing resistance. In the remaining 3% (2/76) there was no change observed.
Immunostaining for EGFR was positive in 32% (10/31) of samples comprising of 1 breast carcinomas, 5 colon carcinomas, 1 ovarian carcinoma, 1 sarcoma, 1 skin melanoma and 1 carcinoma of unknown primary site. Immunostaining for pAkt was positive in 81% (25/31) of samples comprising of 3 breast carcinomas, 10 colon carcinomas, 2 oesophageal carcinomas, 2 ovarian carcinomas, 4 skin melanomas, 3 uveal melanomas and 1 carcinoma of unknown primary site. Of the positive samples, 8 were positive for both antibodies (comprising 1 breast carcinoma, 4 colon carcinomas, 1 ovarian carcinoma, 1 skin melanoma and 1 carcinoma of unknown primary site). In 74% (23/31) of samples that were stained for EGFR and pAkt, there was an IC50, IC90 and IndexSUM value available for comparison. In all cases tested there was no relationship with gefitinib activity and EGFR or pAkt staining.
Discussion
This is the first study in which a TKI has been successfully tested in the ATP-TCA. ATP-TCA has potential to assist drug development for TKIs and possibly to direct therapy for individual patients. It represents one possible answer to the need for predictive oncology testing of these agents, and could be performed alongside clinical trials to obtain correlation data with outcome in patients treated with gefitinib. However, it is difficult to ascertain whether these were specific or non-specific effects of gefitinib and whether similar outcomes would be seen in the clinical setting. This would need to be determined before using this test for routine screening. Gefitinib showed activity in the assay and even though cytotoxic effects were not expected, in some cases the diminution in ATP levels suggests that these may occur. In general, flat concentration – activity curves were observed which are consistent with a cytostatic rather than a cytotoxic effect. Gefitinib alone showed activity in lung, ovarian and colon carcinomas. These results were consistent with previous findings in cell lines [24].
When gefitinib was tested in combination with a limited number of cytotoxic drugs, increases and decreases in the activity of the cytotoxic agent were observed. For example, gefitinib in combination with cisplatin caused 67% of samples to have a decrease in the activity of the cytotoxic. This compares with gefitinib in combination with a second platinum-containing agent, oxaliplatin, where 91% of samples showed an increase in the activity of the cytotoxic. However, it should be noted that oxaliplatin was virtually ineffective against the cells tested and this is therefore likely to reflect the effect of the gefitinib alone (Figure 5). Decreased activity of cytotoxic agents when these were combined with gefitinib was seen in 4 samples with cisplatin, 13 with treosulfan, 1 with oxaliplatin and 11 with treosulfan + gemcitabine. This could be detrimental to patients. It is similar to the effect of tamoxifen treatment on the success of breast cancer chemotherapy [25].
Although there was heterogeneity in the response of tumours to single agent gefitinib, there was no relationship between immunostaining for EGFR and gefitinib activity, consistent with other published studies [26]. Sirotnak et al. [9] showed that gefitinib caused growth inhibition in human tumour xenografts that was not dependent on high levels of EGFR expression. However, EGFR activation leads to activation of at least three separate second messenger cascades. While the ras/raf pathway may mediate the proliferative effects, survival signals are thought be mediated by the PI3/Akt pathway. As cells have to die in the ATP-TCA to register increased inhibition, sensitivity might be related to the degree of activation of the Akt pathway by other mechanisms. Sensitivity to gefitinib and other non-TKI EGFR inhibitors might therefore be related to pathway activation assessed by detection of pAkt, rather than the levels of EGFR expression. However, this study has not found any such relationship and, when EGFR staining was compared to pAkt staining there was no correlation between EGFR levels to pAkt activity. A similar observation was made by Campiglio et al., [27] whose data suggested that neither MAPK nor pAkt were reliable markers of gefitinib activity. It should be noted that many receptors lead to Akt activation and that constitutive activation of the PI3/Akt pathway may be the result of PTEN inactivation.
Of the 4 samples that had an IndexSUM of <300 and the 6 samples that demonstrated >50% inhibition at 100% TDC when tested with single agent gefitinib, 2 samples (a uveal melanoma and an unknown primary) had material available for immunohistochemical staining with EGFR and pAkt. The uveal melanoma was negative for EGFR and positive for pAkt compared to the unknown primary, which was positive for both EGFR and pAkt. However, there were samples with similar IHC results that did not show sensitivity to gefitinib. As the EGFR (HER1) dimerizes with the other HER molecules and mediates greater activity as a heterodimer, it is likely that the expression of these molecules is also important to the activity of gefitinib [12]. Sensitivity to gefitinib is therefore likely to be the end result of a complex series of interactions within the cell.
Conclusion
In this study we have found that gefitinib in combination with different cytotoxic agents (cisplatin; gemcitabine; oxaliplatin; treosulfan and treosulfan + gemcitabine) is a double-edged sword: their effect on growth rate may make some tumours more resistant to concomitant cytotoxic chemotherapy, while their effect on cytokine-mediated cell survival (anti-apoptotic) mechanisms may potentiate sensitivity to the same drugs in tumours from other individuals.
Competing interests
IAC is director of Cantech Ltd.
Authors' contributions
LAK drafted the manuscript and carried out ATP-TCA assays. FDN, PW, SM, SS and SG also carried out ATP-TCA assays. PJ carried out the immunohistochemical work and IAC conceived the study and participated in its co-ordination.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We are grateful to AstraZeneca for providing the supply of gefitinib used in this study. We wish to thank the patients, oncologists and surgeons who provided tumour tissue for this study.
This study was funded by Cantech Ltd.
'Iressa' is a trademark of the AstraZeneca group of companies
Figures and Tables
Figure 1 Median effect of gefitinib on tumour-derived cells compared with a sensitive and non-sensitive colorectal tumour. Error bars show 25th and 75th inter-quartile range.
Figure 2 Frequency histogram showing heterogeneity of the IndexSUM for gefitinib alone in all tumours tested (n = 86).
Figure 3 The effect of gefitinib on tumour-derived cells from recurrent ovarian cancer (n = 13). Legend Some tumours show increased sensitivity (lower IndexSUM), while others show enhanced resistance (higher IndexSUM)
Figure 4 Tumour growth inhibition by gefitinib vs. treosulfan + gefitinib in 2 different tumour types. Legend Figure 4a shows the positive effect of combining treosulfan plus gefitinib in cells derived from a skin melanoma compared with figure 4b in which the combination of treosulfan plus gefitinib has a negative effect in cells derived from an ovarian tumour.
Figure 5 Tumour growth inhibition by gefitinib vs. gefitinib + oxaliplatin in a NSCLC. Legend The combination of oxaliplatin + gefitinib has a positive effect compared with single agent oxaliplatin which is inactive.
Table 1 Drug concentrations used in the ATP-TCA
Drug TDC (microM)
Cisplatin 10.0
Gemcitabine 40.0
Gefitinib 1.0
Oxaliplatin 12.6
Treosulfan 71.9
Table 2 Median IC50 (microM) and IndexSUM values for single-agent gefitinib for all tumours tested.
Tumour N IC50 (microM) IndexSUM
Breast adenocarcinoma 8 7.27 (6.2–16.9) 607 (500–785)
Colorectal adenocarcinoma 18 3.19 (0.1–52.6) 568 (239–818)
Melanoma – cutaneous (CMEL) 7 2.81 (1.9–29.4) 514 (471–587)
Melanoma – uveal (UMEL) 12 17.10 (0.04–69.9) 595 (187–746)
NSCLC 1 - 396
Squamous cell carcinoma 2 2.83 (2.3–3.4) 462 (454–469)
Oesophageal adenocarcinoma 4 3.58 (2.8–4.4) 602 (456–788)
Ovarian carcinoma 26 3.09 (0.2–23.1) 534 (269–777)
Carcinoma of unknown primary site (UPS) 5 4.76 (0.05–14.3) 612 (258–648)
Sarcoma 2 9.8 609 (588–630)
Sweat gland carcinoma 1 24.67 651
(Negative values of IC50 have been excluded as meaningless. Negative values usually resulted from flat concentration – activity curve).
Table 3 Median results for single-agent cytotoxics tested compared with results when tested in combination with gefitinib.
Drug/Combination N AUC IC90 IC50 IndexSUM % showing decrease in IndexSUM when in combination with gefitinib
Gefitinib 86 3943 (40–13212) 646 (243–12614) 399 (4–7008) 570 (187–818) -
Cisplatin 6 7937 (4244–9804) 294 (208–557) 132 (84–309) 434 (382–497) -
Cisplatin + gefitinib 6 9006 (1731–12057) 225 (159–585) 107 (58–325) 486 (351–588) 33% (2/6)
Gemcitabine 2 7127 (498–13756) 764 (206–1321) 382 (30–734) 452 (288–616) -
Gemcitabine + gefitinib 2 12605 (9835–15374) 211 (201–220) 52 (17–86) 315 (207–422) 100% (2/2)
Oxaliplatin 10 3488 (874–8884) 833 (317–2267) 463 (129–1259) 559 (379–681) -
Oxaliplatin + gefitinib 10 5602 (547–12140) 390 (194–775) 217 (60–431) 447 (310–605) 90% (9/10)
Treosulfan 34 13764 (4351–18390) 146 (35–616) 53 (4–342) 353 (65–726) -
Treosulfan + gefitinib 34 13656 (4251–18658) 153 (31–19892) 60 (4–11051) 338 (58–897) 56% (19/34)
Treosulfan + gemcitabine 24 17576 (8107–19164) 57 (6–200) 13 (3–110) 155 (21–456) -
Treosulfan + gemcitabine+ gefitinib 24 17281 (11402–19119) 48 (9–206) 9 (4–70) 146 (25–572) 54% (13/24)
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| 15560844 | PMC535559 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Nov 23; 4:83 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-83 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-381553895210.1186/1471-244X-4-38Research ArticleS-adenosylmethionine (SAM-e) for the treatment of depression in people living with HIV/AIDS Shippy R Andrew [email protected] Douglas [email protected] Kristina [email protected] Irene [email protected] Stephen E [email protected] ACRIA (AIDS Community Research Initiative of America), 230 West 38th St, 17th Floor, New York, NY 10018, USA2004 11 11 2004 4 38 38 25 5 2004 11 11 2004 Copyright © 2004 Shippy et al; licensee BioMed Central Ltd.2004Shippy 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
This study reports on clinical data from an 8-week open-label study of 20 HIV-seropositive individuals, diagnosed with Major Depressive Disorder (DSM-IV), who were treated with SAM-e (S-Adenosylmethionine). SAM-e may be a treatment alternative for the management of depression in a population reluctant to add another "pill" or another set of related side effects to an already complex highly active antiretroviral therapy (HAART) regimen.
Methods
The Hamilton Rating Scale for Depression (HAM-D) and the Beck Depression Inventory (BDI) were used to assess depressive symptomatology from 1,2,4,6 and 8 weeks after initiation of treatment with SAM-e.
Results
Data show a significant acute reduction in depressive symptomatology, as measured by both the HAM-D and the BDI instruments.
Conclusions
SAM-e has a rapid effect evident as soon as week 1 (p < .001), with progressive decreases in depression symptom rating scores throughout the 8 week study.
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Background
The incidence of depression in people living with HIV (PLWH) has consistently been reported to be higher than the 12–15% rates reported for the general population [1]. Depression in this population is largely untreated. Estimates suggest that this comorbid condition of HIV affects 10% – 50% of PLWH [2]. The management of depression in people with HIV is important since it can reduce medication adherence. Depression can be life-threatening, given that the patient can deteriorate medically, and that untreated depression can result in suicide. Untreated depression may also result in substance abuse, a theory based on the 'self medication' theories of addictive behavior. Depression is a pressing problem at this point in the epidemic because the HIV population is aging and the incidence of depression increases with age [3].
In New York City, 27% of all people living with HIV are over 50 years of age and 66% are over 40 years age [4]. In two studies of PLWH age 50 and older, the rate of depressive symptomatology ranged from 58% – 68% [5].
Under-treated depression in the HIV population may be the result of multiple factors. People of color, women, substance abusers, and those living in poverty are less likely to have access to or to accept mental health outpatient services [5-7]. Many patients with HIV who are managing their disease are on complex regimens of medications (HAART) and dietary supplements to treat their illness [8]. Adding another pill with significant side effects is often viewed by both patient and primary treating physician as an unacceptable burden, either in terms of number of pills per day, or in terms of anticipated side-effect profiles, or risk/benefit analysis [9]. Primary care physicians are often unprepared to recognize depression, which can present as fatigue, weakness, insomnia or loss of appetite, and may seek medical explanations for depressive symptoms [7]. HIV-related dementia, which presents with apathy and amotivation, can also be mistaken for depression. Physicians may be unfamiliar with mental health interventions and place depression as a lower priority after management of the primary HIV infection [10]. The stigma of HIV remains large and the addition of the potential stigma of a mental illness (i.e. depression) further contributes to the patient's reluctance to pursue or engage treatments for depression [11]. Many patients fear that substance abuse, homosexuality, or HIV itself may be grounds for discrimination from a mental health provider [12,13].
SAM-e is a compound that has been used for many years in Europe as an antidepressant. It became available in the USA as a non FDA-regulated compound in 1999. SAM-e resembles a naturally occurring compound in the human body, formed by methionine and adenosine triphosphate. SAM-e donates an active methyl group to several acceptor molecules, including catecholamines, fatty acids, phospholipids, proteins and nucleic acids [14]. SAM-e appears to increase central turnover of dopamine and serotonin; for example it increases the main metabolite of central serotonin-5-HIAA (5-hydroxyindoleacetic acid) in the cerebrospinal fluid [15].
The efficacy of SAM-e as an antidepressant has been reported in a number of open-label trials [16] and in two small placebo-controlled clinical trials [17,18]. Kagan et al conducted a placebo-controlled trial that found SAM-e to be effective in the treatment of depressed inpatients [17]. A meta-analysis of SAM-e studies shows a greater response rate with SAM-e compared with placebo, with a global effect size ranging from 17–51% [19]. Side effects reported in these studies are fewer than with SSRIs specifically with respect to nausea, headache, dry mouth, and agitation or activation. In studies of parenteral SAM-e, activation of mania was noted in two cases [20]. SAM-e would seem to offer the potential for effective antidepressant treatment with fewer side effects for patients with medical illnesses. This was shown in a successful open label trial of SAM-e in Parkinson's patients [21] and in a double-blind study of fibromyalgia patients [22]. The aim of the study reported below was to assess efficacy, tolerability, and safety of SAM-e in a group of HIV-positive individuals diagnosed with Major Depressive Disorder (DSM-VI).
Methods
This study is an analysis of the complete dataset from an independently funded, IRB-approved project conducted at ACRIA, a 12-year-old, community-based HIV research and education agency in New York City. A preliminary report, based on a subset of these data, was presented at the 14th International Conference on AIDS. A total of 20 patients were recruited between April 2000 and November 2000.
Recruitment activities included distribution of information brochures to community physicians, announcements at public ACRIA treatment education workshops, and direct calls to persons listed in the ACRIA database. Individuals who responded to recruitment materials were screened via phone by the research coordinator using the PRIME-MD [23]. HIV-positive patients were then invited to be interviewed by a study psychiatrist at ACRIA for a clinical psychiatric examination. Patients who met DSM-IV criteria for Major Depression (assessed with the SCID-IV) and who did not meet any of the exclusion criteria were eligible for participation after giving informed consent [24]. Criteria for exclusion were (a) unstable medical illness, (b) pregnancy, lactation or refusal by participants to employ an acceptable contraceptive, (c) history of substance abuse in the prior month, (d) treatment with another psychotropic medication within two weeks prior to initiation of SAM-e treatment, (e) concurrent MAOI treatment, (f) active suicidal ideation and/or psychotic symptoms, (g) reversible medical pathology that is thought to be causing the depression, and (h) history of mania or diagnosed bipolar disorder. Patients received $15 for each completed study visit.
Treatment was initiated at 200 mg of SAM-e twice a day with daily supplementation of 1,000 mcg Vitamin B12 and 800 mcg Folic Acid, which were provided by Pharmavite, LLC. Over the course of the study, the dose of SAM-e was individually adjusted up to 800 mg, twice daily. Patients reporting nausea or insomnia were not increased above 800 mg daily, while patients with no reported side effects could be increased to a total daily dose of 1600 mg. Dosing was adjusted according to the severity of symptoms and clinical treatment response. We defined clinical treatment response as an improvement in depressive symptomatology of greater than 50% reduction on patient scores on the BDI and HAM-D as our treatment endpoint [25,26]. A statistical data codebook was created and secondary statistical analyses were performed.
Measures
At each study visit (Baseline, Week 1, Week 2, Week 4, Week 6, and Week 8), patients completed a clinical interview and a series of structured questionnaires on medical/physical symptoms, substance abuse, dementia scales, as well as two instruments designed to measure depressive symptoms, self-administered Beck Depression Inventory (BDI and the clinician-administered HAM-D).
BDI
The BDI is a self-report measure for depressive symptoms [25]. Total scores range from 0 – 63 and are calculated by summing the scores to each of the 21 items. Higher scores on this measure indicate greater severity of depression. Scores above 30 indicate severe depression. Scores of 30 – 10 describe moderate depression, while scores less than 10 indicate the absence of depression.
HAM-D
The HAM-D is a 17-item, clinician-rated instrument. It was designed to assess the changes in severity of depressive symptomatology over time in patients who had been diagnosed with Major Depressive Disorder [26]. Scores above 24 reflect severe depression, while scores ranging from 17 – 7 indicate mild symptomatology. Scores below 7 indicate an absence of depression.
Analyses
Two separate analyses (e.g., Baseline -v- Week 1 and Baseline -v- Week 8) were identified as the most critical indicators of SAM-e efficacy. The first analysis represented evidence for acute onset of SAM-e, and the second indicated effectiveness of SAM-e over time. BDI and HAM-D scores were centered and the resulting Z-scores were used in a series of t-tests to assess the agreement between patient and investigator ratings of depression. Mean scores from both depression measures (BDI and HAM-D) were compared between time points, using a series of paired t-tests. Finally, an intent-to-treat analysis, using the last score carried forward, was conducted on patient BDI scores for all subjects who initiated treatment.
Results
Participants
Thirty (30) subjects were screened by telephone and twenty (20) entered into the study. Of these, fifteen (15) were male, five (5) were female and the majority were people of color (50% Black, 30% Hispanic, 20% Caucasian). Table 1 presents a detailed demographic profile of the patients. Although the exclusion criteria included a prohibition against concurrent treatment with conventional antidepressants during the study period, we identified one patient who did not discontinue use of conventional antidepressant until Week 4 of the study. All subjects self-reported that they were not actively substance abusing at the time of the study; however, urine drug screens were not performed. Five (5) patients did not complete the study, but Week 1 data were recorded and were included in the analysis of early onset of therapeutic SAM-e effects. These five (5) subjects were excluded from subsequent analyses. Two (2) subjects did not return for study participation after Week 1: both had a history of IDU; one was co-infected with Hepatitis C and had a comorbid obsessive compulsive disorder (OCD). Three (3) patients did not return after Week 4, one with a history of IDU and one with a history of suicide attempts. Of those who dropped out of the study, two (2) met criteria for AIDS.
Table 1 Demographic characteristics of patients
Variable N % Median Range
Age 20 45 24 – 57
Sex
Male 15 75.0
Female 5 25.0
Race/Ethnicity
Black 10 50.0
Hispanic 6 30.0
Caucasian 4 20.0
HIV diagnosis (year) 20 1992 1987 – 2000
CD-4 count (baseline) 20 320 5 – 1200
Log viral load (baseline) 20 3.40 1.70 – 4.48
Transmission risk
MSM sex 4 20.0
Heterosexual sex 5 25.0
IDU 2 10.0
Multiple risks 9 45.0
Treatment response
Table 2 contains the means, standard deviations and ranges for depression data from each study visit. Of the patients who provided Baseline and Week 1 follow-up data, there was clear evidence for a rapid therapeutic effect of SAM-e. The reduction in mean BDI scores from Baseline (M = 33.5, SD = 11.1; Range: 15 – 55) to Week 1 (M = 18.9, SD = 10.4; Range: 0 – 45) was significant, t(1, 18) = 5.15, p < .001. Mean HAM-D scores declined in a parallel manner, from Baseline (M = 26.5, SD = 6.8; Range: 12 – 39) to Week 1 (M = 16.8, SD = 7.3; Range: 2 – 29). This change was also statistically significant, t(1, 14) = 3.58, p < .01. Both depression assessment instruments reached the defined clinical treatment response threshold of a greater than 50 percent reduction in depression symptom scores.
Table 2 BDI and HAM-D scores recorded at each study visit
BDI HAM-D
Study Visit N M(SD) Range N M(SD) Range
Baseline 20 33.5(11.1) 15–55 20 26.5(6.8) 12–39
Week 1 19 18.9(10.4) 0–45 15 16.8(7.3) 2–29
Week 2 17 14.1(8.2) 0–25 13 10.7(5.5) 0–21
Week 4 17 8.8(7.8) 0–28 15 6.0(4.7) 0–15
Week 6 16 6.4(6.8) 0–20 14 5.2(5.7) 0–20
Week 8 16 5.0(4.7) 0–16 15 3.7(3.3) 1–13
The 15 patients who completed the study also demonstrated clinical treatment response over time. Mean BDI scores dropped significantly from Baseline (M = 35.1, SD = 12.0; Range: 15 – 55) to Week 8 (M = 5.1, SD = 4.8; Range: 0 – 16), t(1, 14) = 8.28, p < .001. Similarly, HAM-D scores were reduced from Baseline (M = 26.7, SD = 6.3; Range: 12 – 36) to Week 8 (M = 3.7, SD = 3.3; Range: 1 – 13). The reduction in HAM-D scores reached statistical significance, t(1, 13) = 9.92, p < .001.
There were no significant differences in patient and physician ratings of depression at each study visit (Table 3). Figures 1 and 2 provide graphic representations of mean BDI and HAM-D scores across each time point, respectively. Remission of depression was defined as a HAM-D score ≦ 7, response to treatment was defined as ≧ 50% decrease in HAM-D scores. For the 15 patients who completed the study, the remission rate was 93%. Fourteen (14) of the 15 patients achieved a HAM-D of 7 or lower, while one patient received a HAM-D rating of 13. The response rate was also 93%, with 14 of the 15 patients achieving 50% or greater reduction in HAM-D scores. The patient who did not meet the response rate had a Baseline HAM-D of 12 and a Week 8 score of 7.
Table 3 Comparison of BDI and HAM-D scores (study completers)
Study Visit t df p
Baseline -0.07 14 .95
Week 1 -1.39 14 .19
Week 2 -0.98 14 .35
Week 4 -0.64 14 .54
Week 6 0.35 13 .74
Week 8 0.71 14 .49
Figure 1 Mean BDI scores at each study visit (95% confidence intervals)
Figure 2 Mean HAM-D scores at each study visit (95% confidence intervals)
The intent-to-treat analysis yielded similar results. The last reported BDI and HAM-D scores for each of the 20 patients who initiated treatment (i.e., received at least one dose of SAM-e) were used in this analysis. Baseline BDI scores (M = 33.5, SD = 11.1; Range: 15 – 55) were higher than Week 8 (M = 6.6, SD = 6.1; Range: 0 – 21). HAM-D scores dropped from Baseline (M = 26.5, SD = 6.8; Range: 12 – 39) to Week 8 (M = 7.7, SD = 10.1; Range: 1 – 39). The reduction in self-reported (i.e., BDI) depressive symptomatology was significant, t(1, 19) = 8.85, p < .001. The results were equivalent for the psychiatrist-rated HAM-D, t(1, 18) = 7.23, p < .001.
The intent-to-treat remission rate was 79%. Fifteen (15) of the 20 patients achieved a HAM-D of 7 or lower, while one patient did not receive a valid HAM-D rating and four (4) received HAM-D ratings greater than 7 (13, 22, 24, 39, respectively). The intent-to-treat response rate was 74%, with 14 of the 19 patients achieving 50% or greater reduction in HAM-D scores.
Adverse effects
No patients terminated their participation due to side effects. Two (2) patients reported nausea and one (1) reported diarrhea; all resolved spontaneously. One (1) patient had insomnia and a subjective feeling of 'high energy,' but did not meet criteria for hypomania.
Discussion
Recent data from the HIV Cost and Services Utilization Study (HCSUS) indicate that almost half the nation's adult HIV patients suffered from symptoms of mental disorders. The report indicates that the prevalence of mental disorders range from four to eight times higher than those found in the general populations. Over 60 percent of HIV-positive adults used mental health or substance abuse services during the period studied; 26% visited mental health specialists, 15% had group mental health treatment, 40% discussed emotional problems with medical practitioners and 30% used psychotherapeutic medications [27].
Depression remains under-reported and under-treated in the HIV population. Patient factors include stigma, fear of more medication, and perhaps the assumption that depression is a normal part of HIV disease, or due to HAART. Physicians tend to focus more on physical symptoms, and may be unaware of how to screen for depression in this population. Both physicians and patients are disadvantaged in accessing gay-affirmative, well-trained HIV mental health specialists who can disentangle HIV medical problems from substance abuse and mental illness. Risk factors for depression (e.g., history of substance use/abuse, lack of social support, stigma, etc.) are prevalent in those populations who are disproportionately affected by HIV. Many patients are reluctant to add more medication to their complex treatment regimens. The need for effective and safe treatments for depression is clear.
Results of this study demonstrate that SAM-e significantly reduces depression in people living with HIV. This finding supports previous research demonstrating the efficacy of SAM-e for use in treating depression. More importantly, the therapeutic effect of SAM-e had an acute onset. There were significant reductions in the severity of depression among study patients within the first week of treatment. Unlike most antidepressants that require several weeks to reach maximum therapeutic efficacy, previous research has shown that serum levels of SAM-e peak within 24 hours of treatment. This may account for the rapid, effects of SAM-e seen on the HAM-D and BDI scores.
Conclusions
SAM-e is a safe, effective treatment for depression among people living with HIV. SAMe has few reported side effects, and in our population of PLWH, was extremely well tolerated. SAM-e in this group had rapid onset of clinical efficacy. Both of these results make it an appealing alternative therapy for the management of depression in the HIV population.
The results of the current study must be interpreted in the context of the study's limitations. Limitations of this study include the lack of a placebo group, the lack of a double-blind design and small sample size. The criteria for participation and the small sample size may limit the generalizability of our findings to other patient populations. Without a placebo control group, we cannot assess two factors that might have affected our results. Although patients were treatment-experienced with other anti-depressants, we cannot rule out the potential placebo-response that may have contributed to the rapid therapeutic effect of SAM-e. Also, meeting with a study psychiatrist may have contributed to improved depressive symptom ratings. Nonetheless, the study results provide useful and clinically relevant information for treating depression in HIV-positive individuals. Future studies employing a double-blind, placebo-control design are warranted.
The absence of depression does not imply wellness. Many people living with HIV/AIDS report feelings of sadness and depression but do not receive treatment for depression. Future studies should also assess health-related quality of life. Such protocols would determine if people with significant, but subclinical depressive symptomatology would benefit from SAM-e.
Authors' contributions
RASrecoded study data, conducted statisticalanalyses andco-wrote the final manuscript. DM provided details of the study design and procedures andwas responsible for extractingclinical data from patient records. IC and KJ served as clinical investigators and provided valuable comments on the final paper. SEKwrote the first draft of the paper and co-wrote the final manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Support for this study and the data verification and analyses was provided by ACRIA. The authors gratefully acknowledge the contributions of Drs. J. Ernst, R. Brown and D. Goldenberg to the original study design. All dietary supplements (SAM-e, Folic Acid, & vitamin B12) were provided by Pharmavite, LLC.
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| 15538952 | PMC535560 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Nov 11; 4:38 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-38 | oa_comm |
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BMC BiochemBMC Biochemistry1471-2091BioMed Central London 1471-2091-5-161554832710.1186/1471-2091-5-16Methodology ArticleThe "Transport Specificity Ratio": a structure-function tool to search the protein fold for loci that control transition state stability in membrane transport catalysis King Steven C [email protected] Integrative Biosciences, Oregon Health & Science University, Portland, Oregon, 97239-3097, USA2004 17 11 2004 5 16 16 7 7 2004 17 11 2004 Copyright © 2004 King; licensee BioMed Central Ltd.2004King; 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
In establishing structure-function relationships for membrane transport proteins, the interpretation of phenotypic changes can be problematic, owing to uncertainties in protein expression levels, sub-cellular localization, and protein-folding fidelity. A dual-label competitive transport assay called "Transport Specificity Ratio" (TSR) analysis has been developed that is simple to perform, and circumvents the "expression problem," providing a reliable TSR phenotype (a constant) for comparison to other transporters.
Results
Using the Escherichia coli GABA (4-aminobutyrate) permease (GabP) as a model carrier, it is demonstrated that the TSR phenotype is largely independent of assay conditions, exhibiting: (i) indifference to the particular substrate concentrations used, (ii) indifference to extreme changes (40-fold) in transporter expression level, and within broad limits (iii) indifference to assay duration. The theoretical underpinnings of TSR analysis predict all of the above observations, supporting that TSR has (i) applicability in the analysis of membrane transport, and (ii) particular utility in the face of incomplete information on protein expression levels and initial reaction rate intervals (e.g., in high-throughput screening situations). The TSR was used to identify gab permease (GabP) variants that exhibit relative changes in catalytic specificity (kcat/Km) for [14C]GABA (4-aminobutyrate) versus [3H]NA (nipecotic acid).
Conclusions
The TSR phenotype is an easily measured constant that reflects innate molecular properties of the transition state, and provides a reliable index of the difference in catalytic specificity that a carrier exhibits toward a particular pair of substrates. A change in the TSR phenotype, called a Δ(TSR), represents a specificity shift attributable to underlying changes in the intrinsic substrate binding energy (ΔGb) that translocation catalysts rely upon to decrease activation energy (). TSR analysis is therefore a structure-function tool that enables parsimonious scanning for positions in the protein fold that couple to the transition state, creating stability and thereby serving as functional determinants of catalytic power (efficiency, or specificity).
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Background
Structure-function analysis seeks to elucidate how the structural attributes of a protein serve its function. The function of a carrier protein is to catalyse transmembrane solute translocation. However, without a productive conspiracy among catalysis-promoting residues in the protein fold, transport proteins would be non-catalytic (i.e., unable to enhance transition state stability). Inasmuch as "... catalytic power will always appear as a result of increased transition state stabilization (lower free energy) ..." [1], a powerful addition to the structure-functionist's arsenal would be a generally applicable method that rapidly identifies sites in the protein fold that control transition state stability (i.e., that control the affinity of substrates for the transition state). What functional characteristics or properties might such a technique probe?
The structure-function technique would be required to provide a keyhole through which to view positions in the protein at which structural perturbations affect transition state binding energy, for it is well-appreciated that a catalyst creates transition state stability by binding substrates tightly in the transition state complex [2]. In fact, binding energy is thought to be significant to the exclusion of all else in carriers that catalyse translocation without any change in the covalent structure of the substrate [3]. Absent changes in substrate structure, it is implicit that the conformational motions of "alternating access" must produce a transition state complex in which the substrate is more tightly bound than in the initial Michaelis complex. Fundamentally, catalysis could not occur without this realization of additional binding energy in the transition state [4].
The present contribution demonstrates use of the Transport Specificity Ratio (TSR) as an analytical keyhole to capture an initial glimpse of positions in the protein fold where structural characteristics control the availability of transition state binding energy. Using the Escherichia coli GabP (gab permease) as a model carrier protein, salient properties and utilitarian features of TSR analysis are demonstrated. The TSR parameter is shown (i) to be calculated from an easily performed dual-label uptake experiment, and (ii) to depend exclusively upon changes in intrinsic substrate binding energy (ΔGb) realized in the transition state. Together these TSR properties should enable transport structure-functionists to obtain rapid, yet incisive, first-pass view of positions in the protein fold where structure influences transition state stability and catalysis per se.
Results
Effect of substrate concentration on the TSR
TSR analysis as implemented in present examples consists of a dual-substrate transport assay in which [14C]GABA and [3H]NA compete for uptake at the GabP active site. Therefore as a practical matter it is necessary to establish conditions under which an adequate signal may be obtained from both isotope channels. This can be accomplished empirically by mixing the labelled substrates in different proportions (Fig. 1A). In the range from 1–10 μM (below expected Km for either substrate) the trading of [3H]NA for [14C]GABA is expected to substantially alter the fraction of active sites occupied by GABA versus NA Nevertheless, it is clear that the calculated TSR parameter is indifferent to the precise substrate concentration ratio. Moreover, at a fixed substrate ratio (7 parts NA to 3 parts GABA), the absolute substrate concentrations may also be varied over a wide range (here 17.5-fold) without affecting the calculated TSR parameter (Fig. 1B).
Figure 1 Results from TSR analysis are valid across a broad range of competing-substrate concentration ratios The Transport Specificity Ratio (TSR) is calculated using results from a dual-label competitive uptake assay in which structurally distinct, labelled substrates are allowed to compete for transport at the same active site. Panel A: Mixtures of 10 μM [3H]NA (0.6 μCi/ml) and 10 μM [14C]GABA (0.2 μCi/mL) were prepared such that [NA] + [GABA] = 10 μM. E. coli strains SK105 (GabP-positive) and SK45 (GabP-negative) were exposed in parallel experiments for 10 seconds at 30°C to substrate mixtures containing the indicated concentrations of [3H]NA. The GabP-dependent (SK105 minus SK45) uptake of either [3H]NA (■) or [14C]GABA (▲) may be read from the left-side ordinate. The calculated TSR (Equation. 6) may be read from the right-side ordinate (○). Panel B: The substrate concentrations were varied in constant proportion such that the GABA concentration (ranging from 1.8–31.5 μM) was always 42.9 percent of the NA concentration (ranging from 4.2–73.4 μM). The radiochemical concentrations for [3H]NA and [14C]GABA were 0.23 μCi/ml and 0.03 μCi/ml, respectively. The indicated concentration ranges produce about 50 percent combined active site occupancy (bound GABA plus NA) – since the affinities for GABA and NA are 40 μM and 200 μM, respectively [25].
Although these data (as well as the underlying theory) indicate that there is great latitude in choosing substrate concentrations for TSR measurements, it is nevertheless pragmatic to select robust initial velocity conditions wherein the substrate concentration ratio is such that equal disintegration rates are seen in both isotope channels (broken line) when the control (e.g., wild type) transporter is studied. Variant transporters, exhibiting relative increases or decreases in specificity for the two substrates, will then be easily visualized as an inequality between the disintegration rates seen in the two isotope channels (so that the data are no longer graphically superimposed).
Effect of protein expression level on the TSR
When distinct transporter variants are studied, it frequently is the case that the strains will express the variants at distinct and unpredictable levels in the plasma membrane, complicating the interpretation of any observed differences in transport velocity. In order to discern how expression-level affects the TSR analysis, IPTG was used to induce to differing levels the lac-controlled expression of the plasmid-borne GabP gene. Growth in the presence of increasing IPTG concentrations caused the uptake of [3H]NA and [14C]GABA to increase in proportion to the GabP expression-level (Fig. 2A), which was monitored by immunoblot (Fig. 2B, inset). Although single-substrate uptake and expression varied over a 40-fold range, the calculated TSR parameter held steady (Fig. 2B), indicating that differing expression levels would have a minimal effect on results obtained by TSR analysis.
Figure 2 Results from TSR analysis are valid across a broad range of carrier expression levels E. coli strains SK11 (GabP-positive) and SK45 (GabP-negative) were grown to early logarithmic phase as described in Methods except that expression was induced by exposing cultures to the indicated IPTG concentrations. The cells were washed with 100 mM potassium phosphate buffer (pH 7.0), and dual-label competitive transport reactions were initiated by exposing the cells to 7 μM [3H]NA (0.42 μCi/ml) and 3 μM [14C]GABA (0.06 μCi/ml) for 10 seconds (initial rate) at 30°C. Error bars represent the S.E.M. (n = 3). Panel A. GabP-dependent uptake (SK11 signal minus SK45 signal) of either [3H]NA (■) or [14C]GABA (▲). Panel B. Transport Specificity Ratio (GABA/NA). Inset. Immunoblot of plasma membrane vesicle protein (2 μg per lane) probed with an anti-pentaHis mAb and developed with a chemiluminiscent alkaline phosphatase substrate (see Methods). Lane 1: Membranes from E. coli strain SK45 (GabP-negative). Lanes 2–10: Membranes from E. coli SK11 (GabP-positive) grown in the presence of 2, 5, 10, 20, 50, 100, 200, 500, or 1000 μM IPTG, respectively.
Effect of assay end-point on the dual-substrate mole ratio
Single-substrate transport velocities may be estimated from the slope of the initial-rate segment of an uptake time course (Fig. 3). Unlike single substrate uptakes, which are linearly affected by deviations from the intended stopping time, the dual-substrate "mole ratio" is time-independent across the linear range studied. Thus, mechanical errors affecting the "stop-time" should be largely self-correcting. Indeed, the red arrow (Fig. 3A) marks the position of an indeterminate error, wherein the single-substrate data points are off the curve suggested by the remaining data. This error is seen to "self-correct" in the dual-label "mole ratio" and TSR calculations (Fig. 3B, red arrow), indicating that dual-label ratio parameters can be more reliably estimated than can single-substrate velocities. In fact, many errors in time and volume will "self-correct" in the TSR calculation (see Discussion section).
Figure 3 Results from TSR analysis are valid across a broad range of reaction times E. coli strain SK11 (GabP-positive) was exposed simultaneously to 6 μM [3H]NA (0.42 Ci/ml) and 4 μM [14C]GABA (0.06 Ci/ml) for the indicated times at 30°C. Parallel experiments were carried out in the presence of 2 mM GABA, which was included to block the GabP. Panel A shows the GabP-dependent component of competitive uptake (difference between the parallel experiments) over a 10-fold time range. The red arrow indicates a probable mechanical error, causing low uptake inconsistent with other points on the curve. The Panel B shows the GABA to NA mole ratio (left-side ordinate) calculated from data shown in the Panel A. The associated TSR values may be read from the right-side ordinate. The red arrow has the same meaning as in the Panel A, and serves here to emphasize the reliability of the TSR analysis, which has self-correcting properties that compensate for many routine sample processing problems that may cause inconsistency in times or volumes (see discussion).
Assignment of TSR phenotypes to GabP variants
When assay conditions conform to recommendations (Fig. 1), then transporters serving as the "parental control" will exhibit superimposed initial rate segments on the uptake time courses for accumulation of the [3H] and [14C] labels [5,6]. Clearly, the GabP variants shown in Figure 4 do not exhibit superimposed initial rate segments, indicating in a highly intuitive visual fashion that the TSR phenotype for these variants will differ from their respective parent transporters. Compared to its Cys-less parent (control TSR = 8), the single-Cys variant, N302C, exhibits a relative increase in preference for NA (TSR = 2.5). Compared to its wild type parent (control TSR = 4), the INS Ala 320 variant (with an extra alanine residue inserted at position 320) exhibits a relative increase in preference for GABA (TSR = 16).
Figure 4 Variants of the E. coli GabP that exhibit Δ(TSR) phenotypes Using data analogous to Figure 1, the concentrations of competing substrates were adjusted empirically such that the initial rates of label accumulation were superimposed for E. coli strains expressing the "control" gab permease (GabP). As a result, any separation between initial rate uptake curves for [14C]GABA (▲) and [3H]NA (■) provides a highly intuitive visual representation of a Δ(TSR) phenotype. Panel N302C shows TSR analysis of the single-Cys GabP variant, N302C. Compared to the Cys-less GabP control (TSR = 8) for which the initial label accumulation rates are superimposed [5], the N302C shows a relative increase in the specificity for NA with a calculated TSR of 2.5. The Panel INS Ala 320 shows TSR analysis of the GabP variant, INS Ala 320, which has an extra alanine residue inserted at position 320. Compared to the wild type GabP control (TSR = 4) for which the initial label accumulation rates are superimposed [6], the INS Ala 320 exhibits a relative increase in specificity for GABA (i.e., opposite of the Panel N302C) with a calculated TSR of 16.
Discussion
TSR phenotyping derives from a concrete definition of catalysis
In order to initiate development of a structure-function relationship for translocation catalysis by GabP [5,6], it was useful to adopt a formalism that describes catalysis in concrete terms [7] so that structural perturbations affecting catalysis might likewise be described in terms of a concrete (quantifiable) phenotype – the TSR. Fundamental to TSR analysis is the notion that transport catalysts use substrate binding energy to lower the translocation energy barrier (activation energy) [2]. Equation 1 states that the catalysed activation energy () is lower than the non-catalyzed activation energy () by an amount equal to the intrinsic substrate binding energy, ΔGb (algebraically negative).
Importantly, Equation 1 (justified by Fig. 5) tells us that to screen for changes in catalytic power (barrier height) per se, one must find an easily measured signal that reports on changes in the intrinsic substrate binding energy (ΔGb) used to stabilize the transition state. That the Transport Specificity Ratio (TSR) analysis fulfills this requirement may be shown as follows.
Figure 5 Changes in catalytic specificity (kcat/Km) reflect underlying changes in transition state binding energy (ΔGb) In this description of catalysis, (i) the magnitude of the non-catalysed activation energy () does not depend on a favourable protein-substrate interaction in the transition state, (ii) the catalysed translocation energy barrier is taken as the Gibbs Energy difference () between the free reactants (C + S) and the transition state complex (CS‡), and (iii) intrinsic substrate binding energy is recognizable as the decisive factor in transition state stabilization. Thus, translocation catalysts (C) will use intrinsic substrate binding energy (ΔGb) to stabilize the transition state (CS‡). The role of ΔGb in lowering the transition state energy barrier compared to a non-catalyzed reaction () may be deduced with aid from the accompanying energy diagrams, which emphasize several instances wherein the thermodynamic distance represented by one coloured arrow equals the summed distance represented by two shorter arrows of the same colour. The illustrated thermodynamic relationships are restated (with proper attention to sign convention) in equations A (red), B (green), and C (blue). Substituting A and C into B yields the fundamental relationship, (boxed), which says that the uncatalysed activation energy (, algebraically positive) is diminished by intrinsic substrate binding energy, ΔGb (algebraically negative), which is the underlying parameter that TSR analysis probes (Eqn. 9). Note: These energy diagrams compare non-catalytic (dots and dashes) and catalytic (solid line) proteins. Imposition of a binding-averse interaction (ΔGR) is seen to de-stabilize the Michaelis complex (CS, red arrows) in the catalytic protein. Subsequent attainment of favourable transition state complementarity (i.e., via conformational transitions that relieve ΔGR , blue arrows) results in use of binding energy to stabilize the transition state complex (CS‡). This internal ''give-and-take,'' involving ΔGR is reflected in its algebraic cancellation when equations A, B, and C are combined to yield the boxed equation (text Eqn. 1), which says that intrinsic substrate binding energy decreases the energy barrier () for a translocation reaction carried out from solution (i.e., directly from the free carrier and substrate (C + S) to the transition state). When C and S are free in solution, the effective second-order rate constant associated with is kcat/Km, the specificity parameter compared in the dual-substrate TSR analysis (Equation.5). That kcat/Km should be associated with the free reactants may be appreciated by considering the Michaelis-Menten Equation when S << Km, and CS complexes do not exist in appreciable amounts (see Discussion).
The Michaelis-Menten equation in two variables
The velocity (v) of a simple translocation reaction, carried out from solution (C + S → Products), is governed by a second-order rate law (Equation 2), wherein the apparent second-order rate constant is k.
v = k[C][S] (2)
Free carrier and substrate (C + S) are dominant under non-saturating, second-order conditions (i.e., [S] << Km), wherein the familiar Michaelis-Menten relationship (Equation 3) reduces to the form of a second-order rate law (Equation 4), and the apparent rate constant may be evaluated as k = kcat/Km (units M-1sec-1).
Although Equation 4 may appear to be a special case, it is actually a generally valid alternative form of the Michaelis-Menten Equation that is little used because it contains two variables, [C] and [S]. Equation 4 is valid at all substrate concentrations, producing the same saturating substrate-velocity curve as Equation 3 (since [C] goes to zero as [S] goes to infinity). The alternative Michaelis-Menten form turns out to be very useful for analysing the uptake of two labelled substrates that compete for transport at the same active site.
Competing substrates equilibrate with the same free carrier concentration
Consider the E. coli GabP exposed simultaneously to arbitrary concentrations of its transported substrates [8,9], [14C]GABA and [3H]NA. These competing substrates, present simultaneously in the same reaction vessel, will necessarily be in equilibrium with precisely the same concentration of free carrier (but unknown concentrations of carrier-substrate complexes), allowing algebraic elimination of [C] (Equation 5) when a ratio is taken between two instances of Equation 4 (one for each substrate).
Catalytic specificity reflects the translocation energy barrier height
That Equation 5 contains the ratio of a pair of (kcat/Km) values has two consequences. First, since (kcat/Km) is formally a measure of catalytic specificity [7,10], we may recast Equation 5 succinctly in terms of the Transport Specificity Ratio (TSR) parameter.
Secondly, since (kcat/Km) is an apparent rate constant (see above), transition state theory holds that its value depends upon the height of the translocation energy barrier () as indicated by this logarithmic form of Eyring's Equation (Equation 7),
wherein k is the Boltzman constant, h is the Planck constant, R is the gas constant, T is the absolute temperature, and a transmission coefficient of unity is assumed.
Specificity ratios depend only upon intrinsic binding energy differences
If catalytic specificity (kcat/Km) depends upon , then by implication the TSR must be related to the intrinsic substrate binding energy – as becomes evident when Equations 1 and 7 are combined,
Equation 8 shows that kcat/Km (synonymous with catalytic power, specificity, and efficiency) varies with the amount of transition state stabilization afforded by ΔGb, which is the intrinsic substrate binding energy (algebraically negative). Taking the ratio between two instances of Equation 8 (e.g., for the two competing substrates, GABA and NA), and combining terms, we obtain
Equation 9 indicates that an experimentally observed change in the TSR parameter would require a change in the underlying intrinsic substrate binding energies that determine the relative height of the translocation energy barriers for two substrates competing at the same active site.
The TSR reflects a change in substrate affinity for the transition state
Figure 6 emphasizes that in the comparison of two substrates, the TSR reflects a difference in substrate affinities for the transition state (at the reaction coordinate peak). This contrasts with true equilibrium binding measurements, which reflect substrate affinities in the initial Michaelis complex (at the reaction coordinate bottom). These affinities are characterized by the dissociation constants, Kd and , which describe the equilibrium position of reactions leading to formation of free reactants from either the Michaelis complex (CS ↔ C + S) or the transition state complex (CS‡ ↔ C + S), respectively. Inasmuch as equilibrium constants (e.g., ) are always determined by Gibbs energy differences (e.g., ΔGb = -RT ln ), it follows (Fig. 6, yellow shading) that a change in transition state binding energy (ΔΔGb) reflects a change in the midpoint separation () between hypothetical curves that describe binding of two test substrates (A and B) to the transition state. Structural features that affect the "tightness" of transition state binding will alter the translocation energy barrier height (Equation 1), which determines synonymously the catalytic power, efficiency, or specificity of a transporter.
Figure 6 Comparison of equilibrium binding versus TSR analysis Envisage a catalytic protein interacting with two substrates (or substrate analogs), one exhibiting high-affinity binding (dashed RED line), and the other low-affinity binding (solid BLUE line). Equilibrium binding to the stable Michaelis complex (LEFT, Panels A and B) would produce concentration-dependent saturation of the binding site (Panel B). From the observed affinity difference (ΔKd) between the two substrates, one can calculate a corresponding difference in binding energy, ΔΔGS (Panel A), for the two substrates interacting with the stable Michaelis complex at the bottom of the reaction coordinate. In contrast, information on the interaction of substrates at the reaction coordinate peak would require a study of binding to the unstable transition state (RIGHT, Panels C and D). Unfortunately, due to the high energy-level and transient nature and of the transition state (denoted by ‡), the relevant binding experiment (Panel D) is technically impossible. However, TSR analysis allows direct calculation (Equation 9) of the transition state binding energy difference, ΔΔGb (Panel C, yellow) between two competing substrates, A and B. A change in the TSR phenotype, or Δ(TSR), thus provides evidence for a change in the graphical separation distance, (Panel D, yellow), for the "impossible experiment" on substrate binding to the unstable transition state. Thus, observation of a Δ(TSR) phenotype reflects underlying structural changes that affect binding discrimination between substrates A and B in the transition state, which are of interest because transition state binding interactions create transport catalysis [2–4, 7] by lowering the activation energy, , and increasing kcat/Km. In summary, the equilibrium binding experiment depicted on the left does not address catalysis per se, whereas the TSR experiment depicted on the right does.
The TSR phenotype is a constant
Unlike first-pass analytical methods that rely on the signal from one labelled substrate, the herein described dual-label analysis leads directly to the TSR parameter – a constant (Equation 9). Constants are intrinsically stable and reliable, reflecting fundamental reaction characteristics that survive changes in ambient conditions (provided temperature and pressure can be held constant). The unique stability and fundamental nature of the TSR phenotype will make it particularly valuable for first-pass analysis in high-throughput screening situations, wherein protein expression levels, duration of the initial rate time course, and degree of saturation by the chosen substrate concentration may be inconsistent across large numbers of transporter variants with differing functional characteristics. This reliability is demonstrated using the E. coli GABA permease (GabP) as a model translocation catalyst. Overall the present study makes clear that the dual-label TSR analysis is insulated remarkably well from many uncontrolled variables that can often compromise the validity of assays that use a single label.
TSR analysis is valid at arbitrary site-saturation levels
Figure 1 shows that the TSR did not change when GabP was exposed to [14C]GABA and [3H]NA in different proportions, or in fixed proportion over a broad concentration range (Fig. 1B). Indeed, the form of Equation 6 suggests that the velocity ratio should self-adjust continuously with changes in the dual-substrate concentration ratio (since the TSR and its component parts, kcat and Km, are all constants). Thus, arbitrary carrier saturation levels are not expected to compromise TSR measurements. Since uncharacterized mutant collections may be expected to contain transporter variants with highly divergent Km values, the saturation-independence of TSR analysis should be of value in high-throughput screening situations where little kinetic information may be available to guide the choice of assay conditions. However, to be of general value the results obtained with GabP must extrapolate to other transporters.
Why the deceptively simple TSR analysis should have broad applicability can be understood from further consideration of Figure 1. When substrate concentrations are varied, carrier saturation levels change, producing new complexes (e.g., [C· GABA] and [C· NA]) in changing proportions. While manipulating these complexes affects single-substrate uptake velocities significantly (Fig. 1), the TSR calculated from these velocities is unaffected because these particular complexes (and complexes of any arbitrary number and description) never have a role in determining the equilibrium – energetic distance () – between the free reactants (C + S) and the transition state (CS‡). This fundamental reality can also be appreciated from the perspective that under non-saturating conditions ([S] << Km), there are no complexes to consider ([C] = Ctotal), and thus even complicated mechanisms reduce to the simple case (Equation 4) in which the reaction proceeds directly from the free reactants in solution to the transition state (C + S → Products). Thus, the simple second-order reaction scheme, C + S → Products, will probably never be "too simple" for the purpose of performing the TSR analysis – even though complicated transport kinetics will feature many complexes that TSR analysis seems to ignore. In truth, the missing complexes are merely irrelevant (not ignored) to the value of (Fig. 5) since these complexes would always lie energetically between (or below) the free reactants (C + S) and the transition state complex (CS‡).
TSR reliability stems from self-correcting properties
It is worth mentioning that TSR analysis has "fool-proof" qualities that derive from its inherent insensitivity to several sources of error that can seriously compromise transport measurements that rely upon a single labelled substrate. TSR calculations may be expected to "self-correct" any sources of error that have proportionally the same effect on the measurement of both isotopes – for such errors cannot affect the isotope ratio used to calculate the TSR parameter.
Figure 3, for example, shows that whereas stop-times affect single-isotope uptake signals in linear fashion, the dual-substrate mole ratio (and TSR calculation) is hardly affected meaning that TSR analysis is inherently insensitive to vast timing errors. In the experiment shown, stopping at arbitrary times across 10-fold range would have impacted the TSR calculation very little. Likewise, most sample handling errors (e.g., pipetting, filtering) will tend to affect both isotopes proportionally so that whereas the single isotope uptakes are affected linearly, the TSR calculation is preserved (Fig. 3, red arrow). Perhaps most importantly, TSR analysis can correct for sample-to-sample variations in protein expression-level (Fig. 2).
In order to demonstrate the expression-independent nature of the TSR parameter, IPTG was used to simulate the wide range of expression levels (40-fold) that might be encountered in an uncharacterized collection of transporter variants. Whereas the single-isotope signals (Fig. 2A) are seen to vary directly with GabP expression, the dual-isotope TSR phenotype (Fig. 2B) varies little. This expression-independent behaviour fully complies with theoretical expectations since (i) the carrier concentration was algebraically eliminated (Equation 5), and (ii) TSR is a "constant" (Equation 9), reflecting fundamental molecular properties of carrier-substrate interaction that do not depend upon the number of carrier molecules expressed in the membrane. The ability to rapidly evaluate a TSR phenotype, formally an expression-independent constant, should be of considerable practical significance for high-throughput screening operations wherein carrier expression levels could be both highly variable and impractical to document in real-time.
Since TSR phenotypes are expression-independent, structure-function information gleaned from a rapid first-pass screen will remain valid irrespective of results that might be obtained from a subsequent immunoblot analysis. Immunoblots do not in any event determine Ctotal in the sense desired for meaningful kinetic characterization, which assumes (Equation 3) that Ctotal consists entirely of active molecules. The possibility of partial denaturation precludes assigning a molecular interpretation to shifts in either velocity or Vmax. In contrast, TSR analysis is unaffected by the presence of inactive molecules, and theoretically will always report reliably on the innate specificity properties of the active site per se – even if the measured signal emanates from a minor fraction of the carrier molecules visualized on an immunoblot.
TSR analysis detects "relative" specificity shifts
The TSR method is inherently capable of detecting new phenotypes that reflect relative specificity shifts favouring either test substrate (Fig. 4). Preliminary to these experiments, dual-substrate ratios were empirically adjusted so that control strains would exhibit superimposed [14C] and [3H] initial rate segments in their uptake time course (shown elsewhere, [5,6]). Plainly, the test cases in Figure 4 do not exhibit superimposed dual-isotope time courses, indicating two distinct Δ(TSR) phenotypes – one relatively favouring NA (Panel N302C), and the other relatively favouring GABA (Panel INS Ala 320).
The Δ(TSR) phenotypes illustrated in Fig. 4 are distinct from one another (and distinct from the control) because there are relative differences in transition state binding energies (Equation 9) that can be visually represented as a change in the relative position of (separation between) the hypothetical binding isotherms for either substrate (Fig. 6D). It is important to emphasize, however, that TSR analysis does not address the absolute magnitude of transition state binding energy shifts, nor the absolute magnitude of shifts in the binding curve midpoint ( shifts). This point is important, and can be illustrated by examining the implications of the figure 4 time courses in more detail.
Calculated TSR values for the N302C and INS Ala 320 variants are, respectively about 2.5 and 16. That these numbers are both greater than 1 indicates (Equation 9) that the hypothetical transition state binding isotherm for GABA would lie to the left (i.e., like the red curve in Fig. 6D) of the NA curve in both variants. If the measured TSR had been unity, then the hypothetical binding curves would be superimposed. If the measured TSR had been below 1, then the NA binding isotherm would lie to the left. Thus, the N302C time course with squares increasing faster than triangles (Fig. 4, Panel N302C) does not indicate an absolute preference favouring NA over GABA, but rather a squeezing down of separation between midpoints on the hypothetical transition state binding isotherms for GABA and NA (relative to the separation in the Cys-less control–TSR = 8). The INS Ala 320 time course with triangles increasing faster than squares (Fig. 4, Panel INS Ala 320) indicates an increase in the separation between midpoints on the hypothetical binding isotherms (relative to the separation in the wild type control – TSR = 4).
Although the ability to measure only relative changes in specificity has limitations, the reader will appreciate that to a mathematical certainty no relative shift can occur in the absence of one or more absolute shifts. TSR analysis thus provides an analytical keyhole through which to scan [5,6] the protein fold, looking for Δ(TSR) phenotypes indicative of loci at which transition state stability can be controlled by amino acid side-chain structure. This conclusion cuts directly to the essence of what a translocation catalyst does – fairly respectable performance for a first-pass, rapid-screening methodology, which minimally can consist of as little as a single datum point for each variant transporter screened.
It is to be noted that since absolute specificity changes can occur in the absence of a relative specificity shift (i.e., equal displacement of the binding isotherms for both substrates), some catalytic residues may be detectable only by more complicated kinetic studies, or possibly through independent TSR experiments with structurally distinct substrate pairs. Since the TSR parameter is a constant that characterizes how the transition state interacts with a particular pair of substrates, different results may be expected with structurally distinct substrate pairs. However, the observation of a Δ(TSR) phenotype always means the same thing – there has been a change in the transition state stability for translocation of one or both substrates.
TSR analysis enables a broad search for the seat(s) of catalytic power
Apart from its delightful simplicity and self-correcting behaviour, the TSR (or rather the ability to observe Δ(TSR) phenotypes) is also attractive as a facile means of expanding interest in "coupled promoting motions" that are networked together in support of catalysis [11]. Such networks (i) are evolutionarily conserved, (ii) undergo conformational oscillations on the timescale of (in synchrony with) the catalyzed reaction, and (iii) collectively can make million-fold contributions to catalytic specificity (transition state stabilization) even though their locations are spatially distant from the active site in enzymes of known structure (e.g., dihydrofolate reductase [1,12-14]]; aspartate aminotransferase [15]). Inasmuch as Equation 8 says that specificity (kcat/Km) is a function of transition state stabilization ( + ΔGb), phenotypic changes in the TSR phenotype should report on structural perturbations that compromise as yet undiscovered networks that couple energetically to the transition state.
Inquiry along this line follows up on a prominent message emerging from recent literature on enzymatic catalysis: structural elements delocalized from the active site can enhance catalytic power (kcat/Km) by many orders of magnitude [1,11] – so that any understanding of enzymatic catalysis based on consideration of the active site in isolation may now be considered incomplete. This delocalization of catalytic power will in all likelihood be true of transport catalysis as well – but even more so since carriers lack a traditional active site (no covalent change in substrate structure). The catalytic power (specificity) of a carrier must therefore derive entirely from conformational motions that lead to tighter ligand binding in the transition state. This crucial catalytic increment in ligand binding energy could be localized within a binding pocket only to the extent that it is possible for a conformational transition to increase carrier-substrate complementarity without at the same time causing a change in conformational energy (structural stability). If conformational energy changes as the transition state forms, then one expects obligatory partitioning of transition state binding energy among multiple interactions (steric, electrostatic, hydrogen-bonding, or solvation forces) at highly delocalized positions throughout the protein fold. Since plasma membrane transport proteins consist mainly of bundled helices that exhibit rigid-body behaviour [16,17] it is unlikely that conformational remodelling of helix-helix interfaces could occur without changing conformational energy. Thus, localized control of translocation specificity (catalytic power) is also quite unlikely, and instead the determinants of specificity ought to be distributed rather broadly at dynamic interfaces throughout the helix-rich structure (a hypothesis that should be broadly testable by TSR scanning approaches [5,6]).
"Alternating Access" vitiates feasibility of localized specificity control
Carrier proteins exhibit a compact tertiary structure in which tightly bundled helical segments span the membrane in a serpentine zig-zag fashion with extensive helix-helix contacts throughout [18,19]. The conformational transitions of "alternating access" (i.e., the general mechanism by which carriers expose a binding site alternately to one side of the membrane and then the other) thus proceed with extensive rigid-body remodelling of helix-helix interfaces. At some point in the translocation process the initial Michaelis complex (CS) is converted to a transition state complex (CS‡) with realization of additional binding energy (i.e., a change in the chemical potential of bound ligand), which creates catalysis. But what part(s) of the protein structure may be held to account for this pro-catalytic increment in binding energy?
Although not concerned with catalysis per se, Tanford set down clear principles from which we can infer that the binding energy used for transition state stabilization should have two obligatory sources in a helix-rich translocation catalyst – one source being dynamic motions in the protein fold. Tanford understood that with "...both translocation and change in chemical potential [of bound ligand] occurring in synchrony ..." [20] via helical tilts and twists, "it is not possible to separate free energy changes attributable to direct bonding to the proteins from free energy changes attributable to rearrangement of the protein structure that may accompany the binding process." [21]. Indeed, Benkovic's recent work on dihydrofolate reductase has provided the first visualization (molecular dynamics simulation) of the dynamic processes by which spatially distal motions in the protein fold can be coupled synchronously with active site rearrangements to create greater transition state stability.
Thus when distal, energy-changing, conformational motions occur in synchrony with (i.e., on the same timescale as) reconfiguration of the bound ligand (as with translocation or covalent structural change), then delocalized contributions to transition state stability must occur. Although the details may vary from case to case, the operable mechanisms will probably be conceptually similar to those that now have been visualized in dynamic simulations as "...coupled promoting motions extending throughout the protein and ligands, where promoting motions refer to equilibrium, thermally averaged conformational changes along the collective reaction coordinate leading to configurations conducive to the reaction." [1].
Importantly, the chance occurrence of a favourable dynamic coupling interaction would be accompanied by an evolutionarily selectable substrate specificity (kcat/Km) shift, suggesting that delocalized coupled promoting motions should be the rule rather than the exception. Engineered structural manipulations that interfere with the operation of coupled networks should impact kcat/Km such that elements of these networks may be rapidly detectable by TSR analysis. Such use of TSR analysis prompts re-examination of philosophical issues concerning the efficacy of mutagenesis in structure-function analysis.
The ambiguity of mutagenesis reflects a truth about catalysis
Many, including this author, have cautioned that mutagenesis is associated with built-in thermodynamic constraints that produce confounding ambiguity when the stated desire is to use engineered structural perturbations as a means to identify residues of an active site [22,23]. However, it needs to be emphasized that Nature, also bound by thermodynamic constraints, relies continuously upon natural selection, taking meaningful advantage of the same ambiguity that the structure-functionist traditionally bemoans. This thermodynamic ambiguity provides that spontaneous mutations affecting structure at locations spatially distinct from the active site may nevertheless have pro-catalytic or anti-catalytic effects that become subject to natural selection. The evolutionary accumulation and coupling together of such pro-catalytic sites has produced now recognizable "networks of coupled promoting motions" that exist far from the active site, yet operate in synchrony with it to promote catalysis [1,11].
That catalysis [1,11] and energy transduction [20-22] appear to rely upon coupled motions in the protein fold raises a question as to whether the structure-function field might benefit from a change in its outlook on the ambiguous characteristics of mutagenesis, henceforth treating ambiguity as a friend that can reveal the location of coupled networks. Widely perceived as a shortcoming, this ambiguity turns out to be an accurate reflection of how Nature uses the protein fold to boost catalytic power. It simply is not the case that a kcal of transition state stabilization emanating from a few residues in the active site is worth (by some visceral rationale) more than a kcal of transition state stabilization emanating from the protein fold.
Conclusions
TSR analysis is a remarkably simple dual-substrate competition assay used to define the TSR phenotype of a translocation catalyst. The TSR phenotype is highly reliable because the TSR parameter is a constant, which renders its value independent of several common variables that, particularly in high-throughput screening, may be poorly controlled or only roughly estimated. A change in the TSR phenotype requires an underlying change in transition state stability (or synonymously an underlying change in catalytic specificity, catalytic power, catalytic efficiency, kcat/Km, or transition state energy barrier) for one or both of the competing substrates. TSR-scanning mutagenesis is thus expected to identify positions in the protein fold that make contributions to transition state stabilization (the essence of catalytic function). The technical simplicity of TSR analysis should enable broad testing of the hypothesis that in carrier proteins the seat of catalytic power will be delocalized along helix-helix interfaces that dynamically enhance structural stability by remodelling in synchrony with transition state formation, thereby promoting translocation catalysis in a manner analogous to recently described networks of coupled promoting motions that allow dynamic interactions in the protein fold to enhance transition state stability in enzymatic catalysis [1,11].
Methods
Strains and plasmids
E. coli strain SK35 is a gabP-negative host strain [8]. E. coli SK45 is a gabP-negative strain harbouring the expression plasmid, pSCK380 [8]. E. coli SK11 expresses a histidine-tagged Cys-less derivative of GabP [5]. E. coli SK105 expresses the Cys-less GabP as a GabP-LacZ hybrid from the plasmid pSCK380Z [24].
Materials
GABA was from Sigma (St. Louis, MO, U.S.A.); NA was from Research Biochemicals International (Natick, MA, U.S.A.); Miller's Luria Broth medium was from Gibco-BRL (Grand Island, NY, U.S.A.); agar and ampicillin were from Fisher Biotech (Fair Lawn, NJ, U.S.A.); bicinchoninic acid protein determination reagents were from Pierce (Rockford, IL, U.S.A.); cellulose acetate filters (0.45 um; 25 mm) were from either Millipore (Bedford, A, U.S.A.) or MicronSep, (cellulosic; 0.45 um, 25 mm) from OSMONICS Inc. (Minnetonka, MN, U.S.A.); [3H] nipecotic acid (40 Ci/mmol) was a custom synthesis from Moravek Biochemicals (Brea, CA, U.S.A.); [14C]GABA was from Dupont-New England Nuclear (Boston, MA, U.S.A.); Ultima Gold ™ scintillation cocktail was from Packard BioScience (Meriden, CT, U.S.A.); the anti-Penta-His monoclonal antibody was from QIAGEN (Valencia, CA, U.S.A.); the goat anti-mouse alkaline phosphatase antibody was from Kirkegaard and Perry Laboratories (Gaithersburg, MD); isopropyl-β-D-thiogalactopyranoside (IPTG) was from Anatrace (Maumee, OH); Immobilon-P™ transfer membranes (0.45 um) were from Millipore (Bedford, MA, U.S.A.); the chemiluminescence reagent for alkaline phosphatase detections, Western Lightning, was from Perkin-Elmer Life Sciences, Inc. (Boston, MA, U.S.A.
E. coli culture conditions
E. coli strains were recovered by streaking glycerol stocks (-80°C) to single colonies on LB agar supplemented with ampicillin (100 μg/ml). LB broth supplemented with ampicillin (100 μg/ml) was inoculated by picking from a single colony and then shaken overnight (16 h) at 37°C. Overnight cultures were diluted 100-fold into fresh medium, shaken for 2 hours at 37°C prior to adding IPTG (0.2 mM), and shaking for two hours more. Cells were then harvested by centrifugation, washed twice with ice-cold KPi Buffer (100 mM potassium phosphate, pH 7.0), and resuspended to 2 mg protein/ml in the same buffer (20 percent of the original culture volume). Cultures treated in this manner are hereafter referred to as washed cells. Washed cells were stored on ice, and then equilibrated to 30°C in a heat block (25 minutes) prior to initiating transport reactions. Cultures treated in this manner are hereafter referred to as prewarmed cells.
Transport conditions
Transport reactions were initiated by mixing 20 μl of a 5-fold concentrated substrate stock solution with 80 μl of prewarmed E. coli cell suspension. TSR analysis of the single-Cys GabP variant, N302C, was performed using a substrate stock containing 35 μM [3H]NA (2.1 μCi/ml) and 15 μM [14C]GABA (0.3 μCi/ml). This solution was found to support equal rates of [14C] and [3H] label accumulation in the Cys-less GabP control strain [5]. TSR analysis of the GabP variant, INS Ala 320, was performed using a substrate stock containing 20 μM [3H]NA (1.2 μCi/ml) and 30 μM [14C]GABA (0.6 μCi/ml). This solution was found to support equal rates of [14C] and [3H] label accumulation by the wild type GabP [6], which contain 5 Cys residues.
A 60 or 120 Hz metronome was used to time the reactions, which were rapidly quenched with 1 ml of ice-cold Stop Solution (KPi Buffer containing 20 mM HgCl2), and then vacuum-filtered (0.45 micron pore). The reaction vessel was then rinsed with 1 ml of Wash Buffer (KPi Buffer containing 5 mM HgCl2) and this was applied to the same filter. Finally, 4 ml of the Wash Buffer was applied to the filter. The filter was then dissolved in Ultima Gold™ scintillation cocktail and the [3H] and [14C] radioactivity (disintegrations per minute, dpm) analyzed with a Packard BioScience Tri-Carb 2900 TR liquid scintillation counter using stored Ultima Gold™ quench curves and automatic quench compensation.
Standard curves for GabP-independent uptake
The GabP-negative E. coli strain, SK45, was grown and prepared for transport experiments as indicated above except that a series of different cell suspensions were prepared spanning a range from 20 to 125 percent of that described above. Dual-label transport experiments carried out with these different suspensions produced a linear standard curve for GabP-independent "background uptake" of [3H]NA and [14C]GABA as a function of protein content. The protein content of GabP-positive test strains could then be used to obtain the appropriate background subtraction by extrapolation from the standard curve. Test strain protein contents were always similar (within 10 percent) because when cell pellets were resuspended steps were taken to assure approximately equal turbidity levels.
Statistics
Replicate (n = 3), background-corrected, dual-substrate uptake velocities (moles/time) were inferred from measured disintegration rates for filter-bound [3H]NA and [14C]GABA. The background-corrected velocity replicates were used to calculate replicate TSR values (Equation 6) from which the mean TSR and standard errors (S.E.M.) shown in the figures were obtained.
Plasma membrane vesicle preparation and immunoblotting
E. coli cells were probe-sonicated to produce plasma membrane vesicles, which were then separated from soluble components and unbroken cells by differential centrifugation as previously described [5]. Plasma membrane proteins were resolved by SDS-PAGE, and transferred to PVDF membranes, which were blocked and then probed with a primary antibody (anti-polyhistidine monoclonal) and secondary antibody (anti-mouse conjugated to alkaline phosphatase) as previously described [5]. Immunoblots were developed with a chemiluminescent alkaline phosphatase substrate (Western Lightning™), and imaged with a cooled CCD camera (Kodak Image Station 440 CF). Chemiluminescent intensities were quantified with Kodak 1D software.
Acknowledgements
This work was supported by National Institutes of Health Grant NS38226. The author thanks Lisa Brown-Istvan, Amy Pugh, and Martha Thompson for providing expert technical assistance.
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| 15548327 | PMC535561 | CC BY | 2021-01-04 16:26:23 | no | BMC Biochem. 2004 Nov 17; 5:16 | utf-8 | BMC Biochem | 2,004 | 10.1186/1471-2091-5-16 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-5-311556337310.1186/1471-2156-5-31Research ArticleDensity-independent population projection trajectories of chromosome-substituted lines resistant and susceptible to organophosphate insecticides in Drosophila melanogaster Miyo Takahiro [email protected] Brian [email protected] Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK2004 24 11 2004 5 31 31 31 8 2004 24 11 2004 Copyright © 2004 Miyo and Charlesworth; licensee BioMed Central Ltd.2004Miyo and Charlesworth; 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
Seasonal fluctuations in susceptibility to organophosphate insecticides were observed in the Katsunuma population of Drosophila melanogaster for two consecutive years; susceptibility to three organophosphates tended to increase in the fall. To examine the hypothesis that variation in fitness among resistant and susceptible genotypes could trigger the change of genetic constitution within the fall population, we investigated density-independent population projection trajectories starting from single adult females with characteristics of chromosome-substituted lines resistant and susceptible to the three organophosphates.
Results
Density-independent population projection trajectories, expressed as the ratios of the number of each chromosome-substituted line to that of line SSS, for which all chromosomes were derived from the susceptible line, showed significant declines in numbers with time for all the resistant chromosome-substituted lines.
Conclusion
The declining tendency in the density-independent population projection trajectories of the resistant chromosome-substituted lines could explain the simultaneous decline in the levels of resistance to the three organophosphates, observed in the Katsunuma population in the fall.
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Background
The development of insecticide resistance in insect pest populations is a population genetic process, in which insecticides select for initially rare resistant mutants within a population [1]. Although the development of insecticide resistance is an inevitable consequence of insecticide application, because the purpose of insecticide usage is to kill a certain portion of the insect pest population, insect populations that have developed resistance to insecticides sometimes exhibit reduction in levels of resistance to insecticides, after having been released from insecticide application. The cause of this reduction in resistance levels has been controversial [2]. Crow [1] said
"Since the genes causing insecticide resistance were at low frequency in the population before the insecticide began to be applied, it must ordinarily be true that they are to some extent disadvantageous; otherwise they would have been common. Therefore the selection for resistance should ordinarily involve the replacement of the original genes with R factors that, in every respect except insecticide resistance, are deleterious from a survival standpoint."
Under this theory, it could be expected that there is variation in fitness among resistant and susceptible genotypes, and that resistant genotypes have lower fitness than susceptible genotypes, which could result in the change of frequencies of resistance factors within a population.
The Katsunuma population (Yamanashi Prefecture, Japan) of Drosophila melanogaster is one of the well-established natural populations in Japan [3]. Katsunuma is famous for its vineyards, which extend continuously through the town, and its wine production. In the fall, masses of squeezed grapes are dumped outside during the process of wine production, and Drosophila flies drastically increase in number on them [4,5]. In this natural population, we have observed seasonal fluctuations in levels of resistance to three organophosphates for two consecutive years; susceptibility to the three organophosphates tended to increase in the fall [5]. To reveal the genetic basis of the seasonal fluctuations in susceptibility to the three organophosphates, we have compared several fitness measures among chromosome-substituted lines, whose chromosomes were derived from a resistant or a susceptible line collected before the population started expanding [6,7]. Lower fitness measures were generally obtained for resistant chromosome-substituted lines, which supports the previous hypothesis that resistant genotypes have lower fitness than susceptible genotypes.
In the Katsunuma population, the drastic change in food and breeding environments occurs from summer to fall. Almost density-independent conditions are suddenly created by masses of squeezed grapes. However, the period of these conditions might not be long enough for the population to attain a stable age-structure; rapid expansion and contraction occur during a rather short period of time. In fact, the Katsunuma population rapidly decreased in mid-November [7]. Therefore, it is also necessary to examine population trajectories during the phase before the population attained a stable age-structure. In this study, we further investigated density-independent population projection trajectories initiated from a single adult female for each chromosome-substituted line, after constructing a Leslie matrix for each line, which may reveal the responses of genotypes similar to each chromosome-substituted line to density-independent environments during a short period of time.
Results
Age-specific fecundity averaged over replications, which were used for constructing a Leslie matrix for each chromosome-substituted line, is shown in Figure 1. Under the density-independent conditions in this study, a genotype similar to line SSS whose chromosomes were all derived from the susceptible line #451-4 could increase the number of individuals totaled over all age-classes to 1.0 × 107 on Day 50 and 3.6 × 1013 on Day 100, starting from a single adult female (Fig. 2). Density-independent population projection trajectories for the other chromosome-substituted lines were expressed as the ratios of the total number of each line to that of line SSS. These are shown in Fig. 3 for resistant lines and in Fig. 4 for susceptible lines, respectively. Because a ratio of 1.0 (dotted lines in these figures) means that the number of individuals of the chromosome-substituted line is equal to that of line SSS, declining curves indicate that the relative number of individuals of that chromosome-substituted line is decreasing, compared to line SSS. All resistant lines showed decreasing ratios with fluctuations, compared to the total number of line SSS (Fig. 3). On Day 50, the ratio of the number of individuals to line SSS was 0.314 (0.848; 95 % upper confidence bound) for line RRR, 0.008 (0.066) for line RSR, 0.036 (0.142) for line SRR, and 0.181 (0.537) for line SSR. On Day 100, the ratio of the number of individuals to line SSS was 0.103 (0.736) for line RRR, 0.000 (0.005) for line RSR, 0.001 (0.014) for line SRR, and 0.022 (0.169) for line SSR. All susceptible lines also showed decreasing ratios with fluctuations, compared to line SSS, but the decline in the ratio was not significant in case of line RSS (Fig. 4). On Day 50, the ratio of the number of individuals to line SSS was 0.001 (0.002; 95 % upper confidence bound) for line RRS, 0.582 (1.345) for line RSS, and 0.018 (0.072) for line SRS. On Day 100, the ratio of the number of individuals to line SSS was 0.000 (0.000) for line RRS, 0.297 (1.479) for line RSS, and 0.000 (0.006) for line SRS.
Figure 1 Age-specific fecundity for each chromosome-substituted line, averaged over replications. Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4. (solid line): resistant line; (dotted line): susceptible line.
Figure 2 Density-independent population projection trajectories from a single adult female for line SSS. (red line): trajectory obtained from observed data; (black line): trajectory obtained from resampled data.
Figure 3 Density-independent population projection trajectories, expressed as ratio to line SSS, for resistant chromosome-substituted lines. Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4. (red line): trajectory obtained from observed data; (blue line): 95% upper confidence bound for the true population projection trajectory; (black line): trajectory obtained from resampled data.
Figure 4 Density-independent population projection trajectories, expressed as ratio to line SSS, for susceptible chromosome-substituted lines. Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4. (red line): trajectory obtained from observed data; (blue line): 95% upper confidence bound for the true population projection trajectory; (black line): trajectory obtained from resampled data.
The distribution of 170 bootstrap replications of the log-transformed ratio of the total number of each line to that of line SSS on Day 100 is shown in Fig. 5 for resistant lines and in Fig. 6 for susceptible lines, respectively. These distributions seem reasonably normal. In fact, for each chromosome-substituted line, the log-transformed ratio obtained using the observed data, and mean and median of 170 bootstrap replications of the log-transformed ratio were very close (Table 1), suggesting that the log-transformed ratio obtained using the observed data was nearly unbiased for each chromosome-substituted line.
Figure 5 170 bootstrap replications of the log-transformed ratio of the total number of each resistant chromosome-substituted line to that of line SSS at Day 100. Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4.
Figure 6 170 bootstrap replications of the log-transformed ratio of the total number of each susceptible chromosome-substituted line to that of line SSS at Day 100. Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4.
Table 1 log (ratio at Day 100) obtained from observed and resampled data.
RRR RRS RSR RSS SRR SRS SSR
Observed -0.987 -5.928 -4.103 -0.527 -2.941 -3.410 -1.652
Mean (Bootstrap replications) -0.967 -5.881 -4.156 -0.463 -3.025 -3.396 -1.685
Median (Bootstrap replications) -0.992 -5.872 -4.061 -0.511 -3.011 -3.378 -1.653
Each chromosome-substituted line is indicated by its chromosome composition (X, second and third chromosome in order). R and S indicate the origin of the chromosome: R for the resistant line #609-10 and S for the susceptible line #451-4.
Discussion
Population projection trajectories of chromosome-substituted lines
Chromosome-substituted lines, whose third chromosomes were derived from the resistant line #609-10, showed resistance to organophosphates [6]. In D. melanogaster, one of the major mechanisms for organophosphate resistance is caused by mutated acetylcholinesterase, the target site of organophosphate insecticides [8]. Mutations in acetylcholinesterase can change the catalytic activity and stability of this enzyme, which may lead to a fitness cost associated with organophosphate resistance [9]. In this study, all the resistant chromosome-substituted lines showed a significant tendency towards a decrease in the ratio of the total number of individuals to that of line SSS, under density independent conditions (Fig. 3). This was especially so for resistant line SSR, for which only the third chromosome was derived from the resistant line #609-10 and the other two major chromosomes were from the susceptible line #451-4. These results suggest that resistant genotypes tend to decrease in frequency under the density-independent conditions, even during the phase before the population attains a stable age-structure.
The calculations we have presented here do not, of course, relate to the changes in frequencies of genotypes in a sexually reproducing population. However, they should provide insight into whether resistance genes are likely to be prevented from spreading to fixation, since the initial rate of change in frequency of a rare gene in a randomly mating population will follow the dynamics that we have studied here ([10] pp. 149–166).
In the previous study, we compared the intrinsic rate of increase among the chromosome-substituted lines used in this study [7]. Resistant lines generally showed lower intrinsic rates than line SSS, but line RRR, whose chromosomes were all derived from the resistant line, had the intrinsic rate of increase that was not significantly different from that of line SSS [7]. Although some interactions among resistant chromosomes were suggested, we could not determine the role of the interactions in the seasonal fluctuations in susceptibility to three organophosphates. Because the decline of line RRR in the ratio was relatively slow, compared to the other resistant lines (Fig. 3), interactions among resistant chromosomes may prohibit rapid decline in the relative number of individuals of resistant genotypes. However, these interactions cannot prevent the decline in frequency of resistant lines under density-independent conditions before the population attains a stable age-structure. Therefore, these results suggest that the interactions among resistant chromosomes may not play a significant role in the seasonal fluctuations in susceptibility to the organophosphates within the Katsunuma population of D. melanogaster.
All the susceptible chromosome-substituted lines also showed a decline in this ratio with respect to line SSS, although the decline in line RSS was not significant (Fig. 4). Because these lines do not have resistance factor(s), these results obviously suggest that other factor(s), rather than the resistance factor(s), may affect the density-independent population projection trajectories of these lines during the phase before reaching a stable structure.
Relatively high frequencies of fitness-related mutations have been maintained in the Katsunuma population of D. melanogaster (viability [3,4]; fertility [3,11-13]; productivity [14]). The frequency of recessive lethal genes on the second chromosome in 1997 was estimated as 25% and that of male sterile genes was 10%, with fluctuations since early 1960s [3]. Because resistance factors were not located on the second chromosome of the resistant line #609-10, the declining tendencies in lines RRS and SRS were not correlated with the resistance factor(s). Therefore, it is possible that the resistant line #609-10 accidentally possessed the above fitness-related mutation(s) in the Katsunuma population on the second chromosome.
Resistant genotype frequency in the Katsunuma population
Following the previous study, which introduced calculating the intrinsic rates of increase for the resistant and susceptible chromosome-substituted lines, this study sheds some more light on the genetic basis of the seasonal fluctuations in susceptibility to three organophosphate insecticides, observed within the Katsunuma population for two consecutive years [5]. Our results support the hypothesis that there is variation in fitness among resistant and susceptible genotypes. In particular, because the fitnesses of the resistant and susceptible chromosome-substituted lines were evaluated by the population projection trajectories generated by a single adult female during the phase before attaining a stable age-structure, which might represent a relatively short period of the density-independent conditions at Katsunuma, this study provide a further genetic basis for understanding the seasonal fluctuations in susceptibility to the three organophosphates from summer to fall.
Variation in fitness among resistant and susceptible genotypes could change the frequencies of the resistant genotypes in the fall Katsunuma population, where almost ampler density-independent conditions are suddenly created by masses of squeezed grapes dumped outside. Because the third chromosome of the resistant lines confers resistance to all of the three organophosphates [6], a significant tendency towards a decline in the ratio of each resistant chromosome-substituted line relative to line SSS could explain the simultaneous decline in the levels of resistance to the three organophosphates, observed in the Katsunuma population [5].
Conclusions
In this study, we calculated population projection trajectories of chromosome-substituted lines resistant and susceptible to three organophosphate insecticides. All the resistant lines showed a significant tendency toward decrease in the numbers relative to line SSS. This study strongly supports the hypothesis that there is variation in fitness among resistant and susceptible genotypes, and that resistant genotypes have lower fitnesses than susceptible ones.
Methods
Drosophila lines
We obtained vital statistics from chromosome-substituted lines, whose chromosomes were derived from a resistant inbred line #609-10 and a susceptible inbred line #451-4, constructed by using a balancer stock, w; Sp/SM1; Pr Dr/TM3 [7,15]. Both of the lines were derived from the same natural population collected at Katsunuma on July 31, 1997, when the D. melanogaster population had not yet started expanding [16]. The resistant line #609-10 had low-to-moderate levels of resistance to three organophosphates, malathion, prothiophos and fenitrothion [16]. Although at least two resistance factors, one on the second chromosome and the other on the third chromosome, were shown to be involved in the Katsunuma population of D. melanogaster [15], the resistance factor(s) for all the three organophosphates were located on the third chromosome in the resistant line #609-10 [6]. Flies used for experiments were grown on glucose-yeast-cornmeal-agar medium in glass vials (3 cm in diameter and 10.5 cm in height) in incubators at 25 ± 0.5°C with a photoperiod of 14: 10 (L: D) h.
In this paper, each chromosome-substituted line was represented by its chromosome composition. For example, SRS indicates the chromosome-substituted line possessing the X and third chromosomes derived from the susceptible line #451-4 (S) and the second chromosome from the resistant line #609-10 (R).
Population projection
Density-independent population projection trajectories from a single adult female were obtained for each chromosome-substituted line, after constructing a 19 × 19 Leslie matrix for each line (Fig. 7; cf. [17]).
Figure 7 A 19 × 19 Leslie matrix, used for calculating density-independent population projection trajectories from a single adult female. Age-specific fecundity, f(x), and survival probabilities, P(x), over 19 days were used to construct a 19 × 19 Leslie matrix for each chromosome-substituted line.
A Leslie matrix consists of elements, which are age-specific fecundity (the first column in this matrix form, f(x)), age-specific survival probabilities (the off-diagonal elements, P(x)), and zero [10]. P(x) values lie between 0 and 1, whereas f(x) values are necessarily more than or equal to zero [18]. For age-specific fecundity f(x), some values are zero, depending on the reproductive schedule of the organisms concerned [18]. By multiplying a row vector n(t), whose components are numbers of each age-class, the number of each age-class at the next time unit, n(t + 1), can be calculated as n(t + 1) = n(t)L [10].
To construct a Leslie matrix, age-specific survival probabilities and fecundity were based on demographic data, collected for each individual adult fly (cf. [19,20]). These were the same data set used for calculating the intrinsic rate of increase for each chromosome-substituted line in the previous study [7]. One pair of adult flies that emerged within six hours was placed in a food vial and transferred into a new vial every day. When the flies were transferred, we checked whether the flies were alive or dead, and counted newly emerged adult flies every day. 16 pairs were prepared for each chromosome-substituted line. Half of the newly emerged adult flies on each day were considered in the estimates of age-specific fecundity in this study. Age-specific survival probability was assigned to be one when the transferred adult fly was alive, and zero when the fly was dead [19,20]. Because adult females were transferred, the age-specific survival probabilities from egg to pupal stages for the adult females were assigned to be one. Ages of the flies placed in the food vials were estimated as the duration from the entry of the adult flies to first emergence of their offspring. Because this length of immature stage varied among replications and among chromosome-substituted lines, ages of the adult flies were not necessarily assigned to be the same among them. Although the ages of adult females that produced no adult offspring cannot be assigned in this study, 14 out of the 16 females producing no adult offspring survived over the 19-day census period; therefore, age-specific survival probabilities for these females were assigned to be one. Two females producing no adult offspring, one RRS and one RSR female, died during the 19-day census period. These females were therefore excluded from the analysis, because the age-specific survival probabilities cannot be defined for these females.
In the previous study, age-specific fecundity and survival probabilities over 19 days, based on each individual, were used for calculating the intrinsic rate of increase for each replication. In this study, the age-specific fecundity and survival probabilities over 19 days were used to construct a 19 × 19 Leslie matrix (Fig. 7), after averaged over replications. Density-independent population trajectories from a single adult female, which just started reproduction, were then projected, using the 19 × 19 Leslie matrix.
Statistical procedures
Density-independent population projection trajectories were estimated as the ratios of the number of each chromosome-substituted line, totaled over all age-classes, to that of line SSS for 100 days. 95% upper confidence bounds (one-sided; [21]) were set for the true population projection trajectories, after standard errors were obtained using the following bootstrap procedures [22]. For each bootstrap replication, 16 (15 for RRS and RSR) replications for each chromosome-substituted line were resampled with replacement from the observed data set, using a random number generator (Fortran 77 program). Age-specific fecundity and survival probabilities were averaged over resampled replications to construct a Leslie matrix for each line. After projecting the Leslie matrix for each line, the ratios of the total number of each line to that of line SSS for 100 days were estimated for each bootstrap replication. We collected 170 bootstrap replications for estimating standard errors for the ratios. Taking the distribution's shape into account, ratios were first log-transformed, and standard errors and 95 % upper confidence bounds were set for the log-transformed values. 95 % upper confidence bounds were then back-transformed into the normal scale.
Authors' contributions
TM carried out data collection and analyses, and drafted the manuscript. BC contributed to the computer programming and provided the theoretical background. All authors read and approved the final manuscript.
Acknowledgments
We would like to thank Yuzuru Oguma (Univ. Tsukuba) for critical reading of the manuscript. We also thank two referees for their helpful comments on the manuscript.
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| 15563373 | PMC535562 | CC BY | 2021-01-04 16:30:34 | no | BMC Genet. 2004 Nov 24; 5:31 | utf-8 | BMC Genet | 2,004 | 10.1186/1471-2156-5-31 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-301554649510.1186/1471-2210-4-30Research ArticleReserpine methonitrate, a novel quaternary analogue of reserpine augments urinary excretion of VMA and 5-HIAA without affecting HVA in rats Sreemantula Satyanarayana [email protected] Krishna M [email protected] Srinivas [email protected] Pharmacology Division, Department of Pharmaceutical Sciences, Andhra University, Visakhapatnam 530003, Andhra Pradesh, INDIA2 Current address: Department of Physiology, University of Tübingen, D 72076 Tübingen, GERMANY2004 16 11 2004 4 30 30 28 2 2004 16 11 2004 Copyright © 2004 Nammi et al; licensee BioMed Central Ltd.2004Nammi 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
Reserpine, an alkaloid from Rauwolfia serpentina was widely used for its antihypertensive action in the past. In later years, its use has been reduced because of precipitation of depression and extra pyramidal symptoms due to its central action. In the present investigation, reserpine methonitrate (RMN), a novel quaternary analogue of reserpine was synthesised and evaluated biochemically for its central and peripheral amine depleting actions in rats while its influence on the blood pressure was measured in anaesthetized rats in comparison with reserpine
Results
Reserpine treatment (5 mg/kg) produced a significant increase in the urinary excretion of VMA, 5-HIAA and HVA while RMN at doses of equal to and double the equimolar doses of reserpine (5 and 10 mg/kg) produced significant increase in VMA and 5-HIAA excretion without producing any effect on HVA excretion compared to control animals. Reserpine in the dose range of 0.5 to15 μg/kg produced significant reduction in blood pressure compared to control. RMN was also found to produce significant decrease in blood pressure at doses of 10, 25 and 50 μg/kg body weight in comparison to control. The results indicated peripheral depletion of biogenic amines by RMN without affecting the central stores of the amines.
Conclusions
The present study clearly indicated that the quaternization of reserpine restricts its transfer across the blood-brain barrier and could be the reason for its selective peripheral action. It is also clear that the hypotensive actions of RMN could be due to peripheral depletion of catecholamines.
Resperine methonitrate (RMN)ResperineBiogenic aminesUrinary metabolitesBlood pressureRats
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Background
Reserpine, an alkaloid isolated from Rauwolfia species, was introduced for the treatment of hypertension and schizophrenia in 1950's but was replaced by more effective drugs by the end of 1970's [1-6]. Reserpine is known to act centrally as well as peripherally by depletion of biogenic amines like noradrenaline, dopamine and serotonin. Mostly, its peripheral depletion of amines is responsible for its antihypertensive effect while its central depletion of amines is responsible for its antipsychotic action [7-13]. However, because of its central action it produces sedation and Parkinsonism when used for the management of hypertension for prolonged periods [14-17]. As a result it has reduced usage for chronic treatment in hypertensive patients and its use is limited to selective patient population only [18,19]. Hence there is a need for structural modification of the drug to make it more acceptable therapeutically for the treatment of hypertension.
Attempts were made in the past to synthesize derivatives of reserpine with possibly higher and/or modified activities or with fewer side effects [20-23]. Compared to reserpine itself, a number of reserpine analogues were found to exert a stronger influence on the amine concentration in the periphery than in brain [24-26].
Based on the poor ability of quaternary compounds to penetrate the blood-brain barrier, a great deal of research has been devoted towards quaternization of existing drugs to achieve preferential peripheral action [27-32]. Earlier reports have demonstrated the synthesis of quaternary derivatives of reserpine and isoreserpine, however their pharmacology was not studied [33,34]. Previous studies by our group also revealed that reserpine methiodide produced selective depletion of peripheral biogenic amines without affecting their central stores [35]. In the present investigation, a quaternary analogue of reserpine viz., reserpine methonitrate (RMN), which was synthesized in our laboratory was evaluated in rats for its amine depleting actions compared to reserpine. For this, the urinary levels of vanillylmandelic acid (VMA), 5-hydroxyindoleacetic acid (5-HIAA) and homovanillic acid (HVA) which are the respective metabolites of noradrenaline, serotonin and dopamine were estimated after reserpine or RMN treatment in rats. The change in the blood pressure response of anaesthetized rats after treatment with RMN was also evaluated in comparison to reserpine.
Results
Biochemical estimations
The main aim of the study was to determine whether RMN was able to deplete the central and peipheral biogenic amines to the same extent as produced by reserpine. Reserpine at a dose of 5 mg/kg body weight produced significant increase in the urinary excretion profile of VMA compared to control animals. The analogue at doses equimolar to reserpine of 5 and 10 mg/kg body weight produced more significant increase in VMA excretion compared to controls and that observed with reserpine (Fig 1). However, the higher dose (10 mg/kg body weight) of RMN did not further enhance the excretion of VMA produced by 5 mg/kg body weight dose.
Figure 1 Diagram illustrating the effect of reserpine and reserpine methonitrate on the 24 h urinary excretion of VMA in rats. Each bar indicates the mean excretion of six animals. Significant difference from control group: *p < 0.05 Significant difference from reserpine treated group: #p < 0.05 NS-No significant difference between 5 and 10 mg/kg treated groups of reserpine methonitrate
Significant increase in 5-HIAA excretion was observed with reserpine at a dose of 5 mg/kg body weight and with the equivalent dose of RMN (Fig 2). The amount of 5-HIAA excreted in animals treated with the analogue/reserpine was found to be more than in the control. However the effect was found to be more with analogue compared to reserpine. The enhancement in dose to 10 mg/kg body weight of RMN did not produce any further increase in 5-HIAA excretion.
Figure 2 Diagram illustrating the effect of reserpine and reserpine methonitrate on the 24 h urinary excretion of 5-HIAA in rats. Each bar indicates the mean excretion of six animals. Significant difference from control group: *p < 0.05 Significant difference from reserpine treated group: # p < 0.05 NS-No significant difference between 5 and 10 mg/kg treated groups of reserpine methonitrate
A marked increase in the HVA excretion was observed in animals treated with reserpine compared to controls while minor change was observed in animals treated with RMN at doses of 5 and 10 mg/kg body weight compared to control (Fig 3).
Figure 3 Diagram illustrating the effect of reserpine and reserpine methonitrate on the 24 h urinary excretion of HVA in rats. Each bar indicates the mean excretion of six animals. Significant difference from control group: *p < 0.05 Significant difference from reserpine treated group: # p < 0.05 NS-No significant difference between 5 and 10 mg/kg treated groups of reserpine methonitrate
Effect on normal blood pressure of anaesthetized rats
The effect of reserpine and RMN on the normal blood pressure of anaesthetized rats was shown in Table 1. Dose dependent hypotension was observed with reserpine as well as with RMN. However, the vehicle (DMSO) also produced hypotension which was approximately 15 mm Hg from basal level. Reserpine at doses of 0.5, 1, 5, 10 and 15 μg/kg produced significant (p < 0.01) reduction in blood pressure compared to control. RMN was also found to produce significant (p < 0.01) decrease in blood pressure at doses of 10, 25 and 50 μg/kg body weight compared to control
Table 1 Effect of reserpine and reserpine methonitrate on the mean arterial pressure of anaesthetized rats.
Drug Dose (μg/kg) Mean arterial pressure (mmHg, n = 6) Reduction due to drug
Before drug After drug Mean reduction
Vehicle 0.05 ml 126.2 ± 2.8 110.0 ± 4.1 16.2 ± 1.4 ---
Reserpine 0.25 138.3 ± 4.6 119.5 ± 4.2 18.8 ± 1.4NS 2.6
0.50 134.1 ± 5.5 91.2 ± 5.0 42.9 ± 0.9** 26.5
1 135.2 ± 4.7 80.5 ± 3.8 54.8 ± 1.6** 38.6
5 130.5 ± 5.1 69.0 ± 3.5 61.5 ± 2.8** 45.3
10 130.0 ± 4.2 58.7 ± 3.9 71.2 ± 1.3** 55.0
15 131.5 ± 5.1 41.1 ± 2.9 90.4 ± 3.1** 74.2
Reserpine methonitrate equivalent to reserpine 10 128.8 ± 6.4 90.0 ± 9.1 38.8 ± 3.6** 22.6
25 135.0 ± 8.5 73.0 ± 7.4 62.0 ± 2.8** 45.8
50 136.8 ± 5.3 42.5 ± 3.7 93.0 ± 8.0** 76.8
Significant difference from DMSO treated group: **p < 0.01
NS – No significant difference from DMSO treated group.
Discussion
The structural modification of existing drugs to achieve selective action is not uncommon in providing better pharmaceutical care to the needy patients. It has been well established that the antihypertensive and tranquilizing actions of reserpine are mediated through the depletion of biogenic amines in the body [12,13,36]. The peripheral depletion of amines is responsible for its antihypertensive effect [11,37] while their central depletion plays a role in sedation and depression of reserpine [38,39]. Reserpine exerts its depleting effect by specifically inhibiting the adenosine triphosphate-Mg2+-dependent incorporation of biogenic amines into their storage vesicles [40,41].
Since reserpine depletes noradrenaline, 5-HT and dopamine from their storage sites, this results in a consequent increase in their metabolite levels in urine. Previous investigators have demonstrated a marked increase in the urinary excretion of peripheral and central metabolites of biogenic amines in animals treated with reserpine [8,42-44].
In the present investigation, a non-invasive biochemical approach was followed to determine the 24 h urinary excretion of VMA, 5-HIAA and HVA in rats treated with reserpine or RMN. Moreover, VMA, the peripheral metabolite of noradrenaline; 5-HIAA, the main metabolite of serotonin; and HVA, the predominant metabolite of dopamine were selected as the biomarkers for evaluation since noradrenaline exists both centrally and peripherally, serotonin exists mainly peripherally while majority of dopamine exists centrally. Since 99% of the total body's content of serotonin is present in the periphery, it is considered that the major part of the excreted 5-HIAA is from the peripheral release [45,46]. Similarly, high levels of dopamine are found in the centre rather than periphery, and any change in the HVA excretion in urine was considered as a corresponding change in dopamine levels at the central regions [47]. These indices provide an indirect evidence for the peripheral and central monoamine depleting effects of reserpine and its quaternary analogue.
The results showed that reserpine increased the urinary excretion of VMA, 5-HIAA and HVA indicating the depletion of peripheral as well as central biogenic amines. These are in agreement with the results observed by previous investigators [8,35,42-44]. The increase in the urinary excretion of VMA and 5-HIAA with RMN is higher than with reserpine at equimolar dose of 5 mg/kg body weight. The localized distribution of the analogue in the periphery could led to higher level of depletion of peripheral noradrenaline and serotonin hence their metabolite levels were found to be increased much more which also substantiate our previous studies [35]. The inability of the analogue to increase HVA excretion unlike reserpine could be due to its non-entry across the blood-brain barrier and into the central nervous system to deplete dopamine which is present predominantly in mesolimbic, nigrostriatal and tuberoinfendibular systems [48].
The increased urinary levels of 5-HIAA observed with RMN could be due to the peripheral release of 5-HT as it is found predominantly at the periphery in enterochromaffin cells. The higher dose (10 mg/kg) of RMN did not produce any further increase in the VMA and 5-HIAA excretion compared to lower dose. The possible reason for this effect could be that 5 mg/kg dose was sufficient to deplete the amines completely from the storage sites.
In order to evaluate whether the quaternary analogue of reserpine (RMN) still retains the peripheral blood pressure lowering activity, further experiments were carried out on the blood pressure of anaesthetized rats. Thus far, the results of RMN on the blood pressure response of anaesthetized rats confirmed that the peripheral actions of reserpine molecule are not affected by quaternization. However, in the present study the vehicle (DMSO) also produced minor hypotensive effect on blood pressure of rats when administered alone with the dose used for the administration of the drugs. Earlier workers [49] also reported hypotension with DMSO supporting the present observations. Reserpine produced dose dependent reduction in blood pressure as demonstrated by previous investigators [50,51]. As indicated in earlier reports [9,11,40,52-54] the hypotensive effect of reserpine observed in rats is due to the depletion of catecholamines from the peripheral stores.
The effect of equimolar doses of RMN also indicated hypotension however, with higher doses compared to reserpine. It is further indicated that quaternization of reserpine not only restricted the entry of RMN to central nervous system but also reduced to the target tissue in the periphery. Hence relatively higher doses were required to produce reserpine like effect. Mechanistically, the hypotensive actions of RMN could also be due to peripheral depletion of catecholamines as evident from the positive correlation with the results of previous section on the peripheral depletion of monoamines.
Conclusion
In conclusion, the present study indicated that the quaternization of reserpine molecule prevents its access into the central nervous system and thereby produces selective peripheral depletion of biogenic amines. Furthermore, the study indicated that quaternization of reserpine had not abolished the hypotensive response but only higher doses were required.
Methods
Chemistry
The synthesis of RMN was done as follows: The solution of reserpine (2 g, 3.3 mmols) in dichloromethane (20 ml) was added to methyl iodide (11 ml, 176 mmols) and the resulting mixture was kept for two days in dark. The solid was filtered and washed with a little cold dichloromethane and dried under vacuum at 70°C for 2 h to yield reserpine methiodide (RMI) [33,34]. Then, to a solution of RMI (0.25 gm, 0.67 mmols) in a mixture of dichloromethane (3 ml) and aqueous ethanol (90%, 2 ml) was added a solution of silver nitrate (56 mg, 0.67 mmols) in aqueous ethanol (90%, 2 ml). The reaction mixture was stirred overnight at room temperature. The solution was filtered and washed thoroughly with chloroform : methanol (1:1). The solid obtained after evaporation of the solvent was passed through a silica gel column and eluted with chloroform : methanol (80:20) to yield RMN, m.p. 292–294°C.
Chemicals used
Reserpine and thiopentone were generous gift samples from Novartis India Limited and Abbott Laboratories, Mumbai respectively. The standard samples of VMA, 5-HIAA, HVA and iso-VMA (internal standard) were purchased from Sigma-Aldrich, St. Louis, USA. All other chemicals used were of HPLC or analytical grade as appropriate.
The solutions of reserpine and RMN under study were prepared in DMSO and the volume of each dose was adjusted to 0.1 ml/100 gm body weight as suggested by Varma et al., [49]. The doses of RMN were calculated on equimolar basis of reserpine.
Animal experiments
Albino rats of either sex weighing between 100–150 gm (Charkaborty Enterprise, Kolkata) were used in the study. They were acclimatized to the laboratory conditions for at least 10 days prior to the experiment and were provided with standard diet and water ad libitum with 12 h light and dark cycle. The animal experiments conducted in this research work were approved by the Institutional Animal Ethics Committee and by the government regulatory body for animal research (Regd. No. 516/01/A/CPCSEA).
Biochemical estimations
Animals were divided into 4 groups of six each and were housed individually in metabolic cages. Funnels of suitable size were arranged at the bottom of the metabolic cages for collection of urine. Perforated plastic discs were arranged in the funnels to retain fecal matter. The animals were maintained at room temperature and acclimatized to metabolic cages for few days prior to drug administration.
The treatment given to the groups of animals was as follows:
Group 1: Control animals treated with DMSO intraperitoneally at a dose of 0.1 ml/100 gm body weight.
Group 2: Animals administered intraperitoneally with reserpine at a dose of 5 mg/kg body weight.
Group 3: Animals administered intraperitoneally with RMN at a dose equivalent to 5 mg/kg body weight of reserpine.
Group 4: Animals administered intraperitoneally with RMN at a dose equivalent to 10 mg/kg body weight of reserpine.
In each group, animals were placed individually in metabolic cages after drug administration and were allowed access to water. The 24 h urine samples from the point of drug administration was collected for each animal in a beaker containing 5 ml of 6 M HCl arranged at the bottom of the funnel. The volumes of the 24 h urine samples collected in the beakers were noted individually and about 2 ml of urine (mixture) from each animal was taken into sample tubes and centrifuged at 3000 rpm for 10 minutes. The supernatants were transferred into another set of clean and dry tubes and stored at -20°C until analysis by HPLC.
Simultaneous HPLC determination of VMA, 5-HIAA and HVA in urine
The procedure described by Wako-chem. Co.,[55] was used for the simultaneous determination of the above metabolites. The urine samples were thawed before analysis. To 0.2 ml of each sample, 0.1 ml of internal standard (iso-VMA, 1000 ηg) and 0.7 ml of mobile phase were added. The solutions were mixed well and filtered through 0.4 μm membrane filter. The filtrate (20 μL) was injected into the column (RP C-18, 250 mm × 4.6 mm I.D; particle size 5 μm; YMC Inc., USA). The mobile phase (filtered through 0.4 μm membrane filter) comprised of 10:90 v/v of acetonitrile and 0.1 M KH2PO4 and the flow rate of the mobile phase was maintained at 0.8 ml/min, which yields a column back pressure of 220–230 kgf/cm2. Detection was done by UV absorption at 230 ηm. The range of the detector was set at 0.001 a.u.f.s. The peak area ratios of VMA, 5-HIAA and HVA to that of internal standard were calculated and substituted in the respective regression equations to estimate the amount of the metabolite present in the sample.
Effect on normal blood pressure of anaesthetized rats
The procedure described by Noble [56] was followed to evaluate the effect of RMN on normal blood pressure of anaesthetized rats in comparison with reserpine. Groups of rats of six each were anaesthetized with an intraperitoneal injection of thiopentone (40 mg/kg body weight). The femoral vein was cannulated for administration of supplementary doses of anaesthetic (if required) and drug solutions.
Haemodynamic setup was used to record the blood pressure of rats. The blood pressure of each animal was recorded from left common carotid artery connected to a mercury manometer on kymograph paper. The normal blood pressure of rats was recorded after stabilization for 30 minutes. The different doses of reserpine (0.25, 0.50, 1, 5, 10 and 15 μg/kg body weight) or RMN (10, 25 and 50 μg/kg body weight) were studied in separate groups (n = 6) to determine the change in blood pressure response.
Statistical analysis
Data are expressed as mean ± standard error of means. Statistical analysis was done using one-way analysis of variance (ANOVA). Post-hoc comparisons were done by using Dunnet's t-test. In all the cases, p < 0.05 was considered statistically significant.
Abreviations
RMN: Reserpine methonitrate
VMA: Vanillylmandelic acid
5-HIAA: 5-Hydroxyindoleacetic acid
HVA: Homovanillic acid
DMSO: Dimethyl sulfoxide
Authors' contributions
SN made significant contribution in designing the studies, conducting the experiments, interpretation of the data, conceptualization of statistical analyses and drafting the final manuscript. KMB assisted in experimental work, data analysis and writing of the manuscript. SS conceived the study, made substantial contributions in data analysis, data interpretation, writing of the manuscript and in coordination of the experiments. All authors read and approved the final manuscript.
Acknowledgements
The authors are indebted to Sri G. Ganga Raju and the scientists of Laila Impex Research Centre, Vijayawada, India for their encouragement and help in the synthesis of RMN. The authors are also thankful to Novartis India Limited and Abbott Laboratories, Mumbai for the supply of reserpine and thiopentone respectively as generous gift samples. The financial support by the Council of Scientific and Industrial Research (CSIR), New Delhi to Srinivas Nammi is gratefully acknowledged.
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| 15546495 | PMC535563 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Nov 16; 4:30 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-30 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-261554648210.1186/1471-2296-5-26Research ArticleThe association of patient trust and self-care among patients with diabetes mellitus Bonds Denise E [email protected] Fabian [email protected] Ronny A [email protected] Vanessa T [email protected] Roger T [email protected] David C [email protected] Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA2 Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA3 Maya Angelou Center for Minority Health, Wake Forest University School of Medicine, Winston-Salem, NC, USA2004 16 11 2004 5 26 26 11 2 2004 16 11 2004 Copyright © 2004 Bonds et al; licensee BioMed Central Ltd.2004Bonds 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
Diabetes requires significant alterations to lifestyle and completion of self management tasks to obtain good control of disease. The objective of this study was to determine if patient trust is associated with reduced difficulty and hassles in altering lifestyle and completing self care tasks.
Methods
A cross-sectional telephone survey and medical record review was performed to measure patient trust and difficulty in completing diabetes tasks among 320 medically underserved patients attending diabetes programs in rural North Carolina, USA. Diabetes tasks were measured three ways: perceived hassles of diabetic care activities, difficulty in completing diabetes-related care activities, and a global assessment of overall ability to complete diabetes care activities. The association of patient trust with self-management was examined after controlling for patient demographics, physical functioning, mental health and co-morbidities.
Results
Level of patient trust was high (median 22, possible max 25). Higher trust levels were associated with lower levels of hassles (p = 0.006) and lower difficulty in completing care activities (p = 0.001). Patients with higher trust had better global assessments of overall ability to complete diabetes care activities (p < 0.0001).
Conclusion
Higher patient trust in physicians is associated with reduced difficulty in completing disease specific tasks by patients. Further studies are needed to determine the causal relationship of this association, the effect of trust on other outcomes, and the potential modifiability of trust
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Background
Diabetes mellitus is a disease associated with significant morbidity and mortality [1,2]. Patients with diabetes have higher rates of coronary artery disease, retinopathy, neuropathy and nephropathy [1]. Many of these complications can be prevented with appropriate medical care[3,4] This care, however, often requires significant alterations in lifestyle and strict adherence to self-care tasks, such as checking blood sugars, and taking medications by the patient [5]. Previous research has shown that patients with diabetes and other diseases often have difficulty in adopting lifestyle changes and completing self-care tasks[6]. The cause of this poor adherence is multi-factorial and includes patient characteristics such as educational level, [7-9] treatment regimen characteristics, [10] and characteristics of the clinical setting [11,12]. Patient's perception on the intrusiveness of treatment regimens and their perceived self-efficacy in completing the task have also been demonstrated to affect adherence to diabetes treatment [13,14].
Aspects of the patient-physician relationship such as communication and empathy have been shown to be important to patient's adherence [15-18] and ability to complete self-care tasks [7,19,20]. Patient trust is another component essential to the doctor-patient relationship [21]. Defined variously as a set of beliefs or expectations that the doctor will act in the patient's best interest, or as a reassuring feeling of confidence in the doctor, [22,23] trust provides the foundation for many of the other aspects of the relationship such as communication and empathy [23]. Despite the significance of trust, there has been relatively little research on this aspect of the doctor-patient relationship. Most studies examining trust have focused on characteristics that predict trust levels [24-28] or the influence insurance characteristics have on trust [29,30]. Limited studies have been done examining the relationship between patient trust and medical outcomes. Safran et al found that adherence to physician recommended lifestyle changes was significantly associated with patient trust levels [31]. Higher levels of patient trust have also been associated with lower hemoglobin A1c levels in patients with type 2 diabetes [32]. None have measured trust and its effect on patient's ability in completing self-care tasks.
Given the underlying importance of trust in the doctor-patient relationship and the effect of the doctor-patient relationship on adherence and self-care, we hypothesized that higher levels of patient trust in physicians may be associated with reduced difficulty of self-care tasks. To explore this hypothesis, we conducted a survey of patients with diabetes to determine if level of trust is associated with the outcomes of difficulty and hassles in completing self-care tasks.
Methods
Data Source
Project IDEAL (Improving Diabetes Education, Access to Care and Living) was an initiative to increase the quality of care and quality of life for underserved patients with diabetes in North Carolina. Full details of the study have been published elsewhere [33]. Briefly, fourteen clinical sites across North Carolina were funded by the Kate B. Reynolds Charitable Trust to establish programs to improve the quality of care provided to underserved patients with diabetes. Each developed its own unique diabetes intervention. Evaluation of the initiative was conducted by the Wake Forest University School of Medicine Department of Public Health Sciences using both quality of care and quality of life indicators. To assess the effectiveness of the initiative, a random sample of patients from each program was selected for inclusion at each point in time (1998 pre-initiative and 2001 post-initiative). Quality of care was assessed through a chart review of randomly selected patient. Diabetes Quality of Care (DQIP) [34] and Health Plan Employer Data and Information Set (HEDIS) [35] measures were used as standards for quality of care. Quality of life was assessed through a telephone survey of the same patients, allowing both quality of care and quality of life indicators to be linked. The telephone survey was composed of portions of the Medical Outcomes Study Short Form 36 (SF-36) [36], the DQIP Patient Measures [34] and Diabetes-39 Health Related Quality of Life instruments [37]. To determine the relationship between self-management and level of patient trust, an established trust instrument was added to the post intervention survey.
Trust Instrument
To measure patient trust, we used the Wake Forest University Trust Scale. This scale has been tested in multiple populations and found to be reliable with good construct validity [38,39]. We used the 5 question institutional trust scale as many of the patients were cared for by multiple physicians. This trust measure is a summation of five items that are adapted from a longer trust instrument to ask the patient's view of doctors in general [39]. The items are: "The doctors at 'this clinic' will do whatever it takes to get patients all the care they need;" "The doctors at 'this clinic' are extremely thorough and careful;" "The medical skills of the doctors here are not as good as they should be;" "You have no worries about putting your life in the hands of the doctors at 'this clinic;" "All in all, you trust the doctors at 'this clinic' completely." A five point scale was used with answers ranging from strongly disagree to strongly agree (1–5), with a higher score indicating a higher level of trust.
Self-Management Measures
Patient self-care measures were taken from the telephone survey. The methods for this survey have been described elsewhere [40]. We examined 3 measures: patients' perceived hassle associated with self-care tasks (hassles), patients' difficulty in completing recommended care activities (difficulty), and a global assessment of their ability to care for their disease (care). Briefly, hassle was measured with a 7-item scale that asked patients to rate how much of a problem or hassle it has been to complete diabetes-related tasks over the past 4 weeks. Items in the scale included remembering to take medication and test blood sugar, making meal plans, avoiding particular foods, having to keep a care schedule in mind, organizing the daily routine around a medical care activity, and total time spent in managing their disease. This scale is part of the DQIP instrument [34]. All questions were measured using a 5-point scale from "no problem" (1) to "a major hassle" (5) with a higher value equalling more hassle. Respondents also had the choice of indicating the question "does not apply". This resulted in a variable number of questions being answered for each patient. To account for this variation, we calculated an average response for each patient by summing the responses and dividing by the number of questions answered by that patient.
Our second outcome, difficulty in completing care activities, was measured in a 5-item scale also taken from the DQIP measures [34]. This scale asked patients about the difficulty they had in completing 5 specific care activities exactly as their doctor suggested. Activities included 1) taking their medication as prescribed, 2) exercising regularly, 3) following their diet, 4) checking their blood sugar, and 5) checking their feet for wounds or sores. Answers were scored on a 5-point scale from "not at all difficult" (1) to "extremely difficult" (5) with an option for "does not apply". Again, to account for variation in the number of questions answered, we calculated an average score, excluding measures that did not apply.
The final adherence measure we examined was a global measure of care. This single item asked patients to rate how they "...are at taking care of their diabetes". Possible responses on a 5-point scale ranged from "I stay right on top of it at all times" (1) to "I let it slip way too much" (5).
Independent Variables
We considered several modifying factors that might influence the relationship between trust and self-care measures. These factors were selected either because they have been shown to influence patient self-care or because they have been used in previous studies on patient trust and outcomes. Modifying factors were obtained from either the survey or through the chart review. Patient's age, gender, race (white, non-white) and insurance (any, none) were obtained from chart review. A co-morbidity score (none, 1–2, greater than 2) was generated based on the number of additional diabetes-related diseases (hypertension, coronary artery disease, non-traumatic amputation, nephropathy, neuropathy, peripheral vascular disease and smoking) identified during chart review. The Medical Outcomes Study Short Form 36 (MOS SF-36) [36] subscales for physical and mental health were included as part of the patient interview and were used to assess health-related quality of life. The length of the relationship with the health care professional and the number of visits to a provider for diabetes care were obtained from the telephone survey. The use of insulin by the patient was collected from the chart review and was included in the analyses. Imputed values were created for missing measures and analyses were run with and without imputed variables to detect the effect of these variables.
Statistical Analysis
To determine the association of patient trust and our outcomes of patient hassles, difficulty in completing care activities and global assessment, we used Generalized Estimating Equation[41] methods to account for clustering of patients within IDEAL program sites. A regression analysis using the xtgee command in STATA/SE 7.0 (STATA Corporation, College Station, TX) was performed for each outcome of interest. Models adjusted for age, gender, race, insurance status, number of co-morbidities, insulin use, number of visits for diabetes in the last year, length of relationship with the provider, physical functioning, and mental health.
Results
Three hundred and twenty-six individuals with diabetes had information from both the telephone survey and chart review portions of the study. The response rate for the telephone survey was 67%.
The mean age of the respondents was 60, over two-thirds were female and forty percent were non-white (see table 1). Most respondents (75%) had some sort of insurance, primarily Medicare or a Medicare HMO. Most (79%) had least one co-morbid condition and the average number of co-morbid diseases was one. Average score on the MOS-SF 36 physical functioning and mental health scale was 57.0 (sd 29.8) and 73.5 (sd 21.2), respectively. Diabetes control was generally good with the average hemoglobin A1c 7.3%, although the range was wide (4.4%–14.6%). Twenty four percent of the sample had insulin listed as a treatment in their medical records. When asked about the length of time they had been seeing a provider or clinic, 15% responded less than 1 year, 21% 1–2 years, and 65% more than 2 years. The average number of visits to a provider for diabetes care in the last 12 months was 3. The level of patient trust in their physician was high, with an average and median level of trust of 22 (sd 3, range 11–25).
Table 1 Description of the Sample (n = 326)
Age, mean 60 (sd 12.0, range 19–89)
Gender, female 67% (n = 217)
Race, non-white 40% (n = 120)
Insurance, none 25% (n = 82)
Co-morbidities, any (%) 79% (n = 228)
Co-morbidities, mean (%) 1.1 (sd 1.0, range 0–4)
Hemoglobin A1c, mean 7.3% (sd 1.7, range 4.4–14.6)
Physical Health (max 100) 57.0 (sd 29.8)
Mental Health (max 100) 73.5 (sd 21.0)
Number of years with provider/clinic*
Less than 1 year 13% (n = 44)
1 to 2 years 19% (n = 62)
More than 2 years 58% (n = 190)
Number of visit in last 12 months 3 (sd 1.0)
Patient Trust (max 25) 22 (sd 3, range 11–25)
* Number does not equal 100% due to participant who responded that they did not have a regular source of diabetes care
When asked about hassles associated with their diabetes, respondents reported the highest degree of hassle with avoiding foods they enjoyed (higher score equals more hassle: mean 2.4, SD 1.4, range 1–5) (table 2). Taking medication (mean 1.2, SD 0.7, range 1–5) and testing blood sugar (mean 1.4, SD 0.9, range 1–5) were the care activities associated with the lowest level of hassle. Similarly, patients reported low levels of difficulty when asked about taking their medication for their diabetes (higher score equals more difficulty: mean 1.2, SD 0.5, range 1–4), while they reported higher levels of difficulty in following a diet for their diabetes (mean 2.1, SD 1.0, range 1–5) and exercising regularly (mean 2.5, SD1.3, range 1–5). Overall, the patients surveyed reported high global assessments of their self care for their diabetes (higher score equals worse self care: mean 2.0, SD 0.9, range 1–5).
Table 2 Average score for global outcome measures and individual components of each measure
Outcome Measure Mean (SD, Range)
Hassles associated with diabetes care*
Average for entire scale 1.6 (0.7, 1–4.5)
Remembering to take diabetes medicine 1.2 (0.7, 1–5)
Remembering to test blood for sugar 1.4 (0.9, 1–5)
Making meal plans 1.6 (1.2, 1–5)
Avoiding foods you enjoy 2.4 (1.4, 1–5)
Keeping schedule in mind at times 1.7 (1.2, 1–5)
Organizing daily routine around diabetes care 1.4 (0.9, 1–5)
Total time spent managing diabetes 1.5 (1.0, 1–5)
Difficulty in performing self care activities**
Average of entire scale 1.7 (0.5, 1–3.8)
Taking medications as prescribed 1.2 (0.5, 1–4)
Exercising regularly 2.5 (1.3, 1–5)
Following diet 2.1 (1.0, 1–5)
Checking blood for sugar 1.5 (0.9, 1–5)
Checking feet for wounds or sores 1.2 (0.5, 1–5)
Global assessment of ability to care for diabetes*** 2.0 (0.9, 1–5)
* Higher equals more hassles associated with care activity
** Higher equals more difficulty in completing care activities
*** Higher equals lower assessment of ability to care
In bivariate analyses, a higher levels of hassles was associated with younger age (p < 0.001), non-white race (p = 0.03), and lower score for mental health. There was no association with gender, insurance status, co-morbidities, use of insulin, physical health, length of the relationship with the provider, or the number of visits for diabetes in the past year. A higher difficulty in completing care activities was associated with younger age (p = 0.002), female gender (0.003), non-white race (p = 0.03), worse physical health (p < 0.001) and worse mental health (p = 0.001). There was no association with insulin use, co-morbidities, length of relationship, or number of visits in the past year. Better self rated ability to care for diabetes was also associated with older age (p = 0.004), better physical health (p = 0.02), and better mental health (p < 0.0001). There was no association with gender, race, co-morbidities, use of insulin, length of relationship, or number of visits in the past year.
In multivariate regression analysis, level of patient trust was significantly associated with each of the outcomes we examined. Patients with higher levels of trust in their physician reported lower levels of hassles with disease (GEE parameter -0.03, 95% CI -0.05 to -0.008, p = 0.006). Similar finding were seen when self-reported difficulty was examined. Higher level of patient trust was again associated with less reported difficulty in caring for diabetes (GEE parameter -0.02, 95% CI -0.03 to -0.01, p = 0.001). Our final patient outcome, global assessment of ability to care for diabetes, also showed a significant association with higher levels of patient trust associated with higher self-reported ability to care for diabetes (GEE parameter -0.05, 95% CI -0.08 to -0.02, p < 0.0001). Thus, if a patient responded "strongly agree" to all the trust questions rather than "agree" (representing a 5-point increase in level of trust), self-reported hassles would decrease by approximately 0.2 points (about 30% of a standard deviation in hassles). Similarly, for a 5-point increase in trust, self-reported difficulty would decrease by 0.1 points (about 20% of a standard deviation in difficulty) and global ability to care for diabetes would decrease by 0.3 (about 33% of a standard deviation in global ability to care for diabetes). (table 3)
Table 3 General estimating equations regression parameters for difference in outcomes associated with a 5-point difference in trust
Outcome GEE parameter estimates (95% CI)
Hassles associated with diabetes care -0.2 (-0.3 to -0.05)
Difficulty in performing self care activities -0.1 (-0.20 to -0.05)
Global assessment of ability to care for diabetes -0.3 (-0.40 to -0.10)
Discussion
Diabetes is a disease requiring many types of interventions to prevent the associated morbidity and mortality. These interventions include medications to control elevated glucose levels and finger sticks to check blood sugars. Additionally, significant alterations in lifestyle such as increasing exercise and changing the type of food one eats are an essential portion of the treatment regimen. In our sample of low-income patients with diabetes, we found low reported hassle of completion of diabetes tasks, low reported difficulty in completing diabetes related tasks and good self reported ability to stay on top of their disease. The patients in this study did report more difficulty in making lifestyle changes such as exercising regularly and following recommended diets than taking medications. Other studies examining adherence in diabetic patients have found similar findings. Ruggiero et al, in a nationwide survey of individuals with diabetes, found that over 90% reported always or usually taking their medication but only 64% always or usually followed dietary recommendations and less than half always or usually exercised [6].
Higher levels of trust were associated with lower reported levels of hassles, lower self-reported difficulty in completing care activities, and improved self-reported global ability to take care of diabetes. These results support our hypothesis of increased completion of self-management tasks with higher levels of patient trust in physicians. There are several possible causes for this association. Patients who are actively engaged in their medical care and jointly make health care decisions with their provider may have less difficulty and hassles in performing self-care activities that they had input on. These patients may also have higher trust levels because they have been engaged as an active participant in the health care decision by their provider. Additionally, medical regimens used to treat chronic disease are complicated. Patients may not fully understand the medical rationale behind particular recommendations such as exercise and diet. Additionally, exercise and diet may not result in immediate improvement in symptoms and often cause initial discomfort or feelings of deprivation, thereby providing little positive feedback and reinforcement. Patients who have high levels of trust may be willing to overcome the initial discomfort and maintain faith that these difficult changes will ultimately be beneficial to them. Our results indicate that an intervention to improve trust that resulted in an increase of 5-points (moving from agree to strongly agree on all questions) would result in an improvement of 0.1–0.3 in our outcome scores. This improvement is equivalent to the difference in self care scores seen between a 60 year old patient and a 30 year old patient. Additionally, trust was significantly associated with all self care measures while insulin use was not.
Few studies have examined the association of completion of self-care tasks with patient trust. Safran et al surveyed adults employed by the state of Massachusetts using the Primary Care Assessment Survey which includes a section on patient trust [31]. They found that patient trust was significantly associated with patient satisfaction and self-reported adherence to lifestyle changes. Thom et al conducted a longitudinal survey of patients cared for in primary care clinics and found that higher levels of patient trust were associated with greater self-reported adherence to prescribed medications [42]. Our study differs from these by its focus on low-income patients and on measurements of difficulty of self-care in a common, chronic disease.
These results add to growing evidence that patient trust is important to patient outcomes, but are limited by the cross-sectional design of the study. Although the results support the contention that higher levels of trust result in decreased hassles and difficulty in completing self-management task, it is possible that the reverse is true. Longitudinal studies of individuals with newly diagnosed chronic diseases such as diabetes are needed to define the temporal nature of this association. Additionally, our outcome measures did not assess whether patients were actually performing the activities, rather, the instruments were designed to determine the level of difficulty and hassles. It is possible that individuals viewed particular activities as very difficult and having a high degree of hassle and still completed the task. Future studies with measurements of both patient perception and actual completion rates are needed. The shortened trust instrument we used lacked the important dimension of patient-centered care. Patients who feel their concerns are listened to and work in partnership with their health care provider may be more likely to attempt new self care activities. It is likely that the inclusion of this dimension through questions such as the providers' ability to listen to or advocate for the patient would strengthen the association of trust with reduced difficulty in completing self care tasks. We also did not collect information on several important mediators of patient trust and self-care such as the empathy level of the health care provider, educational level of the patient, and patients' overall perceived self-efficacy. These additional factors may provide further elucidation into the relationship of patient trust and ability to complete self care task. Despite these limitations, this study adds to our understanding of the predictors of patient self-management of disease. Further studies comparing strategies for measuring trust, measurement of trust at multiple points in time and the linkage of patient trust with clinical outcomes are needed.
Conclusions
Trust is an integral component of the doctor-patient relationship. Increasingly, external forces such as changes in reimbursement for medical services and increases in the cost of malpractice insurance place pressure on this relationship [43]. These pressures may erode trust [43]. If trust is associated with self-care tasks such as adherence to medication or lifestyle changes, the turbulent changes in the health care industry may result in worse health outcomes. Efforts to define the impact of trust on patient ability and comfort in performing self-care activities and to identify the modifiable determinants of trust may translate into improved health outcomes, especially for vulnerable populations.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DEB wrote the text and conducted with the statistical analysis.
FB assisted with the statistical analysis and the writing of the paper.
RAB was responsible for selection of the quality of care markers, oversaw data abstraction and assisted with writing the article.
VTD was responsible for data collection and clinic interventions and assisted with editing the article.
RTA was responsible for selection of the quality of life markers and assisted with editing the article.
DCG was responsible for the scientific direction of the study, design, and assisted with editing the article
All authors read and approved the final manuscript
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was funded by a grant from the Health Care Division of the Kate B. Reynolds Charitable Trust, John H. Frank, Director, Winston-Salem, North Carolina 27106.
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| 15546482 | PMC535564 | CC BY | 2021-01-04 16:29:00 | no | BMC Fam Pract. 2004 Nov 16; 5:26 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-26 | oa_comm |
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BMC AnesthesiolBMC Anesthesiology1471-2253BioMed Central London 1471-2253-4-81556938610.1186/1471-2253-4-8Research ArticleEndotracheal tube cuff pressure in three hospitals, and the volume required to produce an appropriate cuff pressure Sengupta Papiya [email protected] Daniel I [email protected] Paul [email protected] Spencer [email protected] Alicia [email protected] Jaleel [email protected] Anupama [email protected] Outcomes Research™ Institute, University of Louisville, 501 E. Broadway, Suite 210, Louisville, KY 40202, USA2 Department of Anesthesiology and Perioperative Medicine, University of Louisville, 530 S. Jackson St. University Hospital, Louisville, KY 40202, USA3 School of Medicine, University of Louisville School of Medicine, Louisville, KY 40292, USA2004 29 11 2004 4 8 8 8 6 2004 29 11 2004 Copyright © 2004 Sengupta et al; licensee BioMed Central Ltd.2004Sengupta 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
Cuff pressure in endotracheal (ET) tubes should be in the range of 20–30 cm H2O. We tested the hypothesis that the tube cuff is inadequately inflated when manometers are not used.
Methods
With IRB approval, we studied 93 patients under general anesthesia with an ET tube in place in one teaching and two private hospitals. Anesthetists were blinded to study purpose. Cuff pressure in tube sizes 7.0 to 8.5 mm was evaluated 60 min after induction of general anesthesia using a manometer connected to the cuff pilot balloon. Nitrous oxide was disallowed. After deflating the cuff, we reinflated it in 0.5-ml increments until pressure was 20 cmH2O.
Results
Neither patient morphometrics, institution, experience of anesthesia provider, nor tube size influenced measured cuff pressure (35.3 ± 21.6 cmH2O). Only 27% of pressures were within 20–30 cmH2O; 27% exceeded 40 cmH2O. Although it varied considerably, the amount of air required to achieve a cuff pressure of 20 cmH2O was similar with each tube size.
Conclusion
We recommend that ET cuff pressure be set and monitored with a manometer.
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Background
A critical function of the endotracheal tube cuff is to seal the airway, thus preventing aspiration of pharyngeal contents into the trachea and to ensure that there are no leaks past the cuff during positive pressure ventilation. However, complications have been associated with insufficient cuff inflation. Consequences of micro-aspiration of oropharyngeal secretions include nosocomial pulmonary infections [1]. Conventional high-volume, low-pressure cuffs may not prevent micro-aspiration even at cuff pressures up to 60 cm H2O [2], although some studies suggest that only 25 cm H2O is sufficient [3]. In contrast, newer ultra-thin cuff membranes made from polyurethane effectively prevent liquid flow around cuffs inflated only to 15 cm H2O [2]. In the absence of clear guidelines, many clinicians consider 20 cm H2O a reasonable lower limit for cuff pressure in adults.
Catastrophic consequences of endotracheal tube cuff over-inflation such as rupture of the trachea [4-6], tracheo-carotid artery erosion [7], and tracheal innominate artery fistulas are rare now that low-pressure, high-volume cuffs are used routinely. However, post-intubation sore throat is a common side effect of general anesthetic and may partly result from ischemia of the oropharyngeal and tracheal mucosa [8-10], and the most common etiology of non-malignant tracheoesophageal fistula remains cuff-related tracheal injury [11,12]. In addition, acquired laryngeal stenosis may be caused by mechanical abrasion or pressure necrosis of the laryngeal mucosa secondary to high cuff pressure [13,14]. Animal data indicate that a cuff pressure of only 20 cm H2O may significantly reduce tracheal blood flow with normal blood pressure and critically reduces it during severe hypotension [15]. Similarly, inflation of endotracheal tube cuffs to 20 cm H2O for just four hours produces serious ciliary damage that persists for at least three days [16]. Thus, appropriate inflation of endotracheal tube cuff is obviously important.
Lomholt et al. recommended selecting a cuff pressure of 25 cmH2O as a safe minimum cuff pressure to prevent aspiration and leaks past the cuff [17]; Bernhard et al. supported this recommendation [18]. On the other hand, Nordin et al. studied the relationship between cuff pressure and capillary perfusion of the rabbit tracheal mucosa and recommended that cuff pressure be kept below 27 cm H2O (20 mmHg) [19]. Seegobin and Hasselt reached similar conclusions in an in vitro study and recommended cuff inflation pressure not exceed 30 cm H2O [20]. It is thus essential to maintain cuff pressures in the range of 20–30 cm of H2O
Adequacy of cuff inflation is conventionally determined by palpation of the external balloon. Previous studies suggest that this approach is unreliable [21,22]. One study, for instance, found that cuff pressure exceeded 40 cm H2O in 40-to-90% of tested patients [22]. However, increased awareness of over-inflation risks may have improved recent clinical practice. Our first goal was thus to determine if cuff pressure was within the recommended range of 20–30 cmH2O, when inflated using the palpation method. Because cuff inflation practices are likely to differ among clinical environments, we evaluated cuff pressure in three different practice settings: an academic university hospital and two private hospitals. It is also likely that cuff inflation practices differ among providers. We therefore also evaluated cuff pressure during anesthesia provided by certified registered nurse anesthetists (CRNAs), anesthesia residents, and anesthesia faculty.
Cuff pressure can be easily measured with a small aneroid manometer [23], but this device is not widely available in the United States. It would thus be helpful for clinicians to know how much air must be injected into the cuff to produce the minimum adequate pressure. We designed this study to observe the practices of anesthesia providers and then determine the volume of air required to optimize the cuff pressure to 20 cmH2O for various sizes of endotracheal tubes. Taking another approach to the same question, we also determined compliance of the cuff-trachea system in vivo by plotting measured cuff pressure against cuff volume.
Methods
With approval of the University of Louisville Human Studies Committee and informed consent, we recruited 93 patients (42 men and 51 women) undergoing elective surgery with general endotracheal anesthesia from three hospitals in Louisville, Kentucky: 41 patients from University Hospital (an academic centre), 32 from Jewish Hospital (a private hospital), and 20 from Norton Hospital (also a private hospital).
Patients with emergency intubations, difficult intubations, or intubation performed by non-anesthesiology staff; pregnant women; patients with higher risk for aspiration (e.g., full stomach, history of reflux, etc.); and patients with known anatomical laryngeo-tracheal abnormalities were excluded from this study. The Human Studies Committee did not require consent from participating anesthesia providers. However, no data were recorded that would link the study results to specific providers.
Protocol
Independent anesthesia groups at the three participating hospitals provided anesthesia to the participating patients. Because one purpose of our study was to measure pressure in the endotracheal tube cuff during routine practice, anesthesia providers were blinded to the nature of the study. They were only informed about the second purpose of the study: determining the relationship between cuff volume and pressure. Ninety-three patients were randomly assigned to the study. The groups were not equal for the three different types of practitioners; however, determining differences of practice between different anesthesia providers was not the primary purpose of our study.
General anesthesia was induced by intravenous bolus of induction agents, and paralysis was achieved with succinylcholine or a non-depolarizing muscle relaxant. Male patients were intubated with an 8 or 8.5 mm internal diameter endotracheal tube, and female patients were intubated with a 7 or 7.5 mm internal diameter endotracheal tube. This is a standard practice at these hospitals. Patients who were intubated with sizes other than these were excluded from the study. Anesthesia was maintained with a volatile aesthetic in a combination of air and oxygen; nitrous oxide was not used during the study period.
At the University of Louisville Hospital, at least 10 patients were evaluated with each endotracheal tube size (7, 7.5, 8, or 8.5 mm inner diameter [Intermediate Hi-Lo® Tracheal Tube, Mallinckrodt, St. Louis, MO]); at Jewish Hospital, at least 10 patients each were evaluated with size 7, 7.5, and 8 mm Mallinckrodt Intermediate Hi-Lo® Tracheal Tubes; and at Norton Hospital, 10 patients each were evaluated with size 7 and 8-mm Mallinckrodt Intermediate Hi-Lo® Tracheal Tubes. Consecutive available patients were enrolled until we had recruited at least 10 patients for each endotracheal tube size at each participating hospital. All tubes had high-volume, low-pressure cuffs.
Measurements
We recorded endotracheal tube size and morphometric characteristics including age, sex, height, and weight.
An anesthesia provider inserted the endotracheal tubes, and the intubator or the circulating registered nurse inflated the cuff. This is the routine practice in all three hospitals. Adequacy is generally checked by palpation of the pilot balloon and sometimes readjusted by the intubator by inflating just enough to stop an audible leak. Investigators measured the cuff pressure at 60 minutes after induction of anesthesia using a manometer (VBM, Sulz, Germany) that was connected to the pilot balloon of the endotracheal tube cuff via a three-way stopcock. This type of aneroid manometer is nearly as accurate as a mercury manometer, but easier to use [23]. Pressure was recorded at end-expiration after ensuring that the patient was paralyzed. The cuff pressure was measured once in each patient at 60 minutes after intubation. We did not collect data on the readjustment by the providers after intubation during this hour.
A syringe attached to the third limb of the stopcock was then used to completely deflate the cuff, and the volume of air removed was recorded. The cuff was considered empty when no more air could be removed on aspiration with a syringe. The cuff was then progressively inflated by injecting air in 0.5-ml increments until a cuff pressure of 20 cmH2O was achieved. The entire process required about a minute.
Data analysis
Our primary outcomes were 1) measured endotracheal tube cuff pressures as a function of tube size, provider, and hospital; and 2) the volume of air required to produce a cuff pressure of 20 cmH2O as a function of tube size. Outcomes were compared by tube size, provider, and hospital with either an ANOVA (if the values were normally distributed) or the Kruskal-Wallis statistic (if the values were skewed). Compliance of the cuff system was evaluated by linear regression of measured cuff pressure vs. measured cuff volume. Data are presented as means (SD) or medians [interquartile ranges] unless otherwise noted; P < 0.05 was considered statistically significant.
Results
Morphometric and demographic characteristics of the patients were similar at each participating hospital (Table 1). Measured cuff pressures averaged 35.3(21.6)cmH2O; only 27% of the patients had measured pressures within the recommended range of 20–30 cmH2O. Fifty percent of the values exceeded 30 cmH2O, and 27% of the measured pressures exceeded 40 cmH2O. Thus, 23% of the measured cuff pressures were less than 20 mmHg. Measured cuff volume averaged 4.4 ± 1.8 ml. Neither measured cuff pressure nor measured cuff volume differed among the hospitals (Table 2).
Table 1 Demographic and Morphometric Characteristics as a Function of Hospital.
University of Louisville Hospital Norton Hospital Jewish Hospital P
N 41 20 32 ---
Tube size (N) 7 / 7.5 / 8 / 8.5 10 / 11 / 10 / 10 10 / 0 / 10 / 0 10 / 10 / 12 / 0 ---
Age (yr) 41 (14) 50 (14) 51 (16) 0.006
Weight (kg) 88 (27) 100 (40) 78 (17) 0.020
Height (m) 1.7 (0.1) 1.7 (0.1) 1.7 (0.1) 0.669
Results presented as number or mean (SD).
Table 2 Principal Results as a Function Hospital
University of Louisville Hospital Norton Hospital Jewish Hospital P
Measured Cuff Pressure (cmH2O) 26 [18, 38] 34.5 [20, 50.5] 33 [22.5, 48.5] 0.469
Measured Cuff Volume (ml) 4.0 [3.0, 5.0] 4.3 [3.0, 6.0] 4.5 [3.2, 5.5] 0.646
Volume Required for 20 cmH2O (ml) 2.7 [2.1, 3.8] 2.5 [2.3, 3.3] 2.9 [2.2, 3.7] 0.792
Results presented as median [interquartile range].
There was no correlation between the measured cuff pressure and the age, sex, height, or weight of the patients. Nor did measured cuff pressure differ as a function of endotracheal tube size. Measured cuff volumes were also similar with each tube size. Interestingly, the amount of air required to achieve a cuff pressure of 20 cmH2O was similar with each tube size (Table 3). However, there was considerable variability in the amount of air required.
Table 3 Principal Results as a Function of Tube Size.
7.0 mm 7.5 mm 8.0 mm 8.5 mm P
Patients with measured cuff pressure >30 cmH2O (%) 57 57 47 30 0.444
Measured Cuff Pressure (mmHg) 32 [22, 52] 38 [24, 49] 30 [16, 38] 24 [21, 40] 0.467
Measured Cuff Volume (ml) 3.9 [3.0, 5.0] 4.5 [2.7, 5.0] 4.6 [3.5, 6.1] 3.8 [3.0, 5.0] 0.616
Volume Required for 20 cmH2O (ml) 2.6 [2.0, 3.1] 2.5 [1.8, 3.0] 3.0 [2.5. 4.1] 3.3 [2.0, 3.9] 0.143
Results presented as medians [interquartile range] or percent.
CRNAs (n = 72), anesthesia residents (n = 15), and anesthesia faculty (n = 6) performed the intubations. There were no statistically significant differences in measured cuff pressures among these three practitioner groups (P = 0.847).
The compliance of the tube was determined from the measured cuff pressure (cmH2O) and the volume of air (ml) retrieved at complete deflation of the cuff; this showed a linear pressure-volume relationship: Pressure= 7.5. Volume+2.7, r2 = 0.39 (Fig. 1).
Figure 1 The relationship between measured cuff pressure and volume of air in the cuff. There was a linear relationship between measured cuff pressure (cmH2O) and volume (ml) of air removed from the cuff: Pressure = 7.5. Volume + 2.7, r2 = 0.39.
Discussion
Previous studies suggest that the cuff pressure is usually under-estimated by manual palpation. For example, Braz et al. [22] observed cuff pressure exceeding 40 cm H2O in 91% of PACU patients after anesthesia with nitrous oxide, 55% of ICU patients, and 45% of PACU patients after anesthesia without nitrous oxide. In an experimental study, Fernandez et al. [21] observed that when the cuff was inflated randomly to 10, 20, or 30 cmH2O, participating physicians and ICU nurses were able to identify the group in 69% of the high-pressure cases, 58% of the normal pressure cases, and 73% of the low pressure cases. Our results are consistent in that measured cuff pressure exceeded 30 cmH2O in 50% of patients and were less than 20 cmH2O in 23% of patients. Cuff pressures were thus less likely to be within the recommended range (20–30 cmH2O) than outside the range. It was nonetheless encouraging that we observed relatively few extremely high values, at least many fewer than reported in previous studies [22]. This result suggests that clinicians are now making reasonable efforts to avoid grossly excessive cuff inflation.
Measured cuff inflation pressures were virtually identical at the three study sites: one academic center and two private hospitals. These data suggest that management of cuff pressure was similar in these two disparate settings. Interestingly, there was also no significant or important difference as a function of provider – measured cuff pressures were virtually identical whether filled by CRNAs, residents, or attending anesthesiologists. Our results thus fail to support the theory that increased training improves cuff management.
We evaluated three different types of anesthesia provider in three different practice settings. Although we were unable to identify any statistically significant or clinically important differences among the sites or providers, our results apply only to the specific sites and providers we evaluated. While it is likely that these results are fairly representative, it is obvious that results would not be identical elsewhere because of regional practice differences.
Fernandez et al. [21] found that the volume of air required to inflate the endotracheal tube cuff varies as a function of tube size and type. But interestingly, the volume required to inflate the cuff to a particular pressure was much smaller when the cuff was inflated inside an artificial trachea; furthermore, the difference among tube sizes was minimal under those conditions. We similarly found that the volume of air required to inflate the cuffs to 20 cmH2O did not differ significantly as a function of endotracheal tube size. These data suggest that tube size is not an important determinant of appropriate cuff inflation volume. A caveat, though, is that tube sizes were chosen by clinicians in our study and presumably matched patient size; results may well have differed if tube size had been randomly assigned. We intentionally avoided this approach since our purpose was to evaluate cuff pressures and associated volumes in three routine clinical settings.
A limitation of this study is that cuff pressure was evaluated just once 60 minutes after induction of anesthesia. Because nitrous oxide was not used, it is unlikely that the cuff pressures varied much during the first hour of the study cases. We recognize that people other than the anesthesia provider who actually conducted the case often inflated the cuffs. However, a full hour was plenty of time for the provider to have checked and adjusted cuff pressure to a suitable level.
We observed a linear relationship between the measured cuff pressure and the volume of air retrieved from the cuff. The regression equation indicated that injected volumes between 2 and 4 ml usually produce cuff pressures between 20 and 30 cmH2O independent of tube size for the same type of tube. However, there was considerable patient-to-patient variability in the required air volume. Measuring actual cuff pressure thus appears preferable to injecting a given volume of air.
Conclusions
Cuff pressure should be measured with a manometer and, if necessary, corrected.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SP oversaw day-to-day study mechanics, collected data on many of the patients, and wrote an initial draft of manuscript.
DIS contributed to study design, data analysis, and manuscript preparation.
PM, SW, and AV recruited patients and performed many of the measurements.
JD conceived of the study and participated in its design.
AW contributed to protocol development, patient recruitment, and manuscript preparation.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Supported by NIH Grant GM 61655 (Bethesda, MD), the Gheens Foundation (Louisville, KY), the Joseph Drown Foundation (Los Angeles, CA), and the Commonwealth of Kentucky Research Challenge Trust Fund (Louisville, KY). We appreciate the assistance of Diane Delong, R.N., B.S.N., Ozan Akça, M.D., and Rainer Lenhardt, M.D., (University of Louisville). We also appreciate the statistical analysis by Gilbert Haugh, M.S., and the editorial assistance of Nancy Alsip, Ph.D., (University of Louisville).
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| 15569386 | PMC535565 | CC BY | 2021-01-04 16:28:02 | no | BMC Anesthesiol. 2004 Nov 29; 4:8 | utf-8 | BMC Anesthesiol | 2,004 | 10.1186/1471-2253-4-8 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-481554118610.1186/1471-2334-4-48Research ArticleMolecular and epidemiologic analysis of a county-wide outbreak caused by Salmonella enterica subsp. enterica serovar Enteritidis traced to a bakery Lu Po-Liang [email protected] In-Jane [email protected] Ya-Lina [email protected] Shang-Jyh [email protected] Chun-Lu [email protected] LK [email protected] Department of Internal Medicine, Kaohsiung Medical University Hospital, 100 Tzyou 1st Rd., Kaohsiung, Taiwan2 Infection Control Committee, Kaohsiung Municipal Hsiao-Kang Hospital, 482 Shan-Ming Road, 812 Kaohsiung, Taiwan3 Department of Laboratory Medicine, Kaohsiung Municipal Hsiao-Kang Hospital, 482 Shan-Ming Road, Kaohsiung, Taiwan4 Division of Clinical Research, National Health Research Institute, 128 Yen-Chiu-Yuan Road, Sec 2, Taipei, Taiwan2004 15 11 2004 4 48 48 12 12 2003 15 11 2004 Copyright © 2004 Lu 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
An increase in the number of attendees due to acute gastroenteritis and fever was noted at one hospital emergency room in Taiwan over a seven-day period from July to August, 2001. Molecular and epidemiological surveys were performed to trace the possible source of infection.
Methods
An epidemiological investigation was undertaken to determine the cause of the outbreak. Stool and blood samples were collected according to standard protocols per Center for Disease Control, Taiwan. Typing of the Salmonella isolates from stool, blood, and food samples was performed with serotyping, antibiotypes, and pulsed field gel electrophoresis (PFGE) following XbaI restriction enzyme digestion.
Results
Comparison of the number of patients with and without acute gastroenteritis (506 and 4467, respectively) during the six weeks before the outbreak week revealed a significant increase in the number of patients during the outbreak week (162 and 942, respectively) (relative risk (RR): 1.44, 95% confidence interval (CI): 1.22–1.70, P value < 0.001). During the week of the outbreak, 34 of 162 patients with gastroenteritis were positive for Salmonella, and 28 of these 34 cases reported eating the same kind of bread. In total, 28 of 34 patients who ate this bread were positive for salmonella compared to only 6 of 128 people who did not eat this bread (RR: 17.6, 95%CI 7.9–39.0, P < 0.001). These breads were produced by the same bakery and were distributed to six different traditional Chinese markets., Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) was isolated from the stool samples of 28 of 32 individuals and from a recalled bread sample. All S. Enteritidis isolates were of the same antibiogram. PFGE typing revealed that all except two of the clinical isolates and the bread isolates were of the same DNA macrorestriction pattern.
Conclusions
The egg-covered bread contaminated with S. Enteritidis was confirmed as the vehicle of infection. Alertness in the emergency room, surveillance by the microbiology laboratory, prompt and thorough investigation to trace the source of outbreaks, and institution of appropriate control measures provide effective control of community outbreaks.
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Background
Salmonellosis, resulting from the ingestion of contaminated poultry, beef, pork, eggs, and milk [1], is an important public health problem worldwide [2]. Although the high temperature of the baking process would suggest that baked goods provide a relatively inhospitable environment for colonization with infectious pathogens, there have been numerous reports of food poisoning outbreaks associated with consumption of baked goods [3-8], and such outbreaks can be a major public health concern [4].
Food-borne disease outbreaks due to Salmonella species are relatively uncommon in Taiwan [9] compared to those in the United States [21] and Japan [22]. Thirty-one outbreaks were reported to the Department of Health, Taiwan from 1986 to 1995, which accounted for 5.6% of all outbreaks [9]. Serovar Typhimurium was the leading serovar for Salmonella food-borne disease outbreaks but serovar Enteritidis has emerged as a new serovar in Taiwan [23], consistent with similar findings of a worldwide increase in Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis) infections [12,24,25].
We report the findings of the investigation of an outbreak of S. Enteritidis that was performed after notification of the rapid increase of the number of patients with febrile gastroenteritis in an emergency room and an unusually high percentage of Group D Salmonella isolated since it comprised only 7.4% of the various Salmonella groups in the hospital one year before the outbreak.
Methods
Epidemiological investigation
A rapid increase in the number of attendees due to acute gastroenteritis and fever was noted to have begun on July 28, 2001 at the emergency room (ER) of Kaohsiung Municipal Hsiao-Kang Hospital, and this increase continued for the following six days. Clinical and demographic features and the food reported to have been consumed three days before development of gastrointestinal symptoms for all patients with acute gastroenteritis at the ER from July 28 to August 3, 2001 were reviewed by ER personnel and the infection control team. The infection control team conducted patient interviews and reviewed charts using a standardized case record form. Stool and blood cultures were performed once specimens were available. These specimens were further analyzed by the hospital's clinical microbiology laboratory and the laboratory of the Center for Disease Control, Taiwan (CDC, Taiwan) in Kaohsiung, Taiwan. Charts of all patients with and without acute gastroenteritis who visited the ER from six weeks before until two weeks after July 28, 2001 were reviewed by infection control nurses for the baseline data of the ER. An outbreak-associated case was defined as a patient visiting the ER with the diagnosis of acute gastroenteritis and having Salmonella infection. A cohort study of all patients attending the ER with acute gastroenteritis between July 28 and August 3, 2001 was performed in order to test the hypothesis that illness was caused by a specific food.
Laboratory investigation
Surveillance culture
Methods for sample collection, cultivation and isolation were conducted according to the standard protocol of the CDC, Taiwan for food-borne disease outbreaks as described previously [9].
Serogrouping and serotyping of Salmonella
Salmonella serotypes were determined with the use of antiserum (Difco, Detroit, MI, USA) according to the manufacturer's instructions. Serogrouping and serotyping were performed by the slide agglutination method and tube agglutination method to identify the somatic O antigen and flagellar H antigen, respectively [10].
Testing for antimicrobial susceptibility
Antimicrobial susceptibility was determined by the disk diffusion method according to the National Committee for Clinical Laboratory Standards [11]. The antimicrobial agents tested included ampicillin, amoxicillin/clavulanate, gentamycin, cefazolin, cefmetazone, cefperazone, imipenem, ofloxacin, and trimethoprim/sulfamethoxazole. Escherichia coli ATCC 25922 was used as the quality control organism.
Genomic fingerprinting by pulsed field gel electrophoresis (PFGE)
Total DNA was prepared and PFGE was performed as described previously [12,13]. The restriction enzyme XbaI (New England Biolabs, Beverly, MA) was used at the manufacturer's suggested temperature. Restriction fragments were separated by PFGE in 1% agarose gel (Bio-Rad, Hercules, CA.) in 0.5X TBE buffer (45 mM Tris, 45 mM boric acid, 1.0 mM EDTA, pH 8.0) for 25 h at 200 V at a temperature of 14°C, with ramped times of 2 to 40 s using the Bio-Rad CHEF-DRII apparatus (Bio-Rad Laboratories, Richmond, CA). Gels were then stained with ethidium bromide and photographed under ultraviolet light. The resulting genomic-DNA profiles, or "fingerprints," were interpreted according to established guidelines [14]. All experiments above were performed in duplicate.
Environmental investigation
Food items having a significant relationship with gastroenteritis cases with Salmonella infection were suspected as the vector of infection. A sample of the implicated food was collected from a patient's home within six hours of the patient having symptoms of febrile gastroenteritis and it was stored at 4°C. Further tracing the source of the suspected food and the subsequent environmental investigation were undertaken by the Bureau of Health, Kaohsiung County and CDC, Taiwan.
Data collection and statistics
Differences between groups were compared using Chi-Square analysis. Relative risk and 95% confidence intervals were also calculated when Chi-Square analysis was used. Continuous variables were analyzed with Student's t-test. Significance was considered at a P value < 0.05 (Epi Info. Version 3.2.2 Centers for Disease Control and Prevention (CDC), USA).
Results
Patients
An increase in the number of patients admitted due to acute gastroenteritis and fever was noticed beginning from the early morning of July 28, 2001 with a return to normal levels one week later. Reviewing daily case-visits to the ER due to all causes and due to gastroenteritis six weeks before this outbreak period revealed significantly more cases due to all causes visiting the ER on Saturday and Sunday than on weekdays (From Monday to Friday) (mean ± standard deviation (S.D.): 140.3 ± 38.3 compared with 109.6 ± 16.7, P = 0.004). The number of patients visiting due to gastroenteritis on Saturday and Sunday were also higher than those visiting on weekdays (mean ± S.D.: 15.8 ± 7.3 compared with 10.5 ± 3.6, P = 0.032) (Fig. 1).
A total of 162 (17.2 %) of 942 patients visiting the ER during the outbreak period had acute gastroenteritis. Compared with 85 (9.7%) of 872 patients in the week before the outbreak, significantly higher percentages of patients with gastroenteritis were observed in the outbreak week (Relative risk (RR): 1.92, 95% confidence interval (CI): 1.38 – 2.26, P < 0.001). A comparison of the number of patients with and without acute gastroenteritis (506 and 4467, respectively) during the six weeks before the outbreak week and during it also revealed a significant increase in the number of patients of gastroenteritis during the outbreak week (RR: 1.44, 95% CI: 1.22–1.70, P value < 0.001).
Investigation of food consumed by gastroenteritis patients during the outbreak period revealed 34 (21%) had consumed the same kind of egg covered bread decorated with mayonnaise and fried seasoned pork fiber from six traditional Chinese markets (three located in Kaohsiung City and three in Kaohsiung County). There were 2, 2, 2, 3, 3, and 22 gastroenteritis patients that consumed the bread purchased from 6 markets, respectively. In total, 28 of 34 patients who ate this bread were positive for Salmonella compared to only 6 of 128 people who did not eat this bread (RR: 17.6, 95%CI 7.9–39.0, P < 0.001). The association of consuming the kind of bread and having Salmonella infection was significant (RR: 17.6, 95% CI: 7.9–39.0, P value < 0.001). No other identified food or restaurant exposure was significantly associated with the outbreak.
Regarding the 28 Salmonella cases that consumed the implicated bread, 12 were male. Their age ranged from three to 71 years old. Eleven cases (39.3%) were under 18 years old and one case was over 65. The incubation periods ranged from 4 to 17 (median, mean ± S.D. 10, 9.4 ± 3.2) hours after consumption of the bread. Twenty-seven of the 28 patients were hospitalized. The clinical symptoms among the 28 cases included abdominal pain (100%), fever (100%), diarrhea (100%), vomiting (85.7%), chills (35.7%), and headache (7.1%). The laboratory data showed eight (28.6%) cases had white blood cell counts higher than 10000 /mm3. Positive occult blood reaction was found in stool specimens of 24 of 27 (88.9%) patients tested. Colitis was found in all four patients who received colonoscopy examination. The two bacteremia patients' fever subsided one day and two days after admission, respectively, without antimicrobial therapy. No mortality or sequellae occurred among these cases during hospitalization or in the three months' follow-up by infection control nurses.
Further investigation by the city public health administration found the incriminated bread from the six markets was all produced by the same bakery that was prohibited from production of all kinds of bread on August 1, 2001. Because the bakery had stopped their production one day before the official prohibition, no further samples of the components of bread, such as the egg and mayonnaise, were available. Investigation of the bakery staff with stool cultures for Salmonella and surveillance culture of the workplace surfaces were not performed.
Bacterial strains
In the week of this outbreak, there was an extraordinarily high percentage (94.1%) of group D of Salmonella isolates. Twenty-eight Salmonella isolates were cultured from 32 available stool specimens from 34 patients who consumed the implicated food. These isolates were all of group D and were further identified as S. Enteritidis. This pathogen grew in two of the 34 patients' blood cultures. A Salmonella isolate of the same serovar was cultured from bread provided by a patient. The sample had been stored in the refrigerator because the patient had not finished eating it.
A total of 30 stool specimens from 25 patients not consuming the implicated bread were collected and Salmonella was isolated from six of the 25 patients' stool specimens. For the six Salmonella isolates from cases not consuming the implicated food during the outbreak period, two were of group B and four were of group D. Further serovar analysis and PFGE analysis of the four group D isolates was not performed. Among the four group D isolates, two were resistant to ampicillin and trimethoprim/sulfamethoxazole, which was different to the antibiogram of the isolates from cases consuming the implicated bread.
Antibiogram and PFGE patterns
All 28 stool isolates, two blood isolates and one food isolate were susceptible to the nine antimicrobial agents tested. Only two stool isolates showed unrelated PFGE patterns with more than six band differences compared to the epidemic PFGE pattern of the other isolates. All the other stool, blood, and food isolates had the same PFGE pattern indicating a clonal relationship (Figure 2). The two isolates with different PFGE patterns were from two patients who had purchased and consumed the bread from two different markets.
Discussion
The source of the outbreak was traced to ingestion of egg-covered bread topped with mayonnaise and fried seasoned pork fiber from six traditional Chinese markets, and then back to a bakery. This outbreak had no clear association with the common sources of food poisoning outbreaks in Taiwan, such as commercial lunch boxes, or food from banquets or wedding dinners, and it was caused by an uncommon pathogen of food-borne outbreak in Taiwan [9].
Most of the patients falling ill with gastroenteritis on the 29th and 30th of July (Saturday and Sunday) had not consumed the implicated bread, (Fig. 1) suggesting some other source or vehicle existed. However, it could be due to the typical increase of ER visits on Saturdays and Sundays, compared to weekdays, because most clinics stop services on weekend.
Compared with phage typing [15] and plasmid typing [12,16,17], pulsed-field gel electrophoresis (PFGE) is a reproducible, discriminative, and feasible typing method for S. Enteritidis [18,19] though it has been suggested to have limited value in epidemiological analysis because of the high genetic homogeneity among strains of S. Enteritidis [20]. Analytic epidemiologic study in addition to molecular typing is essential in thoroughly investigating the source of Salmonella isolates.
Outbreaks can be missed easily if the increase in case number is not noticed due to the wide distribution of outbreak sources in different areas or if a relatively small outbreak occurs among a large population and cases are exposed to the outbreak source at different times. Recognition of the outbreak reported in this study was aided by the routine surveillance of Salmonella groups and knowledge that the percentage of group D Salmonella patients was low before. Thus, the unusually high percentage of group D Salmonella seen within the week of the outbreak led to the investigation. The implicated vehicle of infection in the outbreak was an egg-covered bun topped with mayonnaise and fried seasoned pork fiber which had been distributed to different markets after production at the same bakery. Epidemiological study, antibiograms, and serologic and molecular typing patterns revealed that almost all cases of S. Enteritidis infection during the period were the same as that of the bread isolate. Only the implicated baked good from the same bakery caused Salmonella infections while other items from the bakery were not found to be epidemiologically related with the outbreak. This finding suggests contamination of the pathogen did not occur during the common routes of production, transportation, or selling of goods from the bakery. Although the bread sold in the traditional Chinese markets frequently does not conform to sanitary requirements, and although the staff involved in distribution and selling are not subject to the routine hygiene inspections, in food stores, the possibility of contamination of a specific bakery product simultaneously at six markets in the same time period is very low, suggesting that the contamination occurred during the production process. Food contamination in the outbreak was traced to the same bakery but an investigation of infection among the bakery staff and sources of the contents of the bread was not conducted. Although the baking process involves high temperatures sufficient to kill pathogens, the manual addition of toppings or flavors, such as mayonnaise, eggs, and meat products provide possibilities for contamination with food-borne pathogens. In addition, insufficient baking may be a risk factor for human health because it may not destroy microbial contamination.
Different Salmonella serovars have been related specifically with some foods. S. Enteritidis is particularly related to eggs [26-28]. In this outbreak and the other S. Enteritidis outbreaks related to baked goods[4,5,7,8], all of the food products contained eggs and the egg material had not been cooked sufficiently. Whether the topping of this bread with lightly cooked eggs or the under-cooking played a role in the contamination could not be confirmed due to lack of culture of separate parts of the bread. The finding that Salmonella isolated from the bread had the same serovar, antibiogram, and PFGE pattern as isolates from patients and the significant relationship between consumption of this baked good and the isolation of Salmonella implicated this food product as the vehicle of contamination.
This outbreak had a relatively short incubation compared to the 24–72 hour range reported for other Salmonella outbreaks [29,30]. Whether a highly virulent strain or a high inoculum of bacteria [30] during production or rapid growth of bacteria due to hot summer weather, contributed to the short incubation time was not investigated.
For many food-borne outbreaks, the pathogens and transmission vehicles are often not identified, usually because of delayed collection of epidemiologic and microbiologic information [28]. Initiating an outbreak investigation based on surveillance of emergency room admissions would provide useful information which may lead to early recognition of the pathogen and vehicle [31]. The alertness of our emergency room staff resulted in recognition of the unusual increase in cases of gastroenteritis, although the patients came from two districts and had no obvious relationship to common food sources. This led to prompt investigation and containment of a potential source of further infection. Cooperation of the emergency room, microbiology laboratory, the infection control team staff at hospitals and public health administration staff combined with the application of epidemiological and bacterial typing methods is crucial to the success of the source identification to prevent further dissemination during Salmonella Enteritidis outbreaks.
Competing interest
The author(s) declare that they have no competing interest.
Authors' contributions
Po-Liang Lu and L. K. Siu were in charge of the investigation, data handling and writing of the manuscript. Po-Liang Lu and Shang- Jyh Hwang participated in the design of the study. Po-Liang Lu, In-Jane Hwang, Ya-Lina Tung and Shang-Jyh Hwang were in the investigation team for data collecting and data analysis. Po-Liang Lu performed the statistical analysis. Chun-Lu Lin and L. K. Siu carried out microbiological assays. All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank the Center for Disease Control, Taiwan for providing investigation and bacterial identification. This work was supported by a grant from the National Health Research Institutes, Taiwan. The assistance during the investigation by Dr. Chao-Sung Chang is gratefully appreciated.
Figures and Tables
Figure 1 Case number of attendees of the ER due to all causes (line) and due to gastroenteritis (bar) 6 weeks before the outbreak and during the outbreak week. Cases consuming the kind of bread with and without Salmonella isolated in late July and early August, 2001 were indicated in different bars.
Figure 2 Agarose gels of the PFGE profiles of Salmonella Enteritidis isolates. Lane 1–28: Salmonella DNA digested with restriction enzyme XbaI. Except for lane 9 which is the macrorestriction pattern from a outbreak-unrelated strain during the outbreak period from a patient not consuming the index food (bread), all the lanes were from outbreak-related isolates. Lane 24 and lane 25 were from two cases consuming bread from two different markets which showed the only two different PFGE patterns to other outbreak-related isolates having the same PFGE pattern. Lane 20 was from the strain isolated from bread. Lane 21 is from a blood isolate and lane 22 was from a stool isolates of the same case and the two lanes were of the same PFGE pattern.
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| 15541186 | PMC535566 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Nov 15; 4:48 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-48 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-521555506910.1186/1471-2334-4-52Research ArticleSpectrum of clinical disease in a series of 135 hospitalised HIV-infected patients from north India Sharma SK [email protected] Tamilarasu [email protected] Amit [email protected] Tarun [email protected] Indrish [email protected] PK [email protected] Department of Medicine, All India Institute of Medical Sciences, New Delhi, India2004 22 11 2004 4 52 52 30 4 2004 22 11 2004 Copyright © 2004 Sharma 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
Literature on the spectrum of opportunistic disease in human immunodeficiency virus (HIV)-infected patients from developing countries is sparse. The objective of this study was to document the spectrum and determine the frequency of various opportunistic infections (OIs) and non-infectious opportunistic diseases, in hospitalised HIV-infected patients from north India.
Methods
One hundred and thirty five consecutive, HIV-infected patients (age 34 ± 10 years, females 17%) admitted to a tertiary care hospital in north India, for the evaluation and management of an OI or HIV-related disorder between January 2000 and July 2003, were studied.
Results
Fever (71%) and weight loss (65%) were the commonest presenting symptoms. Heterosexual transmission was the commonest mode of HIV-acquisition. Tuberculosis (TB) was the commonest OI (71%) followed by candidiasis (39.3%), Pneumocystis jiroveci pneumonia (PCP) (7.4%), cryptococcal meningitis and cerebral toxoplasmosis (3.7% each). Most of the cases of TB were disseminated (64%). Apart from other well-recognised OIs, two patients had visceral leishmaniasis. Two cases of HIV-associated lymphoma were encountered. CD4+ cell counts were done in 109 patients. Majority of the patients (82.6%) had CD4+ counts <200 cells/μL. Fifty patients (46%) had CD4+ counts <50 cells/μL. Only 50 patients (37%) received antiretroviral therapy. Twenty one patients (16%) died during hospital stay. All but one deaths were due to TB (16 patients; 76%) and PCP (4 patients; 19%).
Conclusions
A wide spectrum of disease, including both OIs and non-infectious opportunistic diseases, is seen in hospitalised HIV-infected patients from north India. Tuberculosis remains the most common OI and is the commonest cause of death in these patients.
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Background
The first case of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) in India was detected in 1986 in the state of Tamilnadu [1] and since then the spread of HIV/AIDS across the nation has been relentless. Cases have been reported from all states and union territories of India. Though the overall prevalence of HIV infection is low (<1%), the total number of cases is high. As per estimates, by the end of 2002, about 4.58 million adults were infected with HIV [2]. Even more ominous has been the shift of the epidemic from high-risk groups such as injectable drug users (IDU) and patients with other sexually transmitted diseases, to low-risk groups like married, monogamous women [3].
Though the majority of HIV-infected population lives in developing nations, there is a paucity of data on natural history, pattern of disease and survival of hospitalised patients with HIV/AIDS from these regions, especially India. It is well established that manifestations of AIDS are influenced by factors such as endemic infections and malnutrition that are widely prevalent in these regions [4]. Conventional disease staging criteria, which were developed in western populations may not hold good in these settings [4,5]. Added to this, resource constraints prohibit evaluation and decision-making based on cost and labour-intensive methods such as CD4+ cell counts and viral RNA load estimation. Timely initiation of prophylaxis for opportunistic infections (OIs) and their prompt recognition and treatment are the only economically viable options [6]. In this scenario, knowledge regarding the pattern of OIs, will be useful. This study was conducted to elucidate the frequency of various OIs and non-infectious opportunistic conditions in hospitalised HIV-infected patients, from north India.
Methods
We describe a series of 135 consecutive patients with HIV/AIDS, aged 13 years and above, admitted to the All India Institute of Medical Sciences (A.I.I.M.S.) hospital, New Delhi during the period of January 2000 through July 2003. A.I.I.M.S. is a large tertiary level teaching hospital and referral centre located in north India, attending to HIV-infected patients apart from others. The catchment area of this hospital includes the New Delhi national capital territory and neighbouring states mainly, Uttar Pradesh, Bihar, Uttharanchal, Haryana, Punjab and Himachal Pradesh. All patients were under the care of a single unit, of the three units existing in the Department of Medicine. Decision to admit was taken by the treating physician and all patients were hospitalised for the evaluation and treatment of a suspected OI or HIV-related disorder. We have earlier reported the determinants of in-hospital mortality in this series [7].
Diagnosis of HIV infection was made using methods described previously [8]. All patients referred to A.I.I.M.S. as 'HIV-positive' underwent retesting. Apart from this, all patients treated for tuberculosis (TB), those reporting risk factors for HIV infection and patients with clinical presentation suggestive of underlying immunosuppression, were offered HIV testing. All except one patient were infected with HIV-1, the latter with HIV-2. CD4+ lymphocyte counts were done by flowcytometry, using a FACSCalibur flowcytometer (Becton Dickinson, USA) in patients who could afford for the same. Patients were evaluated using a predesigned instrument regarding the demographic characteristics, risk factors for HIV infection, presenting symptoms, physical findings and laboratory parameters.
Various laboratory abnormalities were defined using cut-off values as follows: anaemia – haemoglobin <100 g/L; leucopenia – total leucocyte count (TLC) <4 × 109/L; lymphocytopenia – absolute lymphocyte count (ALC) <1.2 × 109/L; decreased CD4+ – <500 cells/μL; thrombocytopenia – platelet count <150 × 109/L; elevated erythrocyte sedimentation rate (ESR) – >20 in the first hour; hypoalbuminaemia – albumin <35 g/L; hyperglobulinaemia – globulin >40 g/L; hyperbilirubinaemia – serum total bilirubin >20.5 μmol/L; elevated liver enzymes – >2 times the upper limits of normal [aspartate aminotransferase (AST) – >100 IU/L, alanine aminotransferase (ALT) – >100 IU/L, alkaline phosphatase (ALP) – >560 IU/L]; hypoxaemia – PaO2 <12 kPa; increased arterial-alveolar oxygen gradient (A-a DO2) – >3.3 kPa, while breathing ambient air (FiO2 = 0.21).
While genuine efforts were made to establish the diagnosis of an OI, patients at times were too sick to undergo invasive diagnostic procedures such as bronchoscopy or tissue biopsy and hence the following criteria were used to define an OI concerned. Pulmonary tuberculosis (PTB): clinical features suggestive of TB with radiological features compatible with TB on chest radiograph or computed tomographic scan (CT) and/or demonstration of acid-fast bacilli (AFB) in sputum smears or growth of Mycobacterium tuberculosis in sputum culture; disseminated tuberculosis (DTB): clinical features suggestive of TB with concurrent involvement of at least two non-contiguous organs, in the presence of bacteriological and/or histopathological evidence of TB and improvement with anti-tuberculosis therapy; miliary tuberculosis (MTB): clinical presentation consistent with TB and bacteriological and/or histopathological evidence of TB and demonstration of bilateral miliary infiltrates on chest radiograph or high-resolution CT; tuberculosis meningitis (TBM): clinical and cerebrospinal fluid (CSF) features consistent with tuberculosis meningitis, with bacteriologic demonstration of M. tuberculosis in CSF or after exclusion of other aetiologies such as cryptococcus and syphilis, with evidence of tuberculosis elsewhere and/or improvement with anti-tuberculosis therapy; cryptococcal meningitis: in CSF, demonstration of Cryptococcus sp. yeast cells by India ink or antigen by latex agglutination or growth in culture; cerebral toxoplasmosis: demonstration of multiple ring-enhancing cerebral parenchymal lesions on contrast-enhanced CT or magnetic resonance imaging (MRI), in the presence of anti-toxoplasma antibody in serum and clinical response to anti-toxoplasma therapy; progressive multifocal leucoencephalopathy (PMLE): compatible clinical presentation with demonstration of characteristic cerebral white matter changes by MRI; Pneumocystis jiroveci pneumonia (PCP): bilateral, diffuse interstitial infiltrates on chest radiograph or high-resolution CT, with hypoxaemia (PaO2 <12 kPa) and sputum smears/cultures negative for aerobic bacteria and AFB and/or demonstration of Pneumocystis jiroveci in induced sputum [9].
Patients received primary chemoprophylaxis for PCP and toxoplasmosis and received treatment for specific OIs followed by secondary prophylaxis, as per recommendations [10]. For patients with PCP, whenever hypoxaemia was severe (PaO2 <9.3 kPa), corticosteroids were given in addition to oral co-trimoxazole. None of these patients received assisted ventilation. All patients were counselled and offered antiretroviral therapy, whenever indicated. Upon discharge from the hospital, patients were advised to follow-up as outpatients, on a regular and as needed basis.
Statistical analysis
Entry and analysis of all data were done using a statistical software package (SPSS for Windows, Version 10.0, SPSS Inc., Chicago, IL). Entered data were double-checked for discrepancies. Data are presented as mean ± standard deviation (SD) when distributed normally and as median with interquartile range (IQR), if the distribution was skewed. Frequency of various clinical and laboratory findings and the frequencies of individual OIs are expressed as proportions (%). The relation between ALC and CD4+ count was analysed by Pearson's product moment correlation. Paired-samples t-test was applied to assess the improvement in CD4+ cell counts following antiretroviral therapy.
Results
Over a period of about three and a half years, 135 patients with HIV/AIDS were admitted and all these patients were included in the study. Mean age of the study group was 34 ± 10 (range: 13–73) years. Females constituted less than one fourth of the study group (17%). Details of other demographic characteristics and transmission categories are presented in Table-1. More than two-thirds of patients (76%) were in the second and third decades of their lives. Labourers were the commonest occupational group (40%) and long-haul truck drivers constituted a considerable proportion of the study group. Professionals were the least common occupational group. An identifiable risk factor for HIV infection was present in 59% of patients, extramarital heterosexual contact being the commonest (41.5%). None of the patients reported homosexual practices. About 40% of patients denied any risk factor for the acquisition of HIV infection. A large number of patients (44.4%) were from states other than Delhi.
Three symptoms namely fever, weight loss and diarrhoea were the common symptoms at presentation (70.4%, 65.2% and 23.7% respectively). Productive cough and dyspnoea were present in about a fourth of patients (Table-2). Two-thirds of patients (66.7%) were malnourished (body mass index <19 kg/m2) and generalised lymphadenopathy was present in a considerable proportion of patients (16.3%). Poor performance on mini mental status examination (MMSE score <23) was not uncommon (20.7%). Altered sensorium and focal neurologic deficit were encountered occasionally. Anaemia was present in about half of the patients (50.5%) and among those who where anaemic, 17 (21.8%) patients were on zidovudine. A considerable number of patients were leucopenic and in 22% of patients ALC was less than 1200/μL (Table-3). CD4+ cell counts were done in 109 patients. The distribution of CD4+ cell counts is shown in Figure-1. Most of the patients (n = 90; 82.6%) had CD4+ counts less than 200 cells/μL. Fifty patients (46%) had CD4+ counts less than 50 cells/μL. The correlation of ALC with CD4+ count was not significant (r = 0.14; P = 0.18). HIV viral load estimation was done in only four patients (range 33752–289176 RNA copies/mL). Twenty patients (14.8%) had hypoxaemia. Of these, five patients had PCP (Figure 2-A), ten had DTB, three had extensive PTB and one patient had massive unilateral tuberculosis pleural effusion.
The mean number of OI was 1.4 per patient. The commonest OI was TB (71.1%), followed by oral candidiasis (39.3%). Most of the cases of TB were disseminated (64%; Figure 2-B) and PTB was seen in only a small number of patients (8.1%). Eleven patients had MTB (Table-4). Of the 53 patients with oral candidiasis, five patients had concomitant oesophageal involvement. Cryptosporidium sp. and Giardia sp. infection causing diarrhoea was found in three and two patients respectively. Of the 13 cases of meningitis, eight were tuberculosis and the rest due to Cryptococcus sp.; no case of acute, pyogenic meningitis was seen. Cerebral toxoplasmosis (Figure 2-C) and one case of PMLE were the other central nervous system infections seen.
Of the five cases of cerebral toxoplasmosis, one patient presented as recent onset dilated cardiomyopathy with no features to suggest a central nervous system pathology. On further investigation, the patient was found to be seropositive for anti-toxoplasma antibodies and contrast enhanced CT revealed multiple ring enhancing cerebral lesions. The cerebral lesions cleared and cardiac ejection fraction improved with anti-toxoplasma treatment. The cardiac dysfunction was probably due to toxoplasma myocarditis. Ten patients presented with PCP (Figure 2-A) and three patients had cytomegalovirus (CMV) retinitis (Figure 2-D). One patient was diagnosed as disseminated histoplasmosis. This patient presented as fever of unknown origin. Bone marrow biopsy revealed caseating granulomas and subsequently fungal culture grew Histoplasma capsulatum. Non-infectious AIDS-defining conditions were not common. One case of B-cell non-Hodgkin's lymphoma presenting as unilateral maxillary swelling (Figure 2-E) and another case of Hodgkin's lymphoma that presented as generalised lymphadenopathy with hepatosplenomegaly, were seen.
Only 50 patients (37%) received antiretroviral therapy, of which three patients were taking protease inhibitor (PI) based regimens. All other patients received non-nucleoside reverse transcriptase inhibitor (NNRTI) based (nevirapine – 46 patients, efavirenz – one patient) triple-drug regimens. Follow-up CD4+ counts were done in 16 patients after three to six months of antiretroviral therapy. The mean CD4+ count increased to 224 ± 178 cells/μL from a baseline mean of 131 ± 122 cells/μL, which was statistically significant (P = 0.003). Myelosuppression during antiretroviral therapy developed in four patients, who were taking regimens containing zidovudine. One patient who was on didanosine developed acute pancreatitis (Figure 2-F). Another patient presented with severe, uncompensated, wide anion-gap metabolic acidosis with no apparent cause. This was probably due to lactic acidosis caused by stavudine, which he had been taking.
Twenty one patients (15.6%) died in hospital, most of them due to TB (16 patients) and PCP (4 patients). All patients who died in hospital except for one, had CD4+ counts less than 200 cells/μL (Figure-1).
Discussion
Opportunistic infections (OIs) are the major cause of morbidity and mortality in patients with HIV infection. In resource-limited settings, knowledge regarding the prevalence of various OIs might aid in making decisions regarding empirical treatment and would help to prioritise limited resources. To this end, we studied the frequency of various opportunistic conditions in this series of 135 HIV-infected patients. Nearly a half of these patients came from places other than New Delhi, being referred by medical practitioners or approached on their own. In India, HIV-infected patients are usually treated in tertiary level centres, due to lack of expertise and facilities in primary and district level hospitals. Moreover, for reasons of privacy patients and their kin prefer treatment at places far off from their home towns. This series comprises predominantly of patients from the northern states of India. Hence the findings of the present study may not apply to south India and north-east Indian states. Moreover, being a hospital based study, patients with severe illness causing rapid death and patients with milder symptoms who prefer treatment from local health facilities may not have been proportionately represented in the study group.
Most of the patients were in the age group 21–40 years and males were predominantly affected. This is similar to nation-level statistics in which, of the 57781 cases of HIV/AIDS reported to the National AIDS Control Organisation (NACO), 89% of the cases were in the age group 15–44 years and 74% were males [11]. This section of the population is more affected because they are sexually more active and the social structure is patriarchal. Unfortunately, these patients also happen to be in the economically most productive years of their lives. The male preponderance might have been due to the fact that in the existing social milieu, females do not seek medical care fearing ostracism and loss of family support. Interestingly, a large number of patients (41%) did not give a history of any of the known risk factors for the acquisition of HIV infection and homosexual practice was singularly not seen. It is likely that these patients were reluctant to reveal, due to the prevailing social values, which discourage polygamy and homosexuality. For the same reason, despite a history of prior blood transfusion in a considerable proportion of patients, it may not be possible to ascribe causation; not all these patients would have acquired HIV infection through blood transfusion. Injectable drug users (IDU) constituted only a minority of the study group as has been observed in other parts of India except for the north-eastern states where IDU is widely prevalent [12].
The three symptoms, namely fever, weight loss and diarrhoea were common presenting features. In this series of patients, all of whom had some opportunistic condition at presentation indicating advanced HIV disease, wasting is anticipated. The association of severe weight loss at presentation with TB in HIV-infected patients has been reported before [13]. TB was very common in this series and was the commonest OI. Previous studies also confirm the high prevalence of TB in HIV-infected individuals in India [4,9,14,15]. Most of these cases of TB were either disseminated or extra-pulmonary. We have previously reported the increased prevalence of HIV seropositivity among cases of extrapulmonary TB as compared to PTB and as such, HIV co-infection is now on the rising trend among cases of TB, in this population [16]. This trend is in consonance with findings from across the globe. The cases of TB attributable to HIV are increasing and have reached alarming proportions in certain countries. An example of such a nation is the United States of America, where almost 26% of cases of TB are now attributable to HIV infection [17]. Although these figures are from countries where the incidence of TB has otherwise been low, it is bound to be similar in countries where TB is endemic. PCP was seen in only a small number of patients. This is in sharp contrast with western populations where PCP is the commonest AIDS-defining illness [18]. Earlier studies from India also have reported a low prevalence (6–7%) of PCP [9,14]. The low prevalence of PCP in developing countries is well known and the incidence is probably increasing [19].
The frequencies of various OIs as observed in the present study are similar to those reported earlier from other parts of India [4,9,14,15]. Some of the opportunistic conditions were conspicuous by their absence. Disseminated infection caused by the fungus Penicillium marneffei has been reported to be the third commonest OI in southeast Asia [20] and is known to be highly prevalent in the north-east Indian state, Manipur [21] which shares common geographic, ecologic and climatic conditions with the south-east Asian regions of P. marneffei endemicity. No case of P. marneffei infection was found in the present study. Kaposi's sarcoma, atypical mycobacterial infection and disseminated CMV disease were not seen, as has been observed by a previous study from south India [14]. It appears that, TB being an endemic infection in this population, takes its toll before other less virulent pathogens could come into play. Development of TB per se might have afforded some protection against atypical mycobacterial disease [22]. It is also possible that TB masked the recognition of other OIs, which might have been present since it is well known that a large number of potentially fatal OIs are not diagnosed antemortem [23,24]. In India, Kaposi's sarcoma occurring in the setting of HIV infection and other immunocompromised conditions is a rarity, though not undescribed [25-27]. As such the prevalence of human herpes virus-8 (HHV-8) infection among the healthy population in India is low when compared with African nations and this is also likely to be responsible for the rarity of Kaposi's sarcoma [28]. Apart from the well-recognised OIs, infection unique to the local population namely, visceral leishmaniasis occurs in association with HIV and this has to be borne in mind.
Overall in-hospital mortality in this series is considerable and is probably reflective of the advanced nature of disease at presentation. This is evidenced by the fact that more than 80% of patients had CD4+ counts less than 200 cells/μL and all but one deaths occurred in this group. However, as we had reported earlier, CD4+ cell counts do not determine the immediate outcome of patients with an OI, whereas the latter does [7]. The proximate causes of death in these patients are OIs, which are amenable to effective treatment. Many of these patients were from poor socioeconomic strata and were not able to afford for antiretroviral therapy. Cost constraints directly or indirectly, probably also resulted in poor compliance with scheduled follow-up visits. With the implementation of World Health Organisation sponsored '3 by 5' initiative, it is hoped that antiretroviral therapy will be within the reach for a large number these patients and also compliance is likely to become better.
Conclusions
A wide spectrum of OIs is seen in hospitalised patients with HIV/AIDS from north India. Tuberculosis is the most common OI in this group of patients. Though uncommon, non-infectious opportunistic conditions also occur. Many patients suffer from more than one OI. Hospital mortality is significant, with TB and PCP being responsible for most of these deaths. Whenever a definite diagnosis is not established quickly, empirical treatment should be considered. Such an approach is likely to improve immediate outcome in hospitalised patients with HIV/AIDS.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SKS conceived of the study, designed and coordinated the study and revised the manuscript critically for important intellectual content. TK participated in the design of the study and statistical analysis and drafted the manuscript. AB participated in the design of the study, performed the statistical analysis and revised the manuscript for important intellectual content. TG participated in the design of the study, collected the clinical data and contributed significantly to the content of the manuscript. IB participated in the design of the study, collected the clinical data and participated in drafting the manuscript. PKS participated in the design of the study and coordination and also contributed significantly to the content of the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Figure 2E is reproduced with the consent of the patient. The patient provided verbal consent at the time the photograph was taken, for use of the information on a medical publication. The patient had succumbed at the time of writing this report and the relatives were not traceable to obtain signed, written consent.
Figures and Tables
Figure 1 Distribution of CD4+ cell counts and in-hospital deaths in the respective groups
Figure 2 A – Chest radiograph of a patient with Pneumocystis jiroveci pneumonia showing bilateral, diffuse interstitial infiltrates B – Contrast enhanced computed tomographic (CT) scan of chest showing mediastinal lymphadenopathy in a patient with disseminated tuberculosis. Typical central necrosis evident as low attenuation areas (arrows) is seen C – Contrast enhanced CT scan of brain showing ring enhancing lesions in the basal ganglia bilaterally (arrows). Serology was positive for toxoplasma infection D – Ophthalmoscopic image of a patient with cytomegalovirus retinitis E – Non-Hodgkin's lymphoma in a HIV-infected lady presenting as unilateral maxillary swelling F – Contrast enhanced CT scan of abdomen reveals an oedematous and enlarged pancreas (asterisk) suggestive of acute pancreatitis. The patient was on didanosine and improved following withdrawal of the same and supportive treatment.
Table 1 Demographic characteristics and transmission-risk categories in a series of 135 patients with HIV/AIDS
Parameter Number of patients, n (%)
Age, in years (mean ± SD) 34 ± 10
Males 112(83)
Outside Delhi (n = 108) 48(44.4)
Occupation (n = 106)*
Unskilled labourer 31(29.2)
Skilled labourer 11(10.4)
Driver 11(10.4)
Police and armed forces 9(8.5)
Businessmen 19(17.9)
Clerical 5(4.7)
Professional 2(1.9)
Others 18(17)
Risk categories
Heterosexual contact 56(41.5)
IDU 5(3.7)
Blood transfusion 19(14.1)
Unknown† 55(40.7)
* 29 patients were unemployed; † Patients denied any of the known risk factors; IDU – Injectable drug users.
Table 2 Presenting features and physical findings in a series of 135 patients with HIV/AIDS
Symptoms and signs Frequency* n (%)
Fever 95(70.4)
Weight loss 88(65.2)
Cough 57(42.2)
Expectoration 38(28.1)
Dyspnoea 34(25.2)
Diarrhoea 32(23.7)
Abdominal pain 31(23)
Vomiting 22(16.3)
Night sweats 19(14.1)
Chest pain 17(12.6)
Haemoptysis 4(3)
Altered sensorium 5(3.7)
Seizures 4(3)
Body mass index <19 kg/m2 (n = 78) 52(66.7)
Midarm circumference†, in cm (mean ± SD) (n = 76) 18.9 ± 4.2
Triceps skin-fold thickness†, in mm (mean ± SD) (n = 78) 6.9 ± 1.7
Lymphadenopathy – cervical 39(28.9)
– axillary 32(23.7)
– mediastinal 18(13.3)
– retroperitoneal 15(11.1)
– generalised 22(16.3)
Hepatomegaly 29(21.5)
Splenomegaly 24(17.8)
Pleural effusion 15(11.1)
Ascites 3(2.2)
Pericardial effusion 3(2.2)
Jaundice 9(6.7)
MMSE score <23 28(20.7)
Nuchal rigidity 12(8.9)
Focal neurologic deficit 8(5.9)
* The denominator is 135, except for anthropometric parameters, where the number of patients is given in parentheses; † Measured at midpoint between acromian and olecranon processes of non-dominant arm: mean of three consecutive readings was taken; MMSE – Mini mental status examination.
Table 3 Laboratory findings in a series of 135 patients with HIV/AIDS
Variable Unit Mean ± SD Range Abnormality*%
Haemoglobin g/L 98 ± 27 33 – 161 50.5
TLC × 109/L 6.2 ± 2.8 0.9 – 15.3 17.3
Platelet count × 109/L 267 ± 183 56 – 960 19.3
ESR mm/hr 56 ± 18 12 – 91 96.2
ALC × 109/L 1.7 ± 0.8 0.1 – 3.6 22
CD4+ cell count (n = 109) per μL 121 ± 205 0 – 1425 98.2
Serum albumin g/L 30 ± 8 16 – 51 70.7
Serum globulin g/L 45 ± 11 21 – 76 70.3
Serum bilirubin – total μmol/L 18.8 ± 32.5 3.4 – 220.6 6.7
AST IU/L 77 ± 113 2 – 790 17
ALT IU/L 76 ± 92 2 – 682 22.3
ALP IU/L 360 ± 419 44 – 2355 11.1
PaO2 (n = 31) kPa 10.6 ± 3.7 4.6 – 19.4 14.8†
A-a DO2 (n = 31) kPa 2.8 ± 2.4 0 – 7.5 7.4†
* Defined by cut-off values as mentioned in the text; † Denominator was taken as 135; TLC – Total leucocyte count; ALC – Absolute lymphocyte count; ESR – Erythrocyte sedimentation rate; AST – Alanine aminotransferase; ALT – Aspartate aminotransferase; ALP – Alkaline phosphatase; PaO2 – Partial pressure of oxygen; A-a DO2 – Arterial-alveolar oxygen gradient.
Table 4 Opportunistic infections and HIV-related conditions in a series of 135 patients with HIV/AIDS and in-hospital outcomes
Diagnosis Frequency n*(%) CD4+ count median (IQR) In-hospital deaths, n†(%)
Tuberculosis – any organ 96(71.1) 46 (12–121) 16(16.7)
PTB 11(8.1) 46 (5–218) 3(27.3)
Pleural 4(3) 52 (5–276) --
LNTB 1(0.7) 37‡ --
TBM 8(5.9) 58 (5–165) 1(12.5)
DTB 61(45.2) 47 (15–127) 11(18)
MTB 11(8.1) 27 (5–57) 1(9.1)
Candidiasis 53(39.3) 54 (12–77) 9(17)
PCP 10(7.4) 38 (8–69) 4(40)
Herpes Zoster 8(5.9) 91 (59–151) 1(12.5)
Cryptococcal meningitis 5(3.7) 135 (14–320) 2(40)
Toxoplasmosis 5(3.7) 72 (21–132) 1(20)
Cryptosporidiosis 3(2.2) 61 (5–116) --
CMV retinitis 3(2.2) 127 (37–150) 1(33.3)
Giardiasis 2(1.5) 59 (1–116) --
Visceral leishmaniasis 2(1.5) 41 (5–77) --
Lymphoma 2(1.5) 145 (29–261) 1(50)
Disseminated histoplasmosis 1(0.7) 51‡ --
CMV oesophagitis 1(0.7) 8‡ --
PMLE 1(0.7) 62‡ --
* Not mutually exclusive; † Total will exceed the mortality of the study group; ‡ Single value; – No deaths; IQR – interquartile range; PTB – pulmonary tuberculosis; Pleural – tuberculosis pleural effusion; LNTB – lymph node tuberculosis; TBM – tuberculosis meningitis; DTB – disseminated tuberculosis; MTB – miliary tuberculosis; PCP – Pneumocystis jiroveci pneumonia; CMV – Cytomegalovirus; PMLE – progressive multifocal leucoencephalopathy.
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| 15555069 | PMC535567 | CC BY | 2021-01-04 16:03:31 | no | BMC Infect Dis. 2004 Nov 22; 4:52 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-52 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-791554118210.1186/1471-2407-4-79Research ArticleG-protein inwardly rectifying potassium channel 1 (GIRK 1) gene expression correlates with tumor progression in non-small cell lung cancer Takanami Iwao [email protected] Yoshimasa [email protected] Masatoshi [email protected] First Department of Surgery, Teikyo University School of Medicine, Tokyo, Japan2004 13 11 2004 4 79 79 13 8 2004 13 11 2004 Copyright © 2004 Takanami 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
G-protein inwardly rectifying potassium channel 1 (GIRK1) is thought to play a role in cell proliferation in cancer, and GIRK1 gene expression level may define a more aggressive phenotype. We detected GIRK1 expression in tissue specimens from patients with non-small cell lung cancers (NSCLCs) and assessed their clinical characteristics.
Methods
Using reverse transcription-polymerase chain reaction (RT-PCR) analyses, we quantified the expression of GIRK1 in 72 patients with NSCLCs to investigate the relationship between GIRK1 expression and clinicopathologic factors and prognosis.
Results
In 72 NSCLC patients, 50 (69%) samples were evaluated as having high GIRK1 gene expression, and 22 (31%) were evaluated as having low GIRK1 gene expression. GIRK1 gene expression was significantly associated with lymph node metastasis, stage (p = 0.0194 for lymph node metastasis; p = 0.0207 for stage). The overall and stage I survival rates for patients with high GIRK1 gene expressed tumors was significantly worse than for those individuals whose tumors had low GIRK1 expression (p = 0.0004 for the overall group; p = 0.0376 for stage I).
Conclusions
These data indicate that GIRK1 may contribute to tumor progression and GIRK1 gene expression can serve as a useful prognostic marker in the overall and stage I NSCLCs.
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Background
Lung cancer is one of the leading causes of cancer death in North America [1]. Lung cancer is divided into two morphological types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The results of surgery still remain unsatisfactory; even in stage I NSCLC (no lymph node metastasis and no distant metastasis) about 30% of patients die due to disease recurrence within 5 years after curative resection [2]. Despite major advances in cancer treatment in the past decades, the prognosis of patients with NSCLC has improved only minimally [1]. A new knowledge of the molecular pathogenesis of cancer has emerged from investigative advances in the field of molecular biology [3]. Increased knowledge of the biologic role of genetic changes has provoked an intriguing search for clinical applications of these alterations [4], and has enabled the more aggressive tumors from the less aggressive ones to be distinguished [5].
G-protein inwardly rectifying potassium channels (GIRK) are found in both the heart and the brain, where they are associated with a slowing of the heart rate and suppression of neuronal response [6]. The channel found in the sinoatrial node and on atrial myocytes is formed from the homologous channel subunits GIRK1 and GIRK4 [7]. Although the function of GIRK1 still remains unclear except in cell proliferation in cancer, GIRK1 gene expression has been found to correlate with lymph node metastasis in breast carcinomas [8], but the correlation with GIRK1 gene expression and prognosis has never been analyzed in NSCLC. To our knowledge, this is the first report to analyze the prognostic influence of GIRK1 gene expression in NSCLC and the possible associations between this parameter and other clinical factors. In this study, we used RT-PCR for detecting GIRK1 in tumor tissues. We compared with GIRK1 gene expression with autocrine motility factor-receptor (AMF-R) gene expression, known as a marker of lymph node metastasis and tumor progression, and we investigated how GIRK1 gene expression is related to tumor progression and prognosis in a series of 72 cases of curatively resected NSCLC.
Methods
Tissue specimens
Tumor tissue was collected from 72 patients with NSCLC who underwent curative surgery between 1993 and 1995 at Department of Surgery, Teikyo University School of Medicine. Patients who died within one month after surgery and patients with a past history of another cancer were excluded from the study. Of the 72 patients included, 52 were men and 20 were women and their ages ranged from 34 to 80 years (mean of 66 years). With regard to histological type, 41 were adenocarcinomas, 28 were squamous cell carcinomas and 3 were large cell lung carcinomas. The lesions of these 72 patients were staged on both operative and pathologic findings according to the UICC TNM classification (1997) [9]. There were 24 patients with stage IA, 11 patients with stage IB, 1 patients with stage IIA, 13 patients with stage IIB, and 23 patients with stage IIIA. These patients were performed curative operation with lymph node dissection. The mean follow-up time was 52.0 months (range, 2.7–120.0 months). Freshly removed pulmonary cancer tissues for RNA extraction were immediately frozen in liquid nitrogen and stored at -80°C until further use. And in five of the cases, adjacent normal pulmonary material from the same patient was also used in this study. Tissue samples to be used for hematoxylin-and-eiosin were fixed in formalin and paraffin embedded.
Reverse transcription-polymerase chain reaction (RT-PCR) analysis
Total RNA was purified from fresh soft tissues by the acid guanidinium-thiocyanate procedure [10]. The human pulmonary adenocarcinoma cell line PC-14 (Riken Gene Bank Co., Ltd. Tokyo, Japan) was used as a positive control. All the RNA (5 μg) was used for cDNA synthesis, and the first-standard cDNA solution was then used for the PCR, with primers designed to amplify a 230 bp sequence (sense primer sequence: 5'-GGGATTTGGACATGGCTAAGTC-3' ; antisense primer sequence: 5'-GGCCTGTTTTCATTCTCTTAACTGATAC-3'). The reaction mixture was overlaid with 20 μl of mineral oil. PCR was performed for forty cycles (10 s at 95°C; 60 s at 60°C, and 120 s at 72°C) as previously described [8]. S14 cDNA amplification using the same temperature profile for 30 cycles served as the internal control [11]: the sense and antisense primers for S14 cDNA amplification were 5'-GGCAGACCGAGATGAATCCTC-3' and 5'-CAGGTCCAGGGGTCTTGGTCC-3'. The amplified DNA samples were electrophoresed on 1% agarose gels, and photographed with a Polaroid camera. Densitometric analysis of the photographic negatives was used for band quantification.
Specimen classification based on RT-PCR results
The densitometric value obtained for the GIRK1 band of a given tumor tissue sample was divided by the corresponding S14 value, and was referred to as the GIRK1 gene expression rate. The level of the GIRK1 mRNA expression in PC-14 cell line is elevated. The expression ratio of the tumor was then divided by that of the human pulmonary adenocarcinoma cell line PC-14 to obtain the GIRK1 conservation rate. When the conservation rate of a given specimen was ≧ 0.8, it was considered to indicate high expression of the GIRK1 gene, and if the rate was < 0.8, it was defined as low expression.
Comparison with GIRK1 gene expression and AMF-R gene expression
To ascertain whether links between GIRK1 gene expression and another gene expression, known as a marker of lymph node metastasis and tumor progression in NSCLC, we examined the relationships between GIRKI gene expression and AMF-R gene expression in 46 cases [12]. The studies of AMF-R gene expression were performed as described previously [12]. The expression ratio of the tumor was divided by that of the cell line PC-14, and the conservation rate of a given specimen was larger than the mean ratio, it was considered to indicate high expression of the AMF-R gene, and if the rate was lower than the mean ratio, it was defined as low expression of the AMF-R gene.
Statistical analysis
All data regarding the clinical and histopathological variables were stored in a Macintosh computer. The Stat View program (Aracus Concepts, Berkeley, Ca, USA) was used for all statistical analyses. The relationship between the incidence of GIRK1 expression and clinico-pathologic factors, and AMF-R gene expression was examined by the chi-squared test with Fisher correlation. Survival curves were calculated using the Kaplan-Meier method and analyzed by the log-rank test. Statistical significance was identified as p < 0.05.
Results
Detection of GIRK1 using RT-PCR in NSCLC tissues
To determine the number of PCR cycles appropriate for quantification, from 20 and 50 cycles of PCR were performed, in 5-cycle increments. The expression ratios of GIRK1 to S14 were reasonably constant 35 to 45 cycles (data not shown). Therefore, in the subsequent experiments the values obtained at 40 cycles were defined as the expression of the target genes. Using forty RT-PCR cycles, we found that the ratio of GIRK1/cell line PC-14 expression ranged from 0 to 2.2 (means, 0.8) in tumor specimens (Fig. 1). Of 72 NSCLCs studied, 50 (69%) were classified as GIRK1 high gene expression, and 22(31%) were classified as having GIRK1 low gene expression. Five adjacent normal pulmonary materials ranged from 0 to 0.1 (means = 0.1), and all of them were classified as GIRK1 low gene expression.
Relationship between GIRK1 gene expression and clinicopathological factors
The relationships between GIRK1 gene expression and various clinicopathological factors are shown in Table 1. There were no statistically significant relationships between gene expression and age, gender, T factor and histology. In contrast, GIRK1 gene expression was associated with N factor and stage (p = 0.0194 for lymph node metastasis, p = 0.0207 for stage).
The relationships between GIRK1 gene expression and AMF-R gene expression
Of 46 NSCLCs studied, 33 (72%) were classified as AMF-R high gene expression, and 13(28%) were classified as having AMF-R low gene expression. In most GIRK1 high gene expression cases, AMF-R gene was also expressed. As shown Table 2, the results of GIRK1 gene expression agreed significantly with AMF-R gene and 72% of cases had no discrepancy (p = 0.0303).
Association of tumor GIRK1 gene expression and survival
The survival was compared between the high GIRK1 gene expression group and the low GIRK1 gene expression group in overall, stage I and stage II/III. Figures 2 and 3 show the significance in survival between the high GIRK1 gene expression group and the low GIRK1 gene expression group in overall and stage I groups (P = 0.0004 for the overall group; P = 0.0376 for the stage I group). Although the 5-year survival in low GIRK1 gene expression group is better than that in the high GIRK1 gene expression group in the stage II/III, there is not a difference in survival between the 2 groups (Figure 4).
Discussion
In spite of significant advances in surgery and the use of new, more effective chemotherapeutic regimens, the overall 5-year survival of patients with NSCLC is 17% [13]. Identification of new prognostic factors might be of value in directing therapy and intensifying follow-up for a select group of patients. Lymph node metastasis and stage are the most powerful prognostic markers for NSCLC. Identifying new genes that are associated with tumor growth, metastasis, and prognosis is very important in advancing the understanding of cancer biology.
Human carcinomas exhibit hyperpolarized membrane potential as compared with surrounding normal tissue [14,15]. GIRK1 acts to conduct potassium ions into the cell rather than out of the cell, and play a role in maintaining membrane potential. Though GIRK1 act to hyperpolarizing the cell membrane, the function of GIRK1 still remains completely unclear in cancer. Cell proliferation and the density of intracellular potassium are controlled at specific stages of the cell cycle [16], and cell membrane potential indeed changes during the cell cycle [14]. And GIRK1 is reported to play a role in cell proliferation [3]. Receptors known to activate GIRK1 belong to the family of G-protein coupled receptors, and the G-protein-coupled receptors are reported to be able to induce cell proliferation and activate a pathway leading to angiogenesis in tumor [17]. GIRK1 gene overexpression is reported to follow a general trend of increasing expression if lymph node metastasis is involved in breast carcinomas [8]. Though the mechanical role of GIRK1 in lymph node metastasis in cancer is not clear, angiogenesis is reported to be correlated with lymph node metastasis, tumor progression and poor prognosis in most of solid tumors [18,19]. Therefore, GIRK1 is thought to be able to act not only in cell proliferation but also as an angiogenesis activator as well as G-protein-coupled receptors. S-kinase-associated protein 2 (Skp2) plays a critical role in regulating cell cycle progression and human factor-8-related antigen (F8RA) is assessed to show angiogenesis by microvessel density. So we determined whether or not expression of GIRK1 mRNA correlated with immunohistochemical assays of Skp2 and F8RA. Patients with high expression of GIRK1 mRNA were tendency to show high MVD and positive Skp2 expression without significance (data not shown). GIRK1 could be a candidate for a pharmaceutical target, depending upon further functional studies.
In this study, we used human pulmonary adenocarcinoma cell line PC-14 as a positive control for GIRK1 gene expression. The patients were classified into two groups according to the cutoff point of mean ration of GIRK1/cell line PC-14 expression in tumor specimens. The mean number has been widely used as the cutoff point to divide the patients into two groups [20,21]. GIRK1 was expressed at higher levels in cancer tissue than in adjacent normal lung tissue. It was shown that a high GIRK1 gene expression was detected in 69% of the tumor samples in our patient population with NSCLC. Furthermore, GIRK1 gene expression was also associated with nodal status, and tumor stage. These results, in correlation with nodal status, were similar to a previous report on breast carcinomas [8]. We examined the relationships between GIRKI gene expression and AMF-R gene expression, known as a marker of lymph node metastasis and tumor progression in NSCLC [12], and the results of GIRK1 gene expression agreed significantly with AMF-R. Statistical associations between GIRK1 expression and clinicopathological variables (age, T-factor, histology, and AMF-R) were examined by regression analysis. This analysis also showed that GIRK1 was correlated with AMF-R (data not shown). It was observed that patients with high GIRK1 expression NSCLC showed an unfavorable prognosis compared with those whose tumors had low GIRK1 expression in overall. Many patients in stage II/III disease had high GIRK1 expression than low GIRK1 expression. Therefore the poorer survival in overall was possible to be due to stage. So we compared patients in each stage, and we found a positive correlation between GIRK1 expression and surgical outcome in stage I cancer but not a positive correlation in stage II/III disease. Our results suggest that a high GIRK1 gene expression was strongly associated with an increased recurrence in stage I cancer and that patients with high GIRK1 gene expression may be prone to metastasis, or may already have occult micrometastasis to the lymph node in stage I cancer. On the other hand, GIRK1 expression does not seem to be a prognostic predictor for stage II/III disease individuals. GIRK1 gene expression level may play one of a key role in the biology of lung cancer and define a more aggressive tumor phenotype. Further studies are needed on GIRK1 to evaluate the mechanism of GIRK1 and more studies with a larger group of patients will be necessary to substantiate these data. A real quantative PCR amplication is now the standard approach, and more sensitive and accurate than RT-PCR. We would use the real-time quantative PCR amplication instead of RT-PCR in the next study for estimating the gene expression.
In conclusion, the present study suggests GIRK1 may be contribute to tumor progression and could be a useful prognostic marker in patients with overall and stage I NSCLC. Thus the current findings provide evidence to support a potential utility of this gene in developing a diagnostic test for NSCLC patients.
Competing interests
The author(s) declare that they have no competing interest.
Authors' contributions
IT: Data analysis and writing of manuscript. YI, MG: Critical appraisal of manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Agarose gel electrophoresis of RT-PCR amplified 230 bp GIRK1 cDNA, and 143 bp S14 DNA as internal PCR control. Lane 1, size marker; Lane 2, human pulmonary adenocarcinoma cell line PC-14 (positive control); Lane 3, pulmonary adenocarcinoma with high GIRK1 expression. Lane 4, pulmonary adenocarcinoma with low GIRK1 expression. Lane 5, squamous cell carcinoma with high GIRK1 expression. Lane 6, squamous carcinoma with low GIRK1 expression. Lane 7, large cell carcinoma with high GIRK1 expression. Lane 8, large cell carcinoma with low GIRK1 expression. Lane 9, and Lane 10, normal pulmonary tissue with low GIRK1 expression.
Figure 2 Overall survival of 72 lung cancer patients according to GIRK1 amplification. Survival curves were calculated by the Kaplan-Meier method, and statistical evaluation was determined by the log-rank test (p = 0.0004).
Figure 3 Survival curves of the patients with stage I NSCLC on the basis of GIRK1 amplification. A significant difference was seen between the 2 groups (p = 0.0376).
Figure 4 Survival curves of the patients with stage II / III NSCLC on the basis of GIRK1 amplification. A significant difference was not seen between the 2 groups.
Table 1 Correlation between GIRK1 gene expression and various clinicopathologic factors in patients with lung cancer
Variable GIRK1 p value
Low expression (n = 22) (31%) High expression (n = 50) (69%)
Age at surgery (yrs)
≤60 8 (11 %) 19 (26 %)
>60 14 (20 %) 31 (43 %) 0.90
Sex
Male 17 (24 %) 35 (48 %)
Female 5 (7 %) 15 (21 %) 0.53
Tumor status
T1 10 (14 %) 11 (15 %)
T2 10 (14 %) 25 (35 %) 0.07
T3 2 (3 %) 14 (19 %)
Nodal status
N0 17 (24 %) 26 (36 %)
N1 3 (4 %) 3 (4 %) 0.02
N2 2 (3 %) 21 (29 %)
Stage
IA/IB 16 (22 %) 19 (26 %)
IIA/IIB 4 (6 %) 10 (14 %) 0.02
III A 2 (3 %) 21 (29 %)
Histology
Adenocarcinoma 15 (21 %) 26 (36 %)
Squamous cell carcinoma 6 (8 %) 22 (30 %) 0.40
Large cell carcinoma 1 (2 %) 2 (3 %)
Table 2 Relationship between GIRK1 gene expression results and autocrine motility factor receptor (AMF-R) gene expression results
GIRK1 low expression GIRK1 high expression Total
AMFR low expression 7 (15 %) 6 (13 %) 13 (28 %)
AMFR high expression 7 (15 %) 26 (57 %) 33 (72 %)
P = 0.03
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| 15541182 | PMC535568 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Nov 13; 4:79 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-79 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020372Community PageScience PolicyNoneRegional Societies: Fostering Competitive Research Through Virtual Infrastructures Community PageJennings Steven F Ptitsyn Andrey A Wilkins Dawn Bruhn Russel E Slikker William JrWren Jonathan D 12 2004 14 12 2004 14 12 2004 2 12 e372Copyright: © 2004 Jennings et al.2004This 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 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) describes its efforts to provide local opportunities for researchers to learn and connect with colleagues
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The rich get richer while the poor get poorer. This is as true for research in the life sciences as it is for society in general. Why is this? To put it simply: because existing resources can be leveraged to generate new ones. In life science research, these resources are often expressed in terms of people, equipment, and funding. Those who have more in these areas—large research organizations, support staff, extensive facilities, and pipelines of funding—can be more productive, and thereby more successful in maintaining and even expanding their infrastructure. In the United States, this “scientific wealth” is concentrated in certain geographic areas, particularly along the east and west coasts. While there are many highly competent individuals in other, less wealthy, regions, they often struggle to attract funding because they lack this infrastructure.
Fortunately, scientific progress is also made through collaboration and cooperation—activities fostered through the exchange of viewpoints and the exploration of ideas. Given a framework in which this networking can occur, some of the problems that result from a lack of infrastructure can be overcome. By definition, collaboration involves the sharing of ideas, capabilities, and resources. While it may not be possible to access all necessary resources locally, finding partners—who have their own local capabilities and resources—can enhance one's own research environment. Through these kinds of efforts, “virtual infrastructures” grow. And through collaborative efforts, those of us in some of the “poorer” regions of the United States have opportunities to successfully compete for national sources of funding by becoming better able to address the “existing research environment” consideration on most grant applications.
Building a virtual infrastructure is the primary purpose behind the formation, in the United States, of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS; www.MCBIOS.org), created to serve a geographical area that includes Arkansas, Louisiana, western Tennessee, Missouri, Mississippi, Oklahoma, and east Texas. By its very nature, bioinformatics involves people from many different backgrounds and is therefore ideal for collaborative efforts. Like many other regional societies, our primary goal is to provide a framework in which collaboration and cooperation can occur. By sponsoring regional activities (at the present time, primarily our annual conference) we hope to bring educators, researchers, and especially students together with others who have similar and/or complementary interests. Through the society, not only can medical scientists make contact with computational experts, but researchers in Arkansas can connect with researchers in Oklahoma, educators from Missouri can interact with educators from Louisiana, and students from Mississippi can find others from Tennessee who are working in the same area. Communication across specialties and areas of expertise is especially important for fostering interdisciplinary efforts and exposing students to a broader range of topics than might be available at their own institution. Nowhere is this truer than in the application of informatics to a variety of disciplines.
Regional societies need not be in competition with national or international societies. In fact, many of these larger organizations are finding that it is to their advantage to encourage close relationships with their regional counterparts. The International Society of Computational Biology (ISCB; www.ISCB.org), for example, has recognized MCBIOS under their “regional affiliate” program. Likewise, MCBIOS is encouraging its members to form “local chapters” (for example, the Oklahoma chapter of MCBIOS, www.OKBIOS.org). These local chapters are eligible to host the annual MCBIOS conference and are able to support even closer interactions among their local participants. The hierarchy of affiliations provides an abundance of opportunities for members to participate based upon their interests, financial resources, and tolerance for travel.
Regional events are by nature more inclusive, since smaller investments of time and money are required to participate. This, in turn, creates a more diverse group of attendees, since it enables those whose primary research or educational focus may be outside the subject matter to participate. While not everyone in our region may be able to attend the annual ISCB conference (in 2004 it was held in Glasgow, Scotland), we have deliberately chosen locations for the MCBIOS annual conference that would be within a day's drive for all members. At the inaugural 2003 MCBIOS conference, for example, with a theme of “Building Networks,” a number of computer scientists attended who were able to find out more about bioinformatics and their possible contribution to life science research; they would probably not have been able to make the investment to attend a national or international meeting on bioinformatics.
While still maintaining high standards, regional events also increase the number of venues for the presentation of research results and creative efforts in the educational arena. While the ISCB program, which is already highly self-selecting, accepts less than 15% of their submissions, almost all submissions at the first annual MCBIOS conference could be accommodated, at least in poster form. These regional events can also attract well-respected keynote speakers and provide training opportunities that might not otherwise be available to the membership. For example, Dr. Alan Leshner, CEO of the American Association for the Advancement of Science, gave the keynote address of the 2004 MCBIOS conference. In 2003, we were also able to host a special training session by the National Center for Biotechnology Information on their GenBank and molecular biology tools.
While MCBIOS is still new, we have plans to extend our activities—and thus our impact—beyond our annual conference. We are dedicated to supporting our local chapters, and have plans to develop a speakers' bureau to serve them. Plans to collaborate on regional technology efforts (e.g., a regional computing grid) and on multi-institutional educational programs are also in the works. Through the auspices of the society, we hope to increase regional credibility and attract national funding in support of these infrastructure improvements.
In the long term—and in addition to supporting the intellectual efforts of our members through a vibrant organizational community—our goal is to increase extramural funding for our represented institutions. We hope to achieve this by fostering competitive research. And this competitive research will be possible on a larger scale through our development of a virtual infrastructure—one that comes about through regional collaboration and a pooling of resources. In conjunction with other infrastructure-building efforts (such as the Biomedical Research Infrastructure Network program sponsored by the National Institutes of Health's National Center for Research Resources), we hope to see externally funded research in the life sciences significantly increase within our region, the American “Midsouth.”
(The 2003 and 2004 MCBIOS conferences have been supported in part by National Institutes of Health grant P20 RR-16460 from the Arkansas Biomedical Research Infrastructure Network Program [http://BRIN.UAMS.edu] of the National Center for Research Resources through a grant to underwrite student participation and awards at the conferences. The 2004 MCBIOS conference was also supported in part by a grant from the National Center for Toxicological Research [www.FDA.gov/NCTR].)
Steven F. Jennings is with the Department of Applied Science at the University of Arkansas at Little Rock; Andrey A. Ptitsyn is with the Pennington Biomedical Research Center in the Louisiana State University System; Dawn Wilkins is with the Department of Computer and Information Science at the University of Mississippi; Russel E. Bruhn is with the Department of Information Science at the University of Arkansas at Little Rock; William Slikker, Jr., is with the National Center for Toxicological Research/FDA; Jonathan D. Wren is with the Advanced Center for Genome Technology at the University of Oklahoma's Department of Botany and Microbiology. E-mail: [email protected]
Abbreviations
ISCBInternational Society of Computational Biology
MCBIOSMidSouth Computational Biology and Bioinformatics Society
| 15597114 | PMC535569 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2004 Dec 14; 2(12):e372 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020372 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020419ObituaryMolecular Biology/Structural BiologyNeuroscienceNoneFrancis Crick's Legacy for Neuroscience: Between the α and the Ω ObituarySiegel Ralph M [email protected] Edward M 12 2004 14 12 2004 14 12 2004 2 12 e419Copyright: © 2004 Siegel and Callaway.2004This 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 legacy of Francis Crick is explored by two scientists who were influenced by his work
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‘You’, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells…” —Crick (1994, p. 3)
Francis Crick was an evangelical atheist. He believed that scientific understanding removed the need for religious explanations of natural phenomena. From James Watson's and his early work, the structure of DNA explained the α, the origins of life. This was a starting point; from the elucidation of the structure of DNA, there was an explosion, a massive diversity of science that in part removed the need to postulate a creator or a creation myth. Francis still felt that life was no less astonishing just because it was biological and natural in origin. He had a consistent and completely rational world view without a need to invoke vitalism, or any non-material force (M. Crick 2004). And in the last decades of his life, he applied this philosophy to the Ω, consciousness.
Once the structure of DNA was known, the physicist George Gamow formed the RNA Tie Club, with Francis and eighteen others including his close friends Leslie Orgel and Sydney Brenner (2001); it was an ingathering that sowed seeds for future molecular biologists (Judson 1996). DNA had become the “α,” the beginning (Bronowski 1978), not just of Francis's career, but of a whole new culture of scientific life and understanding (Crick 1966).
Ten years later, the secrets of DNA transcription and translation unmasked, Francis turned to consciousness. He admitted he knew little at first, only that the structure of consciousness was as tough a problem as DNA's structure. DNA was certainly not played out, but the Ferrier Lectures in the Proceedings of the Royal Society of London by David Hubel and Torsten Wiesel were just available, tempting Francis with an almost physicist's view of neurons in action. Hubel and Wiesel wrote of functional architectures, embedded in beautiful, almost crystalline structure. The comprehension of mind invoked by a biological mechanism appeared ripe for the sort of thoughtful, theoretical science he had applied to DNA. Francis was now sixty years old and moved from Cambridge to the Salk Institute in La Jolla, California. Francis began with the brightest young minds he could find.
David Marr was a young mathematician and physiologist whose doctoral thesis on a theory of mammalian brain function at Cambridge had brought him into some contact with Brenner and Francis. A professor at the Massachusetts Institute of Technology, he began working with Tomasio Poggio of the Max Plank Institute in Tübingen on a computational theory of neuroscience. Following an invitation from Francis, Poggio and Marr spent the month of April, 1979 extending their intense examination of the core problems of visual perception. They spent hours sitting at the most western end of the Salk Institute, at the cafeteria or in Francis's office, gazing into the Pacific Ocean with all its daily changes, discussing not only architecture of visual cortex and visual perception, but the ramifications of a good theory of brain function. We know of these conversations, as the probing of Marr by Francis is captured in the final chapter of Marr's now classic book “Vision” (Marr 1982). (Although Marr speaks of a three-way conversation, judging from our own experiences as Francis's younger colleagues, the interlocutor simply seems to be Francis.)
Marr had been diagnosed with acute leukemia in the winter of 1978 (Marr and Vaina 1991). The one-month visit to the Salk Institute was an intellectual gift, for eighteen months later, Marr died. Francis had simultaneously lost a young friend and colleague who had brought an “incisive mind and creative energy” (Crick 1994, p. 77) and his best new ideas of a theoretical neurology to the brain (Marr 1969, 1970). And he saw the tragedy of Marr being cut off from solving the big problems for which he was so clearly destined.
During those early years, Francis must have thought that consciousness was tractable—if only the right way of thinking was brought to bear on it. Francis's brain was capable of collecting and filing away many disparate data, which he could then combine uniquely and imaginatively, leading to that “dramatic moment of sudden enlightenment that floods the minds when the right idea clicks into place” (Crick 1990, p. 141). Whatever his initial thoughts about the nature of the problem, Francis soon came to realize that the problem of consciousness was even tougher than he imagined, that the “click” was not happening with consciousness. In 1988, he wrote, “I have yet to produce any theory that is both novel and also explains many disconnected facts in a convincing way” (Crick 1990, p. 162).
Over the quarter century he was at the Salk Institute, Francis did propose solutions to some smaller problems in neuroscience (Sejnowski 2004) and brought consciousness into the scientific fold (Rich and Stevens 2004). But something else was going on quietly and behind the scenes. Francis was building an army to help him take on consciousness. This was not empire building with Francis as the head of a group of directed scientists in the Cambridge or German model. Francis continually encouraged and assisted young scientists to approach the hardest problems of the brain. Marr and Poggio were just the first recruits he helped embolden. He started his long-time collaboration with Christof Koch, once a post-doctoral trainee with Poggio, on “The Problem of Consciousness” (Crick and Koch 1990, 1992). His door was always open to graduate students, postdoctoral trainees, faculty who wanted to discuss those problems as many others and we can attest. Francis could be found daily at tea time, an ingathering of the Salk Institute computational and vision laboratories of Simon LeVay, Terry Sejnowski and Thomas Albright, surrounded by graduate students and post-doctoral trainees, with conversation ranging across science—Francis listening to their stories of their explorations and encouraging them to reach beyond their horizons. Francis had a “love of the truth and helped others to move to the truth” (Watson 2004).
Francis Crick in his office.
Behind him is a model of the human brain that he inherited from Jacob Bronowski. (Photo: Marc Lieberman)
When Francis worked on the structure of DNA, he had some simple facts, such as Chargaff's Laws, and means to make point mutations from which it could be determined how function followed structure. But not a single neuroanatomist knew how many neurons actually converged in their input to a particular single cell. No one knew how to eliminate a specific cell type from a circuit— to make a point mutation, so to speak, in the structure of consciousness. His 1979 article in Scientific American, “Thinking about the Brain,” did not have much impact at the time, even when it explicitly described three needed methods: first, a method by which all the connections to a single neuron could be stained; second, a method by which “all neurons of just one type could be inactivated, leaving the others more or less unaltered”; and third, a means to differentially stain each cortical area, “…so that we could see exactly how many there are, how big each one is and exactly how it is connected to other areas.” By the mid-1980s, Francis had realized that these massive holes in our understanding of the most simple brain facts were not being filled. Something needed to be done.
Over the twenty years since the RNA Tie Club, molecular biology had matured. Francis actively began encouraging the inclusion of the critical tools of molecular biology in the study of neural circuits and perception; in his thinking, molecular biology was critical to understand how the brain worked because it provided tools. He would encourage junior scientists, postdoctoral trainees, and faculty—all those who had visited him over the years—to think about using these tools. He would give short homilies about the plethora of sub-types of neurons in the retina; would not the cortex be at least as rich in possibilities? Molecular tools could unravel this knot.
As we reminisced after Francis's death, we discovered that Francis had spoken with each of us on these molecular methods, across a twenty-year interval. In the mid-1980s, Francis spoke with Ralph, pressing him to consider how he might do highly specific lesions of single neuron types in motion cortex using molecular identifiers. At the time, the only tools imaginable were some sort of killer antibody approach. Twenty years later, Ed recalls Francis continuing to encourage this cross-disciplinary molecular and systems approach. It was absolutely imperative to Francis's vision of the maturation of neuroscience that there would be a conjoining of molecular biology and systems neuroscience. We are sure we were not unique in hearing this call; with how many others had he shared his vision?
The science of the mind is a thinker's game. It is chess against the grandest masters, biological evolution and natural selection—and we are just learning to move the pieces. Our viewpoint is often myopic, with our noses pressed against the back row of the chessboard. It is hard to see the pieces, let alone their arrangement or the strategies they are forming. Francis may not have had the overview needed to reveal evolution's gambit, but he knew the moves needed to clear the “tangle of difficulties” (Crick 1994, p. 77) that prevented an unfogged view of his opponent's pieces.
Francis hoped for simplicity. He wrote, “Curiously enough, in biology it is sometimes those basic problems that look impossibly difficult to solve which yield most easily…. The biological problems that are really difficult to unscramble are those where there is almost infinity of plausible answers and one has to painstakingly attempt to distinguish between them.” (Crick 1990, p. 157–158). Watson and Crick had picked the right pieces of information to construct their model. Francis early on had had the same hopes to open the doors of consciousness (to paraphrase Huxley 1963). Watson and Crick used their intuition to fill in the gaps. But Francis found that there were just too many possibilities, and the gaps in knowledge were still just too big for consciousness.
In 1999, Francis felt that gentle and informal direction was not enough. Thus, he convened a meeting of molecular biologists and neuroscientists at the Salk Institute to encourage them to work together. He brought scientists including Tom Albright, Ursula Bellugi, Ed Callaway, Rusty Gage, Steve Heinemann, Terry Sejnowski, Chuck Stevens, and Inder Verma into one room and said it was time to get serious. He reminded them of the advantages of genetic methods for targeting specific cell types within complex neural circuits, and he reiterated the need for methods that could be used to identify, manipulate, and observe neural circuits in action. Not only were methods to be used in transgenic mice, but also methods based on viral vectors were needed to study the visual system of monkeys. From this, a number of initiatives moved forward, with studies ranging from the molecular biology of Williams syndrome to basic molecular tool building (Naldini et al. 1996; Blomer et al. 1997; Bellugi et al. 1999; Pfeifer et al. 2001; Zhao et al. 2001; Kaspar et al. 2002a, 2002b, 2003; Lechner et al. 2002; Lein et al. 2004).
Today the tools are emerging at an ever faster pace, at least in part due to Francis's maneuvers behind the scenes and his encouragement of junior scientists. Time is curing Francis's bout of scientific prematurity (Stent 1972). Individual cell types will soon be reversibly inactivated (Johns et al. 1999; Lechner et al. 2002; Slimko et al. 2002; Ibanez-Tallon et al. 2004); viral methods of tracing connections will start to fill in the gaps; new sensor methods for simultaneously recording from hundreds and thousands of identified neurons are coming (Guerrero and Isacoff 2001; Zemelman and Miesenbock 2001; Tsien 2003). There is a new field Francis termed “molecular psychology” or “molecular biology of systems neuroscience”; Albright simply calls it Neuroscience.
In 2001, Francis was diagnosed with colon cancer. He realized that the problem of the neural correlates of consciousness might outlast him. Francis was walking with a cane, still not waiting for anyone, nor allowing anyone to wait for him. He continued to find time for new faces in the field and continued to work on consciousness. While he had made many strides forward, he saw the race for him was winding down. He had had his hope for understanding the structure of consciousness. He had laid the groundwork. He decided to encapsulate his ideas in a “Framework” paper with Koch (Crick and Koch 2003). For many of us it was clear that he was laying out where he would go, had he enough time.
Each of the points of the Framework could form a major research initiative. Perhaps they should. But the central point is the approach to understanding consciousness; it is both structural and functional, peering forward into the future into what the shape might be. It was clear to his friends and colleagues that Francis was leaving a last testament.
As the cancer finally caught up with Francis, he focused on the role of the rarely studied claustrum (Sherk 1986). He wrote internal memos, brought friends and colleagues to working lunches at home with Odile, his wife of fifty-five years. Why do this? Why all this focus on another part of the brain, when only months remained? (Indeed it turned out to be weeks.) Was it his way of saying goodbye, of bringing his extended family close again? We think not. Francis wanted to make sure his plan went forward. He stressed to his visitors queries about the origins of the claustrum, its molecular biology, its role in consciousness. He was using his framework, pointing out the route to understanding the Ω of his career.
Francis was doing what he truly loved to his last moments. He needed to be doing science, perhaps more than ever, to take him away from the physical pain that he surely felt. He had built his army. Perhaps none of us even knew we had enlisted, but we had. And he was setting us off on the long march forward into a time that soon would not be for him. Francis died on the cusp of a new age of molecular systems neuroscience. Soon, we will have the tools and the data, but we will not have Francis. Francis had existed between the α of DNA and the Ω of consciousness. And for a man who never believed in the afterlife, he had indeed achieved immortality.
Ralph M. Siegel is an associate professor in the Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America. Edward M. Callaway is an associate professor in the Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
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| 17593891 | PMC535570 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 14; 2(12):e419 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020419 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020420PrimerAnimal BehaviorEcologyEvolutionGenetics/Genomics/Gene TherapyDrosophilaInsectsThe What and Why of Research on Reinforcement PrimerServedio Maria R 12 2004 14 12 2004 14 12 2004 2 12 e420Copyright: © 2004 Public Library of Science.2004This 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 Genetics of Speciation by Reinforcement
Reinforcement - a process that helps prevent interbreeding between hybridising populations - is an important and little understood mechanism driving the completion of speciation
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Reinforcement, like sympatric speciation (see Box 1), has charisma. Evolutionary biologists are still deeply uncertain about how often these processes take place, and hence how important they are in explaining the biological diversity we see today. Empirical and theoretical support for both ideas has waxed and waned over recent decades. Yet both ideas have consistently garnered an unusual amount of attention.
Box 1. Glossary
Sympatry—Area(s) of overlap in the ranges of populations, enabling potential interbreeding.
Allopatry—Area(s) of population ranges that do not overlap with one another, preventing interbreeding.
Sympatric speciation—Speciation that occurs within a range of sympatry.
Reinforcement—The evolution of mechanisms that prevent interbreeding between newly interacting incipient species, as a result of selection against hybrids (narrow definition) or interspecific matings (broad definition) (See Figure 1).
Conspecific sperm precedence—Disproportional fertilization of a female by sperm of a conspecific male, when that female has mated with both conspecific and heterospecific males.
Much of the appeal of both reinforcement and sympatric speciation lies in the way they unite micro- and macroevolution. Reinforcement, a concept popularized by Dobzhansky (1937), is a process by which speciation, a macroevolutionary process, can be driven directly by natural selection, one of the primary microevolutionary forces. Sympatric speciation can make the same claim. Because of this close linkage between the concepts, the study of one can tell us a great deal about the other (see Kirkpatrick and Ravigné 2002). Such studies can also reveal a lot about the general role of microevolution in species divergence.
Reinforcement provides a pathway toward the completion of the speciation process. Imagine that two divergent populations (potentially even classified as separate species) come into contact after a period of allopatry (Figure 1). If the populations have been apart for a long time, evolved differences between them will cause a certain degree of incompatibility when the populations come together. Often, this incompatibility comes in the form of low hybrid fitness (postzygotic isolation) or mismatched mating characteristics (premating isolation). The degree of the development of these isolating mechanisms is roughly proportional to the genetic distance between the populations, reflecting the fact that incompatibilities accumulate over time (Coyne and Orr 1989).
Figure 1 Schematic Diagram of Reinforcement
(A) Populations diverge by evolving separately for a period of time in allopatry (separated by a geographic barrier). Populations may still be completely compatible in their mating characteristics, or these may also have diverged slightly (represented in the figure by the differences in color). (B) Secondary contact commences. This is shown through the removal of a geographic barrier that allows individuals to migrate between populations (migration is represented by a bidirectional arrow). Secondary contact can also occur by range expansion to produce an area of sympatry, or through other similar mechanisms. Due to the prior divergence between populations, hybrids have low fitness. (C) Selection to avoid producing low fitness hybrids causes the evolution of further divergence in the mating traits (represented by color) of the two populations, reducing interbreeding.
If the isolating mechanisms between these populations are only partially complete, extensive hybridization may occur. This can result in fusion back into a single population, or in the swamping of one population's gene pool by the genes of the other (extinction). But there is another possibility, one that can cause the speciation between the two populations to proceed. Remember that if the populations have been separated for long enough, it is likely that hybrids between them will have relatively low fitness. Individuals who mate with members of the opposing population will therefore produce offspring of poor quality, and hence have lower fitness than individuals that mate within their own population. This favors the evolution (or further divergence) of characteristics that cause mating within, rather than between, populations (Figure 1C). Speciation between the populations is driven further towards completion through this increase in premating isolation.
This process, the evolution of premating isolation after secondary contact due to selection against hybrids, is reinforcement sensu Dobzhansky (1937). Recent authors have broadened the definition of reinforcement to include as a driving force any form of selection against mating between populations (e.g., Servedio and Noor 2003). This could include, for example, lower fertility, or higher mortality of females that mate with members of other populations. In all definitions, however, the microevolutionary process of selection is essential for reinforcement. In fact, in reinforcement, speciation itself can be thought of as an adaptive response to selection. It is little wonder that this causal linking of micro- and macroevolution has appeal for many evolutionary biologists.
Reinforcement in the 21st Century
Despite the substantial progress in our understanding of reinforcement that has been achieved over the last few decades, many questions remain about the process. These questions lend themselves to exploration by a broad variety of disciplines (evolution, ecology, behavior, phylogenetics, phylogeography, genetics), approaches (experimental, observational, comparative, theoretical) and taxonomic systems.
Doubtless, the most important unanswered question about reinforcement is how often it occurs. It is very difficult to prove that reinforcement is occurring, or has occurred, between two species. Reinforcement occasionally leaves a signature, called reproductive character displacement, in which mating characteristics have diverged between populations in areas of sympatry but not areas of allopatry (Figure 2) (the relationship between reinforcement and reproductive character displacement, and controversy over the definition of the latter, is reviewed in Howard 1993). In sympatric areas, populations are capable of producing hybrids, which drives reinforcement, while in allopatry hybrid production, and hence the selection for reinforcement, is absent. Reproductive character displacement has been found to be common, suggesting to some that reinforcement may be common as well (Howard 1993). It is universally acknowledged, however, both that reproductive character displacement can be caused by processes other than reinforcement, and that reinforcement can occur without leaving this signature (e.g., when population ranges are completely sympatric). Proving that reinforcement has occurred requires the ruling out of several alternative hypotheses, which are themselves difficult to assess (Noor 1999; Coyne and Orr 2004).
Figure 2 The Pattern of Reproductive Character Displacement
(A) Reproductive character displacement due to the presence of an area of overlap between two populations (differences in mating characteristics are represented by color changes, with the hatched area showing divergence in sympatry). Reproductive character displacement can also occur when the sympatric and allopatric areas are not contiguous. (B) Reproductive character displacement can appear as a cline in mating cues or mating preferences (y-axis), with divergence originating in the area of sympatry and spreading into areas of allopatry.
Several isolated examples of reinforcement between specific pairs of species have been demonstrated, fairly conclusively, in a variety of taxa including Drosophila pseudoobscura and D. persimilis (Noor 1995), flycatchers (Sætre et al. 1997), sticklebacks (e.g., Rundle and Schluter 1998), spadefoot toads (Pfennig 2003), and walking-stick insects (Nosil et al. 2003) (see also reviews of Noor 1999; Coyne and Orr 2004). These studies involve a variety of behavioral tests of mate choice, analyses of hybrid fitness and the production of hybrids in the wild, and controls for alternative explanations.
While examples such as these provide essential information about reinforcement, their slow rate of compilation and biased reporting do not provide efficient ways to assess how often reinforcement occurs in general. Comparative approaches, which examine patterns across a broader taxonomic group, can also provide support for reinforcement without these detailed mechanistic analyses (review in Coyne and Orr 2004). The revival of reinforcement in the late 1980s began with one such study in the genus Drosophila (Coyne and Orr 1989). By comparing patterns across a wide number of species, such studies can give a better assessment of the potential frequency with which reinforcement occurs—without, however, providing conclusive evidence for reinforcement between specific species pairs.
Another area where further research is essential is the determination of which biological factors promote reinforcement, as opposed to population fusion. Theoretical studies, using mathematical models and computer simulations, are proving useful in pinpointing the effects of many factors such as migration rates and patterns, the type of selection against interspecific mating, and the genetic basis of premating isolation (reviews in Turelli et al. 2001; Servedio and Noor 2003). Fortunately, some of the cases of reinforcement in specific species pairs are now being developed to the point where they can address similar questions (e.g., sex linkage of mating genes; Sætre et al. 2003). Both theoretical studies and these well developed empirical systems are also starting to address a third important area of research: how reinforcement interacts with other forces, such as ecological selection pressures, that promote speciation (e.g., Servedio 2004; Nosil et al. 2003). These integrated studies are essential to the correct placement of reinforcement within the bigger context of speciation processes.
In recent years, exciting developments have started to take place in the analysis of the genetics of reinforcement (reviewed in Servedio and Noor 2003). These developments both parallel and overlap with progress made on the genetics of speciation and species differences in general. For example, significant progress has recently been made in identifying the genetic control of hybrid incompatibilities (e.g., Presgraves et al. 2003; Barbash et al. 2003). This progress has been accompanied by a new understanding of how chromosomal rearrangements may allow these incompatibilities to be maintained despite hybridization in sympatry (Rieseberg 2001; Navarro and Barton 2003; Brown et al. 2004). Sympatric maintenance of incompatibilities, of course, has profound implications for reinforcement, which requires these incompatibilities as the force driving divergence (Noor et al. 2001).
Genetic analysis is also allowing a new understanding of the mechanisms by which reinforcement might be taking place in specific cases. Work by Ortiz-Barrientos et al. (2004) in this issue of PLoS Biology illustrates the extent of the insights that can be made with this approach. Using high-resolution genetic mapping the authors have identified the locations of genes that cause increased discrimination against Drosophila persimilis males by D. pseudoobscura females, due to reinforcement in sympatry. Surprisingly, these genes map to very different areas of the chromosomes than do genes that cause a basal level of mating discrimination between the species in allopatry. Among other insights, the position of these genes suggests that the reinforced discrimination is based on odor, not on the mechanism used in allopatry, male song. This leads to the novel conclusion that reinforcement is not just increasing the strength of an already existing mechanism of species discrimination, but is occurring through the development of a new discrimination system. These kinds of developments can also motivate more realistic theoretical models of the reinforcement process.
Implications and Extensions of Reinforcement
What if, when our assessment of the frequency of reinforcement is improved, it turns out to have been a rare occurrence in the generation of current biological diversity? The study of reinforcement is broad and varied enough that many of our findings about the process would still have wide-reaching implications.
First, recall the claim, at the start of this article, that studying reinforcement reveals much about the role of microevolution in the macroevolutionary process of speciation. Knowledge gained about this relationship is not only directly applicable to the very similar process of sympatric speciation, but can also tell us a great deal about speciation caused by ecological adaptation and sexual selection, which are critical components of reinforcement in many systems (e.g., Nosil et al. 2003; Haavie et al. 2004). Studies looking for reinforcement have also led to insights into the formation and maintenance of hybrid zones (e.g., Butlin 1998; Britch et al. 2001). Situations where reinforcement fails to occur likewise teach a lesson, elucidating possible mechanisms of extinction when secondary contact occurs between species.
Second, analysis of reinforcement clarifies the interactions between levels of reproductive isolation that occur at different stages in the life cycle. Reinforcement, broadly defined, can be driven by isolation at the postzygotic level or by incompatibilities that occur between mating and zygote production (postmating-prezygotic incompatibilities; Servedio 2001). Postzygotic isolation can likewise cause divergence at the premating stage (reinforcement) or potentially at the postmating-prezygotic stage, through the evolution of conspecific sperm precedence (Marshall et al. 2002). These various stages of isolation have different degrees of importance among plants, free-spawning marine invertebrates, and other internally and externally fertilizing animals (Bernasconi et al. 2004). Analysis of these stages of isolation, their interactions, and the evolutionary pressures they are under therefore has broad implications for comparative reproductive biology across these varied groups.
Finally, regardless of whether reinforcement has been a common pathway in speciation, its relevance may be increasing. Reinforcement is a possible outcome anytime species that are capable of hybridization come into contact. Human activity is increasing the incidence of secondary contact by altering habitat and introducing invasive species. This contact often results in hybridization (reviews in Rhymer and Simberloff 1996; Mooney and Cleland 2001). It is important to identify and understand the properties of species pairs that make extensive introgression, extinction, stable hybrid zones, or reinforcement likely outcomes of such contact. If reinforcement has played a small role in the generation of current diversity, it may be because secondary contact itself has historically been a rare occurrence. It is the frequency of reinforcement among incidences of secondary contact that will determine its importance in the near future.
This work is supported by National Science Foundation grant 0234849. A. Chunco, P. Lorch, M. Noor, and C. Willett provided helpful comments.
Maria R. Servedio is at the Department of Biology at the University of North Carolina, North Carolina, United States of America. E-mail: [email protected]
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Brown KM Burk LM Henagan LM Noor MAF A test of the chromosomal rearrangement model of speciation in Drosophila pseudoobscura
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| 15597115 | PMC535571 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 14; 2(12):e420 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020420 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020430Book Reviews/Science in the MediaBioengineeringNeuroscienceNeurology/NeurosurgeryHomo (Human)Brain versus Machine Control Book Review/Science in the MediaCarmena Jose M 12 2004 14 12 2004 14 12 2004 2 12 e430Movie Reviewed Raimi S, director (2004) Spider-man 2 [film]. Sony Pictures Copyright: © 2004 Jose M. Carmena.2004This 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.Dr. Octopus, the villain of the movie "Spiderman 2", is a fusion of man and machine. Neuroscientist Jose Carmena examines the facts behind this fictional account of a brain- machine interface
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“Dr. Octopus,” the villain that terrorizes the city in the most recent film of the popular Spider-Man comic, is the ultimate characterization of a brain–machine interface (BMI) on the big screen. In Spider-Man 2, the brain is that of nuclear physicist Dr. Otto Octavius, who dreams of harnessing nuclear fusion. The machine is a harness of four mechanical arms designed with tentacle-like flexibility, gripping and vision capabilities, and an artificial intelligence module that gives them some autonomy. The interface between the machine and the brain is at the spinal cord level, with an “inhibitor chip” to prevent the artificial intelligence module in the mechanical arms from taking over Octavius's brain. Controlling this mechanical device with his own thoughts, Octavius is able to manipulate hazardous materials during his fusion experiments. However, things go terribly wrong during the exhibition of one of these experiments: the mechanical arms fuse to Octavius's body while the inhibitor chip is disabled, resulting in the machine gaining partial control of his brain. Unable to subvert the machine to his will and conscience, Octavius, together with the BMI, becomes the villainous Dr. Octopus. At the end of the movie, in a flicker of sanity and heroism, Octavius dramatically sacrifices his life as the only way to terminate the evil machine and save the world.
Although Dr. Octopus is a fictional character, a figment of a vivid imagination, audiences are fascinated by the fact that he is a human BMI. BMIs straddle the worlds of fact and fiction. While the entertainment industry has focused primarily on applications for augmenting cognitive and sensorimotor function, as seen in Star Trek, Firefox, and many other science-fiction scenarios, the scientific community has targeted clinical applications, such as neuroprostheses for restoring motor function after traumatic lesion of the central nervous system. The current BMI approach is based on the idea that a human user could enact voluntary motor intentions through a direct interface between his brain and an artificial actuator in virtually the same way that we see, walk, or grab an object with our own natural limbs. Proficient brain control of an external device or actuator should be achievable through training using any combination of visual, tactile, or auditory feedback. As a result of long-term use of the BMI, the brain should be able to “incorporate” (or adapt to) the artificial actuator as an extension of its own body. With these goals in mind, the last five years have witnessed a dramatic increase in BMI-related studies in academic institutions around the world. Subjects have learned to utilize their brain activity for different purposes, ranging from electroencephalogram- and electrocorticographic-based systems (Wolpaw et al. 2002; Leuthardt et al. 2004), in which human subjects control computer cursors, to multielectrode-based systems, in which nonhuman primates control the movements of cursors and robots to perform different kinds of reaching and grasping tasks (Serruya et al. 2002; Taylor et al. 2002; Carmena et al. 2003; Musallam et al. 2004).
These examples of what could be called the first generation of BMIs have something in common: they have been exclusively controlled by neural signals. Even with BMIs that use neural activity recorded with invasive electrodes to yield higher bandwidth and thus allow for the execution of more complex tasks, it remains unclear whether the quality of the signal will ever suffice for a patient to freely, safely, and effectively control a prosthetic arm to perform daily tasks. For instance, the level of motor skill required for dexterous finger manipulation is outstanding. Planning paths and avoiding obstacles while reaching and grasping in unconstrained environments requires similarly fine motor control. Thus, realistic motion through a complex environment with a BMI is extremely challenging and, perhaps, not feasible with the relatively low bandwidth (∼10 Hz) of current BMIs. Even if significant improvements are made in the algorithms used to decode neural activity by, for example, incorporating knowledge from neurophysiological experiments of how motor signals that underlie movements are encoded in the brain, current BMI bandwidth still may not be sufficient to reach the performance level an injured patient would desire.
What does this mean for second-generation BMIs? We may find some inspiration in Dr. Octopus. The fictional BMI in Spider-Man 2 is innovative in the sense that it is a hybrid system that incorporates both neuronal and artificial control signals. It makes perfect sense to take advantage of the fields of engineering (control theory) and artificial intelligence to build better BMIs—part brain and part robot. In principle, these hybrid BMIs would allow a patient to accomplish a task more efficiently than those relying on neuronal signals alone. For example, in a common task such as reaching for and grasping a glass of water, a hybrid BMI would be fed with both brain and machine control signals; the intention of movement would be decoded directly from neuronal signals, leaving obstacle avoidance and grasping stabilization to the artificial control module of the system. Such a module would get inputs from sensors embedded in the robot, and would produce a control signal that would fuse with the neuronal control signal to augment the final output command.
What ratio of neuronal versus artificial signal would be needed for optimal control of a BMI? In the movie, Octavius's crisis is a severe unbalance in favor of machine control. Science fiction aside, we see the more realistic potential problems of having a physical device gaining autonomous control. Technically, this could be analyzed as too much gain in the artificial control signal, which, in a realistic scenario, would likely result in oscillating behavior, jerky grasping, etc. Hence, safeguarding measures (characterized in the movie as the inhibitor chip in Octavius's brain stem) would be needed to avoid dangerous situations when a chronic neuroprosthesis freely interacts with the real world. For both science and science fiction, the question is the same. Brain and machine: which one gets the power?
Jose M. Carmena is at the Center for Neuroengineering, Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, United States of America. E-mail: [email protected]
Abbreviations
BMIbrain–machine interface
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| 0 | PMC535572 | CC BY | 2021-01-05 08:21:22 | no | PLoS Biol. 2004 Dec 14; 2(12):e430 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020430 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020431PrimerBioengineeringIn VitroThe Emergence of Complexity: Lessons from DNA PrimerMao Chengde 12 2004 14 12 2004 14 12 2004 2 12 e431Copyright: © 2004 Chengde Mao.2004This 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.
Algorithmic Self-Assembly of DNA Sierpinski Triangles
Using Biology to Create Complex Patterns
The same molecular qualities that endowed DNA with its capacity to carry hereditary information make it a powerful tool to explore the self-assembly of complex nanostructures
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How did life emerge from a soup of chemicals? How do patterns such as schools of fish form from individuals? How do voting patterns emerge? Such a diverse array of problems seems completely unrelated. However, they all involve “emergence of complexity.” When individuals come together, they form patterns, structures, and organizations that cannot be discerned from the individuals alone. The study of the emergence of complexity is one of the most active and important areas of research. It is important not only for understanding nature, but also for technological applications, including the fabrication of large-scale integrated nanocircuits using a bottom-up approach, and the preparation of multifunctional “smart” nanomaterials.
DNA evolved to be the primary carrier of genetic information because of its extraordinary chemical properties. These same properties also make DNA an excellent system for the study of self-assembly and self-organization. Two complementary molecules of single-stranded DNA have paired bases that bond with each other and form the well-known double helix structure. Two molecules of double-stranded DNA (duplexes) can further associate together if they have complementary single-stranded overhangs (sticky ends). Intermolecular interactions can be precisely predicted by Watson–Crick basepairing (adenine to thymidine and guanine to cytosine). And, these interactions are structurally understood at the atomic level. Given the diversity of the DNA sequences, we can easily engineer a large number of pairs of DNA duplexes that associate with each other with sequence specificity and in a well-defined fashion. This property is not common among other molecular systems. Small organic and inorganic molecular pairs can interact with each other with specificity and in well-defined structures, but the number of such pairs is limited and their chemistry varies greatly. Protein molecules, such as antibody–antigen pairs, have great diversity and high specificity. However, it is extremely difficult, if not impossible, to predict how proteins interact with each other. In contrast, DNA as a molecular system fulfills all the aforementioned criteria.
In nature, DNA occurs predominantly as a linear molecule, and if its conformations were limited to linearity, it would not be very useful for studying self-assembly. Fortunately, branched DNA structures can be engineered. Holliday junctions, for example, are intermediates that occur during genetic recombination. To model Holliday junctions, a stable four-arm junction has been constructed in which, by design, no two strands are fully complementary to each other (Kallenback et al. 1983; Seeman 2003) (Figure 1A). For example, the 5′ half of strand 2 is complementary to the 3′ half of strand 1, but the 3′ half of strand 2 is complementary to the 5′ half of strand 3 instead of that of strand 1. Combining branched structures and the excellent molecular recognition of DNA, we are ready to engineer complicated DNA nanostructures and use them for studying self-assembly.
Figure 1 Basic DNA Structures for Self-Assembly
(A) A four-arm junction and (B) its three-dimensional structure; (C) a DNA DX; and (D) a DNA TX.
Extensive studies have shown that the four-arm junction adopts an X-shape structure (Figure 1B) under physiological conditions, and the angle between its two helical domains can vary widely (Lilley 2000). It is impossible to construct well-defined large structures from flexible components. To overcome this problem, several well-behaved DNA motifs have been engineered. Double crossover (DX) (Fu and Seeman 1993) and triple crossover (TX) (LaBean et al. 2000) molecules are two early examples (Figure 1C and 1D). In such molecules, two or three DNA duplexes lie side by side. Two neighboring duplexes are joined by two crossovers, which prevent any duplex from twisting against its neighbor duplex. Thus, the interhelical angles become fixed at 0°. Other motifs quickly followed, including the paranemic crossover motif (Shen et al. 2004), rhombus/parallelogram motif (Mao et al. 1999), cross motif (Yan et al. 2003), and several triangle motifs (Chelyapov et al. 2004; Ding et al. 2004; Liu et al. 2004). They all are stable, rigid, and readily designed for self-assembly.
One simple example of self-assembly is the formation of two-dimensional (2D) periodic arrays or 2D crystals. This is also one of the greatest successes in the field of DNA self-assembly (Figure 2). The first 2D DNA crystals were assembled from DX motifs (Winfree et al. 1998). In a 2D crystal, each DX molecule contains four sticky ends (A–A′ and B–B′) distributed on its two component duplexes. The complementarity of the sticky ends is designed in such a way that a DX molecule will interact with another four DX molecules through its four sticky ends. Any two DX molecules can interact with each other though only one pair of sticky ends. Any pair of sticky end interactions will position the two DX molecules in a conformation such that no other sticky ends from these two molecules are in sufficient proximity to interact. As a result of this design, regularly ordered 2D arrays have formed (Figure 2). Following similar strategies, others have designed DNA motifs to assemble into 2D arrays, whose symmetries include tetragonal (Yan et al. 2003), pseudohexagonal (Mao et al. 1999; Liu et al. 2004), and hexagonal (Chelyapov el al. 2004; Ding et al. 2004).
Figure 2 Self-Assembly of a 2D DX Array
Each rod represents a DNA duplex. The geometric complementarity represents the sequence complementarity of sticky ends.
Inspired by early theoretical suggestions (Winfree 1998), experimental exploration of aperiodic self-assembly immediately followed. One study applied algorithmic self-assembly to TX molecules (Figure 3) (Mao et al. 2000). The assembling rule “exclusive OR” (XOR) is encoded in the TX molecules. Consider the value of all inputs and outputs as either 1 or 0. For XOR operations, if two inputs are the same, the output will be 0; otherwise, the output will be 1. If molecules X and Y are the input and output, respectively, Yi molecule takes the input from the Xi and Yi−1 molecules. In other words, the values of the Xi and Yi−1 molecules determine what Y molecule will be incorporated. There are four different types of Y molecules, whose inputs are (1, 1), (1, 0), (0, 1), and (0, 0). These four, and only four, Y molecules are enough to satisfy any input combination. Two C molecules connect the input and output molecules, which is necessary for the characterization but not essential for the self-assembly process. Sticky ends between the X molecules and the C molecules are longer than those between Y molecules and between Y and X or C molecules. Thus, the C and X molecules assemble first to form the inputs because the association between longer sticky ends is more stable than those between the shorter ones. Then the output Y molecules assemble to the assembled C and X molecules. In that study (Mao et al. 2000), two different input combinations were used, and one of them is shown in Figure 3. The resulting DNA structures are periodic with respect to the backbones, but they are aperiodic in their sequences. Though the resulting four-byte one-dimensional (1D) structures are quite simple, this study demonstrated that aperiodic structures are achievable through self-assembly.
Figure 3 Self-Assembly of a 1D Aperiodic TX Array Based on XOR Operation
The value of any input or output is binary, either 1 or 0. If two inputs are the same, the output is 0; otherwise, the output is 1.
Winfree and co-workers in this issue of PLoS Biology have extended the algorithmic self-assembly strategy from 1D to 2D (Rothemund et al. 2004). This achievement is certainly a milestone in the field of self-assembly. It overcomes a great challenge, as the structural complexity dramatically increases from 1D to 2D structures. These researchers have applied the same XOR algorithms to DX molecules in their study and achieved fractal structures, Sierpinski triangles (Figure 4). External inputs are in the bottom row. Each row takes inputs from the row immediately below, and sends the operation outputs to the row immediately above. Each position takes two inputs (identical or non-identical) from lower left and lower right positions, and sends identical output to both upper left and upper right positions. The arrows indicate the direction of information flow, or assembly sequences. In their experiment, the rules are encoded in DX molecules. This study is conceptually straightforward, but the experimental challenges are tremendous. One key challenge is assembly fidelity. The right molecules have to compete with partially matched molecules. The concentrations of the competing molecules further complicate the fidelity issue, as some molecules could be rapidly depleted from the solution. In that sense, the current work is quite stunning even though the assembly is far from perfect.
Figure 4 Schematic Representation of Self-Assembly of a Sierpinski Triangle Based on XOR Operation
The values in the bottom row are the inputs.
In principle, a wide range of 2D patterns could be generated with the same set of molecules and the same strategy, changing only the first row of the assembly, which specifies the external inputs. Realization of this goal will critically rely on the elimination of assembly errors, or the introduction of error corrections (Winfree and Bekbolatov 2004).
The current work represents a neat approach to understanding the emergence of complexity. It integrates both simulation and wet chemistry. It also provides a plausible approach to nanofabrications. Over the last decade, a variety of methods have been developed, which use biomacromolecules as templates to fabricate nanostructures (Braun el al. 1998; Douglas and Young 1998; Mucic et al. 1998; Fu et al. 2004). Limited by the complexity of the available biomacromolecular templates, simple nanostructures are the usual result: mostly nanowires, nanoparticles, and simple aggregates of nanoparticles. The current work illustrates the possibility of generating more complicated structures and promises unprecedented structural complexity for nanomaterials.
Chengde Mao is in the Department of Chemistry, Purdue University, West Lafayette, Indiana, United States of America. E-mail: [email protected]
Abbreviations
1Done dimensional
2Dtwo dimensional
DXdouble crossover
TXtriple crossover
XORexclusive OR
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References
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| 15597116 | PMC535573 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2004 Dec 14; 2(12):e431 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020431 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020439EssayOtherNoneMathematics Is Biology's Next Microscope, Only Better; Biology Is Mathematics' Next Physics, Only Better EssayCohen Joel E 12 2004 14 12 2004 14 12 2004 2 12 e439Copyright: © 2004 Joel E. Cohen.2004This 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.Joel Cohen offers a historical and prospective analysis of the relationship between mathematics and biology
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Although mathematics has long been intertwined with the biological sciences, an explosive synergy between biology and mathematics seems poised to enrich and extend both fields greatly in the coming decades (Levin 1992; Murray 1993; Jungck 1997; Hastings et al. 2003; Palmer et al. 2003; Hastings and Palmer 2003). Biology will increasingly stimulate the creation of qualitatively new realms of mathematics. Why? In biology, ensemble properties emerge at each level of organization from the interactions of heterogeneous biological units at that level and at lower and higher levels of organization (larger and smaller physical scales, faster and slower temporal scales). New mathematics will be required to cope with these ensemble properties and with the heterogeneity of the biological units that compose ensembles at each level.
The discovery of the microscope in the late 17th century caused a revolution in biology by revealing otherwise invisible and previously unsuspected worlds. Western cosmology from classical times through the end of the Renaissance envisioned a system with three types of spheres: the sphere of man, exemplified by his imperfectly round head; the sphere of the world, exemplified by the imperfectly spherical earth; and the eight perfect spheres of the universe, in which the seven (then known) planets moved and the outer stars were fixed (Nicolson 1960). The discovery of a microbial world too small to be seen by the naked eye challenged the completeness of this cosmology and unequivocally demonstrated the existence of living creatures unknown to the Scriptures of Old World religions.
Mathematics broadly interpreted is a more general microscope. It can reveal otherwise invisible worlds in all kinds of data, not only optical. For example, computed tomography can reveal a cross-section of a human head from the density of X-ray beams without ever opening the head, by using the Radon transform to infer the densities of materials at each location within the head (Hsieh 2003). Charles Darwin was right when he wrote that people with an understanding “of the great leading principles of mathematics… seem to have an extra sense” (F. Darwin 1905). Today's biologists increasingly recognize that appropriate mathematics can help interpret any kind of data. In this sense, mathematics is biology's next microscope, only better.
Conversely, mathematics will benefit increasingly from its involvement with biology, just as mathematics has already benefited and will continue to benefit from its historic involvement with physical problems. In classical times, physics, as first an applied then a basic science, stimulated enormous advances in mathematics. For example, geometry reveals by its very etymology (geometry) its origin in the needs to survey the lands and waters of Earth. Geometry was used to lay out fields in Egypt after the flooding of the Nile, to aid navigation, to aid city planning. The inventions of the calculus by Isaac Newton and Gottfried Leibniz in the later 17th century were stimulated by physical problems such as planetary orbits and optical calculations.
In the coming century, biology will stimulate the creation of entirely new realms of mathematics. In this sense, biology is mathematics' next physics, only better. Biology will stimulate fundamentally new mathematics because living nature is qualitatively more heterogeneous than non-living nature. For example, it is estimated that there are 2,000–5,000 species of rocks and minerals in the earth's crust, generated from the hundred or so naturally occurring elements (Shipman et al. 2003; chapter 21 estimates 2,000 minerals in Earth's crust). By contrast, there are probably between 3 million and 100 million biological species on Earth, generated from a small fraction of the naturally occurring elements. If species of rocks and minerals may validly be compared with species of living organisms, the living world has at least a thousand times the diversity of the non-living. This comparison omits the enormous evolutionary importance of individual variability within species. Coping with the hyper-diversity of life at every scale of spatial and temporal organization will require fundamental conceptual advances in mathematics.
The Past
The interactions between mathematics and biology at present follow from their interactions over the last half millennium. The discovery of the New World by Europeans approximately 500 years ago—and of its many biological species not described in religious Scriptures—gave impetus to major conceptual progress in biology.
The outstanding milestone in the early history of biological quantitation was the work of William Harvey, Exercitatio Anatomica De Motu Cordis et Sanguinis In Animalibus (An Anatomical Disquisition on the Motion of the Heart and Blood in Animals) (Harvey 1847), first published in 1628. Harvey's demonstration that the blood circulates was the pivotal founding event of the modern interaction between mathematics and biology. His elegant reasoning is worth understanding.
From the time of the ancient Greek physician Galen (131–201 C.E.) until William Harvey studied medicine in Padua (1600–1602, while Galileo was active there), it was believed that there were two kinds of blood, arterial blood and venous blood. Both kinds of blood were believed to ebb and flow under the motive power of the liver, just as the tides of the earth ebbed and flowed under the motive power of the moon. Harvey became physician to the king of England. He used his position of privilege to dissect deer from the king's deer park as well as executed criminals. Harvey observed that the veins in the human arm have one-way valves that permit blood to flow from the periphery toward the heart but not in the reverse direction. Hence the theory that the blood ebbs and flows in both veins and arteries could not be correct.
Harvey also observed that the heart was a contractile muscle with one-way valves between the chambers on each side. He measured the volume of the left ventricle of dead human hearts and found that it held about two ounces (about 60 ml), varying from 1.5 to three ounces in different individuals. He estimated that at least one-eighth and perhaps as much as one-quarter of the blood in the left ventricle was expelled with each stroke of the heart. He measured that the heart beat 60–100 times per minute. Therefore, the volume of blood expelled from the left ventricle per hour was about 60 ml × 1/8 × 60 beats/minute × 60 minutes/hour, or 27 liters/hour. However, the average human has only 5.5 liters of blood (a quantity that could be estimated by draining a cadaver). Therefore, the blood must be like a stage army that marches off one side of the stage, returns behind the scenes, and reenters from the other side of the stage, again and again. The large volume of blood pumped per hour could not possibly be accounted for by the then-prevalent theory that the blood originated from the consumption of food. Harvey inferred that there must be some small vessels that conveyed the blood from the outgoing arteries to the returning veins, but he was not able to see those small vessels. His theoretical prediction, based on his meticulous anatomical observations and his mathematical calculations, was spectacularly confirmed more than half a century later when Marcello Malpighi (1628–1694) saw the capillaries under a microscope. Harvey's discovery illustrates the enormous power of simple, off-the-shelf mathematics combined with careful observation and clear reasoning. It set a high standard for all later uses of mathematics in biology.
Mathematics was crucial in the discovery of genes by Mendel (Orel 1984) and in the theory of evolution. Mathematics was and continues to be the principal means of integrating evolution and genetics since the classic work of R. A. Fisher, J. B. S. Haldane, and S. Wright in the first half of the 20th century (Provine 2001).
Over the last 500 years, mathematics has made amazing progress in each of its three major fields: geometry and topology, algebra, and analysis. This progress has enriched all the biological sciences.
In 1637, René Descartes linked the featureless plane of Greek geometry to the symbols and formulas of Arabic algebra by imposing a coordinate system (conventionally, a horizontal x-axis and a vertical y-axis) on the geometric plane and using numbers to measure distances between points. If every biologist who plotted data on x–y coordinates acknowledged the contribution of Descartes to biological understanding, the key role of mathematics in biology would be uncontested.
Another highlight of the last five centuries of geometry was the invention of non-Euclidean geometries (1823–1830). Shocking at first, these geometries unshackled the possibilities of mathematical reasoning from the intuitive perception of space. These non-Euclidean geometries have made significant contributions to biology in facilitating, for example, mapping the brain onto a flat surface (Hurdal et al. 1999; Bowers and Hurdal 2003).
In algebra, efforts to find the roots of equations led to the discovery of the symmetries of roots of equations and thence to the invention of group theory, which finds routine application in the study of crystallographic groups by structural biologists today. Generalizations of single linear equations to families of simultaneous multi-variable linear equations stimulated the development of linear algebra and the European re-invention and naming of matrices in the mid-19th century. The use of a matrix of numbers to solve simultaneous systems of linear equations can be traced back in Chinese mathematics to the period from 300 B.C.E. to 200 C.E. (in a work by Chiu Chang Suan Shu called Nine Chapters of the Mathematical Art; Smoller 2001). In the 19th century, matrices were considered the epitome of useless mathematical abstraction. Then, in the 20th century, it was discovered, for example, that the numerical processes required for the cohort-component method of population projection can be conveniently summarized and executed using matrices (Keyfitz 1968). Today the use of matrices is routine in agencies responsible for making official population projections as well as in population-biological research on human and nonhuman populations (Caswell 2001).
Finally, analysis, including the calculus of Newton and Leibniz and probability theory, is the line between ancient thought and modern thought. Without an understanding of the concepts of analysis, especially the concept of a limit, it is not possible to grasp much of modern science, technology, or economic theory. Those who understand the calculus, ordinary and partial differential equations, and probability theory have a way of seeing and understanding the world, including the biological world, that is unavailable to those who do not.
Conceptual and scientific challenges from biology have enriched mathematics by leading to innovative thought about new kinds of mathematics. Table 1 lists examples of new and useful mathematics arising from problems in the life sciences broadly construed, including biology and some social sciences. Many of these developments blend smoothly into their antecedents and later elaborations. For example, game theory has a history before the work of John von Neumann (von Neumann 1959; von Neumann and Morgenstern 1953), and Karl Pearson's development of the correlation coefficient (Pearson and Lee 1903) rested on earlier work by Francis Galton (1889).
Table 1 Mathematics Arising from Biological Problems
The Present
To see how the interactions of biology and mathematics may proceed in the future, it is helpful to map the present landscapes of biology and applied mathematics.
The biological landscape may be mapped as a rectangular table with different rows for different questions and different columns for different biological domains. Biology asks six kinds of questions. How is it built? How does it work? What goes wrong? How is it fixed? How did it begin? What is it for? These are questions, respectively, about structures, mechanisms, pathologies, repairs, origins, and functions or purposes. The former teleological interpretation of purpose has been replaced by an evolutionary perspective. Biological domains, or levels of organization, include molecules, cells, tissues, organs, individuals, populations, communities, ecosystems or landscapes, and the biosphere. Many biological research problems can be classified as the combination of one or more questions directed to one or more domains.
In addition, biological research questions have important dimensions of time and space. Timescales of importance to biology range from the extremely fast processes of photosynthesis to the billions of years of living evolution on Earth. Relevant spatial scales range from the molecular to the cosmic (cosmic rays may have played a role in evolution on Earth). The questions and the domains of biology behave differently on different temporal and spatial scales. The opportunities and the challenges that biology offers mathematics arise because the units at any given level of biological organization are heterogeneous, and the outcomes of their interactions (sometimes called “emergent phenomena” or “ensemble properties”) on any selected temporal and spatial scale may be substantially affected by the heterogeneity and interactions of biological components at lower and higher levels of biological organization and at smaller and larger temporal and spatial scales (Anderson 1972, 1995).
The landscape of applied mathematics is better visualized as a tetrahedron (a pyramid with a triangular base) than as a matrix with temporal and spatial dimensions. (Mathematical imagery, such as a tetrahedron for applied mathematics and a matrix for biology, is useful even in trying to visualize the landscapes of biology and mathematics.) The four main points of the applied mathematical landscape are data structures, algorithms, theories and models (including all pure mathematics), and computers and software. Data structures are ways to organize data, such as the matrix used above to describe the biological landscape. Algorithms are procedures for manipulating symbols. Some algorithms are used to analyze data, others to analyze models. Theories and models, including the theories of pure mathematics, are used to analyze both data and ideas. Mathematics and mathematical theories provide a testing ground for ideas in which the strength of competing theories can be measured. Computers and software are an important, and frequently the most visible, vertex of the applied mathematical landscape. However, cheap, easy computing increases the importance of theoretical understanding of the results of computation. Theoretical understanding is required as a check on the great risk of error in software, and to bridge the enormous gap between computational results and insight or understanding.
The landscape of research in mathematics and biology contains all combinations of one or more biological questions, domains, time scales, and spatial scales with one or more data structures, algorithms, theories or models, and means of computation (typically software and hardware). The following example from cancer biology illustrates such a combination: the question, “how does it work?” is approached in the domain of cells (specifically, human cancer cells) with algorithms for correlation and hierarchical clustering.
Gene expression and drug activity in human cancer.
Suppose a person has a cancer. Could information about the activities of the genes in the cells of the person's cancer guide the use of cancer-treatment drugs so that more effective drugs are used and less effective drugs are avoided? To suggest answers to this question, Scherf et al. (2000) ingeniously applied off-the-shelf mathematics, specifically, correlation—invented nearly a century earlier by Karl Pearson (Pearson and Lee 1903) in a study of human inheritance—and clustering algorithms, which apparently had multiple sources of invention, including psychometrics (Johnson 1967). They applied these simple tools to extract useful information from, and to combine for the first time, enormous databases on molecular pharmacology and gene expression (http://discover.nci.nih.gov/arraytools/). They used two kinds of information from the drug discovery program of the National Cancer Institute. The first kind of information described gene expression in 1,375 genes of each of 60 human cancer cell lines. A target matrix T had, as the numerical entry in row g and column c, the relative abundance of the mRNA transcript of gene g in cell line c. The drug activity matrix A summarized the pharmacology of 1,400 drugs acting on each of the same 60 human cancer cell lines, including 118 drugs with “known mechanism of action.” The number in row d and column c of the drug activity matrix A was the activity of drug d in suppressing the growth of cell line c, or, equivalently, the sensitivity of cell line c to drug d. The target matrix T for gene expression contained 82,500 numbers, while the drug activity matrix A had 84,000 numbers.
These two matrices have the same set of column headings but have different row labels. Given the two matrices, precisely five sets of possible correlations could be calculated, and Scherf et al. calculated all five. (1) The correlation between two different columns of the activity matrix A led to a clustering of cell lines according to their similarity of response to different drugs. (2) The correlation between two different columns of the target matrix T led to a clustering of the cell lines according to their similarity of gene expression. This clustering differed very substantially from the clustering of cell lines by drug sensitivity. (3) The correlation between different rows of the activity matrix A led to a clustering of drugs according to their activity patterns across all cell lines. (4) The correlation between different rows of the target matrix T led to a clustering of genes according to the pattern of mRNA expressed across the 60 cell lines. (5) Finally, the correlation between a row of the activity matrix A and a row of the target matrix T described the positive or negative covariation of drug activity with gene expression. A positive correlation meant that the higher the level of gene expression across the 60 cancer cell lines, the higher the effectiveness of the drug in suppressing the growth of those cell lines. The result of analyzing several hundred thousand experiments is summarized in a single picture called a clustered image map (Figure 1). This clustered image map plots gene expression–drug activity correlations as a function of clustered genes (horizontal axis) and clustered drugs (showing only the 118 drugs with “known function”) on the vertical axis (Weinstein et al. 1997).
Figure 1 Clustered Image Map of Gene Expression–Drug Activity Correlations
Plotted as a function of 1,376 clustered genes (x-axis) and 118 clustered drugs (y-axis). From http://discover.nci.nih.gov/external/CIM_example3/cgi_user_matrix.html. (updated 27 April 2000; accessed 7 October 2004). This image is more recent than the published image (Scherf et al. 2000). Used by permission of John N. Weinstein.
What use is this? If a person's cancer cells have high expression for a particular gene, and the correlation of that gene with drug activity is highly positive, then that gene may serve as a marker for tumor cells likely to be inhibited effectively by that drug. If the correlation with drug activity is negative, then the marker gene may indicate when use of that drug is contraindicated.
While important scientific questions about this approach remain open, its usefulness in generating hypotheses to be tested by further experiments is obvious. It is a very insightful way of organizing and extracting meaning from many individual observations. Without the microscope of mathematical methods and computational power, the insight given by the clustered image map could not be achieved.
The Future
To realize the possibilities of effective synergy between biology and mathematics will require both avoiding potential problems and seizing potential opportunities.
Potential problems.
The productive interaction of biology and mathematics will face problems that concern education, intellectual property, and national security.
Educating the next generation of scientists will require early emphasis on quantitative skills in primary and secondary schools and more opportunities for training in both biology and mathematics at undergraduate, graduate, and postdoctoral levels (CUBE 2003).
Intellectual property rights may both stimulate and obstruct the potential synergy of biology and mathematics. Science is a potlatch culture. The bigger one's gift to the common pool of knowledge and techniques, the higher one's status, just as in the potlatch culture of the Native Americans of the northwest coast of North America. In the case of research in mathematics and biology, intellectual property rights to algorithms and databases need to balance the concerns of inventors, developers, and future researchers (Rai and Eisenberg 2003).
A third area of potential problems as well as opportunities is national security. Scientists and national defenders can collaborate by supporting and doing open research on the optimal design of monitoring networks and mitigation strategies for all kinds of biological attacks (Wein et al. 2003). But openness of scientific methods or biological reagents in microbiology may pose security risks in the hands of terrorists. Problems of conserving privacy may arise when disparate databases are connected, such as physician payment databases with disease diagnosis databases, or health databases with law enforcement databases.
Opportunities.
Mathematical models can circumvent ethical dilemmas. For example, in a study of the household transmission of Chagas disease in northwest Argentina, Cohen and Gürtler (2001) wanted to know—since dogs are a reservoir of infection—what would happen if dogs were removed from bedroom areas, without spraying households with insecticides against the insect that transmits infection. Because neither the householders nor the state public health apparatus can afford to spray the households in some areas, the realistic experiment would be to ask householders to remove the dogs without spraying. But a researcher who goes to a household and observes an insect infestation is morally obliged to spray and eliminate the infestation. In a detailed mathematical model, it was easy to set a variable representing the number of dogs in the bedroom areas to zero. All components of the model were based on measurements made in real villages. The calculation showed that banishing dogs from bedroom areas would substantially reduce the intensity of infection in the absence of spraying, though spraying would contribute to additional reductions in the intensity of infection. The model was used to do an experiment conceptually that could not be done ethically in a real village. The conceptual experiment suggested the value of educating villagers about the important health benefits of removing dogs from the bedroom areas.
The future of a scientific field is probably less predictable than the future in general. Doubtless, though, there will be exciting opportunities for the collaboration of mathematics and biology. Mathematics can help biologists grasp problems that are otherwise too big (the biosphere) or too small (molecular structure); too slow (macroevolution) or too fast (photosynthesis); too remote in time (early extinctions) or too remote in space (life at extremes on the earth and in space); too complex (the human brain) or too dangerous or unethical (epidemiology of infectious agents). Box 1 summarizes five biological and five mathematical challenges where interactions between biology and mathematics may prove particularly fruitful.
Box 1. Challenges
Here are five biological challenges that could stimulate, and benefit from, major innovations in mathematics.
Understand cells, their diversity within and between organisms, and their interactions with the biotic and abiotic environments. The complex networks of gene interactions, proteins, and signaling between the cell and other cells and the abiotic environment is probably incomprehensible without some mathematical structure perhaps yet to be invented.
Understand the brain, behavior, and emotion. This, too, is a system problem. A practical test of the depth of our understanding is this simple question: Can we understand why people choose to have children or choose not to have children (assuming they are physiologically able to do so)?
Replace the tree of life with a network or tapestry to represent lateral transfers of heritable features such as genes, genomes, and prions (Delwiche and Palmer 1996; Delwiche 1999, 2000a, 2000b; Li and Lindquist 2000; Margulis and Sagan 2002; Liu et al. 2002; http://www.life.umd.edu/labs/Delwiche/pubs/endosymbiosis.gif).
Couple atmospheric, terrestrial, and aquatic biospheres with global physicochemical processes.
Monitor living systems to detect large deviations such as natural or induced epidemics or physiological or ecological pathologies.
Here are five mathematical challenges that would contribute to the progress of biology.
Understand computation. Find more effective ways to gain insight and prove theorems from numerical or symbolic computations and agent-based models. We recall Hamming: “The purpose of computing is insight, not numbers” (Hamming 1971, p. 31).
Find better ways to model multi-level systems, for example, cells within organs within people in human communities in physical, chemical, and biotic ecologies.
Understand probability, risk, and uncertainty. Despite three centuries of great progress, we are still at the very beginning of a true understanding. Can we understand uncertainty and risk better by integrating frequentist, Bayesian, subjective, fuzzy, and other theories of probability, or is an entirely new approach required?
Understand data mining, simultaneous inference, and statistical de-identification (Miller 1981). Are practical users of simultaneous statistical inference doomed to numerical simulations in each case, or can general theory be improved? What are the complementary limits of data mining and statistical de-identification in large linked databases with personal information?
Set standards for clarity, performance, publication and permanence of software and computational results.
This paper is based on a talk given on February 12, 2003, as the keynote address at the National Science Foundation (NSF)–National Institutes of Health (NIH) Joint Symposium on Accelerating Mathematical–Biological Linkages, Bethesda, Maryland; on June 12, 2003, as the first presentation in the 21st Century Biology Lecture Series, National Science Foundation, Arlington, Virginia; and on July 10, 2003, at a Congressional Lunch Briefing, co-sponsored by the American Mathematical Society and Congressman Vernon J. Ehlers, Washington, D.C. I thank Margaret Palmer, Sam Scheiner, Michael Steuerwalt, James Cassatt, Mike Marron, John Whitmarsh, and directors of NSF and NIH for organizing the NSF–NIH meeting, Mary Clutter and Joann P. Roskoski for organizing my presentation at the NSF, Samuel M. Rankin III for organizing the American Mathematical Society Congressional Lunch Briefing, and Congressman Bob Filner for attending and participating. I am grateful for constructive editing by Philip Bernstein, helpful suggestions on earlier versions from Mary Clutter, Charles Delwiche, Bruce A. Fuchs, Yonatan Grad, Alan Hastings, Kevin Lauderdale, Zaida Luthey-Schulten, Daniel C. Reuman, Noah Rosenberg, Michael Pearson, and Samuel Scheiner, support from U.S. NSF grant DEB 9981552, the help of Kathe Rogerson, and the hospitality of Mr. and Mrs. William T. Golden during this work. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF.
Joel E. Cohen is at the Laboratory of Populations, Rockefeller and Columbia Universities, New York, New York, United States of America. E-mail: [email protected]
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| 15597117 | PMC535574 | CC BY | 2021-01-05 08:28:09 | no | PLoS Biol. 2004 Dec 14; 2(12):e439 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020439 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020440FeatureBiophysicsEcologyEvolutionEcology's Big, Hot Idea FeatureWhitfield John 12 2004 14 12 2004 14 12 2004 2 12 e440Copyright: © 2004 John Whitfield.2004This 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.Can the emerging field of metabolic ecology explain all life's patterns in one unifying theory, from the metabolic rate of a shark to why biodiversity peaks at the equator?
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Life is complicated. It comes in all sorts of shapes, sizes, places, and combinations, and has evolved a dizzying variety of solutions to the problem of carrying on living. Yet look inside a cell and life takes on, if not simplicity, then at least a certain uniformity—a genetic system based around nucleic acids, for example, and a common set of chemical reactions for turning food into fuel. And looked at in broad swathes, life shows striking generalities and patterns. Every mammal's heart will beat about one billion times in its lifetime. Both within and between species, the density of a population declines in a regular way as the size of individuals increases. And the number of species in all environments declines as you move from the equator towards the poles.
Wouldn't it be good if there were a simple theory that used life's shared fundamentals to explain its large-scale regularities, via its diversity of individuals? In the past few years, a team of ecologists and physicists have come up with just such a theory. At its heart is metabolism: the way life uses energy is, they claim, a unifying principle for ecology in the same way that genetics underpins evolutionary biology. They believe that energy use, in the form of metabolic rate, can be understood from the first principles of physics, and that metabolic rate can explain growth, development, population dynamics, molecular evolution, the flux of chemicals through the environment, and patterns of species diversity—to name a few.
The work, its originators insist, is not a theory of everything for biology, or even ecology. But it can often seem that way. “We're making advances on a broad range of questions almost on a weekly basis,” says James Gillooly, of the University of New Mexico, Albuquerque. “We've been having an awful lot of fun.”
Beneath the Surface
Metabolic ecology, as it has become known, is still controversial. Some think its mathematical foundations are unsound, and that it explains nonexistent trends. It also divides researchers on philosophical lines—those that see life's patterns as fundamental versus those who think that variation is the key, those who think that simple, general ideas can help us understand nature versus those who think that complicated problems require complicated answers. A lot is riding on the debate: “If the theory is right, it's one of the most significant in biology for a long time,” says ecologist David Robinson of the University of Aberdeen. “It would provide a common functional basis for all biodiversity.”
Scientists have known for nearly two centuries that larger animals have relatively slower metabolisms than small ones. A mouse must eat about half its body weight every day not to starve; a human gets by on only 2%. The first theories to explain this trend, developed in the late nineteenth century by the German nutritionist Max Rubner and the French physiologist Charles Richet, were based on the ratio between an animal's surface area, which changes with the square of its length, and its volume, which is proportional to its length cubed. So large animals have proportionately less surface area, lose heat more slowly, and, pound for pound, need less food. The square-versus-cube relationship makes the area of a solid proportional to the two-third power of its mass, so metabolic rate should also be proportional to mass2/3. For many years, most biologists thought that it was.
But in 1932, Max Kleiber, an animal physiologist working at the University of California's agricultural station in Davis, re-examined the question, and found that, for mammals and birds, metabolic rate was mass0.73—closer to three quarters than two thirds. Kleiber looked at animals ranging in size from a rat to a steer. By the mid-1930s, other workers had put together a “mouse to elephant” curve that supported the three-quarter-power law, and by the 1960s, the plot had been extended for everything from microbes to whales, still seeming to show the same relationship. Quarter-power scaling also began to stretch beyond metabolic rate. Biological times, such as lifespan and heart rate, were found to be proportional to mass1/4, and fractions related to one-quarter show up in other scaling relationships: the diameter of the aorta and tree trunks is proportional to mass3/8, for example.
It was, however, much harder to find a theoretical reason for why metabolic rate should be proportional to mass3/4—and more generally, why quarter-power scaling laws should be so prevalent in biology. The impasse meant that by the mid-1980s interest in scaling had waned. But it sparked back into life in 1997, when two ecologists—James Brown of the University of New Mexico, Albuquerque, and his graduate student Brian Enquist, now at the University of Arizona, Tucson—and a physicist, Geoffrey West of the Santa Fe Institute, developed a new explanation of why metabolic rate should equal the three-quarter power of body mass.
West, Brown, and Enquist's theory is based on the structure of biological distribution networks, such as blood vessels in vertebrates and xylem in plants. The trio assumed that metabolic rate equals the rate at which these networks deliver resources, and that evolution has minimized the time and energy needed to get materials from where they are taken up—the lungs or roots, for example—to the cells. They also assumed that, although organisms vary greatly in size, the terminal units in their distribution networks, such as blood capillaries or leaf stalks, do not.
Bigger plants and animals take longer to transport materials, and so use them more slowly. In West, Brown, and Enquist's model, the maximally efficient network that serves every part of a body has a fractal structure, showing the same geometry at different scales. And the number of uniform terminal units in such a network—and so the rate at which resources are delivered to the cells—is proportional to the three-quarter power of body mass.
Pattern versus Variation
Whether metabolic rate really varies with the three-quarter power of body mass is still debated—some researchers still favor two-thirds, others think that no one exponent fits all the data—but a majority of biologists favor three-quarters. And whether the fractal theory really explains the relationship of metabolic rate to body size is also still contentious. In the most wide-ranging critique so far, published this April, two Polish researchers, Jan Kozlowski, of Jagiellonian University, Krakow, and Marek Konarzewski of the University of Bialystok, claimed that the theory's maths could not simultaneously contain both uniform terminal units and three-quarter-power scaling, that large animals built along such lines would have more blood than their bodies could contain, that biological scaling laws were not built around quarter powers, and that biological networks were not generally branching fractals.
“I don't believe there's anything to explain—there's no universal scaling exponent,” says Kozlowski. He is also struck by what is left unexplained when size is accounted for: animals of the same size can still show more than an order of magnitude variation in metabolic rate. “What's striking in nature is the variability,” he says. “There are regularities that call for explanation, but that doesn't mean ignoring the variability is correct.” Kozlowski is the co-author of a theory that relates metabolic rate to cell size and the amount of DNA an organism has, one of several alternative explanations of the scaling of metabolic rate published since West, Brown, and Enquist's model.
The criticisms are serious, says Robinson. “The jury is out—questions about the fundamental maths are worrying a few people.” On the other hand, he says, West, Brown, and Enquist's model seems a plausible template for designing an organism, and its predictions fit real-world data remarkably well. Whether this fit truly captures the physical and chemical mechanisms underlying the patterns remains to be seen; Robinson hopes that criticism can strengthen West, Brown, and Enquist's model, perhaps leading to a new, improved theory.
The metabolic theory's authors are not budging. “We've yet to see a criticism we feel we can't answer pretty readily,” says Brown. Kozlowski and Konarzewski's arguments are based on a misreading of the work, he says, and criticisms that focus on one aspect, such as the structure of mammalian vascular systems, miss the key point, which is generality: “If we're wrong on quarter powers, why do they keep showing up in everything from life-history processes to evolutionary rates?”
From Sharks to Tomatoes
After accounting for size, Brown's group turned its attention to the second most important influence on metabolism: temperature. The effect is exponential, and a 5 °C rise in body temperature equals a roughly 150% rise in metabolic rate. The team built an equation for metabolic rate that combined the mass3/4 term with the Boltzmann factor. The latter is an expression of the probability that two molecules bumping into each other will spark a chemical reaction. The higher the temperature, the greater the probability, and the faster the reaction.
Adding temperature explained much of the variation in metabolic rate that remained after adjusting for size. It also explained some of the metabolic differences between groups. For example, a reptile has a slower metabolic rate than a mammal of the same size. But adjusting for its lower body temperature removes much of the difference, suggesting that the two groups share fundamental metabolic processes. The same even goes for plants and animals. “When you correct for size and temperature, the metabolic rates of a shark, a tomato plant and a tree are remarkably similar,” (Figure 1) says Gillooly, who joined Brown's group as a grad student to work on the temperature question. It's not yet clear what the activation energy represents, says Gillooly. It could be a kind of average for all the hundreds of chemical reactions in metabolism, or maybe the energy needed to get over one crucial hump in the path.
Figure 1 After Correcting for Body Size and Temperature, the Metabolic Rates of a Shark, a Tomato Plant, and a Tree Are Remarkably Similar
(Illustration: Sapna Khandwala)
The metabolic theory's third component, resources, is also something of a black box at this stage. Nutrient supply, the team reasons, is the next most important determinant of metabolic rate, and will account for some of the remaining unexplained variation. As with temperature, the overall effect could be a balance of many processes, or it could be due to one limiting element—the growth of lake phytoplankton is often limited by phosphorus, for example, while for marine phytoplankton iron is usually the crucial nutrient. “It's a work in progress,” says Brown. “But our vision for a metabolic theory of life is ultimately going to include material resource limitation.”
These three things still do not account for all the variation in metabolic rate, but more detailed knowledge of species can yield more precise predictions. Using body size, altitude, and diet, Brian McNab, of the University of Florida, Gainesville, has explained 99.0% of the variation in metabolic rate for birds of paradise (Figure 2), and 99.4% of the rate variation in leaf-nosed bats. Nevertheless, when McNab sees attempts to explain variation in metabolism using a few parameters applied across a wide range of sizes and taxonomic groups, what isn't explained strikes him as forcefully as what is.
Figure 2 Adult Male Raggiana Bird of Paradise (Paradisaea raggiana)
Body size, altitude, and diet account for 99.0% of the variation in the metabolic rates of birds of paradise.
(Photo: Brian McNab)
“I have serious reservations as to whether there is a single relationship for body size and metabolic rate,” he says. “I think we will be able to find generalizations in ecology, but they're not going to be simple—there will be a bunch of clauses and restrictions, and animals have a lot of ways to bend the rules.”
No theory matches data exactly, Brown points out; having a baseline prediction for metabolism lets you identify exceptional cases worthy of further investigation. Viewed from this angle, the metabolic theory is a kind of null hypothesis of how organisms work. “Until you have a theory that makes a prediction, you don't know how to interpret any of the variation,” says Brown. And, he adds, despite this variation, the underlying trends are also meaningful. “There are themes of life that are deep-seated and fundamental.”
All the business of life needs energy. So if you know the rate at which an organism burns fuel—or if you know how big and hot it is, and apply the metabolic theory—you can make a suite of predictions about its biology, such as how fast it will grow and reproduce, and how long it will live.
By correcting for mass and temperature, Brown, Gillooly, and their colleagues believe they have revealed underlying similarities in all the rates of life. The hatching times for egg-laying animals, including birds, fish, amphibians, insects, and plankton, turn out to follow the same relationship—if a fish egg were the same size and temperature as a bird egg, it would take equally long to hatch (Figure 3). The same goes for growth: a tree and a mammal of equal size and temperature would gain mass at the same speed. And size and temperature even explain much of the variation in mortality rates between species—which one might have thought to be strongly dependent on external factors such as predators—perhaps through metabolism's influence on aging processes, such as free-radical damage to the genome.
Figure 3 Apple Snail Eggs
The hatching times for egg-laying animals, including birds, fish, amphibians, insects, and plankton—or even these Apple Snail eggs—turn out to follow the same relationship. (Photo: Gary M. Stolz, U. S. Fish and Wildlife Service)
One Rule for All?
If all organisms work in the same way, understanding individual biology offers an obvious route to explaining nature's patterns—ecological processes become a kind of meta-metabolism. Indeed, the team has used their theory to predict the flux of carbon dioxide through forests—a measure more usually used to determine individual metabolic rate. They have also found that body size and temperature predict the densities and growth rates of populations. So hotter environments should support lower population densities, as each individual consumes resources more quickly, leaving less to go round. One thing that does not scale with a quarter power of body size is the area of animals' home ranges; this increases more or less linearly with body size. But in October, Brown, along with researchers from Princeton University and the Institute of Zoology in London, published a model that brought this, too, into the metabolic theory. They borrowed another trick from physics, using an equation that describes colliding gas molecules to model the interactions between neighboring animals.
Temperature could also explain why biodiversity peaks at the equator, the team believes. Organisms with faster metabolisms have faster mutation rates. So the genomes of smaller, hotter animals change more quickly, and they will also get through their generations more rapidly. One would therefore expect to see more new species created in small organisms and warm environments. The large-scale trend in all these rates—hatching time, individual and population growth, ecosystem metabolism, DNA substitution—is closely proportional to a quarter power of body mass.
In the future, Brown's group plans to examine the dynamics of colonial organisms and societies through the lens of metabolic ecology; instead of capillaries, the terminal units of the networks would become ants, or people. There are also many applied problems within the theory's scope, including some of the most significant human impacts on the biosphere. Carbon emissions and the consequent global warming are increasing both the temperature and nutrient supply. And exploited populations, such as fisheries often show a decrease in individual size, as larger animals are preferentially killed. Both these would tend to speed up biological processes. Another team of United States and Italian researchers has found that the same model that describes the growth of individuals can also predict the growth of tumors, hinting that metabolic ecology may have medical applications.
Brown hopes that metabolic ecology will one day become an uncontroversial part of researchers' toolkits, like the theories population geneticists use to predict changes in the frequencies of genes. Before that happens, both the theory's proponents and its opponents have years of work ahead of them. Adopting the theory may also require a shift in ecologists' worldview. Most ecologists work by carrying out experimental manipulations on small groups of similar organisms: the warblers in a woodland, for example, or the grasses of a meadow. When they build models, they do so from empirical data, not from physical first principles. The philosophy behind metabolic ecology disconcerts many researchers, says Robinson. “A lot of traditional biologists are uncomfortable with thinking about data in these terms.”
Kozlowski doubts that simple theories can make precise predictions about the behavior of biological systems on large scales. He believes that metabolic ecology risks leading the discipline up a blind alley: “If I'm right, and the basic model contains an error, correcting the results will be a very long process. If they're not right, they'll have done a disservice to ecology.”
But many ecologists are more optimistic that some unifying principles of nature can be found, and that metabolic ecology, and the debate around it, is a step in the right direction. Some think the theory may be part of an even grander idea. Stephen Hubbell, of the University of Georgia, is one of the architects of another idea causing a stir among ecologists. Called neutral ecology, it proposes a general explanation of how competition between individuals produces the dynamics of birth, death, and migration seen in ecosystems, and its predictions match closely the abundance and diversity of species in the wild. He believes that metabolic and neutral ecology can become elements of some larger theoretical framework.
“I've never been more excited in my life,” says Hubbell. “Ecology now is like quantum mechanics in the 1930s—we're on the cusp of some major rearrangements and syntheses. I'm having a lot of fun.”
John Whitfield is a freelance science writer based in London, United Kingdom. He is writing a book on energy, biology, and the laws of nature. E-mail: [email protected]
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Further Reading
Allen AP Brown JH Gillooly JF Global biodiversity, biochemical kinetics and the energy equivalence rule Science 2002 297 1545 1548 12202828
Brown JH Gillooly JF Allen AP Savage VM West GB Towards a metabolic theory of ecology Ecology 2004 85 1771 1789 (Part of a forum on metabolic ecology in the August 2004 issue of Ecology .)
Enquist BJ Economo EP Huxman TE Allen AP Ignace DD Scaling metabolism from organisms to ecosystems Nature 2003 423 639 642 12789338
Gillooly JF Brown JH West GB Savage VM Charnov EL Effects of size and temperature on metabolic rate Science 2001 293 2248 2251 11567137
Gillooly JF Charnov EL West GB Savage VM Brown JH Effects of size and temperature on developmental time Nature 2002 417 70 73 11986667
Gillooly JF Allen AP West GB Brown JH Metabolic rate calibrates the molecular clock: Reconciling molecular and fossil estimates of evolutionary divergence Proc Natl Acad Sci U S A 2004 In press
Guiot C Degiorgis PG Delsanto PP Gabriele P Deisboeck TS Does tumor growth follow a “universal law”? J Theor Biol 2003 225 147 151 14575649
Hubbell SP The unified neutral theory of biodiversity and biogeography 2001 Princeton (New Jersey) Princeton University Press 448
Jetz W Carbone C Fulford J Brown JH The scaling of animal space use Science 2004 306 266 268 15472074
Kozlowski J Konarzewski M Is West, Brown and Enquist's model of allometric scaling mathematically correct and biologically relevant? Funct Ecol 2004 18 283 289 (The April 2004 issue of Functional Ecology contains several more papers on scaling and metabolism.)
Kozlowski J Konarzewski M Gawelczyk AT Cell size as a link between noncoding DNA and metabolic rate scaling Proc Natl Acad Sci U S A 2003 100 14080 14085 14615584
McNab BK Ecology shapes bird bioenergetics Nature 2003 426 620 621
McNab BK Standard energetics of phyllostomid bats: The inadequacies of phylogenetic-contrast analysis Comp Biochem Physiol A 2003 135 357 368
West GB Brown JH Enquist BJ A general model for the origin of allometric scaling models in biology Science 1997 276 122 126 9082983
West GB Brown JH Enquist BJ A general model for ontogenetic growth Nature 2001 413 628 631 11675785
Whitfield J All creatures great and small Nature 2001 413 342 344 11574846
| 15597118 | PMC535575 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Dec 14; 2(12):e440 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020440 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020454SynopsisAnimal BehaviorEcologyEvolutionGenetics/Genomics/Gene TherapyZoologyDrosophilaInsectsGetting a Whiff of Speciation by Reinforcement Synopsis12 2004 23 11 2004 23 11 2004 2 12 e454Copyright: © 2004 Public Library of Science.2004This 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 Genetics of Speciation by Reinforcement
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Creating a new species is a bit like climbing a greased flagpole—it's hard to get started and even harder to keep going. Random genetic variations may introduce slight differences between two groups but, without some means to keep them apart, sexual interbreeding will quickly remix the genes and obliterate the differences. Accidents of geography—the rising of mountains or a course-changing river, for instance—can provide physical isolation, which then enables genetic divergence through the accumulation of mutations, either through natural selection or genetic drift.
In contrast, speciation without geographic separation relies on the direct action of natural selection to complete the speciation process by strengthening behavioral differences, a process called reinforcement. One of the most powerful means of completing speciation is through the evolution of mate discrimination.
A study by Daniel Ortiz-Barrientos and colleagues focuses on the genetic underpinnings of mate discrimination in Drosophila. They identify two loci that influence the likelihood that a female will choose to mate with a conspecific male, rather than one of a closely related species.
Drosophila pseudoobscura and D. persimilis exist together along the west coast of the United States (sympatry), but separately elsewhere (allopatry). When together, they hybridize and produce sterile males. While D. pseudoobscura males will court females of both species, females prefer conspecific males. This female preference is stronger in sympatric females, an enhancement that presumably evolved by the direct action of natural selection to prevent females from wasting their reproductive efforts producing sterile sons. This variation allowed the authors to conduct a series of genetic crosses among flies of the same species but from different locations. Because the daughters of discriminating D. pseudoobscura females were just as discriminating as their mothers, Ortiz-Barrientos and colleagues concluded that female mating discrimination was inherited as a dominant trait. Further crosses showed that genes responsible for female preference were on the X and fourth chromosomes, and high-resolution mapping refined their locations sufficiently to allow the identification of likely candidate genes. While more work remains to be done, the most promising genes in both regions appear to be involved with olfaction. The fact that one of them, CG13982, is known to up-regulate the other, bru-3, strengthens the case that these are indeed promising candidates.
Drosophila choosing their mates
According to their findings, the authors propose a novel model of mating discrimination in D. pseudoobscura based on the combined response to auditory and olfactory cues. The first of these two layers, weak, or “basal” mating discrimination, has previously been associated with a set of traits for acoustic recognition and mapped to chromosomal regions that are inverted between the two species. Such inversions prevent recombination from purging alleles, thereby contributing to hybrid male sterility. As a consequence, when these species interbreed, they inexorably produce sterile males. The second layer, elucidated in the current study, is “reinforced” mating discrimination, which appears to be related to olfactory cues. This additional system of discrimination helps the first layer to fully eliminate the inevitable cost of producing sterile males. Once the nascent species have started up that slippery pole, reinforced discrimination could provide the traction needed to reach its top.
| 0 | PMC535576 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2004 Dec 23; 2(12):e454 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020454 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1560259110.1371/journal.pmed.0010062Research ArticleDiabetes/Endocrinology/MetabolismEpidemiology/Public HealthEndocrinologyNutrition and MetabolismObesityCohort studiesPublic HealthShort Sleep Duration Is Associated with Reduced Leptin, Elevated Ghrelin, and Increased Body Mass Index Leptin, Ghrelin, and SleepTaheri Shahrad
1
¤Lin Ling
1
Austin Diane
2
Young Terry
2
Mignot Emmanuel
1
*1Howard Hughes Medical Institute, Stanford UniversityPalo Alto, CaliforniaUnited States of America2Department of Population Health Sciences, University of WisconsinMadison, WisconsinUnited States of AmericaFroguel Philippe Academic EditorCentre National de la Recherche Scientifique Institut de Biologie de LilleFrance
Competing Interests: The authors have declared that no competing interests exist.
Author Contributions: ST, TY, and EM designed the study. ST, DA, TY, and EM analyzed the data. ST, LL, and EM performed the experiments. TY enrolled patients. ST, LL, DA, TY, and EM contributed to writing the paper.
*To whom correspondence should be addressed. E-mail: [email protected]¤Current address: Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology, University of Bristol, Bristol, United Kingdom
12 2004 7 12 2004 1 3 e6226 8 2004 21 10 2004 Copyright: © 2004 Taheri et al.2004This 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.
Sleep, Appetite, and Obesity- What Is the Link?
Sleep Duration Affects Appetite-Regulating Hormones
Background
Sleep duration may be an important regulator of body weight and metabolism. An association between short habitual sleep time and increased body mass index (BMI) has been reported in large population samples. The potential role of metabolic hormones in this association is unknown.
Methods and Findings
Study participants were 1,024 volunteers from the Wisconsin Sleep Cohort Study, a population-based longitudinal study of sleep disorders. Participants underwent nocturnal polysomnography and reported on their sleep habits through questionnaires and sleep diaries. Following polysomnography, morning, fasted blood samples were evaluated for serum leptin and ghrelin (two key opposing hormones in appetite regulation), adiponectin, insulin, glucose, and lipid profile. Relationships among these measures, BMI, and sleep duration (habitual and immediately prior to blood sampling) were examined using multiple variable regressions with control for confounding factors.
A U-shaped curvilinear association between sleep duration and BMI was observed. In persons sleeping less than 8 h (74.4% of the sample), increased BMI was proportional to decreased sleep. Short sleep was associated with low leptin (p for slope = 0.01), with a predicted 15.5% lower leptin for habitual sleep of 5 h versus 8 h, and high ghrelin (p for slope = 0.008), with a predicted 14.9% higher ghrelin for nocturnal (polysomnographic) sleep of 5 h versus 8 h, independent of BMI.
Conclusion
Participants with short sleep had reduced leptin and elevated ghrelin. These differences in leptin and ghrelin are likely to increase appetite, possibly explaining the increased BMI observed with short sleep duration. In Western societies, where chronic sleep restriction is common and food is widely available, changes in appetite regulatory hormones with sleep curtailment may contribute to obesity.
The less people sleep the higher their risk for being overweight. This study shows that sleep duration affects appetite-regulating hormones
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Introduction
In population studies, a dose-response relationship between short sleep duration and high body mass index (BMI) has been reported across all age groups [1–7]. In the largest studied sample, elevated BMI occurred for habitual sleep amounts below 7–8 h [2]. A U-shaped curvilinear relationship between sleep duration and BMI was observed for women, but for men, there was a monotonic trend towards higher BMI with shorter sleep duration. Importantly, a recent prospective study identified a longitudinal association between sleep curtailment and future weight gain [6]. How sleep curtailment may interact with body weight is unknown, but hormones regulating appetite and energy expenditure may be involved.
A number of hormones may mediate the interactions between short sleep duration, metabolism, and high BMI. We hypothesized that the two key opposing hormones in appetite regulation, leptin and ghrelin [8,9], play a significant role in the interaction between short sleep duration and high BMI. Leptin is an adipocyte-derived hormone that suppresses appetite [10]. Ghrelin is predominantly a stomach-derived peptide that stimulates appetite [9,11]. Other mediators of metabolism that may contribute include adiponectin and insulin. Adiponectin is a novel hormone secreted by adipocytes and is associated with insulin sensitivity [12,13]. We investigated the associations among sleep duration (acute and habitual), metabolic hormones, and BMI in the population-based Wisconsin Sleep Cohort Study [14].
Methods
Overview
The institutional review board of the University of Wisconsin Medical School approved all protocols for the study, and informed consent was obtained from all participants. The Wisconsin Sleep Cohort Study is an ongoing longitudinal study of sleep habits and disorders in the general population [14]. Briefly, to construct a defined sampling frame, all employees aged 30–60 y of four state agencies in south central Wisconsin were mailed a survey on sleep habits, health, and demographics in 1989. Mailed surveys were repeated at 5-y intervals. A stratified random sample of respondents was then recruited for an extensive overnight protocol including polysomnography at baseline. Stratification was based on risk for sleep-disordered breathing (SDB), with an oversampling of habitual snorers to ensure an adequate distribution of SDB. Analyses were adjusted for the weighted sampling when appropriate. Recruitment for baseline studies was staggered to conduct seven studies per week; study entry and follow-up time thus varied within the cohort. Exclusion criteria included pregnancy, unstable cardiopulmonary disease, airway cancers, and recent upper respiratory tract surgery. The baseline response rate was 51%, with most refusals due to the inconvenience of sleeping away from home. Follow-up studies have been conducted at 4-y intervals, with up to three follow-up studies to date. Collection of morning, fasted blood was added to the protocol in 1995. Extensive survey and other data available from the sampling frame have been used to evaluate the potential for response and drop out biases.
Figure 1 provides an overview of the study population and the data collected. The sample comprised 1,024 participants with an overnight study and blood sample. A 6-d diary of sleep duration was added as part of a protocol to assess daytime sleepiness after the initiation of the cohort study; these data were available for 721 participants.
Figure 1 Sample Construction and Data Collected
All employees (aged 30–60 y) of four state agencies in south central Wisconsin were mailed surveys starting in 1989 regarding general health and sleep habits. From this population, a stratified random sample of respondents was recruited for an extensive overnight protocol providing polysomnography and sleep questionnaire data and morning, fasted serum for hormone and metabolite measurement. The metabolic hormones measured were ghrelin (856 participants), leptin (1,017 participants), adiponectin (1,015 participants), and insulin (1,014 participants) (see Table 1). Based on scheduling availability, 721 participants completed an added protocol to measure daytime sleepiness that included a 6-d sleep diary, of which 714 reported on naps (see Table 1). See text for further description of the study population and definitions of the sleep measures used.
Table 1 Characteristics of the Sample
n = 1,024 except as noted
a Median (first quartile, third quartile)
LDL, low-density lipoprotein
DOI: 10.1371/journal.pmed.0010062.t001
Data Collection
This investigation is based on Wisconsin Sleep Cohort Study data collected from the mailed sleep surveys, overnight studies, and 6-d sleep diaries. Overnight studies were conducted in laboratory bedrooms, with participants setting their own sleep and rise times. After informed consent was obtained, questionnaires on lifestyle and health history were administered, and height and weight measured. A blood sample was collected shortly after awakening from overnight polysomnography.
Polysomnography
An 18-channel polysomnographic system was used to assess sleep states, and respiratory and cardiac variables [14]. Sleep was studied using electroencephalography, electro-oculography, and chin electromyography (Grass Instruments, Quincy, Massachusetts, United States). Continuous measurement of arterial oxyhemoglobin saturation by pulse oximetry (Ohmeda, Englewood, Colorado, United States), oral and nasal airflow, nasal air pressure, and thoracic cage and abdominal respiratory motion (Respitrace Ambulatory Monitoring, Ardsley, New York, United States) were used to assess SDB [14]. Each 30-s interval of the polysomnographic record was scored for sleep stage and SDB using standard criteria [14]. The average number of apneas and hypopneas per hour of measured sleep, the apnea-hypopnea index (AHI), was the measure for SDB. For analyses including AHI as a covariate, participants were excluded if they had sleep studies with less than 4 h of usable polysomnography, or if they were receiving treatment for SDB.
Polysomnographic measures of acute sleep
Polysomnographic measures of sleep duration just prior to blood sampling were used to evaluate degree of “acute sleep restriction.” “Total sleep time” was total hours of polysomnographically defined sleep. “Wake after sleep onset” (WASO) was hours of wake time after three epochs of sleep had occurred. “Sleep efficiency” was total sleep time divided by time from lights out until arising in the morning.
Self-reported sleep measures of chronic sleep
Two variables were used to evaluate degree of “chronic sleep restriction” by estimating average nightly sleep duration: (i) “usual sleep” (from questionnaires) and (ii) “average nightly sleep” (from sleep diaries).
Questionnaire
Usual sleep was estimated from the following questions: how many hours of sleep do you usually get in (a) a workday night? (b) a weekend or non-work night? These questions were included in all mailed surveys and were added to questionnaires completed at the overnight study in 1998. For participants studied after 1998, data from questionnaires administered at the overnight study were used (58%); for the remainder of the sample, data from the mailed survey closest in time to the overnight study were used. Usual sleep was calculated as (5 × workday sleep + 2 × weekend sleep)/7.
Sleep diary
Average sleep duration was also estimated using a 6-d sleep diary, kept by 721 participants as part of an added protocol to measure daytime sleepiness. The median time between the blood collection and completion of the diary was 18 d. Almost all diaries (97%) were completed within 6 mo of blood sampling. In diaries, participants recorded the time they went to bed and arose each day, and the duration of any naps. “Average nightly sleep” was calculated as the sum of the hours between bedtime and arising divided by six. “Average nightly sleep plus naps” added naps to the above sum.
The Relationship between BMI and Sleep Duration
For the analysis of the association of BMI and sleep duration, the sample comprised 1,040 participants with at least one 6-d sleep diary. Of these, 4-, 8-, and 12-y follow-up studies had been completed by 397, 179, and 11 participants respectively (1,828 visits), providing repeated measures data for greater analytic efficiency and precision.
Hormone Assays
Following overnight fasting, serum was collected soon after awakening and stored at −70 °C. All samples were assayed in duplicate. It was not possible to assay samples from all participants in all assays because of the volume of serum available; this particularly affected the ghrelin assay, which required the most volume. Leptin and insulin were determined using enzyme-linked immunoassays (ELISA; Linco Research, St. Charles, Missouri, United States). Total ghrelin and adiponectin were measured by radioimmunoassay (Linco Research). Sensitivity for the leptin and insulin enzyme-linked immunoassays was 0.5 ng/ml and 2 μU/ml, respectively. Sensitivity for the ghrelin and adiponectin radioimmunoassays was 10 pg/ml and 1 ng/ml, respectively. Intra- and inter-assay variations were all less than 5% and less than 10%, respectively. The quantitative insulin sensitivity check index (QUICKI) was 1/(log (I) + log (G)), where I is fasting insulin and G is fasting glucose [15,16].
Statistical Analysis
All analyses were cross-sectional and performed using SAS/STAT 8.2. Leptin, ghrelin, and adiponectin were square-root transformed and insulin log transformed based on the distribution of residuals from the multivariate regression models. We evaluated the relationship of age, sex, BMI, and time of storage of blood sample on hormones using multiple regression. Partial correlations adjusted for age, sex, and BMI were calculated for hormones and QUICKI, with and without control of other potential confounders. The relationships between hormones and sleep were evaluated using multiple linear regression after control for potential confounders including age, sex, BMI, SDB, and morningness tendencies (as measured using the Horne-Ostberg questionnaire, an indirect surrogate of earlier rising time). In all analyses involving insulin, glucose, and QUICKI (but not leptin, ghrelin, and adiponectin), participants with diabetes (self-reported diagnosis, or currently taking insulin or diabetic medications, or with glucose >300 mg/dl) were excluded. Participants with SDB were not removed from the analyses shown. When controlling for AHI in models, participants who used continuous positive airway pressure or who had inadequate sleep were excluded. Because controlling for AHI did not significantly change the sleep-hormone regression coefficients, these analyses are not shown. The relationship of BMI with average nightly sleep was evaluated using a quadratic fit. This was examined using multiple visits (n = 1,828) from 1,040 participants with sleep diary data available. Mixed modeling techniques were used to account for within-subject correlation for participants with multiple visits. SAS procedure mixed was used for modeling and hypothesis testing using robust standard errors and a compound symmetric within-subject correlation structure. All reported p values are two-sided. For illustrative purposes, changes in leptin, ghrelin, and BMI for different sleep amounts were calculated at the average values and sex distribution of the relevant sample.
Results
Table 1 shows the characteristics of the sample, unadjusted for the weighted sampling scheme. Figure 2 shows the mean BMI for 45-min intervals of average nightly sleep after adjustment for age and sex. We found a significant U-shaped curvilinear relationship between average nightly sleep and BMI after adjustment for age and sex (average nightly sleep coefficient = −2.40, p = 0.008; (average nightly sleep)2 coefficient = 0.156, p = 0.008; the two coefficients define a curve). The minimum BMI was predicted at 7.7 h of average nightly sleep. The most striking portion of the curve was for persons sleeping less than 8 h (74.4% of the sample), where increased BMI was proportional to decreased sleep. An increase in BMI from 31.3 to 32.4 (+3.6%) corresponded approximately to an average nightly sleep duration decrease from 8 h to 5 h, as estimated at the mean age (53.1 y) and sex distribution (54.4% male) of the sample with available sleep diary data.
Figure 2 The Relationship between BMI and Average Nightly Sleep
Mean BMI and standard errors for 45-min intervals of average nightly sleep after adjustment for age and sex. Average nightly sleep values predicting lowest mean BMI are represented by the central group. Average nightly sleep values outside the lowest and highest intervals are included in those categories. Number of visits is indicated below the standard error bars. Standard errors are adjusted for within-subject correlation.
Table 2 shows the association of each of the hormones, glucose, and QUICKI with age, sex, and BMI. Serum ghrelin, leptin, adiponectin, insulin, and glucose were significantly correlated with BMI and sex. Storage time had significant effects on some but not all variables; in all cases, however, effect size was small, and the effect was corrected for in all calculations. Adiponectin and glucose were correlated with age. Table 3 shows partial correlations among the measured and calculated variables, adjusted for age, sex, and BMI. All correlations agree with previous studies and validate our assays and population sample. We also examined several potential confounders to be later controlled for, if needed, in our models. Identified relationships included: ghrelin with high-density lipoprotein (HDL), alcohol intake, and creatinine; leptin with diastolic blood pressure, smoking, and blood urea nitrogen (BUN); adiponectin with HDL, triglycerides, uric acid, and BUN; insulin with HDL, triglycerides, uric acid, and smoking; glucose with HDL, triglycerides, uric acid, alcohol intake, BUN, and creatinine; QUICKI with HDL, triglycerides, uric acid, smoking, and BUN. When diabetics (diagnosed) and participants with high glucose (glucose >300 mg/dl) were removed from this analysis, all relationships remained significant except for the correlation between ghrelin and adiponectin, and ghrelin and insulin.
Table 2 Relationships among Metabolic Hormone Levels, Age, Sex and BMI
Each row represents a single regression analysis
a All models also included a term for time since sample was drawn (time of storage). A change of one unit of the predictor variables (age, female indicator) results in a change of the coefficient's size and direction in the transformed variable
b Square-root transformation used in these models
c Natural logarithm transformation used in these models
d Participants with diabetes were excluded (self-reported diagnosis, currently taking insulin or diabetic medications, or glucose >300 mg/dl)
DOI: 10.1371/journal.pmed.0010062.t002
Table 3 Partial Pearson Correlations of Metabolic Hormones and QUICKI after Adjustment for Sex, Age, and BMI
Shown are unstandardized correlation coefficients
a Square-root transformation
b Natural logarithm transformation
c A total of 843 participants had data for all variables; correlations were calculated within this subset
d QUICKI = 1/(log(I) + log(G)) where I is fasting insulin and G is fasting glucose
DOI: 10.1371/journal.pmed.0010062.t003
Using polysomnography to measure objective sleep immediately prior to blood sampling, ghrelin correlated significantly with total sleep time, sleep efficiency, and WASO (Table 4). Using questionnaire and diary data estimating chronic sleep, significant correlations were found between leptin and average nightly sleep (with and without naps) and usual sleep amounts (Table 4). A significant correlation was also observed between ghrelin and average nightly sleep plus naps. These relationships were consistently found when other possible confounding factors such as medications, hypertension, AHI, and factors listed above were included in the statistical model (analysis not shown). In unadjusted models, leptin was significantly correlated with total sleep time and average weekly sleep with naps, and ghrelin was significantly correlated with sleep efficiency and WASO. Figure 3A shows the mean leptin levels for half-hour increments of average nightly sleep after adjustment for age, sex, BMI, and time of storage (see Table 2). In the multiple regression model (see Table 4), there was a significant increasing trend in leptin for average nightly sleep duration (p = 0.01). When evaluated at the average values and sex distribution of our sample, a decrease from 8 to 5 h in average nightly sleep was associated with a predicted 15.5% decrease in leptin. Figure 3B shows the mean ghrelin levels for half-hour increments of total sleep time after adjustment for age, sex, BMI, and time of storage (see Table 2). In the multiple regression model (see Table 4), there was a significant decreasing trend in ghrelin with total sleep time (p = 0.008). When evaluated at the average values and sex distribution of our sample, a decrease from 8 to 5 h of polysomnographically defined total sleep time was associated with a predicted 14.9% increase in ghrelin. There was no significant correlation between sleep duration (acute or chronic) and serum adiponectin, insulin, glucose, or QUICKI. Results of our analyses were unchanged after adjusting for the weighted sampling scheme.
Figure 3 The Association between Sleep Duration and Serum Leptin and Ghrelin Levels
(A) Mean leptin levels and standard errors for half-hour increments of average nightly sleep after adjustment for age, sex, BMI, and time of storage (see Table 2). Average nightly sleep values outside the lowest and highest intervals are included in those categories. Sample sizes are given below the standard error bars. The y-axis uses a square-root scale. Data derived from 718 diaries because three participants had missing leptin data.
(B) Mean ghrelin levels and standard errors for half-hour increments of total sleep time after adjustment for age, sex, BMI, and time of storage (see Table 2). Total sleep time values outside the lowest and highest intervals are included in those categories. The y-axis uses a square-root scale. Note that ranges for total sleep time amounts are typically shorter than those for average nightly sleep amounts (A; see Figure 1), and do not correlate strongly (see text).
Table 4 Relationships between Sleep Variables and Metabolic Hormones, Adjusted for Age, Sex, BMI, and Time of Sample Storage
Each coefficient is from a separate regression model. The first three sleep variables were derived from nighttime polysomnography data; average nightly sleep (with and without naps) was derived from sleep diary data, and usual sleep was derived from questionnaire data
a Sample sizes for all polysomnography-derived sleep variables (sleep efficiency, total sleep time, and WASO)
b Square-root transformation used in these models
c Outliers excluded. One participant was removed from all models because of a very high leptin level and low BMI (21 kg/m2). For the leptin/average nightly sleep and leptin/average nightly sleep with naps models, two participants were removed: one was a large outlier (very high leptin level), and one had 6-d diary sleep of less than 12 h, which was influential. Removing these two points resulted in a slightly smaller, less significant coefficient. For the leptin/usual sleep model, one outlier with a large leptin value was removed. Again, this resulted in a slightly smaller, less significant coefficient
d Natural logarithm transformation used in these models
e Participants with diabetes were excluded (self-reported diagnosis, currently taking insulin or diabetic medications, or glucose >300 mg/dl, n = 78)
DOI: 10.1371/journal.pmed.0010062.t004
Discussion
We found that habitual sleep duration below 7.7 h was associated with increased BMI, similar to findings in other studies including children [1,17], adolescents [5], and adults [2,3]. We also report a significant association of sleep duration with leptin and ghrelin that is independent of BMI, age, sex, SDB, and other possible confounding factors (analysis not shown for SDB and other confounders). Short sleep duration was associated with decreased leptin and increased ghrelin, changes that have also been observed in reaction to food restriction and weight loss and are typically associated with increased appetite. These hormone alterations may contribute to the BMI increase that occurs with sleep curtailment.
Previous studies have shown that both acute sleep deprivation [18] and chronic partial sleep deprivation (sleep restriction) [19] can cause a decrease in serum leptin concentrations. These studies, however, were performed under highly controlled laboratory circumstances. Our results validate the association of decreased leptin with decreased sleep time in a large sample of adults under real-life conditions and, now, indicate a role for ghrelin. Leptin deficiency increases appetite and produces obesity [8,20]. Leptin administration suppresses food intake and reduces energy expenditure [21,22]. Importantly, low leptin as observed with sleep loss has a greater impact on appetite than high leptin levels, which are associated with leptin resistance, as occurs with obesity [8].
Levels of ghrelin, a potent stimulator of appetite [23,24,25], were higher in those with shorter sleep. Ghrelin levels are also positively associated with hunger ratings [26], but decrease with increased BMI (see Table 2). In one study, after 3 mo of dietary supervision, a reduction in BMI of approximately 5% was associated with a 12% increase in ghrelin and a 15% decrease in leptin [27]. These changes, in participants of similar BMI to our sample and presumably producing increased appetite, are comparable to those observed with sleep loss of 2–3 h/night. With sleep loss, however, relatively high ghrelin and low leptin levels are associated with increased BMI. These changes can be hypothesized to play a contributory, rather than compensatory, role in the development of overweight and obesity with sleep restriction.
Our findings are strengthened by the large and well-characterized population-based sample, attention to bias and confounding factors, and in-laboratory polysomnographic data. The changes in hormones with sleep duration were consistent and of significant magnitude. They also represent the first demonstration of a correlation between peripheral hormone levels and both self-reported (questionnaire and diary data) and polysomnographically determined sleep amounts in a general population sample. While these data are more comprehensive than previous studies on this topic, some misclassification error may exist because of intra-person variability or limitations of polysomnographic measurement. Little is known about the stability of self-reported sleep duration and polysomnographic measures of sleep duration over time. We examined the stability of the self-reported sleep duration data, and found these measures to be stable. For 860 participants who completed three surveys, the mean (standard deviation) of intra-person differences in usual sleep for two 5-y periods was 0.10 (0.47) h. For 190 participants with at least three sleep diaries, the mean (standard deviation) of intra-person differences in average nightly sleep for two 4-y intervals was 0.09 (0.41) h. Furthermore, the subjectively reported hours of usual sleep and the diary-derived average nightly sleep values were highly correlated (r = 0.55, p < 0.001). One-night polysomnographically defined total sleep time had a similar intra-person mean difference (0.10 h), with a somewhat larger standard deviation (0.68) for 713 participants with at least three sleep studies.
Elevated ghrelin mainly correlated with acute sleep loss as measured by polysomnography immediately prior to blood sampling (see Table 4; Figure 3B), while reduced leptin correlated with chronic sleep restriction indicated by self-reported sleep measures (see Table 4; Figure 3A). Measures of usual and one-night polysomnographically defined sleep time were only weakly, but statistically significantly, correlated (r = 0.12, p < 0.001), supporting the concept that these measures reflect long-term and short-term changes in sleep amounts, respectively. Our findings are in agreement with the current view that leptin is important in signaling long-term nutritional status while ghrelin has a more significant role in acute hunger. The changes in leptin and ghrelin with sleep restriction could, therefore, provide a powerful dual stimulus to food intake that may culminate in obesity.
Longitudinal and intervention studies will be necessary to define further the link between sleep curtailment and increased BMI. Only total ghrelin was measured, since active octanoylated ghrelin is unstable. Although both total and active ghrelin appear to be regulated in a similar and parallel manner, future studies will need to focus on measurement of the biologically active form. Other potentially important appetite regulatory hormones, such as PYY 3–36 [28], were not measured. Measures of appetite were not included in the Wisconsin Sleep Cohort Study overnight protocol; therefore, a direct examination of the relationship between the observed hormone changes with sleep duration and alterations in appetite was not possible.
Hormone measurements were all performed on a single fasted, morning sample and may not reflect the 24-h profile. It is possible that participants with shorter sleep woke up earlier and that hormone differences may be partially related to circadian time. Leptin and ghrelin levels rise slightly during the night [29], and this could result in higher hormone levels in short sleepers. This may be an issue for ghrelin, as levels increased with acute sleep restriction. It is, however, unlikely to play a role in the leptin finding, since lower levels were found with chronic but not acute sleep restriction. Additionally, studies have shown a high correlation between morning, fasting leptin and ghrelin levels and 24-h mean profile [29,30]. We also found that the ghrelin and leptin changes were unaffected by morningness tendencies. The fact that studies investigating the diurnal profile of these hormones found similar hormonal changes over the entire 24-h period after experimental sleep restriction also corroborates our results [18,19]. The robustness of our findings and similar observations from smaller controlled studies [18,19] also suggest that our statistically significant results are unlikely to be a reflection of the number of analyses carried out.
Animal studies have suggested a link between sleep and metabolism [31,32]. In rats, prolonged, complete sleep deprivation increased both food intake and energy expenditure. The net effect was weight loss and, ultimately, death [33]. Rats fed a high protein-to-calorie diet had accelerated weight loss, compared to sleep-deprived rats fed calorie-augmented (fatty) diets [32]. Food consumption remained normal in sleep-deprived rats fed protein-rich diets, but increased 250% in rats fed calorie-rich diets. Preference for fatty foods has also been reported anecdotally in sleep-deprived humans [32,34]. Sleep deprivation may thus increase not only appetite but also preference for lipid-rich, high-calorie foods. Animal experiments that have found weight loss after prolonged sleep deprivation have to be interpreted in the context of a stressful procedure producing intense sleep debt [35,36], which may interfere with adequate food intake. From our study, we hypothesize that the moderate chronic sleep debt associated with habitual short sleep is associated with increased appetite and energy expenditure. In societies where high-calorie food is freely available and consumption uncontrolled, after milder chronic sleep restriction, the equation may be tipped towards food intake for high-calorie food rather than expenditure, culminating in obesity. Short sleepers may also have more time to overeat.
Sleep loss from a baseline of 7.7 h was associated with a dose-dependent increase in BMI. This was the predominant effect in a population increasingly curtailing sleep [37]. Sleep greater than 7.7 h, however, was also associated with increased BMI. Patients with SDB (a pathology associated with increased BMI) may spend a longer time in bed to compensate for fragmented sleep; however, controlling for AHI did not change the curvilinear BMI–sleep association. Another possibility is that in long sleepers, reduced energy expenditure due to increased time in bed has a greater impact than reduced food intake. In favor of this hypothesis, long sleepers exercise less [38]. In our data, we found that the odds ratio of high levels of self-reported exercise (>7 h/wk), based on a single survey question, decreased with increased sleep time, but controlling for this variable also did not change our findings (analyses not shown).
Insulin resistance with sleep deprivation has been reported in a laboratory study of young, healthy volunteers [39]. When controlling for BMI, we found no significant correlation between insulin, glucose, or adiponectin levels and various measures of sleep duration. Also, there was no significant correlation between QUICKI (or the homeostatic model assessment HOMA [16]; data not shown) and sleep duration. This may be due to difficulties in detecting small effects on glucose tolerance under less-controlled conditions of large population studies.
Our results demonstrate an important relationship between sleep and metabolic hormones. The direct effect of chronic partial sleep loss on food intake, energy expenditure, and obesity now needs to be explored. Altering sleep duration may prove to be an important adjunct to preventing and treating obesity.
Patient Summary
Why Was This Study Done?
Recent studies have shown that there is a link between sleeping less and gaining weight. It isn't clear why there is this link—perhaps, for example, those who are awake in the middle of the night tend to head for the refrigerator for a snack. Another possibility is that the amount of sleep that we have might affect the hormones that control our appetite. We know that extreme sleep deprivation affects the level of leptin, a hormone that controls appetite. Here, the researchers wanted to study levels of leptin and other appetite hormones under more normal conditions, in people with a range of sleeping habits.
What Did the Researchers Do?
The researchers studied participants in a large sleep study that has been going on in Wisconsin for over 15 years. These participants have been filling out questionnaires about their sleep habits and their health in general, have kept sleep diaries, and have occasionally spent a night in the laboratory, where researchers studied their sleep in more detail. After sleeping overnight in the laboratory, the participants gave blood samples, which were tested for hormones.
What Did They Find?
The researchers found that people who slept less were on average heavier. And people who slept less had lower levels of leptin and higher levels of ghrelin, another hormone that controls food intake.
What Does This Mean?
The combination of low leptin and high ghrelin is likely to increase appetite. In other words, short sleep might stimulate appetite, which increases weight.
What Next?
Future studies need to examine the effect of regular short sleeping hours on appetite, food intake, and obesity. These studies could help to answer the question of whether the rise in obesity in many societies is partly due to the fact that people are sleeping less. And it seems well worth testing whether increasing sleep to seven or eight hours per night could help people to lose weight.
Additional Online Information
National Sleep Foundation Web page on obesity and sleep: http://www.sleepfoundation.org/features/obesity.cfm
World Federation of Sleep Research Societies: http://www.wfsrs.org/homepage.html
Red en Medicina del Sueño: http://www.rems.com.ar/
European Sleep Research Society: http://www.esrs.org/
This work was funded by National Institutes of Health grants HL071515 and MH073435 (EM) and HL62252, AG14124, and RR03186 (TY). ST (research associate) and EM (investigator) are funded by the Howard Hughes Medical Institute. The grant-awarding bodies had no role in the study design, data analysis, decision to publish, or manuscript preparation and content. We thank participants in the Wisconsin Sleep Cohort Study. We are grateful to L. Evans, J. Zhang. R. A. Bell, and J. Paterno for technical assistance.
Citation: Taheri S, Lin L, Austin D, Young T, Mignot E (2004) Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med 1(3): e62.
Abbreviations
AHIapnea-hypopnea index
BMIbody mass index
BUNblood urea nitrogen
HDLhigh-density lipoprotein
QUICKIquantitative insulin sensitivity check index
SDBsleep-disordered breathing
WASOwake after sleep onset
==== Refs
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| 15602591 | PMC535701 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 7; 1(3):e62 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010062 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0010068SynopsisDiabetes/Endocrinology/MetabolismEpidemiology/Public HealthNutritionSleep disordersNutrition and MetabolismObesityCohort studiesSleep Duration Affects Appetite-Regulating Hormones Synopsis12 2004 7 12 2004 1 3 e68Copyright: © 2004 Public Library of Science.2004This 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.
Short Sleep Duration Is Associated with Reduced Leptin, Elevated Ghrelin, and Increased Body Mass Index
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Some of us, when awake in the middle of the night, feel an urge to visit the kitchen. This could explain results of previous studies that have shown a link between short sleep duration and high body mass index (BMI). But a study by Emmanuel Mignot and colleagues suggests that it's not just the additional snacking opportunities that make short sleepers more likely to be overweight.
Intrigued by the connection between sleep and BMI, and by recent studies showing that sleep deprivation in laboratory settings can cause a decrease in serum levels of leptin, a hormone known to control appetite, Emmanuel Mignot and colleagues set out to study the levels of various hormones known to regulate appetite and energy expenditure under “real life” conditions.
They took advantage of the Wisconsin Sleep Cohort Study, an ongoing longitudinal study of sleep habits and disorders in the general population. The study began in 1989, when researchers mailed state employees aged 30–60 years a survey on sleep habits, health, and demographics. Mail surveys were repeated at 5-year intervals, and some of the respondents were recruited to sleep a night in the laboratory and undergo various tests. A number of participants were also asked to keep a sleep diary for 6 days. The study has already shown connections between sleep apnea and hypertension, and between menopause and sleep-disordered breathing.
For their study, Mignot and colleagues measured sleep duration (habitual and immediately prior to blood sampling), BMI, and pre-breakfast blood hormone levels in 1,024 participants. Consistent with previous studies, they found that in individuals who sleep less than 8 hours (74% of all participants), BMI was inversely proportional to sleep duration. In addition, short sleep was associated with low leptin and high ghrelin levels (ghrelin is a hormone thought to stimulate food intake).These hormonal differences are likely to increase appetite, which could be responsible for the increased BMI in short sleepers.
Peaceful sleep (Photo: Sharad Taheri)
These findings could explain, at least in part, why societies in which excess calories are much easier to come by than a good night's sleep are more prone to obesity. Mignot and colleagues plan to test this in intervention studies where they make people sleep more and measure the effects on body mass. “Good sleep, healthy eating habits, and regular exercise each may have important roles in fighting obesity in modern society,” suggests Mignot.
| 0 | PMC535702 | CC BY | 2021-01-05 10:38:00 | no | PLoS Med. 2004 Dec 7; 1(3):e68 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010068 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-411556657610.1186/1471-244X-4-41Research ArticleFamily structure and risk factors for schizophrenia: case-sibling study Haukka Jari K [email protected] Jaana [email protected]önnqvist Jouko [email protected] Department of Mental Health and Alcohol Research, KTL, National Public Health Institute, Mannerheimintie 160, FIN-00300 Helsinki, Finland2004 27 11 2004 4 41 41 12 3 2004 27 11 2004 Copyright © 2004 Haukka et al; licensee BioMed Central Ltd.2004Haukka 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
Several family structure-related factors, such as birth order, family size, parental age, and age differences to siblings, have been suggested as risk factors for schizophrenia. We examined how family-structure-related variables modified the risk of schizophrenia in Finnish families with at least one child with schizophrenia born from 1950 to 1976.
Methods
We used case-sibling design, a variant of the matched case-control design in the analysis. Patients hospitalized for schizophrenia between 1969 and 1996 were identified from the Finnish Hospital Discharge Register, and their families from the Population Register Center. Only families with at least two children (7914 sibships and 21059 individuals) were included in the analysis. Conditional logistic regression with sex, birth cohort, maternal schizophrenia status, and several family-related variables as explanatory variables was used in the case-sibling design. The effect of variables with the same value in each sibship was analyzed using ordinary logistic regression.
Results
Having a sibling who was less than five years older (OR 1.46, 95% CI 1.29–1.66), or being the firstborn (first born vs. second born 1.62, 1.87–1.4) predicted an elevated risk, but having siblings who were more than ten years older predicted a lower risk (0.66, 0.56–0.79).
Conclusions
Several family-structure-related variables were identified as risk factors for schizophrenia. The underlying causative mechanisms are likely to be variable.
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Background
Several factors relating to family structure have been suggested as risk factors for schizophrenia. They include sibship size, number of older siblings, birth order in the sibship, age difference to older siblings, and age-at-onset, and sex of the affected siblings. These family-related risk factors may provide useful hints about the nature of environmental and genetic risk factors for schizophrenia [1].
In several studies, siblings of female probands have had a higher risk of developing schizophrenia than siblings of male probands in several studies, with the number of cases varying from 123 to 500 [2-7]. The effect of the age-at-onset of proband is less clear. Some studies have detected no effect [5,8], but in one, early age-at-onset of proband predicted a higher risk for siblings [4].
There are mixed results about the effect of birth order on risk. Two studies reported lowest risk for the firstborns [9,10], and in a Swedish population based study with 167 cases, male subjects fourth or higher in birth order had over a three-fold risk compared to males with lower birth order [11]. On the other hand, a very large scale Danish cohort study involving 2669 cases showed no effect for birth order [12]. A significantly higher risk for first-born males was reported from a Finnish cohort study with 100 cases [13], and in accordance with this, a recent study from Pakistan with 453 cases reported highly significant excess of firstborns among patients with schizophrenia [14].
The number and age of older siblings at the time of birth is hypothesized to be connected to the risk of schizophrenia through infection pressure. In a Swedish register-based study (270 cases), having siblings who were 3–4 years older was associated with increased risk (relative was 2.02) [10]. The Danish cohort showed no effect of birth order on risk, but found that having less than two years age difference to the nearest older or younger sibling increased the risk of schizophrenia [12]. However, in an Irish case-control study with 2969 cases and 5904 controls, the number of older siblings did not predict a diagnosis of schizophrenia over neurosis [15]. Several recent studies have found that advanced paternal age is a risk factor for schizophrenia [16-18].
All risk factors concerning birth order, family size, number of older or younger siblings, and paternal and maternal age are correlated. For example, the number of older siblings is dependent on both the individual's birth order and family size, which are connected to maternal age. If all these correlated variables are not in the model simultaneously, spurious associations may be found. Therefore, a large, population-based data set that contains a sufficient number of sibships with variable structures is needed to analyze family-related variables. Our aim was to investigate which of these variables were risk factors for schizophrenia in Finland among those who were born between 1950 and 1976.
Methods
Register data, study design and statistical analyses
Using the Finnish Hospital Discharge Register of Finland, we obtained information on all hospitalizations with a diagnosis of schizophrenia (ICD-8 295*, ICD-9 295*, ICD-10 F20*). The information was sought for all Finns born between 1950 and 1976 for the period between 1969 and 1996. Sibship was defined as a group of individuals having the same mother. Since many of the variables we examined are related to maternal characteristics, we considered it essential that each sibship has the same mother. It should be noticed that generally the mother has a stronger influence on the child than the father especially in early childhood: e.g. mother's womb provides the fetal environment, and after birth the mother has more of a close physical relationship to the child through breast feeding. Siblings and parents of the probands were identified using the data from the Population Register Centre, and their information was then linked back to the Hospital Discharge Register to obtain data on relatives' hospitalizations. Only those who were born between 1950 and 1976 were qualified as probands, but their younger siblings were included in analyses as family members.
We assumed our ascertainment was complete, because the two registers used, the Population Register and the Finnish Hospital Discharge Register, are nationwide and accurate [19]. The reliability of register diagnoses of schizophrenia is good [20-22] and according to previous studies, more than 95 percent of individuals with schizophrenia receive hospital treatment at some point in their lives [22].
Altogether, we identified 17549 sibships with 35481 siblings, of whom 31004 were born between 1950 and 1976. There were 12 siblings in the largest family, and the highest number of cases in one family was six (Table 3). All families that contained only one child (n = 7947) were excluded from the study because almost all variables we investigated required there to be more than one child in the family.
Table 3 A family study of schizophrenia in Finland in birth cohorts born from 1950 to 1976. Family structure of families (N = 2605) with at least one sibling with schizophrenia; also siblings born before 1950 and after 1976 are included.
N. of cases in the family Family size
2 3 4 5 6 7 8 9 10 11 12
1 3289 2012 984 369 171 62 22 9 5 1 0
2 282 271 190 82 35 16 12 5 1 0 0
3 0 22 26 11 10 8 4 0 1 0 1
4 0 0 4 2 1 1 1 0 0 0 0
5 0 0 0 1 0 1 0 0 0 0 0
6 0 0 0 0 0 0 1 1 0 0 0
We used two types of explanatory variables: variables that were unique for each person, and variables that had the same value for all members of the sibship. The unique variables were sex, birth cohort, birth order, number and ages of older siblings at the time of birth, and the age of mother and father at the time of birth, and the shared variables were the lowest age-at-onset in sibship, sex of the proband with lowest age-at-onset in the sibship, and the sibship size, and whether the mother had schizophrenia or not.
Study design and statistical analyses
We investigated the effect of unique variables using the case-sibling design [23], in which each case was matched with one or more unaffected siblings. We modeled the data with conditional logistic regression using each case in the family as the conditioning unit, which is the standard method for case-sibling design [23].
The sibling with the lowest age-at-onset of schizophrenia was the first case or proband and all unaffected siblings born between 1950 and 1976 and alive at age when first sibling had his/her first illness episode, were chosen as controls. Thus, the matching was carried out with respect to age-at-onset. If there were later cases in the family, controls were chosen in the same way but excluding those siblings who already had developed schizophrenia. Thus, a sibling could serve as a control before developing schizophrenia. The number of cases with at least one control was 9013 and the number of controls was 12046. As shown in Table 4, our data included a substantial number of families with more than 5 siblings.
Table 4 Basic characteristics of sibships.
no schizophrenia (%) schizophrenia (%) All
Sex male 4636 47 5207 52 9843
female 7410 66 3806 34 11216
Siblings under 5 years no 5496 52 4876 48 10372
yes 6550 61 4137 39 10687
Siblings 5–10 years no 7727 52 7172 48 14899
yes 4319 70 1841 30 6160
Siblings over 10 years no 10094 55 8377 45 18471
yes 1952 75 636 25 2588
Birth order 1 2934 44 3781 56 6715
2 4297 57 3260 43 7557
3 2629 68 1262 32 3891
4+ 2186 75 710 25 2896
Birth cohort 1950–1955 3756 51 3648 49 7404
1956–1960 3566 57 2689 43 6255
1961–1965 2825 62 1748 38 4573
1966–1976 1899 67 928 33 2827
Maternal age under 20 441 45 546 55 987
20–25 2509 50 2462 50 4971
26–30 3419 56 2692 44 6111
31–35 2896 62 1778 38 4674
over 35 2781 64 1535 36 4316
Paternal age under 20 101 49 106 51 207
20–25 1281 49 1355 51 2636
26–30 2727 53 2413 47 5140
31–35 2765 60 1879 40 4644
over 35 4159 63 2407 37 6566
not available 1013 54 853 46 1866
Sex of youngest proband male 6880 57 5187 43 12067
female 5166 57 3826 43 8992
Lowest age-at-onset in sibship under 20 2797 54 2351 46 5148
20–25 4410 57 3350 43 7760
26–30 2930 59 2014 41 4944
over 30 1909 60 1298 40 3207
Family size 2 3623 46 4233 54 7856
3 3623 59 2551 41 6174
4 2373 65 1284 35 3657
5+ 2427 72 945 28 3372
Mother schizophrenic no 11537 57 8561 43 20098
yes 509 53 452 47 961
Shared variables, which were the lowest age-at-onset in sibship, sex of the proband with the lowest age-at-onset in the sibship, and the sibship size, were modeled using ordinary logistic regression without conditioning. The probands, who were the affected siblings with the lowest age-at-onset in the families, were not included in this model.
We tested the significance of the interactions between sex and other variables, and interactions between sex of the proband and other variables using likelihood ratio tests.
Results
In the conditional logistic regression, females had lower risk of developing schizophrenia (Table 1). Having siblings who were less than five years older was associated with increased risk of schizophrenia, while having siblings who were more than ten years older was associated with a lower risk of schizophrenia. The firstborn had a higher risk of schizophrenia than later-borns. Young maternal age was associated with increased risk of schizophrenia, while young paternal age decreased risk.
Table 1 The effect of unique family-related variables on the odds of developing schizophrenia: results from the multivariate conditional logistic regression model which used the matched case-sibling design, odds ratios with 95% confidence intervals.
Sex (female vs. male) 0.507(0.478, 0.537)
Siblings who were less than 5 years older (yes vs. no) 1.463(1.288, 1.661)
Siblings who were 5–10 years older (yes vs. no) 0.905(0.800, 1.025)
Over 10 years older siblings (yes vs. no) 0.661(0.556, 0.785)
Birth order
1 (reference)
2 0.618(0.535, 0.715)
3 0.542(0.441, 0.666)
4+ 0.504(0.382, 0.667)
Birth cohort
1950–1955 (reference)
1956–1960 1.143(1.032, 1.267)
1961–1965 1.199(1.015, 1.415)
1966–1976 0.956(0.746, 1.224)
Maternal age
under 20 (reference)
20–25 0.794(0.671, 0.939)
26–30 0.688(0.555, 0.852)
31–35 0.614(0.470, 0.803)
over 35 0.605(0.433, 0.845)
Paternal age
under 20 (reference)
20–25 1.382(0.990, 1.927)
26–30 1.432(1.004, 2.043)
31–35 1.340(0.915, 1.963)
over 35 1.332(0.880, 2.015)
not available 0.213(0.018, 2.491)
In the ordinary logistic regression, siblings had higher risk if the proband had early, under 20 years, age at onset (Table 2). A sibship size of four or more was associated with increased risk of schizophrenia, as was having a mother with schizophrenia. The effect of parental age and having siblings who were less than 5 years older in the sibship disappeared in the ordinary logistic regression, but the effect of being the first born and having siblings who were more than 10 years older remained the same.
Table 2 The effect of shared family-related variables on siblings' odds of developing schizophrenia: results from the multivariate logistic regression where the proband was removed from the analysis, odds ratios with 95% confidence intervals. Unique family-related variables (sex, ages of other siblings, birth order, birth cohort, maternal age, and paternal age) were also included in models as background variables.
Sex of the youngest proband (female vs. male) 0.983(0.864, 1.117)
Lowest age-at-onset in the sibship
under 20 (reference)
20–25 0.574(0.497, 0.664)
26–30 0.323(0.267, 0.391)
over 30 0.156(0.116, 0.209)
Family size
2 (reference)
3 1.128(0.943, 1.349)
4 1.282(1.043, 1.575)
5+ 1.286(1.019, 1.623)
Mother schizophrenic(no vs. yes) 1.822(1.431, 2.321)
None of the tested interactions were significant.
We investigated the effect of proband's sex and age-at-onset, birth order, age difference to siblings, birth cohort, and parental ages on the risk of developing schizophrenia. Proband's early age-at-onset and female sex increased the risk of schizophrenia among siblings. Male sex, having siblings who were less than 5 years older, and being the firstborn also increased the risk of schizophrenia, while having over 10 years older siblings and small family size decreased it.
The higher risk of schizophrenia among the firstborns confirms the findings from some other recent studies. The most likely explanation is the increased risk of pregnancy and delivery complications among primiparous (first delivery) women. In the 1966 and 1986 Northern Finland birth cohorts, primiparity was a risk factor for having a low birth weight infant and for perinatal mortality [24]. In an earlier Finnish cohort study, infants of multiparous women were significantly heavier than infants of primiparous women [25]. As low birth weight and some obstetric complications increase the risk of later development for schizophrenia [26], they may explain the increased risk of schizophrenia among the firstborns. Having siblings who were less than five years older was a risk factor for schizophrenia in our study. Sham et al. found that having siblings who were 3–4 years older was a risk factor for schizophrenia [10], while Westergaard et al. found that a short interval to the nearest older sibling was a risk factor for schizophrenia [12]. Both suggested that common infections brought to the family by young siblings, causing fetal or early childhood infections, could lie behind the association [10,27]. Consistently with this, Brown et al found in a cohort study that second trimester exposure to respiratory infections increased the risk of later development of schizophrenia spectrum disorders two-fold [28].
Discussion
Our results concerning the effect of paternal and maternal age are not comparable to previous studies [16-18], because all patients who did not have siblings were excluded from the analysis. Our findings suggest that in families with at least two children, young maternal age at birth is a risk factor for schizophrenia. This could be linked to obstetric complications. However, although some studies have found that obstetric complications are more common among adolescent mothers [29], this is not the case in Finland [24,30]. Thus, obstetric complications probably do not explain why the offspring of young mothers have an increased risk of schizophrenia.
Consistently with a recent meta-analysis [31], the risk of schizophrenia was 50 percent lower for females than males. If risk factors for schizophrenia are equally distributed among females and males but females are for some reason more resilient to them, it could be assumed that females who develop schizophrenia have more schizophrenia-predisposing genes than males. This would explain why siblings' risk of schizophrenia was higher when the proband was female.
Our results showed that the higher was the age-at-onset of the first proband in the family, the lower was the siblings' risk (Tables 1 and 2), which is in line with previous family studies [5,32,33] and also with studies that found an earlier age-at-onset among patients with familiar rather than sporadic schizophrenia [34]. Also, a study of parents of patients with childhood- and adult-onset schizophrenia found that parents of patients with childhood-onset schizophrenia had a significantly higher morbid risk of schizophrenia spectrum disorders than parents with adult-onset schizophrenia [35]. All these findings are consistent with the hypothesis that in schizophrenia, as in many other multifactorial neuropsychiatric diseases [36], early age at onset is a marker of greater genetic vulnerability to the disorder.
A case-sibling design has some limitations. It can only provide approximate estimates of relative risk. In our analyses only families with two or more siblings were informative, and therefore our results are not applicable to one child families. However, whether matching was taken into account or not did not largely affect the results.
Our study was based on a nationwide, representative sample of patients with schizophrenia and their siblings, and our sample size was considerably larger than in any of the previous studies. Register-based information is not as accurate as information based on face-to-face interview, but Finnish registers are quite reliable. The two registers we used, the National Population Register and the National Hospital Discharge Register, are nationwide. The National Hospital Discharge Register covers all public and private hospitals in Finland, and the National Population Register registers all Finnish citizens. However, as information on parents enabling the identification of sibships first appeared in the Population Register only in 1974, sibship data may be more accurate in the younger cohorts. Information in both registers is accurate. Some proportion of siblings of the study population are really half-siblings, but even in this case, the case-sibling design is applicable, because it does not assume that familial aggregation is due to a genetic mechanism. When entries in the National Hospital Discharge Register were compared with data on hospital case notes, the primary diagnosis in the register and hospital case notes was identical in 99 percent of cases for schizophrenia and in 98 percent of cases for all mental disorders [19]. The reliability of register diagnoses of schizophrenia is also good, with a greater propensity for false-negatives than false-positives [20-22,37].
Conclusions
We investigated concurrently several family-related factors which independently have been suggested to be risk factors for schizophrenia. We were able to verify some of the previous associations, while others were refuted. The risk factors we identified probably have different underlying causative mechanisms. For example, the risk associated with being the firstborn might be explained by increased risk to obstetric complications, while the combination of decreased risk among females but increased risk among siblings of female probands might be explained by genetic factors. The risk factors we identified should be studied further in samples that include more detailed information on possible causative factors, such as infections and obstetric complications.
Competing interests
The author(s) declare that they have no competing interests.
Authors contributions
JH participated in the design of the study, performed the data-analysis and drafted the manuscript. JS participated in the design of the study, and the drafting of the manuscript. JL participated in the design of the study and its coordination. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15566576 | PMC535802 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Nov 27; 4:41 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-41 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-341551129310.1186/1742-4690-1-34ResearchSeroprevalence of simian immunodeficiency virus in wild and captive born Sykes' monkeys (Cercopithecus mitis) in Kenya Ellis Brett R [email protected] Elephas [email protected] Debra [email protected] James [email protected] Moses G [email protected] Scott F [email protected] Department of Tropical Medicine, Tulane University, New Orleans, LA 70112, USA2 Institute of Primate Research, P.O Box 24481, Karen, Nairobi, Kenya3 Division of Pediatric Infectious Diseases, Tulane University, New Orleans, LA 70112, USA4 Biotechnology Program, Florida Gulf Coast University, Fort Myers, FL 33965, USA2004 28 10 2004 1 34 34 28 9 2004 28 10 2004 Copyright © 2004 Ellis et al; licensee BioMed Central Ltd.2004Ellis 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
The Sykes' monkey and related forms (Cercopithecus mitis) make up an abundant, widespread and morphologically diverse species complex in eastern Africa that naturally harbors a distinct simian immunodeficiency virus (SIVsyk). We carried out a retrospective serological survey of SIV infection from both wild and captive Sykes' monkeys from Kenya. We compared two commercially available, cross-reactive ELISA tests using HIV antigens with a novel SIVsyk antigen-specific Western blot assay and analyzed the data by origin, subspecies, age and sex.
Results
The SIVsyk antigen-specific Western blot assay detected more serum samples as positive than either of the cross-reactive ELISA assays. Using this assay, we found that seroprevalence is higher than previously reported, but extremely variable in wild populations (from 0.0 to 90.9%). Females were infected more often than males in both wild and captive populations. Seropositive infants were common. However, no seropositive juveniles were identified.
Conclusion
We have developed a specific and sensitive Western blot assay for anti-SIVsyk antibody detection. Sykes' monkeys are commonly infected with SIVsyk, but with extremely variable prevalence in the wild. Higher infection prevalence in females suggests predominantly sexual transmission. High infection prevalence in infants, but none in juveniles, suggests maternal antibodies, but little or no vertical transmission.
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Background
Both human immunodeficiency virus-1 (HIV-1) and human immunodeficiency virus-2 (HIV-2) have been evolutionarily linked to zoonotic transmissions of naturally occurring simian immunodeficiency viruses (SIVs) from Central African chimpanzees (Pan troglodytes troglodytes) and West African sooty mangabey monkeys (Cercocebus atys), respectively [1,2]. In addition, the SIVsmm from sooty mangabeys has been transmitted in captivity to Asian macaques (Macaca mulatta) and is pathogenic in that species, producing an acquired immunodeficiency syndrome-like condition [3-6]. Evidence of infection by naturally occurring SIVs has been documented in a variety of different non-human African primate species [7-9] and additional types of SIV are likely to exist in other African primates as well [9].
Despite the importance of naturally occurring SIVs as reservoirs for human disease as well as infections in other primate species, few studies have addressed the distribution or prevalence of these viruses in wild host populations [10]. Research in this area has been limited by the fact that many primate species are endangered in the wild and therefore samples used for SIV studies have often been acquired from research centers and zoos, not wild animals. The patterns of transmission in captive animals may have been influenced by altered contacts or behaviors in captivity and may not reflect natural transmission patterns. Furthermore, not all SIVs grow well in cell culture and it has consequently been difficult to generate specific reagents for their study.
The Sykes' monkey (Cercopithecus mitis) belongs to an abundant, widespread and morphologically diverse species complex found throughout East Africa [11-13]. Previous investigations have indicated that these monkeys are infected with a unique virus: SIVsyk. Both wild and captive C. mitis have previously been shown to have antibodies that cross-reacted with SIVsmm and SIVagm [14]. A virus isolate (SIVsyk173) was made from one SIVsyk infected animal and an infectious clone was produced, sequenced and shown to form a distinct phylogenetic group from other known SIVs [15]. Very recently, several additional, related SIVsyk sequences have also been reported [16]. These viruses apparently do not grow well in human or rhesus macaque PBMCs, but have been shown to replicate to some degree in immortalized human lymphoid cell lines [17]. SIVsyk also does not produce detectable disease in either the natural host or in rhesus macaques [15,17] (personal observation M. G. Otsyula), consistent with other observations that infection of many natural host species with their specific SIV is non-pathogenic [15].
Here we report the results of a retrospective serosurvey for the prevalence of SIVsyk in wild Sykes' monkey populations in comparison to captive animals. We compare the use of two different cross-reactive ELISA assays with a novel SIVsyk-specific Western blot assay based on the isolate SIVsyk173. We provide a breakdown of seroprevalence results between captive and geographically isolated wild populations, subspecies, sexes and age groups. These data support the utility of using an antigen-specific assay rather than a cross-reactive assay and indicate that the prevalence of SIVsyk infection is highly variable in wild populations and higher in adult animals than previously reported.
Results
Comparison of SIVsyk Western blot assay to cross-reactive ELISAs
Two hundred and seventy six serum samples from both wild and captive C. mitis were screened for the presence of antibodies cross-reactive to HIV-1 and HIV-2 using two commercial ELISA tests. The 276 samples included 100 sequential (duplicate or triplicate) serum samples from the same animals taken at different times. From these sera, the Innotest HIV-1/HIV-2 antibodies test (Innogenetics, Belgium) detected a total of 132 positive samples and 144 negative samples. The Genescreen HIV-1/2 version 2 test (Biorad, Japan). detected 129 positive samples and 147 negative samples. Five samples gave disparate results using these two tests, four of these samples were positive using the Innotest kit, but negative using the Genscreen test and one sample was positive using the Genscreen test, but negative using the Innotest kit.
To confirm these results and compare the sensitivity and specificity of these assays, all 276 samples were screened by an SIVsyk viral antigen-specific Western blot assay. Serum samples were considered positive by SIVsyk Western blot assay if they showed reactivity to ENV, GAG or CA proteins. The SIVsyk Western assay identified a total of 138 samples as positive and 138 as negative. Ten samples that were negative by the Genscreen assay and seven samples that were negative by the Innotest assay were identified as positive by the SIVsyk Western assay (Figure 1). One sample that was identified as positive by both ELISA tests was consistently negative by SIVsyk Western. Using the SIVsyk Western assay as the standard, this results in a 99.3% specificity for both ELISA assays and 93.9% and 95.8% sensitivity for the Genscreen and Innotest assays, respectively, for detection of anti-SIVsyk serum antibodies. Using McNemar's repeated measures test, the difference between the Western and the Innotest is just under the 95% confidence limit (p = 0.0703) and the difference between the Western and the Genescreen test is statistically significant (p = 0.0117).
Figure 1 Representative SIVsyk Western blot results. Blank (B), Negative serum sample (-), Positive serum sample (+). Lanes 1–10, samples negative by at least one ELISA test, but positive for ENV, GAG or CA by Western. Lane 11, sample positive by both ELISA tests, but negative by Western.
Serosurvey of wild animals
The results from the SIVsyk Western assay were analyzed according to geographic origin, subspecies, age and sex. Figure 2 shows a map of Kenya indicating the locations of the wild collection sites as well as the approximate ranges of the three Sykes' monkey subspecies. Sequential serum samples were excluded from this analysis, reducing the number of samples considered to 176 (109 wild and 67 captive). Of the 109 individual samples from wild populations, 51 positives were detected, for a total wild seroprevalence of 46.8%. This includes both sexes and all age classes from all locations. Between individual wild populations, the seroprevalence level was quite variable, with a high of 90.9% on the coast at Lamu, to 0% at Kwale, also on the coast. Of wild populations, 25 of 38 adult highland Sykes', 11 of 13 adult lowland Sykes' and 1 of 2 adult blue Sykes' were positive, yielding prevalences of 65.8%, 84.6% and 50.0%, respectively for adults of each subspecies. Among the wild born adult animals, 17 of 26 (65.4%) males and 20 of 27 (74.1%) females were positive. Although 1 of 2 (50.0%) wild born infant males and 1 of 1 (100.0%) wild born infant females were positive, 0 of 3 (0.0%) wild born juvenile males were positive. No wild female juveniles were available for testing. Table 1 provides a list of the seroprevalence for all samples from each wild collection site, as well as a list of prevalence among adults only at each site. Tables 2 and 3 provide a breakdown of wild animal seroprevalence according to subspecies, and age and sex, respectively. A Fisher's exact (2 × 2) contingency test for sex-related differences in the total wild population yielded a p value of 0.559, indicating that sex-related differences in the wild are not statistically significant. An exact 3 × 2 contingency test for age-related differences yielded a p value of 0.069, indicating that age-related differences in the wild population are just slightly not statistically significant at the 95% confidence level. A chi squared 8 × 2 contingency test for differences between the eight wild populations yielded a p value of 0.00099, indicating that the differences between populations was highly significant, although the small size of some of the samples violates the assumptions of this test. Repeating this test using only adult animals is also statistically significant (p = 0.0293), however, the further decrease in sample size using only the data from adult animals further violates the assumptions of this test.
Figure 2 Map of Kenya, East Africa. Wild population locations and approximate subspecies ranges are shown.
Table 1 Anti-SIVsyk serum antibodies among wild Sykes' monkeys according to location. Data for all age groups as well as for adults only in each population is provided. Serum samples from animals with no recorded location are listed as unknown.
Location Total Positive/Tested Total Percent positive Adults Positive/Tested Adults Percent positive
Aberdares 5/7 71.4% 4/6 66.7%
Karen 1/4 25.0% 0/3 0.0%
Kibwesi 1/7 14.3% 1/2 50.0%
Kwale 0/9 0.0% 0/1 0.0%
Lamu 10/11 90.9% 9/9 100.0%
N. Kinaangop 3/7 42.9% 0/0 -
Ololua 20/34 58.8% 17/23 73.9%
Thika 3/4 75.0% 3/4 75.0%
Unknown 8/26 30.8% 3/5 60.0%
Total 51/109 46.8% 37/53 69.8%
Table 2 Anti-SIVsyk serum antibodies among wild and captive born Sykes' monkeys according to subspecies. Serum samples from wild animals of unrecorded subspecies are listed as unknown. Colony animals of either mixed breeding or unknown origin are listed as unknown.
Wild born animals Captive born animals
Subspecies Positive/Tested Percent positive Positive/Tested Percent positive
Highland 25/38 65.8% 9/17 52.9%
Lowland 11/13 84.6% 3/4 75.0%
Blue 1/2 50.0% 0/0 -
Unknown 14/56 25.0% 12/46 26.1%
Total 51/109 46.8% 24/67 35.8%
Table 3 Anti-SIVsyk serum antibodies among wild and captive born Sykes' monkeys according to age and sex. Serum samples from wild animals of undetermined age or sex are listed as unknown.
Wild born animals Captive born animals
Age Sex Pos./Tested Percent pos. Pos./Tested Percent pos.
Adult Male 17/26 65.4% 4/8 50.0%
Female 20/27 74.1% 9/13 69.2%
Subtotal 37/53 69.8% 13/21 61.9%
Juvenile Male 0/3 0.0% 0/6 0.0%
Female 0/0 - 0/5 0.0%
Subtotal 0/3 0.0% 0/11 0.0%
Infant Male 1/2 50.0% 4/16 25.0%
Female 1/1 100% 7/19 36.8%
Subtotal 2/3 66.7% 11/35 31.4%
Unknown Unknown 12/50 24.0% 0/0 -
Total 51/109 46.8% 24/67 35.8%
Serosurvey of captive animals
Sixty seven individual serum samples were available from captive animals. The overall seroprevalence in captivity was 35.8% (24/67). This includes both sexes and all age classes. Ten of 17 captive adult highland Sykes' and 3 of 4 captive adult lowland Sykes' were positive, yielding prevalences of 59.0% and 75.0%, respectively for these two subspecies. No known captive Blue Sykes' were available for testing. Among captive adult animals, 4 of 8 (50.0%) males and 9 of 13 (69.2%) females were positive. Four of 16 (25.0%) captive infant males and 7 of 19 (36.8%) captive infant females were positive. However, no captive juveniles (0 of 6 (0.0%) males and 0 of 5 (0.0%) females) were positive. Tables 2 and 3 provide a breakdown of captive animal seroprevalence according to subspecies, and age and sex, respectively. A Fisher's exact (2 × 2) contingency test for sex-related differences in the captive population yielded a p value of 0.646, indicating that sex-related differences in captivity are not statistically significant. An exact 3 × 2 contingency test for age-related differences yielded a p value of 0.001, indicating that age-related differences in captivity are statistically significant.
Serosurvey comparisons
The overall captive seroprevalence was 35.8%, which is slightly lower than the overall wild seroprevalence of 46.8%. Infection prevalence was highest in adults at 69.8% for wild populations and 61.9% in captive populations. Adult females were infected at a higher prevalence than adult males in both the wild (74.1% (20/27) for wild adult females and 65.4% (17/26) for wild adult males) and captive populations (69.2% (9/13) for captive adult females and 50.0% (4/8) for captive adult males). Infants were also found to have a high seroprevalence at 66.7% (2/3) from wild populations and 31.4% (11/35) from captivity, although few wild infants were surveyed. No seropositive juveniles were identified from either wild (0/3) or captive (0/11) populations. A Fisher's exact (2 × 2) contingency test for sex-related differences in the combined wild and captive populations yielded a p value of 0.455. This indicates that while the there is a clear trend in each data set showing higher prevalence among females, the small numbers of samples do not show a statistically significant difference between infection in males and females. An exact 3 × 2 contingency test for age-related differences in the combined wild and captive populations yielded a p value of 0.00001, indicating that age-related differences in the combined populations are highly statistically significant.
Discussion
Previous SIV serosurveys of Cercopithecus mitis have used assays relying on cross-reactivity between antibodies against SIVsyk and other HIVs or SIVs. To assess the relative effectiveness of the use of cross-reactive tests, we compared serological results using two commercially available HIV-1/HIV-2 ELISA kits with an authentic SIVsyk antigen Western blot assay. The cross-reactive ELISA assays were very specific and in only one out of 276 samples gave a false positive result compared to the SIVsyk antigen Western assay. Curiously, this one particular captive juvenile female was repeatedly positive by both ELISAs but repeatedly negative by Western. This animal may possibly have been infected in captivity with a different SIV that produces cross-reactive antibodies to HIV-1 and HIV-2, but not to SIVsyk. On the other hand, the cross-reactive ELISAs failed to detect between 4.2 to 6.1% of the animals that were positive by the SIVsyk antigen Western assay. This result may be due to increased sensitivity using a Western blot format, but is also possibly due to increased sensitivity to the authentic SIVsyk viral antigens.
Variable SIV infection prevalences have previously been reported in this species. However, information on age, sex and captive or wild status was not provided in any previous survey. Lowenstine et al, 1986 [18] found no evidence of infection in two captive Sykes' monkeys at US zoos using an HIV-1 antigen based ELISA. Ohta, et al, 1988 [19], found 4 out of 18 (22%) Sykes' monkeys from East Africa with evidence of infection, presumably by Western using SIVagm antigen, but no further details were given. Three other reports specifically tested Sykes' monkeys at the Institute for Primate Research in Kenya, but it is unknown which of the samples tested in this survey are from the same animals. Emau, et al, 1991 [17], reported 59 out of 100 (59%) positive using a radioimmuno-precipitation assay cross-reactive with SIVsmm and SIVagm. Tomonaga, et al, 1993 [20], reported 9 out of 35 (25%) lowland Sykes', 13 out of 24 (54%) highland Sykes', and 1 out of 14 (7%) blue Sykes' positive using an immunofluorescence assay with SIVagm antigen. Otsyula et al, 1996 [14], reported 12 out of 35 (34%) positive by Western blot using SIVagm antigen and 8 out of 35 (23%) positive by Western blot using SIVsmm antigen. The low incidence of infection reported by Otsyula, et al 1996 [14] using Western blots with SIVagm as well as SIVsmm antigens supports the hypothesis that the increased sensitivity in the present study is due to the use of SIVsyk antigens and is not likely to be simply due to the use of Western blot assay vs. ELISA.
To avoid age-related bias, a comparison between captive and wild populations should be made using only adult animals because the captive population contained many more samples from infants and juveniles than did the wild population (see Table 3). Both captive and wild adults showed a much higher prevalence than previous reports. Just over 60% of captive adults and nearly 70% of wild adults were seropositive. However, in wild populations, seroprevalence varied greatly according to location (from 0 to 90.9%) and this variability was statistically significant. The differences in seroprevalence may be due to small sample sizes, however, the average troop size in this species is approximately 10 animals [12] and we have sampled between 4 and 34 animals from each individual site (some sites have more than one troop present). Alternatively, host immunological parameters, genetic differences in virus strains, troop size, individual or troop behavior, and habitat may all affect the seroprevalence of these viruses among certain populations.
Although the small sample sizes did not provide statistical significance, in both captive and wild animals, a higher infection prevalence was seen in females than in males. This possibly indicates a predominantly sexual transmission route, rather than transmission by aggressive behavior between males as has been shown in mandrills [21]. Sykes' monkeys usually live in a harem arrangement where a dominant male controls sexual access to the females in the troop. This may explain the variability in seroprevalence among wild populations, since the infection status of a dominant male would heavily influence the status of the entire troop. However, other breeding strategies exist in this species and influxes of multiple solitary males into troops have been documented during periods when multiple females are sexually receptive [22]. Unfortunately, we have no data on the breeding situations in the sampled groups or which samples came from dominant versus lower status males.
In addition to the adults, a high seroprevalence was also observed in infants. However, no seropositive juveniles were identified, even though 14 juvenile animals were sampled from both wild and captive populations. A possible explanation is that infants are seropositive due to the presence of maternal antibodies in breast milk, but little or no actual maternal-fetal transmission occurs either in utero, during birth or through breastfeeding. An alternative possibility to consider is that SIVsyk infections could be cleared by infants and that adult seroprevalence is due to later exposure.
Conclusions
In order to investigate the infection prevalence of SIVsyk in the common and widespread Sykes' monkey in East Africa, we used two commercially available ELISA assays and a novel SIVsyk antigen-specific Western blot assay to perform a retrospective serosurvey of 276 previously collected serum samples from both wild and captive animals. To develop the antigen-specific Western assay, we propagated SIVsyk strain 173 in a human CEMx174 lymphoid cell line expressing human CCR5. Comparisons between the cross-reactive ELISAs and the SIVsyk Western assay indicate that the antigen-specific Western assay is more specific and sensitive, supporting the utility of using antigen-specific assays in this and other SIV serosurveys. We report that using this Western assay, infection prevalence is higher on average than previously reported, but that the prevalence in wild populations is variable. In captivity and in the wild, females are more commonly infected than males, although these differences are not statistically significant. Positive serology is also common in infants, but no positive juveniles were identified. We interpret this as evidence of maternal antibodies infants, but a low incidence of maternal-fetal transmission.
Methods
Specimens
The sera used in this study were obtained between 1981 and 1996 from animals that had been previously trapped in the wild or from previously sampled captive animals housed at the Institute of Primate Research in Nairobi, Kenya. Serum samples were stored continuously at -20°C. All animals were captured, housed and sampled in accordance with humane animal use guidelines established by the Kenya Institute of Primate Research. Wild born monkey samples used in this study were derived from eight locations. In general, these sites were rural or suburban areas that bordered forested areas with natural flowing water. These sites are typical of areas in which humans have encroached on natural habitats and now may come in contact with these monkeys as they forage for food. The geography of the sites varied considerably from heavily forested areas in the highlands of Kenya to drier forests along the lowland coast of Kenya. The range of the lowland Sykes' (C. mitis kibonotensis Lonnberg) includes coastal and eastern regions of Kenya [11,13]. The range of the highland Sykes' (C. mitis kolbi Neumann) includes the upland central portion of the country [11,13]. The range of the blue Sykes' (C. mitis stuhlmannii Matschie) includes western Kenya and the border with Uganda [11,13]. Some specimens from the wild came from unknown locations and these are noted as such. Captive animals used for breeding were housed in an area composed of nine outdoor enclosures. Typically, breeding groups were composed of approximately ten individuals. Each breeding group consisted of a harem of one male and four to six females, plus several infants. Infants were left with mothers for a period of time and were usually moved to another group between the age of six months and two years to prevent inbreeding. Individuals not involved in breeding were kept in groups composed of approximately ten juvenile males and females in larger outdoor enclosures. Animals were aged according to dental inspection and according to sexual maturity. Animals believed capable of mating, usually over two years of age, were considered adults. Infants were less than six months of age.
ELISA Analysis
Serum samples were tested using the Innotest HIV-1/HIV-2 Antibodies test (Innogenetics, Ghent, Belgium) and Genscreen HIV1/2 version 2 (Biorad, Tokyo, Japan). The Innotest ELISA kit uses synthetic peptides representing HIV-1 subtype M envelope, HIV-2 envelope, and HIV-1 group O envelope antigens. The Genscreen uses purified gp160 envelope and p25 nucleocapsid recombinant protein of HIV-1 and a peptide that mimics the immunodominant epitope of HIV-2 envelope. Each kit was used according to the manufacturers' recommendations. Photometric measurements were performed with a Dynatech MRX plate reader at 450 and 650 nm wavelengths. Positive and negative determinations were based upon cutoff values as determined by the manufacturers' protocols.
Cells and Virus
CD4+ CEMx174 cells were transduced with a CCR5-encoding vector (pBABE-puro-CCR5, a gift of David Dorsky, University of Connecticut) as previously described [23]. The resulting CCR5 expressing cell line, designated CEM-R5, was maintained in RPMI 1640 with 10% FBS and 1 ug/ml puromycin. The SIVsyk-1.2 isolate (obtained from the AIDS Reference and Reagents Program) was inoculated into CEM-R5 and the CEMx174 parent cell lines. After 7–14 days in culture, widespread syncytia formation was observed in CEM-R5 cells while few syncytia were observed in the parental CEMx174 cells. Cell associated viral proteins were extracted from SIVsyk-1.2 infected CEM-R5 cells grown for 7–10 days post-inoculation.
Western Blot Analysis
Serum samples were screened by Western blot using SIVsyk-specific viral antigens. SIVsyk-1.2 infected cells were centrifuged for 5 min at 1,000 × g and resuspended in lysis buffer containing 1% triton, 50 mM tris pH 7.5, 2 mM EDTA. Cell debris was pelleted for 2 min. at 12,000 × g and the supernatant was added to loading buffer containing 50 mM Tris-HCl pH 6.8, 100 mM DTT, 2% SDS, 10% glycerol, 0.1% bromophenol blue. Viral antigens were separated on a 12% polyacrylamide gel and subsequently transferred to a PVDF membrane. Membrane strips were blocked with a solution of PBS-T containing 5% dry milk. Serum samples were diluted 1:500 and incubated for one hour at room temperature with individual membrane strips. Primary serum antibodies were detected with a 1:4,000 dilution of goat-anti-rhesus HRP conjugated secondary antibody (Nordic Immunology, Tilburg, Netherlands). Colorimetric detection was performed using the Immunopure Metal Enhanced DAB Substrate Kit (Pierce Endogen, USA).
Competing interests
The author(s) declare that the have no competing interests.
Authors' contributions
BRE and EM conducted the ELISA and Western blot experiments and BRE wrote the draft manuscript. DE and JR cultured the SIVsyk virus. MGO provided serum samples and oversaw the ELISA experiments. SFM assisted with and oversaw the Western blot experiments and prepared the final manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported in part by an NIH ICIDR grant to SFM and NIH AI 24030 and AI 46275 to JR.
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| 15511293 | PMC535803 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Oct 28; 1:34 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-34 | oa_comm |
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BMC UrolBMC Urology1471-2490BioMed Central London 1471-2490-4-131554648110.1186/1471-2490-4-13Case ReportTesticular seminoma after the complete remission of extragonadal yolk sac tumor : a case report Kuroda Isao [email protected] Munehisa [email protected] Tomoko [email protected] Ken [email protected] Hitoshi [email protected] Takuji [email protected] Nobuhiro [email protected] Department of Urology, Saitama Medical School, Moroyama, Saitama, Japan2 Department of Pathology, Saitama Medical School, Moroyama, Saitama, Japan3 Department of Urology, Keio University, School of Medicine, Shinanomachi, Shinjyuku, Tokyo, Japan2004 16 11 2004 4 13 13 31 5 2004 16 11 2004 Copyright © 2004 Kuroda 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
Between 2% and 5% of malignant germ-cell tumors in men arise at extragonadal sites. Of extragonadal germ cell tumors, testicular carcinoma in situ (CIS) are present in 31–42% of cases, and CIS are reported to have low sensitivity to chemotherapy in spite of the various morphology and to have a high likelihood of developing into testicular tumors. A testicular biopsy may thus be highly advisable when evaluating an extragonadal germ cell tumor.
Case presentation
A 36-year-old man was diagnosed as having an extragonadal non-seminomatous germ cell tumor, that was treated by cisplatin-based chemotherapy, leading to a complete remission. In the meantime, testicular tumors were not detected by means of ultrasonography. About 4 years later, a right testicular tumor was found, and orchiectomy was carried out. Microscopically, the tumor was composed of seminoma.
Conclusions
We herein report a case of metachronous occurrence of an extragonadal and gonadal germ cell tumor. In the evaluation of an extragonadal germ cell tumor, a histological examination should be included since ultrasonography is not sufficient to detect CIS or minute lesions of the testis.
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Background
Between 2% and 5% of malignant germ-cell tumors in men arise at extragonadal sites [1]. Cytogenetically most extragonadal germ-cell tumors (EGGCTs) i.e., the seminomas and non-seminomas, are similar to their testicular counterparts [2,3]. But there are CIS in 31–42% of EGGCT patients' testes [4]. Ultrastructural studies indicate that CIS originate from rather primitive cells and can develop into different categories of germ cell carcinomas. Furthermore, since CIS are reported to respond poorly to chemotherapies, a metachronous development of testicular cancer will possibly occur, in spite of the various morphologies of testicular cancer [5,6]. In the present case, the etiology of a metachronous appearance of EGGCT and testicular cancer is discussed.
Case presentation
A 36-year-old man was admitted to our hospital with the chief complaint of right-sided scrotal enlargement. He had previously received treatment for an extragonadal germ cell tumor. At the age of 32, he presented with lumbago. CT showed a retroperitoneal tumor (Figure 1), and a transabdominal needle biopsy was carried out. Microscopically, the tumor was composed of a yolk sac tumor (Figure 2A,2B). We performed two courses of systemic chemotherapy using bleomycin, etoposide, and cisplatin, leading to a partial response. As the tumor size was not seemed to decrease after the two courses of the chemotherapy, retroperitoneal lymph node dissection was performed, but failed to show any residual viable cells. An ultrasonic study did not reveal any testicular tumors. About 4 years after the previous treatment, he presented with scrotal enlargement and tumor markers such as AFP and HCG β were within normal limit. A right orchiectomy was performed on 23rd July. Pathology showed the resected tumor was a seminoma with CIS (Figure 3A,3B). No recurrence has been seen since the surgery (Figure 1B).
Conclusions
There are reports that approximately 4% of patients with EGGCT develop a metachronous testicular cancer despite the use of cisplatin-based chemotherapy [7], and the cumulative risk of developing a metachronous testicular cancer 10 years after a diagnosis of EGGCT is 10.3% [8]. However, there is disagreement over whether EGGCT is a primary disease or metastatic from the burned-out primary testicular lesion. Actually, burned-out tumors have been detected in 76% of cases of EGGCT [9]. CIS is also found via biopsy in 31–42% cases [4,10]. Testicular CIS is thought to have resistance to systemic chemotherapies and to develop later to metachronous testicular cancer. In the present case, the EGGCT was a non-seminomatous germ cell tumor including a yolk sac component, whereas the testicular cancer was a seminoma. We believe CIS was present at the time of the treatment of EGGCT and testicular CIS is so primitive that it could differentiate into any type of germ cells. It is also possible that these metachronously developing germ cell tumors developed independently. A testicular biopsy could clarify the relationship between these tumor cells and the expansion of the disease. In the present case no biopsy was done, but an ultrasonic examination ruled out the possibility of testicular CIS. Giwercman et al. [11] emphasized the necessity of histological examinations of the testis upon an evaluation of EGGCT [12-14] and also urged a careful follow-up for patients with EGGCT who do not have simultaneous testicular cancer.
On the other hand, there is an opinion that any patients with retroperitoneal masses should undergo scrotal ultrasound. Comiter et al. [15] showed definite pathological evidence of a burned-out testicular carcinoma in 5 of 6 patients (83%) with presumed extragonadal germ cell tumors and concluded that scrotal ultrasound studies are useful for the evaluation of the palpably normal testes [15]. Kitahara et al. reviewed the incidence of scrotal echogenic leisions with testicular cancers or burned-out tumors of 22 EGGCT patients and found echogenic changes in 17 patients (77.3%) [16]. This means that disease was overlooked by ultrasonic examinations in 22.7% of cases.
In our case, it is possibile that metachronous testicular cancer oriented in testicular CIS, grew from a burned-out tumor, or was independent of the EGGCT. We should have performed testicular biopsies at the time of the diagnosis of EGGCT and reflect the strategies of treatments of EGGCT.
Now we propose a surveillance protocol of EGGCT as Table 1, concerning with following four points.
1. As we mentioned, the overall risk of development a testicular tumor is not so high(4–10.3%).
2. The side effect of CIS therapy (whether irradaition, orchiectomy or chemotherapy) are significant, especially concerning fertility and androgen production.
3. Testicular tumors early detected by adequate surveillance respond well to treatments.
4. Testicular biopsy is not entitled to detect all the CIS.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contribution
IK, MU, HY, KN, TT and ND carried out clinical treatments.
TM carried out histopathological studies.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank the patient for giving consent for publication of this case report.
Figures and Tables
Figure 1 CT shows a retroperitoneal bulky mass. (B) The clinical course from the treatment of EGGCT to that of seminoma.
Figure 2 Photomicrograph of extragonadal non-seminomatous germ cell tumor with a focus of yolk sac tumor. A: H&E section shows characteristic structures suggestive of Schiller-Duval bodies (×200). B: Alpha-fetoprotein immunohistochemical stain is focally positive at the above area (×400).
Figure 3 Photomicrograph of classic seminoma of the testis. A: H&E section shows compact nests of large tumor cells are separated by thin fibrous septa infiltrated by lymphocytes. B: Intratubular germ cell neoplasia in H&E section (×100). A row of atypical germ cells with clear cytoplasm is seen against a thickened basement membrane. No spermatogenesis is occurring in this tubule (×200).
Table 1 Our proposed surveillance protocol of EGGCT is as below.
Testicular Biopsy At the time of the diagnosis
Self-check of the testis Every months
Scrotal ultrasound study -3 years -5 years -10 years
every 3 months every 6 months every year
Tumor markers -3 years -5 years -10 years
every 3 months every 6 months every year
CT(chest-pelvic) -3 years -5 years -10 years
every 3 months every 6 months every year
==== Refs
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Scholz M Zehender M Thalmann GN Borner M Thoni H Studer UE Extragonadal retroperitoneal germ cell tumor: evidence of origin in the testis Ann Oncol 2002 13 121 4 11863093 10.1093/annonc/mdf003
Fossa SD Aass N Heilo A Daugaard G Skakkebaek NE Stenwig AE Nesland JM Looijenga LH Oosterhuis JW Testicular carcinoma in situ in patients with extragonadal germ-cell tumours: the clinical role of pretreatment biopsy Ann Oncol 2003 14 1412 8 12954581 10.1093/annonc/mdg373
Giwercman A von der M aase H Skakkebaek NE Epidemiological and clinical aspects of carcinoma in situ of the testis Eur Urol 1993 23 104 10 discussion 111-4 8477770
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Gleich P Testicular carcinoma in situ and nonpalpable seminoma eight years after contralateral teratocarcinoma Urology 1990 36 181 182 2385889
Kimura F Watanabe S Shimizu S Nakajima F Hayakawa M Nakamura H Primary seminoma of the prostate and embryonal cell carcinoma of the left testis in one patient: a case report Jpn J Urol 1995 86 1497 1500
Comiter CV Benson CJ Capelouto CC Kantoff P Shulman L Richie JP Loughlin KR Nonpalpable intratesticular masses detected sonographically J Urol 1995 154 1367 9 7658540 10.1097/00005392-199510000-00029
Kitahara K Hori J Tokumitsu M Saga Y Hashimoto H Kaneko S Yachiku S Retroperitoneal germ cell tumor with testicular calcification indicating tiny testicular origin: consideration of the origin of retroperitoneal germ cell tumors: report of two cases Hinyokika Kiyo Review Japanese 2003 49 291 5
| 15546481 | PMC535804 | CC BY | 2021-01-04 16:03:53 | no | BMC Urol. 2004 Nov 16; 4:13 | utf-8 | BMC Urol | 2,004 | 10.1186/1471-2490-4-13 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-221556337610.1186/1465-9921-5-22ResearchA comparison of biologically variable ventilation to recruitment manoeuvres in a porcine model of acute lung injury Funk Duane J [email protected] M Ruth [email protected] Linda G [email protected] James A [email protected] Bruce M [email protected] Elizabeth KY [email protected] Edward S [email protected] Craig [email protected] J Elliott [email protected] W Alan C [email protected] Department of Anaesthesia, University of Manitoba, Winnipeg, Manitoba, Canada2 Department Human Anatomy and Cell Science, University of Manitoba, Winnipeg, Manitoba, Canada3 James Hogg iCapture Centre, McDonald Research Laboratories, University of British Columbia, Vancouver, British Columbia, Cananda4 Department of Immunology, University of Manitoba, Winnipeg, Manitoba, Canada5 Department of Oral Biology, University of Manitoba, Winnipeg, Manitoba, Canada2004 24 11 2004 5 1 22 22 13 9 2004 24 11 2004 Copyright © 2004 Funk et al; licensee BioMed Central Ltd.2004Funk 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
Biologically variable ventilation (return of physiological variability in rate and tidal volume using a computer-controller) was compared to control mode ventilation with and without a recruitment manoeuvre – 40 cm H2O for 40 sec performed hourly; in a porcine oleic acid acute lung injury model.
Methods
We compared gas exchange, respiratory mechanics, and measured bronchoalveolar fluid for inflammatory cytokines, cell counts and surfactant function. Lung injury was scored by light microscopy. Pigs received mechanical ventilation (FIO2 = 0.3; PEEP 5 cm H2O) in control mode until PaO2 decreased to 60 mm Hg with oleic acid infusion (PaO2/FIO2 <200 mm Hg). Additional PEEP to 10 cm H2O was added after injury. Animals were randomized to one of the 3 modes of ventilation and followed for 5 hr after injury.
Results
PaO2 and respiratory system compliance was significantly greater with biologically variable ventilation compared to the other 2 groups. Mean and mean peak airway pressures were also lower. There were no differences in cell counts in bronchoalveolar fluid by flow cytometry, or interleukin-8 and -10 levels between groups. Lung injury scoring revealed no difference between groups in the regions examined. No differences in surfactant function were seen between groups by capillary surfactometry.
Conclusions
In this porcine model of acute lung injury, various indices to measure injury or inflammation did not differ between the 3 approaches to ventilation. However, when using a low tidal volume strategy with moderate levels of PEEP, sustained improvements in arterial oxygen tension and respiratory system compliance were only seen with BVV when compared to CMV or CMV with a recruitment manoeuvre.
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Background
A negative consequence of mechanical ventilation using lower tidal volumes (VT) in patients with acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) is alveolar collapse [1-3]. Numerous strategies to recruit these collapsed units have been advocated, but the efficacy of various recruitment manoeuvres for improving and sustaining gas exchange is controversial. Increased PEEP levels have been advocated to maintain patency of the recruited lung, but higher levels of PEEP can cause regional overinflation [4], potentially contributing to ventilator associated lung injury [5]. Moreover, recent evidence finds that when patients with ALI/ARDS are managed with a low tidal volume (VT) approach the addition of higher PEEP levels offers no further improvement in outcome [6]. Thus, high levels of PEEP may no longer have the same relevance for ALI/ARDS management as before. Independent of increases in FIO2, it remains unclear how best to improve and sustain oxygenation, during low VT ventilation strategies for ALI/ARDS management.
Buchman [7] and others [8,9] have highlighted how decreased physiological variability can negatively impact critically ill patients. When such patients require assisted ventilation, physiological variability or "noise" can be restored to the respiratory rate and VT through use of biologically variable ventilation (BVV), a unique computer-controlled version of control mode ventilation (CMV). With BVV, gas exchange and respiratory mechanics improved in animal models, with [10] and without PEEP [11], during low VT protocols using an ARDSNet algorithm [12] and in healthy lungs during prolonged ventilation under anaesthesia [13]. After deliberate collapse with one lung ventilation, recruitment was accelerated [14]. A recent clinical trial showed BVV improved gas exchange and respiratory mechanics in patients undergoing abdominal aortic aneurysmectomy [15]. Other investigators showed noisy ventilation increased surfactant phospholipid levels compared to CMV [16] and a mathematical model of how BVV can enhance recruitment and gas exchange has been advanced [17]. While previous work has indicated BVV results in superior gas exchange and respiratory mechanics compared to CMV with added sighs, this was a post-hoc comparison in a model of deliberate alveolar collapse [14], not a model of ALI/ARDS. As well, the sigh breaths were not equivalent to the larger sustained breaths customarily seen with a recruitment manoeuvre. Thus, it remains unknown if BVV is inferior, comparable or superior to conventional low VT ventilation with a recruitment manoeuvre in ALI/ARDS using a low VT approach.
Therefore, in this study in pigs with oleic acid lung injury, we compared BVV to conventional CMV or CMV with a recruitment manoeuvre (CMV-RM) of 40 cm H2O of continuous positive airway pressure for 40 sec performed hourly for 5 hrs. This approach has been shown to improve oxygenation in patients with early ARDS who do not have any chest wall impairment [18]. A multimodal approach was used to compare the three ventilation strategies. We measured gas exchange and respiratory mechanics. Bronchoalveolar lavage (BAL) fluid was collected to determine cell counts, inflammatory mediators and surfactant function. Tissue was examined by light microscopy to assess lung injury with an established scoring system [19,20] at end experiment.
Methods
Experimental Preparation
Pigs (weighing 20–30 kg) received 0.6 mg atropine, 15 mg midazolam, and 300 mg ketamine intramuscularly for sedation. Isoflurane 5% in 100% oxygen was delivered via facemask to induce anaesthesia. When sufficient depth of anaesthesia was achieved, the pigs were intubated orotracheally with a 6.0 mm cuffed endotracheal tube. Mechanical ventilation was instituted with an Ohio 7000 ventilator (Ohio Medical, Madison WI) with minute ventilation adjusted to maintain a PaCO2 of 35–45 mm Hg. Anaesthesia was maintained with 2% isoflurane in 100% oxygen during surgical preparation. Intravenous rocuronium bromide (1 mg/kg/hr) was administered by continuous infusion for muscle relaxation. Lactated Ringer's solution was given intravenously during the surgical preparation and for the duration of the experiment.
A thermodilution pulmonary artery catheter (7.5-Fr) was inserted and advanced into the right external jugular vein until a satisfactory pulmonary capillary wedge tracing was obtained. Temperature was measured from the tip of the pulmonary artery catheter. The right femoral artery was cannulated for continuous pressure transduction and arterial blood gas (ABG) analysis. A 5-Fr single lumen femoral venous catheter was advanced into the inferior vena cava for infusion of oleic acid. A surgical tracheotomy was performed and the animal was switched to an Esprit® ventilator (Respironics Inc., Palo Alto CA) capable of delivering either CMV or BVV. The ventilator was set to deliver a square wave inspiratory flow pattern with an I:E ratio of 1:2. Isoflurane was discontinued, and a propofol/ketamine infusion at 10/2.5 mg/kg/hr substituted to maintain anaesthesia.
After a 30 cm H2O recruitment manoeuvre for 30 sec, animals were ventilated with a VT of 10 ml/kg at an FIO2 of 0.3, with 5 cm H2O of PEEP. Respiratory rate was adjusted to maintain PaCO2 between 35–45 mm Hg.
After 15 min to stabilize, baseline measurements were obtained. Haemodynamic measurements included mean arterial pressure (MAP), heart rate, central venous pressure (CVP), mean pulmonary artery pressure (MPAP), and pulmonary artery occlusion pressure (PAOP). All haemodynamic data were continuously recorded on a Gould 2600 Oscillograph (Gould, Cleveland, OH). Cardiac output (CO) was measured by thermodilution, in triplicate, at stated measurement periods.
A pneumotachograph (Model 3700; Hans Rudolph, Kansas City, MO) with the sensor immediately proximal to the tracheotomy was used to measure airway pressures and VT intermittently; this data was recorded using an advanced CODAS (Dataq Instruments, Akron, OH) data acquisition system.
Arterial and mixed venous gases were analyzed using a Radiometer ABL 500 (Copenhagen NV, Denmark). Arterial and mixed venous oxygen content, oxygen saturation, and haemoglobin concentrations were measured with a Radiometer OSM3 set for porcine blood.
Static respiratory system compliance (Crs) was measured in triplicate by clamping the expiratory limb of the ventilator circuit at the end of inspiration for 1 sec to obtain a plateau pressure. The VT used was that which the animal was receiving at that time.
Oleic Acid Lung Injury
Oleic acid was infused via the 5-Fr femoral venous catheter at 0.2 ml/kg/hr until PaO2 <60 mm Hg for two consecutive measurements (PaO2/FIO2 <200). Dopamine was started at 5 μg/kg/min and was titrated to keep the MAP >60 mmHg. When the oxygenation target was achieved, the oleic acid infusion was stopped and the PEEP was increased to 10 cm H2O. Ten minutes after the increase in PEEP, an arterial blood gas sample was obtained to determine if PaO2 had increased. PaO2 had to be >75 mm Hg but <90 mm Hg. This was considered to represent adequate lung injury, but indicate that collapsed alveoli could be recruited with the additional PEEP. If the PaO2 did not increase, the experiment was terminated. If the PaO2 increased to >90 mm Hg, additional oleic acid was infused until the PaO2 decreased to <60 mm Hg.
Ventilation Protocol
A low VT (7 ml/kg) protocol was initiated and the respiratory rate increased to 30 bpm. After 10–15 min arterial blood gas sampling was done to assess the stability of the PaO2. If the PaO2 remained stable, the animals were then randomized into one of three groups: conventional ventilation with a VT of 7 ml/kg (CMV); conventional ventilation with VT of 7 ml/kg with a 40 sec, 40 cm H2O recruitment manoeuvre performed hourly (CMV-RM). The recruitment manoeuvre was performed at end-expiration with the PEEP level maintained at 10 cm H2O at an FIO2 of 0.3; or biologically variable ventilation (BVV) with a mean VT of 7 ml/kg.
Following stable oleic acid lung injury, haemodynamic, gas exchange and respiratory system compliance (Crs) measurements were recorded and obtained hourly thereafter. Measurements in the CMV-RM group were obtained 5 min after each recruitment manoeuvre (RM). An additional measure of gas exchange and Crs was made in the CMV-RM group 50 min after the RM, to ascertain the duration of effect of the RM.
Wet:Dry Lung Weight Ratios
At the end of the experiment, a sternotomy was performed. Animals were sacrificed with a lethal dose of thiopental. The trachea was then clamped at end inspiration and the heart and lungs were removed en bloc. Following removal, the lungs were suspended upside down for 10 min to collect bronchoalveolar (BAL) fluid. Samples of BAL fluid were collected in heparinized saline and then frozen immediately at -80oC. Fluid was then sent for cytokine analysis, measures of surfactant function and flow cytometry. The lungs and previously collected BAL fluid were weighed and the lungs were suspended and aerated overnight. The following day, the lungs were placed in an oven to dry, and following a stable dry measurement, wet:dry weight ratio was calculated.
Surfactant Function Assays
Surfactant function was assessed on BAL fluid samples using a capillary surfactometer (Calmia Medical, Toronto, Canada) in the manner of Enhorning and colleagues [21,22]. Such an approach can delineate differences in surfactant function with lung inflammation [23]. Surfactometry was performed under two conditions:
1. Raw BAL fluid surfactant function analysis – centrifuging at 200 g for 5 min to rid large debris, then the supernatant spun at 10,000 g and pellet resuspended in 100 μL saline and analyzed using the surfactometer.
2. Surfactant resuspended in 100 μL of saline after chloroform/methanol extraction. Chloroform/methanol extraction permits the lipid and phospholipid fraction to dissolve in the organic nonpolar solvent (chloroform) and the solvent evaporated to dryness. Volume of the final ratio for chloroform:methanol:water was 1:1:0.9. Following extraction, the pellet was resuspended and analyzed using the surfactometer.
The percentage of time that the capillary tube was open for 2 min was determined for each sample, in triplicate, then averaged. Standards were saline; 0% patent for a 2 min time period and bovine surfactant; >98% patent for a 2 min time period. Each sample was measured in a blinded fashion in both conditions.
Flow Cytometry Analysis
The BAL fluid sample was passed through a cell strainer and 100 μL was transferred into a 5 × 75 mm tissue culture tube. Red cells were lyses by the addition of 500 μL Optilyse C (Beckman Coulter, Mississauga, Canada) and following 15 min incubation at room temperature, the cell suspension was diluted by the addition of 500 μL Isoton II and 100 μL FlowCount Fluorospheres (Beckman Coulter). The cell suspension was immediately analyzed on a Beckman Coulter EPICS Altra cell sorter configured with a high-speed quartz flow cell tip and a water-cooled argon laser emitting 150 mW at 488 nm. Forward and side light scatter signals were employed to derive 2-parameter histograms which clearly defined the fluorescent beads and three populations of cells, subsequently defined as mononuclear cells, neutrophils and eosinophils. At least 20,000 cells were analyzed for each sample. The acquisition software provided with the instrument automatically calculated the concentrations of each cell population based on the number of events in each of the 4 analysis regions.
Cytokine Assays for IL-8 and -10 ELISA
The incubation times and washes were preformed as specified in each respective kit: (BioSource International, Camarillo, CA). Immunoassay kits for swine IL-8 and IL-10 were used. Sample incubation times were kept as constant as possible by pre-plating on a blank 96 well plate before transferring to the coated assay plate. ELISA plates were read at 450 nm by an SLT Rainbow plate reader (Lab Instruments, Research Triangle Park, NC). Standard curves and concentration calculations were performed according to kit directions.
Histological Assessment
For light microscopy, four blocks of tissue from the right lung – upper lobe, middle lobe, nondependent and dependent lower lobe, fixed in buffered formalin, processed and embedded in formalin. Sections were cut and stained with haematoxylin and eosin. Under light microscopy, a lung injury scoring system modified from Rotta et al. [19,20] was used, based on the following variables: alveolar and interstitial inflammation, alveolar and interstitial haemorrhage, oedema, atelectasis and necrosis. The severity of injury was graded for each of the seven variables: no injury = 0; injury to 25% of the field = 1; injury to 50% of the field = 2; injury to 75% of the field = 3; and diffuse injury = 4; for a maximal total score of 28.
Statistical Analysis
Parametric data were analyzed by repeated measures ANOVA using least squares means test matrices to identify differences within and between groups from group × time or group effects. Bonferroni's correction was applied where appropriate. Non-parametric comparison of the lung injury scores was by Kruskal-Wallis test. In all circumstances a p-value ≤0.05 corrected for multiple comparisons was considered significant.
Results
Twenty-five experiments were undertaken. One animal was discarded because oleic acid injury could not be established after 1.5 hrs of infusion. In the CMV-RM group, one animal died 2 hr into the experiment but data are included in analysis in this experiment up to time of the animal's demise. Complete experiments were done in twenty-three animals; (n = 8 for all three groups).
Temperature and Haemodynamic Data
Temperature and haemodynamic data are shown in Table 1: see Additional File 1. All data reported as mean ± SD, unless stated. A significant group × time interaction was seen for temperature. A crossover in temperature was seen with greater temperature at baseline in the BVV group that decreased over time by a mean of 0.8°C by end experiment. In contrast in both of the CMV groups the temperature increased modestly. Least squares means test showed MAP lower in CMV-RM at one hr and beyond after oleic acid compared to the other 2 groups. MPAP was increased significantly following administration of oleic acid in all groups. Between groups MPAP was lower with BVV from 1 hr after oleic acid. No group × time interaction was seen for PAOP (p = 0.755) but by least squares means test at baseline, PAOP was lower in the BVV group at baseline and at one hr following oleic acid. In all groups CO decreased following oleic acid and remained depressed beyond (group × time interaction p = 0.521).
Respiratory Gas and Derived Data
Significant differences were seen between groups for PaO2 over time (Figure 1a). The group × time interaction was p = 0.0001 with greater PaO2 seen with BVV, compared to either CMV or CMV-RM. There was no difference in PaO2 between groups at baseline, following oleic acid administration or during the first hour of the experiment. A clear separation in PaO2 levels became apparent after 2 hr following oleic acid injury in the BVV group. There was no difference between CMV and CMV-RM over time. Respiratory system compliance for the 3 groups is shown in Figure 1b. The group × time interaction was p = 0.089 between groups. Comparison of compliance between groups with least squares means showed significant differences between BVV with CMV and CMV-RM after 2 hr.
Figure 1 1a and b – Arterial Oxygenation and Respiratory System Compliance. Arterial oxygen tension (PaO2) over time for the 3 groups (BVV in red; CMV in blue and CMV-RM in green) (a). The group × time interaction is p = 0.0001. No difference is seen between groups at baseline and following oleic acid infusion. By 2 hr the PaO2 is greater with BVV (+). There is no difference between CMV and CMV-RM. Respiratory system compliance over time for the 3 groups (b). The group × time interaction is p = 0.089. Least squares means tests revealed BVV had significantly greater compliance after 2 hr (+). There was no difference between CMV and CMV-RM.
Table 2: see Additional File 2, shows respiratory gas and derived data for each group. There was no group × time interaction for PaCO2 (p = 0.660). In all groups, the PaCO2 increased following oleic acid injury and was essentially stable at these elevated levels for the remainder of the experiment. The group × time for PvO2 was significant at p = 0.002 with significantly lower PvO2 over time in the CMV-RM group. Shunt fraction was lower in the BVV group (group × time interaction p = 0.003).
Recruitment Effects on Oxygenation and Compliance
PaO2 tended to decrease immediately following a recruitment manoeuvre, but increased over time as measured at 50 min. PaO2 significantly decreased in RM4 and 5 (-11.1 ± 5.2 mm Hg and -5.2 ± 4.4 mm Hg respectively). Following recruitment, PaO2 increased slowly early in the experiment at RM2 and RM3 (10.1 ± 5.5 mm Hg and 11.8 ± 9.1 mm Hg respectively), but failed to do so in the later time periods. For respiratory system compliance, no statistically significant differences were seen at any time period over the course of the experiment in the CMV-RM group.
Airway Pressure Data
A group × time interaction for mean Paw was seen (p = 0.002) with Paw lowest with BVV (Paw at 5 hr was 14.3 ± 0.4 cm H2O with BVV; 15.1 ± 0.2 with CMV-RM and 15.1 ± 1.0 with CMV respectively). In all 3 groups, mean Paw increased following oleic acid. A more pronounced effect was seen with peak Paw (group × time; p = 0.0001) with mean peak pressures over time least with BVV (peak Paw at 5 hr was 23.2 ± 1.9 cm H2O with BVV; 28.8 ± 0.8 with CMV-RM and 28.6 ± 2.8 with CMV respectively). There was no group × time interaction for VT in ml/kg measured over time (p = 0.617).
Lung Histology
When the lung was scored in the upper, middle, nondependent and dependent lower lobes in the right lung, no differences were seen for any region by Kruskal-Wallis test between the 3 groups. In the dependent region of the lower lobes, injury was considerable with scores ranging between 9/28 and 22/28. Wet:dry weight ratios for the three groups were BVV; 8.5 ± 4.0, CMV; 8.9 ± 1.2, and CMV-RM; 11.5 ± 4.8; not significant between any group.
Capillary Surfactometry
Capillary surfactometry was done on raw BAL fluid and following chloroform/methanol extraction. There was no difference in surfactometry for raw BAL fluid between groups; the duration of capillary tube patency as an index of surfactant function was: 6.7 ± 14.3%, 0.7 ± 0.8% and 0.9 ± 1.7%; for BVV, CMV and CMV-RM respectively – not significant. Following chloroform/methanol extraction, surfactant function of the same fluid usually improved dramatically – see Figure 2. Here, duration of capillary patency was 57.6 ± 40.1%, 47.4 ± 43.8% and 81.3 ± 33.8%; for BVV, CMV and CMV-RM respectively – not significant between any groups. In 4 animals, the chloroform/methanol extraction had no discernible effect on surfactant function, in all others surfactant function improved markedly.
Figure 2 Capillary Surfactometry. Box and whisker plots for capillary surfactometry results from both raw (blue) and chloroform/methanol extracted (red) BAL fluid and patency time (%) over 2 min – log scale. In most circumstances the raw BAL fluid has minimal surface activity – low capillary patency. The one animal with a high raw value was from the BVV group. This patency markedly improved with extraction suggesting reactivation of surfactant with removal of BAL oedema fluid, proteins, including cytokines and cellular debris. In 4 animals there was essentially no change in function with extraction. There were 2 animals in the CMV group, and one each in the BVV and CMV-RM in this population. No statistically different behaviour in surfactant function was seen between groups, either for raw or extracted surfactant function.
Flow Cytometry
The flow cytometry of BAL fluid indicate no significant difference between cell counts either in terms of neutrophils, monocytes or eosinophils between groups. Neutrophils were the predominant cell type, at 74% ± 15% of the totals. The total BAL cell counts were highly variable however, with neutrophil counts varying from a low of 187 to a high of 26,189 cells/μL – a 140-fold difference. There was no group effect for neutrophils; p = 0.741. The mean BAL neutrophil counts were 8237 ± 8738/μL for BVV, 12024 ± 12328/μL for CMV and 6492 ± 4219/μL for CMV-RM. BAL fluid volume was also measured based on gravity drainage from the lung over a 10 min period. Volume was not significantly different between groups and was 13.6 ± 16.8 mL with BVV, 15.1 ± 10.6 mL with CMV and 28.4 ± 23.2 mL with CMV-RM. The total neutrophil count in the BAL fluid was calculated as neutrophils/μL × BAL volume in μL for each animal. Mean total neutrophil counts were 1.38 × 108 for BVV, 1.82 × 108 for CMV and 2.34 × 108 for CMV-RM. There was no significant difference between groups.
ELISA Results and Cytometry Correlations
The ELISA results to assess the cytokine IL-8 concentration in BAL fluid indicated no statistically significant difference for the 3 groups: 564 ± 551 pg/mL with BVV; 653 ± 639 pg/mL with CMV and 300 ± 235 pg/mL with CMV-RM. As in the measurement of neutrophil counts, there was significant variability with IL-8 levels ranging over a 385-fold concentration difference. Such a broad range of data in both neutrophil counts and IL-8 concentration suggested the possibility of power law behaviour and correlation between these variables. A very strong power law relationship was found between IL-8 pg/mL and absolute neutrophil count/mL for pooled data; y = 77609x0.75; R2 = 0.85, n = 13. This power law relationship exceeded the linear (R2 = 0.60) and exponential (R2 = 0.41) curve fits.
The number of mononuclear cells from cell cytometry of BAL fluid was not different between groups: for BVV, 1346 ± 743 cells/μL; for CMV, 1767 ± 905 cells/μL; and for CMV-RM, 1068 ± 579 cells/μL. These cells presumably in large part represent alveolar macrophages. A power law relationship was seen between pooled data for a correlation between IL-8 concentration in pg/mL and monocyte count/mL – 1.0 × 10-6x1.38; n = 13, R2 = 0.83. In this situation the linear and exponential fits were R2 = 0.61 and R2 = 0.69 respectively. In this analysis, IL-8 concentration is presumed to be the dependent variable – being released by the monocytes (counts on the x-axis).
The IL-10 results from BAL fluid, as well, indicate no statistically significant difference for the 3 groups: 119 ± 173 pg/mL for BVV; 124 ± 146 pg/mL for CMV; and 149 ± 168 pg/mL for CMV-RM. Unlike IL-8 concentrations the range of variation for IL-10 concentrations was significantly less – the maximum range differing by only 16-fold. There was no power-law relationship found between IL-10 and absolute monocyte count for pooled data: y = 7.5x0.15; n = 13, R2 = 0.07.
Discussion
The main finding in this study of acute lung injury in a porcine model is that BVV significantly improved oxygenation and respiratory mechanics with no difference in indices of lung injury, inflammation or surfactant function compared to the more conventional ventilation techniques – CMV or CMV with a standard recruitment manoeuvre. The improvements seen with BVV were sustained over the course of the experiment; an effect not seen with recruitment. Such improvement was obtained at similar measured mean VT but at lower peak and mean airway pressure and greater respiratory system compliance.
These findings are similar to previously published results from our laboratory showing that BVV was superior to both CMV and CMV with sigh breaths. These sigh breaths were of no advantage in a model of healthy lung recruitment – lung reinflation after a controlled collapse for one hr with contralateral one-lung ventilation [14]. In that model, the sigh breaths were not as large or as sustained as the recruitment manoeuvre studied here. An important difference from our previous work is that the current experiment examined recruitment in a low VT model of ALI/ARDS; not in healthy lung. As well, we have confirmed the differences between BVV and CMV seen in other work from the laboratory using the same model [12]. No important differences were seen for gas exchange or respiratory mechanics between CMV and CMV-RM in the current study.
Shunt fraction was significantly lower with BVV. Lynch et al. [24] and Sandoval et al. [25] showed that shunt fraction is directly related to cardiac output and PvO2. Thus, lower PvO2 in the CMV-RM group should have minimized shunt but here the shunt fraction is greatest. The lowest shunt with BVV indicates that the numerator of the shunt equation is less in this group, indicating enhanced blood flow in aerated lung units.
A multimodal approach to assess lung injury, lung inflammation and surfactant function indicated no discernable differences between the 3 approaches to ventilation:
1. Light microscopy studies demonstrated no significant differences between groups for lung injury in any region examined – from upper to dependent lower lobes. This finding indicates no difference between groups in lung injury as assessed by histology, with the caveat that only small areas of lung in each region were examined in a condition known to be heterogeneous.
2. No difference in surfactant function was seen between groups as assessed by capillary surfactometry. Following oleic acid lung injury, surfactant function was markedly depressed when compared to a surfactant standard – see Figure 2. The raw surfactant from the BAL fluid was not usually active beyond a few percent of normal function, with no difference between groups. In contrast, following chloroform/methanol extraction, surfactant function improved toward normal in most circumstances, again with no difference between groups. It is well known that surfactant is inactivated in the presence of oedema fluid, inflammatory proteins and cellular debris, but that the surfactant can be reactivated in the right circumstances as seen here following chloroform/methanol extraction. No correlation was seen for surfactant function of pooled data for PaO2, wet:dry weight ratios, respiratory system compliance, or peak airway pressure. Arold et al. [16] have shown that noisy ventilation similar to BVV can result in greater surfactant phospholipid concentration over time in healthy lungs. We do not address this specific issue.
3. No difference between groups is seen for cell counts in the BAL fluid. There is considerable variation in this analysis and a full data set is not present. A significant number of studies of cell count could not be undertaken because of the nature of the BAL fluid. When thick with proteinaceous debris the samples often were unable to be prepared for meaningful cytometry analysis. Of the data represented, no difference in the proportion of neutrophils, monocytes or eosinophils is apparent for any of the 3 approaches to ventilation. Allen and Bates [26] have shown that the number of neutrophils in BAL fluid – a 500 fold difference between groups in their study – did not correlate with changes in respiratory mechanics as assessed by changes in elastance with deep inflation in mice. Their findings suggest that the non significant differences in neutrophils counts – 70% greater total counts with CMV-RM than BVV – should be of no influence on respiratory mechanics.
4. IL-8 is considered to be the major neutrophil chemoattractant cytokine in lung diseases like ARDS [27]. IL-10 markedly inhibits lymphocyte and phagocytic function, essential for an adequate immune response to invading microbes [28]. Initial IL-10 serum levels have been shown to be significantly higher in patients with ARDS who died as compared to survivors [29]. No differences were seen between groups in the levels of the inflammatory cytokines IL-8 and IL-10 in BAL fluid. This is not surprising given similar monocyte and neutrophil counts between groups. A large coefficient of variation for both cytokine concentrations is demonstrated, a common finding in most such studies [30]. Such large variation makes meaningful comparisons between groups problematic. A power law relationship between IL-8 concentrations and monocyte and neutrophil counts in BAL fluid is a new insight. We found a very positive correlation relating IL-8 concentration and cell counts. The markedly variable IL-8 concentrations and cell counts in BAL fluid under controlled experimental conditions, suggests a highly nonlinear process has been initiated following oleic acid administration. Despite the observed variation in cytokine and cell counts a strong linkage is suggested by the power-law correlation. This observation, in and of itself is not surprising, given that IL-8 is a known chemoattractant. But the power law descriptor suggests that both low and high concentrations of IL-8 and corresponding cell counts are more frequent than expected. These data also suggest that this interaction is "scalable" with no specific mean value to be anticipated [31]. In this circumstance, large coefficients of variation are to be expected, making meaningful standard comparisons between groups problematic. Looking for similar power law correlations may help to elucidate the nature of the inflammatory process in the future. We have seen differences in temperature between BVV and CMV in the past with greater IL-8 levels with CMV [12]. We cannot confirm that finding in this study. As well, we were unable to confirm a previous observation of an inverse correlation of IL-8 concentration and wet:dry weight ratios in this model (R2 = 0.007 in this experiment, n = 24). Failure to reconfirm these findings may, in part relate to the nonlinear relationships highlighted above. Small changes in initial conditions may preclude similar findings at end experiment.
The above observations, collectively, indicate no fundamental differences between the ventilation strategies studied in regards oleic acid or superimposed ventilator associated lung injury. No one technique seems clearly advantageous. However, BVV alone improves oxygenation significantly following acute lung injury over time, an effect that was sustained, suggesting it is the best technique of the 3 studied to recruit atelectatic lung as assessed by greater PaO2 and respiratory system compliance. BVV has been shown to recruit in a pure model of lung collapse – re-expansion of lung following cessation of one lung ventilation [14]. With BVV, the addition of a noisy end-inspiratory pressure has been advanced as the mechanism to improve oxygenation in the face of the nonlinear characteristics of alveolar recruitment [17].
It could be argued that we have not chosen the optimal recruitment strategy to compare to the two other modes of mechanical ventilation. That said, we have chosen a well recognized approach to recruitment [18], one associated with improved patient outcome in a carefully conducted clinical trial. Amato and colleagues [32] did not state how often their chosen recruitment manoeuvre was used in their clinical study – just that it was used frequently – usually in association with suctioning the tracheobronchial tree. We have applied the Amato recruitment strategy in a comprehensive manner – hourly in an acute model of ARDS. But, in contrast to Amato and colleagues, where PEEP was increased from 5 cm H2O to 2 cm H2O above Pflex in an attempt to prevent derecruitment, we maintained PEEP at 10 cm H2O throughout. In a porcine model of lung lavage, lung remained recruited if PEEP was at 10 cm H2O: the value chosen in this study [33]. This level of PEEP is that recommended in a recent review of mechanical ventilation in ARDS, especially in the context of patchy ARDS where the risk of overdistention of patent alveoli increases the potential for volutrauma [34]. Furthermore, in the ARDSNet study, the Amato recruitment manoeuvre failed to sustain an improved arterial oxygenation, so was abandoned [6]. Failure to demonstrate an increase in oxygen with recruitment suggests either redistribution of blood flow to poorly ventilated regions or potentially increased alveolar flooding with alveolar-capillary disruption. Such causes may account for our failure to demonstrate improved oxygenation at 5 min after individual recruitment manoeuvres. Positron emission tomography indicates that PaO2 will not increase if the sustained inflation does not restore aeration to the atelectatic regions because a significant fraction of pulmonary blood flow is shunted to nonaerated regions [35]. Such may be the case in this study as shunt fraction was greatest in the CMV-RM group. Fujino and colleagues [36] have shown that a recruitment manoeuvre similar to the Amato manoeuvre – 40 cm H2O for 60 sec – was not beneficial in another animal model and that a more aggressive recruitment manoeuvre – PEEP 40 cm H2O and pressure control ventilation of 20 cm H2O, with a respiratory rate of 10, and I:E ratio 1:1 for 2 min – was successful only after the second hour. Allen et al. [37] have shown in a mouse model with deep inflation that PEEP at various, albeit low levels (1, 3, 6 cm H2O) did not influence the time constants for recovery in elastance both before and after lung lavage. They conclude that for a deep inflation to be beneficial in their model that it may be necessary to apply the inflation several times a minute. Thus, the appropriate PEEP level, with and following recruitment, remains controversial [38,39], but what is clear from the current study is that at the same PEEP level significantly better gas exchange and respiratory mechanics occurs with BVV over either mode of CMV – with or without recruitment, with no differences in lung injury, inflammation or surfactant function.
The human variability file for respiratory rate used to program the ventilator is shown in Figure 3. Mathematical analysis indicates that this file is not fundamentally different in characteristics from previous files used to program the ventilator. Such variability files have fractal time sequences and are associated with health. Whether or not "fractal noise" is superior to random or "white noise" is as yet unproven, however, such fractal variability is characteristic of human health. Suki et al. [17] showed the importance of noise to increase the volume recruited when alveoli are collapsed and provide a mathematical framework for how BVV can result in better gas exchange and respiratory mechanics in the context of alveolar collapse. This study confirms previous findings that BVV can deliver the same minute ventilation over time at lower mean peak airway pressure and greater respiratory compliance.
Figure 3 Human Respiratory Rate Variability File. Human breathing variability file used for the study. This is the raw data from a spontaneously breathing healthy female volunteer. The mean rate was 13.4 ± 2.0 breaths/min (shown as the red line). There are 1587 breaths in this file. With BVV, the ventilator is configured as a volume divider at a fixed minute ventilation so that respiratory rate × tidal volume product is constant. Thus the breath-by-breath volume related to instantaneous respiratory rate obtained from sequentially reading the above file in any given experiment is obtained from the minute ventilation/[(instantaneous breath rate/13.4) × chosen mean rate]. Analysis reveals that these data have fractal characteristics.
Conclusions
This study shows that BVV with a human variability file was superior to either CMV or CMV with a recruitment manoeuvre (CMV-RM) for sustained improvement in gas exchange and respiratory mechanics in a porcine model of acute lung injury. Prior work has demonstrated an advantage of BVV in a clinical setting of atelectasis [15]. The current study suggests that BVV may be superior to more conventional approaches to improve oxygenation without increasing the risk of volutrauma when low VT strategies are used for management of ALI/ARDS. A well accepted recruitment strategy has been compared to BVV in this animal study. It is possible that other approaches for recruitment could be superior to the one chosen. However, all chosen modes of recruitment would increase airway pressures over and above those seen with BVV, potentially increasing the risk of volutrauma.
Authors' contributions
D.J.F. was responsible for conduct of the experiments as a fellow in the Anesthesia Laboratory. M.R.G. supervised conduct of the experiments, helped analyze data and helped write the paper. L.G.G. helped with the experiments, data retrieval and collation and table and figure production. J.A.T. did the histological assessments and analysis. B.M.M. supervised the cytokine assays and their interpretation and helped write the paper. E. K-Y. W. conducted the cytokine assays. C.H. did the surfactant assays while a fellow in the Oral Biology Laboratory. J.E.S. supervised and interpreted the results of the surfactant assays. W.A.C.M. conceived the study, analyzed and interpreted data and helped write the paper.
Supplementary Material
Additional File 1
Table 1: Temperature and Haemodynamics
Click here for file
Additional File 2
Table 2: Respiratory Gas and Derived Data
Click here for file
Acknowledgements
The authors thank Respironics Inc. for providing the Esprit® ventilator for the study and the hardware and software development for BVV as well as financial support. The authors also thank Dr. Gerald Lefevre, CEO of Biovar Life Support Inc. for financial support. The CIHR also provided financial support through their regional partnership program.
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| 15563376 | PMC535805 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Nov 24; 5(1):22 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-22 | oa_comm |
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BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-4-201554648810.1186/1472-6947-4-20Research ArticleTreatment decision-making and the form of risk communication: results of a factorial survey Hembroff Larry A [email protected] Margaret [email protected] Celia E [email protected] Institute for Public Policy and Social Research, 321 Berkey Hall, Michigan State University, East Lansing, MI, 48824, USA2 College of Human Medicine, Michigan State University, East Lansing, MI, USA3 College of Nursing, Michigan State University, East Lansing, MI, USA2004 16 11 2004 4 20 20 11 5 2004 16 11 2004 Copyright © 2004 Hembroff 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
Prospective users of preventive therapies often must evaluate complex information about therapeutic risks and benefits. The purpose of this study was to evaluate the effect of relative and absolute risk information on patient decision-making in scenarios typical of health information for patients.
Methods
Factorial experiments within a telephone survey of the Michigan adult, non-institutionalized, English-speaking population. Average interview lasted 23 minutes. Subjects and sample design: 952 randomly selected adults within a random-digit dial sample of Michigan households. Completion rate was 54.3%.
Results
When presented hypothetical information regarding additional risks of breast cancer from a medication to prevent a bone disease, respondents reduced their willingness to recommend a female friend take the medication compared to the baseline rate (66.8% = yes). The decrease was significantly greater with relative risk information. Additional benefit information regarding preventing heart disease from the medication increased willingness to recommend the medication to a female friend relative to the baseline scenario, but did not differ between absolute and relative risk formats. When information about both increased risk of breast cancer and reduced risk of heart disease were provided, typical respondents appeared to make rational decisions consistent with Expected Utility Theory, but the information presentation format affected choices. Those 11% – 33% making decisions contrary to the medical indications were more likely to be Hispanic, older, more educated, smokers, and to have children in the home.
Conclusions
In scenarios typical of health risk information, relative risk information led respondents to make non-normative decisions that were "corrected" when the frame used absolute risk information. This population sample made generally rational decisions when presented with absolute risk information, even in the context of a telephone interview requiring remembering rates given. The lack of effect of gender and race suggests that a standard strategy of presenting absolute risk information may improve patient decision-making.
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Background
In contemporary U.S. society, prospective users of preventive therapies often must evaluate complex information about therapeutic risks and benefits. While health information is increasingly available, it is frequently incomplete and sometimes inaccurate [1,2]. For people to be active participants in health care decisions, information regarding the benefits and risks of therapies must be presented in ways that support high-quality, effective decision-making [3,4].
Prior research on presentation formats that are most effective, comprehensible, and likely to lead to effective decision-making suggests that a critical element is the format of risk information [5]. Mazur [6] suggests that how patients use and react to information are important dimensions for assessing patients' understanding. For example, prior studies have demonstrated the importance of the visual aspects of information presented [7,8] on shaping preferences and use of information.
A key format difference is absolute versus relative gain or loss [9,10]. Absolute risk is a rate. It expresses the actual number of people experiencing an outcome, e.g., 1 in 1,000 women. Relative risk is a ratio (risk among exposed divided by the risk among the non-exposed). Thus, in relative risk terms, those who smoke are described as twice as likely as non-smokers to get lung cancer; taking medication X is described as cutting the risk of disease in half. However, relative risks do not reveal the actual rates of occurrence. Doubling a 2% risk is very different in its consequences from doubling a 40% risk. It has been previously demonstrated that relative risk presentation can produce errors. By "errors," we mean that people's perceptions of risk and benefit information does not conform to normative Expected Utility (EU) theory assumptions. Central to EU theory is the assumption that individuals who make "rational" choices should prefer an option with the highest expected utility relative to all options being considered.
Prior clinical studies have found that presenting information in absolute versus relative risk formats can affect the interpretation of information about medications and personal risks of developing diseases. For example, in a sample of family practice patients, Hux and Naylor [10] found that patients were less willing to take a lipid-lowering drug when risks were described in an absolute compared to a relative risk format (42% versus 88%). Likewise, in a relative risk assessment task, Woloshin et al. [11] found that about two-thirds of a sample of women who were actually at high risk for breast cancer classified themselves as being at less risk than they really were. However, results of clinical studies may or may not be generalizable to the general population. Without cross-sectional samples, it is difficult to develop a general population profile of those most likely not to understand health information, i.e., to not use it "correctly." This study tested the effects of alternative information presentation formats (absolute risk versus relative risk formats) on medication decision-making of adults in the general population.
Methods
Design
Data were collected as a part of a cross-sectional telephone survey of the adult, non-institutionalized resident population of Michigan. The telephone survey was part of a continuing series of quarterly surveys in Michigan known as the State of the State Survey (SOSS) conducted by Michigan State University's Institute for Public Policy and Social Research (IPPSR). 952 interviews were completed within the designated interviewing period. The margin of sampling error for the sample as a whole is ± 3.2%. The average interview lasted approximately 22.8 minutes (median = 22 minutes; standard deviation = 4.8 minutes). A more detailed description of the SOSS methodology, sample design, procedures, and content, is provided in Hembroff and Silver [12].
Sample
The sampling design for the State of the State Surveys is a disproportionate stratified random sample of phone numbers across the state's regions, over-sampling the less populated regions, and the city of Detroit. Trained interviewers called the random-digit dial (RDD) sample of telephone numbers to identify those that were actually working household phone numbers. Within identified households, interviewers interviewed a single adult selected using the "next birthday" technique [13] in which the adult in the household who would have the next birthday was chosen to be the respondent.
The data being reported have been weighted to adjust appropriately for the unequal probabilities of respondent selection based on the number of phone lines to the household and the number of adults within the household. Cases were also weighted to adjust for differential response rates among categories of race, gender, age and to adjust for regional disproportionate sampling. The final weighted data file very closely matches the demographic profile of Michigan's adult population.
Procedures and response rate
Interviews were conducted using the computer assisted telephone interviewing facility (CATI) of IPPSR's Office for Survey Research (OSR). Interviewing took place over a six week time period during June and July of 1997. The overall completion rate among eligible households was 54.3%. The calculated response rate is based on assigning final outcome dispositions codes according to Standard Definitions, the American Association of Public Opinion Research [14] guidelines for calculating response rates using Formula RR4.
Presenting health chances and patient treatment decision-making
The interview contained a split-ballot experiment to examine the effects of alternative ways of communicating risks. All respondents were presented with a baseline scenario. In this case, we asked respondents to ...
"Suppose you had a friend who was told that she was very likely to get a bone disease that would gradually make her crippled. Suppose the doctor said that there was a medication she could take on a daily basis that would greatly reduce her chances of getting the bone disease."
Respondents were asked if they would recommend that their friend take or not take the medication. Respondents were then randomly assigned to be presented one of four follow-up questions. Each follow-up question provided additional information about the hypothetical medication in terms of its benefits or risks. Half of the respondents were told that the medication would increase the patient's risk of breast cancer while the other half were told that the medication would decrease the patient's risk of heart disease. For each half, however, the information was presented in one of two randomly assigned alternative formats. Regarding a decreased risk of heart disease, respondents were told either that:
"by taking the medication she would also reduce her risk of heart disease by more than half," or that
"by taking the medicine she would also reduce her risk of heart disease from 1 in 200 to 1 in 500."
Regarding the increased risk of breast cancer, respondents were told either that
"by taking the medication she would also double her risk of breast cancer," or that
"by taking the medication she would also increase her risk of breast cancer from 1 in 10,000 to 2 in 10,000."
Based on the additional information received, respondents were asked what they would recommend that their friend do. Subsequently, each of these four groups was randomly divided again and given the additional information about increased risk of breast cancer or decreased risk of heart disease that they had not yet received. Then they were asked one more time to indicate what they would recommend to the friend about taking the medication. Thus, respondents were asked to indicate their treatment recommendations three separate times, under three of nine sets of conditions represented by the table below.
An initial treatment recommendation was made based on the information given in condition 1, and then again based on the combinations of information represented by conditions 2, 3, 4, or 7. A third and final recommendation was made based on the combination of information represented in conditions 5, 6, 8, or 9. Comparing the results for conditions 2 through 9 to the results from the baseline in condition 1 allows an assessment of the incremental effect of each additional piece of information on the treatment recommendation.
The dependent variable is the decision to recommend the medication or not. We hypothesized responses to vary by risk format and number of competing risks. For example, the number of "don't know" responses would be greater in conditions where complete information about competing risks was given. The benefits-only format would produce a majority of positive recommendations. However, we could not predict how large those proportions might be. Direction of change was expected to be positive for added benefit information, negative for more risk information, and reflecting trade-offs when risks and benefits were present. That is, we expected respondents to make a "rational" choice based on the perceived greatest expected utility.
Analyses
To assess whether additional risk information about breast cancer or heart disease affected decisions in conditions 2 through 9, we compared the distributions of change responses for each condition. A "non-change response" occurs when the respondent gives the same answer to the follow-up question as to the baseline question. All other patterns of answers, such as a "don't know" or a "don't take the medication response to the follow-up after giving a "take the medication" response to the baseline, or vice versa, constitute change responses. Although some error variance would be expected, analyzing the distributions of change responses across baseline decisions for the individual treatment conditions determines whether or not the experimental manipulations had any systematic effect.
If the added information supports rational decision-making, the percentage of "don't knows" and recommendations against taking the medication would go down if the net effect of the additional information is further risk reduction. Conversely, if the net effect of the additional information is to increase the risk of health problems, the percentage of change responses would be greater among those who initially said "don't know" or to take the medication. The greater the magnitude in the new absolute risk, the greater should be the percentage of respondents who should change their decisions. To test for the effect of risk information format, we aggregated the respondents' decisions. We dummy-coded whether the risk information had been presented in absolute risk terms or not. We also dummy-coded whether the information was given for breast cancer (= 1) or heart disease (= 0). To test the effect of format using logistic regression, we also aggregated recommendations. A dummy variable was created to indicate whether or not the risk information for breast cancer was presented in absolute risk terms (= 1), and similarly for heart disease risk.
To develop a profile of those who could not make a recommendation, we dummy-coded recommendations to the baseline scenario into those recommending either to take or to not take the medication (DONTKNOW = 0) and those who said they did not know what to recommend (= 1). To develop a profile of those who made seemingly non-rational recommendations, we dummy-coded recommendations to the baseline scenario into those recommending to take the medication (WRONG = 0) and those who recommended not to take the medication or said they did not know what to recommend (WRONG = 1).
All analyses were performed using SPSS for Windows 7.5.
Results
A total of 952 individuals were interviewed. In the weighted datafile, 54% were female; 12% were African Americans and 3% were Other non-whites; 26% were under age 30, 40% were 30–49, 18% were 50–64, and 16% were 65 or older; and 43% had a high school education or less, 31% had some college, and 26% had a college degree or more education. The reported median household income was between $40,000 and $50,000. In terms of health indicators, 15% rated their health as only "fair" or "poor;" 14% reported engaging in no leisure time exercise; 25% were current smokers; 38% were overweight or obese; and 1% were heavy drinkers. This profile shows that the sample obtained was representative of the general population of Michigan. All demographic and clinical characteristics were within the survey's margin of sampling error for the prevalence rates for these reported by the Centers for Disease Control and Prevention (CDCP) for Michigan in 1997 except for leisure time exercise.
Information-based decisions
In the baseline condition, two-thirds of the respondents (66.8%) said they would recommend their friend take the medication, one in nine (11.2%) said they did not know what to recommend, and 22.0% recommended that their friend not take the medication.
Table 2 shows the percentages of respondents who changed their recommendation under each of the eight additional information conditions from their baseline decision. For each column, only the percentage of respondents who changed is shown, along with the total number of cases who made each of the three possible baseline choices in that condition.
As shown in Table 2, the experimental manipulations were effective and influenced treatment decisions as predicted. The table shows that the additional information presented did significantly effect respondents' decisions in conditions 2, 3, 4 and 7. In conditions 2 and 3, with additional breast cancer risk information, initial positive or "don't know" recommendations were 4 to 6 times more likely to change with new information. In conditions 5 and 7, where new information was about reduced risk of heart disease, those who had initially recommended not taking the medication or did not know were 7 to 10 times more likely to change their decision than those who had already made positive recommendations.
Similarly, in conditions 5, 6, 8, and 9, the percentage of respondents who changed their recommendation from baseline differed. That is, the information in conditions 5, 6, 8 and 9 were similarly effective at creating systematic changes in respondents' recommendations. Furthermore, those who changed a response from "don't know" at baseline, made changes consistent with the health outcome logically effected. This was true across all eight non-baseline conditions.
Format of risk presentation
Table 3 shows treatment recommendations for all nine conditions. Added information about reduced risk of heart disease increased the percentage of positive recommendations. The format appeared to make no difference, perhaps because the absolute risk of heart disease was roughly consistent with respondents' prior assumptions. However, the percentage of "don't knows" was greater under absolute risk conditions.
Table 3 demonstrates that the added information about increased risk of breast cancer dramatically reduced the percentage of positive recommendations. When presented only in relative risk terms, the percentage of positive recommendations declined by more than two-thirds. When increased risk was presented in absolute risk terms, and risks were very low, the decrease was smaller. The difference appears to be that, although doubled, the risk of breast cancer was still very small in absolute risk terms.
Table 3 shows the results for the conditions in which various combinations of additional information were given. Additional information that the medication would both double the risk of breast cancer and cut the risk of heart disease by more than half reversed decisions in the baseline condition. Virtually the same percentage of respondents chose to recommend against the medication as when only the additional breast cancer information was given. The change was much greater than when only the additional information about the reduced heart disease risk was given. On the other hand, when absolute risk for breast cancer increased and absolute risk of heart disease decreased, the percentage recommending medication was the same as the percentage recommending against medication. Both fall roughly midway between the corresponding relative risk percentages.
Where the increased absolute risk of breast cancer due to the medication was given along with information about the decreased relative risk of heart disease, decisions to recommend the medication were nearly as great as in the baseline condition. However, the evidence clearly suggests respondents paid attention to both additional pieces of information. Where the very low absolute risk of breast cancer was presented, the increased risk appeared to be more acceptable in the face of the benefit in reducing the risk of heart disease. Where respondents were given information in absolute risk terms for both breast cancer and heart disease, twice as many chose to recommend the medication as did where both risks were given only in relative terms. This was only two-thirds as many as did so in the baseline condition. Compared to the baseline condition, respondents seemed to use both additional pieces of information. This is consistent with Expected Utility theory predictions, but was clearest under absolute risk conditions.
Table 4 shows the overall results of risk format. The logistic regression equation predicted the correct outcome for 70% of the cases choosing to recommend not taking the medication and 68% of the cases recommending taking the medication, and 69% overall. The interaction term was significant, indicating that communicating the information in absolute risk terms significantly affected the chances that the respondents would elect to recommend taking the medication, but that the effect was different depending on the type of disease. Receiving the risk information in absolute risk terms for breast cancer significantly increased the likelihood of a positive recommendation, but was not so for heart disease. For heart disease, the information regarding the absolute risk of breast cancer appears to have suggested that the overall risk was lower than previously assumed. In this case, knowing the absolute risk made medication-induced risk more acceptable.
Table 5 shows the effect of absolute risk on decisions. Controlling for demographic, geographic, health, and health risk variables did not change the results (data not shown). The logistic regression equation predicted the correct outcome for 85% of the cases choosing not to recommend taking the medication, 36% of the cases recommending medication, and 69% overall.
The table (again) shows that the interaction term is significant, indicating that the effect of the risk information format for each disease differs depending on what is known about the other disease. In this case, the table indicates that communicating risk information about both heart disease and breast cancer in absolute risk terms significantly changes positive recommendations compared to scenarios in which respondents received risk information in other combinations of formats.
Non-rational decisions or no decisions
A third of the cross-sectional sample chose something other than to recommend taking the medication in the baseline condition. While the primary purpose of this experiment was to explore the effect of information format on treatment decisions, additional analyses were performed to better understand individuals who seem to make decisions contrary to the evidence given, or who indicated an inability/unwillingess to make a decision. The two dummy variables constructed for this purpose, DONTKNOW and WRONG were analyzed against a broad set of demographic and clinical variables, including gender, race, ethnicity, living as a couple or not, age (in categories), rural/urban dwelling, education (in 4 categories), current smoker, current heavy drinker, perceived health as only fair or poor, physical activity, overweight, employed, and avoids medical care or uses alternative sources of care. (Heavy drinker and overweight definitions were taken from the Centers for Disease Control and Prevention (CDCP) Behavioral Risk Factor Surveillance System.
Table 6 indicates that those most likely to report not knowing what to do (DONTKNOW) were respondents who were not employed and those who had children under 18 living in the household. Other than these factors, none of the other variables were significant predictors. Those who were not working and those who had children at home under 18 were somewhat more likely than others to say they received most of their health information from newspapers or teachers. They were also somewhat less likely to say they received most of their information from doctors. Somewhat more than other respondents, these individuals also more frequently reported believing information in the media to be clear and not confusing. The number of cases on which this analysis is based is quite small (105 of the 937 respondents answered "don't know" to the baseline question) making the findings only suggestive. Nevertheless, it is interesting that those who were uncertain how to proceed were typically more likely to rely on newspapers and teachers rather than doctors as their major source of health information [17,18].
Table 6 also shows predictors of WRONG, i.e., those who gave either the "don't know" or "do not take the medication" responses. The table indicates that five variables were significant predictors. Hispanics were more likely to give the "non-rational" answer, as were those who smoke, older respondents, those with children living in the household, and those who had more education. Those who said their health was only "fair" or "poor" were actually more likely than others to give the "rational" answer.
Conclusions
In general, the risk presentation format appeared to influence the resulting decisions. Without knowing the absolute risks, respondents appeared to assume the risk of breast cancer was much greater than the risk of heart disease, or that the perceived likely consequences of breast cancer were significantly less acceptable than those of heart disease. The pattern of recommendations was made to avoid the greater perceived risk. When the competing risks were stated in absolute risk terms, respondents changed their recommendations appreciably. The results of the experiment strongly suggest that people's prospective preventive treatment decisions can be greatly affected by the manner in which potential risks and benefits are communicated to them. When given information regarding the absolute risks associated with a proposed treatment, individuals make remarkably "rational" decisions, clearly indicating the weighing of risks and benefits.
It appears that presenting risks in absolute rather than relative risk formats may be more effective in helping respondents make decisions that are consistent with maximization of expected utility. It appears, in the scenarios devised here, this may be due to the previously documented finding that people exaggerate the risk of cancer [15-17]. They appear to draw on such preconceptions when phrases such as "double your risk" or "cut risk in half" are used in describing possible side effects of treatments. To some degree, exaggerated cancer risk perceptions may result from media reports, public health cancer screening campaigns, and defensive medicine protocols for screening for rare diseases [18].
Our findings seem to indicate that, in the absence of explicit information regarding the absolute risks of particular outcomes, patients interpret information about relative benefits or risks based on whatever preconceptions they may have about their chances of getting the disease in question. That is, they make reasonable use of the relative risk information if they do not know the base risk rates. However, to the extent that these preconceptions may be quite inaccurate, relative risk information may lead them to make decisions that seem irrational and inconsistent with their actual preference. Without information about the absolute risks, health information may become misleading and individuals may reach conclusions that go quite counter to what they actually think about the appropriate balance of benefits and risks of treatments.
Framing the scenarios as recommendations to a friend made the decision-making relevant to both males and females. The logistic regression analyses found no significant differences in decisions based on race, gender, or other demographic characteristics in this relatively large and representative sample. This suggests that these results may represent a general population response pattern. It is clear from the results that members of the public will generally use explicit absolute risk information in quite rational ways when it is available. Consequently, providers of health information, both public and clinical, would be well-advised wherever possible to provide patients with the necessary risk/benefit information in terms of the absolute risks associated with treatment options. Making the best information readily available and comprehensible may reduce reliance on errant preconceptions and opinions. This research suggests that having absolute risk information routinely available to patients and physicians for use in making clinical decisions may be an important part of improving health care decisions.
This study is unique in surveying the general public rather than clinical populations on the effects of risk formats on decision-making. However, it was limited to presenting risks only in terms of the chances of an outcome occurring. It did not address the magnitude of the outcome's impact on the individual's life or the acceptability to the individual of the relevant alternatives. Future research should address these aspects of risk as well as explicitly investigating assumptions about risks of specific diseases. Research that explicitly assesses general public risk perceptions may be valuable in determining the need for absolute risk information in specific disease contexts. Providing baseline absolute risk information in certain disease contexts may lead to improved patient treatment decision-making.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LAH conceived and carried out the survey. MHR and CEW provided medical decision making context for interpretation and discussion. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
The data reported here were collected as a part of the series of quarterly surveys of the State of Michigan constituting Michigan State University's State of the State Survey program (1). The funding for the survey reported here was provided by the M.S.U. Department of Radiology as a part of its contribution to the SOSS program. While IPPSR accepts responsibility for the quality of the data, the interpretation and conclusions presented are solely those of the author(s). Preparation of the manuscript was supported in part by a grant to the third author from the National Institute of Mental Health (MH01721).
Figures and Tables
Table 1 Representation of the baseline and incremental information conditions of the decision experiment
Medication will reduce friend's risk of crippling bone disease. And ... Information given about heart disease risk
None Risk is reduced by more than half Risk is reduced from 1 in 200 to1 in 500
Information given about breast cancer risk None 1 Baseline Condition 4 7
Risk Doubles 2 5 8
Risk increases from 1 in 10,000 to 2 in 10,000 3 6 9
Table 2 Percentage of respondents who changed recommendation decision from baseline choice, by additional information condition
Recommendation decision at baseline
CONDITIONa Take medication Do not take medication Don't Know χ2
2. BC doubles % Changed 70.6% 12.5% 53.6% 56.74**
(n) (160) (56) (28)
3. BC (1/10,000–2/10,000) % Changed 34.7% 9.1% 59.3% 20.11**
(n) (144) (44) (27)
4. HD < half % Changed 5.8% 37.2% 51.7% 45.47**
(n) (137) (43) (29)
7. HD (1/200 – 1/500) % Changed 4.5% 36.5% 28.6% 42.97**
(n) (177) (63) (21)
5. BC doubles; HD < half % Changed 67.6% 12.5% 25.0% 52.43**
(n) (136) (56) (16)
6. BC (1/10,000–2/10,000); HD < half % Changed 19.1% 35.3% 64.9% 29.93**
(n) (141) (34) (37)
8. BC doubles; HD (1/200 – 1/500) % Changed 58.0% 4.3% 58.6% 43.65**
(n) (181) (46) (29)
9. BC (1/10,000–2/10,000); HD (1/200 – 1/500) % Changed 42.8% 27.5% 54.5% 6.92*
(n) (159) (69) (22)
* p < .05; ** p < .001
a The numbers in the left margins of these rows refer to the condition numbers as identified in Table 1.
Table 3 % Recommending a friend take treatment for bone disease by nature of information given regarding effects on chances for breast cancer or heart disease
Information given about ______ beyond baseline scenario Heart disease
Breast cancer None Risk is reduced by more than half Risk is reduced from 1 in 200 to 1 in 500
None % who recommend Take 66.8% 73.3% 71.6%
Don't take 22.0% 15.2% 17.0%
Don't know 11.2% 11.5% 11.5%
(n) (937) (210) (262)
Risk doubles % who recommend Take 21.2% 22.3% 31.3%
Don't take 59.8% 60.8% 49.6%
Don't know 19.0% 16.9% 19.1%
(n) (245) (209) (257)
Risk increases from 1 in 10,000 to 2 in 10,000 % who recommend Take 49.0% 62.8% 42.9%
Don't take 40.0% 26.1% 42.9%
Don't know 11.0% 11.1% 14.2%
(n) (217) (214) (252)
Table 4 Results of Logistic Regressions of Recommending Taking Medication on Type of Disease and Format of Risk Communication (n = 930)
Independent Variable Coefficient (B) (s.e.) Exp(B)
Format Risk Communication (Absolute Risk = 1) -.089 (.208) .915
Type Disease (Breast Cancer = 1) -2.326** (.221) .098
Interaction 1.365** (.293) 3.914
Constant 1.012** (.156)
Model χ2 179.781**
**p < .001
Table 5 Results of logistic regressions of recommending taking medication on communicating breast cancer, heart disease risks in absolute terms (n = 926)
Independent Variable Coefficient (B) (s.e.) Exp(B)
Breast cancer (absolute risk = 1) 1.481** (.206) 4.397
Heart disease (absolute risk = 1) .111 (.208) 1.117
Interaction -.715** (.283) .489
Constant -1.075** (.152)
Model χ2 75.102**
**p < .001
Table 6 Results of logistic regression of "don't know" (DONTKNOW) and "not take the medication – don't know" (WRONG) answers on respondent demographic and health profile variables (n = 942)
WRONG Answer DONTKNOW Answer
Variable B Exp(B) B Exp(B)
Sex -.176 .839 -.188 .828
Race
African Americans -.408 .665 -.193 .824
Other non-white -.938 .391 -.539 .584
Hispanic .988* 2.687 -.546 .580
Health fair or poor -.538* .584 -.177 .838
No, only non-mainstream source health care .155 1.167 -.637 .529
Married or living with partner .126 1.134 -.017 .984
Where live
Rural -.054 .947 .242 1.274
Small town -.303 .738 .426 1.531
Suburb -.503 .605 -.235 .791
Currently employed -.000 1.000 -.635* .530
Current smoker .445* 1.561 -.065 .937
Physically active -.197 .821 .354 1.425
Heavy drinker -.496 .609 .094 1.099
Age (in categories) .152** 1.164 .129 1.138
Level of education (in categories) .240** 1.273 .151 1.163
Children < 18 living in household .795** 2.214 .714* 2.042
Currently overweight or obese .062 1.064 .010 1.010
Constant -1.973** -3.271**
Chi-square = 66.806** 29.727*
df = 18 18
* p < .05; ** p < .001
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| 15546488 | PMC535806 | CC BY | 2021-01-04 16:03:41 | no | BMC Med Inform Decis Mak. 2004 Nov 16; 4:20 | utf-8 | BMC Med Inform Decis Mak | 2,004 | 10.1186/1472-6947-4-20 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-321554117610.1186/1472-6963-4-32Research ArticleInconsistent self-reported mammography history: Findings from the National Population Health Survey longitudinal cohort Bancej Christina M [email protected] Colleen J [email protected] Judy [email protected] Centre for Chronic Disease Prevention & Control, Population and Public Health Branch, Health Canada, Ottawa ON, Canada2 Departments of Community Health Sciences and Medicine & Fellow of the Institute of Health Economics, University of Calgary, Calgary AB, Canada3 Tobacco Control Programme, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa ON, Canada2004 12 11 2004 4 32 32 17 4 2004 12 11 2004 Copyright © 2004 Bancej 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
Self-reported information has commonly been used to monitor mammography utilization across populations and time periods. However, longitudinal investigations regarding the prevalence and determinants of inconsistent responses over time and the impact of such responses on population screening estimates are lacking.
Methods
Based on longitudinal panel data for a representative cohort of Canadian women aged 40+ years (n = 3,537) assessed in the 1994–95 (baseline) and 1996–97 (follow-up) National Population Health Survey (NPHS), we examined the prevalence of inconsistent self-reports of mammography utilization. Logistic regression models were used to estimate the associations between women's baseline sociodemographic and health characteristics and 2 types of inconsistent responses: (i) baseline reports of ever use which were subsequently contradicted by follow-up reports of never use; and (ii) baseline reports of never use which were contradicted by follow-up reports of use prior to 1994–95.
Results
Among women who reported having a mammogram at baseline, 5.9% (95% confidence interval (CI): 4.6–7.3%) reported at follow-up that they had never had one. Multivariate logistic regression analyses showed that women with such inconsistent responses were more often outside target age groups, from low income households and less likely to report hormone replacement therapy and Pap smear use. Among women reporting never use at baseline and ever use at follow-up, 17.4% (95%CI: 11.7–23.1%) reported their most recent mammogram as occurring prior to 1994–95 (baseline) and such responses were more common among women aged 70+ years and those in poorer health.
Conclusions
Women with inconsistent responses of type (i), i.e., ever users at baseline but never users at follow-up, appeared to exhibit characteristics typical of never users of mammography screening. Although limited by sample size, our preliminary analyses suggest that type (ii) responses are more likely to be the result of recall bias due to competing morbidity and age. Inconsistent responses, if removed from the analyses, may be a greater source of loss to follow-up than deaths/institutionalization or item non-response.
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Background
In the absence of organized screening, self-report is often the only means to monitor mammography utilization and to investigate trends in uptake at the population level [1]. In Canada, mammographic screening occurs both within organized programs and opportunistically through routine medical practice [2]. The validity of mammography self-report has previously been studied primarily using convenience samples. Despite differences in methodology, design and population characteristics, studies from a variety of settings have found that self-reports of mammography use are valid provided women are not required to precisely recall the timing of a previous mammogram [3-14]. Women generally tend to underestimate the time elapsed since their most recent mammogram by an average of three months or more, though overestimation can also occur [4-6,8-13,15-17]. Greater discrepancies in recall may occur when the mammogram took place longer ago [15,16], though contrary evidence also exists [11]. With some exceptions [4,6,8,9,13,16,17], studies have not been designed to assess false negative reporting due to the challenge of verifying historical data from multiple service providers. Those that have attempted to validate non-use have indicated that women are unlikely to deny having had a mammogram when indeed they have had one [4,6,8,9,13,17], though false-negative self-reports are not always negligible [16]. However, the opposite is often true; women tend to report having had mammograms which are not verified against medical records [3,4,6,7,9-11,13-16,18-21], a particular problem in groups with low screening prevalence [20]. Valid reporting of mammography use has been found to be unrelated to various health behaviors and perceptions, socioeconomic, demographic, and questionnaire administration factors in some studies [4,10,12,13]. However, others provide some evidence that age, ethnicity, education, employment status, family history of breast cancer, recency of the mammogram, and the regularity with which women receive mammograms do affect self-report accuracy [7,10-13,17,22].
Few evaluations of the reliability of mammography self-report are reported in the literature. Excellent test-retest reliability for having ever had a mammogram was reported in interviews conducted 1 week, 6–30 days, or 6–8 months after an initial interview, while reliability within the past year varied from excellent to good [14,23,24]. In a socioeconomically advantaged group of women aged 50–75 followed annually for 3 years, 98 percent provided logically consistent responses to a question on ever/never use of mammography [25]. Self-reports of ever use have been shown to be more reliable among Caucasian women and those with higher income and education [24]. However, in this study, date of last mammogram was not as reliably reported 6–8 months after initial testing [24].
Based on data from the longitudinal panel of the National Population Health Survey (NPHS), the present study examines the prevalence and determinants of inconsistent self-reports of mammography utilization among Canadian women aged 40 years and older and quantifies the extent that inconsistent self-reports of mammography use contribute to biased estimates of mammography utilization and uptake. To our knowledge, this is one of the first studies of mammography utilization to provide specific longitudinal data on the determinants of inconsistent responses over time and the impact of such responses on population screening estimates.
Methods
The National Population Health Survey (NPHS) is a survey of the Canadian household population. Initiated in 1994–95 and repeated biennially, it is a split panel survey, combining repeated cross-sectional components with the longitudinal follow-up of a panel of respondents. A representative sample of Canadian household residents aged 12 and older from all ten provinces was sampled using a multistage probability design with stratification and clustering at various stages. The overall response rate for the baseline 1994–95 survey was 89 percent with a 96 percent response rate for the selected panel respondent [26]. On follow-up to the baseline survey two years later, 94 percent of the panel members responded [26]. Further details of the sampling procedures, design, data collection and response rates are published elsewhere [26,27].
This study evaluated data from longitudinal panel respondents of the 1994–95 (baseline) and 1996–97 (follow-up) waves of the NPHS to examine inconsistencies in reported mammography utilization among women aged 40+ years at first contact. Questions about mammography use were administered to female respondents through a personal interview conducted in 1994–95 and repeated by telephone approximately two years later. In both survey years, women were asked the identical question: "Have you ever had a mammogram, that is, a breast x-ray?". Those with positive responses were further probed for the time and reason of their most recent mammogram. All women provided their own health-related information; no proxy responses were allowed.
Analyses were restricted to women aged 40 and older (at baseline) who participated in the first two waves of the NPHS and consented to share their information with federal and provincial governments. Two types of inconsistent responses were assessed: (i) baseline reports of ever use which were contradicted by follow-up reports of never use; and (ii) baseline reports of never use which were contradicted by follow-up reports of use prior to 1994–95. Multivariate logistic regression techniques were used to evaluate the associations between women's baseline sociodemographic and health characteristics and type (i) inconsistent responses. Variables significant at p ≤ .05 in age-adjusted analyses were eligible for entry in the multivariate logistic models. Sample size constraints permitted only simple bivariate, rather than multivariate exploration of factors associated with reports reflecting inconsistent timing of most recent use at follow-up (type (ii) response). Estimates were weighted to reflect baseline population characteristics. To account for stratification and clustering in the NPHS sampling design, 95% confidence intervals for parameter estimates were calculated using exact standard errors generated through bootstrap re-sampling methods [28]. All statistical analyses were conducted using SAS.
Results
(i) Inconsistent ever/never utilization
Of the 3,535 women aged 40+ years who responded to the ever/never mammography question in both survey waves (Figure 1: 2 women with missing data regarding timing of mammogram were excluded), four percent (95% CI: 3.1–4.9) reported having had a mammogram at baseline and subsequently, on follow-up reported never having had a mammogram (Table 1). Among women who reported having had a mammogram at baseline, 5.9% (95%CI: 4.6–7.3) reported never use at follow-up (estimate not shown). The majority of women with inconsistent responses (64.4%, 95% CI: 54.4–74.4) reported receiving a recent (i.e., <2 years ago) mammogram at baseline and most (85.6%, 95% CI: 78.2–93.1) reported that the mammogram was done as part of a regular check up (Table 1). It should be noted that the percentage estimates in Table 1 have been weighted according to 1994/95 population characteristics whereas the frequency data represent actual numbers of women surveyed.
Table 2 presents the estimated adjusted odds ratios (95% CIs) of inconsistent ever/never responses associated with women's baseline sociodemographic and health characteristics. Among women reporting ever use at baseline (1994–95), those reporting never use in 1996–97 were significantly more likely to be outside the target age group for screening (50–69), to have lower income, to have not used hormone replacement therapy in the past month and to have never had a Pap test, after adjusting for relevant covariates. Women with lower education levels were also more likely to report such inconsistent responses between baseline and follow-up although education failed to remain a significant predictor in the multivariate model. Other variables considered but not found to be significantly associated with this outcome were rural/urban residence, place of birth, languages spoken, marital status and other social support indices and having a regular physician.
(ii) Inconsistent timing
Follow-up interviews were completed, on average, 1.98 years from the baseline survey (range 1.19–3.01 years). Of the 293 women who reported never use at baseline and ever use at follow-up, 17.4 percent (95%CI: 11.7–23.1) reported a time for their most recent mammogram at follow-up that was inconsistent with never use at baseline. Despite baseline reports that they had never had a mammogram, approximately half of these women reported having had a mammogram at least 5 years ago. Although limited by small numbers, determinants of such inconsistent responses were assessed with simple bivariate analyses. Inconsistencies in timing occurred more often in older women. Compared to women aged 50–69, those 70 and older were more likely to report (at follow-up) that their most recent mammogram had occurred prior to 1994–95, despite a report of never use at baseline (OR = 6.96, 95%CI: 2.42–20.0). Women reporting fair or poor self-rated health were also more likely to report a time for their most recent mammogram at follow-up that was inconsistent with never use at baseline (OR = 2.44, 95% CI: 0.99–6.05).
Impact of inconsistent reporting on uptake estimates
Depending on how inconsistent responses are handled, different measures of use and uptake of mammography may be obtained. The lack of a gold standard such as a medical chart for validation makes the choice of a corrective measure unclear. If inconsistent ever/never responses are included in the analysis unchanged, 67.3 percent (95% CI: 65.1–69.5%) of women would be classified as ever having had a mammogram in 1994–95 while 71.7 percent (95% CI: 69.6–73.7%) would be classified as ever users in 1996–97. Conversely, if it is assumed that inconsistent ever/never responses represent false-positive responses at baseline (an assumption supported by our study findings), the 1994–95 prevalence estimate becomes 63.3 percent (95% CI: 61.0–65.6%), demonstrating an absolute increase in mammography use of 8.4 percent (95% CI: 7.1–9.6%) by this cohort of women by 1996–97.
Discussion
Although a limited number of studies have assessed the reliability of mammography self-report [23-25], detailed evaluations have not been conducted for population-based longitudinal surveys. In this study, reliability could not be assessed, per se, as women's status of never having had a mammogram could normally be expected to change over a two year span. However, by examining inconsistencies in responses expected to remain constant and in responses regarding logical timing of mammography use, it is possible to examine potential concerns regarding response reliability and recall bias, respectively. In longitudinal studies, inconsistent data removed during data cleaning can yield significant losses, and may lead to bias, depending on the amount of attrition at each time point and the magnitude of the differences between those retained in the panel and those lost by such attrition [29]. Longitudinal studies of health must be acutely aware of causes of attrition because losses accumulate over survey waves [30].
Although direct comparison with our sample was not possible due to the expectation that behavior might have changed in a 2-year time span, earlier findings from longitudinal studies of fairly affluent [25] and low-income [24] populations and a population-based study [23], indicated that women reliably report having ever had a mammogram with estimated reliability measured by Cohen's kappa ranging from 0.82–0.87 [23,24]. Our finding that 4 percent (95% CI: 3.1–4.9 percent) of the women participating in the second wave of this longitudinal study inconsistently reported ever having had a previous mammogram was surprisingly high. Previous studies have found that initial use refuted on subsequent interviews occurred in 2–2.9 percent of respondents [24,25].
Our analyses of factors associated with inconsistent ever/never responses indicate that women reporting ever having had a mammogram at baseline but never use at follow-up exhibited many of the sociodemographic and health behaviour characteristics (e.g., lower income, outside age groups targeted for screening, non-users of Pap screening and hormone replacement therapy) commonly observed among non-participants in mammography screening in previous studies [31-38]. Such findings provide support for the assumption that the 1994–95 response of ever use is more likely erroneous. Additional factors (e.g. being born in an Asian country) previously associated with non-use of mammography [31,33] also showed a positive association with providing an inconsistent response; however, small numbers resulted in high variability once clustering and stratification were taken into account and precluded further analysis of this variable.
Validation studies also provide support for our assumption that inconsistent ever/never responses (as assessed in the present longitudinal panel) are most likely to be explained by false-positive responses at baseline. Women are more likely to falsely claim having had a mammogram, than not having one [4,6,8,9,13,17,39,40]. The majority of women in our study with inconsistent ever/never responses also indicated (at baseline) that their most recent mammogram had occurred within the last two years, a finding consistent with past research [10]. Imputation, suggested as a remedy for item non-response [30], may equally be used to deal with inconsistencies with evidence, in this situation, favoring treatment of women's earlier responses as false-positive.
Among women who reported never use at baseline and ever use at follow-up, approximately 17.4 percent reported a time for their most recent mammogram (at follow-up) that was inconsistent with never use at baseline. If respondents truly initiated mammography use subsequent to the baseline interview, they overestimated the time elapsed since their mammogram. Such a finding is relatively inconsistent with previous studies that have generally found that women tend to underestimate the time since their last mammogram [4-6,8-13,15,17]. Although McGovern et al. and Caplan et al. found reverse-telescoping in approximately 9 percent of their samples [13,16], only 8 percent of women in this group miscalculated by more than 1 year [16]. In the present study, women reporting inconsistent timing were older and in poorer health, suggesting that competing health events may have interfered with accurate recall.
Unfortunately, no gold standard was available to assess the validity of the responses women generated. Thus, the proportion of consistent responses that may actually represent invalid responses is unclear. Nor was it possible to distinguish errors in recall of timing from false reports of mammography utilization. Further, the reasons for providing inconsistent responses can only be inferred. The possibility of data entry errors is remote. Although data entry checks for consistency between survey cycles were not included among the comprehensive quality control strategies implemented by Statistics Canada, computer assisted interviewing was used by highly trained interviewers. Also, data entry errors would be expected to occur randomly and not disproportionately among women outside of the target age range or with relatively lower socioeconomic status, as observed in the present study. However, several plausible explanations for the inconsistencies exist, including survey methodology changes and deliberate or inadvertent provision of inaccurate responses by the respondent.
We cannot exclude the possibility that interview changes (from a baseline personal interview to a telephone follow-up) prompted women who reported ever use in 1994/95 to alter their response in 1996–97. The NPHS used an initial personal interview to foster a good long-term relationship with the panel representative, but the cost and logistics of traveling to different regions was prohibitive. Therefore, unless the respondent objected or had no phone, future interviews (including the 1996–97 cycle) were conducted by telephone [26]. The need to maintain study procedures over time in longitudinal studies has been stressed [41], but the impact of altering the interview method on the NPHS results has not been investigated [26]. Sensitive questions may be answered more truthfully by phone. Editing of survey responses by the respondent may occur. Social desirability and a tendency to give positive responses are possible sources of over-reporting [3,11]. Such biases remain largely unexplored with respect to mammography use.
Cognitive research has implicated comprehension as a barrier to providing valid, reliable survey responses. Several researchers employ the lead-in question 'Have you ever heard of a mammogram, that is a breast x-ray?' to identify comprehension difficulties [3,16,21,42] but this was not done in the NPHS where women were directly asked if they had ever had a mammogram. One study using focus group testing and in-depth interviews showed that despite some confusion between mammography and breast exams, women generally understood what mammography was [11], a finding further supported by one population-based survey [21]. However other investigations suggest this is not uniformly so [16,42,43]. One possibility cited is that confusion with other tests such as chest x-rays may lead to over-reporting of mammography [3].
There are several possible explanations for the higher rate of inconsistent responses observed in the present study. The focus of the NPHS questionnaire was not limited to preventive practices nor was it designed for reliability testing. The length and comprehensiveness of the NPHS may have contributed to greater respondent fatigue. Also, the longer time interval between surveys, relative to that observed in other studies, may have contributed to instability in responses. A more favourable level of concordance among responses may be obtained by studies that apply eligibility criteria that ensure more accurate responses (e.g. by having the respondent recall where she had her mammogram to validate her ever/never use) [6,10,15]. Finally, the overall response rate of the NPHS was higher than in comparable studies, so it potentially included a more difficult-to-reach population, less able to provide accurate responses.
The inconsistent data reported here, if removed from longitudinal analyses, could yield losses to follow-up equivalent to or greater than other sources of attrition (e.g., deaths, institutionalization, non-response) over the planned 20 year course of the NPHS. The 1998–99 NPHS included probing questions to reduce inconsistencies and it should alleviate many of the problems evident here [44]. However, probes were not designed to address the larger problem of reverse-telescoping observed among some respondents in the NPHS longitudinal cohort. Incorporation of women's previous responses in subsequent interviews to avoid telescoping and stimulate recall can be used to minimize such inconsistencies [30]. A recent critical review of the accuracy of self-reported health behaviors, including mammography, provides further suggestions for enhancing the accuracy of such data [45].
Conclusions
In summary, inconsistent responses represent a challenge to longitudinal, population-based evaluations of breast screening practices. Losses from inconsistent data regarding mammography participation are not negligible and may contribute to inaccurate estimates of mammography uptake. Women reporting inconsistent ever/never use in the present study displayed characteristics typical of never users, favoring treatment of women's baseline responses as false-positive. Inconsistent responses regarding the timing of recent mammography practices, however, may be primarily related to the impact of age and competing morbidity on recall.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
CB and CM contributed to the initial and revised analyses of the NPHS panel data, the interpretation of the results and the initial drafting/editing of the manuscript. CB and JS were responsible for the data linkage with Statistics Canada (for the NPHS Share File) and for the revised bootstrap analyses. JS also contributed to the interpretation of the results and editing of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors thank Mr. Bill Bradley and Ms. Prem Khosla at Health Canada for providing access to and assistance with the NPHS data, Dr. Francoise Bouchard, Dr. Margaret de Groh and Ms. Lynne Belle-Isle at Health Canada for feedback on the manuscript, and Mr. Douglas Yeo and Mr. Claudio Perez at Statistics Canada for guidance on using the bootstrap re-sampling methods. Dr. Maxwell is funded by a Population Health Investigator Award from the Alberta Heritage Foundation for Medical Research and a New Investigator Award from the Canadian Institutes of Health Research – Institute on Aging.
Figures and Tables
Figure 1 Response characteristics of the NPHS longitudinal panel of women aged 40+ years in 1994–95.
Table 1 Mammography utilization characteristics among Canadian women aged 40+ years, 1994–95 and 1996–97 NPHS longitudinal cohort (n = 3535)*
Characteristic Frequency Percent (95% CI)†
Consistent Responders 3325 94.5 (93.5–95.6)
Ever had mammogram (1994–95) and (1996–97) 2097 63.3 (61.0–65.6)
Never had mammogram (1994–95) and (1996–97) 987 24.3 (22.4–26.3)
Never had mammogram (1994–95), Ever had (1996–97)‡ 241 6.9 (5.7–8.1)
Inconsistent Responders 210 5.5 (4.4–6.5)
Never had mammogram (1994–95), Ever had (1996–97)§ 52 1.5 (1.0–2.0)
Ever had mammogram (1994–95), Never had (1996–97) 158 4.0 (3.1–4.9)
Time since last mammogram reported in 1994–95
< 6 months 32 20.3 (9.9–30.7)
≥ 6 months and < 2 years 55 44.1 (33.1–55.0)
≥ 2 years 71 35.6 (25.6–45.6)
Reason for mammogram: check up / no particular problem 132 85.6 (78.2–93.1)
* n = 2 respondents not classified as consistent or inconsistent as they reported initiation of mammography use in 1996–97 but did not report time of most recent mammogram
† weighted according to 1994–95 population characteristics
‡ reported time of mammogram consistent with interval between surveys
§reported time of mammogram inconsistent with interval between surveys
Table 2 Estimated Odds Ratios (95% CIs) of inconsistent responses for ever having had amammogram* according to baseline sociodemographic and health characteristics amongCanadian women (aged 40+) assessed in the 1994–95 and 1996–97 NPHS (n = 2255).
Characteristic Age-Adjusted OR (95%CI) Adjusted OR† (95%CI)
Age Group
40–49 2.40 (1.41–4.09) 2.11 (1.15–3.88)
50–69 1.00 1.00
70+ 3.62 (2.05–6.38) 3.18 (1.83–5.54)
Household Income
not stated 2.02 (0.48–8.49) 1.92 (0.51–7.21)
low ($0–19,999) 1.95 (1.16–3.28) 1.84 (1.04–3.24)
moderate ($20,000–59,999) 1.00 1.00
high ($60,000+) 0.32 (0.10–0.96) 0.34 (0.11–1.05)
Educational Attainment
elementary / some secondary 1.00 1.00
secondary graduate / some post secondary 0.60 (0.34–1.05) 0.80 (0.45–1.42)
post-secondary degree 0.39 (0.21–0.72) 0.59 (0.31–1.13)
Hormone drug use (past month)
no 1.00 1.00
yes 0.41 (0.24–0.71) 0.44 (0.25–0.78)
Ever had Pap test
yes 1.00 1.00
no 3.19 (1.52–6.68) 2.82 (1.39–5.72)
* refers to women reporting yes to question on ever having had a mammogram in 1994–95 survey and no in 1996–97 survey; comparison group represents women reporting yes in both surveys
† obtained from multivariate logistic regression model, adjusted for all variables listed in table; other variables examined but not found to be significant predictors of inconsistent ever/never responses were: rural residence, place of birth, language, marital status and having a regular physician
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| 15541176 | PMC535807 | CC BY | 2021-01-04 16:03:28 | no | BMC Health Serv Res. 2004 Nov 12; 4:32 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-32 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1621550713510.1186/1471-2105-5-162SoftwareExtractor for ESI quadrupole TOF tandem MS data enabled for high throughput batch processing Boehm Andreas M [email protected] Robert P [email protected] Albert [email protected] Protein Mass Spectrometry and Functional Proteomics Group, Rudolf-Virchow-Center for Experimental Biomedicine, Universitaet Wuerzburg, Versbacher Strasse 9, D-97078 Wuerzburg, Germany2 Applied Biosystems, Applera UK, Lingley House, 120 Birchwood Boulevard, Warrington, Cheshire, WA3 7QH, UK2004 26 10 2004 5 162 162 30 8 2004 26 10 2004 Copyright © 2004 Boehm 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
Mass spectrometry based proteomics result in huge amounts of data that has to be processed in real time in order to efficiently feed identification algorithms and to easily integrate in automated environments. We present wiff2dta, a tool created to convert MS/MS data obtained using Applied Biosystem's QStar and QTrap 2000 and 4000 series.
Results
Comparing the performance of wiff2dta with the standard tools, we find wiff2dta being the fastest solution for extracting spectrum data from ABIs raw file format. wiff2dta is at least 10% faster than the standard tools. It is also capable of batch processing and can be easily integrated in high throughput environments. The program is freely available via , and is also available from Applied Biosystems.
Conclusions
wiff2dta offers the possibility to run as stand-alone application or within a batch process as command-line tool integrated in automation and high-throughput environments. It is more efficient than the state-of-the-art tools provided.
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Background
In tandem mass spectrometry proteins are identified by matching the measured fragment ion spectra derived from peptides with theoretical spectra calculated from known DNA or protein sequences, for example the NCBI sequence database [1]. Algorithms used for this purpose usually have their own input formats and are not able to read the proprietary binary file formats of the mass spectrometer manufacturers. Nevertheless, they are able to read a common format, the DTA format introduced by the Sequest™ algorithm [2]. Thus, needs exist for converting mass spectra into this common format in order to feed the different identification algorithms such as Sequest™ or Mascot™ [3]. The conversion must be accomplished efficiently, requiring as few user interaction as possible. Integrated in high-throughput environments, mass data processing must be realized.
Applied Biosystems mass spectrometers are controlled by a software called Analyst™. This software is used for data evaluation purposes, too. It offers a possibility to integrate extensions called "scripts". One of these scripts available from the manufacturer [4] is "Export IDA Spectra.dll", the only known possibility besides the mzStar from SASHIMI Project [5] to export DTA files from Applied Biosystems ESI data. Using the tools provided by SASHIMI results in two steps: first mzStar must be used to create an XML [6] document (mzXML Schema) as intermediate step, then mzXML2Other must be applied for creating DTA or other formats from the mzXML document, and thus conversion consumes a lot of time and computational power. mzStar is not designed for batch processing nor for converting more than one wiff file in a single run. The Analyst™ script itself requires each chromatogram being opened in Analyst™ per conversion, resulting in a lot of user interaction for each single export. This leads to the effect that batch processing is impossible in both cases and only one binary file can be converted at once. A schematic diagram of the conversion method workflows is shown in figure 1.
Another script named mascot.dll provides support for invocating Mascot™ as protein identification algorithm using Applied Biosystems Analyst™. Such a script does not exist for Sequest™. In most proteomics labs support for Mascot™ as well as for Sequest™ is needed, because these two algorithms are most commonly used in this research field. Although the additional information that can be stored in mzXML is needed in the case of quantitative proteomics experiments based on isotopic labelling of peptides (ICAT [7] or SILAC [8]), this format can be read neither by Mascot™ nor by Sequest™.
We decided to develop a tool for converting data obtained from Applied Biosystems QStar™, providing features like batch processing in an operatorless high throughput environment. If no ER, NL or Prec scans are used, data acquired using a QTrap™ 2000/4000 can be converted, too. This tool is named wiff2dta.
Implementation
The implementation was done according to the Analyst™ Cookbook, a documentation available from Applied Biosystems upon request. wiff2dta is implemented in Visual Basic™ (Microsoft Corp.) because ActiveX™ is provided as the one and only application programming interface (API) by Applied Biosystem's Analyst™ software. Therefore, this is needed for accessing the binary wiff files. Thus, this tool is operating system dependant and only runs on Windows™ (Microsoft Corp.) systems. We use the code provided by the Analyst™ software API in order to benefit from new releases and maintain coherence.
The program has two modes of user interaction: one provides a graphical user interface (GUI) and requires user interaction (GUI-mode); the other uses command-line parameters and suppresses the GUI as no user interaction is required (batch-mode). In batch mode, automation of conversion processes can be achieved. The GUI is shown in figure 2. Conversion can be done in two modes. On one hand only a single binary file can be selected for conversion (file-mode). On the other hand, a whole directory tree can be traversed and all binary ESI MS/MS files in all (or only selected) folders can be converted in one run (directory-mode). For example this mode can be used to convert a folder full of MS/MS data at once. In file-mode distinct samples of one data file can be marked for conversion, if desired. In directory-mode, each sample of each ESI MS/MS file is processed. Used in directory-mode, wiff2dta can be forced to save all resulting DTA files in one single folder by checking "all in one folder". Otherwise, the converted files are stored in a single folder with the name derived from the source ESI MS/MS data file. This folder is placed in the same directory where the corresponding binary file was found.
The conversion itself can be controlled by entering appropriate values in the text fields displayed under the title "Parameters", shown in figure 2. Parameters are "Mass tolerance for combining MS/MS spectra", "MS/MS export threshold", "Minimum number of MS/MS ions for export", "Centroid height percentage", "Centroid merge distance", "Minimum charge of exported spectra" and "Maximum charge of exported spectra". These are parameters of identical function as used by the export of DTA provided by Applied Biosystems' script. wiff2dta produces the same values as this tool, as shown in table 1. Support for other formats, like mascot generic format (MGF) [9] and mzXML [10] will be added. We first focussed on high throughput for conversion into DTA in order to be able of feeding our search programs efficiently.
wiff2dta is able to be integrated in automation and high throughput environments. This can be achieved making use of the command line options. All parameters and modes can be controlled by command-line parameters. These are shown in figure 3. Every GUI parameter has a corresponding command line option. Batch-mode is entered by providing the parameter /auto at the command-line. If this is not present, the values provided override the defaults in the GUI and the form will be displayed.
Results
The program can be started in multiple instances, resulting in parallel processing. Using this feature, it is possible to use several processors on one computer. Additional to this, wiff2dta is about 10% faster than the original tool provided by Applied Biosystems and about 20 times faster than mzStar of the Sashimi project. See table 2. During a 24 hour conversion, the 10% performance gain in savings of about 2.5 hours using the tool original tool provided by Applied Biosystems.
Conclusions
wiff2dta demonstrates improvements in reducing computation time by exploiting a range of optimizations in coding and using the COM interfaces to Analyst™. Useful features like the capability of being integrated in batch processes and mass data processing lead to immense time savings, too.
Availability and requirements
wiff2dta has to be installed in the BIN directory of an installed Analyst™ version 1.3 or higher. The installation consists just of copying the file wiff2dta.exe into this directory. If desired, a link to the program file can be created that can be placed onto the desktop or into the start menu.
The program is freely available from Applied Biosystems (UK) upon request and freely available via and for download.
List of abbreviations used
API: application programming interface
DNA: desoxyribonuclein acid
DTA: file extension ms spectra data in Sequest™ format
ER: enhanced resolution
ESI: electron spray ionization
GUI: graphical user interface
MS: mass spectrometry, mass spectrometer
MGF: mascot generic format, file extension used for this format
NL: neutral loss
Prec: precursor ion
TOF: time-of-flight
WIFF: file extension of Applied Biosystems raw data files
Authors' contributions
AB implemented the program and made a draft of the manuscript. RPG and AS contributed with ideas and proofread the manuscript. RPG supervised the final testing. All authors have read and approved the final manuscript.
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft (SI 835/2-1; FZT 82). The authors would like to thank Karl Mechtler at the Research Institute of Molecular Pathology, Dr. Bohr-Gasse 7, 1030 Vienna, Austria for testing and discussion.
Figures and Tables
Figure 1 Schematic diagram of the workflows of the three conversion methods. For conversion of more than one wiff file, the whole process has to be repeated when using Export IDA Spectra.dll or mzStar, but not when using wiff2dta.
Figure 2 a) The graphical user interface of wiff2dta in directory-mode. This is the only form requiring user interaction. By klicking the button "About", a copyright message and the usage for batch-mode will be displayed. The usage is shown in figure 2. Clicking on the button "Convert" starts the conversion immediately. In directory mode, the tree of folders is listed and folders can be selected for being processed. b) The graphical user interface of wiff2dta in file-mode. The samples are listed and can be selected for conversion. The lower half of this form is identical for both the file- and the directory-mode
Figure 3 The parameters for batch-mode enabling wiff2dta being integrated in automated environments. All parameters can be controlled using the command line.
Table 1 Output in DTA format of the original DTA converter provided by the manufacturer (Export IDA Spectra.dll) and mzStar compared with the results of wiff2dta. All three used the same source file. The output of mzStar differs completely because this tool does not use any grouping of spectra as Export IDA Spectra and wiff2dta do. In DTA format, the first line is reserved for the mass of the parent ion and its charge. The other lines consist of pairs of m/z values and the corresponding intensities.
Export IDA Spectra mzStar wiff2dta
1012.5922 2 1013 2 1012.5922 2
211.0680 4.0000 55.0534 3 211.0680 4.0000
221.1128 4.0000 56.0418 2 221.1128 4.0000
273.1110 7.0000 56.0451 2 273.1110 7.0000
274.0969 3.0000 56.0484 2 274.0969 3.0000
281.0214 4.0000 56.0518 2 281.0214 4.0000
281.0846 4.0000 60.0487 2 281.0846 4.0000
291.1109 4.0000 69.0642 4 291.1109 4.0000
294.1899 4.0000 69.0679 7 294.1899 4.0000
312.0073 2.0000 69.0716 9 312.0073 2.0000
493.2674 2.0000 69.0753 4 493.2674 2.0000
506.3299 2.0000 69.079 2 506.3299 2.0000
507.2403 2.0000 72.0658 3 507.2403 2.0000
507.3192 2.0000 72.0696 13 507.3192 2.0000
507.3693 2.0000 72.0734 17 507.3693 2.0000
508.2601 2.0000 72.0772 34 508.2601 2.0000
Table 2 Performance comparison of Export IDA Spectra, mzStar and wiff2dta on the same computer. wiff2dta is generally faster than the other tools.
File Number of MS/MS spectra mzStar Export IDA Spectra wiff2dta
Qstar0803 679 241.5 s 24.5 s 21 s
Qstar2053 992 426 s 28 s 24 s
Qstar2128 1652 1804 s 97 s 87.5 s
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| 15507135 | PMC535808 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Oct 26; 5:162 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-162 | oa_comm |
==== Front
Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-771556085110.1186/1477-7827-2-77ResearchcDNA microarray analysis of bovine embryo gene expression profiles during the pre-implantation period Ushizawa Koichi [email protected] Chandana B [email protected] Kanako [email protected] Satoshi [email protected] Akira [email protected] Toru [email protected] Kei [email protected] Kazuhiko [email protected] Tomoyuki [email protected] Yukio [email protected] Gozoh [email protected] Kazuyoshi [email protected] Reproductive Biology and Technology Laboratory, Developmental Biology Department, National Institute of Agrobiological Sciences, 2 Ikenodai, Tsukuba, Ibaraki 305-8602, Japan2 Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida Shimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan3 Development and Differentiation Laboratory, Developmental Biology Department, National Institute of Agrobiological Sciences, 2 Ikenodai, Tsukuba, Ibaraki 305-8602, Japan4 Laboratory of Animal Reproduction, College of Agriculture, Kinki University, 3327-204 Nakamachi, Nara 631-8505, Japan5 Department of Veterinary Medicine, Faculty of Agriculture, Iwate University, 3-18-8 Ueda, Morioka, Iwate 020-8550, Japan6 Department of Technology, National Livestock Breeding Center, 1 Odakurahara, Odakura, Nishigo, Fukushima 961-8511, Japan2004 24 11 2004 2 77 77 23 9 2004 24 11 2004 Copyright © 2004 Ushizawa et al; licensee BioMed Central Ltd.2004Ushizawa 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
After fertilization, embryo development involves differentiation, as well as development of the fetal body and extra-embryonic tissues until the moment of implantation. During this period various cellular and molecular changes take place with a genetic origin, e.g. the elongation of embryonic tissues, cell-cell contact between the mother and the embryo and placentation. To identify genetic profiles and search for new candidate molecules involved during this period, embryonic gene expression was analyzed with a custom designed utero-placental complementary DNA (cDNA) microarray.
Methods
Bovine embryos on days 7, 14 and 21, extra-embryonic membranes on day 28 and fetuses on days 28 were collected to represent early embryo, elongating embryo, pre-implantation embryo, post-implantation extra-embryonic membrane and fetus, respectively. Gene expression at these different time points was analyzed using our cDNA microarray. Two clustering algorithms such as k-means and hierarchical clustering methods identified the expression patterns of differentially expressed genes across pre-implantation period. Novel candidate genes were confirmed by real-time RT-PCR.
Results
In total, 1,773 individual genes were analyzed by complete k-means clustering. Comparison of day 7 and day 14 revealed most genes increased during this period, and a small number of genes exhibiting altered expression decreased as gestation progressed. Clustering analysis demonstrated that trophoblast-cell-specific molecules such as placental lactogens (PLs), prolactin-related proteins (PRPs), interferon-tau, and adhesion molecules apparently all play pivotal roles in the preparation needed for implantation, since their expression was remarkably enhanced during the pre-implantation period. The hierarchical clustering analysis and RT-PCR data revealed new functional roles for certain known genes (dickkopf-1, NPM, etc) as well as novel candidate genes (AW464053, AW465434, AW462349, AW485575) related to already established trophoblast-specific genes such as PLs and PRPs.
Conclusions
A large number of genes in extra-embryonic membrane increased up to implantation and these profiles provide information fundamental to an understanding of extra-embryonic membrane differentiation and development. Genes in significant expression suggest novel molecules in trophoblast differentiation.
==== Body
Background
Embryogenesis in the period from fertilization to implantation involves various morphological, cellular, and biochemical changes related to genomic activity [1]. These changes include the elongation of embryonic tissues, cell-cell contact between the mother and the embryo, and placentation. The embryo begins to form the placenta around day 20 of gestation in the bovine [2,3], while embryonic trophoblast and endometrial cells tightly unite to form placentomes on day 30 [4,5]. The bovine embryos at the blastocyst stage are 100 to 200 μm in diameter, but become 20 to 30 cm long by the time of implantation. Embryonic cells undergo both proliferation and differentiation to form the fetus and placenta throughout early embryogenesis. Reprogramming of the genome may be completed and reset during these steps, with embryonic development progressing on to temporal and spatial gene expression [6,7]. The literature data indicate that a number of specific genes are expressed during the period from blastogenesis through implantation, such as interferon-τ (IFN-τ) [8,9], matrix metalloproteinases (MMPs) [10], heparanase [11], and retinoid X receptors [12]. Vast numbers of genes change their expression levels during this period to support the complex mechanisms of embryogenesis and implantation. Although numerous molecules participate in trophoblast differentiation and placentation, the precise molecular and genetic pathways which lead to the formation of placenta remain difficult to clarify. Nevertheless, recent data from animal cloning technology where a somatic or stem cell nucleus is transplanted to an enucleated unfertilized egg, strongly suggested that inappropriate expression of genes is possibly the main cause of early embryo loss [13,14]. These studies have shown that aberrant gene expression patterns were associated with placental abnormalities in several species. Thus, it is clear that the differentiation of trophoblastic cells and the formation of placenta are tightly controlled by mechanisms which precisely govern switch on and off of the appropriate set of genes during embryogenesis. Therefore, a detailed gene expression profile in the pre-implantation embryo provides insights into the molecular mechanisms that are vital to farther our knowledge of the embryogenesis and implantation processes. Unlike traditional molecular methods that have been used to look at a single gene at one time point, a complementary DNA (cDNA) microarray is an efficient tool for analyzing tens of thousands of genes in a single experiment and to identify groups of genes that are critical during early development.
We have developed a bovine utero-placental specific cDNA microarray that contains various genes known to play key roles in extra-embryonic membrane development in the bovine [15]. To our knowledge, there has been no report to date that has evaluated the differential gene expression profile in the embryo as well as in extra-embryonic cells of the pre-implantation embryo in the bovine. Thus, we applied our cDNA microarray to detect gene expressions comprehensively during embryogenesis and implantation in the bovine. Furthermore we attempted to address the expression changes of the key genes that are involved in trophoblastic cell proliferation and differentiation. We first compared the gene expression profiles among blastocyst-stage embryos, elongated-stage embryos, and the trophoblastic tissue at the peri-implantation stage. We further characterized gene expression profiles between the trophoblastic tissue and embryo just after implantation. Comparison of expression differences of specific gene(s) provides important information on the potential role of the particular gene at specific time points of trophoblast development. Approaches such as this help identifying specific genes that are up- or down-regulated during trophoblast development.
Although there is no defined standard method that has been developed for the analysis of microarray data, application of k-means [16,17] and hierarchical clustering [18] methods has been shown to yield reliable data. These two methods have been successfully used to obtain information from the embryo, cytotrophoblast, and uterine tissue during the early stages of pregnancy in humans [19-22] and mice [6,23,24]. We therefore applied k-means and hierarchical clustering methods to identify genes involved in trophoblastic differentiation.
Materials and Methods
Animals and sample collection
Totally five Japanese black cows were slaughtered on Days 19, 21, 27 (two) and 28 of gestation (the day of artificial insemination was designated as Day 0), and collected embryonic membrane and fetus. Embryos were collected non-surgically on Days 7 and 14 of gestation from superovulated Japanese black cows. Namely, a follicle-stimulating hormone (FSH; Antrin-R10, Kawasaki Pharmaceutical, Kawasaki, Japan) was injected twice daily at doses of 5 mg, 3 mg, and 2 mg on the first, second, and third day of treatment, respectively. Luteolysis was induced by injecting prostaglandin F2α (cloprostenol 750 μg/cow; Fujita Pharmaceutical, Tokyo, Japan) on three days after the initial injection of FSH. Artificial insemination was performed both on the day of estrus (Day 0 of gestation) and the next morning, using frozen semen from Japanese black bulls. Twelve blastocysts collected on Day 7 were pooled to represent the Day 7 embryo (abbreviated as Day 7E), two expanded and elongated embryos collected on Day 14 were pooled to represent Day 14E, two embryos collected on Days 19 and 21 were used individually but designated as Day 21E (n = 2). Extra-embryonic membranes collected on Days 27 (two samples) and 28 (one sample) were used as Day 28 extra-embryonic membranes (28EEM) (n = 3), and two fetuses, excluding the extra-embryonic membranes, collected on Days 27 and 28 were used as the Day 28 fetus (28F) (n = 2). All samples were snap frozen in liquid nitrogen immediately after collection and stored at -80°C until RNA extraction. The days of collection were selected depending on the macro morphological changes of the embryos during the peri-implantation periods; bovine embryo passes into the uterus around a morula stage and develop to a blastocyst stage by Day 7. Embryo begins to elongate on Day 14 and form a long embryo of 20 to 30 cm in length on Day 20 just before implantation occurs. The embryo is easily identifiable around Day 30 around which placentome formation is visible [3,5]. All procedures for these animal experiments were carried out in accordance with the guidelines and ethics approved by the Animal Ethics Committee of the National Institute of Agrobiological Sciences for the use of animals.
Sample RNA preparation
RNA extraction
The total RNA was isolated from the embryos using ISOGEN (NipponGene, Toyama, Japan) according to the manufacturer's instructions. Briefly, Samples were mixed and homogenized with ISOGEN. The aqueous phase was collected after the centrifugation. This phase was washed by chloroform for the RNA purification. The aqueous phase was collected after the centrifugation again. The total RNA pellet in the aqueous phase was obtained using the isopropanol sedimentation. Total RNA was directly used for the T7-based linear amplification for the cDNA microarray analysis. Messenger RNA (mRNA) were reverse transcribed by using the T7-oligo dT primer in the T7-based linear amplification system.
T7-based linear amplification
Linear-amplified antisense RNA (aRNA) was used for the cDNA microarray experiment. Because the amount of total RNA extracted was small from Day 7 blastocysts for the microarray analysis, mRNA from Day 7 blastocysts was amplified along with all other mRNA samples that were collected from Days 14 to 28 of gestation to eliminate technical variations and to efficiently compare the array data.
We used a MessageAmp aRNA kit (Ambion, Austin, TX, USA) for the T7-based linear amplification of mRNA. Synthesis and purification of cDNA and aRNA were carried out according to the manufacturer's instructions. The aRNA amplification procedure consisted of (i) first-strand cDNA synthesis (reverse transcription of mRNA), (ii) second-strand cDNA synthesis, (iii) cDNA purification, (iv) in vitro transcription, and (v) aRNA purification. The aRNA amplification for Day 7 blastocysts was done with two steps purification. The comparison between first and second round amplification was r = 0.96 (data not shown).
cDNA microarray
Bovine utero-placental cDNA microarray
For the series of present experiments we used a utero-placental custom cDNA microarray developed in our laboratory, as described previously [13,15]. Briefly, a cDNA library was constructed from mRNA isolated from endometrial (caruncular and intercaruncular endometrium) and placental tissues (cotyledonary and intercotyledonary fetal membrane) of Japanese Black cows on days 0 and 10 of the estrous cycle, and days 30, 60, 100 and 245 of gestation, respectively. PCR products of about 4,000 clones from the cDNA library which was normalized by hit-picking method [15] were spotted onto glass slides robotically. Simultaneously, the clones were sequenced by using the MegaBACE 1000 DNA Sequencing System (Amersham Pharmacia Biotech, Piscataway, NJ). The array contained 3,955 spots that were clustered into 1,738 unique genes on the basis of sequence analysis. The sequenced clones were compiled and annotated by basic local alignment search tool (BLASTn), and the clone that was positioned at the top of the hierarchical tree of genes by GenBank database matching was selected. The 1,738 annotated unique genes represented 816 known genes, 530 expressed sequencing tags (ESTs) and 392 unknown novel genes. The unknown sequences were submitted to DDBJ as ESTs. The DDBJ and GenBank accession numbers of genes on this custom microarray are BP106801 to BP113049 and AB098745 to AB099150, respectively. An additional 35 genes that were not included in the cDNA library but also spotted onto the cDNA microarray were used for analysis since these genes have been shown to be characteristically expressed during gestation in bovines and humans [9,25-29].
cDNA microarray hybridization
We have previously reported our microarray hybridization procedures [15]. Briefly, two μg of amplified aRNA was reverse transcribed in the presence of cyanine 3 (Cy3) or Cy5 fluorescence dye (Amersham Biosciences, Buckinghamshire, UK) using SuperScript II reverse transcriptase (Invitrogen, Carlsbad, CA, USA) to make the hybridization probes. The reaction mixture was incubated for 2 hr at 42°C. The labeled probes were concentrated in a Microcon filter device (Millipore, Bendford, MA, USA), diluted in 15 μl hybridization solution (3.4 × SSC, 0.3% SDS, 20 μg poly (A), and 20 μg yeast tRNA), and applied to the microarray. Identical samples were labeled separately with either Cy3- or Cy5-dye. Thus, two hybridization reactions could be carried out with the same sample. The arrays were sequentially washed with 2 × SSC/0.5% SDS, 0.2 × SSC/0.5% SDS, and 0.2 × SSC solutions after 16 hr incubation at 65°C. The arrays were dried by centrifugation at 1,000 × g. Hybridization images were immediately scanned by a GenePix 4000B laser scanner (Axon Instrument, Union City, CA, USA) and analyzed with GenePix Pro 4.0 computer software. The data were viewed as a scatter plot between Cy3 and Cy5 intensities. Each color intensity value was individually normalized, and the average intensity value of each spot (gene) obtained from different hybridization reactions of the same sample was used for data analysis.
Data normalization of the cDNA microarray
Data normalization was performed by following the procedures described previously [30,31]. The local background intensity of each array spot was smoothed by local weight regression (Lowess) and subtracted from the spot intensity data. The subtracted intensity data were subjected to non-parametric regression and local variance normalization since non-parametric regression can reduce intensity-dependent biases. The accuracy is improved over that of linear regression if the points in the scatter plot of Cy3 versus (vs.) Cy5 are not distributed around a straight line. Minimum Information About a Microarray Experiment (MIAME; ) compliance was met by depositing all the data in the Gene Expression Omnibus (GEO) repository . The GEO accession numbers are as follows. Platform: GPL1221; Samples: GSM23324, GSM26510, GSM26511, GSM23327, GSM23328, GSM23329, GSM23330, GSM23331, and GSM23332; Series: GSE1414.
Cluster analysis of the cDNA microarray data
Data for individual genes were obtained by averaging the intensity values of analogous spots on the microarray. Data were log2 transformed and used for cluster analysis. The TIGR (The Institute for Genome Research) MultiExperiment Viewer (MeV) program was used to derive the k-means and for hierarchical tree clustering analysis [32]. The general expression patterns of the 1,773 individual genes (including 35 manually spotted genes) were investigated by k-means algorithm. Data for each gene were represented by an eight-dimensional vector. K-means clustering was performed by division into 12 centroid centers. The distance between gene vectors was calculated by the cosine coefficient (vector angle). Hierarchical tree clustering was performed on the annotated and EST genes that demonstrated up-regulation when the comparison was made between Day 28EEM vs. Day 28F. Genes that displayed two-fold (and greater) differences were selected and the normalized expression of each sample was utilized for the cluster analysis. The distance between gene vectors was also calculated by the cosine coefficient. We adopted average linkage clustering in the hierarchical tree clustering method. Data used for the cluster analyses were as follows: Day 21E (n = 2; r = 0.84; P < 0.05); Day 28EEM (n = 3; r ≥ 0.90; P < 0.05); and Day 28F (n = 2; r = 0.90; P < 0.05). Other samples were used independently for cluster analysis.
Real-time RT-PCR analysis
Gene expression profiles derived from microarray analyses were confirmed quantitatively by real-time RT-PCR analysis. The selected genes for RT-PCR included two key known genes (placental lactogen-Ala (PL-Ala) and prolactin-related protein-1 (PRP-I)) and four apparently up-regulated ESTs, and compared their expression from Day 7 through Day 28. The ESTs selected have the GenBank accession numbers AW464053, AW465434, AW462349, and AW485575.
The details of real-time RT-PCR procedures have been described in previous reports [11,33]. Briefly, fifty ng total RNA was reverse transcribed into cDNA for 30 min at 48°C by MultiScribe™ reverse transcriptase with an Oligo dT primer, dNTP mixture, MgCl2 and an RNase inhibitor. Primer pairs and oligonucleotide probes labeled with a reporter fluorescence dye at the 5' end and a quencher fluorescence dye at the 3' end were designed using Primer Express computer software (Applied Biosystems, Foster City, CA, USA). The primers and probes for the selected genes are listed in Table 1. The thermal cycling conditions included an initial incubation of samples at 50°C for two minutes and at 95°C for ten minutes, followed by forty cycles with each cycle at 95°C for fifteen seconds and at 60°C for one minute. The cycle threshold value (CT) is related to the initial quantity of the target gene in each sample determined in real time using an ABI Prism 7700 sequence detector (Applied Biosystems). The relative difference in the initial amount of each mRNA species (or cDNA) was determined by comparing the CT values between the samples at different stages. Bovine GAPDH was used as endogenous control. The standard curve for each gene was generated by serial dilution of plasmid containing PL-Ala, PRP-I, four ESTs, or GAPDH cDNA to quantify mRNA concentrations. A ratio of PL-Ala, PRP-I and four ESTs mRNA to GAPDH mRNA was calculated to adjust for any variation in the RT-PCR reaction. All values are presented as mean ± SD. Data were analyzed initially by ANOVA and followed by either Tukey-Kramer multiple comparison test. P-values of <0.05 were considered significant.
Table 1 Oligonucleotide primers and TaqMan probes used for real-time RT-PCR analysis
Gene Primer or TaqMan probe Sequence Position
PL-Ala (J02840) Forward 5' GCAACATTGGTGGCTAGCAA 3' 262-281
Reverse 5' GCCCTCGCCAAACTGTTTATTA 3' 339-317
Probe 5' CTATAGGCTCGCCAGGGAAATGTTCAATGA 3' 285-314
PRP-I (J02944) Forward 5' CAGACAGGTTTATGAATGCCGC 3' 458-479
Reverse 5' CGCAGGCAGTAGAACAGGTTAT 3' 541-520
Probe 5' TCCTCTGCATCATCTAGTCACGGAGCTG 3' 483-511
AW464053 Forward 5' AATATGCCCAGGGCAAACTG 3' 296-315
Reverse 5' TCGGGAGTTTGGAGGGAATT 3' 368-349
Probe 5' TCAATGCCATCAAGAGCTGCCACAC 3' 323-347
AW465434 Forward 5' ACATCTCCCTGAAAGTGAACCC 3' 287-308
Reverse 5' TCCATCCTTGCAGAAGTCTCCT 3' 369-348
Probe 5' CCCTGGAAGCTCATCTGCAATGTAAAGC 3' 316-343
AW462349 Forward 5' GCGTGGATGGTGTCCTACTTCTA 3' 216-238
Reverse 5' GCCACAACGAGAAACAGGAAA 3' 301-281
Probe 5' TGTCTGTTTGCCTTTACTGGTGAGCCCT 3' 240-267
AW485575 Forward 5' CCTCTGATGAAAGATTGGGAACAG 3' 195-218
Reverse 5' AAGTGCCAGAGATCTTGGCCT 3' 285-265
Probe 5' TTCTCCAAACCAACCACCACCAGCTG 3' 230-255
GAPDH (U85042) Forward 5' AAGGCCATCACCATCTTCCA 3' 178-197
Reverse 5' CCACTACATACTCAGCACCAGCAT 3' 253-230
Probe 5' AGCGAGATCCTGCCAACATCAAGTGG 3' 200-225
Results
T7-based linear amplification
We confirmed the reliability of this amplification method by comparing unamplified mRNA vs. amplified mRNA (aRNA) derived from the same bovine placental tissue after hybridization into the utero-placental cDNA microarray. We observed a significant correlation (r ≥ 0.81, P < 0.05; data not shown) between the unamplified mRNA and amplified aRNA. Confirmation experiments were conducted twice with reverse labeling (for a total of four times). Previous reports have also described substantial cDNA microarray population correlation between unamplified mRNA and amplified aRNA [34,35]. The correlation of our amplification was equivalent to previous studies [34,35]. Thus, amplified aRNAs can be used as representative materials in cDNA microarray experiments.
Differential gene expression profiles determined by cluster analysis
Sixty-six genes out of a total of 1,773 were exempted from cluster analysis due to low expression intensity values. Consequently, 1,707 genes, including 833 previously annotated, were classified into 12 categories by k-means clustering, as illustrated in Fig. 1. Twelve k-means cluster profiles were summarized into three types, in which the gene-expression intensity (i) increased only from Day 7 to Day 14 and thereafter either remained constant or decreased slightly (clusters 2, 3, 12); (ii) Continued to increase until Day 21 (clusters 1, 6, 7, 8, 9, and 10); and (iii) progressively increased until Day 21 (clusters 4, 5, and 11). Clusters 1 and 7 contained more than 200 genes that included annotated and ESTs, whereas clusters 4, 5, and 11 contained fewer than 100 genes. Clusters 5 and 11 contained mostly members of the cytokine family, and clusters 4 and 5 contained ECM-related genes. In particular, trophoblast-specific genes, such as placental lactogens (PLs), prolactin-related proteins (PRPs), and pregnancy-associated glycoproteins (PAGs), were found in cluster 11.
Figure 1 K-means clusters of the expression patterns of 1707 genes in a bovine embryo. Lines refer to the k-means of gene expression on Days 7E to 21E, Day 28EEM and Day 28F. The solid line refers to the k-means center of gene expression on Days 7E to 21E and Day 28EEM. The dotted line refers to the k-means center of Day 21E to Day 28F.
Temporal specific gene expression in extra-embryonic membranes
Pair-wise comparisons were made to identify genes displaying differential expression; the results are given in Fig. 2. All data considered significant exhibited an increase (up-regulation; yellow) or decrease (down-regulation; blue) of at least two-fold. Top 20 known genes of the most or less expression over two-fold differences were listed in Tables 2 to 5. All data of individual gene changes are available in an additional file on same web site of this paper or as supplemental tables (Suppl. Tables 1–4 in Additional files – see ).
Figure 2 Gene expression correlations of bovine peri-implantation embryo between Day 7E vs. Day 14E, Day 7E vs. Day 21E, Day 7E vs. Day 28EEM, Day 7E vs. Day 28F, Day 14E vs. Day 21E, Day 14E vs. Day 28EEM, Day 14E vs. Day 28F, Day 21E vs. Day 28EEM, Day 21E vs. Day 28F, and Day 28EEM vs. Day 28F. The yellow areas highlight a greater than two-fold gene expression difference (up-regulated) between the X-axis and Y-axis samples. The blue areas highlight a greater than two-fold gene expression difference (down-regulated) between the X-axis and Y-axis samples. The gray areas highlight a 0.5- to 2-fold gene expression difference between the X-axis and Y-axis samples.
Table 2 Top 20 known genes of the most or less expression over two-fold differences between Day 7E vs. Day 14E – see also additional file 1
Accession No. Gene name D14E/D7E K-means Classification
Day 14E/Day 7E down-regulated genes (<0.5)
L07872 Homo sapiens Jk-recombination signal binding protein 0.19 8 Apoptosis & Cell cycle
NM_001294 Homo sapiens CLPTM1 0.23 4 Cytokine family
NM_001021 Homo sapiens ribosomal protein S17 (RPS17) 0.25 8 Ribosomal
M20866 Sus scrofa cofilin 0.25 1 Cytoskeleton
M21683 Sus scrofa nonhistone protein HMG1 0.30 8 DNA binding protein
AF020508 Bos taurus PAG-6 0.36 11 Cytokine family
AF020507 Bos taurus PAG-5 0.39 11 Cytokine family
X91755 Bos taurus cathepsin L 0.42 8 Oncogene & Tumor inhibitor
NM_005722 Homo sapiens ARP2 actin-related protein 2 homolog (yeast) 0.43 8 Cytoskeleton
AF210381 Bos taurus DDVit1 0.44 4 ECM & related
AF020506 Bos taurus PAG-4 0.46 1 Cytokine family
U21660 Bos taurus phosphatidylcholine transfer protein 0.47 4
L02897 Dog nonerythroid beta-spectrin 0.47 9
AF166124 Homo sapiens selenoprotein X 0.49 8
Day 14E/Day 7E up-regulated genes (2<)
X14926 Mus musculus calreticulin 24.00 12
AB009282 Homo sapiens cytochrome b5 16.48 12
NM_004494 Homo sapiens hepatoma-derived growth factor (HDGF) 13.52 12 Oncogene & Tumor inhibitor
AF000137 Bos taurus connective tissue growth factor precursor (CTGF) 12.18 12 Cytokine family
NM_000365 Homo sapiens triosephosphate isomerase 1 (TPI1) 10.52 12
X89984 Homo sapiens BCL7A protein 10.31 12 Cytoskeleton
AF217197 Homo sapiens FBP interacting repressor (FIR) 9.80 12 Transcriptional regulator
NM_005720 Homo sapiens ARPC1B 9.45 3 Cytoskeleton
NM_001404 Homo sapiens EEF1G 9.43 12 Transcriptional regulator
AB003094 Bos taurus ferritin L subunit 9.38 12
X56503 Sus scrofa casein kinase II beta subunit (CKII beta) 8.55 12 Enzyme
X13684 Bos taurus glutathione peroxidase (gpx1) 8.39 3 Enzyme
NM_002436 Homo sapiens membrane protein, palmitoylated 1 (55 kD) 8.33 12 Membrane protein
NM_005022 Homo sapiens profilin 1 (PFN1) 8.09 12 Cytoskelton
X01809 Bos taurus cathepsin 3' terminus 8.08 12 Oncogene & Tumor inhibitor
AF207664 Homo sapiens matrix metalloprotease (ADAMTS1) 8.03 3 ECM & related
X56597 Homo sapiens humFib fibrillarin 7.97 12
AF182001 Bos taurus D4-GDP-dissociation inhibitor (D4-GDI) 7.82 3
NM_007317 Homo sapiens kinesin-like 4 (KNSL4) 7.66 2
M21044 Bos taurus MHC class I BoLA 7.65 2 Cytokine family
All data of individual gene changes are available in Suppl. Table 1 of the Additional file.
Table 3 Top 20 known genes of the most or less expression over two-fold differences between Day 14E vs. Day 21E.
Accession No. Gene name D21E/D14E K-means Classification
Day 21E/Day 14E down-regulated genes (<0.5)
U21661 Rattus norvegicus myotrophin 0.28 2 Cytokine family
Day 21E/Day 14E up-regulated genes (2<)
AF004877 Homo sapiens pro-alpha 2(I) collagen (COL1A2) 16.08 4 ECM & related
U21660 Bos taurus phosphatidylcholine transfer protein 10.13 4
X15112 Bos taurus cytochrome c oxidase subunit VIb (AED) 10.01 4
X59504 Bos taurus prolactin-like protein (PRP-VI) 9.62 11 Cytokine family
AB008683 Bos taurus alpha2(I) collagen (COL1A2) 9.27 4 ECM & related
AF105429 Ovis aries H19 7.81 4
X65210 Bos taurus microsatellite DNA 7.62 11
X15975 Bos taurus PRP-V 7.59 11 Cytokine family
S74761 Bos taurus water channel protein CHIP29 7.44 9 Membrane protein
AF196320 Bos taurus Interferon-tau1C 7.39 2 Cytokine family
NM_001613 Homo sapiens actin, alpha 2 (ACTA2) 6.98 4 Cytoskeleton
L06151 Bos taurus PAG-2 6.86 11 Cytokine family
M32303 Bos taurus metalloproteinase inhibitor 6.82 11 ECM & related
NM_001553 Homo sapiens IGFBP7 6.62 9 Cytokine family
M21683 Sus scrofa nonhistone protein HMG1 6.58 8 DNA binding protein
AF020508 Bos taurus PAG-6 6.54 11 Cytokine family
AF125041 Ovis aries decorin 6.42 4
J02944 Bos taurus PRP-1 6.40 11 Cytokine family
AB004800 Sus scrofa S100C protein 5.68 9 Cytokine family
AF192336 Bos taurus PAG-19 5.47 11 Cytokine family
All data of individual gene changes are available in Suppl. Table 2 of the Additional file.
Table 4 Top 20 known genes of the most or less expression over two-fold differences between Day 21E vs. Day 28EEM.
Accession No. Gene name D28EEM/D21E K-means Classification
Day 28EEM/Day 21E down-regulated genes (<0.5)
AF196320 Bos taurus Interferon-tau1C 0.07 2 Cytokine family
AF033096 Avena sativa nonphototropic hypocotyl 1 (NPH1-1) 0.22 12
L10240 Homo sapiens EMMPRIN 0.24 7 ECM & related
U21660 Bos taurus phosphatidylcholine transfer protein 0.27 4
X15112 Bos taurus cytochrome c oxidase subunit VIb (AED) 0.28 4
L34261 Bos taurus palmitoyl-protein thioesterase 0.29 6 Enzyme
M26576 Homo sapiens alpha-1 collagen type IV 0.29 6 ECM & related
NM_001747 Homo sapiens capping protein, gelsolin-like (CAPG) 0.32 2 Cytoskeleton
U46064 Sus scrofa aldehyde reductase (ALR1) 0.33 3 Enzyme
AF020513 Bos taurus PAG-11 0.33 12 Cytokine family
M11120 Rat 28S rRNA 0.33 2
NM_004718 Homo sapiens COX7A2L 0.34 8 Enzyme
AF144763 Bos taurus TIMP-1 0.35 3 ECM & related
AF034607 Homo sapiens chloride channel ABP 0.36 8 Membrane protein
NM_005556 Homo sapiens keratin 7 (KRT7) 0.36 2 Cytoskeleton
AJ243656 Methanobacterium thermoautotrophicum ehbA-Q 0.37 2
M83104 Bos taurus cytochrome b5 reductase 0.37 2 Enzyme
X59693 Bos taurus ubiquinol-cytrochrome-c reductase (subunit II) 0.38 6 Enzyme
D84557 Homo sapiens HsMcm6 0.38 12
M77234 Homo sapiens ribosomal protein S3a 0.38 6 Ribosomal
Day 28EEM/Day 21E up-regulated genes (2 <)
AF020508 Bos taurus PAG-6 3.45 11 Cytokine family
X01912 Goat epsilon I beta-globin 3.00 9
AF004133 Sus scrofa adipocyte membrane protein 2.66 1 Membrane protein
M73961 Ovis aries PAG-1 2.35 6 Cytokine family
J02840 Bos taurus placental lactogen (PL-Ala) 2.21 5 Cytokine family
AF192336 Bos taurus PAG-19 2.17 11 Cytokine family
AB005148 Bos taurus interleukin 1 (IL-1) receptor antagonist 2.13 1 Cytokine family
D10989 Bos taurus endothelin ETB receptor 2.12 1
X59504 Bos taurus PRP-VI 2.08 11 Cytokine family
M80328 Bos taurus PL-Val 2.01 5 Cytokine family
Z11742 Bos taurus annexin XI 2.00 5 Apoptosis & Cell cycle
All data of individual gene changes are available in Suppl. Table 3 of the Additional file.
Table 5 Top 20 known genes of the most or less expression over two-fold differences between Day 28EEM vs. Day 28F.
Accession No. Gene name D28F/D28EEM K-means Classification
Day 28F/Day 28EEM down-regulated genes (<0.5)
M73961 Ovis aries PAG-1 0.14 6 Cytokine family
X89984 Homo sapiens BCL7A protein 0.14 12 Cytoskeleton
X15975 Bos taurus PRP-V 0.15 11 Cytokine family
AF079545 Ovis aries placental lactogen precursor (PL) 0.16 8 Cytokine family
AF020509 Bos taurus PAG-7 0.19 11 Cytokine family
J02840 Bos taurus placental lactogen (bPL-Ala) 0.20 5 Cytokine family
X59504 Bos taurus PRP-VI 0.20 11 Cytokine family
S72871 Homo sapiens GATA-2 transcription factor 0.20 8 Transcriptional regulator
AF192336 Bos taurus PAG-19 0.22 11 Cytokine family
AF020508 Bos taurus PAG-6 0.23 11 Cytokine family
J02944 Bos taurus PRP-I 0.24 11 Cytokine family
L06151 Bos taurus PAG-2 0.25 11 Cytokine family
AF020512 Bos taurus PAG-10 0.27 5 Cytokine family
AF020514 Bos taurus PAG-12 0.28 11 Cytokine family
AF192334 Bos taurus PAG-17 0.30 11 Cytokine family
AL133034 Homo sapiens clone DKFZp727K171 0.31 11
Z11742 Bos taurus annexin XI 0.31 5 Apoptosis & Cell cycle
U89321 Homo sapiens nucleophosmin phosphoprotein (NPM) 0.32 5
X17614 Bos taurus 3 beta hydroxy-5-ene steroid dehydrogenase/delta 5-delta4 isomerase 0.32 5 Enzyme
AF192333 Bos taurus PAG-16 0.32 11 Cytokine family
Day 28F/Day 28EEM up-regulated genes (2<)
L34261 Bos taurus palmitoyl-protein thioesterase 4.32 6 Enzyme
AF000137 Bos taurus connective tissue growth factor precursor (CTGF) 2.85 12 Cytokine family
NM_004458 Homo sapiens FACL4 transcript variant 1 2.84 7 Enzyme
NM_006491 Homo sapiens NOVA1 transcript variant 3 2.82 1 Oncogene & Tumor inhibitor
NM_006838 Homo sapiens methionine methionyl aminopeptidase 2 2.72 4 Enzyme
AB028449 Homo sapiens helicase-MOI 2.65 9 DNA binding protein
AL080102 Homo sapiens clone DKFZp564N1916 2.64 9
AF057300 Homo sapiens truncated RAD50 protein 2.63 3
AB043994 Bos taurus MMP-2 2.56 7 ECM & related
NM_006719 Homo sapiens transcript variant ABLIM-m 2.53 1 Cytoskeleton
AF195417 Homo sapiens DEAD-box protein abstrakt (ABS) 2.51 2
Z25531 Bos taurus repeat region DNA 2.41 3
AF113682 Homo sapiens clone FLB3436 PRO0868 2.40 4
S76474 Homo sapiens trkB 2.39 1 Cytokine family
Y16533 Ovis aries IGF-II 2.39 7 Cytokine family
M86739 Bos taurus neuropeptide Y receptor 2.38 7 Cytokine family
AF043937 Homo sapiens DHAPAT 2.37 9 Enzyme
AF144763 Bos taurus TIMP-1 protein 2.36 3 ECM & related
NM_005563 Homo sapiens stathmin 1/oncoprotein 18 (STMN1) 2.36 4 Oncogene & Tumor inhibitor
AF198487 Homo sapiens transcription factor LBP-1b 2.34 4 Transcriptional regulator
All data of individual gene changes are available in Suppl. Table 4 of the Additional file.
Day 7 to Day 14
A comparison between Day 7E and Day 14E clearly indicates that only 26 genes were down-regulated (14 annotated and 12 ESTs), with many genes (680) being up-regulated from Day 7 through Day 14E. The most significant genes during this period are listed in Table 2 and Suppl. Table 1 (see Additional files). They included 22 cytokine-related molecules, 44 enzymes, 21 transcriptional regulators, 16 oncogenes or tumor suppressor genes, 12 apoptosis or cell-cycle molecules, 10 heat-shock proteins (HSP), 9 cell-adhesion molecules, and 8 membrane proteins. A striking expression was found for several genes during this period, i.e. calrecticulin (X14926), which is a 55 kd calcium binding protein of the ER lumen, and caldesmon (X89984), an actin-binding protein, were two of the most significantly expressed genes. PAGs and IFN were also strongly expressed. Other cytokine molecules, such as HDGF (NM_00494), had their expression accented. Cell-function-related genes such as integrin, mucin, ADAMTS, keratin, and cytokeratin were found to be significantly increased in their expression.
Day 14 to Day 21
A total of 452 genes were significantly up-regulated from Day 14 through Day 21, and half of these (226 genes) were annotated by BLASTn (Fig. 2). Only two genes were down-regulated, and they contained one annotated gene, myotrophin. The expression of various PL-related genes, including PRPs, was up-regulated during this period. The expression of extra-matrix-related genes, such as collagens, proteoglycans, MMPs, extracellular MMP inducer (EMMPRIN), and heparanase, and cell adhesion molecules, such as mucin, integrins, ezrin, and certain cytokines such as insulin-like growth factors (IGFs) and epidermal growth factor receptor (EGFR), were up-regulated. IFN-τ expression was emphasized. The individual gene expression data is provided in Table 3 and Suppl. Table 2 (see Additional files).
Day 21 to Day 28
A comparison between Day 21E and Day 28EEM revealed that the expression of 14 genes increased slightly in Day 28EEM and 11 of these were annotated, which included PAGs, PLs, and IL-1. A total of 173 genes were significantly down-regulated in Day 28EEM, including 109 annotated genes; these genes were found mainly in k-means clusters 2, 3, 6, 8, and 12. The most significantly down-regulated gene during this critical period for implantation was IFN-. Down-regulation of certain MMP or matrix-related genes was also found (see Table 4 and Suppl. Table 3 in Additional files) in the top 50 genes of lowest expression.
Comparison between extra-embryonic membranes on Day 28 and the fetus on Day 28
A total of 119 genes, including ESTs, decreased significantly in expression, with only 52 annotated genes among them in Day 28EEM (Fig. 2). However, in Day 28EEM, 74 up-regulated genes contained members of the cytokine family and molecules that play a role in cell-to-cell interactions, such as PLs, PRPs, PAGs, MHCs, and mucin, as indicated in Fig. 3. The up-regulated genes included 35 ESTs, which exhibited expression profiles similar to the cytokines during the pre-implantation period in extra-embryonic membranes. In addition to these trophoblast-specific genes, which are known to be specific to extra-embryonic membranes, other interesting genes, such as dickkopf-1, which is a Wnt signal regulation molecule, grancalcin, which is a calcium binding protein, two actin-related proteins (NM_005720, X89984), and cell proliferation related genes (NM_006429, U89321), were specifically found in extra-embryonic membranes when compared to the fetus. The actual roles of these genes in extra-embryonic cell development are as yet unknown. Individual data are provided in Table 5 and Suppl. Table 4 in Additional file 1.
Figure 3 Hierarchical tree cluster of differentially expressed genes in the bovine embryo during the implantation period (Days 7 to 28 of gestation). Two-fold and greater differences in gene expression between Day 28EEM vs. Day 28F are selected in the expression profiles from all samples of Day 7E, 14E, 21E, 28EEM and 28F. Normalized and log2 transformed expression data were used for the clustering analysis. The yellow cells indicate up-regulated genes in the median of the total value; the blue cells indicate down-regulated genes in the median of the total value. The black cells indicate no changes in expression.
Candidate extra-embryonic membrane specific genes
Genes related to trophoblast cell differentiation were anticipated to change their expression when the comparison was made between Day 28EEM and Day 28F. Most of the annotated genes of the 74 genes that increased over two-fold in Day 28EEM were PL-, PRP-, and PAG-related genes. The expression of 35 ESTs showed profiles similar to those of the annotated genes during the pre-implantation period, as determined by three hierarchical analyses (Fig. 3). An attempt was made to find new genes related to extra-embryonic cell lineages. Four ESTs (AW464053, AW465434, AW462349, and AW485575) were selected and analyzed by real-time RT-PCR. The quantitative data coincided with the microarray data as shown in Figs. 3 and 4. The relative intensities of these ESTs, PL-Ala and PRP-I were similar to those of real-time RT-PCR.
Figure 4 Real-time RT-PCR analysis of PL-Ala, PRP-I, and ESTs (GenBank accession No. AW464053, AW465434, AW462349, and AW485575) mRNA in bovine embryos. Gene expression on Days 7E to 21E, and 28EEM or 28F is provided. The expression of each mRNA was normalized to the expression of GAPDH mesured in same RNA preparation. The expression refers "means ± SD". Values with different letters are significantly different (P < 0.05).
Discussion
The major reproductive wastage in farm animals is early embryo loss, i.e. the anomalous development of embryos and/or an aberration of placentation [36]. Various technologies, such as artificial insemination, embryo transfer, and cloning, have been applied to bovine reproduction [13,37]. Precise knowledge of the gene expression profile during peri-implantation is necessary to reduce early losses and to improve the reproductive efficiency of these new technologies [6,38-40]. However, little is known about the complex molecular regulation of embryos and extra-embryonic membrane development in cattle. Thus, the genes to be profiled include new, functional gene candidates. We suggest an assessment method or key gene to help clarify the complex mechanisms in early embryo and trophoblast cell proliferation and differentiation.
Various genes changes during embryonic development, particularly in extra-embryonic membranes, with specific morphological changes occur in bovine: a remarkable elongation of the embryonic membrane, fusion between fetal membrane and caruncular epithelium, endometrial reorganization [3,41,42]. Specific genes, like IFN-τ, PLs, PRPs, PAGs, IGFs, and IGFBPs have been studied as they take important roles around implantation in bovine [3,43-48]. However these molecules are essential for development of the embryo and the formation of extra-embryonic membranes, determination of their specific roles are still difficult to prove.
Microarray analyses provided the time-dependant genes profiles in accord with the progress of gestation during the peri-implantation period. Most genes that expressed from the blastocyst stage to early placentation were up-regulated, while only 14 of the identified genes exhibited down-regulation (Fig 1). For example, selenoprotein X (AF166124) exerts a stimulative function on cell proliferation, particularly in the blastocyst, since its expression decreased on Day 14 compared to Day 7. This result coincided with that in silico work [49].
We selected genes related to heat-shock proteins (HSP) to interpret comprehensive genes expression, because they were frequently found in the list of significant genes and previous data had revealed their importance in early embryo development [50]. Ten HSP-related genes out of 333 annotated genes were up-regulated over two-fold on Day 14E in comparison to Day 7E. Similar genes were detected, i.e. seven on Day 21E and three on Day 28EEM. These results suggest a specific importance for HSPs in the early development of embryos. HSPs may disturb the coordination between the conceptus and endometrium in bovines and be related to the induction of early embryo loss [51]. Aberration of HSP90 expression in mice causes a placental abnormality that is closely related to trophoblast cell differentiation and proliferation [52].
Intensive IFN-τ expression was found during the implantation period (Days 14E to 21E). The trophoblast elongates and grows into a thread-like structure during this period and produces IFN- which inhibits luteolysis and plays a role in embryonic survival by mediating embryo-maternal crosstalk [8,43,53-56]. IFN-τ not only has an anti-luteolytic function enabling the establishment of gestation in cows, but apparently also participates in various other functions necessary for embryo survival [56,57]. It has been reported that IFN-τ has an immunoregulative effect [58] and also interferon modulates the expression of MHC class I antigen in mouse trophoblast cell cultures [59]. It is interesting that MHC I and its related genes, MHC II and β-microglobulin, exhibited increased expression in the present study only during the implantation period.
Cell-adhesion molecules and cytokines, such as the integrin family, have been emphasized their importance for initiation of implantation [60-62]. Recently, Ezrin, a cytoskeletal-membrane linker molecule belonging to the ezrin-radixin-moesin (ERM) family, proved its importance for implantation in mice [63], exhibiting greater expression than in the pre-implantation stage in bovine blastocysts in the present study. The importance of adhesion molecule involvement via the adhesion-regulating molecules (ARM-1), ICAM-1, LECAM-1, Lu-ECAM-1, calcium, and integrin-binding protein (CIB) was suggested by their expression increasing around the expected implantation starting day (Day 21E).
Numerous PAG, PL, and PRP genes, involved in the differentiation of trophoblastic cell lineage, were found among the 39 annotated genes of the 74 that exhibited over a two-fold difference between extra-embryonic membranes and the fetus on Day 28. The expression of most PAG family members increased towards Day 21E. Other binucleate cell-specific genes, such as PL and PRP family members, exhibited a coordinated expression with the PAG family of genes. These results confirm previous reports from this laboratory [3,13,33,64]. PAG-5, -15, -16, -17, and -19 were distributed to k-means cluster 11 in the present study, whereas PAG-8 was distributed to k-means cluster 3 (Fig. 1). The former group of PAG family members is apparently produced by binucleate cells, while those in the latter group may be produced by other cells of the trophectoderm [46].
Microarray data suggested various new candidate genes for extra-embryonic development even they have known function. Dickkopf-1 may inhibit Wnt proteins, which influence many aspects of embryonic development [65]. Annexins are a family of structurally related calcium-dependent phospholipid binding proteins [66]. Grancalcin is a Ca (2+)-binding protein [67]. Actin-related protein 2/3 (Arp2/3) complex may be related to the actin cytoskeleton [68]. Chaperonin containing t-complex polypeptide 1 (CCT complex) is essential for the maturation of cyclin E [69]. Nucleophosmin phosphoprotein (NPM) (U89321), is a major nucleolar protein that is 20 times more abundant in tumors or proliferating cells [70]. Finally, BCL7A exhibits homology with the actin-binding protein caldesmon [71].
The most straightforward explanation for present study is that EST genes that display similar expression patterns are functionally related [72]. AW464053, AW465434, AW462349, and AW485575 gene expression patterns were similar to the expression patterns of PL and PRP-I (Figs. 3 and 4). These ESTs were submitted from the cDNA library of Soares normalized bovine placenta (Lewin et al., unpublished). The EST of AW485575 was submitted from a library made from pooled tissue of Day 20 and Day 40 bovine embryos [73]. However, the function of all four ESTs remains obscure. The AW465434 gene has a 495 bp sequence and was found to be common for 21/21 bp (e-value = 1.1) in Homo sapiens in BAC clone RP11-1246C19 (AC102953). The AW462349 gene has 545 bp sequences and was found to be common for a slight 43/47 bp (e-value = 3 × 10-7) in Sus scrofa clone RP44-519O7 (AC096884). The AW485575 gene has 436 bp sequences and was found to be common for a slight 157/181 bp (e-value = 6 × 10-39) in Homo sapiens hypothetical protein MGC39389 (BC003531). The AW464053 gene expression patterns were similar to the expression pattern of PRP-I, and the gene sequence has a portion in common with an e-value = 1 × 10-102 and 4 × 10-31 (common sequence = 326/371, 87% and 105/116, 90%) for PRP-VI (X59504), or an e-value = 1 × 10-95 (common sequence = 351/408, 86%) for PRP-III (M27240 or NM_174160). We suggest this EST was new member of bPRP.
Conclusions
This study provides developmental expression changes of a large number of genes in the bovine embryo during the peri-implantation period, with particular focus on genes that express in extra-embryonic membranes. The new gene candidates that were discussed here may address a new set of annotated genes and ESTs for embryonic differentiation and development. Participation of several known genes like Ezrin, CIB, Dickkopf-1, Grancalcin and EST (AW464053, AW465434 etc) in extra-embryonic membrane differentiation and development is elucidated by this microarray analysis. Fundamental investigations of this sort contribute significantly to a better understanding of the effects of various cultural conditions and cellular and genetic manipulation of embryos, including in vitro fertilization, in vitro maturation, and embryo cloning technology.
Supplementary Material
Additional File 1
Supplement tables list the whole genes which produced the expression difference in Fig. 2 and complement Tables 2–5 beneath (see text for details). The legends of supplemental tables are as follows: Supplement Table 1; Two-fold differentially expressed genes between Day 7E vs. Day 14E. Supplement Table 2; Two-fold differentially expressed genes between Day 14E vs. Day 21E. Supplement Table 3; Two-fold differentially expressed genes between Day 21E vs. Day 28EEM. Supplement Table 4; Two-fold differentially expressed genes between Day 28EEM vs. Day 28F.
Click here for file
Acknowledgements
This research was supported by grants from the Bio-oriented Technology Research Advancement Institution (BRAIN); and a grant-in-aid (HC-04-2261-1) from the Ministry of Agriculture, Forestry, and Fisheries of Japan.
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-2-101553894910.1186/1477-9560-2-10Case ReportThromboembolic events and haematological diseases: a case of stroke as clinical onset of a paroxysmal nocturnal haemoglobinuria Granata Gianluca [email protected] Tiziana [email protected] Micco Pierpaolo [email protected] Barbara [email protected] Giampiero [email protected] Vito Giuseppe [email protected] Ugo [email protected] Giuseppe [email protected] Alferio [email protected] Internal Medicine of Second University of Naples, Naples, Italy2 University of Molise, Aesernia, Italy2004 11 11 2004 2 10 10 4 6 2004 11 11 2004 Copyright © 2004 Gianluca et al; licensee BioMed Central Ltd.2004Gianluca 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.
Some haematological diseases are associated to an increased risk of thromboembolic events. We report a case of paroxysmal nocturnal haemoglobinuria (PNH) in which a cerebrovascular event represented the first clinical manifestation of disease. PNH is associated to thromboembolic events, generally of venous districts often involving unusual locations such as mesenteric vessels, sagittal veins, inferior vena cava and renal veins.
To our knowledge arterial thrombotic episodes are rare and the involvement of arterial cerebral vessels is exceptional. Then, our case points out the importance of investigating about haematological disorders in all patients presenting with a stroke, in which the common predisposing conditions are excluded.
paroxysmal nocturnal haemoglobinuriastrokethromboembolic eventshaematological disease
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Background
Paroxysmal nocturnal haemoglobinuria (PNH) is an acquired clonal disorder of haematopoietic stem cells clinically characterized by acute intravascular haemolytic crisis, in particular nocturnal, often overlapped to chronic haemolysis, and by thrombotic events and bone marrow failure. It is associated with a somatic mutation in the phosphatidylinositol glycan complementation class A (PIG-A) gene, mapped to the X chromosome; the subsequent deficiency of glycosylphosphatidylinositol (GPI) and of GPI-anchored molecules, as the decay accelerating factor (DAF or CD55) and the membrane inhibitor of reactive lysis (MIRL or CD59), causes an increased susceptibility to complement-mediated lysis of erythrocytes, leukocytes and platelets [1].
The association between PNH and thromboembolic accidents, generally manifesting as thrombotic events of venous vessels sometimes complicated by pulmonary embolism, is well established. Arterial thrombotic episodes, particularly of cerebral vessels are enough rare [2].
We report a case of PNH presenting with thromboembolic events, both venous (proximal deep venous thrombosis of lower limbs) and arterial (stroke).
Case history
Clinical summary
A 56-year-old woman, with history of peptic ulcer and family history for cerebrovascular disease was referred to our Division of Internal Medicine with asthenia and generalized discomfort. She reported a cerebrovascular accident manifesting as a right brachial and crural hyposthenia ten month ago, almost completely receded at observation time; she also referred recurrent episodes of proximal deep venous thrombosis (DVT) of lower limbs in the last seven months.
Pathological findings
In order to identify any hypercoagulable state (i.e. inherited or acquired thrombophilia), in view of her personal and familiar history, we tested prothrombin time, as INR, activated partial thromboplastin time, as ratio, fibrinogen, protein C and S, antithrombin III, activated protein C resistance, anti-cardiolipin antibodies IgG and IgM, lupus anticoagulant, plasminogen activator inhibitor type 1, d-dimer, gene polimorphism of clotting factor II and V, gene polimorphism C9774T and G3775A of apolipoprotein B and gene polimorphism C3932T and C4070T of apolipoprotein E resulted all in normal range; while gene polimorphism of tetrahydrofolate reductase and angiotensin converting enzyme revealed heterozigosity for both. Subsequently, homocysteinemia test revealed mild hyperhomocysteinemia. All thrombophilic tests are summarised in table 1.
Table 1 Thrombophilic tests
Thrombophilic tests (units of measurement) Results Normal range
Protein C (antigen) (%) 99% 60–125
Protein S (antigen) (%) 102% 60–125
Antithrombin (activity) (%) 105% 80–120
Activated protein C resistance (Bertina) 0,90 >0,77
Anti-cardiolipin antibodies IgG (U/GPL) 4 <7
Anti-cardiolipin antibodies IgM (U/MPL) 2 <4
Lupus anticoagulant absent absent
Plasminogen activator inhibitor type 1 (ng/dl) 30 4–44
PTHRA20210 gene polimorphism wild type wild type
Factor V Leiden gene polimorphism wild type wild type
Apolipoprotein B gene polimorphism C9774T and G3775A wild type wild type
Apolipoprotein E gene polimorphism C3932T and C4070T wild type wild type
Methylene-tetrahydrofolate C677T gene polimorphism heterozigosity wild type
Angiotensin converting enzyme deletion gene polimorphism insertion/deletion insertion/insertion
Homocysteinemia (μM) 22 5–15
Prothrombin time (INR) 0.95 0.8–1.2
Activated partial thromboplastin time (ratio) 0.92 0.8–1.2
Fibrinogen (mg/dl) 305 220–400
D-dimer (ug/l) 188 0–198
A magnetic resonance imaging scan showed little and multiple ischemic lesions in particular in left cerebral peduncle (fig 1A), semioval centres (fig 1B), left pons and midbrain. Moreover, a vascular ultrasound examination ruled out the presence of significant stenosis of arterial cerebral vessels and confirmed proximal DVT and post-thrombotic syndrome of lower limbs.
Figure 1 Magnetic resonance imaging scan showing multiple ischemic lesions in left cerebral peduncle (1A) and semioval centres (1B).
Other available data showed: red blood cells 2.470.000/mm3, hemoglobin 7.9 g/dl, hematocrit 25%, mean corpuscular volume 99,6 fl, mean corpuscular hemoglobin 32 pg, mean corpuscular hemoglobin concentration 32 gr/dl, white blood cells 4,040/mm3, platelets 93.000/mm3, reticulocytes 5,4%, serum iron 76 μg/dl, erytro-sedimentation rate 1° hour 40 mm, lactate dehydrogenase 944 UI/l, total bilirubin 0,72 mg/dl, indirect bilirubin 0,36 mg/dl, and presence of hemoglobinuria. Coombs' test, cold agglutinins, antinuclear antibodies, anti-extractable nuclear antigens antibodies, anti-mithocondrial antibodies, anti-smooth muscle antibodies were negative. Laboratory data and their range are summarised in table 2.
Table 2 Other laboratory findings
Laboratory data (units of measurement) Results Normal range
Erytro-sedimentation rate 1° hour (mm) 40 <10
lactate dehydrogenase (UI/l) 944 100–190
total bilirubin (mg/dl) 0,72 0–1
indirect bilirubin (mg/dl) 0,36 0–0,5
antinuclear antibodies absent absent
anti-extractable nuclear antigens antibodies absent absent
anti-mithocondrial antibodies absent absent
anti-smooth muscle antibodies absent absent
An abdominal ultrasonography excluded a hypersplenism and/or Kasabath-Merritt syndrome.
A peripheral blood smear did not show any finding suggestive for haematological disorders. A bone marrow biopsy showed a slight hyperplasia of erythrocytic bone marrow cell line.
These laboratory and morphological findings suggested a non-immune haemolytic anemia. In particular, due to the exclusion of other non-immune haemolityc disorders by means of age and clinical history together with the presence of hemoglobinuria and pancytopenia, we hypothesized paroxysmal nocturnal hemoglobinuria. This diagnosis was confirmed by an immunophenotypic profile of peripheral blood cells, showing a 15% of deficient CD59 erythrocytes, and by the presence of hemosiderinuria. Haematological findings are summarised in table 3.
Table 3 Haematological data
Laboratory data (units of measurement) Results Normal range
red blood cells (cells/mm3) 2.470.000 4.200.000 – 5.400.000
hemoglobin (g/dl) 7,9 12–16
hematocrit (%) 25 37–45
mean corpuscolar volume (fl) 99,6 81–99
mean corpuscolar hemoglobin (pg) 32 27–31
mean corpuscolar hemoglobin concentration (g/dl) 32 32–36
white blood cells (cells/mm3) 4.040 4.800 – 10.800
Platelets (cells/mm3) 93.000 130.000 – 400.000
Reticulocytes (%) 5,4 <2
haemoblobinuria traces absent
Hemosiderinuria present absent
Coombs'test negative negative
cold agglutinins negative negative
Peripheral blood smear Normal
Bone marrow biopsy slight hyperplasia of erythrocytic cell line
Immunophenotypic profile of peripheral blood cells 15% of deficient CD59 erythrocytes
During her hospitalization two haemotrasfusions were necessary in occasion of two concurrent haemolytic crises. Following dismission, in order to prevent further thromboembolic events, the patient began oral anticoagulation therapy with warfarin according with INR value in range of 2–2,5. Moreover she was treated with B12 vitamin and folate supplementation.
Discussion
The association between haematological diseases and thromboembolic events is well established. In particular high thrombotic risk is recognized in patients with essential thrombocythemia, polycythemia vera, PNH and drepanocytosis [3]. PNH is associated to venous thrombosis in approximately one third of cases. The most frequently reported locations are unusual such as mesenteric vessels, sagittal veins, inferior vena cava and renal veins. When thrombosis occurs in the pre-hepatic or hepatic veins, the patient develops a Budd-Chiari syndrome [4]. Arterial thrombosis is rare, even if few cases of cerebral arterial thrombosis [5] and acute myocardial infarction [6] are described in the literature.
The mechanism whereby PNH causes an hypercoagulable state is not clear. PNH platelets lack the GPI-linked proteins CD55 and CD59, and respond to the deposition of terminal complement components by vesiculations of portions of their plasma membrane, resulting an increased procoagulant property. PNH cells also lack the receptor of the GPI-linked urokinase plasminogen activator, which may result in impaired fibrinolysis [4]. Also an increase of membrane-derived procoagulant microparticles (phosphatidylserin) stemming from the platelets of PNH patients has been described [3].
In our case, thrombotic events represented the clinical onset of PNH and involved both venous (DVT) and arterial (stroke) vessels. Neurological manifestations in PNH patients are generally due to cerebral venous thrombosis [7,8], even if a few cases of cerebral arterial episodes, involving large vessels, are described. However, usually cerebral ischaemia in PNH did not occur as presenting sign of the disease nor affect small and middle cerebrovascular arteries [5]. In our patient the relationship between PNH and thrombotic events is strongly suggested, especially after excluding inherited or acquired thrombophilia and atherosclerotic risk factors. Heterozigosities for gene polimorphism of tetrahydrofolate reductase and angiotensin converting enzyme, detected in our patient, are not associated to an increased risk of stroke, while acquired or inherited hyperhomocysteinemia may be involved [9-11].
Also haemotological findings agree with PNH diagnosis because of the association of thrombosis, anemia and thrombocytopenia. We excluded further causes of non-immune haemolityc anemia (i.e. spherocytosis, enzymatic disorders, microangiopathic anemia) and thrombocytopenia (i.e. disseminated intravascular coagulation, haematological malignancies, systemic erythematosus lupus, primary or secondary antiphospholipid syndrome, hypersplenism).
In conclusion, PNH is associated to thromboembolic events, especially in the venous district and should be considered as a possible cause of an hypercoagulable state, in particular when unusual vascular locations are involved. Our case indicates the possibility of arterial thrombotic episodes in a patient with PNH and suggests a thorough evaluation of any haematological disorders in patients presenting with stroke or myocardial infarction, especially in the absence of atherosclerosis risk factors and/or a thrombophilic state.
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-891554648610.1186/1471-2164-5-89Research ArticleThe rehydration transcriptome of the desiccation-tolerant bryophyte Tortula ruralis: transcript classification and analysis Oliver Melvin J [email protected] Scot E [email protected] Joaquin [email protected] Steven A [email protected] Paxton R [email protected] Cropping Systems Research Laboratory, USDA-ARS, 3810 4th St, Lubbock, TX, USA2004 16 11 2004 5 89 89 12 8 2004 16 11 2004 Copyright © 2004 Oliver et al; licensee BioMed Central Ltd.2004Oliver 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
The cellular response of plants to water-deficits has both economic and evolutionary importance directly affecting plant productivity in agriculture and plant survival in the natural environment. Genes induced by water-deficit stress have been successfully enumerated in plants that are relatively sensitive to cellular dehydration, however we have little knowledge as to the adaptive role of these genes in establishing tolerance to water loss at the cellular level. Our approach to address this problem has been to investigate the genetic responses of plants that are capable of tolerating extremes of dehydration, in particular the desiccation-tolerant bryophyte, Tortula ruralis. To establish a sound basis for characterizing the Tortula genome in regards to desiccation tolerance, we analyzed 10,368 expressed sequence tags (ESTs) from rehydrated rapid-dried Tortula gametophytes, a stage previously determined to exhibit the maximum stress induced change in gene expression.
Results
The 10, 368 ESTs formed 5,563 EST clusters (contig groups representing individual genes) of which 3,321 (59.7%) exhibited similarity to genes present in the public databases and 2,242 were categorized as unknowns based on protein homology scores. The 3,321 clusters were classified by function using the Gene Ontology (GO) hierarchy and the KEGG database. The results indicate that the transcriptome contains a diverse population of transcripts that reflects, as expected, a period of metabolic upheaval in the gametophyte cells. Much of the emphasis within the transcriptome is centered on the protein synthetic machinery, ion and metabolite transport, and membrane biosynthesis and repair. Rehydrating gametophytes also have an abundance of transcripts that code for enzymes involved in oxidative stress metabolism and phosphorylating activities. The functional classifications reflect a remarkable consistency with what we have previously established with regards to the metabolic activities that are important in the recovery of the gametophytes from desiccation. A comparison of the GO distribution of Tortula clusters with an identical analysis of 9,981 clusters from the desiccation sensitive bryophyte species Physcomitrella patens, revealed, and accentuated, the differences between stressed and unstressed transcriptomes. Cross species sequence comparisons indicated that on the whole the Tortula clusters were more closely related to those from Physcomitrella than Arabidopsis (complete genome BLASTx comparison) although because of the differences in the databases there were more high scoring matches to the Arabidopsis sequences. The most abundant transcripts contained within the Tortula ESTs encode Late Embryogenesis Abundant (LEA) proteins that are normally associated with drying plant tissues. This suggests that LEAs may also play a role in recovery from desiccation when water is reintroduced into a dried tissue.
Conclusion
The establishment of a rehydration EST collection for Tortula ruralis, an important plant model for plant stress responses and vegetative desiccation tolerance, is an important step in understanding the genome level response to cellular dehydration. The type of transcript analysis performed here has laid the foundation for more detailed functional and genome level analyses of the genes involved in desiccation tolerance in plants.
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Background
The cellular response of plants to water deficits has both economic and evolutionary importance directly affecting plant productivity in agriculture and plant survival in the natural environment. Ramanathan [1] has argued, based on predictions of global environmental changes, that developing crops which are more tolerant to water deficits while maintaining productivity, will become a critical requirement in the early part of the 21st century. Understanding how plant cells tolerate water loss is a vital prerequisite for developing strategies for improving tolerance of, and biomass/seed production under drought conditions.
In the last decade the genes induced by water-deficit stress have been successfully enumerated in plants that are relatively sensitive to cellular dehydration, in particular Arabidopsis thaliana [2-6]. In addition, the mechanisms by which the genetic response to water deficit is controlled by abscisic acid (ABA)-dependent and independent pathways have also been extensively elucidated [6,7]. However, even with the recent addition of in-depth examination of gene expression patterns using Arabidopsis microarrays [8,9] we have little functional knowledge of the genes that respond to water deficits. Of critical importance is the question of which of the genes identified as responding to water deficits actually have an adaptive role in establishing tolerance, in particular tolerance of cellular dehydration, and which are genes that are only responding to the injury incurred by the imposition of the stress. Injury may induce, or repress, specific genes that are not involved in promoting adaptation to cellular dehydration. An indication that at least some of the genes have an adaptive function in dehydration tolerance derives from the observation that they are expressed in tissues that acquire desiccation tolerance, the extreme manifestation of dehydration tolerance, such as in maturing seeds and in leaves of desiccation-tolerant plants during drying [3,10,11]. However, in order to fully address the question of the adaptive importance of genes involved in responses to cellular dehydration it is necessary to gain an evolutionary perspective of the involvement of a gene in dehydration tolerance mechanisms. To this end we have established an ongoing comparative genomics program to study the genetic responses to dehydration in species that span the evolution of dehydration (desiccation) tolerance mechanisms within the land plants. Here we present an analysis of ESTs derived from the desiccation-tolerant bryophyte Tortula ruralis [Hedw.] Gaertn. Meyer & Scherb., that is representative of the primitive genetic strategy for the acquisition of desiccation-tolerance [see [12,13]].
Desiccation tolerance, the ability to recover from the almost complete loss (90%) of protoplasmic water, is a phenomenon common in the reproductive structures of green plants: pollen, spores and seeds. However, the ability to survive vegetative desiccation is a demonstrable but uncommon occurrence in the plant kingdom [13-18]. Within the flowering plants there are only approximately 300 species of flowering plants that are known to tolerate vegetative desiccation [16,17].
Recent physiological phylogenetic analyses indicate that vegetative desiccation tolerance was primitively present in the bryophytes (the basal-most living clades of land plants), but was lost in the evolution of tracheophytes. Desiccation-tolerant bryophytes are found worldwide and occupy a variety of ecological niches, most of which could, during some period of the year, be considered as extreme either on a macro or microhabitat level. In most cases the extremes that these plants experience are both in water availability and temperature [19-23].
Desiccation-tolerant bryophytes, because of their simple architecture, have few, if any, morphological (or indeed physiological) characteristics or adaptations that can limit water loss or regulate plant temperature. As a result of this, the internal water content of their photosynthetic tissues rapidly equilibrates to the water potential of the environment once free water is lost from the surface of the plant. This in turn means that these plants experience drying rates that are much faster than those experienced by their more complex pteridophyte or angiosperm counterparts. In fact, the drying rates that desiccation-tolerant bryophytes experience are generally lethal to desiccation-tolerant ferns and flowering plants [14]. The rapid equilibration of protoplasmic water potential with that of the environment in bryophyte tissues appears to demand a type of desiccation tolerance that is significantly different from that exhibited by desiccation tolerant angiosperms [15]. Rather than acquiring desiccation tolerance in response to a dehydration event as seen in Craterostigma plantagineum, Sporobolus stapfianus, and other desiccation-tolerant angiosperms, desiccation-tolerant bryophytes appear to express this trait constitutively [15,24]. This form of desiccation tolerance is considered the most primitive of those that have received attention so far [13]. In this type of tolerance the primary response to a desiccation event, at least at the level of gene expression, occurs after the fact, during the first hour or two following rehydration. This has led to the suggestion that a major component of the mechanism of desiccation tolerance in bryophytes is a rehydration-induced cellular repair response [15,24]. The implication is that although cellular protection and hence desiccation tolerance is constitutive, it is not sufficient to prevent some damage from occurring (or being manifested) upon rehydration, and thus repair processes are needed and induced when water returns to the protoplasm of the cells.
The repair aspect of the mechanism of desiccation tolerance in these plants, although demonstrated to be a major component of tolerance, is difficult to detail and characterize. Most work has focused on the proteins whose synthesis is induced immediately upon rehydration of desiccated gametophytic tissue. Early work [25] established the ability of T. ruralis and other mosses to rapidly recover synthetic metabolism when rehydrated. The speed of this recovery was inversely dependent upon the rate of prior desiccation: the faster the rate of desiccation, the slower the recovery. In addition, although the pattern of protein synthesis in the first two hours of rehydration of T. ruralis is distinctly different from that of hydrated controls, novel transcripts were not made in response to desiccation [26]. Hence it was suggested that T. ruralis responds to desiccation by an alteration in protein synthesis upon rehydration that is in large measure the result of a change in translational control. Changes in transcriptional activity were observed for nearly all transcripts studied [27] but did not result in a qualitative change in the transcript population during desiccation or rehydration. It thus appears that T. ruralis relies more upon the activation of pre-existing repair mechanisms for desiccation tolerance than it does on either pre-established or activated protection systems.
In a detailed study of the changes in protein synthesis initiated by rehydration in T. ruralis, Oliver [26] demonstrated that during the first two hours of hydration the synthesis of 25 proteins is terminated, or substantially decreased, and the synthesis of 74 proteins is initiated, or substantially increased. Controls over changes in synthesis of these two groups of proteins, the former termed hydrins and the latter rehydrins, are not mechanistically linked. It takes a certain amount of prior water loss to fully activate the synthesis of rehydrins upon rehydration. RNA blots revealed that several rehydrin transcripts accumulate during slow drying [28,29] at a time when it is assumed that transcriptional activity is rapidly declining. These transcripts do not accumulate during rapid desiccation, nor is their accumulation during slow drying associated with an increase in endogenous ABA accumulation. ABA is undetectable in this moss [[30], M. J. Oliver, unpubl data], and T. ruralis does not synthesize specific proteins in response to applied ABA. The accumulation of these transcripts was postulated to be the result of an increase in mRNA stability brought about by the removal of water from the cells [27]. Recent studies clearly demonstrate that these transcripts are sequestered in the dried gametophytes in mRNP particles [29] and that this results in the change in their stability. The implication from this work is that the sequestration of mRNAs required for recovery hastens the repair of damage induced by desiccation or rehydration and thus minimizes the time needed to restart growth upon rehydration.
The major question arising from these studies concerns the identity of rehydrins and what possible functions and roles they may play in regards to the response to desiccation and rehydration and to desiccation tolerance per se. We have some limited knowledge of the functions (or postulated functions) of a few of the rehydrins from classical molecular analyses, [31,32] and from a small-scale EST collection analysis [33]. However, there is an obvious need to extend this base and to develop testable hypotheses that will help us to elucidate the metabolic and genetic mechanisms that control the recovery and repair of dried plant cells and their role in the development and evolution of desiccation tolerance. To gain an appreciation of the number of possible rehydrin genes and the range of possible functions encompassed by their expression we have initiated a genomics level analysis of gene expression during the recovery of Tortula gametophytes from the desiccated state. The first step in this process was to establish an EST collection that is representative of the transcripts available to the moss during the first few hours following rehydration. In this report we present a bioinformatic analysis of 10, 368 ESTs from the early phases of recovery following rehydration of rapidly-dried Tortula ruralis gametophytes; rapid dried gametophytes were chosen in order to maximize the recovery and repair response upon rehydration. The bioinformatics approach we have taken is based on that used by McCarter et al., [34] to conduct a comprehensive analysis of 5,700 Meloidogyne incognita L2 ESTs, and includes cluster analyses, transcript abundancy estimations, and functional classifications based on InterPror domains, Gene Ontology hierarchy, and KEGG biochemical classifications. The overall goal was to gain an appreciation of the Tortula transcriptome during the time period following rehydration when the desiccation driven alteration in gene expression is at its peak. During this period we hypothesize the processes of cellular repair and recovery are the main focus of the metabolism of the gametophytic cells.
Results and discussion
Ten thousand three hundred and sixty eight individual cDNA clones were selected from a Tortula ruralis rehydration library and subjected to single-pass 5' directional sequencing to generate 10,368 primary ESTs of which 9,159 (88%) passed through quality control, vector trimming, E. coli contamination, and cloning artifact removal. The 9,159 ESTs averaged 648 nucleotides in length and totaled 5.93 million nucleotides submitted to Genbank. These submitted ESTs form the basis of the subsequent transcriptome analysis utilizing the High Throughput-Gene Ontology-Genome Annotation Toolkit (HT-GO-GAT), a software developed by S.E. Dowd (unpublished).
Cluster analysis
Utilizing the assembly algorithms incorporated in the SeqManII software (part of the DNASTAR suite from DNASTAR Inc, Madison WI), the 9,159 ESTs were grouped into contigs and clusters by establishing assembly stringencies that generated groupings defined by the analysis protocols of McCarter et al., 2003 [34]. Contigs contain EST members that appear to originate from single transcripts whereas clusters are assemblies of ESTs that could represent transcripts from the same gene but alternate splice isoforms, or in the case of Tortula ESTs, which derive from a population of individual gametophytes, alleles of the same gene. The 9,159 ESTs formed 7,272 contigs and 5,563 clusters, both of which exhibit an average size of 669 nucleotides. However, the longest sequence increased from 1,575 nucleotides for contigs to 2,229 nucleotides for clusters. Clusters varied in content from a single EST (singletons) in 4,362 cases (78%) to 48 ESTs for a single cluster (Figure 1). The elimination of redundancy during contig building and cluster formation reduced the total number of nucleotides for further analyses from 5.93 million to 4.87 million (contigs) and 3.71 million (clusters). Overall the 9,159 ESTs potentially represent 5,563 genes, a discovery rate of 60%, with 47.6% of the ESTs as singletons. This is an overestimation of gene discovery since several non-overlapping clusters can represent a single gene and from our blast search data this appears to be a possibility, at least in the case of clusters 121 and 204 that appear to independently represent a gene that has a weak similarity to the LEA protein of Caenorhabditis elegans. Even though the ESTs derive from a non-normalized library 96.5% of the clusters still have 5 or fewer EST members.
Figure 1 Histogram of distribution of ESTs by cluster size.
Transcript abundance
The consensus Cluster sequences were subjected to a BLASTx style search of a custom curated non-redundant database derived from UNIPROT and annotated according to the degree of similarity of the cluster to the highest scoring match in the database of known gene sequences. The degree of similarity was based on the quality of the BLASTx statistical outputs as well as a visual inspection of the aligned sequences between the query and the target. Clusters that generated an HSP-bit score below 70 and or E values higher than 10-7 were assessed manually for all possible alignments taking into account the alignment length, number of identical matches, gaps, and positive replacements. Utilizing these criteria we were able to annotate 3,321 (59.7 %) of the 5,563 clusters by their similarity to known genes within the database. This also meant that 2,242 of the clusters, or 40.3%, represent sequences that have no known counterpart in the public databases and that we categorize as unknowns.
Table 1 lists the 30 most abundant EST clusters derived from the Tortula rehydration EST collection. These transcripts only account for 8.4% of the generated ESTs, however. Ten of the most abundantly represented transcripts encode proteins that do not match any of the sequences in the databases searched in this study and are designated as unknowns. Seven abundant transcripts appear to encode proteins that belong to a class of proteins known as Late Embryogenesis Abundant (LEA) proteins, although the relatively low HSP bit scores and high E-values for most of these BLASTx matches has to be considered as a caveat in this assessment. LEA proteins have long been ascribed a protective role for cells that are experiencing dehydration [11,35]. This is a conclusion drawn on the strong correlation between LEA transcript accumulation and water loss, rapid decline in transcript levels upon rehydration, and especially as LEA gene expression relates to the programmed desiccation stage of seed maturation. If one can conclude that LEA protein transcripts are abundant in the rehydration transcriptome of Tortula ruralis, which is consistent with some of our earlier findings [32], then it is also possible that these proteins are involved in either protection of cellular integrity during the initial phases following rehydration when cell disruption is apparent in bryophytes [36], or are actively involved in the restoration of cells damaged by a desiccation event. This difference in the response of LEA gene expression between Tortula and what has been reported for angiosperms may also be a reflection of what we believe to be a more primitive mechanism of dehydration tolerance, and perhaps a more primitive role for and control of LEA gene expression. Other abundant transcripts appear to fall into the membrane transport (aquaporin, cysteine rich proteins, channel and pore proteins) and proteins that can be associated with plant stress events (metallothionine, esterase, and rubredoxin). The Early Light Inducible Protein A (ELIP-A) transcript is the only member of the abundant transcripts that we have previously reported [33] as belonging to the group of proteins we have termed, rehydrins [31]. These proteins have been suggested to be synthesized in response to stress-induced photo-damage within the Tortula chloroplast and may play a protective or repair function for the photosynthetic apparatus [37].
Table 1 Thirty most abundant transcripts in the Tortula rehydration library.
Length #ESTs in Cluster Description TrEMBL Accession # Bit Score E-value
962.0 48 Unknown
1102.0 40 ABA-inducible protein WRAB1 (Cold-responsive LEA/RAB-related COR protein) Q9XFD0 63.93 2.88E-09
1000.0 37 Caenorhabditis elegans CE-LEA O16527 75.87 6.51E-13
889.0 35 Unknown
1000.0 34 TspO tryptophan rich sensory protein homologue Q92ZA7 127.87 1.43E-28
994.0 34 Unknown
975.0 32 Hydrophobic LEA-like protein (Oryza sativa) Q9ZRF8 100.91 1.84E-20
964.0 32 Unknown
1201.0 31 Cysteine rich protein (ion transport) Q24977 50.45 3.86E-05
1051.0 28 Late embryogenesis abundant (LEA) protein 76 (Brassica napus) P13934 51.60 1.42E-05
683.0 28 Cysteine-rich non-metallothionein-protein Q24774 47.75 1.05E-04
1078.0 26 Early light-inducible protein ELIPA (Tortula ruralis) Q8RYB6 404.06 1.24E-111
956.0 25 Rb7 (Fragment)-MIP/Aquaporin O04179 215.70 3.62E-55
732.0 25 Stress-inducible membrane pore protein Q93Z87 88.58 6.21E-17
1043.0 24 Ribosomal-protein-alanine acetyltransferase Q8IAE2 116.32 4.73E-25
942.0 24 Unknown
902.0 22 Caenorhabditis elegans CE-LEA O16527 50.83 2.02E-05
1043.0 21 Protein DR1172-LEA type 1 family Q9RV58 55.45 1.01E-06
984.0 20 Unknown
661.0 20 Core protein (Pisum sativum) amino acid-selective channel protein Q41050 79.34 3.13E-14
514.0 20 Pyrus pyrifolia Metallothionein-like protein Q9LUX2 57.38 7.04E-08
1316.0 19 Hypothetical protein K08H10.2a Q9XTH4 92.82 8.12E-18
996.0 19 Unknown
982.0 18 Putative late embryogenesis abundant protein (Arabidopsis) Q9LF88 53.91 2.63E-06
910.0 18 Unknown
790.0 18 Gb|AAF26109.1 (Hypothetical protein) Arabidopsis Q9FFJ0 125.56 5.31E-28
1717.0 17 Lanatoside 15'-O-acetylesterase precursor (Foxglove) O82681 165.62 1.39E-39
1000.0 17 Unknown
1000.0 17 P0577B11.21 protein (Oryza sativa) Rubridoxin-like Q84SC5 60.08 3.85E-08
957.0 17 Unknown
Although transcript abundance may reflect the metabolic or physiological needs of the moss during the rehydration phase of a wet/dry/wet cycle it would be more desirable to know how these transcripts are recruited and utilized by the translational machinery to make the proteins that actually contribute to the recovery of the gametophytes following rehydration. Such information is beyond the scope of this analysis but the identification and isolation of the Clusters that represent the Tortula rehydration transcriptome does represent the first major step for such a pursuit.
Functional classification of transcripts
Physiological, biochemical, and molecular data all point towards an active period of cellular activity, presumably for repair and recovery from desiccation induced damage, during the first two hours following rehydration of dried gametophytes [13,15]. The identity of the more abundant transcripts (Table 1.) does provide some insight into the nature of the metabolic activity that is associated with rehydration, at least it gives an indication of what metabolic processes may be of prime concern as the plant recovers from desiccation. However, the functional classification of the Tortula transcripts present during the initial phases following rehydration using the Gene Ontology (GO) classification system paints a broader view of the possible metabolic activity of the gametophytic cells at this time. This does, of course, come with the understanding that these are transcript based analyses and do not directly reflect protein levels which would offer a more definitive assessment of the metabolic capability of the cells during rehydration.
Functional classification of the Tortula rehydration transcripts was achieved by matching the Tortula clusters to characterized protein domains in a combined protein database, using HT-GO-GAT (Materials and Methods), which allowed us to assign GO terms to each cluster. The assignment of GO terms made it possible to place the clusters into the GO hierarchy which can be viewed by use of an AmiGo browser. Of the 3, 321 clusters that exhibited significant similarity to known genes in the public databases, 2,203 (66% of annotated or 40% of all clusters) represent genes that contain conserved protein domains that have known biochemical and physiological functions in other organisms and map to the GO hierarchy. The GO representations for the Tortula rehydration clusters are presented in Tables 2 through 4. The representations are segregated into the three main organizing principles of GO: biological process (Table 2), cellular component (Table 3), and molecular function (Table 4). A complete listing of the GO mappings is available on our website at .
Table 2 Gene ontology (GO) mappings : Biological processes 1673 clusters
Categories and subcategories Representation % of Total
Cellular Processes 409 24.5
Cell Communication 75 4.5
Signal Transduction 68 4
Cell adhesion 6 0.5
Cell Death 13 1
Cell Growth & Maintenance 334 20
Organization & Biogen 31 2
Cell proliferation 21 1.5
Transport 286 17
Development 25 2
Physiological Processes 1641 98
Cell Growth & Maintenance 334 20
Death 13 1
Metabolism 1345 80
Alcohol metabolism 61 4
Amine metabolism 83 5
Amino Acid metabolism 91 5.5
Aromatic cpd metabolism 51 3
Biosynthesis 498 30
Carbohydrate metabolism 154 9
Carbon utilization 19 1
Catabolism 160 9.5
Coenzyme & Prosthetic grp 38 2
Electron transport 162 9.5
Energy pathways 115 7
Heterocycle metabolism 19 1
Lipid metabolism 55 3
Nitrogen metabolism 8 0.5
Nucleic acid metabolism 174 10
One carbon cpd metabolism 10 0.5
Organic acid metabolism 102 6
Oxidative phosphorylation 13 1
Oxygen and ROS metabolism 26 1.5
Phosphorous metabolism 111 7
Pigment metabolism 7 0.5
Protein metabolism 536 32
Secondary metabolism 16 1
Sulfur metabolism 10 0.5
Vitamin metabolism 8 0.5
Photosynthesis 73 4
Response to Endogenous Stimuli 16 1
Response to External Stimuli 74 4
Perception 14 1
Response to abiotic 22 1.5
Response to biotic 45 2.5
Response to Stress 61 3.5
Response to DNA damage 16 1
Response to oxidative stress 23 1.5
Response to pest/pathogen 8 0.5
Response to water deprivation 6 0.5
Secretion 3
Table 3 Gene ontology (GO) mappings: Cellular component 982 clusters
Categories and subcategories Representation % of Total
Cell 963 98
Intracellular 711 72
Cell Cortex 6 0.5
Chromosome 9 1
Cytoplasm 563 57
Cytoskeleton 34 3.5
Cytosol 27 2.5
Endoplasmic reticulum 29 3
Translation elongation cplx 6 0.5
Golgi apparatus 12 1
Mitochondrion 70 7
Plastid (Chloroplast) 122 12.5
Ribosome 224 23
Nucleus 109 11
Ribonucleoprotein complex 233 24
Ribosome 224 23
Membrane 368 37.5
Endomembrane system 5 0.5
Organelle Inner membrane 40 4
Integral to membrane 159 16
Unclassified 149 15
H+-transporting ATPase 10 1
Mitochondrial membrane 43 4
Organelle Outer membrane 11 1
Plasma membrane 5 0.5
Extracellular 7 0.5
Table 4 Gene ontology (GO) mappings: Molecular function 1992 clusters
Categories and subcategories Representation % of Total
Antioxidant activity 6 0.5
Binding 758 38
Amino acid binding 6 0.5
Carbohydrate binding 6 0.5
Lipid binding 6 0.5
Metal ion binding 161 8
Calcium 43 2
Magnesium 18 1
Transition metal 78 4
Nucleic Acid binding 296 15
DNA binding 125 6
Nuclease activity 15 1
RNA binding 58 3
Translation factor, nucleic acid 54 3
Nucleotide binding 335 17
Purine nucleotide (adenyl/guanyl) 334 17
Protein binding 25 1.5
Catalytic Activity 1162 58.5
Helicase activity 25 1.5
Hydrolase activity 374 19
acting on acid anhydrides 108 5.5
acting on carbon-nitrogen (not peptide) 5 0.5
acting on ester bonds 71 3.5
acting on ether bonds 7 0.5
acting on glycosyl bonds 39 2
Peptidase activity 97 5
Isomerase activity 48 2.5
Kinase activity 137 7
Protein kinase activity 90 4.5
Lipase activity 55 3
Lyase activity 93 4.5
Oxireductase (Ored) activity 264 13.5
disulfide Ored activity 17 1
Monooxygenase activity 28 1.5
Ored activity – CH-OH donors 55 3
Ored activity – acting on NADH/NADHP 13 0.5
Ored activity-acting on paired donors 13 0.5
Ored activity – peroxide acceptor 27 1.5
Ored activity – single donor 17 1
Ored activity – sulfur group donors 16 1
Oredactivity – CH-NH2 group donors 9 0.5
Small protein conjugating enzyme activity 11 0.5
Transferase activity 310 15.5
transferring acyl groups 30 1.5
transferring alkyl or aryl groups 11 0.5
transferring glycosyl groups 35 2
transferring nitrogenous groups 12 0.5
transferring one–carbon groups 38 2
transferring phosphorous containing grps 154 7.5
Chaparone activity 45 2.5
Defense protein activity 7 0.5
Enzyme activator activity 6 0.5
Unknown molecular function 50 2.5
Nutrient reserve activity 8 0.5
Signal Transducer activity 58 3
Receptor activity 41 2
Two-component response regulator 9 0.5
Two-component response sensor molecule 18 1
Structural Molecule activity 261 13
Structural component of ribosome 239 12
Transcription Regulator activity 41 2
Transcription factor 27 1.5
Two-component response regulator 9 0.5
Translation Regulator activity 54 3
Nucleic acid binding 54 3
Transporter activity 286 14
amine/polyamine transport 8 0.5
carbohydrate transport 11 0.5
Carrier activity 109 5.5
electrochemical potential driven 29 1.5
primary active transporter 81 4
Channel/pore class transporter 25 1
Electron transport 61 3
Ion transport 57 3
anion 5 0.5
cation 49 2.5
metal ion 16 1
Organic acid transport 11 0.5
Protein transport 46 2.5
Of the 2,203 clusters, 1673 (76%) map into the Biological Processes classification, 1641 (98%) of these fall into the Physiological Processes category and 409 (24.5%) cross-map into the Cellular Processes category (Table 2). Within physiological processes 80% of the clusters were associated with metabolism and 20% cell growth and maintenance (it is within this group that most of the overlap occurs with the Cellular Processes category). The distribution is not surprising for ESTs (clusters) derived from a tissue that is harvested at a time of metabolic upheaval such as rehydration and recovery from the desiccated state. In support of this notion are the almost identical distributions of ESTs observed for cDNA collections from protonemal tissues of the moss Physcomitrella patens following various hormonal treatments designed to illicit developmental perturbations and metabolic switching (Table 5, and from data reported by Nishiyama et al., 2003 [38]).
Table 5 Comparison of GO mappings for Tortula ruralis and Phycomitrella patens
Relationship level Gene ontology ISC P.pat ISC T.rur T:P
1 9981 % 2203 %
2 biological process 7119 71.33 1673 75.94 1.1
3 physiological processes 6901 69.14 1641 74.49 1.1
4 metabolism 5601 56.12 1345 61.05 1.1
5 biosynthesis 1672 16.75 498 22.61 1.3
5 carbohydrate metabolism 642 6.43 154 6.99 1.1
5 carbon utilization 44 0.44 19 0.86 2.0
5 catabolism 670 6.71 160 7.26 1.1
5 energy pathways 306 3.07 115 5.22 1.7
5 oxidative phosphorylation 28 0.28 13 0.59 2.1
5 oxygen and ROS metabolism 76 0.76 26 1.18 1.6
4 photosynthesis 138 1.38 73 3.31 2.4
5 photosynthesis, dark reaction 21 0.21 9 0.41 2.0
5 photosynthesis, light reaction 56 0.56 41 1.86 3.3
4 response to external stimulus 232 2.32 74 3.36 1.4
5 perception of external stimulus 37 0.37 14 0.64 1.7
5 response to abiotic stimulus 75 0.75 22 1.00 1.3
5 response to biotic stimulus 136 1.36 45 2.04 1.5
4 response to stress 231 2.31 61 2.77 1.2
5 response to oxidative stress 57 0.57 23 1.04 1.8
5 response to water deprivation 8 0.08 6 0.27 3.4
4 cell growth and/or maintenance 1536 15.39 334 15.16 1.0
3 cellular process 1845 18.49 409 18.57 1.0
4 cell communication 310 3.11 75 3.40 1.1
5 signal transduction 254 2.54 68 3.09 1.2
4 cell growth and/or maintenance 1536 15.39 334 15.16 1.0
5 cell organization and biogenesis 222 2.22 31 1.41 0.6
5 cell proliferation 219 2.19 21 0.95 0.4
5 transport 1131 11.33 286 12.98 1.1
3 development 119 1.19 25 1.13 0.9
2 cellular component 4290 42.98 982 44.58 1.0
3 cell 4137 41.45 963 43.71 1.1
5 cell wall 73 0.73 7 0.32 0.4
4 intracellular 2909 29.15 711 32.27 1.1
5 cytoplasm 2041 20.45 563 25.56 1.2
5 extrachromosomal DNA 37 0.37 15 0.68 1.8
5 nucleus 815 8.17 109 4.95 0.6
5 ribonucleoprotein complex 590 5.91 233 10.58 1.8
5 thylakoid 116 1.16 46 2.09 1.8
4 membrane 1628 16.31 368 16.70 1.0
5 mitochondrial membrane 104 1.04 43 1.95 1.9
5 inner membrane 100 1.00 40 1.82 1.8
5 outer membrane 28 0.28 11 0.50 1.8
2 molecular function 8755 87.72 1992 90.42 1.0
3 binding 3530 35.37 758 34.41 1.0
4 nucleic acid binding 1389 13.92 296 13.44 1.0
5 DNA binding 649 6.50 125 5.67 0.9
5 nuclease activity 89 0.89 15 0.68 0.8
5 RNA binding 330 3.31 58 2.63 0.8
5 translation factor activity 176 1.76 54 2.45 1.4
4 nucleotide binding 1478 14.81 335 15.21 1.0
4 protein binding 221 2.21 25 1.13 0.5
4 metal ion binding 616 6.17 161 7.31 1.2
3 chaperone activity 202 2.02 45 2.04 1.0
4 heat shock protein activity 57 0.57 17 0.77 1.4
3 signal transducer activity 233 2.33 58 2.63 1.1
4 two-component sensor molecule activity 38 0.38 18 0.82 2.2
4 receptor activity 167 1.67 41 1.86 1.1
5 transmembrane receptor activity 30 0.30 17 0.77 2.6
4 structural constituent of ribosome 553 5.54 239 10.85 2.0
3 transcription regulator activity 234 2.34 41 1.86 0.8
4 transcription factor activity 150 1.50 27 1.23 0.8
3 translation regulator activity 176 1.76 54 2.45 1.4
4 translation factor, nucleic acid binding 176 1.76 54 2.45 1.4
5 translation elongation factor activity 67 0.67 33 1.50 2.2
5 translation initiation factor activity 97 0.97 20 0.91 0.9
3 transporter activity 1115 11.17 286 12.98 1.2
4 carbohydrate transporter activity 49 0.49 11 0.50 1.0
4 carrier activity 414 4.15 109 4.95 1.2
4 channel/pore class transporter activity 68 0.68 25 1.13 1.7
5 alpha-type channel activity 59 0.59 23 1.04 1.8
3 catalytic activity 5294 53.04 1162 52.75 1.0
4 isomerase activity 183 1.83 48 2.18 1.2
5 intramolecular isomerase activity 41 0.41 10 0.45 1.1
4 kinase activity 735 7.36 137 6.22 0.8
4 lyase activity 352 3.53 93 4.22 1.2
4 oxidoreductase activity 960 9.62 264 11.98 1.2
4 transferase activity 1557 15.60 310 14.07 0.9
4 hydrolase activity 1764 17.67 374 16.98 1.0
The subcategory distributions within the Physiological and Cellular processes also seem to reflect the nature of the cellular disturbances that result from a desiccation-rehydration event. Processes involved in metabolite and ion transport within and between cells are represented by 17% of the clusters that map to Biological Processes, and almost 86% of those that map to the Cell Growth and Maintenance subcategory. Within the Metabolism subcategory of Physiological processes 40% (32% of total) of the clusters map to protein metabolism (synthesis) and 37% (30% of total) map to biosynthetic processes. The considerable representation within the aforementioned three subcategories is consistent with much of our biochemical and physiological evidence concerning the metabolic activity and emphasis in gametophytic cells during rehydration [15,24,25]. In particular, following rehydration the protein synthetic machinery is rapidly reconstituted, having been dismantled during drying, to direct the synthesis of pattern of proteins termed rehydrins that appear to be a crucial aspect of the desiccation tolerance mechanism of Tortula ruralis (as described above). The importance of the protein synthetic machinery and its re-establishment following rehydration is also highlighted by the preponderance of clusters associated with the Ribosomal subcategory of the Cellular Component Classification; 40% of the Cytoplasm category (23% overall).
Only 982 clusters (45% of the 2,203 that constitute the mapped population) map within the Cellular Component Classification and are presumably associated with structural functions (Table 3). Of these 982 clusters, almost all map to the Cell classification within which 72% map to Intracellular components and 38% to the Membrane category. Within these categories, representation is most significant in the ribosomal, integral membrane protein, and plastid subcategories. As discussed above the importance of protein synthesis during recovery from desiccation tolerance may explain the preponderance of clusters associated with ribosomal structural components as well as the number of clusters that are associated with the membrane and plastid subcategories. Ultrastructural studies of dried and rehydrated gametophytes clearly indicate that the inrush of water during rehydration disrupts membranes and causes a disorganization of the internal granal structures and swelling of the large chloroplasts of the Tortula leaf cells [36,39].
Of the clusters that can be mapped into GO hierarchies, 90% can be ascribed molecular functions (Table 4). Of the major categories, Binding activity (38%), Catalytic activity (58.5%), Structural Molecule activity (13%), and Transporter activity (14%) are best represented in the cluster collection. Metal ion, nucleic acid, and nucleotide binding are the most represented subcategories within the Binding activity category perhaps reflective of the need for biosynthetic and repair activity associated with rehydration of moss cells. Within the Catalytic activity category the majority of the clusters are associated with Hydrolase (19%), Transferase (15.5%), Oxireductase (13.5%), and Kinase (7%) activities. Almost half of the clusters associated with the Transferase activity subcategory map as transferring phosphate-containing groups. Each one of these subcategories represent catalytic activities that could be argued as important for a cell to recover from a major metabolic perturbation such as that seen during rehydration. The significant representation within the Kinase and phosphate transfer categories also suggests an active metabolic control "program" occurs when the desiccated cells receive water and attempt to recover from the damage. It is also intriguing that there is a significant representation within the Transporter subcategory as little is known of this group within the context of desiccation tolerance in bryophytes, although solute (osmolytes) and sugar transport in and out of the vacuoles of desiccation tolerant Angiosperms [3] do occur during desiccation and rehydration.
Of particular interest to this study are clusters that represent gene expression control factors both at the transcriptional (41 clusters) and translational level (54 clusters). These clusters, along with those that represent biochemical control mechanisms for signaling and gene expression at the protein level, such as kinases (90 clusters) and phosphate-transfer activities (154 clusters), may represent critical elements in the activation and execution of the cellular recovery processes necessary for the mechanism of desiccation tolerance exhibited by Tortula ruralis. This is of importance because of the key position of bryophytes in the evolution of desiccation tolerance in plants. Our main hypothesis is that an elucidation of the signaling and activation pathways for the rehydration response in this assumed primitive tolerance mechanism could have major implications for the study of stress tolerance mechanisms in all plants and thus these clusters represent important targets for further study at the molecular and biochemical levels.
GO based comparison with Physcomitrella patens EST collections
The representation of the Tortula rehydration clusters throughout the GO mapping system is indicative of the emphases on, but not expression levels of, particular cellular activities represented in the moss gametophytes during this period of a wet and dry cycle. In an attempt to assess if the accents on particular cellular activities indicated for rehydrated gametophytes are characteristic of the rehydration induced metabolic state or are simply indicative of processes associated with normally active bryophyte cells, we compared the representation of the Tortula rehydration clusters within the GO categories with similar "clusters" from Physcomitrella patens, the only other bryophyte that has similar genomic level information. The majority of the Physcomitrella "clusters" are derived from a large EST collection representing transcripts from both untreated and hormone induced (to switch developmental pathways) cells of protonemal cultures. The Physcomitrella ESTs are described by Nishiyama et al., [38] and were obtained from Physcobase as assembled contigs (assembled in an identical fashion to what we designate as clusters). In total 22,885 Physcomitrella contigs, derived from 102553 ESTs obtained from Physcobase and Genbank, were subjected to a BLASTx search, as described for the Tortula clusters using HT-GO-GAT. Of the 22,885 contigs 9,981 (43.6%) represent genes that contain conserved protein domains that have known biochemical and physiological functions in other organisms and map to the GO hierarchy.
There are two caveats for this comparison; 1) differences in representation may simply reflect species differences in the emphasis on individual classes of cellular activities between Tortula and Physcomitrella, or 2) differences in representation may reflect differences in the emphasis on individual classes of cellular activities between mature gametophytes (Tortula) and protonema (Physcomitrella). Until a comparison of GO mapping distributions can be made directly between clusters derived from an EST collection from hydrated control gametophytes from Tortula with those from the rehydration collection these caveats remain important. Nevertheless, even with these difficulties and limitations the comparison is still useful for developing new hypotheses as to what cellular processes might be important in the recovery of moss cells from desiccation.
In general the distribution of the Tortula clusters within the GO mappings are similar if not identical to the distribution of the Phycomitrella contigs gleaned from Physcobase. This can be seen especially at the GO map 2 and 3 relationship-level categories (Table 1 and supplemental material) using the multi-species GO browser (DrZOOview2.0 ). However, there are several notable differences and in general the differences are consistent with what has been determined, in earlier studies, to be important in the recovery of the moss from desiccation following rehydration [4,23] and what is known about plant responses to abiotic stress in general [8,13]. The differences evident in the comparison are presented in Table 5, where the extent of the similarity in the distribution of the clusters are expressed as the ratio of representation for Tortula to representation for Physcomitrella (T:P). In the Biological category the most striking differences in GO representation of Tortula clusters occurs in categories where the percentage representations are relatively low but the numbers of clusters are substantial. In the level 4 relationship, Photosynthesis, the representation for Tortula is 3.31% compared to 1.38% for Physcomitrella, a 2.4 fold difference, the majority of which is accounted for by the difference in the representation levels for the light reaction category. The rapid recovery of photosynthesis is critical for the recovery of bryophyte cells, particularly in regards to the production of energy and reducing power for the metabolic activity associated with repair and reconstitution of the gametophytic cells. Chloroplast structure in Tortula is severely disrupted, especially if desiccation occurred rapidly, in the first few hours following rehydration [36,40] but recovers quickly. The difference in representation in this category is consistent with these observations and thus may reflect the greater need for a supply of a diversity of photosynthetic components in rehydrated Tortula than in Physcomitrella cells that have not experienced a disruption in the photosynthetic apparatus. Similar inferences can be made concerning the differences in representation observed for carbon utilization (T:P of 2.0) and oxidative phosphorylation (T:P of 2.1) for Tortula in that mitochondrial activity and integrity are also compromised during rehydration [25,36]. Other differences in representation between Tortula and Physcomitrella mappings relate to a higher representation for Tortula in the categories that relate to responses to external stimuli and responses to stress both of which would seem consistent with the emphasis that cellular activity for Tortula would have in comparison to unstressed Physcomitrella cells. In particular the increased representation within the response to oxidative stress is of note, as an elevated protection of cellular integrity from the damaging reactive oxygen species (ROS) typically associated with a desiccation rehydration event is a distinctive component of desiccation tolerant bryophytes when compared to their desiccation sensitive relatives such as Physcomitrella [41].
The above observations concerning the more extensive representation of clusters within the GO mappings related to organelle function in Tortula are mirrored in the Cellular Component Level 2 classification mappings. In the Cellular Component classification Tortula exhibits an almost two fold difference in representation within categories associated with either the chloroplast or mitochondria, such as Extrachromsomal DNA (T:P of 1.8), Thylakoid (T:P of 1.8), Mitochondrial membrane (T:P of 1.9), and both Inner and Outer membranes (T:P of 1.8). In this case the categories are generally related to genes representing membrane components and since it is the organelle membranes that exhibit the majority of the damage during desiccation and rehydration it is consistent that these categories would be better represented in the Tortula cluster mappings than those for Physcomitrella. In addition to the organellar related classifications the Tortula clusters also exhibit a higher representation within the Ribonucleoprotein complex category (T:P of 1.8) which in all likelihood reflects an emphasis on ribosomal components since a similar difference in representation is seen in the Structural Constituent of the Ribosome category (T:P of 2.0) within the Molecular Function Level 2 classification. Again such differences in representation in the comparison between Tortula and Physcomitrella GO mappings are consistent with our previous studies on the responses of Tortula gametophytes to desiccation and rehydration and comparisons to non-stressed bryophyte tissues. Protein synthesis is critical to the recovery of Tortula cells following a desiccation event [11,13,15] not only for the synthesis of proteins damaged by the stress of desiccation but also directing the response to the stress at the level of gene expression [26,29]. Early studies determined that the speed at which desiccation occurred has a marked effect on both the rate of recovery of protein synthesis and the rate at which either new ribosomes are formed or pre-existing ones are repaired [42,43], rapid desiccation results in a more prolonged recovery of normal protein synthetic levels and also slows the reconstitution of ribosomes upon rehydration. Since the Tortula clusters are derived from ESTs of rehydrated moss that was dried rapidly, the greater representation in ribosome related GO mappings for this collection compared to the Physcomitrella clusters, that represent transcripts from cells where presumably normal ribosomal turnover and synthetic rates are prevalent, is consistent with the biological state of the Tortula cells. The emphasis on protein synthesis in rehydrated Tortula cells compared to those of Physcomitrella is also evident in the comparison of representations within the Translation regulator, Translation factor, and Translation elongation factor activity GO mappings.
Other differences seen in the Molecular function classification, such as the greater representation within the Tortula collection of clusters involved in Two-component Sensor or Channel/pore class Transporter activity designated mappings, offer novel possibilities for investigation into rehydration metabolism that have not been indicated as important until now. The individual identity of the clusters that map to these categories should offer possible hypotheses that can be tested in our future research.
Functional classification based on KEGG analysis
An alternative classification of clusters, based on biochemical function, involves the use of HT-GO-GAT to assign clusters to individual Kyoto Encyclopedia of Genes and Genomes (KEGG ) metabolic pathways. Of the 2203 clusters that map within the GO hierarchy only 642 clusters had assigned EC numbers generating 325 unique mappings (see additional file 1: KEGG biochemical pathway mappings for Tortula rehydration clusters). The paucity of EC assignments limits this aspect of the analysis but the mapping of clusters to the KEGG metabolic pathways still presents some useful perspectives on the metabolic emphasis of the rehydrated gametophytic cells. Eighty-six of the 123 pathways contained within the Metabolism category (metabolic pathways), were represented by 84.6% of the 642 Tortula clusters. The KEGG metabolic pathways that are well represented by Tortula clusters are Carbohydrate Metabolism (83 enzymes represented), Amino Acid Metabolism (72 enzymes), Energy Metabolism (40 enzymes), Lipid Metabolism (27 enzymes), and Metabolism of Cofactors (24 enzymes). All of these pathways have been previously associated with the cellular recovery processes associated with rehydrated moss gametophytes [14,15]. Within the metabolic activities not represented by the Tortula clusters only the lack of ascorbate metabolizing enzymes appears unusual as ascorbate has been well documented as an important metabolite in the protection of moss gametophytes from oxidative damage during stress. This however appears to be the result of a limitation in the assignment of EC numbers since several Tortula clusters show significant similarities to enzymes involved in ascorbate metabolism in the original BLASTx search used to generate the GO mappings. The limitation of the KEGG based classification can also been seen in the poor representation of Tortula clusters in the other KEGG pathways (Genetic Information Processing, Environmental Information processing, and Cellular Processing) which is surprising given the number of clusters that were identified in the BLASTx search and in the GO databases as being contained within these classifications. As an example, none of the 239 clusters that map in the GO hierarchy as structural components of the ribosome (Table 4) or any of the clusters that mapped to transcriptional and translational components were contained in the corresponding KEGG pathways. Thus although useful information can be gleaned from the KEGG classification system and metabolic pathway mappings, especially in practical terms for functional studies using individual clusters, it has some major limitations for drawing any broad based hypotheses from the representation of Tortula clusters within each pathway.
ORF based assessment of Tortula clusters
Of the 5,563 consensus cluster sequences used in the BLASTx search of our database (see above) 40.3% failed to exhibit sufficient similarity (did not meet set criteria, see above) with known sequences to allow for an accurate annotation of the cluster. It is possible that these contigs, rather than containing novel amino-acid coding regions, contain mainly 3' or 5' untranslated regions (UTRs) or coding regions that are so short as to render them incapable of generating a significant similarity score. In order to investigate this possibility we examined the three classes of contigs, those with significant similarity scores; "good hits", those that generated poor similarity scores; "false hits", and those that failed to generate any scored similarity; "no hits", to determine the longest open reading frame (ORF). We limited the ORF determination to those clusters that contain an AUG codon in the 5' to 3' direction of the clone in any one of the three possible reading frames (the cDNAs were directionally cloned). The results of this analysis are shown in Figure 2. Of the 5,563 clusters generated in the study, 4,789 generated ORFs under the limitations imposed by the analysis. Of these 2,983 were classified as "good hits", 1,564 as "false hits" and 242 as "no hits". Those clusters that are classified as "good hits" exhibit ORFs that are in general evenly distributed from 20–40 amino acids long to 220–240 amino acids long. The clusters that are classified as "false hits" from the BLASTx search do have a relatively larger proportion of shorter ORFs, in the 20–40 and 40–60 amino acid range but also a substantial proportion that are much longer. The distribution of ORFs in this category ('false hits") does not appear to be sufficiently skewed from that for the "good hit" clusters to render them incapable of generating similarity scores in the BLASTx search. This would suggest that these clusters do contain novel amino acid sequences that are not represented in the public protein databases by sequences sufficiently similar to generate significant HSP Bit scores. In addition, the distribution of ORFs in the "no hit" cluster category are distributed in a similar manner to those of the "good hit" classification and so are also likely to represent clusters encoding proteins with novel amino acid sequences. The clusters contained within the "false hit" and "no hit" categories are of particular interest in our search for novel genes and pathways that are associated with the ability of certain plants to acquire vegetative desiccation tolerance.
Figure 2 Distribution of clusters by size of longest ORF (by number of amino acids). Solid line, clusters that have significant similarity with a known sequence in the database by BLASTX. Dotted Line, clusters that showed weak similarity with a known sequence in the database. Dashed Line, clusters without similarity to known sequences in the database.
Conserved gene comparison to Physcomitrella and Arabidopsis
In this analysis the Physcomitrella and Arabidopsis databases were independently used in a BLASTx search using the 5,563 Tortula clusters as query sequences. Even though we used the entire 5,563 cluster sequences as individual queries only 3,321 actually represent sequences that could be annotated using the criteria for similarity discussed previously. When the Tortula clusters were used as queries against the Physcomitrella database, 5,554 generated HSP Bit scores however only 282 exhibited a level of conservation of sequence that passed the criteria established for annotation of our Tortula clusters (HSP Bits score above 70 and or E values of 10-7 or less, see above). Against the Arabidopsis database only 927 Tortula clusters generated HSP Bit scores but 612 had a level of conservation sufficient to be considered reliable matches to Arabidopsis genes. These observations are difficult to rationalize and may simply reflect the inequality of the target databases. The Physcomitrella database, which is generated from a relatively small EST collection, is much smaller than that for Arabidopsis, which is gleaned from the full genomic sequence. This may explain why although most Tortula clusters generated HSP Bit scores only a relative few were capable of producing scores sufficiently high enough, and with low enough error probabilities, for confident assignment of co-identity with a Physcomitrella contig. Obviously Tortula ruralis is more closely related to Physcomitrella patens, as they are both bryophytes, than to Arabidopsis thaliana but the phylogenetic distance between the two bryophytes is still substantial, which may also help explain the paucity of high value matches and the large number of low value hits. The Arabidopsis database is much more comprehensive and would be expected to generate more significant matches as observed and perhaps because of the evolutionary distance between the two plants more queries that do not generate an HSP.
The twenty gene products that exhibit the highest level of conservation for both comparisons, Physcomitrella (E values of 0 to -39) and Arabidopsis (E values of 0 to -55) are presented in Table 6. Several common highly conserved genes are present in both comparisons, including genes involved in cell structure (tubulin and actin), protein synthesis (ribosomal proteins and elongation factors), protein-turnover (polyubiquitins), stress proteins (heat shock), chromosomal proteins (histones), signal transduction (ADP-ribosylation factor), and binding proteins (calmodulin). Interestingly there are differences between the two lists, which may reflect the relative phylogenetic distances between the three species. The three most conserved proteins between Tortula and Physcomitrella did not register as highly conserved between Tortula and Arabidopsis, the most conserved protein between Tortula and Physcomitrella, the rRNA intron-encoded homing endonuclease, did not generate a match at all with an Arabidopsis counterpart. Interestingly, the most conserved photosynthesis related protein between Tortula and Physcomitrella is a chlorophyll a/b-binding protein which generates a HSP Bit score of 205 and E value of 3.0E-53, the Arabidopsis counterpart generates a HSP Bit score of 82 and E value of 8.9E-15. In this case the level of conservation of this protein within the comparisons appears to reflect the phylogenetic relationships that exist between the three plants. However, the most conserved photosynthesis related protein between Tortula and Arabidopsis is a photosystem I P700 apoprotein A2 which generates a HSP Bit score of 1120 and E value of 0, the Physcomitrella counterpart generates a HSP Bit score of 34 and E value of 1.25E-1, which appears contrary to what is expected at what is seen for the chlorophyll a/b-binding protein. This type of relationship is also seen for the Ribulose 1 protein (Cluster 2170) that is conserved between Tortula and Arabidopsis but only to a low degree with the Physcomitrella counterpart. If such relationships can be substantiated with further study and complete sequences (both the Tortula and Physcomitrella clusters are based on ESTs) then some interesting evolutionary questions can be raised concerning both gene function and selection pressures on the individual plants and metabolic processes.
Table 6 The twenty most conserved Tortula gene clusters between Physcomitrella and Arabidopsis
Tortula Cluster Physcomitrella Identity Bit Score e-value
Contig_2145 PPCContig_497 Q9AY32 (Q9AY32) RRNA intron-encoded homing endonuclease 813 0.0E+00
Contig_1352 PPCContig_406 Q9AVH2 (Q9AVH2) Putative senescence-associated protein (Fra 630 0.0E+00
Contig_3546 PPCContig_1008 O04892 (O04892) Cytochrome P450 like_TBP (EC 1.14.14.1) 448 1.8E-126
Contig_602 PPCContig_359 polyubiquitin UBQ10/SEN3 413 1.2E-115
Contig_480 PPCContig_12 Q9SPA1 (Q9SPA1) Elongation factor-1 alpha 3 306 1.4E-83
Contig_966 PPCContig_1903 EF1A_VICFA (O24534) Elongation factor 1-alpha (EF-1-alpha) 284 5.8E-77
Contig_1126 PPCContig_2103 Q41067 (Q41067) Polyubiquitin 250 5.9E-67
Contig_1901 PPCContig_1954 Q8H932 (Q8H932) Alpha tubulin 241 7.4E-64
Contig_1538 PPCContig_4431 heat shock protein hsc70-1 (hsp70-1) (hsc70.1) 233 1.6E-61
Contig_1444 PPCContig_49 ADP-ribosylation factor 1 (ARF1), putative 208 4.6E-54
Contig_919 PPCContig_1049 Q9SXW8 (Q9SXW8) Chlorophyll a/b-binding protein 205 3.0E-53
Contig_1381 PPCContig_2023 Q8S173 (Q8S173) Putative 60S ribosomal protein L37a 197 5.3E-51
Contig_5058 PPCContig_951 Q9SWW8 (Q9SWW8) Actin (Fragment) 195 3.3E-50
Contig_1676 PPCContig_2347 calmodulin 193 8.7E-50
Contig_1366 PPCContig_846 Q84NX8 (Q84NX8) Putative ribosomal protein L19 174 8.0E-44
Contig_2369 PPCContig_2689 O04664 (O04664) Small RAS-like GTP-binding protein (AT5G551 173 9.9E-44
Contig_1934 PPCContig_4083 Q9SJB9 (Q9SJB9) Putative translation initiation factor eIF- 171 3.3E-43
Contig_3793 PPCContig_3975 Q9LFN6 (Q9LFN6) DEAD box RNA helicase RH15 169 1.9E-42
Contig_1721 PPCContig_2679 H33_ARATH (P59169) Histone H3.3 165 2.1E-41
Contig_3462 PPCContig_970 Q43821 (Q43821) Ubiquitin conjugating enzyme (EC 6.3.2.19) 160 1.2E-39
Tortula Cluster Arabidopsis Identity Bit Score e-value
Contig_3469 ATCG00340 (PSAB) photosystem I P700 apoprotein A2 1120 0.0E+00
Contig_882 AT1G07930. elongation factor 1-alpha (EF-1-alpha) 666 0.0E+00
Contig_602 AT4G05320.4 polyubiquitin UBQ10/SEN3 617 1.0E-175
Contig_2170 ATCG00490 (RBCL) riblose 1 435 5.0E-121
Contig_1984 AT5G02960.1 40S ribosomal protein S23 (RPS23B) 431 5.2E-120
Contig_1538 AT5G02500.1 heat shock protein hsc70-1 (hsp70-1) (hsc70.1) 366 3.2E-100
Contig_1124 AT4G05050.1 polyubiquitin UBQ11 341 5.8E-93
Contig_1444 AT1G23490.1 ADP-ribosylation factor 1 (ARF1), putative 307 1.2E-82
Contig_1901 AT1G50010.1 tubulin alpha-2/alpha-4 chain (TUA2) 303 3.7E-81
Contig_52 AT3G12580.1 heat shock protein hsp70 291 1.3E-77
Contig_1060 AT4G38510.1 probable H+-transporting ATPase 285 4.1E-76
Contig_1125 AT4G02890.2 polyubiquitin (UBQ14) 274 8.3E-73
Contig_3069 AT4G40040.1 histone H3.2 270 1.4E-71
Contig_1935 AT5G08290.1 YLS8, Dim1 homolog 264 1.1E-69
Contig_2698 AT5G45775.2 60S ribosomal protein L11 (RPL11D) 260 1.2E-68
Contig_5058 AT5G09810.1 ACTIN 2/7 (sp|P53492) 258 6.8E-68
Contig_1676 AT3G43810.1 calmodulin 252 2.8E-66
Contig_3867 AT3G05530.1 26S proteasome AAA-ATPase subunit RPT5a 232 3.4E-60
Contig_347 AT3G11940.2 40S ribosomal protein S5 (RPS5B) 230 2.2E-59
Contig_1052 AT2G29550.1 tubulin beta-7 chain (TUB7) 218 1.3E-55
Conclusions
Bryophytes have an important and underestimated place in the study of plant responses to water deficits, in particular desiccation tolerance. Bryophytes occupy what we believe to be one of the most primitive states, along with algae, in the evolution of desiccation tolerance and represent, in all probability, the stage in the emergence of plants from a fresh water environment to occupy the various niches available on dry land [13]. Unfortunately, it is only with the advent of the development of Physcomitrella patens as a model plant for molecular genetic studies, fired by its particular ability to perform efficient and homologous recombination in vitro, that bryophyte genomics has become a topic of some interest. Physcomitrella is rapidly becoming the model of choice for developmental and transgenic studies [44] because of its ease of manipulation and indeed there are plans in place to sequence its genome [45]. Tortula ruralis on the other hand has long been established as an attractive model for the analysis of environmental stress tolerance, in particular desiccation tolerance, It has been a very useful model in assessing structural, physiological, biochemical and genetic (gene expression) aspects of severe dehydration of plant cells and mechanisms by which primitive plants respond to and survive protoplasmic water loss [15,25,46]. The progression of the Tortula model into genomics is a critical aspect in the development, along with a transformation system and assessment of its ability for efficient homologous recombination, into a more useful and manipulable model for understanding desiccation tolerance and the nature of extremophiles. In addition to the importance of Tortula for stress biology, the establishment of a second and contrasting bryophyte model, especially with regards to genomics, is essential for the validation and usefulness of the information gained from the analysis of the Physcomitrella genome and the general principles gleaned from its use as a plant model. It is to these ends that we have initiated this study into the transcriptome of Tortula gametophytes as they respond to a major stress event, in this case a combination of desiccation and rehydration.
The rehydration transcriptome of Tortula, as defined by the clusters presented herein, is remarkably consistent with what we know about the desiccation response for this bryophyte and its metabolic activity during the first two hours following rehydration. The GO mapping of the Tortula clusters enabled a broad look at what cellular activities appear to be emphasized in the rehydrated gametophytes and in agreement with our previous biochemical analyses highlighted the prominence of the protein synthetic machinery, both in structure and control, membrane structure and metabolism, and the need to reestablish plastid integrity. These observations were bolstered by the comparative GO analysis using the extensive EST collection generated for the desiccation sensitive moss Physcomitrella patens. The GO analysis has also provided fuel for new investigations and hypotheses into the role of other cellular processes, such as membrane transport, phosphorylation and signal transduction, in the mechanisms that enable desiccation tolerance in plants. Signal transduction is especially intriguing with regards to desiccation tolerance in this bryophyte as it appears to rely on alterations in translational control to effect a response to desiccation in contrast to the well characterized transcriptional responses, and associated signaling pathways, associated with abiotic stress in the Angiosperms [5,7]. In addition to the functional based analyses, the simple abundance estimates has also correlated well with previous work and has given further credence to the notion that LEA proteins may also play a role in maintaining cellular integrity when water is reintroduced into desiccated plant tissues. The strong correlation between what is known about the mechanism for desiccation tolerance employed by Tortula ruralis and what can be inferred from the analyses of the Tortula rehydration EST collection gives a measure of confidence not only in the value of a bioinformatics approach to gain a view of a particular transcriptome but also in the basis for new hypotheses and research directions that are generated from them.
Although the type of analyses presented here are extremely useful in assessing the types of transcripts present in a particular tissue at a particular time and in response to some perturbation, either external or internal in origin, and generating hypotheses concerning the functional aspects of the transcriptome they are intrinsically correlative in nature. In order to gain a more direct picture of the transcriptome and more importantly, with regards to functional assessments, the "translatome", the bioinformatics must be linked to a detailed expression profile of the transcripts represented within the ESTs generated to investigate the particular biological response or mechanism of interest. To this end the clusters described in this report form the basis of a Tortula gametophyte microarray designed for the profiling of both the extant transcriptome and the associated translatome and their response to desiccation and rehydration under various conditions. The array and the experimental design of the expression profiling will allow us to generate a more accurate assessment of both transcript levels and transcript recruitment into the protein synthetic machinery during, and recovery from, desiccation in Tortula ruralis. In combination with the bioinformatics analyses presented here, the expression profiling will allow us to generate a more complete picture of the cellular response of a tolerant plant species, an extremophile, to an extreme abiotic stress event.
Methods
Source material
Tortula ruralis ([Hedw.] Gaertn, Meyer and Scherb), also classified as Syntrichia ruralis, gemetophytes were collected, harvested, and stored as described previously [27]. For experimental purposes, gametophyte tissue was hydrated for 48 h to fully recover from dried storage and trimmed to remove stem material. Rapid-dried moss was prepared by placing the cropped gametophytes in a closed atmosphere of 0% relative humidity (RH) on 3-mm filter paper over activated silica gel in a Petri dish. This drying regime resulted in the attainment of the air-dried state within 30 min. The gametophytes remained in this atmosphere overnight to ensure desiccation and prior to library construction were rehydrated for 2 h in deionized water at 18°C in the light.
Library construction
Total RNA was isolated from the rehydrated gametophytes by a series of phenol extractions as described by Lane and Tumiatis Kennedy [47]. PolyA RNA was isolated from the total RNA fraction by oligo-(dT) chromatography [48], using DynaBeads oligo-(dT25) (Dynal, Inc., Lake Success, NY, USA), through two rounds of selection according to the manufacturer's instruction. The purified polyA fraction was used as a template for double-stranded cDNA synthesis using the Superscript Plasmid System (Invitrogen Life Sciences, Carlsbad, CA, USA). The resultant cDNA population was cloned into the pSPORT1 vector, according to manufacturer's instructions, to construct the unidirectional rehydration cDNA library. Small scale sequencing of 384 random clones confirmed the directional aspect of the inserts, the plant nature of the source cDNAs, and the frequency of positive clones. The average insert length for the library was assessed at 1.2 Kb. A subset of 10, 368 randomly picked positive clones (white in a blue-white X-Gal/IPTG based screen) were transferred to individual wells in 384 well plates containing suitable growth medium for storage, replication, and sequencing.
Sequence analysis
High throughput sequencing of the inserts contained in the 10,368 individual clones was performed using "rolling circle amplification" of the individual plasmids to generate suitable sequencing templates at the Joint Genome Institute, Walnut Creek, CA, U.S.A. Clones were sequenced using primers specific for vector sequence upstream of the multiple cloning site and at the 5' end of the cDNA insert. The sequences were delivered as primary binary files (raw trace files), which were then processed through our sequencing, pipeline as described below.
Sequence preparation
Trace files were entered into SeqMan II and quality screening performed with medium stringency corresponding to a phred threshold value of 12. Vector searching was performed using the pSPORT™ vector both in forward and reverse orientations with minimum match length of 7, connect distance of 3, Maximum register shift of 10, Minimum NW percent match of 90, gap weight of 0 and length weight of 2. Contaminant screening was performed with minimum match of 25. Following the quality screening the sequences were exported as a single FASTA file and polyA tails were removed using a custom PERL script. Several hundred sequences that contained internal polyA stretches were manually identified. These sequences were manually edited to remove polyA and trailing sequences.
Contigs
Edited sequences were then entered into SeqMan for assembly into contigs. Assembly for contigs was performed with match size of 50, minimum match percentage of 97, minimum sequence length of 100.
Clusters
Consensus contig sequences were exported from SeqMan as individual files and entered into a separate SeqMan project and reassembled using the same parameters used for contigs but with a minimum match percentage of 90. These were considered clusters.
EST submission
EST submission to GenBank was performed using the USDA-ARS Livestock Issues Research Unit's (LIRU) High Throughput-Gene Ontology-Genome Annotation Toolkit (HT-GO-GAT). HT-GO-GAT can be obtained from the LIRU website at . A total of 9159 EST sequences were submitted to GenBank and assigned accession numbers CN200321-CN209479
Functional genetics
Functional annotations, Enzyme commission numbers, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway assignments were assigned to cluster sequences using HT-GO-GAT. Consensus sequences derived from Clusters were entered into the software that utilizes custom BLASTx, RPS-BLAST, and relational mySQL databases to identify potential functional assignments based upon sequence and functional domain similarity matching. The software was set to identify high stringency matches and the resulting data manually curated using the high throughput results viewer interface of HT-GO-GAT. HT-GO-GAT produces various reports related to given datasets including ORF analysis, KEGG pathway reports and visualization, and also exports gene ontology association files. These association files were imported into a mySQL database and visualized using the USDA-ARS-LIRU's DrZooView2.0 GO database browser .
Authors' contributions
MJO conceived of the study, generated the libraries and ESTs, annotated the EST database, performed much of the analyses, interpreted the results, and drafted the manuscript. SED designed the bioinformatics software and database, performed the sequence alignments, performed the ORF analysis, and generated the Bryobase website. JZ coded the HT-GO-GAT software and created the mySQL database. SAM participated in the design of the bioinformatics and design of the sequence analysis. PRP participated in the design of the database and sequence analysis, and participated in the annotation of the individual EST sequences.
Supplementary Material
Additional File 1
KEGG biochemical pathway mappings for Tortula rehydration clusters. An alternative functional classification of clusters based on established biochemical activities of gene products. The classification is achieved by the use of HT-GO-GAT to assign clusters to individual Kyoto Encyclopedia of Genes and Genomes (KEGG ) metabolic pathways. This file contains the tabulated results of this classification.
Click here for file
Acknowledgements
The sequencing of the Tortula ESTs was funded under USDA-ARS CRIS project 6208-21000-013-00D and accomplished at the DOE Joint Genome Institute, Walnut Creek, CA with the expert technical guidance of Dr. Jeff Boore and Kristen Kadner. Mention of trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that may be suitable.
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| 15546486 | PMC535811 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Nov 16; 5:89 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-89 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-411556657110.1186/1479-5876-2-41ResearchAdoptive immunotherapy of cancer with polyclonal, 108-fold hyperexpanded, CD4+ and CD8+ T cells Wang Li-Xin [email protected] Wen-Xin [email protected] Hallie [email protected] Peter A [email protected] Julian A [email protected] Suyu [email protected] Gregory E [email protected] Center for Surgery Research, The Cleveland Clinic Foundation, Cleveland, OH, USA2 Dept. of General Surgery, The Cleveland Clinic Foundation, Cleveland, OH, USA2004 26 11 2004 2 41 41 12 10 2004 26 11 2004 Copyright © 2004 Wang et al; licensee BioMed Central Ltd.2004Wang 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.
T cell-mediated cancer immunotherapy is dose dependent and optimally requires participation of antigen-specific CD4+ and CD8+ T cells. Here, we isolated tumor-sensitized T cells and activated them in vitro using conditions that led to greater than 108-fold numerical hyperexpansion of either the CD4+ or CD8+ subset while retaining their capacity for in vivo therapeutic efficacy. Murine tumor-draining lymph node (TDLN) cells were segregated to purify the CD62Llow subset, or the CD4+ subset thereof. Cells were then propagated through multiple cycles of anti-CD3 activation with IL-2 + IL-7 for the CD8+ subset, or IL-7 + IL-23 for the CD4+ subset. A broad repertoire of TCR Vβ families was maintained throughout hyperexpansion, which was similar to the starting population. Adoptive transfer of hyper-expanded CD8+ T cells eliminated established pulmonary metastases, in an immunologically specific fashion without the requirement for adjunct IL-2. Hyper-expanded CD4+ T cells cured established tumors in intracranial or subcutaneous sites that were not susceptible to CD8+ T cells alone. Because accessibility and antigen presentation within metastases varies according to anatomic site, maintenance of a broad repertoire of both CD4+ and CD8+ T effector cells will augment the overall systemic efficacy of adoptive immunotherapy.
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Introduction
Cancer immunotherapy, using T lymphocytes that recognize tumor-specific antigens, holds great promise. Advantageous features include: exquisite specificity for targeted antigens, thereby sparing normal tissues, and the ability of effector T cells to traffic to tumor in all anatomic locations. Although most effector T cells are subject to activation-induced cell death (AICD), a memory response is established leading to sustained protection [1]. Despite the theoretical appeal of T cell-mediated immunotherapy, clinically relevant benefits have been documented in only a small subset of human cancer patients who present with metastatic disease [2-5]. Several factors contribute to the poor host immune response including defective Antigen Presenting Cell (APC) function in cancer patients, and production of immunosuppressive substances by tumors [6-8].
Cognizant of these features, many preclinical studies of active immunotherapy have used a vaccination/challenge scheme to avoid tumor-induced immunosuppression or have alternatively treated hosts with minimal tumor burdens. Several human clinical trials have similarly focused on hosts with minimal residual disease in order to define the magnitude and characteristics of the immune response. These studies have clearly established that immune responses are successfully generated in vaccinated cancer patients. However, the frequency of responding T cells is typically less than one percent even after multiple cycles of vaccination [9-14]. In contrast, the immune response to pathogens generates a tremendous amplification of reactive T cells [15-17]. In the clinical setting, relatively little is yet known about the magnitude of proliferation of individual precursor cells (burst size) as they mature into effector cells, or the flux between lymphoid tissue, peripheral blood, and tumor sites. This results in ambiguity about the optimal time and site to quantify the immune response. Likewise, analysis of apoptosis of effector cells is likely to be important [18,19]. These gaps in fundamental knowledge have made it difficult to identify components of active immunotherapy that could be enhanced to boost the aggregate immune response to a therapeutic level.
Adoptive immunotherapy is another approach to cancer immunotherapy that circumvents some of the limitations of active immunotherapy. Animal tumor models have convincingly demonstrated that hosts bearing progressively growing weakly immunogenic tumors nevertheless generate sensitized T cells in TDLN [20]. Antigen-sensitization causes T cells to downregulate expression of L-selectin (CD62L) providing a convenient phenotypic marker for segregation of primed T cells from the majority of irrelevant T cells [21-23]. Our previous studies have demonstrated that ex vivo activation of purified CD62Llow T cells from TDLNs generates potent effector CD4+ and CD8+ T cells that can mediate regression of advanced tumors in every tested anatomic location [24-26]. The high potency of such cells permitted brief 5-day activation and limited numerical amplification (10-fold) to supply sufficient quantities of cells for the previous mechanistic analysis of the anti-tumor response. Importantly, these experiments demonstrated that there is tight dose dependence, oftentimes with even a three-fold reduction in the number of transferred cells accounting for a difference between minimal treatment effect and complete cure. The relative efficacy of CD4+ versus CD8+ effector cells also varies considerably between pulmonary metastases and intracranial (i.c.) or subcutaneous (s.c.) tumors [27]. This indicates that maintenance of CD4+ as well as CD8+ tumor-reactive effector T cells would be required for optimal adoptive immunotherapy against disseminated metastatic disease.
We investigated whether we could overcome quantitative limitations associated with active immunotherapy through extensive numerical expansion of effector cells. In a previous study, we determined that in vitro activation of tumor-sensitized L-selectinlow precursors with anti-CD3 mAb and high concentrations of IL-2 (100 U/ml) induced rapid proliferation of CD8+ effector cells [28]. Adoptive transfer of such cells cured established tumors in recipients. However, these culture conditions led to maximal proliferation in 9 days with subsequent decline in cell numbers thus limiting the total expansion to approximately 103-fold. In this report, we define ex vivo activation conditions that permit numerical expansion of either CD4+ or CD8+ effector T cells to greater than 108-fold while retaining their high therapeutic potency and preserving a broad T cell receptor (TCR) repertoire.
Materials and methods
Mice and tumors
Female C57BL6N (B6) mice were purchased from the biologic Testing Branch, National Cancer Institute (Frederick, MD). They were maintained in a specific pathogen-free environment according to National Institute of Health guidelines. Mice were used for experiments at 8–10 weeks of age. The MCA 205 and MCA 207 fibrosarcomas, syngeneic to B6 mice were serially passaged in vivo s.c. as described previously [29].
Preparation and culture activation of TDLN CD62Llow cells
Tumors were established by s.c. flank inoculation of 1.5 × 106 MCA 205 cells and 12 days later the TDLNs were removed and mechanically disrupted to obtain a single cell suspension. The TDLN cells were incubated with 100 μl anti-CD62L microbeads per 108 cells and applied to MACS columns (Miltenyi Biotech, Auburn CA) and the flow through fraction was collected. For CD4+ hyperexpansion, the CD62Llow subset was depleted of CD8+ cells by MACS on day 0 and day 36 of culture activation. CD62Llow cells, containing approximately 50% TCR+ and 50% B220+ subsets, were suspended in complete medium (CM) and incubated for 2 days at 4 × 106 per well in 24 well culture plates coated with anti-CD3 (145-2C11) as previously described [28]. Activated cells were washed, counted, and suspended at 0.5 × 105/ml in CM with IL-2 (4 U/ml) (Chiron Corp. Emeryville, CA), with or without rmIL-7 (10 ng/ml) or rhIL-23 (2 ng/ml) (each from R&D Systems, Minneapolis, MN) and then diluted to 105/ml on day 5 of activation. On days 9 and 15, the cell concentration was adjusted to 2 × 105/ml. For experiments with two cycles of anti-CD3 stimulation, T cells were incubated with immobilized anti-CD3 for 14 hrs on day 15 and used for adoptive therapy on day 23. For long-term expansion, cultures were maintained for 23 days after the initial anti-CD3 stimulation in CM with the indicated combination of IL-2 (4 U/ml), IL-7 (10 ng/ml), and IL-23 (2 ng/ml) and then were stimulated with anti-CD3 for 14 hrs on day 23 and every 7 days thereafter.
IFN-γ and FACS analysis
T cells were stimulated with a single cell suspension of either MCA 205 or MCA 207 tumors at a 1:1 ratio, or with immobilized anti-CD3. Brefeldin A was added after five hours of stimulation and the cells were harvested after 20 hrs and stained for intracellular IFN-γ according to the manufacturers instructions (BD Biosciences, San Diego, CA). FACS analysis was performed using FITC or PE conjugated antibodies or isotype control antibodies (BD Biosciences).
RNA isolation and CDR3 size distribution analysis (TCR spectratyping)
TDLN cells were lysed using TRIzol reagent (Invitrogen, Carlsbad, CA) and total RNA was reverse transcribed into cDNA using the SuperScript II RT kit (Invitrogen). cDNA was amplified using PCR with 22 different VB-specific primers paired with a hex-labeled constant region primer which spans the CDR3 region as previously described [30]. CDR3 size distribution analysis was performed by mixing 1.0 μl of hex-labeled PCR amplified cDNAs with 12.0 μl deionized formamide (Sigma) and 0.5 μl size standard (Genescan-400 ROX, ABI 310; Perkin-Elmer, Shelton, CT), heated for 2 minutes at 90°C and chilled on ice prior to analysis. Samples were applied to an ABI 310 sequencer for CDR3 size distribution analysis. Samples were determined to be oligoclonally skewed if the CDR3 size patterns failed to exhibit a Gaussian bell-shaped distribution and were dominated by one or two prominent peaks.
Adoptive immunotherapy
Mice were inoculated with MCA 205 or MCA 207 tumor cells (3 × 105) i.v. to establish pulmonary metastases. Subcutaneous tumors were established by inoculation of 1.5 × 106 cells. Intracranial tumors were established by transcranial inoculation of 105 tumor cells at a depth of 4 mm as previously described [31]. Mice bearing 3-day s.c. or i.c. tumors or 10-day pulmonary metastases were treated with 5 Gy nonmyeloablative total body irradiation (TBI) delivered from a 137Cs irradiator prior to intravenous transfer of the T cells whereas mice with 3-day pulmonary tumors were not irradiated. For pulmonary tumors, mice were euthanized on day 20 post inoculation, the lungs were insufflated with India ink and the number of surface tumor nodules was enumerated using a dissecting microscope. Subcutaneous tumors were measured in two perpendicular dimensions three times per week and mice with progressive tumors were euthanized when the product of dimensions exceeded 200 mm2. Mice bearing intracranial tumors were monitored daily for survival or were euthanized when neurologic symptoms such as decreased grooming and decreased spontaneous movement were apparent.
Statistical analysis
Treatment groups consisted of five individuals. Analysis of tumor size for s.c. tumors was performed by the Mann-Whitney rank sum test. For pulmonary tumors, a t test was performed on paired samples and p < 0.05 was considered significant. Survival of mice bearing i.c. tumors was compared using the Wilcoxon rank sum test.
Results
Ex vivo stimulation with anti-CD3, IL-2, and IL-7 augments effector cell generation
During the progressive growth of weakly immunogenic tumors an immune response, albeit sub-therapeutic, is initiated in TDLNs. In previous studies our laboratory has demonstrated that the CD62Llow subset of T cells contains the tumor-reactive subset whereas the reciprocal CD62Lhigh subset does not have any therapeutic effect and displays suppressor activity [1,24,32]. This finding is consistent with numerous studies documenting the high expression of CD62L on naïve T lymphocytes and its rapid downregulation upon antigen stimulation [21-23]. TDLNs were harvested from mice bearing 12-day subcutaneous MCA 205 tumors and the CD62Llow subset was purified by MACS depletion of CD62Lhigh cells. The typical yield of CD62Llow cells was 1.5 × 106 per TDLN, which represented 7–8.5% of the initial cells. The phenotype of the total TDLN prior to MACS separation and the negatively selected CD62Llow subset is demonstrated in Figure 1A. The separated cells were highly enriched for the CD62Llow fraction consisting of 36% TCR+ cells among which 7% were CD8+ and 22% were CD4+. The cells were activated with immobilized anti-CD3 mAb for 48 hrs during which time aggregates of lymphoblasts developed. The activated cells were resuspended at a low density, 0.5 × 105/ml, in medium supplemented with IL-2 (4 U/ml) with or without IL-7 (10 ng/ml) and the cell concentration was adjusted to 105/ml on day 5 and 2 × 105/ml on day 9. As demonstrated in figure 1B, there was an initial 2-fold decline in cell number during the first 48 hrs of culture, primarily though depletion of CD62Llow B220+ cells. This was followed by a rapid burst of proliferation from day 2 until day 15 when the IL-2 supplemented culture peaked at 175-fold proliferation and the IL-2 + IL-7 cultures reached 1000-fold proliferation.
Figure 1 Proliferation and efficacy of CD62Llow TDLN cells cultured with IL-2 +/- IL-7. (A) Freshly isolated whole TDLN cells were stained for expression of TCR and CD62L (left panel). The purified CD62Llow subset was stained for TCR and CD62L expression (center panel), or for CD4 and CD8 expression (right panel). (B) CD62Llow TDLN cells were activated with immobilized anti-CD3 mAb for 2 days then cultured in medium with IL-2 (4 U/ml) (closed circle) or the combination of IL-2 and IL-7 (10 ng/ml) (open circle). Cells density was adjusted to 105/ml on days 5 and 9 and total proliferation was calculated. (C) Mice bearing 3-day s.c tumors were treated with 5 Gy TBI then received adoptive transfer of 5 × 106 T cells cultured for 9 days with IL-2 alone (open circle), IL-2 + IL-7 (open triangle), or HBSS (closed circle). Each treatment group is significantly different than HBSS control (P < 0.01).
The morphology of the cells changed from lymphoblastoid to small round cells at day 15 and there was no additional proliferation. IL-7 preserved the viability of cells whereas IL-2 alone could not prevent a 8-fold numerical decline between days 15 to 30. Because the TDLN cells were initially segregated based on phenotype rather than antigen specificity and the anti-CD3 stimulation was antigen-independent, it was not known whether the enhanced proliferation achieved in the presence of IL-7 was due to preferential growth of irrelevant T cells or preservation of tumor-reactive T cells. As demonstrated in figure 1C, there was equivalent therapeutic efficacy against s.c. tumors at day 9 using T cells cultured with IL-2 alone or the combination of IL-2 and IL-7. Regression of established tumors requires efficient trafficking and the dose of 5 × 106 CD62Llow TDLN cells is near the lowest threshold dose required to cure 3-day s.c. MCA 205 tumors [26,33]. Thus, the addition of IL-7 during in vitro activation augmented the total number of cells but did not substantially diminish per-cell therapeutic efficacy.
Preservation of effector function after anti-CD3 re-stimulation
The activated T cells were re-stimulated with anti-CD3 at the time of maximal proliferation on day 15 to determine whether additional numerical expansion could be initiated. Re-stimulated cells rapidly regained lymphoblast morphology and as anticipated nearly half of the cells underwent AICD [34]. The surviving cells underwent a 100-fold numerical expansion before achieving a growth plateau (Figure 2A). In addition, the composition of the T cell cultures changed over time due to the more rapid intrinsic proliferative response of CD8+ T cells [35]. Although CD4+ and CD8+ T cells each proliferated in the presence of IL-2 and IL-7, by day 23 CD8+ cells comprised 86% of the culture whereas there were only 4% CD4+ cells (Figure 2B). The re-stimulated cells cultured in IL-2 or the combination of IL-2 plus IL-7 each retained potent therapeutic efficacy against 10-day MCA 205 pulmonary metastases but not against MCA207, demonstrating retention of antigenic specificity (Figure 2C). The hosts bearing 10-day pulmonary tumors were treated with 5 Gy TBI, which causes transient lymphopenia, prior to adoptive transfer. Moreover, hosts did not receive adjunctive IL-2. Thus, the transferred cells were able to function independently of radiosensitive host cells and were not dependent on exogenous cytokine support. Because the combination of IL-2 plus IL-7 promoted greater numerical expansion of CD8+ T cells with preservation of effector function, it was used for subsequent hyperexpansion studies.
Figure 2 Restimulation of activated T cells induces additional proliferation with retention of specific anti-tumor efficacy. (A) CD62Llow TDLN cells were activated with anti-CD3 mAb from day 0–2 and again for 14 hrs on day 15. T cells were cultured in the presence of IL-2 (4 U/ml) (closed circle) or IL-2 plus IL-7 (10 ng/ml) (open circle) and the total proliferation is indicated. (B) FACS analysis of activated T cells on day 23 of culture stained for CD4 and CD8. (C) Mice bearing 10-day pulmonary metastases of either MCA205 or MCA207 tumors were pre-treated with 5 Gy TBI then received adoptive transfer of 2.5 × 107 cells cultured with IL-2 alone, the combination of IL-2 plus IL-7, or control HBSS as indicated. Difference between the groups bearing MCA 205 treated with T cells cultured with IL-2 or IL-2 plus IL-7 and all other groups is (P < 0.01). (D) Mice bearing 3-day s.c tumors were pre-treated with 5Gy TBI followed by injection of; HBSS (closed circles), 5 × 106 T cells activated for 5 days (open circles, P < 0.01), 5 × 106 restimulated T cells at day 23 of culture (closed triangles, P = 0.4), 1.5 × 107 re-stimulated T cells (open triangle, P < 0.01), or 4 × 107 re-stimulated T cells (closed square, P < 0.01). Number of mice showing complete regression in each treatment group of 5 mice is indicated in parentheses.
The relative per-cell potency of re-stimulated cultures on day 23 was compared with the 5-day culture activation approach we have employed in previous studies. The segregated CD62Llow TDLN cells were frozen and one aliquot was thawed and activated for a total of 23 days with anti-CD3 stimulation on days 0–2 and again on day 15. The second aliquot was thawed on culture day 18 of the first aliquot and stimulated with anti-CD3 for 48 hrs and then cultured with IL-2 and IL-7 for an additional 3 days. The two T cell cultures were synchronously harvested and transferred into hosts bearing 3-day s.c. tumors. As demonstrated in Figure 2D, whereas 5 × 106 cells activated for 5 days was curative in 5/5 mice, 5 × 106 cells cultured for 23 days had minimal therapeutic effect. However, a modest increase in the cell dose to 1.5 × 107 cells led to a significant therapeutic effect and at a dose of 4 × 107 cells 2/5 mice were cured. The s.c. tumor model is highly dependent on the presence of tumor-specific CD4+ T cells [36,37]. The relative decrease in percentage of CD4+ cells from 24% on day 5 of culture to 4% on day 23 may account for some of the differential therapeutic effects. In contrast to the modest difference in per-cell efficacy, there was nearly 1000-fold greater proliferation in the 23-day versus 5-day cultures indicating that the aggregate therapeutic effect was substantially greater following extended culture.
Repetitive anti-CD3 stimulation induces hyper-expansion of CD8+ effectors
There are immunologic scenarios that demonstrate exhaustion of the effector response leading to failure of immunologic control of infection or tumor [38-40]. By contrast, selection and extensive propagation of T cell clones indicates that T cells can undergo massive proliferation yet retain antigen-specific function. To assess whether there is an intrinsic limit to the retention of in vivo effector function of CD62Llow cells, they were stimulated with anti-CD3 followed by IL-2 and IL-7 for 23 days. Starting on day 23, the T cells were activated with anti-CD3 every 7 days. The time course between the initial and subsequent anti-CD3 stimulations was chosen based on evidence that T cells undergo changes in gene expression, phenotype, and function over a twenty-day time course in the transition from naïve to memory cells [41].
As demonstrated in Figure 3A, repetitive anti-CD3 stimulation was accompanied by immediate AICD in approximately 50% of the cells followed by rapid proliferation. There was no evidence that the T cells became effete over the 50-day expansion period despite a total proliferation of 2–6 × 108-fold that was consistent in three independent experiments. TCR Vβ expression was determined for freshly harvested TDLN T cells, the CD62Llow subset, and activated cultures from several independent experiments (Table 1) "see additional file 1". As demonstrated, multiple TCR Vβ families are represented prior to and following hyperexpansion. There is a relatively low level of variability in the prevalence of multiple TCR Vβ families between experiments, especially considering that greater than 108-fold total proliferation had occurred. In addition, TCR spectratype analysis for each Vβ family revealed a poyclonal rather than clonal or oligoclonal distribution (data not shown).
Figure 3 CD8+ effector T cells can be hyperexpanded through repetitive anti-CD3 stimulation. (A) CD62Llow TDLN cells were restimulated with anti-CD3 mAb for 14 hrs every 7 days starting on day 23 of culture and overall proliferation was measured for three independent experiments. (B) T cells were harvested on day 50 of culture and adoptively transferred to hosts bearing either MCA 205 or MCA 207 3-day pulmonary metastases (P < 0.01 for MCA 205 tumors treated with either 6 × 106 or 2 × 107 compared with all other groups). (C) Mice bearing 3-day s.c tumors were treated with 5 Gy TBI then received adoptive transfer of the indicated number of T cells hyperexpanded for 50 days (P = 0.06 for 4 × 107 cell dose) (D) Mice bearing 3-day i.c. tumors were treated with 5Gy TBI then received adoptive transfer of the indicated number of T cells hyperexpanded for 50 days. Mice were sacrificed when they developed neurologic symptoms indicating progressive tumor (P = 0.9 for treatment groups versus control).
At day 50, the cultures were >99% TCR+ and CD8+ indicating preferential expansion or survival of CD8+ cells under the conditions employed. As shown in Figure 3B, adoptive transfer of 2 × 107 cells to hosts with 3-day MCA205 pulmonary metastases eliminated tumors and 6 × 106 cells was the threshold dose for complete response whereas 2 × 106 cells were subtherapeutic. In addition, there was no response against the antigenically distinct MCA207 tumor. In an independent experiment, the dose of T cells required to completely eliminate 3-day pulmonary metastases was 2 × 106 indicating some inter-experimental variability in per-cell efficacy. Because of the critical role of CD4+ T cells for therapy of s.c. or i.c. tumors it was not anticipated that the hyperexpanded CD8+ cultures would mediate complete regression of tumors at these anatomic sites. Indeed, there was substantially less efficacy against 3-day s.c.tumors. Adoptive transfer of 4 × 107 cells showed a trend toward response (P = 0.061) with 1/5 mice cured in only one of two identically designed experiments (Figure 3C). In addition, a dose of 2 × 107 cells was subtherapeutic against 3-day i.c. tumors (Figure 3D).
Despite the rapid proliferation of CD8+ T cells in vitro, there was no evidence of lymphoid hyperplasia when the mice were sacrificed to enumerate lung metastases 17 days after adoptive transfer. Moreover, there was no evidence of lymphoproliferative disease even when the hyperexpanded T cells were transferred into 5Gy TBI hosts bearing i.c. or s.c. tumors that had transient lymphodepletion of host cells. Thus, despite extensive proliferation in vitro the T cells did not demonstrate any evidence of transformation.
Hyperexpanded CD4+ T effector cells mediate regression of i.c. and s.c. tumors
The clinical utility of adoptive immunotherapy for patients with metastatic cancer is dependent on the ability of T cells to function at all anatomic sites of disease. To selectively activate CD4+ T cells, the CD62Llow TDLN were depleted of CD8+ cells with magnetic beads prior to anti-CD3 activation and again on day 36 of culture. The CD4+ cells were activated with anti-CD3 mAb for 48 hrs and cultured in the presence of IL-7 and either IL-2 or IL-23 with anti-CD3 restimulation performed on days 26 and day 36 of culture. The rate of proliferation of CD4+ cells was similar in the presence of IL-2 or IL-23 (Figure 4A). On day 43 of culture, cells cultured with IL-7 plus IL-2 were 87% CD4+ and 11% CD8+, whereas cells cultured in IL-7 plus IL-23 were 98% CD4+ and less than 1% CD8+. The T cells were adoptively transferred to hosts with 3-day s.c (Figure 4B) or 3-day i.c. (Figure 4C) tumors demonstrating that a dose of 3 × 107 cells cultured in the presence of either IL-2 or IL-23 was curative.
Figure 4 Hyperexpanded CD4+ T cells mediate regression of intracranial or subcutaneous tumors. (A) CD62Llow TDLN cells were depleted of CD8+ cells prior to anti-CD3 activation and were maintained in medium with IL-2 (4 U/ml) plus IL-7 (10 ng/ml) or alternatively with IL-7 (10 ng/ml) plus IL-23 (2 ng/ml) and were restimulated for 14 hrs with anti-CD3 mAb at the indicated time points. The total proliferation with indicated losses due to AICD or re-purification of CD4+ cells is indicated. (B) Mice bearing 3-day s.c.tumors were treated with 5 Gy TBI followed by adoptive transfer of 3 × 107 CD4+ T cells culture activated for 43 days and tumor size was measured. On day 37, one mouse from IL-2 + IL-7 group was euthanized due to progressive tumor growth, however complete regression was observed in the remaining 4 mice (P = 0.015 versus control). Complete regression was observed in all five recipients of IL-7 + IL-23 cultured CD4+ T cells (P = 0.005 versus control). (C) Mice bearing 3-day intracranial tumors were treated with 5Gy TBI followed by adoptive transfer of 3 × 107 CD4+ T cells culture activated for 43 days. Mice were followed for survival (P < 0.01 for both treatment groups versus control). (D) Mice bearing 3-day subcutaneous tumors were treated with 5 Gy TBI followed by adoptive transfer of 4 × 107 CD8+ T cells hyperexpanded to greater than 108-fold for 50 days, or 1.5 × 107 CD4+ T cells hyperexpanded to greater than 108-fold for 85 days. On day 28, 4 mice from CD8 treatment group (* P = 0.39 versus control) and 2 mice from CD4 treatment group (# P = 0.019 versus control) were euthanized due to progressive tumor growth but complete tumor regression was observed in the remaining mice.
The CD4+ T cells maintained in IL-7 plus IL-23 were subjected to continued repetitive anti-CD3 restimulation every 7 days starting on day 56. These conditions led to exclusive proliferation of CD4+ T resulting in 1.2 × 108-fold total proliferation by day 85 of culture. Despite extensive in vitro proliferation in response to antigen-independent stimulation for 85 days, 1.5 × 107 CD4 cells retained efficacy against 3-day s.c.tumors, with 3/5 mice achieving complete tumor regression (P = 0.019 versus control) (Figure 4D). As previously demonstrated, CD8+ cells synchronously cultured in the presence of IL-2 plus IL-7 for 50 days and expanded to 108-fold demonstrated minimal efficacy against subcutaneous tumors (P = 0.39 versus control) with 1/5 mice achieving complete tumor regression.
Hyperexpanded cultures retain IFN-γ producing cells
The initial antigen priming event in vivo was driven by tumor-specific antigens but the segregation of the CD62Llow subset and all subsequent in vitro activation stimuli were antigen independent. Consequently, reactivity of cultures against tumor antigens might fluctuate over time. This possibility was analyzed by quantifying the percentage of culture activated T cells that produce IFN-γ specifically when exposed to tumor in vitro. For this assay, a single cell digest of in vivo propagated tumor is used which contains MHC class II+ APC as well as tumor cells and is capable of stimulating both CD4+ and CD8+ T cells. As demonstrated in Figure 5, there was minimal spontaneous production of IFN-γ and minimal reactivity against the antigenically distinct MCA 207 tumor. By contrast, on day 8 of culture 39% of CD8+ T cells produced IFN-γ in response to MCA 205 tumor cells. This percentage of IFN-γ positive CD8+ cells decreased to 10% on day 36. CD8+ T cells that were simply maintained in culture with IL-2 and IL-7 cytokine support but without anti-CD3 restimulation did not proliferate but remained viable. Under non-proliferative conditions, the percentage of MCA 205 reactive T cells was maintained at 13% on day 36. Similarly, CD4+ T cells cultured in the presence of IL-7 plus IL-23 had 11% IFN-γ positive cells in response to MCA205 tumor with minimal spontaneous production and minimal response to MCA207 tumor digest. The in vitro assay of IFN-γ production may not be fully reflective of the in vivo capacity of T cells to mediate tumor regression because CD4+ T cells cultured with IL-2 plus IL-7 had only 2% IFN-γ positive cells despite equivalent in vivo anti-tumor efficacy. The vast majority of T cells (73–86%) produced IFN-γ in response to anti-CD3 stimulation throughout the in vitro culture activation period.
Figure 5 Hyperexpanded CD4+ and CD8+ T cells produce IFN-γ in response to tumor stimulation. CD62Llow TDLN cells were culture activated with anti-CD3 and IL-2 plus IL-7 for 23 days then were restimulated with anti-CD3 every 7 days. T cells were removed from culture on day 8, on day 36 for CD8+ cultures, or day 43 for CD4+ cultures. T cells were incubated without additional stimulus to determine spontaneous production of IFN-γ or with single cell digest of MCA205 or MCA207 tumors or with immobilized anti-CD3 mAb and Brefeldin A was added at 5 hrs and cells were harvested after 14 hrs. Intracellular IFN-γ was determined by FACS and the percentage of T cells is indicated.
Discussion
These experiments demonstrate that T cells, sensitized to tumor antigens in vivo, can be activated in vitro under conditions that promote hyperexpansion of either the CD4+ or CD8+ subset while retaining their potent therapeutic efficacy against established tumors. A notable feature of in vitro activation is that it permits selection and enrichment of a minor subset of tumor-reactive precursor cells. The mechanism of antigen sensitization of T cells through cross-priming by APC within draining LNs provides a convenient localized anatomic source that is already highly enriched. When coupled with physical segregation based on phenotypic characteristics that distinguish between antigen-stimulated versus naïve T cells, enrichment to nearly 40% of tumor-specific T cells was achieved. One important aspect of this strategy for selection and enrichment is that it does not require pre-existing knowledge of the immunogenic tumor antigens and does not require freshly acquired T cells to exhibit effector function. These conditions have relevance for many clinical situations where tumor antigens are not yet described or where unique tumor antigens may be immunodominant. Moreover, signaling defects have been observed in freshly acquired T cells from tumor-bearing hosts that might impede segregation based on functional properties [42-44].
Starting with a highly enriched population of T cells we were able to use a powerful, yet antigen-independent, stimulus such as anti-CD3 mAb that preserved the initial TCR repertoire diversity. Interestingly, anti-CD28 stimulation was not required for this experimental model, presumably because the T cells had already received co-stimulation during APC-mediated priming in vivo. The principal advantage to in vitro activation is that the culture conditions can be adapted to optimize proliferation of distinct subsets of responding cells. It is important to note that anti-CD3 activation in the absence of exogenous cytokine support did not lead to T cell proliferation even among CD4+ cells. The low cell density may have prevented secreted cytokines from reaching a critical threshold concentration. Moreover, IL-7, produced by non-hematopoetic cells and IL-23, produced by APC, mandated an exogenous source of these cytokines for in vitro culture activation. The combination of IL-2 and IL-7 provided rapid proliferation of CD8+ T cells and preserved their viability after completion of the initial mitogenic burst. The reason this combination was effective is that IL-7 receptor α chain is constitutively expressed on naïve and memory T cells but is downregulated on activated T cells [45,46]. By contrast, the IL-2 receptor α chain is reciprocally expressed on activated cells in a transient manner. Thus, the combination of these two cytokines ensured continuous mitogenic signal transduction. IL-7 is crucial for development and homeostasis of T cells and is markedly increased following lymphodepletion. Therefore, there is considerable interest in employing lymphodepletion as a strategy to augment active as well as adoptive immunotherapy [47]. Likewise, exogenous IL-2 has been administered in the context of tumor antigen vaccination as well as in nearly every clinical application of adoptive transfer to provide helper function [48]. However, in addition to their mitogenic effects on antigen-stimulated T cells, systemic production of IL-7 or systemic administration of IL-2 has effects on irrelevant T lymphocytes, other hematopoetic cells, and the vasculature. The inability to target cytokine support specifically to the relevant T cells limits the effectiveness of in vivo cytokine administration. More importantly, we have clearly documented that adjunctive IL-2 inhibits trafficking of adoptively transferred T cells into intracranial or subcutaneous tumors [49]. By contrast, cytokine stimulation can be targeted specifically to effector cells under optimal conditions in vitro without adverse systemic effects on the host. Future experiments to adjust the sequence, and concentration of supplemental cytokines using more sophisticated schedules than employed here might provide superior effector function.
These experiments confirm the importance of CD4+ T cells for therapy of tumors in certain anatomic sites, such as the brain and subcutaneous tissue. The slower rate of CD4+ cell proliferation relative to CD8+ cells following the initial anti-CD3 stimulation led to their rapid marginalization in mixed cultures. However, depletion of CD8+ cells and use of cytokine combinations such as IL-7 and IL-23 favored the selective hyperexpansion of CD4+ cells that retained potent in vivo function. IL-23 is a member of the IL-12 family of cytokines and contains the IL-12 p40 subunit that transduces signals through the shared IL-12β1 chain in addition to the unique p19 subunit [50,51]. The IL-23 receptor is expressed on memory but not naive CD4+ cells, thus it is ideal for previously sensitized LN T cells. Myeloid cells, which are the natural source of IL-23, disappear rapidly in the in vitro cultures mandating an exogenous source. There is not a substantial amount of data on the effects of in vivo IL-23 administration, therefore its utility as a systemically administered adjuvant for T cell adoptive immunotherapy is unclear. However, the related cytokine IL-12 has substantial systemic effects that have limited its clinical use [52].
Although CD4+ T cells have been investigated as a source of helper function for CD8+ cytolytic cells our previous experiments have clearly established their stand-alone potential against MHC class II negative tumors [53]. CD4+ T cell anti-tumor function is mediated through cross-presentation of specific tumor-antigens by tumor associated APC [54]. As demonstrated, CD4+ cells cultured with IL-23 produced greater levels of INF-γ that would augment antigen presentation. Indeed, addition of tumor-reactive CD4+ cells to tumor digest increases the reactivity of the CD8+ cells. In addition to their autonomous effector functions, CD4+ cells are required to generate a functional CD8+ memory response in vivo [55,56]. Our recent experiments have demonstrated that adoptive transfer of effector T cells causes tumor destruction and sensitization of a secondary wave of regenerating host T cells [57]. In this regard, IL-23 stimulation of CD4+ cells might be particularly useful because it, unlike IL-12, induces production of the pro-inflammatory cytokine IL-17 [58]. Our observation that such sensitization occurred even in hosts with partial tumor regression indicates that the presence of effector CD4+ T cells and inflammatory conditions of tumor antigen acquisition by host APC are important in perpetuating the anti-tumor response.
Repetitive anti-CD3 stimulation was utilized to drive hyperproliferation of T cells yet the TCR/CD3 complex also activates genetic programs required for effector function. Effector molecules such as Fas, Perforin, and IFN-γ have autoinhibitory as well as paracrine inhibitory effects during culture activation. Viewed purely in operational terms, sequential in vitro activation first under conditions that optimize T cell proliferation, then with conditions that restore effector functions immediately before adoptive transfer, would be advantageous. Future experiments will explore whether it is possible to dissociate proliferative signaling pathways from those mediating effector function through selective transient gene inactivation. The quantitative aspects of hyperexpansion are of less scientific interest but do have some practical implications. The 108-fold extent of proliferation far exceeded what was required to treat the tumor models employed. Moreover, the availability of uniformly primed T cells for mechanistic studies is not numerically limited when using an inbred strain. However these experiments establish an approach to maintain polyclonality and preserve effector function despite extensive antigen-independent proliferation. As such, the quantitative aspects of hyperexpansion may have relevance to certain clinical situations where an autologous source of antigen-primed T cells may be limited and extensive host tumor burden may demand a large number of effector T cells.
Supplementary Material
Additional File 1
This is a table describing the percentage of cells expressing various TCR Vβ family members at three different time points.
Click here for file
Acknowledgements
This work was supported by a grant from NIH, CA 091981.
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-471555506310.1186/1471-2148-4-47Research ArticleWidespread presence of "bacterial-like" PPP phosphatases in eukaryotes Andreeva Alexandra V [email protected] Mikhail A [email protected] Research School of Biological and Molecular Sciences, Oxford Brookes University, Headington, Oxford OX3 OBP, UK2 Present address: University of Illinois, College of Medicine, Department of Pharmacology, 835 S. Wolcott Ave, Chicago, IL 60612, USA2004 19 11 2004 4 47 47 25 8 2004 19 11 2004 Copyright © 2004 Andreeva and Kutuzov; licensee BioMed Central Ltd.2004Andreeva and Kutuzov; 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
In eukaryotes, PPP (protein phosphatase P) family is one of the two known protein phosphatase families specific for Ser and Thr. The role of PPP phosphatases in multiple signaling pathways in eukaryotic cell has been extensively studied. Unlike eukaryotic PPP phosphatases, bacterial members of the family have broad substrate specificity or may even be Tyr-specific. Moreover, one group of bacterial PPPs are diadenosine tetraphosphatases, indicating that bacterial PPP phosphatases may not necessarily function as protein phosphatases.
Results
We describe the presence in eukaryotes of three groups of expressed genes encoding "non-conventional" phosphatases of the PPP family. These enzymes are more closely related to bacterial PPP phosphatases than to the known eukaryotic members of the family. One group, found exclusively in land plants, is most closely related to PPP phosphatases from some α-Proteobacteria, including Rhizobiales, Rhodobacterales and Rhodospirillaceae. This group is therefore termed Rhizobiales / Rhodobacterales / Rhodospirillaceae-like phosphatases, or Rhilphs. Phosphatases of the other group are found in Viridiplantae, Rhodophyta, Trypanosomatidae, Plasmodium and some fungi. They are structurally related to phosphatases from psychrophilic bacteria Shewanella and Colwellia, and are termed Shewanella-like phosphatases, or Shelphs. Phosphatases of the third group are distantly related to ApaH, bacterial diadenosine tetraphosphatases, and are termed ApaH-like phosphatases, or Alphs. Patchy distribution of Alphs in animals, plants, fungi, diatoms and kinetoplasts suggests that these phosphatases were present in the common ancestor of eukaryotes but were independently lost in many lineages. Rhilphs, Shelphs and Alphs form PPP clades, as divergent from "conventional" eukaryotic PPP phosphatases as they are from each other and from major bacterial clades. In addition, comparison of primary structures revealed a previously unrecognised (I/L/V)D(S/T)G motif, conserved in all bacterial and "bacterial-like" eukaryotic PPPs, but not in "conventional" eukaryotic and archaeal PPPs.
Conclusions
Our findings demonstrate that many eukaryotes possess diverse "bacterial-like" PPP phosphatases, the enzymatic characteristics, physiological roles and precise evolutionary history of which have yet to be determined.
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Background
Reversible phosphorylation of proteins is a ubiquitous mechanism, indispensable for regulation of virtually any cellular function. Therefore, protein kinases and phosphatases are of paramount importance for normal functioning of all metabolic and signalling pathways. In eukaryotes, PPP family is one of the two known protein phosphatase families specific for Ser and Thr [1-4]. Unlike eukaryotic (and archaeal [5]) PPP phosphatases, bacterial members of the family have broad substrate specificity [6] or may even be Tyr-specific [7-9]. Moreover, one group of bacterial PPPs are diadenosine tetraphosphatases [10,11]. Unlike eukaryotes, in prokaryotes PPP phosphatases appear to be facultative, since entirely sequenced genomes of some bacteria and archaea do not encode them [5,12]. Nevertheless, when present, they appear to play essential roles [13-15].
Three motifs (GDXHG, GDXXDRG and GNH(E/D)), highly conserved in the N-terminal subdomains of the catalytic domains of all PPP phosphatases [10,11], contain most residues which coordinate metal ions in the active centre [16] and are considered as the signature of the PPP family.
In a previous work [17] we identified an unusual cDNA fragment from a moss Physcomitrella patens, showing no similarities to the known PPP phosphatases beyond the presence of the GDXHG and GDXXDRG motifs. Detection of homologous cDNA sequences from Arabidopsis and rice suggested the presence of an unknown PPP group in plants, distinct from "conventional" eukaryotic PPP phosphatases [17].
We have now taken advantage of a much greater representation (as compared to 1999) of sequence databases for various species to further explore this initial observation. We present the evidence for the existence in eukaryotes of three "non-conventional" branches of the PPP family. We also identify a previously unrecognised conserved motif in the PPP catalytic domain, which can be used as a signature of "bacterial"-type PPP phosphatases.
Results
"Rhizobiales / Rhodobacterales / Rhodospirillaceae – like" PPP phosphatases in plants
Two Arabidopsis sequences, At3g09960 and At3g09970, were retrieved using the P. patens fragment [17] as a query in TBlastN searches. They share 85% identity with each other at the protein level (see Figure 1). Both genes are transcribed, since full-length cDNAs have been detected in a large-scale transcription study [18]. They are arranged on chromosome 3 in tandem, suggesting their origin by a recent duplication. A number of ESTs from other plant species (but none from non-plant eukaryotes) were also detected by TBlastN searches, which in most cases provide no evidence for the existence of more than one isoform.
Figure 1 Comparison of the primary structures of plant Rhilphs, related α-proteobacterial phosphatases and human PP1α as a prototype of "conventional" eukaryotic PPP phosphatases. Amino acid residues conserved in at least all but one Rhilphs and α-proteobacterial phosphatases are shown in white and shaded in black. Residues conserved in at least two thirds of the sequences are shown in white and shaded in dark grey. Residues conserved in at least half of the sequences are shown in black and shaded in light grey. Following substitutions were considered as conserved residues: Ile/Leu, Phe/Tyr, Asp/Glu, Asn/Gln, Arg/Lys and Ser/Thr. Catalytic site residues that interact with metal ions are indicated by asterisks according to [20]. SAPNY motif in PP1, conserved in most eukaryotic PPP phosphatases, is double underlined. Solanum tuberosum sequence is translation of the EST entries BQ516856, BQ516857 and BI435517. Physcomitrella patens sequence is translation of the EST entry BQ039171. Other accession numbers are indicated in Table 1.
Among prokaryotes, related sequences were detected in some α-Proteobacteria, the closest matches being with Rhizobiales, Rhodobacterales and Rhodospirillaceae. Therefore, we designate this group as Rhizobiales / Rhodobacterales / Rhodospirillaceae-like phosphatases, or Rhilphs (Figure 1; see also Table 1 and Figure 4), and the two Arabidopsis genes products At3g09960 and At3g09970 as "Rhizobiales-like" phosphatases 1 (RLP1) and 2 (RLP2), respectively.
Table 1 Species, accession numbers (UniProt, EMBL, NCBI or TIGR Gene Index) and common names (where available) of PPP phosphatase sequences shown in Figure 4. For A. thaliana sequences, gene numbers are also indicated. Sequence No. 67 is available from Chlamydomonas reinhardtii draft genome [65].
No. Accession Species name
Rhilphs
1 Q9SR61; At3g09960 Arabidopsis thaliana
2 Q9SR62; At3g09970 Arabidopsis thaliana
3 BE034080 Mesembryanthemum crystallinum
4 BQ995369 Lactuca sativa
5 AW034786 Lycopersicon esculentum
6 BU875146 Populus balsamifera
7 BG457803 Medicago truncatula
8 AV425727 Lotus japonicus
9 BM731295 Glycine max
10 BF261816 Hordeum vulgare
11 BQ788728 Triticum aestivum
12 AL731641 Oryza sativa
13 CF670562 Pinus taeda
14 BQ039171 Physcomitrella patens
Group I (α-Proteobacteria)
15 Q987U4 Mesorhizobium loti
16 Q8UA33 Agrobacterium tumefaciens
17 ZP_00054691 Magnetospirillum magnetotacticum
18 ZP_00015226 Rhodospirillum rubrum
19 ZP_00014771 Rhodospirillum rubrum
20 CAE28794 Rhodopseudomonas palustris
21 Q9ABQ8 Caulobacter crescentus
22 ZP_00051041 Magnetospirillum magnetotacticum
23 ZP_00093979 Novosphingobium aromaticivorans
24 Q92V37 Sinorhizobium meliloti
Group VII (heterogeneous)
25 Q8YZT4 Anabaena sp.
26 Q9WZK1 Thermotoga maritima
27 O34205 Fervidobacterium islandicum
28 NZ_AABE01000101 Cytophaga hutchinsonii
Group III (γ-Proteobacteria and bacteriophage λ)
29 P03772 Bacteriophage λ
30 P55798 E. coli PrpA
31 Q8VPE2 Salmonella typhimurium PrpA
32 Q8Z487 Salmonella enterica
33 P55799 E. coli PrpB
Group IV (Firmicutes)
34 Q81YR3 Bacillus anthracis
35 Q9FB69 Lactococcus lactis
36 Q97FF3 Clostridium acetobutylicum
Alphs
37 BM291808 Amblyomma variegatum
38 TC9835 Ciona intestinalis
39 BU652795 Chlamydomonas reinhardtii
40 AC091781 Trypanosoma brucei
41 AC084046 Trypanosoma brucei
42 AL499620 Leishmania major
43 BQ143558 Metarhizium anisopliae
44 AC127427 Magnaporthe grisea
45 AA966318 Aspergillus nidulans
46 P40152 Saccharomyces cerevisiae
ApaH
47 Q8Y1K9 Ralstonia solanacearum ApaH
48 Q9JVF4 Neisseria meningitidis ApaH
49 P05637 Escherichia coli ApaH
Group VI (heterogeneous)
50 O31614 Bacillus subtilis
51 O69213 Anabaena sp. PrpA
52 Q93JF4 Streptomyces coelicolor
53 Q9RS78 Deinococcus radiodurans
Group II (Cyanobacteria)
54 O54390 Microcystis aeruginosa PP1-Cyano 1
55 P74150 Synechocystis sp.
56 Q8YP31 Anabaena sp.
57 ZP_00072257 Trichodesmium erythraeum
58 Q8DGA2 Thermosynechococcus elongatus
Shelphs
59 AC119500* Leishmania major
60 Q8EBN0 Shewanella oneidensis
61 Q9S427 Shewanella sp.
62 TIGR_167879 Contig1731 Colwellia psychrerythraea
63 CF394707 Pinus taeda
64 TC31593 Solanum tuberosum
65 Q944L7; At1g18480 Arabidopsis thaliana
66 BF645180 Medicago truncatula
67 Scaffold_45 Chlamydomonas reinhardtii
68 Q9LMJ5; At1g07010 Arabidopsis thaliana
69 AW266595 Mesembryanthemum crystallinum
70 BG644111 Lycopersicon esculentum
71 BG450922 Medicago truncatula
72 BI787505 Glycine max
73 Q8L676 Oryza sativa
74 TC21958 Hordeum vulgare
75 AC007863 Trypanosoma brucei
76 AL499621 Leishmania major
77 TIGR_246197 Contig433 Myxococcus xanthus
78 EAK84303 Ustilago maydis
79 O74480 Schizosaccharomyces pombe
80 EAK87480 Cryptosporidium parvum
81 Q7RIH8 Plasmodium yoelii
82 Q8IKE5 Plasmodium falciparum
83 Q8I5Y5 Plasmodium falciparum
84 Q7RR22 Plasmodium yoelii
Group V (heterogeneous)
85 O87639 Streptomyces coelicolor
86 Q9RVT7 Deinococcus radiodurans
Archaea
87 O28453 Archaeoglobus fulgidus PPA
88 Q8ZW26 Pyrobaculum aerophilum
89 O34200 Methanosarcina thermophila PP1-arch2
90 Y12396 Pyrodictium abyssi
"Conventional" eukaryotic PPP
91 Q9U493 Plasmodium falciparum PPJ
92 Q8I728 Trypanosoma cruzi PPEF
93 BH900132 Ostreococcus tauri
94 O14829 Homo sapiens PPEF1 (PP7)
95 Q8IDE7 Plasmodium falciparum PP5
96 P53041 Homo sapiens PP5
97 P53043 Saccharomyces cerevisiae PPT
98 P32838 Saccharomyces cerevisiae PPG
99 P05323 Homo sapiens PP2A
100 O00743 Homo sapiens PP6
101 Q08209 Homo sapiens Calcineurin (PP2B)
102 P08129 Homo sapiens PP1
103 P32945 Saccharomyces cerevisiae PPQ
104 O49346 Arabidopsis thaliana PP7
* This phosphatase is unlikely to be catalytically active due to replacements of essential residues in the active centre.
Figure 2 Comparison of the primary structures of "Shewanella-like" phosphatases (Shelphs) and human PP1α as a prototype of eukaryotic PPP phosphatases. Designations for conserved amino acid residues are as in Figure 1. For Oryza sativa, dashed underlined C-terminal sequence has been corrected by comparison with ESTs. Accession numbers: Plasmodium falciparum 1, Q8I5Y5; 2, Q8IKE5; Trypanosoma brucei 1, AC007863; 2, AC084046.12. Chlamydomonas reinhardtii sequence is translation of the EST entries BG855683 and BI995255. Other accession numbers are indicated in Table 1.
Figure 3 Characteristic modifications (shaded in black) in the conserved PPP signature motif GDXXDRG in bacterial diadenosine tetraphosphatases and eukaryotic Alphs. Eukaryotic species are shown in bold. Plus signs indicate that gene expression is confirmed by the presence of ESTs.
Figure 4 Neighbor-Net analysis of the conserved N-terminal subdomains (starting 5 amino acid residues before conserved GDXHG and ending 25 residues after GNH(E/D) of 104 bacterial, archaeal and eukaryotic PPP phosphatases. Bootstrap values exceeding 50% (out of 1000 resamplings) were obtained in a separate neihbour-joining analysis and are shown in brackets. Species and accession numbers are listed in Table 1. Note that groups designated as I, IV and VII did not receive significant bootstrap support; corresponding sequences are grouped together for convenience of their representation in Table 1. This image (and bootstrap values for the alternative splits) can be viewed at a higher resolution as the Additional File 1.
Figure 5 Distinct conserved motifs in the C-termini of bacterial and "bacterial-like" PPP phosphatases from eukaryotes as opposed to archaeal and eukaryotic PPP phosphatases. A His residue directly binding a metal ion in the catalytic centre (marked with asterisk), and the elements of secondary structure are shown for bacteriophage λ phosphatase and for human PP1 according to ref. [20] and [19], respectively. The (I/L/V)D(S/T)G motif, highly conserved in bacterial and "bacterial-like" phosphatases, is highlighted. An expanded version of this alignment can be viewed as the Additional File 2.
Structural features of Rhilphs
All residues that are expected to bind metal ions in the catalytic centre are conserved in Rhilphs (Figure 1). Rhilphs do not have N- or C-terminal extensions beyond their catalytic domains, which in many PPP phosphatases have regulatory function and / or interact with regulatory proteins / subunits. Instead, they have characteristic inserts between the conserved motifs GNH(E/D) and HAG (corresponding to HGG in "conventional" eukaryotic PPPs, see Figure 1). Notably, much shorter inserts are found at a similar position in α-proteobacterial phosphatases (group I in Figure 1). Inserts in both plant Rhilphs and α-proteobacterial phosphatases contain a conserved motif LXXAXPXXH (Figure 1). Similarly to bacteriophage λphosphatase (λPP [19]), Rhilphs lack a region corresponding to β8, β9 and α9 of eukaryotic PPPs [20-22]. Like bacterial PPP phosphatases, Rhilphs do not have a SAPNY motif, conserved in the β12-β13 loop of eukaryotic PPPs. Analysis of Rhilph sequences did not reveal targeting or signal peptides.
While this work was in progress, a phosphatase encoded by an Arabidopsis gene At1g07010 was reported in an independent study [4].
"Shewanella-like" PPP phosphatases in plants, red algae, fungi and unicellular parasites
We undertook further TBlastN searches using full-length Arabidopsis RLP2 as a query to see whether Arabidopsis genome encodes additional "bacterial-like" PPPs. These searches identified two more genes for putative PPP phosphatases, only distantly related to Rhilphs and to any other members of the family (Figure 2).
One of these genes is At1g070101. At least three different predicted products of this gene could be found in protein databases. On the basis of comparison with EST sequences, we consider as the correct structure that of Q8RY10 with Asp and Gly at positions 109 and 208, respectively (see Figure 2). The other detected gene, At1g18480, is also represented in the databases by three distinct deduced proteins. Comparison with A. thaliana ESTs confirms Q944L7 as the correct structure.
Genomic and EST database searches provided ample evidence for the presence of related phosphatases in a number of green plants, including multiple angiosperm species, pine and a unicellular green alga Chlamydomonas reinhardtii (Figure 2; see also Table 1 and Figure 4). Related sequences were also identified in some fungi (several basidiomycetes and an ascomycete Schizosaccharomyces pombe, but not other ascomycetes), in Apicomplexa, Trypanosomatidae, and in a red alga Porphyra yezoensis (for available sequence from the latter species, see Figure 5).
The most closely related prokaryotic phosphatases were detected in Myxococcus xanthus (δ-Proteobacteria) and psychrophilic bacteria Alteromonadales (γ-Proteobacteria): uncharacterised phosphatases from Shewanella oneidensis and Colwellia psychrerythraea and a Tyr-specific phosphatase PPI from Shewanella sp. [8]. Therefore, we designate this phosphatase group as "Shewanella-like" phosphatases, or Shelphs, and the products of the two prototype Arabidopsis genes At1g07010 and At1g18480 as "Shewanella-like" phosphatases 1 (SLP1) and 2 (SLP2), respectively.
Structural features of Shelphs
Like in Rhilphs, all residues that are expected to bind metal ions in the catalytic centre are conserved in Shelphs (see Figure 1). Another feature common with Rhilphs is the presence of inserts (as compared to "conventional" eukaryotic PPPs) between the GNHE and H(A/G)G motifs (Figure 1), which are especially long in plant Shelphs. However, these inserts share no sequence similarity between Rhilphs and Shelphs and probably appeared in the two phosphatase groups independently. Like in Rhilphs, a region corresponding to β8, β9 and α9 of eukaryotic PPPs is absent in Shelphs, and the primary structure of the region corresponding to the β12-β13 loop is similar to that of typical bacterial PPPs. A.thaliana SLP1 and corresponding Shelph isoform from rice have chloroplast targeting sequence, which could not be detected in A.thaliana SLP2 and corresponding isoform from Medicago truncatula.
Eukaryotic PPPs distantly related to bacterial diadenosine tetraphosphatases
Identification of Rhilphs and Shelphs prompted us to perform extensive searches of eukaryotic sequence databases. These searches revealed the existence of other "bacterial-like" PPP phosphatases throughout eukaryotes. Sequences only distantly related to Rhilphs, Shelphs or any other PPP phosphatases were detected in several fungi (including a putative S. cerevisiae phosphatase reported previously [23]), in Trypanosomatidae, a tick Amblyomma, an ascidian Ciona, Chlamydomonas, pine and diatoms Fragilariopsis cylindrus and Phaeodactylum tricornutum. Blast searches using these sequences revealed that all of them share higher similarity to bacterial diadenosine tetraphosphatases (ApaH) than to other PPP groups. Therefore, we tentatively designate them as ApaH-like phosphatases, or Alphs (Figures 3 and 4; Table 1; partial sequences available for pine and diatoms are shown in Figure 5).
Alphs share a distinctive common structural feature. In the GDXXDRG motif, absolutely conserved in other PPPs, the second Asp (which stabilises the protonation of a His that directly participates in catalysis [20]) is replaced by a neutral amino acid, and the Arg residue (which coordinates phosphate [24]) is replaced, with one exception, by Lys (Figure 3). The former of these replacements is also found in ApaH, while the latter is unique to Alphs. While higher overall sequence similarity and a common alteration in the GDXXDRG motif are compatible with closer relatedness of Alphs to bacterial diadenosine tetraphosphatases, phylogenetic analysis using full length sequences failed to produce a robust tree due to high sequence diversity (not shown).
Relationship of novel eukaryotic PPP groups to known PPP phosphatases
In order to better understand the relationship of "bacterial-like" PPP phosphatases in eukaryotes to each other and to bacterial PPPs, we attempted to extend our previous phylogenetic analysis of eukaryotic PPP phosphatases [25] by including PPP sequences from a number of bacteria and archaea. Primary structures of bacterial PPP phosphatases are extremely diverse and, outside the relatively conserved N-terminal subdomain of about 100 amino acids containing the GDXHG, GDXXDRG and GNH(E/D) motifs, they share only a few conserved residues. Moreover, many of the sequences have long insertions at different positions. This leads to the failure to produce informative alignments of full-length catalytic domains. Therefore, we aligned more conserved N-terminal subdomains only, an approach applied previously by Kennelly [6] to a much smaller set of PPP sequences available at that time. Phylogenetic reconstruction was attempted with either neighbor-joining (as implemented in PHYLIP [26] or SplitsTree [27]) or maximum likelihood analysis using quartet puzzling (TreePuzzle [28]); in the latter case a smaller dataset consisting of with consisting of 32 representative sequences was analyzed due to the inability of the algorithm to handle large datasets. Due to the relatively short length of the sequences and their high diversity, some of the major clades did not receive significant bootstrap support and were different depending on the method used, although most major clades, including Rhilphs and Shelphs, were recovered by both methods. Alphs tended to be grouped together by neighbor-joining but were split into smaller clades when maximum likelihood analysis was used. However, we still tentatively consider Alphs as a single group due to the characteristic replacements in their catalytic centre.
To circumvent the ambiguity of the results, we used Neighbor-Net [29], a neighbor-joining based method that constructs phylogenetic networks rather than trees and thus represents conflicting signals and visualises feasible trees in a single plot (Figure 4; for a high-resolution image, see Additional file 1). The Neighbor-Net analysis accurately identified the major clades such as eukaryotic and archaeal phosphatases, as well as their closer relationship to each other than to bacterial PPPs [6]. Separation of "conventional" eukaryotic PPPs into two branches, suggested previously from the analysis of the full-length catalytic domains [25], was also recovered. As it was suggested by initial sequence similarity searches, Rhilphs, Shelphs and Alphs represent distinct major clades of the PPP family, as divergent from "conventional" eukaryotic and archaeal PPP phosphatases as they are from major bacterial clades (Figure 4; Additional file 1).
Common structural elements in all "bacterial-like" PPP phosphatases from eukaryotes and bacterial phosphatases
C-terminal regions of the catalytic domain of all "conventional" eukaryotic PPP phosphatases share a highly conserved (with minor variations) SAPNY motif, located in the β12-β13 loop. This loop and the Tyr residue of the SAPNY motif in particular are implicated in interaction with regulators and inhibitors [21,30-32]. β strands (β9 and β10) corresponding to β12 and β13 are conserved in λPP [19]. However, the sequence on the C-terminal side of β9 is dissimilar to SAPNY in λPP and in bacterial PPPs. A conservative replacement of the first Ser of SAPNY by Thr is found in many bacterial sequences (this Thr is however only moderately conserved and is replaced by Glu or Gln in all Rhilphs and by Val or Phe in most Shelphs). The two adjacent positions are occupied by highly conserved Asp and Gly residues, respectively, thus defining a previously unrecognised motif (I/L/V)D(S/T)G. This motif is present in all examined bacterial PPPs, as well as in all "bacterial-like" phosphatases from eukaryotes described above (Figure 5; see also Additional file 2 for a more complete alignment).
In addition, we note the presence of another characteristic feature of bacterial and "bacterial-like" PPPs: the His residue (H248 in PP1; H186 in λPP) coordinating one of the metal ions in the catalytic centre is preceded by an absolutely conserved Gly; this residue is conserved in some archaeal but not in "conventional" eukaryotic PPPs (Figure 5; Additional file 2).
Discussion
In this report, we have documented the presence in different eukaryotic lineages of the genes that encode PPP phosphatases resembling those of bacterial origin, rather than "conventional" eukaryotic members of the family. Catalytic domains of these "bacterial-like" phosphatases are characterised by relatively conserved structure of the N-terminal subdomains, but very diverse organisation of the C-terminal subdomains, where conserved motifs and residues forming the active centre are separated by sequences of various length that share little or no similarity between different clades (Figure 6). In most cases, corresponding EST sequences could be detected, which confirms that these genes are expressed.
Figure 6 Schematic diagram depicting organisation of the catalytic domains of the phosphatase groups discussed in this study. N-terminal subdomains (used in the alignment for the Neighbor-Net analysis, Figure 4) and C-terminal subdomains are shown in red and yellow, respectively. Positions of the conserved motifs in PPλ and the residues forming the active centre (underlined; shown according to ref. [19]) are shown. Positions of the LXXAXPXXH motif in plant Rhilphs and related phosphatases from Rhizobiales are indicated (green boxes). For more detailed information on the position of inserts in Rhilphs and Shelphs relative to the elements of the secondary structure, see Figures 1 and 2, respectively.
The most conspicuous presence of "bacterial-like" PPPs has been detected in plants. Plants possess phosphatases from all three novel groups described in this work. Rhilphs are most closely related to PPP phosphatases from a number of α-proteobacteria, including purple photosynthetic bacteria and Rhizobiales. The absence of related sequences in eukaryotes other than land plants suggests that Rhilphs may have been acquired after plants started colonising land. Although bacterial lineage that could be the source for plant Rhilphs could not be unambiguously identified by phylogenetic reconstruction, Rhizobiales (or purple photosynthetic bacteria from which Rhizobiales are thought to have originated [33,34]) appear to be likely candidates. Indeed, Rhizobiales have transmissible chromosomal elements and some, like Agrobacterium, are able to integrate their plasmid genes into plant genome [35] or even transform animal cells [36], a situation that would be ideally suited for a horizontal gene transfer to occur. Interestingly, the presence of genes of rhizobial origin has been detected in plant parasitic nematodes [37]. Possible origin of plant Rhilphs from α-proteobacterial phosphatases is also supported by the presence in the enzymes of both groups of characteristic inserts in similar positions, which share some sequence similarity (see Results).
Phosphatases of another group, designated as Shelphs, are found in green plants, in a red alga, in Apicomplexa, Trypanosomatidae, as well as in some fungi. The similarity between proteins from Apicomplexa and Trypanosomatidae and those from plants is well documented. Trypanosomatidae are related to photosynthetic euglenoids and are thought to have lost plastids secondarily [38]. Apicomplexan parasites have a relict plastid, originated from the engulfment of a red alga [39]. Thus, the presence of phosphatases shared by plants, red algae, Apicomplexa and Trypanosomatidae is not surprising and probably reflects the presence of Shelphs in a common ancestor of photosynthetic eukaryotes. The presence of chloroplast targeting sequence in SLP1 suggests a possible origin of Shelphs from a bacterial precursor of the chloroplast (however it should be noted that Shelphs are absent from cyanobacteria); alternatively, this sequence may have appeared secondarily. Protein Ser/Thr phosphorylation / dephosphorylation is essential for regulation of photosynthesis, and unidentified okadaic acid-insensitive protein phosphatases in chloroplasts have been reported [40,41]. SLP1 appears to be a good candidate for such a phosphatase.
The origin of fungal Shelphs is unclear. Curiously, they are found in basidiomycetes and in an ascomycete S. pombe, but not in a number of other ascomycetes, whose genomes have been completed. Current data do not permit to discriminate between (i) the presence of Shelphs in a common ancestor of eukaryotes and their loss in such lineages as animals and many fungi, and (ii) independent acquisition of Shelphs from bacteria by an ancestor of photosynthetic eukaryotes and by fungi. Further sequencing of eukaryotic genomes may shed light on the evolutionary history of this PPP group.
The third group of "bacterial-like" phosphatases detected in eukaryotes, designated here as Alphs, appears to be distantly related to bacterial diadenosine tetraphosphatases ApaH. Patchy distribution in several eukaryotic kingdoms suggests that Alphs were probably present in the common ancestor of eukaryotes, but were independently lost in many lineages, including insects, vertebrates and flowering plants. A characteristic modification of the conserved GDXXDRG motif shared only with ApaH further supports a suggestion that Alphs may represent a divergent branch of diadenosine tetraphosphatases, rather than protein phosphatases. However, relatedness of eukaryotic Alphs to bacterial diadenosine tetraphosphatases remains hypothetical, since Alph sequences are too divergent from ApaH, as well as from each other, to permit a reliable phylogenetic reconstruction.
Diadenosine oligophosphates are considered as emerging signalling molecules in both intra- and intercellular signalling in eukaryotes [42,43]. In particular, human diadenosine oligophosphate hydrolase FHIT has been identified as a tumor suppressor [44]. It seems plausible that appearance of eukaryotic diadenosine oligophosphate hydrolases (structurally unrelated to the PPP phosphatases) may have made bacterial-type diadenosine tetraphosphatases redundant, leading to their loss in many eukaryotic lineages. It would be interesting to test experimentally whether Alphs are indeed diadenosine oligophosphatases.
More generally, an important implication of our findings is that many eukaryotes possess PPP phosphatases with yet undetermined substrate specificity. Eukaryotic PPP phosphatases are generally considered as Ser/Thr specific in vivo, although they may be able to dephosphorylate phosphoTyr-containing substrates in vitro (e.g. [45,46]). This is probably true for archaeal PPPs as well [5]. However, Ser/Thr specificity is not a feature of bacterial PPP phosphatases [7-9,13,47-49]. Thus, it would not be possible to predict substrate specificity of uncharacterised "bacterial-like" PPP phosphatases without experimental evidence. In particular, since Shewanella PPI is Tyr-specific [8], it would be interesting to determine substrate specificity of eukaryotic Shelphs. It is also worth noting that interest in tyrosine phosphorylation in plants has recently been stimulated by identification of plant Tyr phosphatase genes and by the finding that Tyr phosphorylation is involved in the regulation of stomatal movement (reviewed by Luan [50]).
Three motifs, GDXHG, GDXXDRG and GNH(E/D) form the diagnostic signature of all PPP phosphatases [10,11]. We detected a (I/L/V)D(S/T)G motif, which appears to be a characteristic signature of "bacterial"-type PPPs. The existence of such a motif is striking per se, taking into account extreme structural diversity of bacterial PPP phosphatases. It indicates that (I/L/V)D(S/T)G was probably present as the fourth "universal" signature motif in the common ancestor of PPP phosphatases, and was lost in the common lineage of archaeal and "conventional" eukaryotic PPPs. An alternative possibility could be that the (I/L/V)D(S/T)G motif was acquired by a bacterium and propagated by lateral gene transfer, replacing the ancestral SAPNY-related motif. However this scenario seems less likely, since the (I/L/V)D(S/T)G motif is present, with minor variations, in virtually all bacterial phosphatases, despite their great diversity, and is replaced by SAPNY-related sequences only in archaeal and "conventional" eukaryotic PPPs.
The Asp residue in the 2nd position of (I/L/V)D(S/T)G is highly conserved and can only be replaced by Glu, indicating that the negative charge is essential. The presence of a highly conserved Gly in the 4th position indicates that flexibility of the polypeptide chain is likely to be important. The crystal structure of bacteriophage λ phosphatase (PPλ; [19]) shows that the Asp residue of (I/L/V)D(S/T)G (Asp202) is just downstream of the β9 strand, which corresponds to the β12 strand in mammalian PP1. In PPλ, Asp202 is hydrogen bonded to a water molecule coordinated to one of the metal ions in the catalytic centre, which probably accounts for its conservation. In eukaryotic or archaeal PPPs, corresponding position is occupied by neutral residues (see Figure 5).
It would be tempting to speculate that this difference in the region just downstream of the β9 (β12) may be responsible for a feature that is common to all "bacterial"-type but not to eukaryotic / archaeal PPPs. One such feature is the Ser/Thr specificity of the latter group. The Tyr residue of the SAPNY motif has been suggested to provide a bulky phenol ring in the β12-β13 loop, sufficient to sterically block access of phosphoTyr-containing substrates to the active site [32]. However this is unlikely to be the sole determinant of Ser/Thr specificity, since residues containing bulky aromatic rings (Tyr, Phe or Trp) are found in the same or adjacent positions in many bacterial phosphatases (Figure 5). Since the (I/L/V)D(S/T)G motif, absent in eukaryotic and archaeal PPPs, is involved in organisation of the catalytic centre [19], it is possible that this difference in the catalytic centre organisation may be one of the determinants of broad substrate specificity vs. Ser/Thr specificity.
Conclusions
So far, eukaryotic PPP phosphatases were considered as a well-defined monophyletic group of enzymes, specifically dephosphorylating phosphoSer and phosphoThr, while a much more structurally and enzymatically diverse PPP phosphatases were known to be present in prokaryotes. Our findings demonstrate that, in addition to "conventional" eukaryotic PPP Ser/Thr-specific protein phosphatases, many eukaryotes possess very diverse "bacterial-like" PPP phosphatases. Enzymatic characteristics, physiological roles and evolutionary history of these phosphatases have yet to be revealed.
Methods
Detection of PPP phosphatase-coding sequences
Sequence similarity searches were conducted using BlastP or TBlastN [51] at NCBI [52] in the following databases: "non-redundant" (NR), "expressed sequence tags" (EST), "genomic sequence survey" (GSS) and "high-throughput genomic sequences" (HTGS). Additional Blast searches of the following databases were performed: finished and unfinished genomes of eukaryotes at the NCBI [53]; fungal genomes at the Broad Institute [54]; plant genomes at The Arabidopsis Information Resource (TAIR) [55]; Gene Index databases of tentative consensus sequences (EST assemblies) at The Institute for Genomic Research (TIGR) [56]; Chlamydomonas reinhardtii draft genome [57]. In all cases, reciprocal searches were used, i.e. hits retrieved by Blast searches were in their turn used as queries in the following Blast searches. Accuracy of gene prediction was examined by comparison of the retrieved sequences with translations of corresponding EST entries. In the absence of available ESTs, closely related sequences from other species were used. Taxonomy of the species from which the phosphatase sequences is given according to the NCBI taxonomy web site [58].
Phylogenetic analysis
Multiple alignments were generated using CLUSTAL W [59] at Kyoto University Bioinformatics Centre [60] and edited manually. During manual editing, particular attention was paid to correct alignment of the PPP family signature motifs and other conserved residues known to constitute the catalytic site of PPP phosphatases. Phylogenetic tree construction by the neighbor-joining method [61] and bootstrap analysis were performed using the PHYLIP package, version 3.573 [26]. Maximum likelihood analysis was performed using TreePuzzle [28]. Possible alternative neighbor-joining based phylogenies were visualised using Neighbor-Net [29] as implemented in SplitsTree, version 4.β10 [62]
Analysis of the primary structure
The presence of signal peptides and targeting sequences was analyzed using TargetP [63] at the the Centre for Biological Sequence Analysis, Technical University of Denmark [64].
Abbreviations
EST, expressed sequence tag, Alph, ApaH – like phosphatase; PPP, protein phosphatases of the P family; Rhilph (RLP), Rhizobiales / Rhodobacterales / Rhodospirillaceae – like phosphatase; Shelph (SLP), Shewanella – like phosphatase.
Authors' contributions
Both authors contributed equally to this work.
Supplementary Material
Additional File 1
Neighbor-Net analysis of the conserved N-terminal subdomains (starting 5 amino acid residues before conserved GDXHG and ending 25 residues after GNH(E/D) of 104 bacterial, archaeal and eukaryotic PPP phosphatases. This version of Figure 4 is the original SplitsTree file that can be viewed using SplitsTree, freely available for download (see Methods). Bootstrap values (out of 100 resamplings) are shown and can be highlighted by selecting corresponding alternative splits. The file also contains the alignment used for the analysis (Input).
Click here for file
Additional File 2
Distinct conserved motifs in the C-termini of bacterial and "bacterial-like" PPP phosphatases from eukaryotes as opposed to archaeal and eukaryotic PPP phosphatases. This is an expanded version of Figure 5.
Click here for file
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| 15555063 | PMC535813 | CC BY | 2021-01-04 16:29:00 | no | BMC Evol Biol. 2004 Nov 19; 4:47 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-47 | oa_comm |
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J NeuroinflammationJournal of Neuroinflammation1742-2094BioMed Central London 1742-2094-1-231554648910.1186/1742-2094-1-23Researchβ-Amyloid promotes accumulation of lipid peroxides by inhibiting CD36-mediated clearance of oxidized lipoproteins Kunjathoor Vidya V [email protected] Anita A [email protected] Lea A [email protected] Tayeba [email protected] Kathryn J [email protected] Lipid Metabolism Unit, Dept. of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114 USA2004 16 11 2004 1 23 23 8 10 2004 16 11 2004 Copyright © 2004 Kunjathoor et al; licensee BioMed Central Ltd.2004Kunjathoor 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
Recent studies suggest that hypercholesterolemia, an established risk factor for atherosclerosis, is also a risk factor for Alzheimer's disease. The myeloid scavenger receptor CD36 binds oxidized lipoproteins that accumulate with hypercholesterolemia and mediates their clearance from the circulation and peripheral tissues. Recently, we demonstrated that CD36 also binds fibrillar β-amyloid and initiates a signaling cascade that regulates microglial recruitment and activation. As increased lipoprotein oxidation and accumulation of lipid peroxidation products have been reported in Alzheimer's disease, we investigated whether β-amyloid altered oxidized lipoprotein clearance via CD36.
Methods
The availability of mice genetically deficient in class A (SRAI & II) and class B (CD36) scavenger receptors has facilitated studies to discriminate their individual actions. Using primary microglia and macrophages, we assessed the impact of Aβ on: (a) cholesterol ester accumulation by GC-MS and neutral lipid staining, (b) binding, uptake and degradation of 125I-labeled oxidized lipoproteins via CD36, SR-A and CD36/SR-A-independent pathways, (c) expression of SR-A and CD36. In addition, using mice with targeted deletions in essential kinases in the CD36-signaling cascade, we investigated whether Aβ-CD36 signaling altered metabolism of oxidized lipoproteins.
Results
In primary microglia and macrophages, Aβ inhibited binding, uptake and degradation of oxidized low density lipoprotein (oxLDL) in a dose-dependent manner. While untreated cells accumulated abundant cholesterol ester in the presence of oxLDL, cells treated with Aβ were devoid of cholesterol ester. Pretreatment of cells with Aβ did not affect subsequent degradation of oxidized lipoproteins, indicating that lysosomal accumulation of Aβ did not disrupt this degradation pathway. Using mice with targeted deletions of the scavenger receptors, we demonstrated that Aβ inhibited oxidized lipoprotein binding and its subsequent degradation via CD36, but not SRA, and this was independent of Aβ-CD36-signaling. Furthermore, Aβ treatment decreased CD36, but not SRA, mRNA and protein, thereby reducing cell surface expression of this oxLDL receptor.
Conclusions
Together, these data demonstrate that in the presence of β-amyloid, CD36-mediated clearance of oxidized lipoproteins is abrogated, which would promote the extracellular accumulation of these pro-inflammatory lipids and perpetuate lipid peroxidation.
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Background
Hypercholesterolemia is an established risk factor for atherosclerosis and a number of recent epidemiological studies have suggested a link between increased circulating cholesterol levels and Alzheimer's disease (AD) [1]. Lipoproteins in the serum and the central nervous system (CNS) mediate cholesterol homeostasis through the delivery and removal of cellular cholesterol. With hypercholesterolemia, these phospholipid and cholesterol rich-particles accumulate abnormally outside the arterial lumen, where they are susceptible to oxidization [2]. Lipoprotein-derived oxidation products (hydroperoxides, lysophosphatidylcholine, oxysterols and aldehydes) initiate the inflammatory response that drives atherosclerotic plaque formation in the artery wall, and these lipid peroxidation products, including malondialdehyde and 4-hydroxynonal (HNE), have also been detected in AD-affected brains [3,4]. AD patients have been reported to have cholesterol profiles known to be pro-atherosclerotic, including increased total serum and low-density lipoprotein (LDL) cholesterol, and increased susceptibility to lipoprotein oxidation [5-9]. Antibodies raised against oxidized LDL (oxLDL) demonstrate reactivity to amyloid plaques and surrounding tissue, indicating that lipid peroxidation epitopes present in oxLDL accumulate in the brains of AD patients [3]. Recently, oxidized cholesterol metabolites identified in both atherosclerotic and senile plaques have been found to accelerate β-amyloid fibril formation [10]. Together, these findings suggest that, as in atherosclerosis, the accumulation of lipoprotein oxidation products in Alzheimer's disease may contribute to chronic inflammation.
Phagocyte expressed pattern recognition receptors (PRR) are the first line of defense of the innate immune system against foreign or modified proteins and lipids. Scavenger receptors are pattern recognition receptors that bind and internalize a wide range of ligands, including certain polyanions, modified forms of LDL, advanced glycation endproducts and apoptotic cells [11]. These receptors are expressed by macrophages and microglia, and are the primary clearance pathway for pro-inflammatory oxidized lipoproteins [12]. In addition to binding oxLDL, several members of the scavenger receptor A (SRA) and B (CD36, SR-B1) class recognize fibrillar β-amyloid (Aβ), which accumulates in the brain and cerebral blood vessels in AD, as well as in coronary atherosclerotic plaques [13-15]. While studies in Sra null mice have failed to show a role for this receptor in the pathogenesis of AD [16], it has recently been demonstrated in our lab, and others, that Aβ activates an inflammatory signaling cascade via CD36 that regulates microglial activation and recruitment in the brain [17-19]. In AD patients, increased CD36 expression was detected in the frontal cortex which correlated with the presence of amyloid plaques and oxidative markers, suggesting that upregulation of this scavenger receptor pathway may also promote inflammation in vivo [20]. Similar to its role in peripheral macrophages, CD36 on microglia is believed to scavenge modified proteins and oxidized phospholipids. We hypothesized that a simultaneous increase in lipoprotein oxidation and accumulation of Aβ in the brain and blood vessels in AD might compromise the ability of this scavenger receptor to effectively clear these modified host ligands.
Aβ has previously been shown to reduce uptake of LDL modified by acetylation, in microglia and SRA- or SR-B1-transfected cells [21]. We have shown that CD36 binds acetylated LDL with very low affinity, indicating that these studies primarily addressed the impact of Aβ on Class A scavenger receptor activity [12]. Unlike SR-A, which binds the modified apolipoprotein B component of acetylated LDL, CD36 recognizes oxidized phospholipids within the oxidized lipoprotein particle [22]. CNS lipoproteins isolated from cerebrospinal fluid, astrocytes or microglia, contain similar amounts of phospholipid, cholesterol, and cholesteryl ester content as their serum counterparts, and a pro-oxidative environment in Alzheimer's disease is believed to accelerate the formation of lipid peroxides in these particles [23]. In this study, we assessed the impact of Aβ on the binding and degradation of oxLDL via CD36, SR-A and CD36/SR-A-independent pathways. The availability of mice genetically deficient in Sra and Cd36 has facilitated studies to discriminate the actions of these individual scavenger receptors. We show that Aβ dose-dependently inhibits oxLDL binding, lysosomal degradation and cholesterol ester accumulation in macrophages and microglia. This inhibitory effect was mediated specifically via CD36 and could be reversed by removal of extracellular Aβ, indicating that the lysosomal degradation pathway was not directly impaired. Furthermore, activation of CD36-signaling by Aβ did not mediate this inhibitory effect, as targeted inactivation of essential downstream kinases did not restore oxLDL degradation. Together, these data demonstrate that Aβ impairs the ability of CD36 to scavenge oxidized lipids by competing for receptor binding. This suggests that accumulation of Aβ in the brain and vessel wall in AD would inhibit the clearance of pro-inflammatory oxidized phospholipids and oxidized-phospholipid-containing particles such as lipoproteins, thereby promoting lipid peroxidation.
Methods
β-Amyloid
Aβ1-42 and reverse Aβ42-1 (revAβ) peptides were obtained from Biosource International (Camarillo, California). To induce fibril formation, Aβ1-42 was resuspended in H2O at 1 mg/ml and incubated for 1 week (37°C) and fibril formation was confirmed by thioflavine S (Sigma-Aldrich Co., St. Louis, Missouri) fluorescent staining as we previously described [17,18].
Mice
The Cd36-/- mice were generated in our laboratory as previously described [17] and SraI/II null (Sra-/-) mice were generously provided from Dr. T. Kodama (University of Tokyo, Japan) [24]. Both mouse lines were backcrossed to C57BL/6 mice for 7 generations (98.6% C57BL/6) prior to intercrossing to generate mice lacking both Sra and Cd36. Double knockout mice (Sra-/-/Cd36-/-) were generated from heterozygote intercrosses at the expected ration of 1:16. Wild type age-matched C57BL/6 mice (The Jackson Laboratory, Bar Harbor, Maine) were used as controls for these three lines. Lyn-/- and Fyn-/- mice were obtained from The Jackson Laboratory and Lyn-/-, Fyn-/- and wild type littermate control mice were generated from heterozygote intercrosses. All mice were maintained in a pathogen-free facility with free access to rodent chow and water. All experimental procedures were carried out in accordance with Massachusetts General Hospital's institutional guidelines for use of laboratory animals.
Primary macrophage and microglial culture
Macrophages were collected from 6–8 week old mice by peritoneal lavage 4 days after i.p. injection with 3% thioglycollate as we previously described [17,25]. Cells were washed in PBS, cultured for 2 h in DMEM with 5% FCS, and washed again to remove non-adherent cells. Adherent cells were incubated in DMEM with 1% FCS overnight prior to use and were routinely >95% CD11b+ and F4/80+ as determined by flow cytometric analysis. Primary microglia were prepared from mixed brain cultures of neonatal mice as we previously described [17]. Briefly, whole brains were incubated in 0.25% trypsin and 1 mM EDTA (10 min, 25°C) and dissociated to obtain a single cell-suspension. Cells were washed in HBSS (4x, 10 min) and cultured in DMEM containing 10% FCS, 1% Fungizone for 10–12 days. Microglia accumulating above astrocyte monolayers were collected after gentle agitation, washed and incubated in DMEM with 1% FCS overnight prior to use. Microglia prepared in this manner were routinely >95% CR3+ and express SR-A and CD36 [14,17,18].
Lipoproteins
Human 125I-LDL and LDL (d = 1.019 - 1.063) were purchased from Biomedical Technologies (Stoughton, Massachusetts) and oxidized as we previously described [12,26]. LDL was diluted to 250 μg/ml, dialyzed against PBS at 4°C to remove EDTA, and then dialyzed against 5 μM CuSO4 in PBS at 37°C for 6 or 10 h. Oxidation was terminated by the addition of 50 μM butylated hydroxytoluene and 200 μM EDTA and oxLDL was used within 2 days of preparation. Moderately oxidized LDL (6 h oxidation) had a relative electrophoretic mobility of approximately 2.5–3 times that of native, unmodified LDL, whereas extensively oxidized LDL (10 h oxidation) had a relative mobility four times that of native LDL.
125I-OxLDL degradation, binding and uptake assays
Measurement of 125I-oxLDL binding, degradation and uptake was performed on confluent monolayers of peritoneal macrophages (7 × 105) and microglia (5 × 105) in 24 well plates as we previously described [12,26]. Briefly, 10 μg/ml of 125I-oxLDL was added to cells in the presence or absence of 30-fold excess unlabeled oxLDL, native LDL, Aβ1-42, or revAβ peptide for 5 h at 37°C. To measure 125I-oxLDL degradation, media were removed and assayed for TCA-soluble non-iodide degradation products. To measure 125I-oxLDL binding in the presence Aβ1-42 or revAβ, cells were washed 3x with 50 mM Tris pH 7.4, 0.15 N NaCl and 2 mg/ml BSA, 1x with 50 mM Tris pH 7.4 and 0.15 N NaCl and treated with 0.4% dextran sulfate to release surface bound 125I-oxLDL [27]. To measure 125I-oxLDL uptake, cells were washed 3x in 50 mM Tris pH 7.4 and 0.15 N NaCl, lysed in 0.1 N NaOH and assayed for 125I and cellular protein content. In some experiments, cell-association of oxLDL (cell-surface bound and endocytosed oxLDL) was measured by omitting the dextran sulfate treatment. Cellular protein content was measured by BCA assay (Pierce, Rockford, IL) and degradation, binding and uptake activity are expressed as ng 125I-oxLDL/mg protein. Specific degradation was calculated as the difference of total cellular degradation of 125I-oxLDL in the presence and absence of 30-fold excess unlabelled oxLDL competitor. All measurements were performed in triplicate and are representative of at least 3 experiments.
Analysis of cellular cholesterol content
Macrophages and microglia were cultured with 40 μg/ml of oxLDL for 48 h in the presence or absence of Aβ1-42 or revAβ. Cholesterol ester accumulation was assessed by gas chromatography-mass spectrometry (GC-MS) and oil red O staining as we previously described [12,26]. For GC-MS analysis, lipids were extracted with hexane:isopropanol (3:2) and stigmasterol (Sigma, St. Louis, Missouri) was added as an internal standard. Lipid extracts were washed once with water and divided equally. One lipid aliquot was saponified for determination of total cholesterol and the second aliquot analyzed for free cholesterol using gas chromatography-mass spectrometry. The samples were injected (splitless) into an Agilent 6890 GC-MS-(G2613A system, Agilent Technologies, Palo Alto, CA) equipped with a J&W DB17 fused silica capillary column (15 m × 0.25 mm inner diameter × 0.5 μm; J&W Scientific, Folsom, CA). The GC temperature program was as follows: the initial temperature was 260°C for 5 min, then increased to 280°C (5°C/min) and held 280°C for 11 min. A model 5973N mass-selective detector (Agilent Technologies) was used in scan modes to identify the samples. Cholesterol measurements were made in triplicate and normalized to cellular protein content. Cholesterol ester content was calculated by subtracting free cholesterol from total cholesterol measured after saponification. To assess neutral lipid accumulation, cells were fixed in 4% paraformaldehyde and stained with oil red O for 30 min. Staining was recorded on an Olympus X10 microscope equipped with a digital camera.
Real time RT-PCR analysis
Total RNA was extracted using Trizol B reagent and real-time quantitative RT-PCR (QRT-PCR) was performed using the QuantiTect SYBR Green PCR kit (Qiagen Inc, Valencia, CA) as we previously described [17,18]. Each reaction contained 0.3 μM of CD36, SRA or GAPDH primers, 3 μl of cDNA, SYBR Green, and HotStarTaq polymerase. PCR was performed using a BioRad iCycler under the following conditions: 15 min at 95°C, followed by 30 cycles of 30 sec at 95°C, 30 sec at 55°C and 30 sec at 72°C. Each sample was analyzed in triplicate and the amount of CD36, SRA and GAPDH mRNA in each sample was calculated from a standard curve of known template. Data are expressed as the mean number of CD36 and SRA molecules normalized to GAPDH.
Western analysis
Cells were washed in ice-cold PBS and lysed in radioimmune precipitation buffer containing protease and phosphatase inhibitors. For detection of CD36, 30 μg of protein was run on an 8% denaturing SDS-polyacrylamide gel, transferred to nitrocellulose and blocked overnight in 5% nonfat dry milk and 3% BSA in Tris-buffered saline containing 0.1% Tween 20 (TBS-T) as we previously described [17,26]. Membranes were incubated with a rabbit anti-CD36 antiserum (1:500 dilution) generated in our laboratory [17] for 2 hours, washed three times in TBS-T, and incubated with horseradish peroxidase-conjugated anti-rabbit IgG (1:10,000 dilution) for 1 hour. Blots were washed 3x in TBS-T, exposed to ECL reagent (Amersham Biosciences, Piscataway, NJ), and signal was recorded and quantified using an Alpha Innotech Fluorchem 8800 image analysis system. Blots were stripped and probed with an anti-actin rabbit polyclonal antibody (Santa Cruz Biotechnology) as described above as an internal standard for equivalent loading.
Results
β-Amyloid blocks oxidized LDL metabolism and cellular cholesterol accumulation in macrophages and microglia
Treatment of peritoneal macrophages with Aβ1-42, but not revAβ, dose-dependently inhibited lysosomal degradation of 125I-oxLDL (Fig. 1a). Half-maximal inhibition of macrophage 125I-oxLDL degradation was achieved with 10 μM Aβ1-42. This was equivalent to the inhibitory effect of 15-fold excess of unlabelled oxLDL competitor (Fig. 1b). At 20 μM, Aβ1-42 reduced macrophage degradation of 125I-oxLDL by up to 90%, while treatment with the same concentration of non-fibrillar revAβ peptide reduced degradation by only 10%, and this concentration was selected for all further experiments. Because engulfment of Aβ1-42 has previously been reported to disrupt endosomal/lysosomal integrity in a neuronal cell line [28], we investigated whether the observed reduction in oxLDL degradation could be attributed to lysosomal accumulation of Aβ1-42 which occurs within 1 h of treatment. After exposure to Aβ1-42 for 3 hours, macrophages were washed extensively to remove extracellular Aβ1-42 and exposed to 125I-oxLDL or 125I-oxLDL + Aβ1-42 for 5 h. While cells continuously exposed to Aβ1-42 showed a profound impairment of oxLDL degradation, cells pre-treated with Aβ1-42 were similar to untreated and revAβ-treated cells, indicating that intracellular accumulation of Aβ1-42 does not block subsequent lysosomal degradation of oxLDL (Fig. 1c).
Figure 1 Aβ inhibits lysosomal degradation of oxidized LDL and cholesterol ester accumulation in macrophages. A. Fibrillar Aβ, but not revAβ, dose-dependently inhibits lysosomal degradation of 125I-oxLDL by macrophages, similar to unlabeled oxLDL competitor (B). C. Intracellular accumulation of Aβ does not block lysosomal degradation of 125I-oxLDL. Macrophages were pretreated with 20 μM Aβ or revAβ for 3 hours to allow intracellular accumulation, washed extensively to remove extracellular peptide and degradation of 125I-oxLDL over 5 h was measured in the absence (PT) or presence of additional peptide. D. Aβ blocks cholesterol ester accumulation in oxLDL treated macrophages. Cellular lipids were extracted from macrophages treated with oxLDL (40 μg/ml) for 48 h in the presence or absence of 20 μM Aβ and analyzed by gas-chromatography mass-spectrometry. Cholesterol ester content was normalized to cellular protein. (A-D) Data are the mean of triplicate samples ± standard deviation, *p ≤ 0.005.
The inhibition of 125I-oxLDL degradation by Aβ1-42 would be predicted to reduce cellular cholesterol ester accumulation. Excess unesterified "free" cholesterol is cytotoxic and is thus rapidly converted by the microsomal enzyme acyl-coenzyme A:cholesterol acyltransferase (ACAT) to cholesterol ester for storage. This neutral lipid is retained in cytoplasmic lipid droplets for storage and/or efflux from the cell. Using gas chromatograpy-mass spectrometry, we quantified the cholesterol ester content of macrophages treated with oxLDL in the presence and absence of Aβ1-42. As expected, untreated cells did not contain measurable cholesterol ester, while macrophages treated with 40 μg/ml oxLDL for 48 h accumulated approximately 80 μg cholesterol ester/mg cellular protein (Fig. 1d). By contrast, macrophages treated with both oxLDL and Aβ1-42 showed no measurable cholesterol ester accumulation after 48 h, similar to untreated cells.
As seen in peripheral macrophages, Aβ1-42 substantially inhibited 125I-oxLDL binding, uptake, and degradation by primary microglia indicating that it has a similar effect on lipoprotein metabolism in these two myeloid cell types (Fig. 2a,2b,2c). In the presence of 20 μM Aβ1-42, microglia demonstrated a 55% reduction in 125I-oxLDL binding, an 80% reduction in 125I-oxLDL uptake and a 95% reduction of 125I-oxLDL degradation. The absence of cholesterol ester in oxLDL treated microglia exposed to Aβ1-42 was confirmed by staining cells with the neutral lipid stain oil red O. Microglia treated with oxLDL alone demonstrate oil red O positive lipid droplets in their cytoplasm characteristic of cholesterol ester storage (Fig. 2d). However, in the presence of Aβ1-42, oxLDL treated microglia show a dramatic reduction in lipid droplets that is not seen with treatment with the same concentration of revAβ. As expected, cells treated with Aβ1-42 or revAβ alone do not accumulate cholesterol ester in the absence of exogenously added oxLDL (Fig. 2d). Similar results were observed in macrophages (data not shown). Together, these data demonstrate that Aβ blocks cholesterol ester accumulation in macrophages and microglia by inhibiting oxLDL clearance.
Figure 2 Aβ inhibits oxLDL binding, uptake and degradation in microglia. Treatment of primary microglia with 20 μM fibrillar Aβ, but not revAβ, inhibits 125I-oxLDL binding (A), cellular uptake (B) and degradation (C). Data are the mean of triplicate samples ± standard deviation, *p ≤ 0.005. (D) Microglia treated with 20 μM fibrillar Aβ fail to accumulate cholesterol ester in the presence of oxLDL. Microglia were incubated with 40 μg/ml oxLDL for 48 h in the presence and absence of 20 μM Aβ or revAβ peptide and stained with oil red O to visualize neutral lipid. Cells treated with oxLDL alone or in the presence of revAβ demonstrate the accumulation of red-stained lipid droplets in the cytoplasm. By contrast, oil red O staining is greatly reduced in oxLDL and Aβ co-treated microglia. Mag. 200X.
fAβ downregulates expression of the OxLDL receptor CD36
To address the mechanism by which Aβ1-42 inhibits oxLDL metabolism, we first evaluated cellular expression of the scavenger receptors SRA and CD36. Fibrillar Aβ1-42 reduced expression of CD36 mRNA by 40 and 60% after 6 and 24 h, respectively (Fig. 3a), but showed no effect on macrophage expression of SRA. Western blotting confirmed a 40% decrease in CD36 protein in Aβ1-42 treated macrophages (Figure 3b), which would be expected to reduce the ability of these cells to bind oxLDL.
Figure 3 Aβ downregulates expression of the oxLDL receptor CD36. A. Analysis of CD36 and SRA mRNA in peritoneal macrophages treated with Aβ (20 μM) by quantitative RT-PCR. Data represent the mean of triplicate samples ± standard deviation, *p ≤ 0.005. B. Western blot analysis confirming CD36 protein downregulation by Aβ. The signal was recorded and the integrated density value quantified using an Alpha Innotech FluorChem Imager and normalized to actin protein. Data are representative of 2 experiments.
fAβ competes for oxLDL binding to CD36, but not SRA
β-Amyloid has previously been reported to bind to the class A scavenger receptors SRA I & II and to block uptake of LDL modified by acetylation [14,21]. We employed Sra and Cd36 single null mice to investigate the role of these receptors in the inhibition of oxLDL clearance by Aβ1-42. In addition, we used Sra/Cd36 double null mice to evaluate the role of SRA/CD36-independent mechanisms, including those of additional scavenger receptor family members. Because of the difficulty of culturing sufficient numbers of primary microglia for binding and degradation experiments, studies involving knock-out mice were performed with peritoneal macrophages. In Sra-/- and wild type macrophages Aβ1-42 blocked cell association (binding and uptake) of 125I-oxLDL by greater than 50%, indicating that this scavenger receptor is not essential for the inhibitory action of Aβ (Fig. 4a). By contrast, in the absence of Cd36, impairment of 125I-oxLDL cell association by Aβ1-42 was reduced to 8%, indicating that this receptor was the primary target of Aβ1-42 inhibition (Fig. 4a).
Figure 4 Inhibition of oxLDL cell-association by Aβ requires CD36, but not CD36-associated signal transduction. A. To determine whether SRA or CD36 was essential for Aβ-inhibition of oxLDL metabolism, cell-association of 125I-oxLDL was measured in wild type, Sra-/- and Cd36-/- macrophages in the presence or absence of 20 μM Aβ. While Aβ blocked oxLDL association by approximately 50% in wild type and Sra-/- macrophages, this effect was lost in Cd36-/- macrophages indicating that CD36 is required for this inhibition. B. Inhibition of 125I-oxLDL degradation by Aβ does not utilize the Aβ-CD36 signaling pathway involving Lyn and Fyn kinases. Aβ impaired oxLDL degradation to a similar extent in wild type, and Lyn-/- or Fyn-/- macrophages in which CD36-signaling is impaired, indicating that this signal transduction pathway is not required, Data are the mean of triplicate samples ± standard deviation, *p ≤ 0.005.
The finding that CD36 is required for Aβ1-42 inhibition of oxLDL suggests two possible mechanisms of action: (1) direct competition for CD36 binding, or (2) inhibition of oxLDL metabolism as a result of Aβ/CD36 signal transduction. To address whether CD36 signaling inhibits cellular oxLDL degradation, we used macrophages with targeted deletions in two kinases in this pathway, Lyn and Fyn, which have previously been shown to be required for CD36-mediated p44/42 activation, MCP-1 secretion and ROS production [17]. However, as in wild type macrophages, Aβ1-42 effectively inhibited 125I-oxLDL degradation in Lyn-/- and Fyn-/- macrophages, suggesting that this signaling pathway does not inhibit oxLDL metabolism (Fig. 4b). Furthermore, treatment of macrophages with the general phosphotyrosine kinase inhibitor genistein did not reverse Aβ1-42 inhibition of 125I-oxLDL degradation, confirming that phosphotyrosine kinase signaling does not mediate this effect of Aβ1-42 (data not shown). Interestingly, in untreated Fyn-/- macrophages 125I-oxLDL degradation was increased 2-fold (Fig. 4b) indicating that this kinase may play a role in regulating oxLDL uptake. However, despite a doubling of oxLDL degradation in Fyn-/- macrophages, this process was still inhibited by Aβ1-42 by up to 90%. Together, these experiments suggest that Aβ1-42 inhibition of oxLDL metabolism is not the result of CD36-Lyn/Fyn signal transduction and support the hypothesis that Aβ1-42 competes for oxLDL binding to CD36. Analysis of 125I-oxLDL cell-surface binding showed that Aβ inhibited 125I-oxLDL binding by approximately 60% in wild type macrophages (Fig. 5). This inhibitory effect was lost in Cd36-/- macrophages, confirming that Aβ inhibited oxLDL binding to this receptor. Of note, wild type macrophages bound 60% more oxLDL than macrophages lacking Cd36 as has previously been reported, and this correlated with the percentage reduction of oxLDL binding by Aβ in wild type macrophages (57%), suggesting that the CD36-dependent contribution to oxLDL binding was totally inhibited. To confirm that other myeloid scavenger receptors were not inhibited by Aβ, assesed 125I-oxLDL binding in Sra-/-Cd36-/- macrophages. No effect of Aβ was observed in these cells, demonstrating the specificity of Aβ inhibition of oxLDL binding to CD36.
Figure 5 Inhibition of oxLDL binding requires CD36, but not other scavenger receptors. Binding of 125I-oxLDL was measured in wild type, Cd36-/- or Cd36/Sra-/- macrophages in the presence or absence of 20 μM Aβ to assess the role of CD36 and CD36/SRA-independent pathways. In the absence of CD36, oxLDL binding was not reduced by Aβ, indicating that this receptor is the target of Aβ inhibition. Binding of oxLDL via other scavenger receptors, which is measurable in Cd36/Sra-/- macrophages, was not inhibited by Aβ. Data are representative of triplicate samples ± standard deviation, *p ≤ 0.005.
Discussion
Numerous studies have demonstrated elevated markers of lipid peroxidation in the brains, CSF and plasma of Alzheimer's disease patients, including thiobarbituric acid-reactive substances, 4-hydroxy-2-nonenal (HNE), acrolein and F2-isoprostanes, which are suggestive of a persistent pro-oxidant environment [3,4,9,29,30]. Lipoprotein particles are especially vulnerable to free-radical mediated lipid peroxidation and the resulting peroxy fatty acids are highly unstable, readily decomposing to form peroxy and alkoxy radicals that further promote oxidation. This self-propagating cycle of lipid peroxidation is particularly damaging in lipid-rich tissues such as the brain, and as a result, the innate immune system has evolved mechanisms to rapidly recognize and clear oxidized lipids. The myeloid scavenger receptors are the first lines of defense against these non-native lipids, as well as modified host proteins such as β-amyloid [11,31]. This dual responsibility prompted us to evaluate whether macrophages and microglia would be compromised in their ability to metabolize oxidized lipoproteins in the presence of Aβ. We found that fibrillar Aβ specifically inhibited all phases of oxLDL metabolism, including binding, uptake, degradation and accumulation of cellular cholesterol ester. This was mediated by a selective inhibition of CD36 binding by Aβ, as well as a decrease in CD36 mRNA and protein expression. However, inhibition of oxLDL metabolism was independent of the recently identified Aβ-CD36-signaling cascade, as targeted inactivation of essential downstream kinases did not restore cellular oxLDL degradation. Together, these data demonstrate that oxidized lipoprotein metabolism by CD36 is profoundly impaired in the presence Aβ, and suggest that accumulation of Aβ in the brain and blood vessels in AD would foster the extracellular persistence of these pro-inflammatory lipids, thereby perpetuating lipid peroxidation. Thus, Aβ binding of CD36 in the brain would promote inflammation via two specific mechanisms: (1) through its engagement of signal transduction and microglial recruitment, and (2) through its abrogation of this important clearance pathway for oxidized phospholipid-containing ligands.
In addition to CD36, two other scavenger receptor family members have been shown to be expressed in the brain and to bind Aβ. The Class A scavenger receptors, SRA I and II, and the class B SR-BI are expressed by neonatal microglia, but unlike CD36, these receptors are not expressed by microglia in the normal adult brain [14,15]. However, microglial expression of SRA is increased during AD, and this receptor can mediate both adherence to Aβ and its phagocytosis [14,32,33]. In Sra-/- mice, there is a 60% impairment in microglial binding of Aβ and reactive oxygen production, however, AD-associated brain pathology is not reduced [16,33]. SRA ligands, including acetylated LDL and fucoidan, reduce Aβ uptake by microglia, however these ligands may also affect other receptors [34]. Conversely, Aβ and its soluble precursor protein, sAPPα, inhibit macrophage and microglial uptake of acetylated LDL [14,21,35]. While acetylated LDL is not believed to occur physiologically, other modifications of LDL, such as oxidation, that allow binding to SRA may also be competed by Aβ. However, in our assays Aβ inhibition of oxLDL binding and degradation did not occur via this pathway, similar effects were seen in wild type and Sra-/- cells. By contrast, the effect of Aβ was abolished in the absence of CD36, indicating that this receptor is the target of Aβ action.
The difficulty in isolating human lipoproteins from the CNS has limited their experimental use, however, several groups have shown that oxidized serum lipoproteins, including LDL, HDL and VLDL, are toxic to neurons [36-39], and both oxLDL and oxidized CSF lipoproteins disrupt neuronal microtubule organization, a pathogy characteristic of the AD brain [6,38,40]. Thus, the loss of CD36-mediated oxidized lipoprotein clearance in the presence of Aβ1-42 would be predicted to foster inflammation and tissue injury. While we have shown that Aβ blocks CD36 binding of oxLDL, and its subsequent degradation, we would predict that similar results would be found with oxidized lipoproteins isolated from the CNS, astrocytes or microglia. Although serum and brain lipoprotein particles differ in their apolipoprotein composition [23,41-44], they contain similar amounts of cholesterol, cholesterol ester and phospholipid. CD36 has been shown to recognize a phospholipid moiety of oxidized lipoproteins, primarily oxidized phosphatidylcholine, which is abundant in CSF lipoproteins [22,41]. The presence of a pro-oxidant environment in AD would be expected to generate similar modifications of CSF lipoproteins and lipoproteins isolated from AD-affected individuals have, in fact, been shown to be more susceptible to oxidation [5,6]. Inhibition of the primary clearance pathway for oxidized lipoproteins would be predicted to promote inflammation and persistence of lipid peroxidation.
Disruption of oxidized lipoprotein metabolism by Aβ may also be relevant in the context of atherosclerosis. Cholesterol oxidation products generated during the inflammatory component of atherosclerosis have been shown to accelerate β-amyloid fibril formation [10,45]. Furthermore, a recent study identified Aβ advanced human atherosclerotic plaques [46]. Our data suggests that the presence of Aβ in the artery wall may both prevent macrophage oxidized LDL uptake via CD36, thereby promoting β-amyloid fibril formation and activating CD36-signaling [47]. It has recently been shown that Aβ-CD36-signaling leads to the expression of cytokines and chemokines, including IL-1β, TNFα, MCP-1, MIP-1α and β and MIP-2 [17-19]. Activation of this signaling cascade would be predicted to promote inflammation, as well as atherosclerotic plaque progression. Indeed, overexpression of a mutant human amyloid β-precursor protein in an atherosclerosis-susceptible mouse strain (B6Tg2576) led to significantly increased levels of atherosclerosis, which correlated positively with cerebral Aβ deposits [48]. Of particular interest, when these mice were maintained on a normal chow diet that did not induce atherosclerosis in wild type littermates, B6Tg2576 mice developed early atherosclerotic lesions in the aortic root, suggesting that Aβ promotes atherogenesis. The convergence of risk factors for AD and atherosclerosis suggest that these chronic inflammatory diseases may have overlapping mechanisms of pathogenesis in which cholesterol levels and lipid peroxidation play a central role.
List of abbreviations used
Aβ, β-amyloid peptide 1–42; ACAT, acyl-coenzyme A:cholesterol acyltransferase; AD, Alzheimer's disease; CSF, cerebral spinal fluid; DMEM, Dubelcco's modified Eagle medium; FCS, fetal calf serum; fAβ, fibrillar Aβ; GC-MS, gas chromatography-mass spectrometry HNE, 4-hydroxy-2-nonenal; oxLDL, oxidized low density lipoprotein; revAβ, reverse β-amyloid peptide 42-1; SRA, scavenger receptor A; SR-BI, scavenger receptor B I.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
VVK performed the measurements of 125I-oxLDL binding, uptake and degradation, and participated in the design of the study and analysis of results. LAM and TK isolated the primary microglia and macrophages, performed western blots, quantitative RT-PCR, and measurements of 125I-oxLDL binding, uptake and degradation. AAT performed measurements of 125I-oxLDL binding, uptake and degradation. KJM conceived of the study, participated in its design and wrote the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by NIH AG20255 and an award from the Ellison Foundation (KJM).
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| 15546489 | PMC535814 | CC BY | 2021-01-04 16:38:19 | no | J Neuroinflammation. 2004 Nov 16; 1:23 | utf-8 | J Neuroinflammation | 2,004 | 10.1186/1742-2094-1-23 | oa_comm |
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Theor Biol Med ModelTheoretical Biology & Medical Modelling1742-4682BioMed Central London 1742-4682-1-111554649110.1186/1742-4682-1-11ResearchApplication of methods of identifying receptor binding models and analysis of parameters Gurevich Konstantin G [email protected] UNESCO Chair in healthy life for sustainable development, Moscow State Dentistry Medical University (MSDMU), Delegatskaya street 20/1, 127473, Moscow, Russian Federation, Russia2004 16 11 2004 1 11 11 15 8 2004 16 11 2004 Copyright © 2004 Gurevich; licensee BioMed Central Ltd.2004Gurevich; 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
Possible methods for distinguishing receptor binding models and analysing their parameters are considered.
Results and Discussion
The conjugate gradients method is shown to be optimal for solving problems of the kind considered. Convergence with experimental data is rapidly achieved with the appropriate model but not with alternative models.
Conclusion
Lack of convergence using the conjugate gradients method can be taken to indicate inconsistency between the receptor binding model and the experimental data. Thus, the conjugate gradients method can be used to distinguish among receptor binding models.
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Background
Most medicinal preparations and biologically active substances do not penetrate into cells and must therefore exert their influence on intracellular processes by interaction with specific protein molecules at the cell surface [1-3], for which the name "receptors" is in common use. Hormones and drugs that interact with receptors are known as "ligands". Data from research in molecular biology, and also results from indirect studies, have established the following schemes of ligand-receptor interaction [see [4-6] represented by the general models:
Non-cooperative interaction between ligand and receptor:
where R is the receptor molecule, L is the ligand molecule, RL is the ligand-receptor complex, and k+1 and k-1 are respectively the kinetic constants of formation and dissociation of the complex.
Cooperative interaction between ligand and receptor
Interaction of one ligand with N types of binding sites
Let us note that the ligand-receptor interaction can also involve a combination of all three of these schemes. The most frequently used method for studying ligand-receptor interactions is the radioreceptor method [7], based on measuring the amount of radioactively labelled ligand bound in some defined manner to the appropriate receptor. Thus, experimentally, direct measurements of ligand-receptor complex concentration, [RL] are determined. The investigator has to solve two basic interrelated problems [6]:
1. discrimination among the ligand-receptor binding models (1–3 or modifications thereof);
2. determination of parameters that adequately relate the model to the experimental data.
From a pharmacological point of view, the most important parameters are the following:
[R0] (initial receptor concentration), and
Kd = k-1/k+1 (dissociation constant) [7]
The concentration of receptors and the dissociation constant can be changed. Modification of these parameter values can occur in many physiological and pathophysiological situations. For instance, the receptor concentration can reflect functional receptor modifications, and the dissociation constant can reflect genetic alterations of the receptor [6].
To solve the two interrelated problems a series of graphic methods can be deployed, of which the most frequently used is the Scatchard method [7,8]. However, the application of graphic methods in many cases is limited because of experimental errors and/or receptor binding complexity [9,10]. In particular, graphic methods are inapplicable for definition of the cooperative binding parameters and for analysis of non-equilibrium binding.
Regression methods can be found for the measurement of ligand-receptor interaction constants [11]. As a matter of fact, these procedures computerize the graphic methods. Therefore, both regression methods and graphic methods are of limited applicability. The present paper argues that it is very difficult or impossible to discriminate reliably among receptor binding models or to analyse the parameters by traditional analytical methods.
Materials and methods
Let us write the law of mass action for each ligand-receptor interaction scheme as:
For the scheme (1)
But [R] = [R0] - [RL], [L] = [L0] - [RL].
So equation (4) can be rewritten:
This differential equation relates to the class of Rikkatty equations. It can be solved analytically with the help of a special substitution [12], but in all other cases the substitutions [R] = [R0] - [RL], [L] = [L0] - [RL] do not generate analytically soluble equations. Therefore, all equations of this form were solved numerically using the Runge-Kutta method [13,14].
The differential equations are as follows:
For scheme (2):
For scheme (3):
Numerical solution of equations (5–7) was carried out to determine [RL]u. Random error assuming the normal distribution law was superimposed on the magnitude of [RL]u, and was calculated at 5, 10, 20 or 100 points.
The magnitude [RT]m was calculated using parameters other than [RL]u from models (1–3). These parameters were applied to the determination of [RL]u by the following functional minimization:
Φ = ([RL]u - [RL]m)2. (8)
For functional minimization as per equation (8), Newton's method and its variants (the conjugate gradients method and coordinate descent method in various modifications) were used [15-17]. The iteration procedure stopped, when Φ/[RL]u was constant on the next iteration step.
It is clear from the literature [6] that [R0] and Kd cannot be <10-15 M or >10-5 M. Hence the iteration procedure could be improved by re-scaling these parameters logarithmically, making 10-15 M equivalent to -1 on the new scale and 10-5 M equivalent to 1.
Results and discussion
The functional (8) contour plots are shown in fig. 1. From this figure, the degree of correlation between the parameters [R0] Kd can be seen. Therefore the magnification of the random error in evaluating the magnitude of [RL]u displaces the functional (8) global maximum from its true values. In a sufficiently large neighbourhood of the global maximum, the functional magnitude (8) is practically invariant. However, this modification becomes more essential for evaluating the ratio of the functional (8) to basis vector of values [RL]u. Therefore this ratio was used with the inhibiting criterion choice.
Figure 1 The functional (8) contour plot. The various methods of functional minimization are illustrated: a. The second derivative Newton method b. The conjugate gradients method c. The coordinate descent method
The Newton method converges only in the close neighbourhood of the global maximum. However, modifications of the Newton method using second derivatives allow convergence to the global maximum after 1–2 iterations (fig. 1, line 1).
The conjugate gradients method converged after 2–3 iterations (fig. 1, line 2). When magnification of the random error in the evaluation of [RL]u was taken into account, the convergence of the conjugate gradients method varied less than that of the Newton method.
The coordinate descent method required an indeterminately large number of iterations before satisfactory convergence was reached. Use of the exhausting coordinate descent method accelerated the convergence procedure, but the number of iterative steps remained large (fig. 1, line 3).
It can be shown that 5 points suffice to identify the parameters of model (1) using the conjugate gradients method, whereas this method required >10 points for identifying the parameters in a more complicated model. The Newton methods required >7 and 12 points respectively, and the coordinate descent method required >10 and 18 points.
Functional (8) behaviour was analysed with respect to the evaluation of [RL]m using an incorrect binding model. In particular (see fig. 2), the functional (8) contour plot for model (1) with the attempt to approximate the given model by scheme (2). It follows from the figure that a discordant receptor binding model results in functional (8) contour plot modification.
Figure 2 The functional (8) contour plot with an inadequate choice of receptor-binding model.
Thus, the modification of the functional (8) contour plot from the type in fig. 1 to the type in fig. 2 can be used as the criterion for choosing a receptor binding model. With the right choice, the contour plot is similar to that represented in fig. 1. With the incorrect choice, the contour plot is similar to that shown in fig. 2.
It appears that when an incorrect choice of the receptor binding model has been made, the conjugate gradients method does not lead to convergence, whereas in some cases the Newton method converges to one of the local minima. Therefore, lack of convergence using the conjugate gradients method suggests an incorrect choice of receptor binding model.
Conclusion
Possible methods have been explored for discriminating among models for receptor binding model and for defining the relevant parameters. The procedure devised allows one to determine the receptor binding model and its parameters, even when the application of graphical methods is difficult or impossible. As seen here, lack of convergence in the conjugate gradients method indicates that an incorrect choice of model has been made. It is also shown that for the defining the parameters of the correct model, 5–10 data points are sufficient.
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| 15546491 | PMC535815 | CC BY | 2021-01-04 16:39:22 | no | Theor Biol Med Model. 2004 Nov 16; 1:11 | utf-8 | Theor Biol Med Model | 2,004 | 10.1186/1742-4682-1-11 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-451554832910.1186/1471-2202-5-45Research ArticleElevated responses to constant facial emotions in different faces in the human amygdala: an fMRI study of facial identity and expression Gläscher Jan [email protected]üscher Oliver [email protected] Cornelius [email protected]üchel Christian [email protected] Neuroimage Nord, Department of Neurology, University Hospital Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany2 Functional Neuroimaging Laboratory, Department of Psychiatry, Weill Medical School of Cornell University, 1300 York Ave, New York, NY, 10021, USA2004 17 11 2004 5 45 45 12 7 2004 17 11 2004 Copyright © 2004 Gläscher 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
Human faces provide important signals in social interactions by inferring two main types of information, individual identity and emotional expression. The ability to readily assess both, the variability and consistency among emotional expressions in different individuals, is central to one's own interpretation of the imminent environment. A factorial design was used to systematically test the interaction of either constant or variable emotional expressions with constant or variable facial identities in areas involved in face processing using functional magnetic resonance imaging.
Results
Previous studies suggest a predominant role of the amygdala in the assessment of emotional variability. Here we extend this view by showing that this structure activated to faces with changing identities that display constant emotional expressions. Within this condition, amygdala activation was dependent on the type and intensity of displayed emotion, with significant responses to fearful expressions and, to a lesser extent so to neutral and happy expressions. In contrast, the lateral fusiform gyrus showed a binary pattern of increased activation to changing stimulus features while it was also differentially responsive to the intensity of displayed emotion when processing different facial identities.
Conclusions
These results suggest that the amygdala might serve to detect constant facial emotions in different individuals, complementing its established role for detecting emotional variability.
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Background
Facial expressions and facial identities are important cues for the evaluation of social contexts [1,2]. Two main types of information have to be processed while seeing other persons' faces: a face has to be identified as belonging to a unique individual, establishing facial identity, while facial expressions have to be interpreted for emotional context, which is crucial for the social interaction [3]. Facial expression itself conveys two levels of information: first, facial emotions of others signal information about the emotional state and the benevolence or hostility of that person towards oneself; second, they convey information about the person's evaluation of the environment. The same emotional expression on several other persons' faces often signals a high degree of consistency in their evaluation of the current environment; they are therefore a particularly valid cue for one's own situational appraisal of this environment. In particular, the existence of specialized brain systems for the perception of fear expressions as a form of threat related to physical attack [4], points to the importance the brain attaches to social signals of potential environmental threats [5,6].
Visual analysis of human faces has been suggested to be achieved by a core system comprising the fusiform gyrus together with the inferior occipital gyrus, the superior temporal sulcus, and the amygdala [7,8]. The amygdala in particular has been shown to play a pivotal role in the processing and recognition of emotional facial expression, especially fear [9,10]. Since early functional imaging studies [11,12] showed amygdala activation to fearful face stimuli, a variety of imaging studies have led to a more detailed understanding of amygdala function in facial expression. Temporal properties of amygdala responses to stimulus repetition (i.e. habituation, e.g. [11,13-15]), the influence of attentional state [16] and awareness (e.g. [17-19]), as well as the amygdala's response to different types of emotional expression (most recently e.g. [20]) have been explored. Despite many studies investigating facial affect, only few have addressed the interaction of facial affect and identity. Preceding experiments on face processing were not explicitly designed to investigate differences in processing of facial identity and facial expressions, rather they examined the effects of changes in appearance, viewing angle, selective features and physical stimulus properties on face processing (e.g. [14,21,22]).
Recent advances to assess the effects of facial identity and emotional expression include one report on increased left amygdala activation to blocks of multiple novel vs. single identical faces displaying neutral or emotionless expressions [23]. Animated emotional expressions or identities, morphing either from neutral to emotional expression or from one neutral face to another, have been shown to elicit stronger activations in bilateral amygdala than static displays of these same stimuli [24]. Notably, these studies were not designed to investigate the interaction of identity and emotional expression as identity was only varied within the neutral expression condition. Most recently, Winston and colleagues have employed a factorial design to explore fMRI-adaptation to repeated presentations of two facial expressions in the context of an event-related fMRI experiment [25]. In contrast to earlier findings (e.g. [11,12]), the authors did not observe any significant signal changes in the amygdala in the context of their factorial design, suggesting that sparse stimulus presentations interleaved with a checkerboard might not be potent enough to evoke a task-related fMRI activation in this region.
Variability and constancy of identity and emotional expression are concepts that require stimulus integration over time, thus a sufficient number of stimulus presentations might be critical for a brain region such as the amygdala in order to detect stability or changes in the stimulus sequence. Therefore, we tested this interaction in a 2 × 2 factorial design (see Figure 1), with either constant or variable facial identity (factor 1), and either constant or variable emotional expression (factor 2), in a block design fMRI study. The conditions used were: (a) constant identity, constant expression (CICE), (b) variable identity, constant expression (VICE), (c) constant identity, variable expression (CIVE), (d) variable identity, variable expression (VIVE). To control for attentional effects during the procedure, an oddball task was included to avoid confounds by an emotional judgment or a gender differentiation task (the latter being a property of facial identity, [26]).
Given that numerous reports demonstrate amygdala activation in the processing of sequential facial expressions [11,12,27-29] and given the importance of different facial identities as a valid cue for making social inferences (see above), we hypothesized that constant emotional expressions displayed in different faces would elicit strong responses in the amygdala despite its well-described habituation to repeated stimuli [13,15,23]. Furthermore, we expected that constant fearful expressions (shown in different identities) would elicit the strongest amygdala activation given the evidence from lesion and imaging studies that this expression is a particularly potent activator of the amygdala [11,12,17,30,31]. Because we expected to find a stimulus repetition effect in the fusiform gyrus [14,32] we systematically examined responses to changing versus constant stimulus features, i.e., facial expression and identity in this area.
Results
Analysis of the behavioral data showed that subjects maintained a high degree of task attention (98.03 % hits to oddball stimuli (facial stimuli at reduced luminance)). There was a trend of a main effect of identity in the reaction times (RTs) to the oddball stimuli (F1,10 = 4.859, p < 0.06), with longer RTs in the conditions of changing identities, but no significant effects of emotion or an interaction (emotion: F1,10 = 0.755, p > 0.40; interaction: F1,10 = 1.656, p > 0.20).
For the analysis of the imaging data we chose a statistical threshold of p < 0.05, corrected for a reduced search volume of interest for regions with a priori hypotheses (amygdala, fusiform gyrus). For other brain areas the threshold was set to a threshold of p < 0.05, corrected for the entire brain. Post-hoc contrasts of specific experimental conditions for detailed characterization of the experimental effects were carried out at an uncorrected significance threshold.
In the amygdala we expected to find higher responses to constant emotional expressions in different facial identities. Thus in our 2 × 2 factorial design, this can be formally tested by an interaction. Data for all experimental conditions in the peak activation voxels found in our masked interaction contrast (thresholded at p < 0.01, for the purpose of visualization) are shown in the top left and right panels of Figure 2 with the corresponding significant voxels in the SPMs (arrows in middle panels). Applying a reduced search volume corresponding to an anatomical mask of the amygdala (see Methods) to these activations we found a highly significant peak in the right and another peak approaching significance in the left amygdala. We report the latter peak in light of bilateral amygdala activation in similar tasks shown previously [11,14]. Locations and statistics for this analysis for each peak are displayed in Table 1.
Post-hoc contrasts of the experimental conditions revealed that amygdala responses to VICE were only significantly more active than responses to CICE (left amygdala (-15 0 -15): T = 3.75, p < 10-4; right amygdala (18 0 -18): T = 4.76, p < 10-4), and also more active than the other two experimental conditions (CIVE and VIVE): left amygdala (-15 0 -15): VICE vs. CIVE: T = 1.77, p < 0.05; VICE vs. VIVE: T = 2.40, p < 0.01; right amygdala (18 0 -18): VICE vs. CIVE: T = 2.19, p < 0.01; VICE vs. VIVE: T = 3.30, p < 0.001.
In a further post-hoc analysis of the response to VICE we decomposed this condition with respect to different emotional expressions. This analysis revealed that the VICE activation is primarily caused by the response to maximally fearful expressions and (to a lesser degree) to other emotional expressions, but only marginal to neutral expressions (Figure 2, bottom graphs). Post-hoc pair-wise comparisons between maximally fearful and the other emotional expressions exceeded statistical thresholding (left amygdala (-15 0 -15): Fear Max vs. Fear Min: T = 1.95, p < 0.05, Fear Max vs. Neutral: T = 2.45, p < 0.01, Fear Max vs. Happy Min: T = 2.53, p < 0.01, Fear Max vs. Happy Max: T = 1.91, p < 0.05; right amygdala (18 0 -18): Fear Max vs. Neutral: T = 1.85, p < 0.05, Fear Max vs. Happy Max: T = 2.17, p < 0.05).
In order to characterize the time-course of the amygdala responses to the emotion-specific responses in condition VICE we calculated the fitted responses for the most intense and the neutral expressions by multiplying the parameter estimates (regression coefficients) for the respective boxcar and exponential decay regressor with the canonical response function created by SPM during design specification. Figure 3 shows the time course for each emotion-specific response for each peak voxel in both amygdalae. While the overall height of the fitted responses parallel the parameter estimates of the boxcar regressors in Figure 3, we found a trend toward within-block habituation to fearful expressions, and an increase in the fitted responses for happy expressions (especially in the right amygdala) while neutral expression maintained the level of activation across the entire block.
Based on previous work [22,32] we expected activation in the fusiform gyrus to be dependent on stimulus changes irrespective of whether emotional expression, facial identity, or both were varied. We also expected to detect an influence of emotion type and intensity on fusiform activity [12,22]. Accordingly, we compared conditions with at least one changing stimulus feature (identity, emotion) to the condition in which the same picture was repeated for the entire block. Here we found evidence for an effect of changing stimulus features. Figure 4 depicts significantly activated voxels in the lateral fusiform gyrus for this contrast (thresholded at p < 0.001 for the purpose of visualization), with arrows pointing to the voxels of peak activation. Location and statistics corrected for a reduced search volume for this region reported by Vuilleumier and colleagues [33]; see Methods) are shown in Table 1. These locations correspond well to previously reported regions linked to human face processing [34].
Pair-wise post-hoc contrasts of the four experimental conditions revealed that the lateral fusiform gyrus was significantly more activated by all experimental conditions with at least one changing stimulus feature (VICE, CIVE, VIVE) than by the condition with repeated presentation of the same picture (CICE): left lateral fusiform gyrus (-39 -54 -24): VICE vs. CICE: T = 2.75 p < 0.01, CIVE vs. CICE: T = 3.09, p < 0.01, VIVE vs. CICE: T = 3.77, p < 0.001; right lateral fusiform gyrus (36 -54 -24): VICE vs. CICE: T = 2.98, p < 0.01, CIVE vs. CICE: T = 2.79, p < 0.01, VIVE vs. CICE: T = 3.01, p < 0.01.
A further post-hoc analysis of the condition VICE revealed an effect of emotion intensity with the maximal intensity expressions eliciting stronger activations than those with reduced intensities and the neutral expression. Pair-wise contrasts of the emotion-specific VICE condition revealed that maximally fearful expressions elicited significantly larger activation than neutral and emotional expressions of minimal intensity (left lateral fusiform gyrus (-39 -54 -24): Fear Max vs. Fear Min: T = 2.6, p < 0.01; Fear Max vs. Neutral: T = 2.79, p < 0.001; Fear Max vs. Happy Min: T = 2.38, p < 0.01; right lateral fusiform gyrus (36 -54 -24): Fear Max vs. Fear Min: T = 1.72, p < 0.05; Fear Max vs. Neutral: T = 1.94, p < 0.05; Fear Max vs. Happy Min: T = 2.27, p < 0.01; indicated by asterisks (*) in the bottom panels of Figure 4). However, only in the left lateral fusiform gyrus the same post-hoc tests were significant when contrasting maximally happy expressions with all others (Happy Max vs. Happy Min: T = 1.84, p < 0.05; Happy Max vs. Neutral: T = 2.54, p < 0.01; Happy Max vs. Fear Min: T = 1.99, p < 0.05, indicated by pluses (+) in the bottom panels of Figure 4).
Discussion
We investigated the interaction of facial identity and expression in 2 × 2 factorial blocked fMRI study which uniquely enabled us to compare all combinations of facial identity and expression within the same experiment, therefore the results of the present study complement and extend the results of previous studies on facial identity and expression processing in the human brain. In support for our hypotheses we found distinct response patterns in bilateral amygdala and bilateral lateral fusiform gyrus. While the lateral fusiform gyri can be characterized by a binary response pattern corresponding to an effect of changing stimulus feature, the amygdala responded maximally to constant emotional facial expressions in combination with changing facial identity. In addition, in the VICE experimental condition we found an effect of emotion intensity in the fusiform gyrus, with stronger activations to maximally intense expression irrespective of valence when compared with neutral or modestly intense expressions. While we found a (non-significant) trend for the same modulation of the VICE activation by emotion intensity in the left amygdala the general pattern when including the right amygdala rather conforms to a specific sensitivity for maximally fearful expression which elicited the strongest activation.
Fusiform gyrus activations
The binary response pattern conforming to sensitivity for changes in stimulus features in the lateral fusiform gyrus might be explained by reference to the proposed network of face processing in humans [7]. Haxby and colleagues hypothesize that this part of the distributed network is especially sensitive to facial stimulus configurations (and the variability of these configurations). Evidence supporting this conclusion has been reported in a study by Vuilleumier and colleagues who demonstrated a significant repetition suppression effect in the fusiform gyrus in response to the second compared to the first presentation of a given facial identity [22]. Similarly, Rotshtein and colleagues demonstrated stronger activation in face related voxels in the lateral occipital complex (LOC) for blocks with different identities (resembling the condition VICE of the present study) compared to blocks of the repetition of the same identity (our condition CICE) [14]. Another study investigating the influence of facial expression and identity on fusiform activation found reduced activation to facial identity but not to expression and no interaction of these two factors [25]. Our data support and extend these findings with respect to identity in the fusiform gyrus, as we found evidence for an effect of changing stimulus features in the lateral fusiform gyrus irrespective of the dimension of variation (facial identity, emotional expression collapsed over all emotions, or both).
Additionally, we found an effect of emotion intensity in the fusiform gyrus activity in condition VICE with higher activation to maximally fearful and happy expressions relative to with neutral expressions, paralleling earlier findings which report stronger activation to fearful than to neutral expressions [22,24,27,33,35]. Rotshtein and colleagues show significantly larger activation for aversive versus happy expressions in repeated presentations (CICE), suggesting an identity repetition × emotion interaction [14], while others fail to find an effect of negative (or positive) expression in the fusiform gyrus [25]. This apparent negativity bias might be a confound of stimulus selection as researchers use negative material more frequently than positive stimuli. In fact, the underlying mechanism might be an enhanced attentional processing of arousing facial expressions [36], which are usually also the most negative faces. Our finding of an effect of emotion intensity supports this notion more convincingly as we also show increased activation in the fusiform gyrus to happy expressions. An underlying arousal dimension that exerts a modulatory effect on the activation in this region might also explain the lack of an effect of expression in the findings of Winston and colleagues [25], as they did not include a neutral (low arousal) expression in their stimulus set.
Two recent studies suggest that the low frequency information of facial stimuli might drive the modulatory effect of arousal, which is thought to be a feedback influence of the amygdala, which also displays a preference for low frequency information [22,35].
Amygdala activations
Our 2 × 2 factorial blocked fMRI design allowed us to compare between the possible conditions of constant and variable facial identity and expression. We show for the first time a maximum of activation of the amygdala to variable facial identities displaying the same emotion (VICE) compared to all three other main conditions (CICE, CIVE and VIVE). Furthermore, within this condition (VICE) maximally fearful expressions elicited the strongest amygdala activity. The result of the present study replicates and extends, in part, the earlier findings that also utilized blocked presentations of facial expression with stimulus configurations resembling one or more of our experimental conditions (VICE blocks: [11,12,28,37,38]; CICE and VICE blocks: [14]; VICE and VIVE blocks: [39]). In light of our findings, the significant amygdala activation to fearful expression in the aforementioned studies might have been obtained because they employed a stimulus presentation conforming to our condition VICE. Interestingly, in a study comparing dynamically morphed and static presentations of facial stimuli, LaBar and colleagues [24] also report amygdala activation when neutral faces are morphed into each other (a dynamic version of our condition VICE) as compared to static and repeated presentation of the same facial stimuli (the condition CICE in the present study). A closer inspection of our findings in Figure 2 reveals a similar trend, as the neutral expression in the VICE condition still elicited a larger signal change than the condition CICE. Thus, our comprehensive factorial design can relate these earlier findings in the broader context of constancy and variability of identity and expression.
Many imaging and lesion studies have documented activations or behavioral impairments following the presentation of fear-related stimuli (for review see [9]). Our decomposition of the amygdala activation in condition VICE supports this claim. However, recent studies, which used different sensory modalities and carefully controlled for the often confounded dimensions of valence and intensity, argued for an effect of emotion intensity (arousal) in the amygdala [40-42]. Further support for this interpretation comes from a comprehensive study investigating the effects of different expressions of basic emotions during direct and incidental stimulus processing [20]. The authors found no specific effect for a particular emotional expression; they rather report amygdala activations when comparing high vs. low intensity exemplars of the facial stimuli. Although we also find trends for an effect of emotion intensity (Figure 2, bottom left panel), the question of specific amygdalar fear sensitivity or a more general arousal sensitivity remains equivocal based on our findings.
Interestingly, Winston and colleagues [25], employing a similar design as in the present study, failed to observe significant signal changes in the amygdala. The divergent findings might be explained by the difference between our blocked and their event-related design, in which they sequentially presented pairs of facial stimuli. Sparse stimulus presentations in that study might not be sufficient to trigger activation in the amygdala, as this structure might decode a more sustained stimulus train for assessing the biological relevance of the current situation (see below). Furthermore, in their study, the pairs of faces presented were separated by an face-outlined checkerboard [25] which might have prevented the induction of an emotional state that would be encoded by the amygdala.
The temporal properties of amygdala responses to facial expressions are crucial. Many studies show habituation of the amygdala response to blocks of directly adjacent fearful vs. neutral or happy expressions with constant identities over the course of 30 to 80 sec [13,15,38]. This is comparable to the CICE condition in the present study in terms of presented stimuli but not with respect to block length and block order. Interestingly, when the CICE condition in the present study is analyzed for the comparison of maximum fear vs. neutral blocks a non-significant trend for left ventro-lateral amygdala activity (data not shown) is seen, indirectly supporting those findings.
Other studies used fearful and neutral VICE conditions showing within-block and across-block habituation with fixed alternating block order [11] or in the comparison of VICE vs. CICE blocks [14,23] in designs with pseudo-randomized block order. Likewise, we also show a highly significant difference between the VICE and CICE conditions in the amygdala. The response profile of the amygdala for maximal fearful expressions in the VICE condition trends toward a within-block habituation effect (Figure 3). In contrast, the response profile for neutral expressions exhibits a sustained response during the entire block albeit at a much lower overall level. This aspect parallels findings by Breiter and colleagues [11], although direct comparison is limited because that study showed habituation on a larger timescale (between blocks). The within-block increase of amygdala activation to happy expressions, however, suggests that the temporal nature of signal changes in the amygdala might be more complex. This delayed onset of activation to happy faces is in accordance with an evolutionary interpretation of our findings. The amygdala is located in a critical position on the efferent pathway that is involved in the preparation of autonomic responses to threatening situations [43]. Because happy expressions are usually a valid signal for non-threatening situations that do not require fight or flight responses, amygdala activation is not needed at an early stage. The explanation also holds for the early but overall attenuated response to neutral expressions that are inherently ambiguous, which thus prompt for sustained perceptual processing and, potentially, for preparation of defensive behavior [44]. Additionally, Wright and colleagues [23] showed greater amygdala activation to neutral VICE blocks compared with neutral CICE blocks. Clearly, further research is needed to characterize the temporal evolution of amygdala activation during this type of blocked stimulus presentation in more detail.
One might argue that the responses in the conditions with variable emotional expression (CIVE, VIVE) were reduced because within those blocks variable emotional and neutral expressions were intermixed, possibly leading to smaller signal changes within those blocks. However, subjects were presented with the same number of stimuli of each emotional expression in every experimental condition. Therefore, differences cannot be attributed to a varying number of emotional expressions seen in different conditions. Thus, if the conditions with variable emotions elicit a smaller signal change because of the neutral faces within them, this effect should also apply to the blocks of constant neutral expressions in different faces (VICE condition). This effect is in fact shown in the lower bar graphs in Figure 2, especially in comparison to the other emotional VICE conditions. Because we found an overall elevated response in the amygdala to the condition with constant emotions in changing identities (VICE) compared to conditions with variable emotions (CIVE, VIVE; see Figure 2 upper bar graphs), we argue that the observed differences between our experimental conditions represent a true effect of the sequential stimulus configuration within this experimental design. Although we can not ultimately exclude the possibility of confounding order effects within the categories of variable emotion conditions (CIVE, VIVE), this does not diminish the main point of this study, namely that the human amygdala is most responsive to the sequential presentation of faces with constant emotion (fearful) and varying identity. To further confirm and generalize this finding future studies are needed using additional emotions (e.g. anger, sadness, etc.).
Novelty effects of changing facial identity and stimulus order of the different conditions might also be claimed as an explanation for the strong responses to the condition VICE [23]. But in contrast to the latter study, subjects in our study were familiarized with the stimuli before the experiment and the same facial identities were presented repeatedly throughout the experiment, thus minimizing stimulus novelty. Furthermore, if novelty detection were the main factor driving the amygdala response, the condition VIVE should have also elicited a strong amygdala response which it did not (see Figure 2). There was also no systematic sequence of blocks of constant facial identities followed by variable facial identities in the present study which has been shown to relatively increase the activation to the multiple identity condition presented secondly [23].
It is interesting to note that the peak activation within the amygdala is located in the medio-dorsal part of this structure. Previous imaging studies of the processing of facial affect have predominantly reported their activations in the dorsal amygdala (for review see [10], Figure 3). The dorsal amygdala has also been associated with the representation of ambiguous stimuli, such as fearful expressions that do not signal a potential threat directly [10,29,44]. However, given the resolution and post scan smoothing of the functional images in this and other functional imaging studies, the localization of amygdala activity should be discussed with caution.
The potential biological relevance of constant facial emotion
Sensitivity for constant facial emotions in different identities can be seen as a process that integrates stimuli with respect to the conveyed emotion over a certain amount of time. Thus, environmental stimuli (such as facial expressions) are compared to each other to detect changes to stability in the emotion, a concept that can be described as emotion constancy. In this context, constant facial emotions in different individuals signal a high degree of consistency in others' appraisals of the environment, and constitute a more valid cue for one's own appraisal.
Furthermore, facial expressions differ in their degree of saliency which often reflects the biological relevance of the stimulus that evoked the expression [18,44]. For example, a fearful face as a reaction to a threatening stimulus is more salient and calls more immediately for appropriate action than a neutral facial expression. We found elevated response in the amygdala especially to these salient (fearful) expressions because these constant salient facial emotions call for an immediate situational appraisal and subsequent action.
Our findings gain support and extend the findings of earlier imaging studies on the processing of human facial emotions which employed an experimental design similar to our condition VICE and found significant activation in the left dorsal amygdala with constant fearful expressions [11,12]. Taken together, these results, as well as our own findings in this experiment, suggest that the same emotional expressions displayed in many different faces are potent stimuli that activate the amygdala and might serve the detection of emotion constancy. Conceptually, conditions with variable emotionality (CIVE, VIVE) can be seen as noise in the context of the detection of constant emotions among others and thus, it is not surprising that the amygdala shows less signal change in these conditions.
The perception of emotional facial expressions in others yields insights into their evaluations of the environment and guides one's own emotional and behavioral reactions. Encountering the same emotional expression in many different people (emotion constancy) is an especially valid and readily available cue for making subsequent inferences about the potential harmfulness of an environmental situation. These inferences would have direct implications for evolutionary survival and must have been a central feature of human ancestral cognitive abilities [45]. They also remain essential for safe locomotion in our current complex environment as they reduce the time spent in a potentially threatening situation, because the perceiving subject can avoid energy-intensive and time-consuming search for potential threats in the environment [46]. For example, encountering the same fearful expression in several different people (emotion constancy) strongly implies an activation of the fear system [45,47] and that precaution and avoidance behavior are adequate reactions in this situation. Our data, in particular the large response to constant maximally fearful expressions in different individuals, suggest that the amygdala plays a central role in the neurobiological realization of this environmental evaluation.
Conclusions
Emotional facial expressions are an important cue for the appraisal of an environmental situation. Our study has demonstrated a new perspective on the functional characterization of the amygdala involved in the perceptual processing of human faces by incorporating the dimensions of constancy and variability detection in these stimuli. These are essential for the assessment of the temporal dynamics of social situations.
Methods
Experimental design
A 2 × 2 factorial design (identity × emotional expression) across 4 different block types was used: (a) constant identity, constant expression (CICE), (b) variable identity, constant expression (VICE), (c) constant identity, variable expression (CIVE), (d) variable identity, variable expression (VIVE). Figure 1 schematically displays our experimental design showing three consecutive stimuli from one block within each cell of the factor table.
Subjects
13 Subjects (8 females, 10 right-handed) participated in this fMRI study. The mean age was 25.6 (SD 7.8). The data sets of two subjects were excluded from further image analysis because of radio frequency artifacts caused by the scanner leaving a total of 11. All subjects were fully informed about the experimental procedure and signed a consent statement which was approved by the local ethics committee.
Experimental procedure
The procedure was completed in one imaging session with 4 runs each containing 20 blocks of stimulus presentation. Within one run the blocks were interleaved with 15 s of central fixation (rest period). Each run started with a rest period of 20 s. 20 stimuli were presented per block at a rate of 1 Hz. Stimuli were shown for 900 ms interposed with a 100 ms gray blank of mean luminance to make transitions between stimuli less abrupt. Numbers and types of both facial expressions and facial identities were counterbalanced across the entire experiment. Conditions varied only in terms of the sequential configuration of the stimuli within the blocks. Stimulus order within each block was pseudo-randomized and fixed. The sequence of blocks within each run was counterbalanced, pseudo-randomized and fixed to minimize stimulus order effects. Additionally, the sequence of the four runs was pseudo-randomized across subjects assuring different orders of runs in each subject.
Stimuli consisted of 4 different faces (facial identities) drawn from the Ekman series of facial affect [48] with 5 different emotional expressions ranging from fearful to happy expressions, including neutral. Two intermediate expressions displayed fearful and happy emotions at reduced (50 %) intensities. Those face pictures were interpolations using computer morphing procedures [49] similar to those in other studies. Subjects were instructed to fixate on a small red fixation dot presented in the middle of the viewing monitor while simultaneously attending to the entire stimulus presentation.
In order to maintain and control for attentional effects during the procedure we included an oddball task as we were seeking to avoid confounds by an emotional judgment or a gender differentiation task (latter being a property of facial identity, [26]). As oddball stimulus we occasionally presented the facial stimuli with reduced luminance of the entire face while leaving the stimulus visible which was not expected to affect activation in high-order visual areas [50]. We chose to manipulate the entire facial stimulus for the oddball task in order to keep the subjects attention on the entire stimulus rather than on some small feature. Subjects were instructed to respond with a button press whenever an oddball target appeared. The number of oddball targets per block ranged from 2 to 4 to avoid subjects' expectancy effects. In order to prevent a systematic effect of the number of oddballs in the data analysis, the number of oddball stimuli was counterbalanced across blocks and conditions. Subjects were familiarized with the oddball task in a practice session prior to the first scanning run.
Image acquisition
Imaging was performed on a 1.5 T Magnetom Vision (Siemens, Erlangen, Germany) scanner. 43 transversal slices of echo-planar (EPI) T2* weighted images in each volume with a slice thickness of 2 mm and 1 mm gap (TR = 3.5 s, TE = 40 ms, flip angle 90°, FoV 192 × 192 mm2, matrix 64 × 64) were acquired. A total of 204 volumes were collected per run.
Image processing
Image processing and statistical analysis were carried out using SPM99 for the single subject analysis and SPM2 for the group analysis [] All volumes were realigned to the first volume, spatially normalized to a standard EPI template [51] using sinc interpolation and finally smoothed with a 11 mm isotropic full width at half maximum (FWHM) Gaussian filter to account for anatomical differences between subjects and to allow statistical inference using Gaussian Random Field theory.
Statistical analysis
The data of 11 subjects were included in the statistical analysis. Data analysis was performed using the mass univariate general linear model as implemented in SPM99 and commenced by specifying the design matrix for each subject using a boxcar and an exponential decay regressor for modeling the hemodynamic response to each experimental condition. The boxcar regressor models the mean activation within the block while the exponential decay regressor (time constant 4 s) models decreases and increases (through negative contrast weights) within the block. The conditions with constant emotional expressions (CICE, VICE) allowed a decomposition into emotion-specific components. Thus each of these two conditions involved 10 regressors (boxcar and exponential decay for 5 different expression), while each of the other conditions (CIVE, VIVE) were specified with two regressors. Data were high-pass filtered at 1/120 Hz. Serial autocorrelation was controlled by superimposing a known autocorrelation in form of temporal smoothing using a low-pass filter at 4 sec filter width. Successively, contrasts for each experimental condition were computed by averaging the same block type across runs and multiplying the design matrix with the contrast vectors.
These single-subject contrast images were then taken to the second level oneway ANOVA [52,53] in SPM2 allowing for an appropriate non-sphericity correction [54]. This correction is equivalent to the Greenhouse-Geisser procedure in multivariate ANOVA analyses and allows for correct assessment of the error covariance matrix, hence securing valid inference in the group comparisons.
In order to detect voxels that show elevated responses to the same expression displayed in different individuals we constructed the interaction contrast of our 2 × 2 design. Hence, we created the contrast [(VICE > CICE) > (VIVE > CIVE), p < .01 for the purpose of visualization] for the amygdala and masked it with the contrast [(VICE > CIVE), p < .05] to exclude regions showing higher activations to CIVE than to VICE. For our hypothesis in the fusiform gyrus we used a contrast that compared conditions with at least one changing stimulus feature (facial identity, emotional expression, or both) with the condition in which the same stimulus was shown for the entire block [(VICE + CIVE+ VIVE) > 3 × CICE].
T-statistics for the assessment of significant regional activation were assembled into Statistical Parametric Maps (SPMs) which refer to the probabilistic behavior of Gaussian random fields [55]. Our threshold was set at p < .05 (corrected). Because we had region-specific hypotheses for the amygdala and the lateral fusiform gyrus, we applied a reduced search volume to our amygdala activation which was derived by an anatomical mask created with MRIcro [56] on the template brain of the Montreal Neurological Institute (MNI, [57]). With additional visual reference to a high-resolution anatomical atlas [58] we outlined the amygdala on each slice of the MNI template brain. Thus, the amygdala search volume comprised 77 voxels, or 2071 mm3 on the right side and 74 voxels, or 2005 mm3 on the left side. Similarly, we applied a 10 mm radius sphere to our activation peaks in the lateral fusiform gyrus centered on coordinates reported by Vuilleumier and colleagues when contrasting faces vs. houses in a functional localizer task [33]; see Table 1). For additional brain areas not included in our volumes of interest we corrected for the entire brain volume.
For the emotion-specific analyses of condition VICE (Figure 2 and 4, bottom panels) we referred to specific contrasts for each emotional expression created at the single-subject level. These contrast images were raised to another second level one-way ANOVA to test, for example, for significant activations to maximally fearful faces within this condition. These statistical comparisons were carried out at an uncorrected significance threshold.
List of abbreviations
EPI: echo-planar imaging
fMRI: functional magnetic resonance imaging
LOC: lateral occipital complex
RT: reaction time
SPM: statistical parametric map
VIVE: variable identity, variable emotion
VICE: variable identity, constant emotion
CICE: constant identity, constant emotion
CIVE: constant identity. variable emotion
Fear/Happy Max: maximally fearful/happy expression
Fear/Happy Min: minimal fearful/happy expression.
Authors' contributions
J.G. and O.T. designed, coordinated, and conducted data collection, analysis, and interpretation. C.B. and C.W. conceived of the study and participated in its design, analysis and interpretation. All authors read and approved the final manuscript.
Acknowledgements
This research was supported by grants from the BMBF and the Volkswagenstiftung to C. B., and a doctoral fellowship by the Studienstiftung des Deutschen Volkes to J. G. We thank Dr. James Root for helpful comments on the manuscript.
Figures and Tables
Figure 1 Schematic display of the experimental design We used a 2 × 2 (facial identity × facial expression) factorial design with either constant/variable identity or constant/variable expression as factor levels. Each cell represents one condition in the experiment. Facial identity varied between 4 faces (2 male, 2 female), expressions varied between 5 facial emotions ranging from maximally fearful over neutral to maximally happy with moderately fearful and happy in between. Pictures were presented sequentially at 1 Hz (900 ms stimulus duration, 100 ms blank screen).
Figure 2 Responses to different experimental conditions in both amygdalae Coronal and transversal views of the activation foci in both amygdalae. These SPMs are derived from the interaction contrast identity × emotion (see Methods) and are superimposed on an averaged T1-weighted MR image of all our subjects. Arrows are pointing to the maximally activated voxel in the region. We report left amygdala activation in the light of bilateral activation shown previously in similar tasks [11,14]. Top left and right panels. Group mean parameter estimates (regression coefficients ± s.e.m.) to each of the experimental conditions for bilateral amygdala are shown. Condition VICE elicits the largest activation of the peak voxels in both amygdalae and is significantly larger than all other experimental conditions. Asterisks (*) indicate significant difference of respective condition compared with condition VICE at the specified uncorrected significance level. Bottom left and right panels. Detailed (emotion-specific) analysis of condition VICE. Category labels refer to maximally fearful, neutral, and maximally happy facial expression. Intermediate categories are minimally fearful and minimally happy facial expression. Largest activations are elicited by maximally fearful faces. Asterisks (*) indicate significant differences between the respective emotion compared with maximally fearful faces at the specified uncorrected significance level.
Figure 3 Fitted time curves of BOLD-responses of the emotion-specific analysis of condition VICE Category labels refer to maximally fearful, neutral, and maximally happy facial expression, respectively. The fitted time courses from the peak voxels of SPMs derived from the interaction contrast identity × emotion (see Methods) are presented.
Figure 4 Responses to different experimental conditions in left and right lateral fusiform gyri Middle panels. Coronal view of the activation foci in the lateral fusiform gyri bilaterally. These SPMs are derived from the contrast that tested for a simple effect of changing stimulus features (see Methods) and are superimposed on an averaged T1-weighted MR image of all our subjects. Arrows are pointing to the maximally activated voxels in the region. Left and right panels. Group mean parameter estimates (regression coefficients ± s.e.m.) to each of the experimental conditions for bilateral lateral fusiform gyrus are shown. All conditions with changing stimulus features (identity, emotion; VICE, CIVE, VIVE) elicit significantly larger activation of the peak voxels in both fusiform gyri than condition CICE in which the same pictures were shown for the entire block. Asterisks (*) indicate significant differences of respective condition compared with condition CICE at the specified uncorrected significance level. Bottom left and right panels. Detailed (emotion-specific) analysis of condition VICE. Category labels refer to maximally fearful, neutral, and maximally happy facial expression. Intermediate categories are minimally fearful and minimally happy facial expression. Largest activations are elicited by maximally fearful faces. Asterisks (*) indicate significant differences between the respective emotion compared with maximally fearful faces at the specified significance level, pluses (+) indicate significant differences between the respective emotion compared with maximally happy faces at the specified uncorrected significance level.
Table 1 Location of significant voxels in amygdala and lateral fusiform gyrus.
Region x y z Z-value
Left Amygdala -15 0 -15 2.65
Right Amygdala 18 0 -18 3.55**
Left Fusiform Gyrus -39 -54 -24 3.43*
Right Fusiform Gyrus 36 -54 -24 3.19*
Statistics in the amygdala are based on a reduced search volume based on an anatomical mask (see Methods). In the fusiform gyri we employed a search volume with a 10 mm radius sphere centered on coordinates reported by Vuilleumier and colleagues [33] which derived from a comparison between face and house stimuli in a functional localizer [± 45 -54 -21]. All reported activations are corrected for the volume of interest. MNI coordinates. * p < .05 (corrected); ** p < .01 (corrected).
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| 15548329 | PMC535816 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Nov 17; 5:45 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-45 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-251554470110.1186/1471-2296-5-25Research ArticleFamily physicians' perspectives on practice guidelines related to cancer control Zitzelsberger Louise [email protected] Eva [email protected] Ian D [email protected] Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa Hospital, Canada2 Cancer Care Nova Scotia, Halifax, Canada3 School of Nursing, University of Ottawa, Canada2004 15 11 2004 5 25 25 15 6 2004 15 11 2004 Copyright © 2004 Zitzelsberger et al; licensee BioMed Central Ltd.2004Zitzelsberger 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
Family physicians (FPs) play an important role in cancer control. While FPs' attitudes towards, and use of guidelines in general have been explored, no study has looked at the needs of FPs with respect to guidelines for the continuum of cancer control. The objective of this study was to understand which guideline topics FPs consider important.
Methods
Five group interviews were conducted by telephone with FPs from across Ontario, Canada. Transcripts were analyzed inductively. Content analysis identified emergent themes. Themes are illustrated by representative quotes taken from the transcripts.
Results
The main areas where FPs felt guidelines were needed most included screening – a traditional area of responsibility for FPs – and treatment and follow-up – areas where they felt they lacked the knowledge to best support patients. Confusion over best practice when faced with conflicting guidelines varied according to disease site. FPs defined good guideline formats; the most often cited forms of presentation were tear-off sheets to use interactively with patients, or a binder. Computer-based dissemination was acknowledged as the best way of widely distributing material that needs frequent updates. However, until computer use is a common aspect of practice, mail was considered the most viable method of dissemination. Guidelines designed for use by patients were supported by FPs.
Conclusions
Preferred guideline topics, format, dissemination methods and role of patient guidelines identified by FPs in this study reflect the nature of their practice situations. Guideline developers and those supporting use of evidence-based guidelines (e.g., Canadian Strategy for Cancer Control) have a responsibility to ensure that FPs are provided with the resources they identify as important, and to provide them in a format that will best support their use.
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Background
Family physicians (FPs) play an important role in cancer control. Their traditional involvement has been primarily focused on opposite ends of the cancer control continuum: prevention, screening and diagnosis at the beginning of the continuum, and provision of palliative care at the other end. Treatment and follow-up have typically been the responsibility of secondary or tertiary care physicians.
There are indications that FPs would like their traditional roles to include involvement in treatment and follow-up [1,2], and that they are, in fact, becoming more involved in these areas [3,4]. A survey of Canadian FPs showed that they felt a significant proportion of long term follow-up care could be transferred to the FP decreasing the burden on consultants [5]. For example, in a randomized trial of routine follow-up of breast cancer patients, the care provided by FPs was found to be equivalent to that of an oncology specialist [6]. A concern, however, with increased involvement is that FPs receive little oncological training in medical school [4], and thus, are not adequately prepared for involvement in certain aspects of cancer care particularly as treatment practices change as new evidence emerges.
Clinical practice guidelines (CPGs) can provide FPs with information and guidance on evidence-based best practices. While FP attitudes on the use of CPGs have been shown on the whole to be positive [7-11], FPs have suggested that involvement of FPs in the development of guidelines that take into account the nature of primary care would improve their uptake among FPs [11-14]. Dowswell et al[11] suggested that rather than ask how to get physicians to follow guidelines, it would be more productive to ask physicians about their information needs and how they would like them met.
A broad partnership of key stakeholders known as the Canadian Strategy for Cancer Control (CSCC) is currently working towards a national cancer control strategy. The CSCC initiative builds upon previous work done in Canada, and has the goal of developing, adopting and implementing a national strategy. A focus on "Guidelines and Standards" is one of the CSCC's top priorities with an aim to establish mechanisms and improve capacity for collaborative guideline and standards development [15]. In order to ensure wise use of resources, it will be important to ascertain the needs of FPs in Canada with respect to CPGs relevant to cancer control. While many studies have focused on cancer screening [16-18] and palliative care [19,20], to our knowledge, no study has looked at what FPs consider important in terms of content, format and dissemination of guidelines related to cancer control activities. The objective of this study was to learn the views of Ontario FPs for the furtherance of cancer control efforts by the provincial cancer agency, Cancer Care Ontario. The information derived from the study is also of benefit for national cancer control efforts through the CSCC.
Methods
In order to learn FPs' views on guidelines on cancer control, a qualitative methodology was considered the most appropriate. To facilitate FPs' involvement (e.g., eliminate travel and time barriers) and ensure representation of FPs practicing in remote areas, the most feasible option was to hold interviews by teleconference [21]. Thus, we conducted the process as key informant interviews involving between two and four participants [22,23] and using a semi-structured interview format.
With respect to recruitment, FPs from various regions in Ontario identified through the Canadian Medical Directory were recruited via an information letter and follow-up telephone call. In addition, FP colleagues identified potential participants or individuals who might suggest potential participants. Sampling was purposeful [23]. We felt that it was important to involve FPs from the different regions in Ontario (northern, eastern, central east, southwest and central west), as well as have both urban and rural representation, as needs with respect to guidelines may differ based on these characteristics. Once a commitment to participate was made, a time and date for the teleconference were established. FPs were then faxed a consent form and a list of questions that would be asked during the interview.
To allow for in-depth discussion, three cancer disease sites were selected: lung, colorectal and cervix. The first two were included because of high incidence; the latter because conflicting screening guidelines [21] currently exist [24-27]. Questions focused on preferred topics for guidelines along the cancer control continuum, preferred format and method of dissemination of guidelines, and perspectives on guidelines written for patients. Questions were tested for clarity and coverage of important issues in a pilot session involving four FPs and the moderator (LZ). As a result of the pilot, it was decided that four participants were the maximum number for each session to ensure that the session was not too long – an important consideration for physicians with busy schedules.
Five group interviews were held. There was a minimum of 2 and maximum of 4 FP participants in each session. Each of the five interview sessions lasted between 45 and 60 minutes. In order to accommodate FPs' schedules, three of the five interviews were held in the early evening, and two in the morning. FPs were asked to select either a lottery ticket or telephone card as a small acknowledgement of their participation. Each session was led by the same individual (LZ), an experienced qualitative researcher. Interviews were audio-taped.
Audio tapes were transcribed immediately after each interview, and underwent a preliminary analysis allowing for emergent issues or ideas to be explored in future sessions. Transcripts were read by one of the researchers (LZ); latent content analysis (coding and classification into themes) was done manually [22]. As each question addressed a specific topic, the question topics themselves acted as broad organizing categories. Relevant transcript sections were marked and assigned code words. Codes of similar type and content were combined into sub-categories within each question topic [28].
A second researcher (EG) was given two transcripts to read and code independently using the list of codes and categories. Coding was compared. Where necessary, definitions of code words were refined and categories expanded upon [29].
Disagreements and differences were resolved through discussion and data was re-examined where necessary [30]. Representative quotes were selected from the transcripts in order to illustrate key issues raised by the participants.
Ethical approval for this study was obtained from the Ottawa Hospital Research Ethics Board.
Results
Of the 13 physicians participating in the study, seven were male and six were female; five practiced in a rural setting, the remainder in an urban setting. The majority (9/13) were in group practice. Approximately 5% of FPs contacted agreed to participate in the study. Overall, topics raised by urban and rural FPs were similar except with respect to guidelines on cancer treatment.
Guideline topics
Using lung, colorectal and cervical cancers as exemplars, FPs were asked for which topics along the cancer control continuum (i.e., prevention, screening, diagnosis, treatment, follow-up, or palliation) they most wanted guidelines. Screening was the topic most frequently mentioned, although reasons behind requests for screening guidelines differed for each disease site. For lung cancer, there are currently no evidence-based screening tools or maneuvers, and no screening guidelines. FPs were uncertain whether to routinely screen for lung cancer in their practices. For colorectal cancer, on the other hand, there are a number of screening tools and a number of recommendations made by different organizations. Conflicting guidelines resulted in FPs being uncertain about what to do in practice.
I'm certainly much less certain of the area of screening for colorectal cancer. I mean there's a lot of different guidelines out there, it depends on who you read and I regard that as an area very much in flux...I still remain a bit confused as to who should have what. (FP6)
FPs also commented that a similar confusion was prevalent with regard to screening guidelines for cervical cancer.
After three normal [screens], every two years and discontinue at the age of 70, that's what it says. And then this other one says start at age 18, and after three normals then do it every three years except high risk patients should have annual smears...the American College of OB/GYN recommends its smears always continue annually. The American Cancer Society and the Canadian Task Force recommends screening until age 65 and 69 respectively. So, it's a dog's breakfast. (FP5)
However, in comparison to colorectal cancer screening, there was much less ambiguity about what to do in practice. FPs readily adapted cervical cancer screening guidelines to suit individual patient situations or demands.
I would say I have people who I am willing to see every 3 years because I feel quite confident that they'll be back; they're good at keeping up and the ones that I'm more uncertain about in terms of their follow up, I'll make sure I do it more frequently just in case...For me it varies very much between 1 and 3 years and it is very much a decision of my own. (FP11)
In discussing the issue of conflicting guidelines, FPs raised the point that where more than one guideline exists, the credibility of all come into question. For example, if there are multiple guidelines on a topic, can any be the 'right' one to use?
FPs were also very interested in treatment guidelines. Interest centered around two situations: decision making with patients, and dealing with side-effects of treatment. In the first, FPs wanted to know about treatment available to their patients diagnosed with cancer. They saw their role as helping patients and families make informed choices. As such, they wanted information on treatment goals, survival rates for different treatments, quality of life issues as well as risk of and dealing with potential side-effects.
A lot of the patients I have who go out to a cancer clinic come back and make sure I agree with what they're choosing and the problem is I don't have the information to be able to even aid them in making their decision....So if we had a little bit more information than those flow charts that are 'yes/no' to say [this] is the most recent information for survival rates...that kind of thing would be helpful. (FP7)
The second area regarding treatment was raised by rural physicians who often saw cancer patients in emergency departments when, for example, patients were home between chemotherapy cycles. FPs mentioned the difficulty caring for patients when they knew little about their treatment plan.
Areas where we really need specific evidence-based guidelines are in treatment and follow-up. I mean, although the patient may disappear to the cancer clinic...they certainly do show up in emergency, and sometimes the husband or wife calls us as well and says, "Well you know they're getting this drug or they're getting this radiation, they're really sick and what do you think about this?" And if we don't even know what they're getting or what the potential side-effects are, it's really hard to be helpful. So, we need specific guidelines. (FP5)
Rural physicians were also interested in guidelines for follow-up.
...we are going to be doing more and more of our own follow-up, that's the trend, that's the next century, so we need good guidelines. (FP5)
Guidelines were seen by some FPs as a potential communication device between cancer centres and community-based FPs. They suggested that guidelines could be sent to the FP from the cancer centre and include notations by oncologists regarding individual patients. In this way, FPs would feel they had the tools to provide on-going support and care for patients in the treatment or follow-up stages.
Format and dissemination
Two themes were identified related to guideline format: format aspects and presentation. Regarding format, Table 1 presents FPs' 'definition' of what attributes comprise a good guideline. The outstanding features requested were a combination of brevity, and formatted in such a way that FPs were able to quickly identify relevant content.
Table 1 Components of a 'Good' Guideline
Dated
Has a clearly defined, reputable source
Involves FPs in the creation process to ensure its clinical practicality
Not too text based (graphics, tables, flowcharts)
Clear, non-ambiguous recommendations
Well organized
Clearly graded as to levels of evidence
One guideline from one authorative body (to reduce confusion)
Readable in a few minutes
Designed so that FPs will use the guideline frequently and become familiar with it
One to two pages long
The most popular forms of presentation suggested by FPs were a binder that would be easy to update, and tear-off sheets that could be given to the patient but which also provided a review opportunity for the physician through the act of explaining the guideline to the patient. CD ROMs, posters or software packages (guidelines and a recall system for screening) were other suggestions.
I like [the] idea of the tear off sheet to use in discussion with patients. I think guidelines are only useful to the extent that we can go over them again and again and actually be familiar with them ourselves, rather than just having them tucked in a big binder with many other guidelines. So, something that can be used with the patient. (FP3)
Computer-based dissemination was acknowledged as the best way of distributing material widely and addressing the difficulties and expense of updating material. Presentation by local leaders, CME, fax, small group meetings, and mail were other suggestions. While computers were mentioned most often, FPs emphasized that information needs to be widely disseminated to all physicians. For this reason, mail was still seen as the most viable form of dissemination.
Patient guidelines
All FPs agreed that guidelines written for patients would be useful, although there was concern that they should be written very clearly, only be available for topics for which there is good evidence, and not be conflicting. They felt patient guidelines would be useful in that they would act as an added voice, giving weight to the FP's recommendation. Guidelines were also seen as useful in countering misinformation brought in by patients (e.g., from the Internet) to the consultation. On the whole, FPs felt that the more information patients had, the better. Three FPs felt that guidelines would encourage patients to take responsibility for their own care; patients could remind their FP if they were due for screening.
So I think the biggest effort is to establish the proper guidelines that are accepted by a group of authorities in Canada and then that would make it easier for me to say, "Well, look, this is the actual guideline that is the result of a great deal of research and in fact you really don't need that mammogram at the age of 40"...I think there has to be an effort to make sure that patients are not given conflicting guidelines. (FP8)
In terms of content, FPs felt the guideline should echo the FP message. In addition to the tear-off sheets mentioned earlier, ideas for presentation included an educational message played on the telephone when a patient calls, or video messages broadcast on office televisions.
Discussion
FPs have traditionally been responsible for prevention, screening, and early detection, and palliative care. Of the topics along the cancer control continuum, screening guidelines were most frequently identified by FPs in this study. Screening for cancer is primarily the responsibility of FPs who need to stay informed of changes or conflicts in recommendations. British general practitioners, when interviewed about use of guidelines, said that they referred to guidelines for cases that they encountered either most commonly or most rarely in practice [12].
FPs' preferences for screening guidelines addressed two different information needs. In the case of lung cancer, where a familiar screening maneuver was not recommended (i.e., chest x-ray), FPs wanted guidelines that addressed what they should do for routine screening. In the case of colorectal cancer, FPs received conflicting messages about screening, and sought guidance as to which recommendation to use.
One barrier to guideline use is if guidelines are considered controversial [21,31,32]. FPs identified conflicts in recommendations for colorectal and cervical cancer screening. However, they expressed less difficulty in making decisions regarding cervical cancer screening for their patients in comparison with colorectal cancer screening. This may be because cervical cancer screening is a long established practice with good evidence of benefit. Differences between guidelines for cervical cancer relate to the length of interval between routine screening [24,25]. Conversely, colorectal screening is a new practice for which there have been long standing recommendations against routine screening. Currently, differences in recommendations relate to the type of screening maneuver [26,27].
Decisions regarding screening practice also depended on patient factors such as a patient's motivation to adhere to a screening routine. Physician factors (e.g., perceptions of guidelines and clinical experience) and patient factors have been identified as two of the three determinants of a decision making model for cancer screening in the case of unclear or controversial guidelines. The third determinant was the perceived quality of the physician-patient relationship and the clarity of the recommendation being discussed [21].
As with Feightner et al.[33], FPs in this study encouraged development of patient versions of FP guidelines. Guidelines were seen as an opportunity to counter misinformation brought in by patients and also as a means having of both physician and patient participate in the patient's care. The literature with respect to improving cancer screening practice shows that interventions that target both the physician and the patient have the greatest impact [34].
While the FPs in this study play a role in treatment and follow-up, they do not feel they have the information they need to help their patients. Practice location appears to influence the type of treatment information FPs want, although generalizations can not be made on the basis of the qualitative methods used in this study. In urban settings, FPs' roles along the continuum of cancer care principally involve prevention, screening, diagnosis, and palliation. In addition to the above roles, rural physicians are involved in treatment and follow-up, and this involvement is perceived as increasing in the future. Consequently, they state that guidelines on treatment and follow-up would be helpful. Rural physicians participating in a Canada-wide focus group and interview study on FP-oncologist communication also indicated a need for treatment and follow-up information [2].
While FPs expressed a need for guidelines on cancer follow-up, several guidelines have been published. In the case of colorectal cancer for example, a guideline has been published in Ontario under the auspices of Cancer Care Ontario [35]. The challenges with respect to guidelines that are not created specifically for FPs include a need to find the best ways of making FPs aware of the existence of such guidelines, and also to provide implementation strategies geared towards the FP practice [36-39].
FPs in this study preferred guidelines in a paper format disseminated by mail rather than electronically. The 2001 Janus survey conducted by the College of Family Physicians of Canada found that only approximately one quarter of FPs across Canada have access to and use computerized CPGs in their office [40]. The FPs in this study prefer a format that they could use interactively with patients. Recurrent use with patients was seen as a way of helping FPs assimilate the knowledge. Guidelines as a 'look-up' resource and as a general educational tool could become part of the general practitioner's knowledge base [12].
The findings of this study are limited by several factors. The purpose of the study was to gather information on FPs' perspectives on guidelines along the cancer control continuum. As with all qualitative research, the results of the study are not generalizable beyond the sample. However, the intent of sampling in qualitative research is to identify key informants who will illuminate particular aspects of the research topic [23]. FPs agreeing to participate in this study are likely those who have a strong interest in guidelines. The perspectives shared by FPs offer insight into the guideline topics, format, and dissemination of guidelines that FPs consider important in caring for their patients with cancer.
Further research should focus on identifying the guideline needs of a larger, nation-wide sample of Canadian FPs to ensure that efforts by initiatives such as the CSCC result in CPGs that will be both viewed positively and adopted by FPs. However, perception of the value of a guideline is not enough to ensure adoption. While much effort has gone into guideline development, the focus on dissemination has been through traditional dissemination strategies (e.g., publication in professional journals). Knowledge transfer is known to be more complex [41] requiring multifaceted strategies to encourage adoption and taking into account the environment, the potential user and characteristics of the innovation (e.g., guideline) [36,42,43,39,38].
Conclusion
Guideline topics, format, dissemination, and patient guidelines discussed by FPs in this study reflect their particular practice situations. FPs' strongest preferences were for guidelines on cancer screening, followed by guidelines on treatment that would help them support and provide care for patients. The conflicting messages of some guidelines did not necessarily make decision making problematic for FPs. Rather, it was the reason behind the conflict that created difficulties. FPs saw patient guidelines as an educational tool for both themselves and their patient. Guidelines on treatment and follow-up are available, although they are generally geared towards specialists not FPs. This suggests a need for FP versions to be created. Challenges for Canadian guideline developers/implementers include not only ensuring that the evolving needs of FPs are met, but also that they address the needs of FPs with respect to how that information is formatted, delivered to FPs and how FPs are supported in its use.
Competing interests
Dr. Graham is a CIHR New Investigator.
Authors' contributions
LZ participated in the design of the study, conducted the interviews and analysis, and drafted the manuscript. EG participated in the design of the study, the analysis, and contributed to the manuscript. IG participated in the design of the study, and assisted with the pilot test of interview questions. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank the following physicians for their willingness to share their perspectives: Adam Chen, Ron Peddle, Susan Boron, Marie Gear, Ray Dawes, Carolyn Brown, Greg Cooper, Don Patten, Shayna Watson, Lori Chalkin as well as the other participants. Thank-you as well to Robert Sauls, Marjorie Wood and Ray Viola for their help in designing the modified focus group questions. Funds for this research were provided by the Lung Disease Site Group, Program in Evidence-based Care, Cancer Care Ontario and the Cervical Cancer Screening Programme, Cancer Care Ontario.
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| 15544701 | PMC535864 | CC BY | 2021-01-04 16:29:00 | no | BMC Fam Pract. 2004 Nov 15; 5:25 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-25 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-351549149510.1186/1475-2875-3-35ResearchAssociation of house spraying with suppressed levels of drug resistance in Zimbabwe Mharakurwa Sungano [email protected] Susan L [email protected] Robert [email protected] Tawanda [email protected] Steven K [email protected] Karen P [email protected] Department of Molecular Microbiology & Immunology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore MD 21205, USA2 The Malaria Institute at Macha, P.O. Box 630166, Choma, Zambia3 Blair Research Institute, P.O. Box CY 573, Causeway, Harare, Zimbabwe4 Provincial Medical Director (Manicaland), 24 'C' Avenue, Box 323, Mutare, Zimbabwe5 Postgraduate Studies School, Faculty of Health Sciences, The University of the Witwatersrand, 7 York Road, Parktown, 2193, Johannesburg, South Africa6 Peter Medawar Building for Pathogen Research, Department of Zoology, South Parks Road, Oxford University, Oxford OX1 3SY, UK2004 18 10 2004 3 35 35 14 7 2004 18 10 2004 Copyright © 2004 Mharakurwa et al; licensee BioMed Central Ltd.2004Mharakurwa 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
Public health strategies are needed to curb antimalarial drug resistance. Theoretical argument points to an association between malaria transmission and drug resistance although field evidence remains limited. Field observations, made in Zimbabwe, on the relationship between transmission and multigenic drug resistance, typified by chloroquine, are reported here.
Methods
Periodic assessments of the therapeutic response of uncomplicated falciparum malaria to chloroquine in two selectively sprayed or unsprayed health centre catchments, from 1995 – 2003. Cross-sectional analysis of in vivo chloroquine failure events for five sites in relation to natural endemicity and spraying history.
Results
During selective house spraying, the chloroquine failure rate for the sprayed catchment decreased, such that, after four years, the odds of chloroquine failure were 4× lower than before start of spraying in the area (OR 0.2, 95% CI 0.07 – 0.75, p = 0.010, n = 100). Chloroquine failure odds for the sprayed area became 4× lower than contemporaneous failure odds for the unsprayed area (OR 0.2 95% CI 0.08 – 0.65, p = 0.003, n = 156), although the likelihood of failure was not significantly different for the two catchments before selective spraying started (OR 0.5, 95% CI 0.21 – 1.32; p = 0.170, n = 88). When spraying ended, in 1999, the drug failure odds for the former sprayed area increased back 4 fold by 2003 (OR 4.2, 95%CI 1.49 – 11.78, p = 0.004, n = 146). High altitude areas with naturally lower transmission exhibited a 6× lower likelihood of drug failure than low-lying areas (OR 0.16 95% CI 0.068 – 0.353, -2 log likelihood change 23.239, p < 0.001, n = 465). Compared to sites under ongoing annual spraying, areas that were last sprayed 3–7 years ago experienced a 4-fold higher probability of chloroquine failure (OR 4.1, 95%CI 1.84 – 9.14, -2 log likelihood change 13.956, p < 0.001).
Conclusion
Reduced transmission is associated with suppressed levels of resistance to chloroquine and presumably other regimens with multigenic drug resistance. It seems the adoption of transmission control alongside combination chemotherapy is a potent strategy for the future containment of drug-resistant malaria.
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Background
The escalation of parasite drug resistance has persisted as a major obstacle to malaria control for decades [1-3]. Owing to dwindling options for affordable, safe and effective drugs, rising clinical failure rates exact a substantial public health toll, especially in Africa [4,5]. In countries that recently replaced chloroquine with sulfadoxine/pyrimethamine as first line treatment, there are signs of increasing resistance to the antifolate combination [1,2,6-9]. Partly because of the spectre of drug resistance, pharmaceutical companies reduced investment in new antimalarial drug research. Fortunately, official calls in the mid 1990's led to renewed public-private sector initiatives for the development of new compounds, as well as the improvement of existing ones [10,11].
However, Plasmodium falciparum has repeatedly demonstrated the ability to develop resistance to practically any drug upon wider introduction, as illustrated by multi-drug resistance, especially in South East Asia [12-15]. Thus, public health strategies that delay or minimize the escalation of drug resistance are urgently required. To date, the only approach that has been widely evaluated and is currently being introduced is the use of combination chemotherapy [11,16,17] which protects constituent drugs from resistance through a multigenic mechanism of resistance and strategic pharmacological properties such as short half-life. In poor countries the effectiveness of this method is hampered by increased cost of medication. Furthermore, even the new combinations are not totally protected from the development of resistance, as illustrated by the recent confirmations of clinical failure and in vitro resistance to proguanil/atovaquone [18-22]. Additional strategies are, therefore, needed to ensure the successful containment of drug-resistant malaria.
Mathematical models have been proposed suggesting a relationship between malaria transmission and the evolution of drug resistance, though some workers suggest a positive association [23,24] while others propose a negative one [25,26]. Major implications for control pertain to this question. It may mean that vector control programmes are counterproductive by aggravating drug resistance, or, it could be that they complement chemotherapy by alleviating resistance. Although this interaction between transmission and drug resistance is further addressed in a review [27], the exact answer still remains uncertain against a background of limited field evidence. The present paper presents observations on the field relationship between transmission variations (both natural and vector control induced) and the levels of in vivo multigenic drug resistance, typified by chloroquine.
Methods
Study areas and population
Zimbabwe, on the southern fringes of malaria in Africa, experiences seasonal and potentially epidemic transmission characterized by a non-immune population with high probability of drug treatment [28-30]. The country has sustained a national malaria vector control programme for decades, based on intradomicilliary application of residual insecticide. From the early 1990s selective vector control was introduced, in which areas with moderate transmission are of less priority and spraying is focused in zones of high transmission/high malarial incidence. Chloroquine has remained the first line treatment for uncomplicated malaria, although a combination of chloroquine and sulfadoxine/pyrimethmanine is currently being introduced in some areas. A tiered drug distribution policy has been implemented in the country, so that, until 1997, chloroquine was the only antimalarial available at the peripheral level. Thereafter, policy revisions allowed wider distribution of sulfadoxine/pyrimethamine to treat chloroquine failure cases.
The study was based at five health centres located in the low-lying (<600 m above sea level) hyperendemic transmission zone as well as those in the higher altitude (600 – 1200 m asl) mesoendemic transmission zone bordering the malaria-free central watershed (Table 1, Fig 1). All the study locations experience seasonal, single peak (February-May) malaria transmission typical for Zimbabwe. Acute symptoms and complications occur across all ages in this non-immune population, where asymptomatic carriage of asexual parasitaemia is rare [28,29]. The study was conducted on uncomplicated falciparum malaria cases of all age groups presenting at the health centres for treatment.
Table 1 Study area characteristics
Site Elevation Endemicity *Population Estimate Villages of patient origin Spraying status Treatment drug
Burma Valley 683 m mesoendemic 11764 25 Last sprayed 1999 CQ+S/P since 2001
Chitakatira 1211 m mesoendemic 13245 28 Last sprayed 1998 CQ+S/P since 2003
Sahumani 784 m mesoendemic 5950 24 Last sprayed 1992 CQ+S/P since 2003
Madhuku 471 m hyperendemic 11583 39 Ongoing CQ+S/P since 2001
Mola ≈500 m hyperendemic 13000 28 Ongoing CQ+SP since 2001
*2002–2003 population census.
Figure 1 Location of study sites, shown in relation to altitudinal zones that govern malaria endemicity. Central watershed (elevation > 1200 m above sea level) experiences nil – hypoendemic malaria transmission, and endemicity increases with falling altitude towards the north and south of the country.
Study design
The study was a prospective assessment of the therapeutic response of P. falciparum malaria to chloroquine from 1995–2003. Consecutive assessments of therapeutic response were conducted in two mesoendemic sites during the presence or absence of selective indoor residual insecticide spraying (house spraying). Transverse assays for Pfmdr1 and Pfcrt mutations associated with chloroquine resistance were carried out in these two sites during the 1998–99 transmission season. Further assessments of in vivo chloroquine therapeutic response were carried out cross-sectionally in another three sites where treatment change to chloroquine (CQ) + sulfadoxine/pyrimethamine (SP) was not yet being implemented due to temporary unavailability of SP. Malarial incidence was determined retrospectively for all sites using available health centre records.
In vivo antimalarial therapeutic efficacy assessments
The in vivo therapeutic efficacy of chloroquine was assessed using the standard WHO (1996) protocol [31]. Since this protocol was primarily targeted for regions of intense malaria transmission, two modifications were adopted to suit the seasonal/epidemic conditions of Zimbabwe. These were (i) inclusion of febrile patients of all age groups and (ii) adoption of radical asexual parasite elimination as a criterion for adequate response to treatment.
Inclusion of all age groups was on the rationale that there is no premunition in the population. Recruited patients were thus a representative sample of the symptomatic population which presents for treatment with chloroquine in the primary health care system. The radical asexual parasite elimination criterion was adopted because persistent asexual parasitaemia poses a risk of complications in non-immunes.
Molecular detection of Pfmdr1 and Pfcrt polymorphisms
Amino acid polymorphisms at codons 86 and 1246 of the P. falciparum Pfmdr1 gene and at codon 76 of the P. falciparum chloroquine resistance transporter gene (Pfcrt), which are associated with chloroquine resistance [32,33], were detected by PCR and codon-specific restriction enzyme digestion [34,35]. Appropriate positive and negative control strains were used in interpretation and, except for the Pfcrt codon, additional restriction sites were included in the target PCR product to serve as internal controls for complete digestion.
Ethics
The study was approved by respective provincial medical health authorities and by the Medical Research Council of Zimbabwe. Patient participation was by the informed consent of the patients themselves or guardians, in the case of children.
Results
Association of house-spraying with reduced levels of chloroquine resistance
(i) Burma Valley and Sahumani follow-up study
On the grounds of low malarial incidence, the catchments of Sahumani clinic, in Mutasa district and Burma Valley clinic, in Mutare district (Fig 1), were removed from the spraying programme in 1992, with the advent of selective control to save on inseciticide. However, the Burma catchment, which is situated on commercial farms, was re-allocated to annual spraying from 1995 – 1999 when, for economic reasons, local farmers agreed to supply the malaria control authorities with insecticide. The Burma catchment subsequently reverted to no spraying after the 1999 spraying operation, due to disagreements between commercial farmers and the government. In contrast, the Sahumani catchment, which is located in villages, remained unsprayed from 1992.
There were no malaria statistics for the two health centres prior to 1998, (1997 for Sahumani). However, during the selective annual spraying, the risk of contracting malaria in the sprayed Burma Valley catchment was at least 2.6 fold lower than for Sahumani (Fig 2) from 1998 – 2000. After the selective spraying operation ended in 1999, the malarial incidence became uniform for the two catchments by 2001 (Fig 2).
Figure 2 Monthly malarial incidence in Burma valley (BV) and Sahumani (SH) catchments during and after selective spraying (boxed terms, Risk Ratios (95% CI) for peak malaria transmission period (February – May)).
The therapeutic failure rate of Burma Valley decreased during selective spraying (Table 2) such that by 1999, the odds of chloroquine failure were 4× lower than they were before spraying resumed in this area (OR (95% CI) 0.233 (0.072 – 0.747), for 1999 compared to 1995; 0.482 (0.270 – 0.861), for each year of spraying: -2 log likelihood ratio change 6.432, df = 1, p = 0.011). Therapeutic failure rates were not significantly different in the Burma Valley and Sahumani catchments (1995 season) prior to selective spraying of Burma Valley (Table 2). However, by 1999 the odds of drug failure had become 4× lower in the annually sprayed Burma catchment (Table 2). The failure rate in Sahumani did not significantly change during the 4-year period (OR (95%CI): 0.62 (0.25 – 1.57), for 1998 compared to 1995; 0.53 (0.26 – 1.08) for 1999 compared to 1995; 0.85 (0.71 – 1.02) for each successive year, -2 × log likelihood ratio change, 3.080, df, 1, p = 0.0793).
Table 2 Chloroquine therapeutic failure (TF) rates in Sahumani and Burma Valley from 1995–2003.
Therapeutic failure rate (n)
1995 1997/98* 1999 2003
Burma Valley 27.0% (37) 15.2% (33) 7.9% (93) 26.5% (83)
Sahumani 41.2% (51) 30.3% (33) 26.9% (65) -
Odds ratio (95% CI) 1.89 (0.76 – 4.72) 2.4 (0.73 – 8.14) 4.3 (1.54 – 11.85) -
P 0.170 0.142 0.003 -
*Burma Valley 1997 compared to Sahumani 1998.
After selective spraying ceased in 1999, the odds of drug failure in Burma valley increased back 4-fold by 2003 (OR (95%): 4.18 (1.485 – 11.782), p = 0.004, n = 146) Chloroquine efficacy assessments for 2003 were not conducted in Sahumani as the treatment was changed that year to chloroquine plus sulfadoxine/pyrimethamine.
(ii) In vivo prevalence of mutations in Pfmdr1 and Pfcrt genes
Amino acid polymorphisms on Pfmdr1 and Pfcrt codons associated with chloroquine resistance were examined in pre-treatment patient samples from 1998 and 1999 in Burma Valley and Sahumani (i.e. 3–4 years after re-start of spraying in Burma Valley). Resistance-associated mutations at amino acid codons 86 and 1246 of Pfmdr1, and codon 76 of Pfcrt, were more prevalent in the Sahumani area (Table 3, Fig 3). Interestingly, mixed infections containing both mutant and wild type variants tended to be more frequent in the Burma Valley area (Fig 3), despite lower transmission in this catchment. The same distribution pattern observed with individual codons was mirrored in mutated haplotypes comprising two or more amino acid codons (Table 3). Three-codon haplotypes from the sprayed area exhibited significantly more mixed mutant and wild variants at one or more codons than corresponding haplotypes from the unsprayed area (odds ratio 5.4, 95 % CI: 1.89 – 15.54, p = 0.001, n = 131).
Table 3 Relative abundance of mutated P. falciparum genotypes in Sahumani and Burma Valley (1998 and 1999 transmission seasons).
Mutant genotype OR (95%CI) of mutants (Sahumani : Burma Valley) P N
Pfmdr1 Tyr-86 2.4 (1.2 – 4.7) 0.013 137
Pfmdr1 Tyr-1246 4.2 (1.7 – 10.7) 0.001 135
Pfcrt Thr-76 2.2 (1.1 – 4.6) 0.028 144
Pfmdr1 Tyr-86 + Pfmdr1 Tyr-1246 3.9 (1.5 – 10.1) 0.003 132
Pfmdr1 Tyr-86 + Pfmdr1 Tyr-1246 + Pfcrt Thr-76 4.0 (1.6 – 10.3) 0.002 131
Figure 3 Distribution of mutated (m) and wild (w) P. falciparum variants in Burma Valley (BV) and Sahumani (SH), at Pfmdr1 codons 86 (Pfmdr-86) and 1246 (Pfmdr-1246), and Pfcrt codon 76 (Pfcrt-76).
Drug failure as a function of transmission
A scatterplot of chloroquine therapeutic failure rate with malarial incidence suggested a positive association (Fig 4). In stead of using parametric tests on arcsine transformed data (perhaps better done with more data points), the probability of chloroquine failure was examined as a function of transmission zone, and spraying history, using a logistic model. The health centre catchments naturally falling in the mesoendemic zone according to altitudinal classifications [28], did exhibit significantly lower malarial incidence, for at least the previous 10 years, than those in the hyperendemic zone (Fig 5, Table 4). In the logistic regression, the probability of therapeutic failure was 6.4-fold lower in these mesoendemic catchments than in hyperendemic ones (Table 5, 6, 7). At any time point, catchments that were under ongoing annual spraying experienced 4-fold lower likelihood of drug failure than those that were last sprayed 3–7 years ago (Table 5).
Figure 4 Scatter plot of therapeutic failure prevalence with malarial incidence.
Figure 5 Monthly malarial incidence per thousand population in hyperendemic (hyper) and mesoendemic (meso) catchments.
Table 4 Risk of clinically diagnosed malaria in hyperendemic and mesoendemic catchments during the peak malaria season (February – May).
Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Hyperendemic population (elev. <600 m) 21306 21925 22643 34298 35733 27647 25578 22651 27174 27989
Mesoendemic population (elev. ≥600 m) 65664 66497 69788 75761 88181 90910 93721 48244 96057 61625
Malarial RR (95% CI) 1.9 (1.72 – 1.99) 1.7 (1.57 – 1.81) 5.3 (5.09 – 5.55) 2.8 (2.67 – 2.88) 1.1 (1.09 – 1.17) 5.0 (4.82 – 5.12) 2.0 (1.89 – 2.04) 2.7 (2.56 – 2.75) 1.6 (1.55 – 1.69) 3.3 (3.06 – 3.48)
P <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Table 5 The probability of chloroquine therapeutic failure as a function of transmission level and spraying history
: Independent variable coding
Parameter coding
(1) (2) (3) (4)
Study year 2003 1.000 .000 .000 .000
1999 .000 1.000 .000 .000
1998 .000 .000 1.000 .000
1997 .000 .000 .000 1.000
1995 .000 .000 .000 .000
Last annual spraying 3+ yrs ago 1.000
0 yrs/ongoing .000
Transmission level mesoendemic 1.000
hyperendemic .000
Table 6 The probability of chloroquine therapeutic failiure as a function of transmission level and spraying history: variables in the equation
B S.E. Wald df Sig. *Exp(B) 95.0% C.I. for EXP(B)
Lower Upper
Step 1(a) Transmission level (1) -1.863 0.419 19.780 1 0.000 0.155 0.068 0.353
Last annual spraying (1) 1.411 0.409 11.879 1 0.001 4.099 1.838 9.142
Study year 13.037 4 0.011
Study year (1) -0.728 0.290 6.317 1 0.012 0.483 0.274 0.852
Study year (2) -0.400 0.317 1.589 1 0.207 0.671 0.360 1.248
Study year (3) -0.224 0.440 0.259 1 0.611 0.799 0.338 1.892
Study year (4) 0.262 0.445 0.346 1 0.556 1.299 0.543 3.109
Constant -0.157 0.407 0.149 1 0.700 0.855
a Variable(s) entered on step 1: Transmission, Last annual spraying, Study year.
* corresponds to odds ratio for therapeutic failure
Table 7 The probability of chloroquine therapeutic failiure as a function of transmission level and spraying history: Model if term removed
Variable Model Log Likelihood Change in -2 Log Likelihood df Sig. of the Change
Step 1 Transmission level -331.110 23.239 1 .000
Last annual spraying -326.469 13.956 1 .000
Study year -326.142 13.303 4 .010
Discussion
The build up of drug-resistant P. falciparum malaria calls for public health strategies to maximize the useful life of antimalarials. According to the findings of the present study, reduced transmission, due to vector control or high altitude, was associated with suppressed levels of in vivo therapeutic failure and genotypic resistance to chloroquine. Assuming that chloroquine resistance has a multigenic mechanism, as is the general consensus [12,36,37], this association between transmission and drug resistance presumably governs other drugs or drug combinations that have polygenically encoded resistance.
From the Burma Valley and Sahumani cross-sectional assays, there was, in the sprayed catchment, a higher likelihood of infections carrying mixed mutated and wild type codons, for both Pfcrt and Pfmdr1, despite the lower transmission. This paradoxical result suggests that the sprayed area probably favoured more genetic out-crossing, resulting in recombination break down of drug-resistant haplotypes. The genetic out-crossing may partly explain the association of low drug resistance with the house spraying. Further studies are needed to verify this relationship in more areas.
In Burma Valley, despite drug pressure, the proportion of resistant parasites decreased during spraying, and subsequently resurged after the spraying stopped. This is reminiscent of the decrease in proportion of chloroquine-resistant parasites reported in China [38], and more recently in Malawi [39,40], following suspension of chloroquine use. From these observations it would seem that chloroquine-resistant parasites bear a fitness cost as drug selection advantage is removed or counteracted.
What is distinct about the current results is that the fitness cost for resistance appeared to occur in the sporogonic phase, as distinguished from an in vivo fitness burden that is thought to ensue following cessation of drug use. In the present results, drug selection advantage for the resistant parasites appeared to be directly counteracted by independent survival limiting factors, such as vector control and high altitude. This has important implications for control as it means that drug-resistant P. falciparum can be contained during drug use. Furthermore, costly acquisition of immunity in the resident population is, presumably, not the only prerequisite for curbing drug resistance.
The present results afford field evidence supporting the continuation of sustainable malaria vector control programmes. Similar findings were reported for Uganda [41], although the same workers found a difference between chloroquine (multigenic resistance) and sulfadoxine/pyrimethamine (monogenic resistance) below a critical threshold of transmission [37]. These papers may further support the findings of the present study. It has been cautioned that resistance might exacerbate as eradication is approached [42]. However, in the current study, the low transmission levels associated with high altitude and spraying showed no signs of this counterproductive effect. Moreover, in poor countries, which are the de facto stronghold for malaria, eradication so far remains only an academic prospect, as the malaria burden continues to increase [43]. It seems that the adoption of sustainable transmission control with combination chemotherapy is a potent approach for the future containment of drug-resistant malaria.
Conclusions
Reduced transmission due to house spraying or high altitude is associated with suppressed levels of phenotypic and genotypic resistance to chloroquine and presumably other multigenically encoded drug regimens. Transmission control implemented with combination chemotherapy seems a potent approach for the future containment of drug-resistant malaria.
Authors' contributions
SM was the principal investigator responsible for the study design, data collection, analysis and drafting of the manuscript. SLM carried out essential co-ordination of project activities. RM and TC afforded technical input on the manuscript and facilitated field data collection. SKC made vital contributions in original proposal development, seeking of funding, and edited the manuscript. KPD provided crucial inputs in the study design, genetic analysis, general direction and co-ordination of the study and the writing up of the manuscript.
Acknowledgements
Mr V. Rusinga (Manicaland Provincial Medical Office), provided vital information on history of provincial selective vector control strategies. Dr. B. Mabaera commented on the manuscript. The authors are thankful to medical staff of Sahumani, Burma Valley, Chitakatira, Madhuku and Mola clinics, for their unwavering support to the study. Acknowledgement is also due to the residents of the study areas for participating in follow-up intensive drug efficacy assessments and sample collections. Mr A.C. Murahwa and J. Karisa carried out much of the microscopy for the study and played a central role in field data collection. Dr. Tim Anderson gave invaluable advice on molecular aspects of the study and edited the manuscript. Control strains were kindly donated by Dr. M.T. Duraisingh in Professor D.C. Warhurst's group and Dr. Lisa Ranford-Cartwright from Professor David Walliker's labs. This study was sponsored by WHO/TDR and the EU INCO-DC programme. The Beit Trust contributed to funding of field data collection and travel. SM was a Beit Fellow. Molecular work and part of travel expenses were met by The Wellcome Trust.
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| 15491495 | PMC535889 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Oct 18; 3:35 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-35 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-451556662610.1186/1475-2875-3-45ResearchAutodissemination of the entomopathogenic fungus Metarhizium anisopliae amongst adults of the malaria vector Anopheles gambiae s.s. Scholte Ernst-Jan [email protected] Bart GJ [email protected] Willem [email protected] Laboratory of Entomology, Wageningen University Research Centre, Binnenhaven 7, P.O. Box 8031, Wageningen, the Netherlands2 Entomology Unit, Agency's laboratories Seibersdorf, International Atomic Energy Agency, A-2444 Seibersdorf, Austria2004 28 11 2004 3 45 45 25 8 2004 28 11 2004 Copyright © 2004 Scholte et al; licensee BioMed Central Ltd.2004Scholte 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
The entomopathogenic fungus Metarhizium anisopliae is being considered as a biocontrol agent for adult African malaria vectors. In the laboratory, work was carried out to assess whether horizontal transmission of the pathogen can take place during copulation, as this would enhance the impact of the fungus on target populations when compared with insecticides.
Methods
Virgin female Anopheles gambiae sensu stricto were exposed to conidia whilst resting on fungus-impregnated paper. These females were then placed together for one hour with uncontaminated males in proportions of either 1:1 or 1:10 shortly before the onset of mating activity.
Results
Males that had acquired fungal infection after mating indicate that passive transfer of the pathogen from infected females does occur, with mean male infection rates between 10.7 ± 3.2% and 33.3 ± 3.8%. The infections caused by horizontal transmission did not result in overall differences in survival between males from test and control groups, but in one of the three experiments the infected males had significantly shorter life spans than uninfected males (P < 0.05).
Conclusion
This study shows that autodissemination of fungal inoculum between An. gambiae s.s. mosquitoes during mating activity is possible under laboratory conditions. Field studies are required next, to assess the extent to which this phenomenon may augment the primary contamination pathway (i.e. direct contact with fungus-impregnated targets) of vector populations in the field.
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Introduction
Control of the main African malaria vector Anopheles gambiae (Diptera: Culicidae) continues to rely heavily on application of residual insecticides, either for indoor residual house spraying [1] or bednet impregnation [2]. These approaches have been highly effective in reducing malaria morbidity and mortality [2], but associated problems regarding environmental pollution [3,4], acceptability and cost [5,6] and the now widespread and continuing development of resistance [7-10] underscore the need for alternative strategies, such as vector control with biological agents [1,11,12].
Entomopathogenic fungi are among the biological control agents used against insect pests. Interest in using the hyphomycete Metarhizium anisopliae against adult African malaria vectors has recently increased [13]. The fungus has proven to be highly virulent for this vector, both in the laboratory [14] as well as in the field (Scholte et al., in preparation). The principal method of contamination of the target insect population with the fungus is through application of conidia on indoor resting targets. However, in order to achieve the highest possible impact on the target population, it is desirable that contamination pathways other than the primary mode of contamination are utilised, for instance horizontal transmission. Horizontal transmission of pathogens within the same host/target species is called autodissemination, and this phenomenon has been suggested for biocontrol of several insect pests [15,16]. Successful transmission of M. anisopliae by honeybees for infection of the pollen beetle Meligethes aeneus [17], of Beauveria bassiana between adult flies of Delia radicum [18] and of M. anisopliae and B. bassiana between adult tsetse flies, Glossina morsitans morsitans [19] confirms the capability of insects to transmit fungi horizontally. Autodissemination of insecticidal biocontrol agents, such as insect-pathogenic fungi, provides an additional advantage over pesticides, as the impact on pest populations increases beyond direct contact. In several cases, autodissemination of entomopathogenic fungi within populations of insect pests, using attractant traps as the initial source of infection, has succeeded [18,20-22]. The strategy envisaged for the use of M. anisopliae against adult An. gambiae is that host-seeking females, and occasionally also males that rest indoors, will receive primary infections while resting indoors on fungus-impregnated resting targets. Under optimal circumstances, prior to death, this infection may be transmitted to conspecifics upon contact (e.g. during mating). These mosquitoes are, therefore, not infected through direct contact with fungus-impregnated materials, but indirectly, upon physical contact with infected counterparts. It is estimated that approximately half of newly hatched, virgin females take a blood meal before mating [23,24]. A female, with contaminated legs and mouthparts following the blood-feeding visit to a house containing fungus-impregnated resting targets, may contaminate male counterparts when she mates the following dusk period, thereby spreading the fungus through the population.
The objective of this study was to investigate whether adult An. gambiae infected with M. anisopliae can transmit the fungus to uncontaminated mosquitoes of the opposite sex through physical contact during the mating process.
Materials and Methods
Bioassays
Three bioassays were conducted to assess whether autodissemination of M. anisopliae can occur during the process of mating:
1) 30 fungus-contaminated virgin females and 30 uncontaminated males were placed in a standard (30 cm3) netting cage for one hour during the normal mating period;
2) a single fungus-contaminated virgin female and a single uncontaminated male where placed together in a glass tube for one hour during the normal mating period;
3) a single fungus-contaminated virgin female and 10 uncontaminated males were placed in a standard (30 cm3) netting cage for one hour during the normal mating period.
A fourth bioassay assessed whether or not:
4) males that became infected with the fungus had acquired the infective propagules from contaminated females or rather from contact with the substrate where those females had rested previously.
Mosquitoes
The Anopheles gambiae sensu stricto strain used originates from Suakoko, Liberia (courtesy Prof. M. Coluzzi) and has been maintained in the Wageningen laboratories since 1989, under standardized conditions described by Mukabana et al. [25]. Experimental mosquitoes were four (females) or seven day old (males). Virginity of the females was assured by collecting them within 24 hrs after emergence from cages and keeping them separate from the males prior to the experiments. In all experiments, mosquito mortality was checked daily, the mosquito cadavers placed on moist filter paper and placed in Petri dishes that were sealed with parafilm. These were kept in an incubator at 27 ± 2°C and checked for fungal sporulation after three days.
Fungus
Metarhizium anisopliae var. anisopliae isolate ICIPE30 (courtesy Dr. N.K. Maniania) was originally isolated in 1989 from a stemborer, Busseola fusca Fuller, near Kendu Bay, Western Kenya. Conidia were inoculated on oatmeal agar (OA) and placed in an incubator to grow. Fungal virulence was maintained by passing it through An. gambiae mosquitoes every five cycles after growing on OA. Third instar larvae were infected by applying dry conidia on the water surface. After 48–72 hours, moribund larvae were removed and their thorax opened to remove tissue with blastospores. These were plated on OA and placed in a dark incubator at 27°C. One week after the onset of sporulation of the colonies, conidia were harvested using a 0.05% Triton-X solution and a glass rod. The solvent with conidia was concentrated by removing the supernatant after centrifuging for three minutes at 5000 rpm. Dilutions were made using 0.05% Tween80 to obtain a conidial concentration of 108 conidia ml-1. Vegetable (sunflower) oil was added to obtain an 8% adjuvant oil formulation. Five ml of this suspension was pipetted evenly over a 240 cm2 piece of filter paper resulting in conidial densities of 1.6 × 1010 conidia m-2. The impregnated paper was left to dry at 20°C and 75 ± 5% RH for 48 hours and was then placed on the inside of a plastic cylinder (height 11.3 cm, diameter 3.4 cm) in such manner that the paper neatly lined the upright wall of the tube. The top of the tube was covered with netting material. This setup was used only to infect female mosquitoes. Before any contamination, the viability of the impregnated conidia was checked by placing a 1 cm2 piece of the impregnated paper on a Sabourad Dextrose Agar in an incubator at 27°C in the dark for 16–20 hours. After incubation, the piece of paper was carefully removed and placed under a microscope (X 400) to determine the proportion germinated. For direct contamination of the female mosquitoes with M. anisopliae, around 30 individuals were placed in the cylinder for 24 hours.
Experimental procedures
Bioassay 1
Thirty uninfected males were placed in a 30 cm3 netting cage three hours before the onset of mating. Half an hour before (artificial) dusk, which for An. gambiae is the time when mating activity occurs [26], thirty contaminated females were added to this cage by releasing them from the cylinder (see above) where they had spent the previous 24 hours. By that time a large percentage of the males had the fibrillae on their antennae erect, which is considered a sign for impending mating activity [27]. One hour after introduction of the females, all males were gently removed from the cage using a 2 cm diameter glass vial and placed in a clean cage where they had access to 6% glucose ad libitum. The experiment was replicated three times, on different days. Control groups were treated similarly, with the difference that the paper lining the contamination tube was void of conidia. Mortality of males and females was checked daily to measure longevity. Dead mosquitoes (both sexes) were removed from the cages and placed on moist filter paper in a Petri dish, which was sealed and examined for fungal growth three days later. An additional similar experiment with only five females and five males was performed to determine whether conidia could be seen on the cuticle of the test males directly after the females were removed. Males were killed by a droplet of chloroform and placed under a microscope (X 400) and examined for attached conidia.
Bioassay 2
A single, uncontaminated 7-day old male was placed in a clean glass vial (diameter 2 cm, height 10 cm, sealed off with netting), to which one M. anisopliae contaminated female was added 30 minutes before the onset of mating activity. After one hour the couple was separated by gently removing the male, which was placed and kept in a separate glass vial until it died. A wad of cotton wool moist with 6% glucose was placed on top of the vial. Females remained in the vial and were provided with glucose in the same way. Mortality of both sexes was recorded daily. The control group consisted of an equal number of pairs that were handled equally, with the difference that the females were not infected with the fungus. This was repeated with 35 male-female pairs on three different days.
Bioassay 3
Ten uncontaminated 7-day old males were placed in a 30 cm3 netting cage. Half an hour before the onset of mating activity, a single infected female mosquito was added to this cage with the males. After one hour each male was gently removed using a clean 2 cm diameter glass vial and kept alive individually as in bioassay 2. This was done 14 times for the test group and six times for the control group.
Bioassay 4
To assess whether the males in the above bioassays had acquired fungal infection from the contaminated females during the process of mating, or from resting on the substrate where fungus-contaminated females had rested previously (glass and netting), two experiments were carried out. In one experiment a total of 46 contaminated females (in two separate trials) were gently transferred to a 500 ml glass beaker, sealed of with a glass Petri-dish. The females were killed after one hour by applying a droplet of chloroform in the beaker. They were then removed and placed into a Petri-dish on a piece of moist filter paper. The dish was sealed with parafilm, to be checked three days later for fungal infection. Directly after removal of the contaminated females, a total of 47 uncontaminated males (in two separate trials) were placed in the beaker for three days, after which they were killed, removed and checked for fungal infection as described above. During the three days inside the beaker they had continuous access to a cotton wool wad moist with a 6% glucose solution. The second experiment differed only in that a standard netting cage was used instead of a glass beaker, with 64 fungus-contaminated females and 67 uncontaminated males in two separate trials.
Data analysis
Mortality data were subjected to Kaplan-Meier pair-wise comparison survival analysis. Mosquito mortality data closely fitted the Gompertz distribution [28]. Comparisons of the average ages at the time of death were calculated and analyzed using GLM analysis. All analyses were carried out by using Genstat 7.0.
Results
In all four bioassays, female mosquitoes that had been exposed to conidia in the cylinder setup died significantly faster than the control females (F = 104.4; p < 0.001), with an average of 96.4 ± 2.0% of the cadavers having sporulating M. anisopliae.
All three replicates of bioassay 1 indicated transfer of the fungus from contaminated females to uninfected males, with an average male infection rate of 26.1 ± 5.3% (Table 1). There was no difference in survival between the males of the control and test groups (F = 0.30; p = 0.5844), but when the group of test males was split into those that had been infected and those that had not, survival analysis showed a difference in survival approaching significance (F = 2.73; p = 0.098). Under a compound microscope, conidia were observed on four of the five males. Most were found on the lower parts (tibia, tarsi, uncinus (claws) and arolium) of the first and second pair of legs (Figure 1). A few conidia were found attached to the hairs of the tip of the wings. No conidia were found on the head, thorax, abdomen or the hind legs. Per mosquito 0–25 conidia were found. Female mosquitoes that had spent 24 hrs on fungus-impregnated paper had conidia on legs, tips of their wings and mouthparts, but not on the thorax or abdomen.
Table 1 Autodissemation of M. anisopliae from females to males An. gambiae s.s.. The proportions of male mosquitoes infected are shown with the differences in survival of fungus-infected males compared to uninfected males within the test groups.
Bioassay Ratio Average %(± SEMa) of males infected Survival analysis (Kaplan Meier) Average age of males at time of death (days)
infected uninfected
1 1:1 26.1 ± 5.3 F = 2.73;
p = 0.098 10.3 ± 0.7 18.6 ± 0.8b
2 1:1 34.0 ± 0.6 F = 3.39;
p = 0.065 11.3 ± 1.9 15.6 ± 1.6b
3 1:10 10.7 ± 3.2 F = 13.02;
p = 0.001 13.1 ± 1.3 17.9 ± 0.6b
a: SEM = standard error of mean
b: Significant difference (p < 0.05) between infected and uninfected males using GLM analysis (Genstat 7.0).
Figure 1 Conidia of the entomopathogenic fungus Metarhizium anisopliae on the lower part of a male Anopheles gambiae s.s. leg, after having been horizontally transferred from an infected female during copulation.
In the second bioassay, with individual pairs in glass vials, an average of 34.2 ± 0.6% of the males acquired and died of the fungal infection. Survival of these males as a group was not significantly different from the control group (F = 1.27; p = 0.259). When the males from the test group were split into those that became infected and those that did not, a decrease in survival of the infected test males compared to the uninfected males was observed, although this effect was not significant (F = 3.39; p = 0.065). However, the average age at death of 11.3 ± 1.9 days of the fungus-infected males was significantly lower than the 15.6 ± 1.6 days of the uninfected test males (p = 0.001).
In the third bioassay, horizontal transmission occurred in nine of the 14 replicates (64.3%). In those nine trials an average of 16.7 ± 3.7% of the 10 males acquired an infection. Calculated over the 14 trials, the infection rate was 10.7 ± 3.2%. As in the other two bioassays, there was no significant difference in survival rates between the test and control groups (F = 2.19; p = 0.139). However, those males in the test group that were found with sporulating fungus died significantly faster than uninfected males (F = 13.02; p = 0.001) with the average age at the time of death of 8.4 ± 1.1 days for infected and 17.9 ± 0.6 days for uninfected males, respectively.
None of the 114 males in bioassay 4 were found infected with the fungus.
Discussion
It is generally believed that fungal dissemination within a host population occurs due to activities and movements of the host. The fungus can exploit host behaviour like trophallaxis, tactile communication, grooming (in social insects) [29,30] and mating [31] to spread through a host population. Taking into account the physiological state of the females and the natural display of behaviour at the time of the bioassays, it is assumed that the observed autodissemination of M. anisopliae from female to male An. gambiae s.s. was the result of mating. This is strongly supported by the findings from experiment 4 where none of the males that had stayed on the surface area where fungus-contaminated females had rested previously acquired an infection. The average age at death of fungus-infected mosquitoes was quite high when compared to mosquito survival in Scholte et al. [14]. This is probably due to the relatively low level (a maximum of 25 conidia) of inoculum transferred. From those mosquitoes that were checked under the microscope for the presence of conidia, four out of five males contained conidia. It is thus likely that many males become contaminated, but that only a relatively low proportion of these males will actually succumb to the infection: in many cases the number of conidia was low, resulting in marginal infections that were successfully countered by the immune responses staged by the males.
In order to achieve the highest possible impact of the fungus on the mosquito population, it is desirable that other pathways besides the primary mode of (direct) contamination are utilized. The results of this study show that under laboratory conditions horizontal transmission can occur, which suggests that it may occur in the field. When these experiments were carried out, it was presumed that predominantly females would be infected directly from the indoor resting targets in the field. From a recent field experiment (Scholte et al., in preparation), however, it was found that a large proportion (44.9%) of the An. gambiae s.l. found indoors were males. This suggests that not only females can deliver fungal inoculum to uninfected males, but that also infected males may infect uninfected females. Further research is needed to determine to what extent this secondary pathway of fungal contamination may contribute to spreading the fungus within mosquito field populations.
Conclusion
This study has shown that horizontal transfer of fungal inoculum between mosquitoes is possible during copulation and may contribute to spreading of the fungus within target mosquito populations in the field. However, since conditions under which horizontal transmission is likely to occur are quite specific, field experimentation is required to measure the real impact that autodissemination may have. For now it is concluded that the relatively low infection levels recorded in this study suggest that the impact of biological control with M. anisopliae against African anophelines will predominantly depend on direct contamination of adult mosquitoes from conidia-impregnated resting targets such as walls, ceilings and sheets.
Authors' contribution
E-JS was directly involved in the experimental work, analysis of the data and drafting of the manuscript. BGJK conceived of the study, obtained funding for it in collaboration with WT and revised the manuscript prior to submission.
Acknowledgements
We thank Dr. Patrizia Scarpulla for assistance with the experiments and Leo Koopman, André Gidding and Frans van Aggelen for supplying mosquitoes. This study was supported by the Netherlands Foundation for the Advancement of Tropical Research (WOTRO), grant number W83-174.
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| 15566626 | PMC535890 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 28; 3:45 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-45 | oa_comm |
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Comp HepatolComparative Hepatology1476-5926BioMed Central London 1476-5926-3-91556656810.1186/1476-5926-3-9ResearchThe establishment and characterization of the first canine hepatocellular carcinoma cell line, which resembles human oncogenic expression patterns Boomkens Sacha Y [email protected] Bart [email protected] Jooske [email protected] Ronald [email protected] Herman F [email protected] den Ingh Ted SGAM [email protected] Jan [email protected] Louis C [email protected] Department of Clinical Sciences of Companion Animals, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 8, P.O. Box 80154, 3508 TD Utrecht, The Netherlands2 Department of Pathobiology, Division of Pathology, Faculty of Veterinary Medicine, Utrecht University, The Netherlands3 Department of Infectious Diseases and Immunology, Division of Virology, Faculty of Veterinary Medicine, Utrecht University, The Netherlands2004 26 11 2004 3 9 9 13 8 2004 26 11 2004 Copyright © 2004 Boomkens et al; licensee BioMed Central Ltd.2004Boomkens 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
Hepatocellular carcinoma (HCC) is one of the most worldwide frequent primary carcinomas resulting in the death of many cirrhotic patients. Unfortunately, the molecular mechanisms of this cancer are not well understood; therefore, we need a good model system to study HCC. The dog is recognized as a promising model for human medical research, namely compared with rodents. The objective of this study was to establish and characterize a spontaneous canine tumor cell line as a potential model for studies on HCC.
Results
Histomorphological, biochemical, molecular biological and quantitative assays were performed to characterize the canine HCC cell line that originated from a dog with a spontaneous liver tumor. Morphological investigations provided strong evidence for the hepatocytic and neoplastic nature of the cell line, while biochemical assays showed that they produced liver-specific enzymes. PCR analysis confirmed expression of ceruloplasmin, alpha-fetoprotein and serum albumin. Quantitative RT-PCR showed that the canine HCC cell line resembles human HCC based on the measurements of expression profiles of genes involved in cell proliferation and apoptosis.
Conclusions
We have developed a novel, spontaneous tumor liver cell line of canine origin that has many characteristics of human HCC. Therefore, the canine HCC cell line might be an excellent model for comparative studies on the molecular pathogenesis of HCC.
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Background
Hepatocellular carcinoma (HCC) is one of the most worldwide frequent primary tumors in man, with an estimated 564,000 new cases and almost as many deaths in 2000 [1]. It almost always develops in the setting of chronic hepatitis or cirrhosis, conditions in which many hepatocytes are destroyed, inflammatory cells invade the liver, and connective tissue is deposited. Unlike colorectal carcinoma, for example, for which a model can be generated based on known molecular events occurring during the process of carcinogenesis [2], the pathogenesis of HCC is largely unknown [3]. Although many risk factors have been reported to be involved in the transformation from a normal cell into a malignant tumor cell, such as HBV, HCV, alcohol, aflatoxin B, cirrhosis, older age, and male gender, the molecular mechanisms of neoplastic transformation and progression in HCC are not yet well understood.
However, the study of those mechanisms is hampered because the liver tissue of patients with HCC has only limited value and primary hepatocytes are difficult to maintain in culture. Furthermore, primary hepatocytes rapidly lose detoxifying P450 isoenzymes. In addition, and because of the heterogeneity of the molecular genetic changes that can lead to HCC across species, molecular genetic studies in animals have not yet provided a precise general model for the molecular pathogenesis of HCC in humans. The dog is a valuable model for human comparative studies, since it has a comparative life span and habitat and thus similar risk factors and its domestication started over 10,000 years ago [4]. Moreover, and like rodents, the dog develops spontaneous hepatocellular tumors. However, these tumors are not associated with hepatitis and cirrhosis, and develop in normal livers. Furthermore, the entire genome of the dog is currently being sequenced, what will allow further detailed species-specific molecular analyses.
Here, we describe the establishment and morphological, immunohistochemical, biochemical, and molecular characterization of the first canine hepatocyte cell line derived from a spontaneous HCC of a dog. The objective of this study was to investigate whether this cell line could be used as a potential model for studies on human HCC. Therefore, we investigated whether this canine hepatocyte tumor cell line had features similar to human HCC with respect to mutations in the hepatocyte growth factor receptor (c-MET) gene and the differential gene expression of several oncogenes, proto-oncogenes and proteins involved in proliferation, apoptosis and cell survival.
Results
Histopathology of the donor dog
The primary neoplasm was histologically characterized by broad trabeculae, 2 – 6 cells in thickness, of well-differentiated hepatocytes and separated by sinusoidal structures lined by endothelium. The hepatocytes had uniform moderately sized nuclei and small nucleoli; mitotic figures were very rare. Regularly areas with marked steatosis or glycogen accumulation within the neoplastic hepatocytes were observed. Locally within this well differentiated tumor there was a carcinomatous area characterized by broad trabeculae of more basophilic cells with large nuclei, moderate anisokaryosis, usually one or more large nucleoli and 3–5 mitotic figures per high power field (Figure 1). The non-affected liver histology of the donor dog showed no abnormalities, such as inflammation.
Figure 1 Histopathological characteristics of the original liver tumor from which the cHCC cell line is derived. A) A well-defined tumor area (*), as well as a carcinomatous area (**); B) An enlargement of the carcinomatous area (**).
Development and histomorphological characterization
Directly after establishment of the initial cell suspension (June 19, 2002), the cells appeared pleiomorphic. After approximately 10 weeks of culturing, the cells formed clusters as rounded, vital cells, which are non-adherent. This characteristic has remained ever since. Freezing and re-culturing of the cell line had no effect on cell growth. A 1:10 splitting and medium refreshment of the culture by careful trypsinization once a week (a "passage") is optimal. The tryspinized cell clusters were further cultured with fresh DMEM culture medium. The cells rather grow in these smaller clusters and not as single cells.
The cell clusters were collected, fixed and handled as described. As shown in Figure 2, histology revealed solid cell clusters of large epithelial cells, with papillary projections at the periphery and extensive central necrosis. The cells were polygonal and moderately pleiomorphic, 10 – 20 μm in the largest diameter, showed marked anisocytosis and sometimes vacuolation of the cytoplasm. The nuclei were large (5–10 μm) and centrally located, with large and often multiple prominent nucleoli, and many, sometimes bizarre, mitotic figures (Figures 2A and 2B). Immunohistochemical staining for both HepPar1 and CK7 was best after Bouin fixation. In the Bouin fixed material, the hepatocyte marker HepPar1 (Figure 3A) revealed moderate granular cytoplasmic staining in the majority of the cells; CK7 (Figure 3B) showed slight to moderate granular cytoplasmic staining in a minority of the cells. As controls for the two stainings, liver tissue and kidney tissue of a healthy dog was used. These were positively and negatively stained, respectively. HepPar1 staining shows throughout the liver tissue samples, whereas the CK7 is localized in the bile ducts.
Figure 2 Histomorphological characterization cHCC cell line. A) Solid cell clusters of large epithelial cells with papillary projections at the periphery and extensive central necrosis. Bouin fixation, HE staining; B) Papillary growth of moderately pleiomorphic cells with anisocytosis and anisokaryosis, prominent nucleoli, and multiple mitotic figures. Carnoy fixation, HE staining.
Figure 3 Immunohistochemical characterization cHCC cell line. A) Immunohistochemical staining for HepPar1. Moderate granular cytoplasmic staining in the majority of the cells. Bouin fixation; B) Immunohistochemical staining for CK7. Slight to moderate granular cytoplasmic staining in a minority of the cells. Bouin fixation.
Biochemical characterization
The activity of ALT, GLDH and AST was measured to investigate whether the cHCC cells produced liver characteristic enzymes. In order to compare the amount of hepatic enzymes produced by the cHCC cell line, those measurements were also performed for the widely used human hepatocyte cell line HepG2 (enzyme activity of HepG2 was set at 100%), and the commonly used canine kidney cell line MDCK. The results showed the cHCC cell line produced 25% of the highly liver-specific ALT compared to HepG2, whereas the MDCKs did not produce ALT at all (Table 3). Of another specific liver enzyme, GLDH, the cHCC cell line produced 19% of the activity of HepG2, whereas the MDCK cells produced only 10%. The cHCC cell line produced 28% of AST compared to HepG2, whereas MDCK produced only 8%.
Table 3 Liver enzyme activity measurement of the cHCC cell line compared with HepG2 and MDCK.*
ALT (%) AST (%) GLDH (%)
HepG2 100 100 100
cHCC 25.8 27.7 19
MDCK 0 8 10
* Note: The activity of the enzymes is given as a percentage compared to the activity of proteins in HepG2.
Molecular characterization
To further examine whether the cell line truly consists of hepatocytes, we isolated RNA from the cHCC, made cDNA, and performed PCRs for the gene expression of hepatocyte markers. The cHCCs proved to be PCR-positive for canine serum albumin, alpha-fetoprotein and ceruloplasmin. All obtained products were sequence confirmed.
Mutations in the c-MET gene
Mutations in exons 15–21 of the c-MET gene have been described in human HCC. A PCR was therefore performed with primers based on this region of the c-MET gene. The products were analyzed and aligned with known canine and human c-MET sequences (Gen Bank Accession numbers AB118945 and NM_000245, respectively). At nucleotide position 4089, a thymine (T) instead of an adenine (A) was observed, which resulted in a serine in the cHCC cell line versus a threonine in healthy tissue at codon 1363 (T1363S) (see Figure 4).
Figure 4 Mutation in the c-MET gene of cHCC (in figure as "c-MET cHCC") compared to healthy liver tissue (canine c-MET; Gen Bank accession number AB118945; "canine c-MET" in figure) and human c-MET (Gen Bank accession number NM_000245; "c-MET human" in figure) sequences. nt 4021–4219 of the canine c-MET is shown. The mutation is marked in grey at nt 4089 (A to T) of the canine c-MET, which corresponds to a change of amino acid 1363, from a threonine to a serine (T1363S). The STOP codon in the canine and human c-MET is also marked in grey. Alignment was performed by SECentral CLUSTAL W (1.7) multiple sequence alignment.
Quantitative measurements of mRNA levels of gene products differentially expressed in cHCC
To explore whether the cHCC cell line has similar expression profiles of genes involved in neoplastic growth and apoptosis as human HCC, the mRNA levels of the following genes were measured by means of quantitative RT-PCR: c-MET, PTEN, p27kip, Bcl-2, beta-catenin, SOCS3, ODC, TGF-alpha and collagen-1. The expressions of these genes were normalized by relating them to the housekeeping genes, HPRT and beta-actin. As shown in Figure 5, c-MET, a receptor tyrosine kinase involved in cell survival and growth, was down-regulated 33-fold compared to the control group. PTEN, an inactivator of the Akt/PKB pathway was down-regulated over 200-fold. To further substantiate activation of Akt/PKB, we measured two downstream targets, p27kip and Bcl-2. They were indeed down-regulated 5-fold and up-regulated 3-fold, respectively. HGF, the ligand of c-MET, induces mRNA levels of beta-catenin and TGF-alpha, both involved in cellular growth. The latter two were down-regulated 6- and 7-fold, respectively. From two novel proteins associated with hepatocellular carcinomas in man [5], suppressors of cytokine signaling type 3 (SOCS3) and collagen-I, a non-significant 2-fold elevation of SOCS3, were found and a 7-fold, significant down-regulation of collagen-I was observed. The gene expression of a proliferation factor, ornithine decarboxcylase (ODC), also proved to be elevated 3-fold.
Figure 5 Differential gene expression profiles of the cHCC cell line as compared to liver tissue of healthy dogs, measured by quantitative Real-Time PCR. Data represent mean ± SE of the groups. The "n" for these figures stands for the fold change of the gene expressions of the cell line as compared with our control group.
Discussion
We have described the establishment and characterization of a canine hepatocyte tumor cell line, derived from a spontaneous HCC in a dog. Both immortalized hepatocytes and hepatic progenitor cells come from various transgenic mouse lines [6], are drug-induced [7], or are obtained after SV40 large T-antigen transfection [8]. However, immortalization with SV40 induces HGF/c-MET activation via an autocrine HGF loop [9]. This clearly contrasts with the cHCC, where HGF-induced growth is absent (data not shown), most likely because of severely reduced c-MET levels.
Our morphological study has accumulated good evidence – but no definitive proof of the hepatocytic and neoplastic nature of the cHCC cells. Positive staining for hepatocyte marker HepPar1 strongly indicates the hepatocytic origin of the cultured cells [10]. The neoplastic nature of the cells can be deduced from the pleiomorphism of the cells, the number of sometimes bizarre mitotic figures. The simultaneous presence of CK7 and HepPar1 positive cells is also consistent with the neoplastic nature of the cells [11], and suggests the presence of both fully differentiated hepatocytes and progenitor cells in the cHCC cell culture. The hepatocytic nature of the cHCC cell line is further indicated by the activity of the liver-specific enzyme ALT and the expression of hepatocyte markers, like serum albumin, ceruloplasmin, and alpha-fetoprotein, as it is also the case for the human tumor liver cell line HepG2.
Binding of HGF to c-MET triggers tyrosine autophosphorylation of the intracellular domain in the c-MET receptor and induces responses that account for mitogenesis and growth. In human c-MET, point mutations have been described in the tyrosine kinase domain, which may be associated with the development of primary liver carcinomas [12]. In our study, we detected an unknown point mutation near the tyrosine kinase domain, which results in a conserved change from a threonine to a serine. Whether this mutation has any influence on the autophosphorylation of two adjacent tyrosines [13] remains to be proven.
In quantitative RT-PCRs, we measured the differential gene expressions of several gene products involved in proliferation/growth and cell survival. In human HCC, the c-MET gene-expression was observed to be induced in 60% of the cases [14], whereas we found a down-regulation of the c-MET gene-expression. Although we only measured mRNA levels of c-MET, we can correlate them to their protein expression levels [15]. Furthermore, lack of HGF-responsiveness was also observed by reduced expression of beta-catenin and TGF-alpha in the cHCC cell line, which is in accordance with findings in human HCC [16].
It has also been observed that the tumor-suppressor PTEN, the Akt/PKB pathway inhibitor [17], was down-regulated drastically, leading to an increased activity of Akt/PKB, as shown by the anti-apoptotic protein Bcl-2 and the cell-cycle inhibitor p27kip, which were induced and inhibited, respectively. Both p27kip and PTEN are inhibited in human HCC as well [17,18]. In addition, the gene expressions in cHCC of SOCS3 and collagen-I were elevated (although not significantly) and inhibited, respectively, as it was also detected in human HCC [19]. Moreover, we found an up-regulation for ODC. This proliferation factor, induced by several growth factors, is responsible for proliferation in many cell types [20]. Taken together, the expression data show elevated proliferation, increased cell survival, and reduced apoptosis, which explains the neoplastic nature of cHCC.
Conclusions
From the morphological, biochemical, and molecular biological assays performed in this study, we conclude that the cHCC cell line clearly represents hepatocytes. In addition, cHCC has neoplastic characteristics comparable to HCC in man. Therefore, this cell line can be used as a model not only to study the molecular pathogenesis of human HCC, but also to investigate possible etiological agents of canine hepatitis.
Methods
Donor dog
Our material was taken from the liver of an eleven-year old, privately owned female Cairn Terrier dog, diagnosed with HCC by histological analysis. With the owners' consent, the dog was routinely anesthetized, parts were taken from various areas of the neoplastic liver tissue. In addition, liver tissue was collected for histological analysis, fixed in 10% buffered formalin and paraffin embedded. Sections were routinely stained with hematoxylin and eosin (HE). Then, the dog was immediately euthanized.
Isolation and culturing conditions
Immediately after resection, the liver samples were kept in DMEM culture medium supplemented with 10% fetal calf serum (FCS; Fetal Calf Serum Gold, PAA Laboratories GmBH, Pasching, Austria), penicillin and streptomycin (P/S; 100 IU/ml and 100 μg/ml final concentration, respectively) and were kept on ice. Under sterile conditions, liver samples of various areas were cut into small pieces (5 × 5 mm) and trypsinized with 30 ml of trypsin/EDTA (0.5 g/l trypsin 1:250 and 0.2 g/l EDTA; BioWhittaker Europe, Verviers, Belgium) in a sterile Erlenmeyer flask placed on a stirring platform for 30 minutes. The cell suspensions were filtered with a 70 μm nylon filter (Falcon; Becton Dickinson Labware, Franklin Lakes, NJ). Erythrocytes were lysed from the filtered suspension. The remaining cell suspension was resuspended in DMEM supplemented with 10% FCS and P/S and was cultured at 37°C with 5% CO2 and 95% air under a humidified atmosphere in non-coated flasks. It was observed every day for any changes. The medium was refreshed twice a week.
Histomorphological characterization
After 3 weeks of culturing with medium refreshment only and no splitting of the culture, the content of a T80 cm2 flask with the cHCC cell culture was harvested by transferring the entire contents of the flask to a tube, which was centrifuged for 10 minutes at 1,500 g. The supernatant was replaced by freshly made fixation fluid for 4 hours. For optimal immunohistochemical staining, four different fixatives were used: zinc sulfate formalin, Bouin, Carnoy, and 10% neutral buffered formalin. The fixated cell pellet was transferred to a foam leaf-protected plastic embedding cassette. After fixation, samples were manually dehydrated and embedded in paraffin. Sections (3 μm thick) were cut and stained with hematoxylin and eosin (HE). For immunohistochemical staining, paraffin sections were mounted on poly-L-lysine coated slides, post-fixed into ice-cold acetone fixation fluid for 10 minutes, air dried and stored at room temperature (RT) until use.
For the detection of HepPar1, slides were deparaffinized, immersed in 10 mM Tris, 1 mM EDTA buffer (pH 9), heated in a microwave oven for 10 minutes for antigen retrieval, cooled down for 10 minutes at RT and washed in PBS buffer. Endogenous peroxidase activity was blocked by 0.3% H2O2, in methanol, for 30 minutes at RT. After washing with PBS buffer containing 0.1%Tween-20, background staining was blocked by incubating the sections with normal goat serum (1:10 diluted in PBS), for 30 minutes. Sections were incubated overnight at 4°C with the primary antibody HepPar1 (clone OCH1E5, Dakocytomation, Glostrup, Denmark) diluted 1:50 in PBS. After washing in PBS-Tween, slides were incubated in DAKO EnVision™ + reagent, HRP-labeled (Dakocytomation,) for 45 minutes at RT. After washing in PBS buffer, sections were developed using 3,3-diaminobenzidine as chromogen, and counterstained with hematoxylin.
For the detection of CK7, the slides were treated as described above but without antigen retrieval, and incubated overnight at 4°C with mouse anti-human CK7, clone OV-TL 12/30 (Dakocytomation), diluted 1:25 in PBS with 1% bovine serum albumin. For both HepPar1 and CK7, formalin-fixed paraffin-embedded canine liver and kidney tissue controls were incubated with and without the primary antibody. In contrast to the cell culture, in the liver tissue antigen retrieval for CK7 was necessary and, therefore, a 40-minute proteinase-K (Dakocytomation) digestion at RT was performed before blocking of the endogenous peroxidase, in methanol.
Biochemical characterization
The content of a T80 cm2 flask with the cHCC cell culture was harvested, spun down for 5 minutes at 1,500 g, the cell pellet was washed in 10 ml PBS, centrifuged for 5 minutes at 1,500 g and the cells were resuspended in 1 ml PBS. Two hundred μl of the cell suspension were lysed and homogenized with a pestle in RIPA buffer containing 1% Igepal, 0.6 mM phenylmethylsulfonyl fluoride, 17 μg/ml aprotinine and 1 mM sodium orthovanadate (Sigma Chemical Co., Zwijndrecht, The Netherlands), for 30 minutes on ice. Total protein concentrations were calculated using a Lowry-based assay (DC Protein Assay, BioRad, Veenendaal, The Netherlands).
For the liver enzyme measurement, 800 μl of the cell suspension was centrifuged at 12,100 g for 5 minutes. The pellet was lysed in milliQ by vortexing, centrifuged again for 5 minutes at 12,100 g and the supernatant was analyzed in a Beckman Synchron CX7 analyzer. The following enzymes were measured: ALT, AST and GLDH. AST and ALT were measured at 37°C with the Tris-pyridoxal phosphate method with Beckman-Coulter reagent. GLDH was measured with Roche reagent. All samples were subjected to the external quality control mission of the Dutch Foundation for Quality Assessment in Medical Laboratories.
As comparison, the widely used human hepatoma cell line HepG2 (Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, DSMZ, Germany) and MDCK canine kidney cell line (own collection) were used. These were grown to 80–100% confluency (T80 cm2 flask) under standard culturing conditions, as described for the cHCC cell line.
RNA isolation and reverse-transcription PCR
Total cellular RNA was isolated from each frozen canine liver tissue in duplicate and from all the cell cultures used in this study using the Qiagen RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The RNA samples were treated with DNase-I (Qiagen RNase-free DNase kit). In total 3 μg of RNA was incubated with poly (dT) primers at 42°C for 45 min, in a 60 μl reaction, using the reverse transcription system (Promega Benelux, Leiden, The Netherlands).
Molecular characterization
To examine whether the cell line consisted of hepatocytes, we isolated total RNA and made cDNA as described above. PCRs were performed to investigate the gene expressions of hepatocyte (albumin, alfa-fetoprotein, ceruloplasmin) markers. All reactions were performed in a 50 μl volume with a thermal cycler (MJ Research Inc., Watertown, MA). Reaction mixtures contained 0.2 μM of each oligonucleotide primer (Isogen Life Science, Maarssen, The Netherlands), PCR buffer (Invitrogen Corporation, Carlsbad, CA), 2.5 U of Platinum Taq polymerase (Invitrogen), 2 mM MgCl2 (Invitrogen) and 250 μM of each nucleotide (Promega Corporation, Madison, WI). The PCR conditions were: initial denaturation at 95°C for 4 min, followed by 40 cycles consisting of denaturation at 95°C for 1 minute, annealing at 60°C for 1 minute, elongation at 72°C for 1 min, and, finally, an elongation step at 72°C for 10 min. The PCR products were analyzed on a 1.5% agarose gel, and the DNA fragments were visualized with ethidium bromide. The primers used for these PCRs are depicted in Table 1.
Table 1 Oligonucleotides for RT-PCR used in this study.
Primer Target Primer sequence (5'-3')
mutMETF1 C-terminal part of canine cMET CCT TGG AAA AGT AAT AGT TC
mutMETR1 C-terminal part of canine cMET GTT TCA TGT ATG GTA GGA C
mutMETF2 C-terminal part of canine cMET GAA GTT TCC CAG TTT CTG AGC
mutMETR2 C-terminal part of canine cMET AAG GGT ATG GAG CAA CAC AT
CSA U Serum albumin GTT CCT GGG CAC GTT TTT GTA TGA
CSA L Serum albumin CTT GGG GTG CTT TCT TGG TGT AAC
Ceruloplasmin U Ceruloplasmin GGA ATA TGA GGG GGC CAT CTA TC
Ceruloplasmin L Ceruloplasmin GCA CGT CCA CTT CAT TAC CCA TGC C
alpha-fetoprot U alpha-fetoprotein GGC TGC TCC GCC ATC CAT CC
alpha-fetoprot L alpha-fetoprotein TTT TCC CCA TCC TGC AGA CAC TCC
Mutations in c-MET
To investigate mutations in the tyrosine kinase domain of c-MET, a PCR was performed with two overlapping primer sets for this domain (Table 1), both resulting in an approximately 750 bp product. PCR conditions were as described above with an annealing temperature of 50°C. The products were sequenced using an ABI 3100 Genetic Analyzer (Applied Biosystems, Nieuwerkerk a/d IJssel, The Netherlands). Sequence analysis and alignments were performed with Lasergene software (DNASTAR Inc., Madison, WI).
Samples for Real Time PCR
Quantitative gene expression measurements of the cHCC cell line were compared with a group of four healthy liver tissues. Liver biopsies from the healthy dogs, which included two Cairn terriers (breed of the donor dog), were obtained under local anesthesia with a 16G biopsy needle and immediately snap-frozen and stored at -70°C until further analysis.
Quantitative measurements of mRNA levels of gene products involved in neoplastic growth and apoptosis
Real-Time PCR based on the high affinity double-stranded DNA-binding dye SYBR green I (SYBR® green I, BMA, Rockland, ME) was performed in triplicate in a spectrofluorometric thermal cycler (iCycler®, BioRad). Per reaction, 1.67 μl of cDNA was used in a 50 μl volume containing 1 × manufacturer's buffer, 2 mM MgCl2, 0.5 × SYBR® green I, 200 μM dNTP's, 0.4 μM of each oligonucleotide primer, 1.25 units of AmpliTaq Gold (Applied Biosystems), on 96-well iCycler iQ plates (BioRad). Primers (Table 2) were designed using PrimerSelect software (DNASTAR Inc.). All PCR protocols included 40 cycles consisting of denaturation at 95°C for 20 sec, annealing for 30 sec, and elongation at 72°C for 30 sec. Melt curves (iCycler, BioRad), gel electrophoresis, and sequencing were used to examine each sample for purity and specificity. For each experimental sample, the amount of the genes under study, and of the housekeeping genes HPRT and beta-actin, were determined from the appropriate standard curve in autonomous experiments. Results were normalized according to the average amount of housekeeping genes and the values divided by the normalized values of the healthy group to generate relative expression levels [5]. Statistical analysis was performed using the Student T-test, and the level of significance was set to a p value 0.05.
Table 2 Oligonucleotides for quantitative RT-PCR used in this study.
Primer Extension Primer sequence (5'-3') Tm Product size (bp) Accession Number
HPRT U AGCTTGCTGGTGAAAAGGAC 56 100 L77488/ L77489
L TTATAGTCAAGGGCATATCC
Beta-actin U GATATCGCCGCGCTCGTCGTC 55 350 Z70044
L GGCTGGGGTGTTGAAGGTCTC
c-MET U TGTGCTGTGAAATCCCTGAATAGAATC 58 112 AB118945
L CCAAGAGTGAGAGTACGTTTGGATGAC
PTEN U AGATGTTAGTGACAATGAACCT 64,5 110 U92435
L GTGATTTGTGTGTGCTGATC
P27Kip U CGGAGGGACGCCAAACAGG 59 90 AY455798
L GTCCCGGGTCAACTCTTCGTG
Bcl-2 U TGGAGAGCGTCAACCGGGAGATGT 62 87 AB116145
L AGGTGTGCAGATGCCGGTTCAGGT
Beta-catenin U ATGGGTAGGGCAAATCAGTAAGAGGT 64 107 AY485996
L AAGCATCGTATCACAGCAGGTTAC
TGF-alpha U CCGCCTTGGTGGTGGTCTCC 63 122 AY458143
L AGGGCGCTGGGCTTCTCGT
SOCS3 U ACACCAGCCTGCGCCTCAAGACCT 63 119 AY485997
L CGCCTCGCCGCCCGTCA
Collagen I U GTGTGTACAGAACGGCCTCA 61 111 AF056303
L TCGCAAATCACGTCATCG
ODC U GTGGGCGATTGGATGCTCTTTG 59 111 BI395954
L TGTTGGCCCCGACATCACATAGTAG
Ethics
All our procedures concerning animal use were approved by the owners and by the Utrecht University's Ethical Committee, as required by the Dutch law. The animals received human care in line with the University's guidelines.
Abbreviations
ALT – alanine aminotransferase; Akt/PKB – ser/thr protein kinase B (also PKB/Akt); AST – aspartate aminotransferase; CK7 – cytokeratin 7; GLDH – glutamate-lactate dehydrogenase; HCC – hepatocellular carcinoma; HepPar1 – hepatocyte paraffin-1; HGF – hepatocyte growth factor; HPRT – hypoxanthine phosphoribosyl transferase; MDCK – Madin-Darby canine kidney cells; ODC – ornithine decarboxcylase; P27KIP – kinase inhibitor protein 27 kDa; PTEN – phosphatase and Tensin homolog deleted on chromosome TEN; SOCS3 – suppressors of cytokine signalling type 3; TGF-alpha – transforming growth factor alpha.
Author's contributions
SYB carried out the establishment, the biochemical and molecular characterization, and the mutations in c-Met study. BS carried out the quantitative RT-PCR, whereas JY and RK carried out the histomorphological part. TvdI performed the description of the initial tumor. SYB, HFE, TvdI, JR and LC participated in the study design and coordination of the study. All authors read and approved the final manuscript.
Acknowledgments
We appreciate the technical assistance given by Brigitte Arends, Jos Vossen and Ronald Molenbeek, of the Faculty of Veterinary Medicine, Utrecht University, The Netherlands.
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| 15566568 | PMC535891 | CC BY | 2021-01-04 16:38:24 | no | Comp Hepatol. 2004 Nov 26; 3:9 | utf-8 | Comp Hepatol | 2,004 | 10.1186/1476-5926-3-9 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-391556657810.1186/1477-7819-2-39ResearchOdontogenic tumors in Nigerian children and adolescents- a retrospective study of 92 cases Ajayi Oluseyi F [email protected] Akinola L [email protected] Wasiu L [email protected] Mobolanle O [email protected] Department of Oral Pathology and Biology, College of Medicine, University of Lagos P.M. B. 12003, Lagos, Nigeria2 Department of Oral and Maxillofacial Surgery, College of Medicine, University of Lagos, P.M. B. 12003, Lagos, Nigeria2004 27 11 2004 2 39 39 27 8 2004 27 11 2004 Copyright © 2004 Ajayi et al; licensee BioMed Central Ltd.2004Ajayi 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
Tumours arising from odontogenic tissues are rare and constitute a heterogenous group of interesting lesions. The aim of this study was to determine the relative frequency of odontogenic tumors (OT) among Nigerian children and adolescents 19 years or younger.
Patients and methods
The histopathology records were retrospectively reviewed for all the tumors and tumor-like lesions of the oral cavity and the jaws seen in children and adolescents ≤ 19 years seen between January 1980 and December 2003. Hematoxylin and eosin-stained sections were re-evaluated and the diagnosis in each case was confirmed or modified according to World Health Organization (WHO) classification, 1992; and were subjected to analysis of age, sex, site of tumor and histopathologic type.
Results
A total of 477 tumors and tumor-like lesions were seen in patients ≤ 19 years during the period of the study. Of these, 92 (19.3%) were odontogenic tumors. Benign odontogenic tumors constituted 98.9% of the cases seen, while only 1 case (1.1%) of malignant variety was seen during the period. The mean (SD) age of patients was 14.9 (± 3.1) years (range, 4–19 years). Male-to-female ratio was 1:1; and mandible-to-maxilla ratio was 2.7:1. OT's were most frequently seen in patients aged 16–19 years (46.7%) and the least number (2.2%) were found in patients aged 0–5 years. Among nine histologic types of OT seen, ameloblastoma (48.9%), adenomatoid odontogenic tumor (19.6%) and odontogenic myxoma (8.7%) were predominant. Multicystic/solid and unicystic variants of ameloblastoma were diagnosed in 40 (89%) and 5 (11%) cases respectively.
Conclusions
Odontogenic tumors are relatively common in children and adolescents in Nigeria. One out of every 5 children and adolescents with tumors and tumor-like lesions of oral cavity and the jaws seen in this study had a diagnosis of odontogenic tumor.
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Background
Tumors and tumor-like growths arising from the odontogenic tissues constitute a heterogenous group of particularly interesting lesions, as they display the various inductive interactions that normally occur among the embryologic components of the developing tooth germ [1]. In humans, tumors of odontogenic tissues are comparatively rare, comprising of about 1% of all jaw tumors [2]. In children and adolescents, neoplastic lesions are often benign and are of mesenchymal origin [3,4]. Choung and Kaban [5] were of the opinion that tumor histology in this age group did not correspond to their clinical behaviour.
There are few reports especially from Africa, which have specifically reported the high frequency of odontogenic tumors (OT) in children and adolescents in the literature. In most previous African reports, odontogenic tumours in children were presented as part of orofacial [3] or oral tumors in this age group [3,6]; or presented as specific tumours e.g. ameloblastoma or adenomatoid odontogenic tumor [7,8]. We could find only a single report on odontogenic tumors in children and adolescent in African environment [9]. The aim of this study was to determine the relative frequency of odontogenic tumors among children and adolescent ≤ 19 years seen over a period of 24 years (1980–2003).
Patients and methods
The histopathology records of the Department of Oral Pathology and Biology, College of Medicine, University of Lagos, Lagos, Nigeria, were reviewed for all the tumors and tumor-like lesions of the oral cavity and the jaws seen in children and adolescent ≤ 19 years from January 1980 to December 2003. Hematoxylin and eosin-stained sections was re-evaluated and the diagnosis in each case was confirmed or modified according to World Health Organization (WHO) 1992 classification [10]. The data was subjected to analysis of age, sex, site of tumor and histopathologic type. The age of the patients was divided in four groups: Group 1 (0–5 years); group 2 (6–10 years); group 3 (11–15 years); group 4 (16–19 years). Data was analyzed using SPSS for Window (version 11.0; SPSS Inc., Chicago, IL) and frequency tables and cross tables were prepared.
Results
A total of 477 tumors and tumor-like lesions were seen in patients ≤ 19 years during the period of the study. Of these, 92 (19.3%) were odontogenic tumors. Benign odontogenic tumors constituted 98.9% of the cases seen, while only 1 case (1.1%) of malignant variety was seen during the period. Table 1 shows the various histologic types and their relative frequency. There were 47 males and 45 females; a male-to-female ratio of 1:1 was observed. The mean (SD) age of patients was 14.9 (± 3.1) years (range, 4–19 years). Most of the patients (46.7%) were in age group 4 and the least number of patients were found in age group 1 (2.2%) Table 2.
Table 1 Relative frequency of odontogenic tumors in children and adolescents (≤ 19 years)
Histological Types Frequency (%)
Ameloblastoma 45 (48.9)
Calcifying epithelial odentogenic tumor 1(1.1)
Ameloblastic fibroma 5 (5.4)
Adenomatoid odontogenic tumor (AOT) 18 (19.6%)
Calcifying odontogenic cyst (COC) 3 (3.3)
Odontoma 4 (4.3)
Odontogenic fibroma 7 (7.6)
Myxoma 8 (8.7)
Ameloblastic carcinoma 1 (1.1)
Total 92 (100)
Table 2 Age distribution of patients with odontogenic tumors (years)
Histological Types Group 1 Group 2 Group 3 Group 4 Total
Age 0–5 Age 6–10 Age 11–15 Age 15–19
Ameloblastoma 1 2 22 20 45
Calcifying epithelial odontogenic tumour 0 0 1 0 1
Ameloblastic fibroma 0 1 1 3 5
Adenomatoid odontogenic tumor (AOT) 0 3 6 9 18
Calcifying odontogenic cyst 0 2 1 0 3
Odontoma 0 1 1 2 4
Odontogenic fibroma 1 0 2 4 7
Myxoma 0 1 4 3 8
Ameloblastic carcinoma 0 0 0 1 1
Total 2 10 38 42 92
Table 3 shows the gender and site distribution of various histologic typing of odontogenic tumors. The tumors occurred more often in the mandible (67) than in the maxilla (25) giving a maxilla-to-mandible ratio of 1:2.7.
Table 3 Distribution of histologic types of odontogenic tumors according to gender and site of tumor
Histological type Number (%) Gender Site
Male Female Mandible Maxilla
Ameloblastoma 45 (48.9) 28 17 40 5
Calcifying epithelial odontogenic tumor 1 (1.1) 1 0 0 1
Ameloblastic fibroma 5 (5.4) 1 4 5 0
Adenomatoid odontogenic tumor 18 (19.6) 6 12 8 10
Calcifying odontogenic cyst 3 (3.3) 2 1 1 2
Odontoma 4 (4.3) 2 2 0 4
Odontogenic fibroma 7 (7.6) 5 2 5 2
Myxoma 8 (8.7) 2 6 7 1
Ameloblastic carcinoma 1 (1.1) 0 1 1 0
Ameloblastoma constituted almost half (48.9%) of the odontogenic tumors with female-to-male and maxilla-to-mandible ratios of 1:1.7 and 1:8 respectively. The mean (SD) age of patients in this group was 15.1 (± 3.0) years (range, 4–19 years) with most patients (49%) in age group 3. Multicystic/solid and unicystic variants were diagnosed in 40 (89%) and 5 (11%) cases respectively.
Adenomatoid odontogenic tumor (AOT) was the second most common tumor in this series (Table 1) accounting for 19.6% of odontogenic tumors in this population. All the patients were between 8 and 19 years (Mean ± SD; 14.6 ± 3.2) with most of them (50%) in age group 4. More females (12) were affected than males (6); a male-to-female ratio of 1:2 was observed. Maxillary lesions (10) were commoner than mandibular lesions (8).
Odontogenic myxoma accounted for 8 cases (8.7%) of odontogenic tumors with a male-to-female ratio of 1:3. Only 1 case occurred in the maxilla, the rest (7) were found in the mandible. The patients were found between 10 and 19 years.
Odontogenic fibroma accounted for 7 cases (7.6%) of tumors in this series. More cases were found in males and in the mandible. Patients were seen between 5 and 19 years in this group.
Ameloblastic fibroma accounted for 5.4% of cases seen. The lesion occurred exclusively in the mandible with a male-to-female ratio of 1:4.
Odontomas accounted for 4.3% of OT seen. They were exclusively found in the maxilla with equal sex predilection.
Table 3 shows the gender and site distribution of other less common odontogenic tumors in children and adolescent ≤ 19 years of age.
Discussion
We present a report of odontogenic tumours in children and adolescents aged ≤ 19 years. This report represents the largest series of odontogenic tumors in children and adolescents in Africa. We found that 19.3% of tumors and tumor-like lesions of the oral cavity and the jaws in children and adolescent in this study were odontogenic tumors. This is similar to the findings of Arotiba [6]. However, other authors have reported higher [9,11-13] or lower [4,14] frequency of these heterogenous group of lesions in children and adolescents.
A major problem in comparing our report with previous studies is the lack of uniformty in the age group studied in those reports. Some studies were restricted to children under the age of 14 years [3,15], or 15 years [4,6,11-13,16], while others included higher age groups [9,14]. Odontogenic tumors were most frequently seen in patients in group 4 in this study (> 15 years) in agreement with Adebayo et al [9]. Al-Khateeb et al [14] reported that odontogenic tumors were most commonly (72%) seen in patients aged 12–18 years in their report. Other authors have reported that odontogenic tumors were most frequently seen in patients aged 11–15 years [4,11,13]; these authors, however considered patients aged 0–15 years in their studies.
Odontogenic tumors were less frequently seen in patients aged 0–5 years in this study in agreement with most reports in the literature [4,9,11-14,17]. About 98% of OT in the present series was found in patients older than 5 years. Many odontogenic tumors are thought to arise from the tooth germ [18]. In most permanent teeth, crown formation is completed by the age of 4 or 5 years; odontogenic tumors seemed to develop after crown formation [12,13]. This strengthens the impression that the majority of odontogenic tumors arise from quiescent remnants of the tooth germ [14].
Odontogenic tumors in children are known to have predilection for the mandible [11-13]. This is also corroborated by our findings with 73% of the tumors in this series found in the mandible. Al-Khateeb et al [14] however found 64% of OT in the maxilla. Odontomas were exclusively found in the maxilla in the present series. Males were slightly affected by OT in our study in agreement with Adebayo et al [11]; whereas Ulmansky et al [4] reported female preponderance in their study.
Ameloblastoma was the most common tumor in this study in agreement with reports from Africa [6,11]. Other authors reported odontoma as the most frequently seen OT in children [12-14]. Ulmansky et al [4] found odontogenic myxoma as the most common OT in children in their study. The gender and site distribution of ameloblastoma in this study is in agreement with other reports [6,11-14]. Ameloblastoma was found in all the age groups considered in this study, unlike other histologic types of OT. Few cases of unicystic variant of ameloblastoma (11%) were seen in the present series. Unicystic ameloblastoma has been reported to be more common in Western children than African children [19]. Previous data from Africa as corroborated by the present series, have shown a low percentage of unicystic ameloblastomas in their patient population compared to other parts of the World [20,21]. Unicystic tumors have a different prognosis to the multi cystic type and are said to be more common in children [19,22,23].
Adenomatoid odontogenic tumor (AOT) was the second most common OT in this study and 50% of this tumor was found in patients >15 years. Asamoa [3] reported AOT as the most frequent pediatric odontogenic tumor in Nigeria; whereas Arotiba [6] found AOT as the second most common OT after ameloblastoma in Nigerian children. The relative frequency of 19.6% found in this study is higher than in other reports [6,9,13,14]. More of this lesion was found in the maxilla in concordance with previous reports [6,7,9,24,25]. OT is reported to be more common in females [1,7,17,26]; and this is also confirmed by our findings.
In children, the reported incidence of myxoma ranges from 1.2% to 39% [4,6,9,13]. Myxoma was the third most common OT in this study with an incidence of 8.7%. Ulmansky et al [4] reported that odontogenic myxoma was the most common OT in Israeli children. The gender predilection favors females in previous publications [4,9,24,26,27]. This study found that the male-to-female ratio was 1:3, in agreement with previous reports. Previous publications [9,24,27] reported that both jaws were equally affected in their reports; a ratio of 1:7 was found in the maxilla and mandible respectively in the present study. Odukoya [26] also found maxilla-to-mandible ratio of 1:3.
Odontogenic fibroma was reported to be rare in children with incidence ranging from 0% to 1.3% of OT [4,9,11,13]. An incidence of 7.8% found in our study was however, similar to that of Al-Khateeb et al [14]. Arotiba [6] reported that 12.5% of odontogenic tumors in his study were odontogenic fibroma. More cases were found in males and in the mandible in agreement with the report of Lu et al [17]; whereas others [1,26] have reported females and maxillary predilection. Ameloblastic fibroma was exclusively seen in the mandible, accounting for 5.4% of OT seen in this study. Females were also affected more than males in the ratio of 1:4. Other authors [17,26] have also reported predilection of ameloblastic fibroma for the mandible but with both jaws affected equally.
Odontomas are often regarded as dental hamartomas, rather than odontogenic neoplasms [14,28,29]. Odontoma is relatively rare in Nigerian children as confirmed in our report and others from Nigeria [6,9,11], accounting for 4.3% of OT in this study. This contrasts the findings of Al-Khateeb et al [14], Tanaka et al [12] and Sato et al [13] that reported odontoma as the most frequently seen OT in North Jordanian and Japanese children respectively. Most odontomas are discovered on routine radiograph and do not produce clinical symptoms [1]. This may be responsible for the low incidence observed in African population, because most patients in our environment do not seek medical consultation unless there are symptoms suggesting an obvious pathology. While some authors reported that odontomas commonly affect the mandible [12,17], others have reported predilection for the maxilla [1,13,14] and some authors have reported equal distribution in both jaws [9,11,25]. Odontomas were found exclusively in the maxilla in the present series.
Malignant odontogenic tumors are rare, most especially in children [1,4,6,9,11,12,15,30]. No cases of malignant odontogenic tumors were found in African children and adolescents [6,9,11,30], Israeli children [4], North Jordanian children and adolescents [14] and Japanese children [12,13]. A case ameloblastic carcinoma of the mandible in a 16-year old girl was, however seen in this study.
Conclusions
Odontogenic tumors are relatively common in children and adolescents in Nigeria. One out of every 5 children and adolescents with tumor and tumor-like lesions of oral cavity and the jaws seen in this study had a diagnosis of odontogenic tumor.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contribution
OFA conceived the study, coordinated the write-up and submission of the article, and also reviewed the slides.
ALL, WLA and MOO did the literature search and participated in the writing of the manuscript.
All the authors read and approved the final manuscript.
Funding Source
None declared
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| 15566578 | PMC535892 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Nov 27; 2:39 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-39 | oa_comm |
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Reprod HealthReproductive Health1742-4755BioMed Central London 1742-4755-1-71555507310.1186/1742-4755-1-7ResearchMale Reproductive Health: A village based study of camp attenders in rural India Dunn Kate M [email protected] Susmita [email protected] Rumeli [email protected] Primary Care Sciences Research Centre, Keele University, Staffordshire, ST5 5BG, UK2 Family Health International (FHI), India Country Office, Opposite Convention Hall, Ashok Hotel, Chanakyapuri, New Delhi 110021, India3 Child in Need Institute (CINI), PO Pailan via Joka, Kolkata 700104. West Bengal, India2004 22 11 2004 1 7 7 5 5 2004 22 11 2004 Copyright © 2004 Dunn et al; licensee BioMed Central Ltd.2004Dunn 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
A paucity of information about male reproductive health and a perceived interest in involvement among local men provided the impetus for carrying out a village based male reproductive health camp. The aim was to investigate men's willingness to participate in such camps, and to describe reproductive health problems in men.
Methods
Structured interviews were carried out with 120 men attending a reproductive health check-up in a village in rural West Bengal, India. General information, details of family planning methods used and data on reproductive health complaints were collected. Clinical examinations were also carried out. Socio-demographic characteristics were compared for men with and without reproductive health and urinary complaints.
Results
Three quarters of the married men were using contraception, but the majority stated that their wives were responsible for it. The most common reproductive health complaint was urinary problems; 28% had burning on urination, and 22% reported frequent and/or difficult urination. There were few social or demographic differences between men with and without problems. Seventeen percent of the men had clinically diagnosed reproductive health problems, the most common being urethral discharge. None of the men with diagnosed problems were using condoms.
Conclusions
This study highlights the interest of men in their reproductive health, but also highlights the high proportion of men with problems. In addition, a number of men with clinically diagnosed problems had not reported them in the interviews, illustrating either the reticence to report or the lack of knowledge about symptoms of reproductive health problems. Recommendations for future programmes and research in this field are given.
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Background
Reproductive health is a major world priority, with particular problems in developing countries. However, as Ndong [1] states, "reproductive health generally has been synonymous with women's health", and reproductive health of men has received little attention [2]. Researchers and health planners have pointed out that better outcomes for reproductive health programmes would be expected if men were involved [2-6], and there are a number of mechanisms by which this might occur. Hawkes [7] indicated that the treatment of male reproductive health problems might actually encourage more women to seek treatment, and therefore improve the overall level of reproductive health. Other studies have highlighted that women often need the support of their husbands, including financial support, to attend reproductive health services [4,8,9] and that the health status of couples, in particular the reproductive health status, is strongly linked to the knowledge, attitudes and behaviour of men [6,10].
The documentation of the prevalence of male reproductive health problems has been identified as a research priority by the World Health Organization (WHO) [5]. Until now limited information on reproductive health problems and health care needs in men from developing countries, particularly from rural areas of India, is available [11,12]. Verma [12] studied male sexual problems in a slum population in Mumbai (India) and found that around half of the men studied could identify symptoms such as itching, burning on urination and white discharge, although the prevalence of these symptoms among these men was not reported. One study from Bangladesh reported that over 10% of men had symptoms that were possibly indicative of a sexually transmitted infection, with the main complaints being urethral discharge and pain passing urine [2].
Information about male reproductive health problems may help in the planning of future services, the needs for screening, prevention and treatment. In order to collect this information, a village based reproductive health camp for men was carried out. Health camps are used to provide services in places where the perceived need and demand is high but access to treatment is poor due to lack of information about available services, distance from health facilities, or low economic status of the population. It is one of the preferred methods of service provision in rural villages. The usual model is a temporary clinic set up by a team of health personnel at a community-owned location (e.g. school or community hall) to provide services and medicines usually free of cost based on the health needs of the community. The aim of this study was to investigate men's willingness to participate in such camps, and to describe reproductive health problems in this group of men. This paper reports on the findings from this health camp.
Methods
Study participants
Men aged 15–60 years, both married and unmarried and living in one rural village were invited to attend. The village is relatively small (approximately 4000 residents), mostly consisting of low-income, Muslim population, and had been highlighted by local health workers as having particularly poor knowledge about reproductive health issues.
Camp procedure
The camp was held in a village school on one day in February 2000. The community was consulted about the camp through a variety of routes, including men's groups, Panchayats (local self government), schoolteachers and specially convened public meetings; all of these groups agreed to the holding of the camp in the village. The camp was publicised through peer educators, health workers and group meetings, and was described as a reproductive health check-up, with free treatment if necessary. A male team was used as feedback from health workers and peer educators in the village indicated that men would be more willing to attend if male doctors carried out the examinations.
Health workers carried out a confidential interview on arrival to obtain demographic information (age, marital status, number of children, employment and income), information about personal hygiene, details about family planning issues and about general health complaints (including fever, dizziness, backache, headache, skin problems) by using a structured interview schedule. Male doctors then asked about reproductive health complaints (including urinary problems, genital itching, penile sores/swelling, urethral discharge) and current sexual activity (sex outside marriage or with a prostitute), and carried out clinical examinations (recording of urethral discharge, genital ulcer, inguinal lymph nodes, penile lesions, scrotal swelling, genital warts or scabies). Blood samples were taken for Venereal Disease Research Laboratory (VDRL) test from all attendees.
Those men detected as having sexually transmitted infections and urinary problems were given the appropriate medication, provided with condoms and referred to hospital when necessary. Advice and instructions for condom use were given and patients were encouraged to bring their partners for treatment.
Setting
The Child in Need Institute (CINI) is a non-governmental organisation (NGO) working in West Bengal, and is one of the four national NGOs in India. The organisation has, for the last 30 years, focused on the health and nutrition of women and children, with the additional involvement of adolescents and men. CINI started working on reproductive health with men in rural areas in 1997. To date, this involvement has entailed orientation and education with men's groups, intensive training of voluntary peer educators and facilitators and individual interaction with health workers. There are at present no specific clinical services for men provided by CINI: men who are identified as having a problem are given individual information and counselling and are referred to other clinics for treatment.
Analysis
The information collected during the interviews, clinical examination and pathology reports was entered into the Epi-Info data entry and analysis package [13]. Statistical analyses were carried out using the SPSS statistical package [14]. The relationship between reproductive health complaints, clinical findings and other health and socio-demographic factors was investigated. P-values for the differences between means were calculated using the independent samples t-test. P-values for the differences between proportions were calculated using the chi-squared test or Fisher's exact test.
Results
One hundred and twenty men attended the camp; all men responded freely to the interview, and all but two agreed to a clinical examination. This sample represents about 10% of the local male population aged 15–60 years.
Demographic information
The mean age in this group of men was 29.6 years (range 16–60). Fifty-nine percent of the men were married (n = 71); the mean age at marriage was 22.6 years (range 14–34). Fifty-two percent of the attendees had children (comprising 90% of the married men); among the men with children, the mean number was 2.8 (range 1–7). The literacy rate was 71% among the attendees, although more than half of these had only studied up to class V or less. Eighty four percent of the attendees (n = 85, data missing for 19 attendees) had a monthly income of less than Rs. 2000 (~$45); 40% (n = 48) were daily wage earners, and a further 30% (n = 36) were in service (usually unskilled office workers), 15% (n = 18) were either unemployed or students. One third of the men worked away from home for at least one night each month, half of these for 1–5 days, and half for more than 5 days per month. Only 11 attendees indicated that they consumed alcohol.
Twenty percent (n = 24) reported good personal hygiene (washing genital areas and changing underwear daily), however 28% (n = 33) had very poor hygiene practices (washing genital area and/or changing underwear less than twice a week).
General health
Four fifths of men (82%) reported at least one problem with their general health, the most commonly reported problems were coughs and colds (45%, n = 54), and weakness (28%, n = 34), other problems included indigestion, backache and headache. Two men were identified as having hypertension.
Family planning
Forty-three percent of all men (n = 51) reported that they or their wives were using some kind of family planning, this represented 73% of the married men; only one unmarried man was using contraception (condoms). Over half of the men stated that their wives are responsible for family planning, they are using the oral contraceptive pill, have an intrauterine device or had sterilisation. Eight men were using condoms, and one man had had a vasectomy. The main reason given for not using family planning was being unmarried. Two men reported a lack of knowledge of family planning, two men had religious objections and five men said that they wanted children.
Sexual behaviour
Six married men admitted to having extramarital sex, three of them with prostitutes. Seven unmarried men (14%) reported having sexual relations; three of them with prostitutes. None of the men visiting prostitutes reported using condoms, however one married man having extra marital sex reported using condoms. It did not appear that the men visiting prostitutes or having extra-marital sex were any different from the other men in terms of monthly income, education or number of nights working away from home.
Reproductive health complaints
Half of the attendees reported at least one reproductive health complaint (n = 60) (table 1). Only three men with a reproductive health complaint reported using condoms (table 2). There were no statistically significant differences in age, monthly income, days of absence from home or marital status between men with and without reporting of sexual health problems. However, men who have complaints have on average fewer years of education (3.3) compared to men who do not have complaints (4.9). Similar proportions of men were married in those with and without reproductive health complaints (table 2). The most common complaints were urinary problems, which were reported by 43% of attendees (n = 51); 28% (n = 33), of men complained of burning on urination and 23 % (n = 27) reported frequent and/or difficult urination.
Table 1 Self reported complaints of reproductive health problems
Complaint No of attendees % (n = 120)
Burning on urination 33 28%
Frequent urination 20 17%
Difficulty urinating 7 6%
Strong urine 4 3%
Any urinary complaint 51 43%
Premature ejaculation 6 5%
Urethral discharge 6 5%
Genital itching 5 4%
Impotence 2 2%
Primary infertility 2 2%
Any reproductive health complaint 60 50%
Table 2 Socio-demographic characteristics in men with and without reproductive health & urinary complaints
Demographic variable Any reproductive health complaint Burning on urination Frequent and/or difficult urination
Yes
n = 60 No
n = 60 p-value Yes
n = 87 No
n = 33 p-value Yes
n = 94 No
n = 26 p-value
Age (mean years) 29.8 29.1 0.761† 28.3 29.9 0.498† 31.7 28.8 0.249†
Monthly income (mean rupees) 1447 1411 0.905† 1283 1493 0.528† 1146 1515 0.307†
Education (mean years) 3.3 4.9 0.009† 3.7 4.2 0.476† 3.2 4.3 0.126†
Time away from home (mean days per month) 2.8 1.9 0.330† 2.5 2.3 0.825† 3.6 2.0 0.141†
Marital status: no., (%) married 35 (58%) 34 (57%) 0.853‡ 17 (52%) 52 (60%) 0.414‡ 16 (62%) 53 (56%) 0.638‡
Condom use: no. (%) using condoms 3 (5%) 5 (8%) 0.717* 3 (5%) 5 (8%) 0.683* 1 (2%) 7 (12%) 1.000*
Sex with prostitute: no. (%) of men 2 (3%) 4 (7%) 0.679* 1 (2%) 5 (8%) 1.000* 0 6 (10%) 0.338*
Good personal hygiene: no. (%) of men** 13 (26%) 11 (19%) 0.492* 10 (32%) 14 (18%) 0.129* 4 (20%) 20 (23%) 1.000*
† p-value for the difference between means using independent samples t-test; ‡ p-value using chi-squared test
* p-value using Fisher's exact test; ** Reporting washing genital areas and changing underwear daily
Clinical reproductive health findings
On clinical examination, 20 men (17%) had findings of reproductive health problems, (table 3), fourteen of these men had reported reproductive health complaints in the interview. None of the men with clinical findings reported using condoms, and nearly half (n = 9) had poor self-reported hygiene. Two of the men with positive findings reported visiting prostitutes, and five reported having extramarital sex. The most common clinical finding was urethral discharge, which was found in 13 men, this included all 6 men who had reported urethral discharge in the interview (table 4); two of these men reported having sex with a prostitute. Painful genital lesions were found in two men, this could be an indicator of the herpes virus; these men were both married and one was visiting a prostitute, neither used condoms and neither reported penile sores in the interview, although both reported genital itching (table 4). Only one man had a positive VDRL result, he had reported genital itching, the clinical examination found a genital ulcer and swollen and painful lymph nodes. He was married, and reported having extra marital sex with a prostitute, and said he did not use condoms.
Table 3 Clinical findings of reproductive health problems
Clinical sign No of attendees % (n = 118)
Urethral discharge 13 11%
Genital ulcer 2 2%
Swollen lymph nodes 5 4%
Painful genital lesions 2 2%
Scrotal swelling 0 -
Genital warts 0 -
Scabies 2 2%
Fungal infection 2 2%
Any clinical sign 20 17%
Table 4 Comparison of self reported complaints and clinical findings of reproductive health problems (no. of attendees)
Clinical finding
Self-reported complaint Urethral discharge
(n = 13) Genital ulcer
(n = 2) Swollen lymph nodes
(n = 5) Painful genital lesions
(n = 2) Scabies
(n = 2) Fungal infection
(n = 2) Any clinical sign
(n = 20)
Burning on urination (n = 33) 4 0 2 1 2 0 7
Frequent urination (n = 20) 4 0 1 0 0 0 4
Difficulty urinating (n = 7) 1 0 2 1 1 0 4
Strong urine (n = 4) 2 0 1 1 2 0 3
Any urinary complaint (n = 51) 8 0 3 1 2 0 11
Premature ejaculation (n = 6) 0 0 1 1 1 1 2
Urethral discharge (n = 6) 6 0 1 0 1 0 6
Genital itching (n = 5) 1 1 2 2 1 0 3
Any reproductive health complaint (n = 60) 10 1 4 2 2 1 15
There was a trend that men having extramarital sex and men having sex with prostitutes were more likely to have positive clinical findings than men not having extramarital sex (5/13 vs. 15/105; odds ratio 3.8, 95% confidence interval 1.1 to 13.0 and 3/6 vs. 17/112; OR 5.6, 95% CI 1.0 to 30.0, respectively). None of the eight men using condoms had positive clinical findings compared with 18% (n = 20) of the men not using condoms.
Discussion
This study indicates that there is a high level of reproductive health problems among those men attending a reproductive health camp in a rural area of India. More than one in ten men had urethral discharge and over one third reported urinary problems. The high number of men reporting urinary symptoms is similar to that of an unpublished study in Uttar Pradesh, India [12]. However, a survey in Bangladesh reported pain passing urine in only 8% of men questioned [2]. In our study, 14% of unmarried men admitted to having sexual relations and 8% of married men reported extra-marital sex in our study, which lies within the expected range from other studies in Southern Asia [11,12].
The enthusiasm from men for involvement in reproductive health programmes has been reported elsewhere in Southern Asia [2,15]. Factors perceived to have maximised the attendance rate may have included the use of purely male staff at the camp, the promotion of a 'check-up' rather than just treatment, the consultation with local people and the placement of the camp in a local school (based on feedback from the health workers and peer educators). The attendees appear to be similar to the local population in terms of demographic factors such as marital status, education and income; however, the attendees may not be representative of all the men in the area. It is possible that men who thought they had a problem might be more likely to attend a reproductive health check-up; conversely, men who perceived that they had problems might be less likely to attend through fear. Studies maximising the generalisability of the sample would improve our understanding of the problems in this section of the population.
Wang [5] identified the level of responsibility that men hold for the consequences of their sexual behaviour as a priority area, and other researchers have put this emphasis primarily on condom use [3]. This study has shown that none of the men with clinical findings of reproductive health problems, nor any of the men visiting prostitutes, reported using condoms. Men who did not use a condom, who had sex with a prostitute or who had extramarital sex also appeared to be more likely to have clinical findings of reproductive health problems. The uptake of condom use was equally low compared to other reports, making the promotion of condom use in this population even more important [11].
The high proportion of men reporting urinary problems has also highlighted the need testing of urinary samples. One study in Bangladesh indicated that among 47 men reporting pain passing urine, only one was infected with Chlamydia trachomatis [2]; we are not aware of similar data for Indian samples.
A significant number of men did not complain of reproductive health problems (such as urethral discharge) during the interview, but had positive clinical findings (table 4). This incoherence has been reported in other studies [2,11], and could reflect a reticence about admitting to such problems, or that the men were unaware of their symptoms which may explain the low proportion of men seeking treatment for their problems [8]. Therefore, the dissemination of information about symptoms of reproductive health problems highlights another important area for further investigation.
There are some limitations to this study. First of all the numbers were relatively small and a larger sample size might have given further insight. Information obtained from urethral smears of all attendees may have provided important information. However, this was not possible in our setting due to technical problems. Further, not all areas of relevance to reproductive health were addressed in this study. Psychosexual problems have been commonly reported elsewhere [2], but were not asked about as part of this study. Some priority areas for male reproductive health, namely HIV/AIDS and male infertility [5], have not been addressed here. These areas were not addressed as CINI did not have sufficient resources, and there were no referral linkages to appropriate facilities at the time. Inclusion of these problems in future research is vital in improving overall reproductive health.
Conclusions
This study provides important information about male reproductive health problems in a sample of men in rural West Bengal, India. The response of the men and the fact that nearly all attenders were willing to undergo a clinical examination is encouraging, and emphasises the possibilities for future research. The high level of reproductive health problems identified on clinical examination, but not reported in interviews, plus the low levels of condom use illustrate the need for reproductive health interventions with such groups. Future research could build on the findings of this exploratory study.
List of abbreviations
CINI = Child in Need Institute
VDRL = Venereal Disease Research Laboratory
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KMD participated in the design of the study, performed the statistical analysis and drafted the manuscript. SD conceived the study, participated in its design, co-ordination, data entry and analysis and contributed to the manuscript. RD conceived the study, and participated in the design, co-ordination and analysis. All authors read and approved the final manuscript.
Acknowledgements
This work was carried out as part of a Reproductive Health Project funded by the MacArthur Foundation, USA. Dr Dunn's post was arranged through VSO, an international development charity. We would like to thank all the staff at the Child in Need Institute for their co-operation and advice.
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Dudgeon MR Inhorn MC Men's influences on women's reproductive health: medical anthropological perspectives Social Science & Medicine 2004 59 1379 1395 15246168 10.1016/j.socscimed.2003.11.035
Pachauri S Male involvement in reproductive health care Journal of the Indian Medical Association 2001 99 138 141 11478756
Wang YF Male reproductive health research needs and research agenda: Asian and Chinese perspective Asian Journal of Andrology 1999 1 13 20 11225899
Wegner MN Landry E Wilkinson D Tzanis J Men as partners in Reproductive Health: From Issues to Action International Family Planning Perspectives 1998 24 38 42
Hawkes S Morison L Foster S Gausia K Chakraborty J Peeling RW Mabey D Reproductive-tract infections in women in low-income, low-prevalence situations: assessment of syndromic management in Matlab, Bangladesh Lancet 1999 354 1776 1781 10577639 10.1016/S0140-6736(99)02463-0
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Bloom SS Tsui AO Plotkin M Bassett S What husbands in northern India know about reproductive health: correlates of knowledge about pregnancy and maternal and sexual health J Biosoc Sci 2000 32 237 51 10765613 10.1017/S0021932000002376
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Dean AD Dean JA Coulombier D Brendel KA Smith DC Burton AH Dicker RC Sullivan K Fagan RF Arner TG Epi Info: a word processing, database and statistics program for epidemiology on microcomputers. [Version 6] 1994 Atlanta, Georgia, USA, Centers for disease control and prevention.
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| 15555073 | PMC535893 | CC BY | 2021-01-04 16:38:10 | no | Reprod Health. 2004 Nov 22; 1:7 | utf-8 | Reprod Health | 2,004 | 10.1186/1742-4755-1-7 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-491554648310.1186/1471-2334-4-49Research ArticleA national survey of the prevalence of schistosomiasis and soil transmitted helminths in Malaŵi Bowie Cameron [email protected] Bernadette [email protected] Bina [email protected] Peter [email protected] Maria [email protected] Department of Community Health, College of Medicine, University of Malawi, Blantyre, Malawi2 Sussex Health Protection Unit, Lewes, East Sussex, UK3 Community Health Sciences Unit, Ministry of Health, Lilongwe, Malawi4 PO Box 345, Mangochi, Malawi5 Department of Microbiology, College of Medicine, University of Malawi, Blantyre, Malawi2004 16 11 2004 4 49 49 30 3 2004 16 11 2004 Copyright © 2004 Bowie 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
Past estimates have put the prevalence of schistosomiasis between 40% and 50% in the Malawi population overall based on studies undertaken ten years or more ago. More recent surveys in known high risk areas find similar levels. However control measures, changing ecology and migration may have led to changes in the prevalence of schistosomiasis in different parts of Malawi. A national schistosomiasis and soil-transmitted helminth (STH) survey was undertaken to measure the distribution, prevalence and intensity of infection in November 2002.
Methods
A school was selected randomly from a random sample of 30 Traditional Authorities stratified by six distinct ecological zones, and 1,664 year 3 pupils (9–10 year olds) were questioned about recent illnesses and "red urine". Samples of urine and faeces were examined for the presence of eggs using the standard Kato-Katz technique for soil-transmitted helminths and intestinal schistosomiasis and urine samples using the filtration technique for Schistosoma haematobium.
Results
The prevalence of Schistosoma mansoni is 0.4% (95% CI 0–1.3%), S. haematobium 6.9% (95% CI 1.9 – 11.9%), hookworm 1.3% (95% CI 0.4–2.3%), Ascariasis 0.5% (95% CI 0.1–1.0%) and trichuriasis 0% in year 3 pupils (modal age 10 years of age). Intensity of infection is low for all infections except for 2.5% who have high intensity S. haematobium infection. The "red urine" question is 67% sensitive and 80% specific for positive S. haematobium microscopy.
Conclusions
The reduction in prevalences may be real as a result of recent control measures, or false if historical results were based on surveys of high risk populations. Another explanation is that this survey used an unrepresentative sample of schools. Detailed analysis suggests this is unlikely.
Recommendations include the use of a 30% positive threshold for the "red urine" screening question to be used in schoolchildren in high prevalence areas.
This survey, based on a national probability sample excluding the northern region lakeside area, finds much lower overall prevalence and intensity of schistosomiasis and STHs than previous estimates based on selected surveys. Disease control featuring chemotherapy may be having a profound effect. The localised nature of the distribution of the infections means that control programmes may work best if undertaken at district level or below. "Red urine" questionnaire surveys may help identify hot spots.
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Background
Chronic infections with soil-transmitted helminths and schistosomiasis are common in Malaŵi and cause considerable morbidity [1]. The Ministry of Health wishes to establish the distribution of these infections prior to a reassessment of national policy regarding their control.
All water bodies in Malawi are considered to be potential transmission sites for schistosomiasis [2]. The most recent national estimate puts the prevalence of schistosomiasis between 40% and 50% of the population [3]. This is based on surveys carried out prior to 1991. Numerous local surveys have been conducted more recently in different parts of the country [4-7]. In a survey carried out by Randall et al in 1996 in northern Malawi, S. mansoni and S. haematobium were detected in 27% and 20% of schoolchildren respectively [8]. A survey performed by the Bilharzia Control Programme in Mangochi reported in 1999 showed between 60–80% of school children living along the lakeshore has S. haematobium [9]. This level of infection was confirmed in a later survey performed by the Lakeshore Schistosomiasis Control Project, which found the prevalence amongst school aged children (5–15 years old) to be as high as 80% along the lake shore, with some areas having 100% infection rates [2].
In Malawi there is little contemporary information available on helminth infection. Furthermore, fragmented control measures, changing ecology [10] (such as considerable water resource development and deforestation in the last 10 years) and migration may have led to marked changes in the prevalence of schistosomiasis within regions in Malawi since previous studies were done.
A national schistosomiasis and soil-transmitted helminth (STH) survey was undertaken to measure the distribution, prevalence and intensity of infection in November 2002. Results from the survey are reported in this paper.
Methods
Methods follow recent WHO advice to allow comparison with other international studies [11]. This includes a symptom and illness questionnaire and urine and faecal sampling. The use of questionnaires as a rapid assessment tool for urinary schistosomiasis has been evaluated in multi-country studies [4].
Seven areas (with the number of Traditional Authorities in each) represent the seven distinct ecology and soil conditions in Malaŵi (figure 1):-
Northern Region highland (23)
Northern Region lakeside
Central Region highland (62)
Central Region lakeside (17)
Southern Region highland (51)
Southern Region lakeside (31)
Urban (3)
The northern region lakeside had recently been surveyed by the Karonga Prevention Study [8]. To save expense this ecological area was not resurveyed. Hence only six areas were included in this survey.
Study population
Primary-school children were chosen as the target population. There are several reasons for conducting the survey in this age group. Children consistently have the highest prevalence and transmission of schistosomiasis and soil-transmitted nematodes (except hookworm); treatment via schools on a national scale is feasible [12] (and primary school enrolment is reasonably high in Malawi) and extremely cost-effective [13]. Third-year school classes (9–10 year olds) are commonly surveyed and this was the age-group for this survey.
Sample frame and selection
The sample was selected using a stratified multi-stage cluster design [14]. The six ecological zones constituted the strata. School children were clustered within schools which were clustered within Traditional Authorities (TA). Five Traditional Authorities (TA) were randomly selected from the total in each ecological zone using a computer generated random numbers technique. A list of all primary schools in these TAs was compiled by contacting the relevant District Education Officer and one school was selected at random. Private schools were included though the vast majority were government schools. In the three urban districts of Blantyre, Lilongwe and Mzuzu five schools were selected. A total of 30 schools were surveyed between 25 September and 14th November 2002. A census of all children attending standard 3 who were present on the day of the survey was taken and all were enrolled. Thus between 36 and 71 third year schoolchildren (aged 8–10 years) in 5 schools selected at random from each of six ecologically distinct regions of the country were sampled (Figure 2). A total of 1664 children were enrolled.
Questionnaire
All children were administered a standard symptom questionnaire by a class teacher in their local language [see Additional file 1]. The questionnaire was completed before specimen collection and was designed to assess general health without biasing responses towards helminthiasis in particular. Children were asked about symptoms during the past month [e.g. cough, itch, headache, fever] and whether they had particular illnesses during the past month [e.g. malaria, diarrhoea, skin, eye disease, bilharzia].
Specimen collection methods
School children were asked to provide a stool sample and at least 10 ml of urine (collected mid-morning). Stool samples were stored in a cool box during transfer to the laboratory, and then kept at 4°C until processed within 24 hrs of collection.
Urine and faecal analysis
Stool samples were tested for the presence of eggs using the standard Kato-Katz technique [15] for soil-transmitted helminths and intestinal schistosomiasis. Samples with ascaris more than 50,000 eggs per gram (epg), hookworms more than 4,000 epg, trichuria more than 10,000 epg and S. mansoni more than 400 epg were defined as high intensity. Urine samples were tested using the filtration technique for S. haematobium. Infection with S. haematobium was classified as high intensity if at least 50 eggs per 10 ml of urine were detected or there was visible haematuria [16].
Data analysis
Data were double entered into EpiInfo and then exported into STATA v7 for initial analysis. SPSS was used for chi squared, paired-t and McNemar tests. EpiInfo 2002 was used to calculate adjusted prevalence rates for ecological zones and national prevalence. The prevalences and 95% confidence intervals were computed incorporating the stratification by ecological zones. Sampling weights were computed as the product of the number of TA's in an ecological zone times the number of schools in the selected TA. For the urban zone, weights were adjusted for the fact that there were only three TA's and two schools were selected from two of them (Complex sample frequency analysis, Epi Info 2002, CDC Atlanta). The relationship between the prevalence and intensity of S. haematobium infection in schools was investigated using Pearson product moment correlation coefficient (SPSS).
Results
The sample
A total of 1,664 children were surveyed. Between 36 and 71 children were included from each school. There were 1,662 completed questionnaires, 1,546 stool samples and 1,638 urine specimens collected. Just over half (50.7%) of the sample was female. The mean age (for 1,658 children where age was recorded) was 10.7 years [range 6–17 years]. The mean age of boys was 10.9 years and of girls 10.5 years.
Prevalence and intensity of infection
Schistosomiasis
A total of 140 children from 19 different schools had S. haematobium detected by urine microscopy, of which 45 had high intensity infection. The prevalence in schools ranged between 0 (11 schools) and 43.1% (a school in the southern lowlands). Twenty one children from eight different schools had visible haematuria. Only 8 children from 2 different schools had S. mansoni detected by stool microscopy and none were high intensity infections.
Overall, the national prevalence of S. mansoni was found to be 0.4%. The national prevalence of S. haematobium was found to be 6.9% (95% CI 1.9–11.9%) (Table 1). 2.5% of these were high intensity infections [range 0–23.3%]. There was a clear association between prevalence and intensity (r = 0.9, n = 30, p = <0.01); schools with high prevalence of S. haematobium also had high intensity of infection.
There was a wide range of ages in standard 3, with mode of 10 years of age. The observed prevalence was higher in older children (see figure 3) and boys (9.0%) compared with girls (8.2%).
Soil transmitted helminths
The number of children with detectable helminth infestation was low. Twenty-six children from 13 schools had hookworm detected from stool, none of which were high intensity. Eighteen children from nine different schools had ascaris eggs detected and no trichuris eggs were detected at all. There was no obvious pattern of co-infection with schistosomiasis and soil-transmitted helminths across the schools sampled; in 16 schools there was concordance with either both or neither type of infection detected; in 14 schools there was no concordance.
The prevalence of STHs was much lower than that of schistosomiasis (Table 1). Only 1.8% [95% C I 0.6–3.1%] of children had evidence of infection with any of hookworm, ascaris or trichuriasis on stool microscopy. There were no heavy intensity STH infestations detected.
Prevalence and intensity of infection by ecological region
Schistosomiasis
S. haematobium infection was detected in all ecological areas (Table 2). The highest rates were in the Southern Lowland (23.2%) and the Central lowland (9.5%); the other four areas examined all showed a prevalence of between 2% and 7.4%. There was a wide range in the prevalence of S. haematobium infection in all areas. S. mansoni was rarely detected, and only found in the Southern Highlands (1.3%).
To assess the sampling method used to select schools we classified the selected schools into high and low expected prevalence of S. haematobium based on ecology and known previous prevalence and compared our survey results with these expectations. Of the 12 expected high prevalence schools seven were found to be high and 5 unexpectedly low. Of the 18 expected low prevalence schools 12 were found to be low and 6 unexpectedly high. Unexpected results were found in the expected low prevalence as much as in the expected high prevalence schools using the McNemar test for symmetry actual (p = 0.29).
We also compared the historical results for Malawi districts compiled by the London School of Hygiene and Tropical Medicine for the Schistosomiasis Control Initiative in 2001 (personal communication with Simon Brooker) with our results. Prevalence has fallen overall by 24% (95% confidence intervals of 16–32%). A formal paired t-test finds this reduction is true for low as well as high prevalence schools (t = 6.4 with 29 degrees of freedom and highly statistically significant).
Soil transmitted helminths
There was also a low prevalence of STHs in all areas, though urban areas had the highest rates for any STH (6.6%) and hookworm was most prevalent in the Northern Highland area (2.6%). There were no STHs detected in the Central lakeshore schools.
Questionnaire responses
Total number of respondents was 1,662 (Table 3). It is possible to calculate the sensitivity and specificity of the "red urine" question in respect of the microscopic presence of S. haematobium (Table 4). The sensitivity of the "red urine" question in respect of S. haematobium being seen by microscopy is 67% and the specificity 80%. At a national average prevalence of 8.5% the "red urine" question has a positive predictive value of 24% and a negative predictive value of 96%. At the higher prevalence (22.5%) found in the Southern Lowlands zone the positive predictive value is 49% and the negative predictive value is 89%.
Discussion
Prevalence and distribution
The overall prevalence of schistosomiasis is well below expectations. Based on previous studies in Malawi the overall prevalence of schistosomiasis was thought to be between 40% and 50% in the population. Of course these were surveys of selected populations, perhaps undertaken in the season of high transmission. Certainly the disease is localised in Malawi. In the study conducted in the northern lakeshore area in 2000 by Randall et al [8], schoolchildren from four schools were screened and there was a wide range of prevalence: 5%–57% on S. haematobium and 6%–42% in the case of S. mansoni. The school located closest to rice fields had a high prevalence of schistosomiasis. In another recent survey carried out at the Cape McClear peninsula in the Southern Lake zone results showed markedly different S. haematobium and S. mansoni prevalence amongst adjacent villages situated on the lake shore and inland (Paul Bloch, personal communication).
Our survey, representative of all schoolchildren in the country, and undertaken just before the rainy season, suggests far lower levels of 7% for S. haematobium and 0.4% for S. mansoni. The finding highlights two important features of schistosomiasis in Malawi. Firstly, these infections are highly localised and secondly they are not common in general. As expected higher prevalence rates are found in the Southern lake/lowlands zone. S. mansoni does not seem to be a generalised problem in any particular ecological area in the country.
The overall prevalence of STHs is lower than expected. The survey conducted in Karonga District (Randall et al., 2000 [8]) finds a higher prevalence of STHs as well as of S. mansoni and S. haematobium. The differences could be due to seasonal variation (the survey in Karonga district was conducted after the rainy season). This possibility is supported by a low prevalence found in Karonga district in samples collected in November 2001, prior to the rains (unpublished observation, MA Perez). Supporting evidence is seen in the prevalence rates found in urban areas where rains would not affect transmission as much as in rural areas. Similar results are found in previous studies in urban Blantyre: S. mansoni (0%), Ascaris (18%) and Hookworm (0.4%) [7]. Another explanation for the higher prevalence rates found in Karonga District could be the different methodology used to process the stools. The Kato-Katz method [16] was used during our survey in order to calculate intensity of infection. Due to time and personnel constraints, only one Kato-Katz slide was read from each sample, and this could have affected the sensitivity of our testing [17,18]. On the other hand the survey in Karonga District was done using a commercial kit (Parasep®, Intercep Ltd) in order to concentrate the stools and achieve high diagnostic sensitivity. Because of the dissimilar laboratory methods we have been unable to include results for the Northern Lakeland Ecological zone in the estimate of overall prevalences for the whole country. The northern lakeshore zone has tended to have high prevalences. The impact of omitting this zone is likely to underestimate the national results. We intend to survey the zone using our own laboratory methods when funds are available to complete the national picture.
Although this survey is designed to be representative of this age group in Malawi, it is limited in scale by the budget ($12,000). This is an important limitation when one considers that the distribution of schistosomiasis in populations is likely to be highly localized [19]. The population sampled is representative of the general population of school children in standard three with respect to age and sex distribution. However, this survey does not represent children in Malawi who do not attend school or who were absent on the day of the survey due to illness, or who live in the northern lakeland zone. Despite the introduction of free primary education, the net enrolment rate in Malawi is 78%. There is little difference between poor and non-poor households in regard to the proportion of primary school-aged children in school [20]. However, poor children are likely to drop out before reaching standard 5, especially girls in rural areas. (Malawi poverty reduction strategy p.7). Children enrolled but absent due to sickness may have higher rates of schistosomiasis. In future if localised surveys in areas of low school enrolment are contemplated surveyors should consider including children who are not attending school or are absent due to illness.
Drug treatment has been widely if intermittently available. The National Schistosomiasis Control Programme does not have documented evidence of where and when universal drug treatment has been offered to communities. Nor does it have details of where and when Praziquantel has been available for prescription to symptomatic individuals at health centres and hospitals. Supplies and universal treatment programmes have tended to depend on intermittent interest and funding from NGOs. However Praziquantel seems to have been widely available (Kayuni SA. National Survey to find out difficulties people face in taking regular anti-schistosomiasis drugs in Malawi. Fourth Year Student Project. College of Medicine, Blantyre, 2003) and may have had an effect. In this survey of 264 people living in high risk villages in eleven randomly selected districts 71% reported receiving anti-schistosomiasis treatment.
Several NGOs such as Save the Children (US) in Mangochi and World Vision in Chikwawa in recent years have studied helminth infections in schoolchildren. Save the Children (US) surveyed and treated schoolchildren in the entire Mangochi District after finding a prevalence of Ascaris lumbricoides (1.9%), Hookworm (24%), and S. mansoni (16%) in four schools. The last survey done in 2000 showed an overall prevalence of A. lumbricoides (0.4%) and Hookworm (1.5%) on the four schools after two consecutive courses of treatment (Mary Mukaka, Save the Children, personal communication). Similar interventions may have had an effect on the prevalence of infections in other parts of the country.
Another possible explanation for the unexpectedly low prevalence results could be due to a sampling method that selected a low preponderance of schools in high risk areas. However the results of the analysis looking at expected and actual prevalence found in each school suggest this did not happen. Unexpected results were found in the expected low prevalence as much as in the expected high prevalence schools using the McNemar test for symmetry actual (p = 0.29).
The reason for the comparative low prevalence rate of S. haematobium now is not due to asymmetric sampling of low risk schools; it is due to a marked trend of a reduced prevalence across the board. It is not just the high risk schools that have lower prevalence now. Low risk schools have lower rates as well. Compared to the historical results for Malawi districts compiled by the London School of Hygiene and Tropical Medicine for the Schistosomiasis Control Initiative in 2001, prevalence has fallen overall by 24%, and this reduction is true for low as well as high prevalence schools. Our interpretation is that the results reflect a widespread reduction in schistosomiasis prevalence and not a biased sample in the current survey which inadvertently missed schools in high risk areas. However bias may still be an explanation for the change. The widespread reduction in prevalence could be due to bias in the selection of high risk schools in the historic studies rather (than in the selection of low risk schools in our survey). Our sampling fraction of 30 of a possible 3,900 schools is sufficient to give statistical confidence in our overall prevalence result of 6.9% (95% CI 1.9 – 11.9%) for S. haematobium so long as our schools and Traditional Authorities were chosen randomly, which they were.
Questionnaire results
Of the questions asked, only the red urine question is sufficiently sensitive and specific to be used as a screening test. It is a cheap way of assessing schistosomiasis prevalence and as such may be useful to identify hot spots within districts.
Implications for the control programme
Past control programmes, despite their fragmentary application in Malawi, seem to have achieved much in recent years. High intensity infections are now rare, and it is of course these that are thought to cause morbidity. There remain some high prevalence locations but these are uncommon now. The survey findings suggest that future approaches to control can focus on the identification of hot spots through local surveys. These should be undertaken in districts in high prevalence ecological zones, probably through "red urine" questionnaire surveys of school children. Because of the low positive predictive value of the "red urine" question, a low threshold should be used to categorise each school. The categorisation as proposed in Table 5 takes this into account and provides a simple way to identify those schools that require a urine microscopy survey.
Conclusions
The highly effective drugs now available and the control methods used in Malawi seem to have reduced the prevalence of both schistosomiasis and soil helminths to low levels.
There remain some localised areas of high prevalence for which local control measures will be required. It seems that the epidemiology of these diseases makes local control at district level or below the preferred public health approach.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
BS conceived the survey and provided logistical advice. CB designed the survey and undertook the final analysis and first drafting. BP supervised the survey and undertook the initial analysis. PM undertook the randomization and led the survey team. MP supervised the quality control of the laboratory work. All contributed to the final report.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
questionnaire; the questionnaire in Chichewa used in the schools to find out about the health of the pupils surveyed.
Click here for file
Acknowledgements
The authors would like to acknowledge the professionalism and contribution to this survey of Mr Webster Nanthambwe and Mr Yusuf Kanamazina from CHSU who provided the laboratory expertise, Dr Sarah White, Dr Anne Stoddard, Professor Reg Marsh and Dr Michael Deming for statistical and Dr Bagrey Ngwira for epidemiological advice. The survey would not have been possible without the help and support of the District Education Managers, the head teachers of the selected primary schools, the teachers of Standard 3 classes and, of course, the pupils themselves. They answered the questions and provided the samples with politeness and good humour. The costs of the survey were financed by WHO and the Schistosomiasis Control Initiative. The authors would like to thank the referees for their, in some cases considerable, advice.
Figures and Tables
Figure 1 Seven distinct ecological zones in Malaŵi
Figure 2 Map of Malawi with selected schools
Figure 3 Prevalence of S. haematobium in Standard 3 schoolchildren by year of age
Table 1 Prevalence of schistosomiasis and Soil Transmitted Helminths in Malawi primary schools survey, 2002
Schistosomiasis Prevalence {high intensity} – (95% confidence intervals)
S. mansoni 0.4 (0–1.3) %
S. haematobium* 6.9 (1.9–11.9)% {2.5 (0.2–4.9) %}
Mixed 7.7 (2.2–13.2)%
Soil transmitted helminthiasis
Hookworm 1.3 (0.4–2.3)% {0}
Ascariasis 0.5 (0.1–1.0)% {0}
Trichuriasis 0.0
Any infection 1.8 (0.6–3.1)% {0}
*Heavy intensity S. haematobium > or = 50 eggs/ 10 ml or visible haematuria
Table 2 Prevalence of helminth and schistosomiasis infections in standard three school children in Malawi, by ecological area.
Nos infected/nos examined [%(range)] Northern Highland Central Highland Central low/lakeshore South Highland South low/lakeshore urban
No. children sampled 257 242 291 261 297 277
Schistosomiasis
S. mansoni 0 - 0 7 [1.3(0–4.3)] 0 0
S. haematobium 15 [7.4(0–14.8)] 6 [2.0(0–5.0)] 28 [9.5(0.4–18.5)] 14 [3.2(0–8.6)] 66 [23.2(3.3–43.1)] 11 [3.4(0–7.8)]
Mixed - - - 1 [0.2(0–0.6)] - -
Any infection 15 [7.4(0–14.8)] 6 [2.0(0–5.0)] 28 [9.5(0.4–18.5)] 20 [4.4(0–11.8)] 66 [23.2(3.3–43.1)] 11 [3.4(0–7.8)]
Soil transmitted helminthiasis
Hookworm 7 [2.6(0.7–4.4)] 4 [1.8(0–3.6)] 0 3 [0.7(0–1.8)] 7 [1.9(0–4.0)] 5 [2.0(0–5.6)]
Ascaris 2 [0.5(0–1.5)] 1 [0.6(0–1.6)] 0 2 [0.4(0–1.1)] 2 [0.3(0–1.2)] 11 [5.1(0.6–9.5)]
Trichuris - - - - - -
Any helminths 9 [3.1 (0–6.4)] 5 [2.4(0–5.2)] 0 5 [1.0(0–2.7)] 8 [2.1(0–4.5)] 15 [6.6(1.3–12.0)]
Table 3 Answers to questions about haematuria, blood in stools in the last month and history of bilharzia and worms
Question Blood in urine Blood in stool Bilharzia Worms
No 1207 1394 1162 1090
Yes 384 179 434 512
Missing answer 71 89 66 59
Total 1662 1662 1662 1661
Table 4 Sensitivity and specificity of the "red urine" question
Answer to Microscopy for S. haematobium Totals
positive negative
Red urine? – yes 90 289 379
Red urine? – no 44 1142 1186
Totals 134 1431 1565
Table 5 Proposed categorisation of and intervention recommended in schools based on "red urine" prevalence
Prevalence of positive "red urine" question Intervention recommended
> 30% Undertake microscopy survey – if prevalence > 50% – Annual universal treatment. If <50% – Annual targeted treatment to children
< 30% "Red urine" screen of schoolchildren every two years
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| 15546483 | PMC535894 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Nov 16; 4:49 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-49 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-491554648310.1186/1471-2334-4-49Research ArticleA national survey of the prevalence of schistosomiasis and soil transmitted helminths in Malaŵi Bowie Cameron [email protected] Bernadette [email protected] Bina [email protected] Peter [email protected] Maria [email protected] Department of Community Health, College of Medicine, University of Malawi, Blantyre, Malawi2 Sussex Health Protection Unit, Lewes, East Sussex, UK3 Community Health Sciences Unit, Ministry of Health, Lilongwe, Malawi4 PO Box 345, Mangochi, Malawi5 Department of Microbiology, College of Medicine, University of Malawi, Blantyre, Malawi2004 16 11 2004 4 49 49 30 3 2004 16 11 2004 Copyright © 2004 Bowie 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
Past estimates have put the prevalence of schistosomiasis between 40% and 50% in the Malawi population overall based on studies undertaken ten years or more ago. More recent surveys in known high risk areas find similar levels. However control measures, changing ecology and migration may have led to changes in the prevalence of schistosomiasis in different parts of Malawi. A national schistosomiasis and soil-transmitted helminth (STH) survey was undertaken to measure the distribution, prevalence and intensity of infection in November 2002.
Methods
A school was selected randomly from a random sample of 30 Traditional Authorities stratified by six distinct ecological zones, and 1,664 year 3 pupils (9–10 year olds) were questioned about recent illnesses and "red urine". Samples of urine and faeces were examined for the presence of eggs using the standard Kato-Katz technique for soil-transmitted helminths and intestinal schistosomiasis and urine samples using the filtration technique for Schistosoma haematobium.
Results
The prevalence of Schistosoma mansoni is 0.4% (95% CI 0–1.3%), S. haematobium 6.9% (95% CI 1.9 – 11.9%), hookworm 1.3% (95% CI 0.4–2.3%), Ascariasis 0.5% (95% CI 0.1–1.0%) and trichuriasis 0% in year 3 pupils (modal age 10 years of age). Intensity of infection is low for all infections except for 2.5% who have high intensity S. haematobium infection. The "red urine" question is 67% sensitive and 80% specific for positive S. haematobium microscopy.
Conclusions
The reduction in prevalences may be real as a result of recent control measures, or false if historical results were based on surveys of high risk populations. Another explanation is that this survey used an unrepresentative sample of schools. Detailed analysis suggests this is unlikely.
Recommendations include the use of a 30% positive threshold for the "red urine" screening question to be used in schoolchildren in high prevalence areas.
This survey, based on a national probability sample excluding the northern region lakeside area, finds much lower overall prevalence and intensity of schistosomiasis and STHs than previous estimates based on selected surveys. Disease control featuring chemotherapy may be having a profound effect. The localised nature of the distribution of the infections means that control programmes may work best if undertaken at district level or below. "Red urine" questionnaire surveys may help identify hot spots.
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Background
Chronic infections with soil-transmitted helminths and schistosomiasis are common in Malaŵi and cause considerable morbidity [1]. The Ministry of Health wishes to establish the distribution of these infections prior to a reassessment of national policy regarding their control.
All water bodies in Malawi are considered to be potential transmission sites for schistosomiasis [2]. The most recent national estimate puts the prevalence of schistosomiasis between 40% and 50% of the population [3]. This is based on surveys carried out prior to 1991. Numerous local surveys have been conducted more recently in different parts of the country [4-7]. In a survey carried out by Randall et al in 1996 in northern Malawi, S. mansoni and S. haematobium were detected in 27% and 20% of schoolchildren respectively [8]. A survey performed by the Bilharzia Control Programme in Mangochi reported in 1999 showed between 60–80% of school children living along the lakeshore has S. haematobium [9]. This level of infection was confirmed in a later survey performed by the Lakeshore Schistosomiasis Control Project, which found the prevalence amongst school aged children (5–15 years old) to be as high as 80% along the lake shore, with some areas having 100% infection rates [2].
In Malawi there is little contemporary information available on helminth infection. Furthermore, fragmented control measures, changing ecology [10] (such as considerable water resource development and deforestation in the last 10 years) and migration may have led to marked changes in the prevalence of schistosomiasis within regions in Malawi since previous studies were done.
A national schistosomiasis and soil-transmitted helminth (STH) survey was undertaken to measure the distribution, prevalence and intensity of infection in November 2002. Results from the survey are reported in this paper.
Methods
Methods follow recent WHO advice to allow comparison with other international studies [11]. This includes a symptom and illness questionnaire and urine and faecal sampling. The use of questionnaires as a rapid assessment tool for urinary schistosomiasis has been evaluated in multi-country studies [4].
Seven areas (with the number of Traditional Authorities in each) represent the seven distinct ecology and soil conditions in Malaŵi (figure 1):-
Northern Region highland (23)
Northern Region lakeside
Central Region highland (62)
Central Region lakeside (17)
Southern Region highland (51)
Southern Region lakeside (31)
Urban (3)
The northern region lakeside had recently been surveyed by the Karonga Prevention Study [8]. To save expense this ecological area was not resurveyed. Hence only six areas were included in this survey.
Study population
Primary-school children were chosen as the target population. There are several reasons for conducting the survey in this age group. Children consistently have the highest prevalence and transmission of schistosomiasis and soil-transmitted nematodes (except hookworm); treatment via schools on a national scale is feasible [12] (and primary school enrolment is reasonably high in Malawi) and extremely cost-effective [13]. Third-year school classes (9–10 year olds) are commonly surveyed and this was the age-group for this survey.
Sample frame and selection
The sample was selected using a stratified multi-stage cluster design [14]. The six ecological zones constituted the strata. School children were clustered within schools which were clustered within Traditional Authorities (TA). Five Traditional Authorities (TA) were randomly selected from the total in each ecological zone using a computer generated random numbers technique. A list of all primary schools in these TAs was compiled by contacting the relevant District Education Officer and one school was selected at random. Private schools were included though the vast majority were government schools. In the three urban districts of Blantyre, Lilongwe and Mzuzu five schools were selected. A total of 30 schools were surveyed between 25 September and 14th November 2002. A census of all children attending standard 3 who were present on the day of the survey was taken and all were enrolled. Thus between 36 and 71 third year schoolchildren (aged 8–10 years) in 5 schools selected at random from each of six ecologically distinct regions of the country were sampled (Figure 2). A total of 1664 children were enrolled.
Questionnaire
All children were administered a standard symptom questionnaire by a class teacher in their local language [see Additional file 1]. The questionnaire was completed before specimen collection and was designed to assess general health without biasing responses towards helminthiasis in particular. Children were asked about symptoms during the past month [e.g. cough, itch, headache, fever] and whether they had particular illnesses during the past month [e.g. malaria, diarrhoea, skin, eye disease, bilharzia].
Specimen collection methods
School children were asked to provide a stool sample and at least 10 ml of urine (collected mid-morning). Stool samples were stored in a cool box during transfer to the laboratory, and then kept at 4°C until processed within 24 hrs of collection.
Urine and faecal analysis
Stool samples were tested for the presence of eggs using the standard Kato-Katz technique [15] for soil-transmitted helminths and intestinal schistosomiasis. Samples with ascaris more than 50,000 eggs per gram (epg), hookworms more than 4,000 epg, trichuria more than 10,000 epg and S. mansoni more than 400 epg were defined as high intensity. Urine samples were tested using the filtration technique for S. haematobium. Infection with S. haematobium was classified as high intensity if at least 50 eggs per 10 ml of urine were detected or there was visible haematuria [16].
Data analysis
Data were double entered into EpiInfo and then exported into STATA v7 for initial analysis. SPSS was used for chi squared, paired-t and McNemar tests. EpiInfo 2002 was used to calculate adjusted prevalence rates for ecological zones and national prevalence. The prevalences and 95% confidence intervals were computed incorporating the stratification by ecological zones. Sampling weights were computed as the product of the number of TA's in an ecological zone times the number of schools in the selected TA. For the urban zone, weights were adjusted for the fact that there were only three TA's and two schools were selected from two of them (Complex sample frequency analysis, Epi Info 2002, CDC Atlanta). The relationship between the prevalence and intensity of S. haematobium infection in schools was investigated using Pearson product moment correlation coefficient (SPSS).
Results
The sample
A total of 1,664 children were surveyed. Between 36 and 71 children were included from each school. There were 1,662 completed questionnaires, 1,546 stool samples and 1,638 urine specimens collected. Just over half (50.7%) of the sample was female. The mean age (for 1,658 children where age was recorded) was 10.7 years [range 6–17 years]. The mean age of boys was 10.9 years and of girls 10.5 years.
Prevalence and intensity of infection
Schistosomiasis
A total of 140 children from 19 different schools had S. haematobium detected by urine microscopy, of which 45 had high intensity infection. The prevalence in schools ranged between 0 (11 schools) and 43.1% (a school in the southern lowlands). Twenty one children from eight different schools had visible haematuria. Only 8 children from 2 different schools had S. mansoni detected by stool microscopy and none were high intensity infections.
Overall, the national prevalence of S. mansoni was found to be 0.4%. The national prevalence of S. haematobium was found to be 6.9% (95% CI 1.9–11.9%) (Table 1). 2.5% of these were high intensity infections [range 0–23.3%]. There was a clear association between prevalence and intensity (r = 0.9, n = 30, p = <0.01); schools with high prevalence of S. haematobium also had high intensity of infection.
There was a wide range of ages in standard 3, with mode of 10 years of age. The observed prevalence was higher in older children (see figure 3) and boys (9.0%) compared with girls (8.2%).
Soil transmitted helminths
The number of children with detectable helminth infestation was low. Twenty-six children from 13 schools had hookworm detected from stool, none of which were high intensity. Eighteen children from nine different schools had ascaris eggs detected and no trichuris eggs were detected at all. There was no obvious pattern of co-infection with schistosomiasis and soil-transmitted helminths across the schools sampled; in 16 schools there was concordance with either both or neither type of infection detected; in 14 schools there was no concordance.
The prevalence of STHs was much lower than that of schistosomiasis (Table 1). Only 1.8% [95% C I 0.6–3.1%] of children had evidence of infection with any of hookworm, ascaris or trichuriasis on stool microscopy. There were no heavy intensity STH infestations detected.
Prevalence and intensity of infection by ecological region
Schistosomiasis
S. haematobium infection was detected in all ecological areas (Table 2). The highest rates were in the Southern Lowland (23.2%) and the Central lowland (9.5%); the other four areas examined all showed a prevalence of between 2% and 7.4%. There was a wide range in the prevalence of S. haematobium infection in all areas. S. mansoni was rarely detected, and only found in the Southern Highlands (1.3%).
To assess the sampling method used to select schools we classified the selected schools into high and low expected prevalence of S. haematobium based on ecology and known previous prevalence and compared our survey results with these expectations. Of the 12 expected high prevalence schools seven were found to be high and 5 unexpectedly low. Of the 18 expected low prevalence schools 12 were found to be low and 6 unexpectedly high. Unexpected results were found in the expected low prevalence as much as in the expected high prevalence schools using the McNemar test for symmetry actual (p = 0.29).
We also compared the historical results for Malawi districts compiled by the London School of Hygiene and Tropical Medicine for the Schistosomiasis Control Initiative in 2001 (personal communication with Simon Brooker) with our results. Prevalence has fallen overall by 24% (95% confidence intervals of 16–32%). A formal paired t-test finds this reduction is true for low as well as high prevalence schools (t = 6.4 with 29 degrees of freedom and highly statistically significant).
Soil transmitted helminths
There was also a low prevalence of STHs in all areas, though urban areas had the highest rates for any STH (6.6%) and hookworm was most prevalent in the Northern Highland area (2.6%). There were no STHs detected in the Central lakeshore schools.
Questionnaire responses
Total number of respondents was 1,662 (Table 3). It is possible to calculate the sensitivity and specificity of the "red urine" question in respect of the microscopic presence of S. haematobium (Table 4). The sensitivity of the "red urine" question in respect of S. haematobium being seen by microscopy is 67% and the specificity 80%. At a national average prevalence of 8.5% the "red urine" question has a positive predictive value of 24% and a negative predictive value of 96%. At the higher prevalence (22.5%) found in the Southern Lowlands zone the positive predictive value is 49% and the negative predictive value is 89%.
Discussion
Prevalence and distribution
The overall prevalence of schistosomiasis is well below expectations. Based on previous studies in Malawi the overall prevalence of schistosomiasis was thought to be between 40% and 50% in the population. Of course these were surveys of selected populations, perhaps undertaken in the season of high transmission. Certainly the disease is localised in Malawi. In the study conducted in the northern lakeshore area in 2000 by Randall et al [8], schoolchildren from four schools were screened and there was a wide range of prevalence: 5%–57% on S. haematobium and 6%–42% in the case of S. mansoni. The school located closest to rice fields had a high prevalence of schistosomiasis. In another recent survey carried out at the Cape McClear peninsula in the Southern Lake zone results showed markedly different S. haematobium and S. mansoni prevalence amongst adjacent villages situated on the lake shore and inland (Paul Bloch, personal communication).
Our survey, representative of all schoolchildren in the country, and undertaken just before the rainy season, suggests far lower levels of 7% for S. haematobium and 0.4% for S. mansoni. The finding highlights two important features of schistosomiasis in Malawi. Firstly, these infections are highly localised and secondly they are not common in general. As expected higher prevalence rates are found in the Southern lake/lowlands zone. S. mansoni does not seem to be a generalised problem in any particular ecological area in the country.
The overall prevalence of STHs is lower than expected. The survey conducted in Karonga District (Randall et al., 2000 [8]) finds a higher prevalence of STHs as well as of S. mansoni and S. haematobium. The differences could be due to seasonal variation (the survey in Karonga district was conducted after the rainy season). This possibility is supported by a low prevalence found in Karonga district in samples collected in November 2001, prior to the rains (unpublished observation, MA Perez). Supporting evidence is seen in the prevalence rates found in urban areas where rains would not affect transmission as much as in rural areas. Similar results are found in previous studies in urban Blantyre: S. mansoni (0%), Ascaris (18%) and Hookworm (0.4%) [7]. Another explanation for the higher prevalence rates found in Karonga District could be the different methodology used to process the stools. The Kato-Katz method [16] was used during our survey in order to calculate intensity of infection. Due to time and personnel constraints, only one Kato-Katz slide was read from each sample, and this could have affected the sensitivity of our testing [17,18]. On the other hand the survey in Karonga District was done using a commercial kit (Parasep®, Intercep Ltd) in order to concentrate the stools and achieve high diagnostic sensitivity. Because of the dissimilar laboratory methods we have been unable to include results for the Northern Lakeland Ecological zone in the estimate of overall prevalences for the whole country. The northern lakeshore zone has tended to have high prevalences. The impact of omitting this zone is likely to underestimate the national results. We intend to survey the zone using our own laboratory methods when funds are available to complete the national picture.
Although this survey is designed to be representative of this age group in Malawi, it is limited in scale by the budget ($12,000). This is an important limitation when one considers that the distribution of schistosomiasis in populations is likely to be highly localized [19]. The population sampled is representative of the general population of school children in standard three with respect to age and sex distribution. However, this survey does not represent children in Malawi who do not attend school or who were absent on the day of the survey due to illness, or who live in the northern lakeland zone. Despite the introduction of free primary education, the net enrolment rate in Malawi is 78%. There is little difference between poor and non-poor households in regard to the proportion of primary school-aged children in school [20]. However, poor children are likely to drop out before reaching standard 5, especially girls in rural areas. (Malawi poverty reduction strategy p.7). Children enrolled but absent due to sickness may have higher rates of schistosomiasis. In future if localised surveys in areas of low school enrolment are contemplated surveyors should consider including children who are not attending school or are absent due to illness.
Drug treatment has been widely if intermittently available. The National Schistosomiasis Control Programme does not have documented evidence of where and when universal drug treatment has been offered to communities. Nor does it have details of where and when Praziquantel has been available for prescription to symptomatic individuals at health centres and hospitals. Supplies and universal treatment programmes have tended to depend on intermittent interest and funding from NGOs. However Praziquantel seems to have been widely available (Kayuni SA. National Survey to find out difficulties people face in taking regular anti-schistosomiasis drugs in Malawi. Fourth Year Student Project. College of Medicine, Blantyre, 2003) and may have had an effect. In this survey of 264 people living in high risk villages in eleven randomly selected districts 71% reported receiving anti-schistosomiasis treatment.
Several NGOs such as Save the Children (US) in Mangochi and World Vision in Chikwawa in recent years have studied helminth infections in schoolchildren. Save the Children (US) surveyed and treated schoolchildren in the entire Mangochi District after finding a prevalence of Ascaris lumbricoides (1.9%), Hookworm (24%), and S. mansoni (16%) in four schools. The last survey done in 2000 showed an overall prevalence of A. lumbricoides (0.4%) and Hookworm (1.5%) on the four schools after two consecutive courses of treatment (Mary Mukaka, Save the Children, personal communication). Similar interventions may have had an effect on the prevalence of infections in other parts of the country.
Another possible explanation for the unexpectedly low prevalence results could be due to a sampling method that selected a low preponderance of schools in high risk areas. However the results of the analysis looking at expected and actual prevalence found in each school suggest this did not happen. Unexpected results were found in the expected low prevalence as much as in the expected high prevalence schools using the McNemar test for symmetry actual (p = 0.29).
The reason for the comparative low prevalence rate of S. haematobium now is not due to asymmetric sampling of low risk schools; it is due to a marked trend of a reduced prevalence across the board. It is not just the high risk schools that have lower prevalence now. Low risk schools have lower rates as well. Compared to the historical results for Malawi districts compiled by the London School of Hygiene and Tropical Medicine for the Schistosomiasis Control Initiative in 2001, prevalence has fallen overall by 24%, and this reduction is true for low as well as high prevalence schools. Our interpretation is that the results reflect a widespread reduction in schistosomiasis prevalence and not a biased sample in the current survey which inadvertently missed schools in high risk areas. However bias may still be an explanation for the change. The widespread reduction in prevalence could be due to bias in the selection of high risk schools in the historic studies rather (than in the selection of low risk schools in our survey). Our sampling fraction of 30 of a possible 3,900 schools is sufficient to give statistical confidence in our overall prevalence result of 6.9% (95% CI 1.9 – 11.9%) for S. haematobium so long as our schools and Traditional Authorities were chosen randomly, which they were.
Questionnaire results
Of the questions asked, only the red urine question is sufficiently sensitive and specific to be used as a screening test. It is a cheap way of assessing schistosomiasis prevalence and as such may be useful to identify hot spots within districts.
Implications for the control programme
Past control programmes, despite their fragmentary application in Malawi, seem to have achieved much in recent years. High intensity infections are now rare, and it is of course these that are thought to cause morbidity. There remain some high prevalence locations but these are uncommon now. The survey findings suggest that future approaches to control can focus on the identification of hot spots through local surveys. These should be undertaken in districts in high prevalence ecological zones, probably through "red urine" questionnaire surveys of school children. Because of the low positive predictive value of the "red urine" question, a low threshold should be used to categorise each school. The categorisation as proposed in Table 5 takes this into account and provides a simple way to identify those schools that require a urine microscopy survey.
Conclusions
The highly effective drugs now available and the control methods used in Malawi seem to have reduced the prevalence of both schistosomiasis and soil helminths to low levels.
There remain some localised areas of high prevalence for which local control measures will be required. It seems that the epidemiology of these diseases makes local control at district level or below the preferred public health approach.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
BS conceived the survey and provided logistical advice. CB designed the survey and undertook the final analysis and first drafting. BP supervised the survey and undertook the initial analysis. PM undertook the randomization and led the survey team. MP supervised the quality control of the laboratory work. All contributed to the final report.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
questionnaire; the questionnaire in Chichewa used in the schools to find out about the health of the pupils surveyed.
Click here for file
Acknowledgements
The authors would like to acknowledge the professionalism and contribution to this survey of Mr Webster Nanthambwe and Mr Yusuf Kanamazina from CHSU who provided the laboratory expertise, Dr Sarah White, Dr Anne Stoddard, Professor Reg Marsh and Dr Michael Deming for statistical and Dr Bagrey Ngwira for epidemiological advice. The survey would not have been possible without the help and support of the District Education Managers, the head teachers of the selected primary schools, the teachers of Standard 3 classes and, of course, the pupils themselves. They answered the questions and provided the samples with politeness and good humour. The costs of the survey were financed by WHO and the Schistosomiasis Control Initiative. The authors would like to thank the referees for their, in some cases considerable, advice.
Figures and Tables
Figure 1 Seven distinct ecological zones in Malaŵi
Figure 2 Map of Malawi with selected schools
Figure 3 Prevalence of S. haematobium in Standard 3 schoolchildren by year of age
Table 1 Prevalence of schistosomiasis and Soil Transmitted Helminths in Malawi primary schools survey, 2002
Schistosomiasis Prevalence {high intensity} – (95% confidence intervals)
S. mansoni 0.4 (0–1.3) %
S. haematobium* 6.9 (1.9–11.9)% {2.5 (0.2–4.9) %}
Mixed 7.7 (2.2–13.2)%
Soil transmitted helminthiasis
Hookworm 1.3 (0.4–2.3)% {0}
Ascariasis 0.5 (0.1–1.0)% {0}
Trichuriasis 0.0
Any infection 1.8 (0.6–3.1)% {0}
*Heavy intensity S. haematobium > or = 50 eggs/ 10 ml or visible haematuria
Table 2 Prevalence of helminth and schistosomiasis infections in standard three school children in Malawi, by ecological area.
Nos infected/nos examined [%(range)] Northern Highland Central Highland Central low/lakeshore South Highland South low/lakeshore urban
No. children sampled 257 242 291 261 297 277
Schistosomiasis
S. mansoni 0 - 0 7 [1.3(0–4.3)] 0 0
S. haematobium 15 [7.4(0–14.8)] 6 [2.0(0–5.0)] 28 [9.5(0.4–18.5)] 14 [3.2(0–8.6)] 66 [23.2(3.3–43.1)] 11 [3.4(0–7.8)]
Mixed - - - 1 [0.2(0–0.6)] - -
Any infection 15 [7.4(0–14.8)] 6 [2.0(0–5.0)] 28 [9.5(0.4–18.5)] 20 [4.4(0–11.8)] 66 [23.2(3.3–43.1)] 11 [3.4(0–7.8)]
Soil transmitted helminthiasis
Hookworm 7 [2.6(0.7–4.4)] 4 [1.8(0–3.6)] 0 3 [0.7(0–1.8)] 7 [1.9(0–4.0)] 5 [2.0(0–5.6)]
Ascaris 2 [0.5(0–1.5)] 1 [0.6(0–1.6)] 0 2 [0.4(0–1.1)] 2 [0.3(0–1.2)] 11 [5.1(0.6–9.5)]
Trichuris - - - - - -
Any helminths 9 [3.1 (0–6.4)] 5 [2.4(0–5.2)] 0 5 [1.0(0–2.7)] 8 [2.1(0–4.5)] 15 [6.6(1.3–12.0)]
Table 3 Answers to questions about haematuria, blood in stools in the last month and history of bilharzia and worms
Question Blood in urine Blood in stool Bilharzia Worms
No 1207 1394 1162 1090
Yes 384 179 434 512
Missing answer 71 89 66 59
Total 1662 1662 1662 1661
Table 4 Sensitivity and specificity of the "red urine" question
Answer to Microscopy for S. haematobium Totals
positive negative
Red urine? – yes 90 289 379
Red urine? – no 44 1142 1186
Totals 134 1431 1565
Table 5 Proposed categorisation of and intervention recommended in schools based on "red urine" prevalence
Prevalence of positive "red urine" question Intervention recommended
> 30% Undertake microscopy survey – if prevalence > 50% – Annual universal treatment. If <50% – Annual targeted treatment to children
< 30% "Red urine" screen of schoolchildren every two years
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Gundersen SG Kjetland EF Poggensee G Helling-Giese G Richter J Chitsulo L Koumwenda N Krantz I Feldmeier H Urine reagent strips for diagnosis of schistosomiasis haematobium in women of fertile age Acta Trop 1996 62 281 7 9028412 10.1016/S0001-706X(96)00029-0
Kjetland EF Poggensee G Helling-Giese G Richter J Sjaastad A Chitsulo L Kumwenda N Gundersen SG Krantz I Feldmeier H Female genital schistosomiasis due to Schistosoma haematobium. Clinical and parasitological findings in women in rural Malawi Acta Trop 1996 62 239 55 9028409 10.1016/S0001-706X(96)00026-5
Phiri K Whitty CJ Graham SM Ssembatya-Lule G Urban/rural differences in prevalence and risk factors for intestinal helminth infection in southern Malawi Ann Trop Med Parasitol 2000 94 381 7 10945048
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| 15569392 | PMC535895 | CC BY | 2021-01-04 16:30:50 | no | BMC Nucl Med. 2004 Nov 29; 4:2 | latin-1 | BMC Nucl Med | 2,004 | 10.1186/1471-2385-4-2 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-891557420310.1186/1471-2407-4-89Case ReportAtypical presentation of hepatocellular carcinoma: a mass on the left thoracic wall Çoban Şahin [email protected]üksel Osman [email protected]öklü Seyfettin [email protected] Koray [email protected] Meltem [email protected]ökmeci Abdulkadir [email protected] Department of Gastroenterology, Ankara University Medical School, 06100, Ankara, Turkey2 Department of Gastroenterology, Türkiye Yüksek İhtisas Hospital, 06100, Ankara, Turkey3 Division of Clinical Cytology, Department of Pathology, Ankara University Medical School, 06100, Ankara, Turkey2004 1 12 2004 4 89 89 6 7 2004 1 12 2004 Copyright © 2004 Çoban 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
Hepatocellular carcinoma is a common malignancy for which chronic hepatitis B infection has been defined as the most common etiologic factor. The most frequent metastatic sites are the lung, bone, lymphatics, and brain, respectively. Metastases to the chest wall have been reported only rarely.
Case presentation
We report a patient with hepatocellular carcinoma who presented with an isolated metastatic mass on the left anterolateral chest wall in the axillary region.
Conclusions
Metastasis of HCC should be included in the differential diagnosis of rapidly growing lesions in unusual localizations, particularly in patients with chronic liver disease even if a primary tumor can not be radiologically identified.
hepatocellular carcinomametastaseschest wallaxilla
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Background
Hepatocellular carcinoma (HCC) is the most frequent primary malignant tumor of the liver [1]. It is usually seen in the 6th and 7th decades of life, and the most common etiologic factor is chronic viral hepatitis, particularly in the presence of cirrhosis. Chronic hepatitis B has been described as the most frequent cause [2-4]. Hematogeneous extrahepatic metastases are common, with lungs, regional lymph nodes, kidneys, bone marrow and adrenals being the most frequent sites [3,4]. Rarely HCC may present as a mass without a primary liver tumor being identified [5-7].
This report presents an unusual metastasis of HCC which presented as a mass on the left chest wall in the axillary region.
Case presentation
A 71-year-old male was admitted to our clinic with complaints of left shoulder pain, swelling in the left anterolateral chest wall, jaundice, weight loss, dyspnea and weakness. At three months prior to admission, he had noticed a less movable mass approximately 2 cm in diameter and then the tumor had grown rapidly, becoming increasingly painful during exertion. There was no history of expectoration, fever, or night sweats. The patient's physical examination revealed yellow discoloration of the sclerae, pitting edema up to the knees and a 5 cm × 6 cm fixed mass in the left axillary region. There was mild splenomegaly but no hepatomegaly, liver masses or ascites. Laboratory findings were as follows: sedimentation rate: 90 mm/hr; Hb: 11.5 mg/dL; WBC: 3900/mm3; Plt: 126 000/mm3; PT: 15.6 sec; INR:1.3; total protein: 6.5 g/dL (N: 6.4–8.5 g/dL); albumin: 2.2 g/dL (N: 3.4–4.8 g/dL); AST: 124 iu/L (N: 15–40); ALT: 52 iu/L (N: 10–40); ALP: 173 iu/L (N: 37–147); GGT: 213 iu/L (N:0–40); total bilirubin: 3.8 mg/dL (N: 0.1–1.2), and direct bilirubin: 1.57 mg/dL (N: 0–0.3). Hepatitis B virus surface antigen, IgG antibody to the core antigen, anti-HBe and HBV DNA with polymerase chain reaction were positive. HBe antigen, anti-Delta and serological markers of hepatitis C were negative.
Abdominal ultrasonography showed ascites, splenomegaly and diffusely nodular and heterogeneous echogenic patterns in the liver. There was no history of chronic liver disease. Upper gastrointestinal endoscopy was normal except for esophageal varices. Computerized tomography of the thorax revealed a mass on the left anterolateral chest wall (Figure 1). Cytological examination of a fine needle aspirate taken from the mass was consistent with metastatic hepatocellular carcinoma (Figure 2). Abdominal computerized tomography detected thrombosis in the right portal vein. The liver parenchyma was diffusely heterogeneous with irregular borders and without a clear mass or infiltrating lesion. There was no lymphadenopathy on thoracic, abdominal or pelvic computerized tomography. The patient had an elevated serum alpha-feto protein (AFP) level of 60 000 ng/mL (0 – 13.6 ng/ml). Cytological examination of the liver confirmed the diagnosis of hepatocellular carcinoma. The patient was discharged on palliative treatment, and he died 21 days later.
Discussion
Although extrahepatic metastasis of HCC was reported in 18% of untreated patients in a retrospective study, metastatic lesions were found at a higher incidence in an autopsy study of deaths related to primary liver cancer [8,9]. The most common sites of extrahepatic involvement are the lungs, lymph nodes, adrenal glands, and bones [3,8,10-12]. Metastasis of HCC occurs frequently by way of intrahepatic blood vessels, lymphatic permeation, or direct infiltration. Hematogenous spread occurs with the involvement of hepatic or portal veins or the vena cava. Metastases have also been found in collaterals and varices, and this appears to have been the route of metastasis in the patient reported here. His right portal vein was thrombosed, most likely due to tumoral infiltration. Tumor cells might have passed through the left thoracic wall via portosystemic collaterals, the azygous system and finally intercostal veins. Another possible route is through subcutaneous collaterals communicating to thoracoepigastric veins and draining into the axillary vein.
Although bone metastases are seen in 10% of HCC patients [13], it infrequently appears as the first manifestation of HCC. The most common sites are the vertebra and pelvis [14]. Isolated metastases of HCC to the ribs have been rarely reported [5,6,15-17].
The establishment of the diagnosis of metastatic HCC can occasionally be problematic, particularly when the primary tumor has not been identified. To the best of our knowledge there are only two reports in the literature describing cases of solitary metastasis to the chest wall from an unknown primary HCC. One patient had an HCC metastasis involving the sternum, and the other patient had a metastasis on the right 4th rib [5,6].
In the present patient, the etiology of HCC was due to chronic hepatitis B, and the HCC was diffuse in nature. Yuki et al. reported an association between HBs antigen positivity and diffuse-type HCC. Intrahepatic, hematogenous and lymphogenous metastases have been frequently observed in diffuse-type HCC [18]. Nevertheless, diffuse-type HCC is often difficult to detect on imaging studies because of its permeative appearance and heterogeneity of background chronic liver disease [19]. In our patient, although HCC was of the diffuse type, metastasis involvement was isolated. There was also no regional lymph node infiltration.
AFP, when elevated, usually correlates with tumor size. AFP doubling time is also closely related to tumor doubling time. A rapidly growing axillary mass, diffuse tumoral infiltration of the liver and a very high level of AFP in our patient are consistent with these observations. However, nearly one-fourth of patients with HCC may have normal AFP values [19].
The need for liver biopsy might be questionable in the our patient. The presence of chronic liver disease with a very high AFP level was highly suggestive for HCC. However, there are several reports in the literature describing ectopic HCC without a liver origin. These patients can be treated with surgical resection and have a good prognosis [7,20]. Furthermore, several reports have claimed that ectopic livers are particularly predisposed to developing HCC [20-22].
In conclusion, metastasis of HCC should be included in the differential diagnosis of rapidly growing lesions in unusual localizations, particularly in patients with chronic liver disease even if a primary tumor cannot be radiologically identified.
Competing interest
The author(s) declare that they have no competing interests.
Authors contributions
SC, involved in the patient active management and preparation of the figures. OY, carried out the literature search. SK, revised it for scientific content. KC, performed the cytological examination. MB, involved in the patient active management. AD, edited the manuscript and coordinated the submission. All authors contributed to the preparation of the manuscript. All authors read and approved the final version of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Thoracic computerized tomography showing a lesion of 7 cm × 6.5 cm in diameter on the left anterolateral chest wall in the left axillary region at the level of 4.–5. ribs with minimal compressive atelectasis and destruction of the ribs.
Figure 2 The cell block section of the axillary mass is composed of atypical hepatocytes forming thickened trabeculae and solid nests lined by endothelial cells (H& E, ×100).
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| 15574203 | PMC535896 | CC BY | 2021-01-04 16:02:59 | no | BMC Cancer. 2004 Dec 1; 4:89 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-89 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-111556084710.1186/1743-422X-1-11ResearchPrevention of genital herpes in a guinea pig model using a glycoprotein D-specific single chain antibody as a microbicide Chen Jianmin [email protected]é Sanat K [email protected] Anthony [email protected] University of Texas Medical Branch, Galveston, Texas, USA2004 23 11 2004 1 11 11 11 11 2004 23 11 2004 Copyright © 2004 Chen et al; licensee BioMed Central Ltd.2004Chen 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
Genital herpes (GH) is a recurrent sexually transmitted infection (STI) that causes significant morbidity and is also the major source of herpes simplex virus (HSV) in cases of neonatal herpes. Vaccination is a current goal which has had limited success so far in preventing GH and microbicides offer an attractive alternative. Treatment of primary disease cannot prevent establishment of latent infections and thus, cannot prevent subsequent recurrent disease. Recently, many of the molecular events leading to entry of HSV into cells have been elucidated, resulting in the description of a number of herpesvirus entry mediators (HVEMs) that interact with HSV glycoprotein D (gD) on the surface of virions. Described here is a strategy for interrupting the spread of HSV based on interfering with these interactions. The hypothesis addressed in the current report was that single chain antibody variable fragments (scFv) that interrupt associations between gD and HVEMs would not only prevent infection in vitro but could also be used as microbicides to interfere with acquisition GH.
Results and Conclusions
Here we show that a scFv derived from a particular hybridoma, DL11, not only inhibits infection in vitro but also prevents development of GH in a guinea pig model when applied intravaginally in an inert vehicle. Comparison of different anti-gD single chain antibodies supported the hypothesis that the activity of DL11-scFv is based on its ability to disrupt the associations between gD and the two major receptors for HSV, nectin-1 and HveA. Further, the results predict that bacterial expression of active single chain antibodies can be optimized to manufacture inexpensively a useful microbicidal product active against HSV.
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Background
GH is generally caused by HSV type 2 (HSV-2), though HSV type 1 (HSV-1) is increasingly recognized as a significant cause of primary infections [1]. Throughout the last few decades there were substantial advances in understanding the epidemiology of genital HSV infections. Primary infection is almost always self-limited but on healing virus is not eliminated from the host but rather, viral genomes remain in a latent (dormant) state in sensory neurons innervating initially infected skin and mucous membranes [2,3]. The significance of latency is that it is a reservoir of infection that can periodically reactivate, causing virus to travel down nerve fibers to skin or mucous membranes in the dermatome of primary infection. This may be manifest clinically as recurrent GH or more frequently, causes unrecognized shedding of infectious HSV [4-7] which despite being unrecognized is responsible for the majority of new HSV-2 infections [8]. The epidemiology is further complicated by the fact that many primary infections are asymptomatic or unrecognized, which has the important implication that the first clinical presentation of GH, often referred to as the initial episode, may be caused by a recurrence of a prior asymptomatic primary infection [9].
In the latter half of the 20th century, there were great strides in antiviral therapy for GH but unfortunately, treating primary disease does not prevent establishment of infection [10] and thus, cannot prevent subsequent recurrent disease. Barrier contraception provides some protection but its efficacy remains unclear [11] owing to the complex nature of HSV pathogenesis, in which virus is shed frequently and asymptomatically from multiple sites below the waist [5]. Hence condoms are not as effective at preventing transmission of GH as they are for other sexually transmitted infections. Vaccination is a current goal which has had limited success to date. A recent trial of a glycoprotein D-based sub-unit vaccine protected only double (HSV-1 and 2) seronegative women but not men [12]. Further, protection was mainly measured by prevention of primary disease rather than infection. It is known that treating primary disease does not prevent establishment of latency and consequently, the long term efficacy of this vaccine against subsequent recurrences remains unknown.
Thus, the number of strategies for preventing sexual transmission of GH is limited. Recently, there has been considerable interest in topical microbicides as a potentially attractive alternative to vaccination for prevention of sexually transmitted infections, including GH [13]. Women are able to control the use of vaginal microbicides and several products are currently being used or tested, including acid buffers and sulphated polymer-based inhibitors or surfactants [14] like nonoxynol-9 (N-9) [13]. N-9 has been used as a spermicide for 30 years and was thought to provide some protection against gonorrhea and chlamydia, a view was recently proven to be erroneous [14]. A major factor limiting the efficacy and long-term viability of N-9 and similar chemical compounds as topical agents is their irritant effects on the vaginal epithelium [15]. Further, recent data suggest that N-9, contrary to prior belief, is not effective at protecting against HIV but rather it was shown to increase rather than decrease the risk of acquiring HIV in some populations studied, particularly women at high risk of infection [14].
An evolving strategy that may be useful for preventing specific sexually transmitted viral infections is blocking of virus entry into cells or subsequent inhibition cell-to-cell spread. Many of the molecular events leading to entry of HSV into cells have now been unraveled, resulting in the description of two prominent cell-surface herpesvirus entry mediators (Hve-A and nectin-1, also known as Hve-C) that interact with HSV glycoprotein D (gD) on the surface of virions [16-20]. In a recent study [21], nectin-1 was shown to be expressed in the vaginal epithelium of humans throughout the estrous cycle. In contrast, in mice nectin-1 was expressed in vaginal epithelium only during the stage of estrous at which they are susceptible to HSV. Using a mouse model of GH, pre-incubation of HSV-2 with soluble recombinant nectin-1 was shown to block entry of virus through vaginal mucosa [21], suggesting the importance of nectin-1 in mediating entry of HSV into the female genital tract. Hve-A and nectin bind to conformationally overlapping regions of gD and we were able exploit this information together with the results of prior studies that had mapped the sites on gD recognized by a panel of monoclonal antibodies [22-26]. Antibody DL11 was of particular interest because it binds to an epitope on gD that blocks the interactions between gD and both Hve-A and nectin-1 [19] (figure 1). We show here that a single chain antibody variable fragment (scFv) constructed from DL11 neutralizes HSV infection in vitro, inhibits cell-to-cell spread of virus and can be used to prevent infection in a guinea pig model of GH.
Figure 1 Panel A: Hypothetical model illustrating the antigenic structure of gD and demonstrating the clustering of antigenic sites into seven groups, as defined by locations of amino acids bound by various monoclonal antibodies. Disulphide bonds location defined by braces. Diagram adapted with permission from Nicola et al, 1998 [22]. Of particular relevance to this study are the locations of sites VII (amino acid residues 11–19), which is bound by antibody 1D3, and site Ib, a discontinuous epitope that includes residues 222 to 252 that is bound by antibody DL11. Panel B: Diagram showing interface between N-terminal amino acids of gD and HveA and the approximate residues (blue) bound by monoclonal antibody 1D3 and, by inference, 1D3 scFv (adapted with permission from Connolly et al, 2003 [19].
Results
Construction and expression of single chain antibodies against gD
Four from the panel of anti-HSV gD hybridomas available were selected for scFv construction based on the known locations of their epitopes [22] (summarized in figure 1) and knowledge about the neutralization properties of the antibodies produced by them. Of particular note are the properties of DL11, which neutralizes both HSV-1 and HSV-2 in the absence of complement and antibody binding to its conformational epitope is known to disrupt the interactions of gD both with Hve-A and nectin-1. Also 1D3 binds to a group VII neutralizing epitope that directly interferes with the interface between gD and HveA (figure 1B). A fifth scFv cassette, against carcinoembryonic antigen (CEA) was excised from a plasmid encoding an anti-tumor chimeric T-cell receptor, kindly provided by Hinrich Abken (Cologne University, Germany). For production of cDNAs, individual VL and VH regions from each hybridoma were reverse transcribed using primers near the VH-CH and VL-CL junctions. For PCR cloning these primers were paired with a panel of degenerate primers derived from VH or VL signal sequences (Table 1) that were able to amplify all hybridoma heavy and light chains tested so far (14/14) irrespective of antibody class or subclass (data not shown). PCR products were sequenced directly to facilitate design of new primer sets allowing, on re-amplification of hybridoma cDNAs, elimination of degenerate primer sequences introduced in the first reaction and introduction of 2/3 of a 15 amino acid hinge region comprising three repeats of four glycine and one serine residues (Figure 2). VL and VH are not covalently linked in nature but flexible hinges of this design and length were shown previously [27] to allow reconstruction of antibody binding sites when VL and VH are linked end-to-end (figures 3, 4). Finally, the PCR products containing the overlapping hinge regions were ligated, PCR amplified and the resultant scFv cassette was TA cloned into pCR2.1TOPO. To generate the desired single chain antibodies, the cassettes were subcloned into the bacterial expression vector pET101-D. An antibody modeling algorithm, verified by the locations of the complementary determining regions, was used to predict the 3-D structures of all four of the anti-gD single chain antibodies. The results were consistent with reconstitution of the original antigen binding sites (e.g. figure 3, DL11; others not shown).
Table 1 Degenerate PCR primers used for amplification of VL (kappa) and VH (gamma).
Nomenclature Primer sequences used for PCR reactions
Signal sequence/framework primers
Kappa 1 GGTGATATCGTGATRACMCARGATGAACTCTC
Kappa 2 GGTGATATCWTGMTGACCCAAWCTCCACTCTC
Kappa 3 GGTGATATCGTKCTCACYCARTCTCCAGCAAT
Kappa 4 CTGWTGTTCTGGATTCCTG
Kappa 5 GTGCTCTGGATTCGGGAA
Kappa 6 TCAGCTTCYTGCTAATCAGTG
Kappa 7 TGGGTATCTGGTRCSTGTG
Kappa 8 GTTTCMAGGTRCCAGATGT
Kappa 9 TGTTTTCAAGGTRCCAGATGT
Kappa 10 CTSTGGTTGTCTGGTGTTGA
Kappa 11 TGCTKCKCTGGGTTCCAG
C region kappa primer TGGTGGGAAGATGGA
Signal sequence/framework primers
Gamma 1 GAGGTGAAGCTGCAGGAGTCAGGACCTAGCCTGGTG
Gamma 2 AGGTVMAACTGCAGVAGTCWGG
Gamma 3 AGGTVVAGCTGCAGVAGTCWGG
Gamma 4 ACTGCAGGTRTCCACTCC
Gamma 5 RCTACAGGTGTCCACTCC
Gamma 6 GCYACAGMTGTCCACTCC
Gamma 7 ACTGCAGGTGTCCTCTCT
Gamma 8 RCTRCAGGYGTCCACTCT
Gamma 9 CCAAGCTGTGTCCTRTCC
Gamma 10 TGTTGACAGYCVTT CCKGGT
Gamma 11 TAYTTTAAAARGTGTCMAGTGT
Gamma 12 CTYTTAAAAGGKGTCCAGWG
Gamma 13 CYTTTAMATGGTATCCAGTGT
Gamma 14 ATGGCAGCWGCYCAAAG
Gamma 15 CTTTTAAAAGWTGTCCAGKGT
Gamma 16 CTTCCTGATGGCAGTGGTT
C region gamma primer TAACCCTTGACCAGGCATCC
Key to degenerate nucleotides: R = A+G; M = A+C; W = A+T; K = G+T; S = G+C; Y = C+T; H = A+T+C; B = G+T+C; D = G+A+T; N = A+C+G+T; V = G+A+C
Figure 2 Panel A: Structure of an scFv cassette spliced using a (Gly4Ser)3 hinge. Panel B. Alternative glycine codons were used in the overlapping region of the hinge to avoid production of completely overlapping regions, thereby generating a sub-optimal (Gly4Ser)2 hinge.
Figure 3 Single plain view of a 3-D model of DL11 scFv, showing its predicted structure. Panel A: Strand view, colored by group, demonstrates relative orientation of the kappa (top) and gamma (bottom) chains, which shows the positions of residues to which the (Gly4Ser)3 hinge is attached. Panel B: Wireframe image illustrating hinge attachment sites on one side of the molecule (linked by dashed line) and clustering on the opposite side (inside the circle) of the complementary determining regions (CDRs) predicted by the Kabat antibody database. The clustering of CDRs suggests correct conformation of the molecule with formation of an antigen binding site.
Figure 4 Western blot demonstrating expression of DL11 scFv by E, Coli. BL21 cells were transfected with p-TOPO10 containing the scFv cassette. Bacterial lysates were purified using a nickel chelation column and the reaction with anti-V5 of total lysates and various fractions from the column are shown. Lane 1, unpurified total bacterial lysate; lane 2, nickel column flow through; lanes 3 and 4, saline washes; lane 6, eluate from Ni beads; lane 7, bacterial supernatant; lane 8, scFv remaining on nickel column after elution; lane 9: supernatant from un-induced bacteria.
Bacterial expression and extraction of anti-gD single chain antibodies
The single chain antibodies were expressed in E. Coli strain BL21 using pET101-D (Invitrogen), which attaches hexa-His and V5 tags to expressed proteins for their isolation and identification. Bacteria were induced with IPTG, centrifuged and the supernatants tested for the presence of scFvs by western blotting using anti-His antibody (figure 4). Bacterial pellets were sonicated in phosphate buffered saline to release inclusion bodies and proteins were solubulized by addition of 6 M guanidine (BL21). Nickel bead chelation was used to extract the His-tagged protein. Western blots of eluates from nickel beads (e.g. DL11 scFv from DL21; Fig. 4, lanes 6 and 7) identified scFvs that were released by this procedure. They were generally isolated at concentrations of 500–1000 µg/ml from BL21. Re-folding and intra-chain disulphide bond formation were maximized by gradually reducing guanidine concentration by step-wise dialysis from 6 M initially to 3 M, then 2 M, 1 M, 0.5 M and finally 0 M, with addition of L-arginine and oxidized glutathione (GSSG) in final two steps [28]. The ability of the single chain antibodies produced in this way to bind their target antigen was tested by determining their reaction with plastic bound gD by ELISA. Binding ratios were calculated in relation to the background binding of CEA scFv (e.g. DL11-based scFv; figure 5)
Figure 5 Binding of scFv to plastic bound gD. Binding ratios of DL11 scFv to gD compared with an irrelevant (CEA) scFv at the same protein concentrations.
Selected anti-gD single chain antibodies neutralize HSV in vitro
The hypothesis that selected single chain antibodies can block infection of cells in vitro by reacting with an epitope that disrupts the interface between gD and HVEMs was tested by comparing the activities of the various bacterially expressed anti-gD scFv in a Vero cell-based HSV-1 plaque reduction assay. A scFv directed against an epitope on carcinoembryonic antigen was included as an unrelated control. The results showed that pre-incubation of virus with DL11 and 1D3 scFvs inhibited plaque formation with decreasing efficiency. DL6 scFv showed minimal but reproducible activity (data not shown), whereas the other scFvs tested (DL2 and CEA) had no plaque reducing capability at all (figure 6). Against HSV-2, only DL11 showed neutralizing activity in a similar plaque reduction assay (data not shown), confirming the type common nature of its epitope. In addition to inhibition of plaque formation, pre-incubating HSV-2 with 100 µg/ml DL11 caused an 80% reduction in plaque numbers and a ~50% reduction (figure 7) in the size of plaques (0.95 ± 0.3 mm with DL11scFv vs. 1.9 ± 0.4 mm without, respectively). The same was true for HSV-1 and DL11 (not shown). It was concluded that DL11scFv could not only block infection of cells with HSV but also was able to inhibit cell-to-cell spread of virus.
Figure 6 Specific reduction of HSV-2 plaque numbers by incubation of virus with anti-gD scFv. Vero cells were pre-incubated with approximately 120 PFU HSV-2, strain G with single chain antibodies generated from hybridomas D11 (¦), 1D3 (?), DL2(?) and an irrelevant CEA-specific construct (?).
Figure 7 Reduction in plaque size in the presence of DL11 scFv. Mean plaque size in absence of scFv (Panel A) was 1.9 ± 0.4 mm compared with 0.95 ± 0.3 mm in presence of 100 mg/ml DL11 scFv (Panel B). Figures represent mean of 100 plaques ± standard deviation. Bar = 1 mm.
Protection against HSV type 1 and type 2 GH by administration of a DL11-based single chain antibody before infection with virus
The HSV type-common and startling in vitro activities of single chain antibodies derived from hybridoma DL11 prompted us to examine the ability of DL11scFv to protect against vaginal HSV disease, using a well established guinea pig model of GH [29,30]. The vehicle selected for these preliminary studies was 1% carboxymethylcellulose because this is an inert compound that is used for its viscosity in our routine plaque assays.
A pilot experiment was done with HSV-1, in which BL21 produced DL11 and DL2 single chain antibodies (0.5 mg/ml) were each instilled into the vaginas of guinea pigs (1 ml/animal). Approximately 20 seconds later the guinea pigs were challenged with 5 × 106 PFU HSV-1, strain SC16 and monitored for development and severity of primary disease. The result (figure 8) showed that DL11-based scFv completely protected the animal from lesion development whereas DL2-based scFv appeared to have, as expected, no effect.
Figure 8 Effect of DL11 scFv on HSV-1 genital disease in guinea pigs. Panel A: Blisters of GH 5 days after instillation of HSV-1 into vaginal vault. Several areas of ulceration with surrounding erythema are visible bilaterally (e.g. arrows); Panel B: Complete protection against HSV-1 by prior instillation, immediately before HSV challenge, of 1 ml CMC containing DL11 scFv (500 µg/ml).
Next a more ambitious test of microbicidal activity was attempted, using HSV-2 rather than HSV-1 and a longer interval (20 minutes) between scFv instillation and challenge (Table 2). Two groups of 20 guinea pigs were each administered either DL11 or DL2 (control) scFv (1 ml/guinea pig). All animals were challenged with 106 PFU of HSV-2, strain G and monitored daily as before. All except one animal were completely protected by DL11 scFv compared with DL2 scFv, all of which developed moderate to severe disease, scored as described in methods (p = <0.0001; Mann Whitney test).
Table 2 Prevention of GH in guinea pigs by DL11 scFv.
Pre-treatment Severity of lesions Mean lesion score (n = 20)
500 µg/ml DL2 scFv 20 minutes prior to infection 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4. 3.55 ± 0.153 *
500 µg/ml DL11 scFv 20 minutes prior to infection 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0. 0, 0, 0, 3. 0.15 ± 0.15 *
* p < 0.0001 (Mann-Whitney test)
Discussion
In practice, the rapidity of isolation and cloning of scFv into a bacterial expression vector (approximately one week) by the procedure described here allowed expeditious activity assessments to be made for the different constructs. Bacterial protein expression systems are widely used for the production of recombinant proteins and problems are often encountered with disulphide bond formation. Whilst there are no covalent bonds between heavy and light chain sequences in immunoglobulin hypervariable domains, intra-chain disulphide bonds can, to varying degrees among different antibodies, influence conformation of the antigen binding site [31]. Thus, failure to form intra-chain disulphide bonds has the potential to disrupt antigen binding and could also detrimentally affect stability of the molecule. For this reason, a previously reported method [28] was adapted to promote formation of intra-chain disulphide bonds in vitro, using protein extracted from bacterial inclusion bodies. After isolation of inclusion bodies by sonication of bacteria, proteins were solubulized with 6 M guanidine hydrochloride, the concentration of which was gradually reduced to zero by a stepwise daily dialysis routine. In all cases we tried, this procedure generated soluble hexa-His tagged single chain antibody fragments, which have an approximate molecular weight of 34 kD, at concentrations of approximately 750 – 1000 µg/ml. Precipitation of proteins in the final dialysis step tended to occur above concentrations of 1000 µg/ml and was prevented by careful monitoring of the sample volume to ensure concentrations stayed below this critical threshold.
Entry of HSV into cells is known to be mediated through interactions between gD and 3-O-sulfated heparan and one or more specific entry mediators, HveA, nectin-1 and nectin-2 [32]. Overall, the results of plaque reduction assays in vitro were compatible with the hypothesis that significant interference with the binding of gD with HVEMs can be achieved with a single chain variable fragment selected according to the known properties of their parent antibodies. This is the first direct evidence that neutralization of HSV can be a property of certain antigen binding domains alone, a corollary of which is that no other regions of the antibody need be required to neutralize the virus. Of the five scFvs tested in this study, the most effective was produced from antibody DL11, which is known to interfere with binding both to HveA and nectin-1. Neutralization in vitro by a scFv constructed from antibody 1D3, which binds to a site on gD that overlaps the binding site for HveA, was significantly less efficient than that seen with DL11 scFv, presumably because, in the presence of 1D3 scFv, HSV was still able to utilize nectin-1 as a receptor. As expected, scFvs derived from DL2, a non-neutralizing monoclonal antibody, and a scFv reactive against CEA showed no activity in the plaque reduction assay. The weak activity of the construct made from DL6 correlates with the known weak neutralizing property of the native antibody, which is presumed to be the result of conformational changes induced it's by its binding to gD.
The data presented here correlate with the prior finding that mice can be protected against HSV-2 by topical administration of antibody [33,34] and a subsequent report from Zeitlin et al [35] that mice were protected against HSV-2 transmission by intravaginal administration of an IgG2a monoclonal anti-gD antibody and its IgA switch variant. Here, these observations are extended in several respects. The epitope on gD recognized by the most effective scFv, that constructed from antibody DL11, was defined as one that interferes with binding of gD with two major mediators of herpesvirus entry into cells, namely HveA and nectin-1. In this respect, attention is drawn to the recent report of Linehan et al [21] that nectin-1 is expressed in the genital tracts of mice and humans and soluble nectin-1 can block entry of HSV into vaginal epithelium. These data [21] together with the unprecedented protection of guinea pigs by DL11scFV shown here strongly implicate nectin-1 as a critical mediator of HSV the entry genital epithelium and in fact it is suggested here that nectin-1, rather than other herpesvirus entry mediators, likely play a dominant role in genital tract infection. The protective activity of a scFv established with certainty that the constant regions of anti-gD antibody molecules are not required for protection against HSV. This finding has the important consequence of eliminating the complement binding activity of IgG, which will greatly limit the potential for unwanted inflammatory side effects of topically administered anti-gD preparations, an important advantage if they are to be used clinically. The specific nature of anti-herpes scFv and the ability to choose an inert formulation has two potential advantages over other microbicides. First is selected high specific activity against HSV and second is that they are not irritating to the genital tract. Their murine derivation is not anticipated to be a problem with topical use, but humanization of the hypervariable region is possible by grafting the complementary determining regions onto a human framework, This is an option should their systemic use ever be considered. Of particular interest may be the use of microbicidal gels prior to delivery for the prevention of neonatal herpes. The inert nature of single chain antibodies, combined with a suitable vector, should enable their widespread use in this context among HSV-2 seropositive mothers. These are important considerations given the high prevalence of GH and its frequent asymptomatic nature.
In summary, we believe that single chain antibodies against HSV merit further study and development as topical microbicides. The production of active molecules in bacteria makes their use a feasible and relatively inexpensive prospect.
Conclusions
Single chain antibodies against HSV gD could be synthesized readily from several IgG secreting hybridomas using degenerate immunoglobulin heavy and light chain immunoglobulin primers that hybridized to regions flanking the complementary determining regions, which determine antigen specificity.
Two mechanisms of interference with infection were evident when DL11 scFv was examined in detail. First, the number of plaques produced by virus could be inhibited by up to 90% when reacted with HSV prior to infection of Vero cells, indicating that scFv neutralized virus prior to establishment of productive infection. This result also suggested that nectin-1 and HveA, the binding of which are both blocked by DL11, are the main mediators of virus entry into Vero cells. 1D3, which interferes specifically with the interface of gD with HveA, was effective to a lesser extent. Second, in addition the striking ability of DL11 scFv to neutralize virus inoculums, this particular construct reduced plaque size significantly, from which it was concluded that cell-cell spread of HSV was also inhibited. This observation could have implications for therapeutic use of single chain antibodies in the future and may have enhanced the performance of DL11 scFv as a microbicide in the guinea pig model. This result was mediated by suboptimal scFv concentrations for virus neutralization, implying that lower concentrations of DL11 scFv may be required to interfere with intercellular spread of virus than to block entry.
The finding that DL11 scFv was active for 20 minutes, the maximum time tested, when instilled into the vaginal vault was considered encouraging for future development of scFv as microbicides and the observation merits further consideration of the vehicle used. Slow release formulations may be appropriate depending on their cost. Overall, it appears that selected single chain antibodies are promising candidates for interfering with binding of gD to HVEMs and studies in a guinea pig model of GH suggest that they may comprise a plausible strategy for preventing transmission of GH.
Methods
Generation of scFvs
Single chain antibodies were constructed from four anti-gD secreting hybridomas, DL11, DL6, DL2 and 1D3. An additional scFv, directed against carcinoembryonic antigen (CEA) served as an independent control. Messenger RNAs from 5 × 105 - 106 hybridoma cells were isolated using Trizol (Invitrogen, CA) and cDNAs were generated by reverse transcription with Taq polymerase ('Expand High Fidelity Taq polymerase' ; Roche, IN). RT was primed with anti-sense oligonucleotides designed to anneal either to mouse kappa light chain or heavy chain constant region sequences, just downstream of the J-C junction (table 1). Light and heavy chain hypervariable regions (VL and VH) were amplified by priming 'sense' PCR reaction products with panels of oligonucleotides (OGNs) designed from Kabat database sequences to be complementary to kappa (light chain) and gamma (heavy chain) signal or framework sequences (table 1). In practice, pools of 11 degenerate OGN sequences were found to be sufficient to prime 100% of kappa chain reactions (14/14 hybridomas regardless of subclass). Similarly, a pool of 14 degenerate OGNs successfully amplified the gamma chains from these hybridomas. From each hybridoma, the resulting VL and VH cDNAs were sequenced and new specific primers were designed each of which included 2/3 of the fifteen amino acid (Gly4Ser)3 flexible hinge region, allowing the variable regions to be amplified and spliced together reconstituting the antigen binding site on reconformation (figures 2, 3). To prevent complete overlap of the complementary hinge sequences, which would result in the introduction of a sub-optimal 10 amino acid (Gly4Ser)2intervening segment, alternative glycine codons were used in each component of the hinge. Four of the scFvs were TA cloned into the bacterial expression vector pET101/D-TOPO (Invitrogen, Carlsbad, CA) which generates hexa-His tagged proteins after expression in vitro.
Expression of single chain antibodies in bacteria
Proteins were expressed in IPTG-induced E. Coli BL21 [DE3] (Invitrogen), released by sonication in PBS and inclusion bodies were separated by centrifugation. Proteins in inclusion bodies were solubulized with 6 M guanidine HCl and purified by metal chelation. A stepwise dialysis procedure with addition of GSSG (oxidized glutathione; Sigma) and L-arginine in the final two steps was used to assist in the formation of intra-chain disulphide bonds in order to optimize re-conformation and stability of the scFvs [28]. Protein concentrations were measured using the BCA method (Pierce).
ELISA to quantify binding of scFv to gD
Microtiter plate wells were coated with soluble gD (6 µg/ml) and then blocked with 1% skimmed milk. After incubation with serial two-fold dilutions of scFv, binding was detected with anti-V5, the alternative tag on the scFv, because the recombinant gD used in the assay was, like the single chain antibodies, tagged with hexa-His. Binding ratios were calculated in relation to an irrelevant (CEA-specific) scFv.
Virus growth, titration and plaque neutralization assays
HSV-1 (strain SC16) and HSV-2 (strain G) were grown and titrated in Vero cells as described [36,37]. Titers were determined using a standard plaque assay [38]. Cells were grown and maintained in Dulbecco modified Eagle medium supplemented with 10% (growth medium; GM) or 1% (maintenance medium; MM) fetal bovine serum. A plaque reduction assay was done in Vero cells to assess the neutralizing capabilities of each scFv. Briefly, 100–200 plaque forming units (PFU), diluted in MM, of either HSV-1 (strain SC16) or HSV-2 (strain G) were incubated at room temperature for 1 hour with serial ten-fold dilutions of each scFv in a total volume of 1 ml. After gentle shaking with 3 × 106 Vero cells for a further 1 hour the samples were plated in 6 cm dishes (Nunc) in a total volume of 5 mls of GM containing 2% carboxymethylcellulose (CMC). Plaques were enumerated after 3 days incubation at 37°C in a 5% CO2 atmosphere.
Guinea pig model of GH
The microbicidal properties of scFv were tested using a guinea pig model of GH. Female outbred Hartley guinea pigs weighing 350–400 grams were obtained from Charles River laboratories (Wilmington, MA). Prior to inoculation of each guinea pig with virus, the introitus was opened with a calcium alginate swab moistened in physiological saline and 1 ml of 1% CMC containing either DL2 scFv or DL11 scFv at a final concentration of 500 µg/ml, was instilled using a pipette with a plastic tip. CMC was used as a vehicle to facilitate retention of the scFv in the vaginal vault. At various times thereafter, animals were challenged with 106 PFU HSV-1 (strain SC16) or HSV-2 (strain G). Over the ensuing two weeks lesions were scored on a scale of 0–4 (0 = no lesion; 1 = erythema and swelling only; 2 = small vesicles <2 mm; 3 = coalescent or large vesicles >2 mm; 4 = ulceration and maceration). All experiments were done according to the guidelines laid down in The NIH Guide for Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AS conceived and coordinated the work described and wrote the manuscript. JC was responsible for the experiments described and SKD provided technical support.
Acknowledgements
Doctors Roselyn Eisenberg and Garry Cohen are thanked for providing anti-gD hybridomas and for their helpful discussion during the project. JC was supported by the Sealy Endowment Fund. The authors thank the Gillson-Longenbaugh Foundation for supporting the work described.
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| 15560847 | PMC535897 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Nov 23; 1:11 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-11 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-471548813810.1186/1471-2458-4-47Research ArticleVerbal autopsy of 80,000 adult deaths in Tamilnadu, South India Gajalakshmi Vendhan [email protected] Richard [email protected] Epidemiological Research Center, Chennai, India2 Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), University of Oxford, Oxford, UK2004 15 10 2004 4 47 47 13 5 2004 15 10 2004 Copyright © 2004 Gajalakshmi and Peto; licensee BioMed Central Ltd.2004Gajalakshmi and Peto; 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
Registration of the fact of death is almost complete in the city of Chennai and not so in the rural Villupuram district in Tamilnadu, India. The cause of death is often inadequately recorded on the death certificate in developing countries like India. A special verbal autopsy (VA) study of 48 000 adult (aged ≥ 25 yrs) deaths in the city of Chennai (urban) during 1995–97 and 32 000 in rural Villupuram during 1997–98 was conducted to arrive at the probable underlying cause of death to estimate cause specific mortality.
Methods
A ten day training on writing verbal autopsy (VA) report for adult deaths was given to non-medical graduates with at least 15 years of formal education. They interviewed surviving spouse/close associates of the deceased to write a verbal autopsy report in local language (Tamil) on the complaints, symptoms, signs, duration and treatment details of illness prior to death. Each report was reviewed centrally by two physicians independently. Random re-interviewing of 5% of the VA reports was done to check the reliability and reproducibility of the VA report. The validity of VA diagnosis was assessed only for cancer deaths.
Results
Verbal autopsy reduced the proportion of deaths attributed to unspecified and unknown causes from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu for adult deaths (≥ 25). The sensitivity of VA to identify cancer was 95% in the age group 25–69.
Conclusion
A ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25–69 years) and less reliably for older ages (70+). Thus VA is practicable for deaths in early adult life or middle age and is of more limited value in old age.
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Background
In developed countries, data on disease-specific mortality by age are readily available from national vital registration. In developing countries, where 80% of the world's deaths occur, estimation of cause of death is more difficult because the levels of coverage of vital registration and reliability of cause of death stated on the death certificate are generally low (especially in rural areas).
A reliable assessment of disease-specific mortality rates is not yet possible in many parts of India, either because the underlying cause of the terminal illness was never known or because the relevant information was not recorded. For legal purposes death records do usually subdivide the causes of death into medical and non-medical (external) causes. But once non-medical causes have been excluded, specification of the underlying cause of a death from disease may be inaccurate, misclassified or missing for about 50% of adult deaths. For example, in Chennai, Tamilnadu, (south India) about half of those who died at home soon after the diagnosis of cancer (and whose deaths were therefore, in almost all cases, likely to have been caused by their cancer) do not have cancer mentioned on their death certificate [1], and for other diseases the problems might well be even worse.
Elsewhere, 'Verbal Autopsy' i.e., 'systematic retrospective inquiry of family members about the symptoms and signs of illness prior to death' has been used to help determine the underlying medical cause of death, particularly in childhood [2,3]. For childhood deaths in populations that are not covered by adequate medical services such "verbal autopsies" are now of established value in helping to classify the broad patterns of mortality. Verbal autopsies have also been used to assess the medical causes of maternal deaths [4-7]. Although in India there are about as many deaths in middle age as in childhood, there is less experience with verbal autopsies of adult deaths.
A special study on 'verbal autopsy' of adult deaths was conducted in urban and rural areas in the state of Tamil Nadu, south India during 1998–2000. The aims of the study were (a) to develop a verbal autopsy instrument and test its utility to determine the underlying cause of death and (b) to estimate cause specific mortality using underlying cause of death arrived based on verbal autopsy reports. Now we report the type of training given to the field interviewers to interview and write the verbal autopsy report for adult deaths, the procedure followed to arrive at the probable underlying cause of death, the accuracy of the instrument developed and change in the proportions of cause of death based on the underlying cause of death arrived by reviewing 80 000 verbal autopsy reports.
Methods
A: Training of field interviewers to write a 'Verbal autopsy' report
Male non-medical graduates with at least 15 years of formal education were selected. A ten days training was given on verbal autopsy interview techniques and writing verbal autopsy reports. There are four steps in training; 1). introduction to anatomy, signs and symptoms of various diseases, 2). mock interviews, 3). hands-on-training on writing verbal autopsy reports and 4). feed back session.
Step 1
This consisted of a basic three day introduction to anatomy, collecting data on history of past illness (refer Appendix I in Additional file 1), using symptoms/signs checklist of various diseases (refer Appendix II in Additional file 1) and to interview the surviving spouse/close associates or relatives of the deceased, the other members of the community such as neighbours to get data on train of events or circumstances preceding the death. Reports are to include complaints, symptoms, signs, duration of illness and treatment details of the illness prior to death.
The following data are to be ascertained (for all deaths due to medical causes) from the respondent to write the verbal autopsy narrative report:
• onset of illness prior to death: sudden or gradual
• major symptom(s) and associated symptom(s) – in chronological order
• If a symptom was present it was used as a filter to define what questions to be asked. For example, the filter symptom for heart attack was chest pain and the associated symptoms were breathlessness, sweating, vomiting and pain in the retrosternal area radiating to hand, shoulder, back etc. Cough for more than 4 weeks was a filter for lung cancer and tuberculosis. For each symptom, the duration should be recorded. Details of additional symptoms are built into the narrative in chronological order, by prompting, if necessary.
• progress of the illness
• any treatment received : Yes/No
• If yes, type of treatment received
• details of hospitalization prior to death:
○ name of the hospital (e.g. tuberculosis hospital, cancer hospital, coronary care unit etc),
○ duration of hospitalization,
○ whether discharged from the hospital against medical advice or not.
○ Status at the time of discharge from the hospital: alive/dead
• history of similar episodes and treatment(s) given
• abstract information related to the illness prior to death from the investigation reports done for any illness close to the time of death (within 6 months prior to the death) / hospital discharge summary etc, if available
• If a death certificate is available, copy the cause of death given on the death certificate (In the Tamilnadu study death certificates were available for only 20% of total deaths).
• While recording history of adults with long standing illness, the description should include details that occurred in the month preceding the death, with other information recorded in the past history section (Appendix I in Additional file 1).
• For deaths that occur during pregnancy, delivery, or within six weeks of delivery: use Appendix II (A and B) in Additional file 1
If the respondent is able to give the major symptoms and circumstances leading to death, then additional probing questions are asked about the associated symptoms using the symptoms/signs checklist (Appendix II(A & B) in Additional file 1) If the respondent is not able to give sufficient information on the symptoms of the illness prior to death or have difficulty in remembering any major symptom, then get necessary information to rule out non-medical causes of death. When the interviewer is sure that the death was not due to unnatural cause, the following procedure is used to collect necessary data on the symptom.
○ read out the filter symptom/sign of each module in the symptom/sign checklist
○ check responses to each, and note down positive responses
○ Where there is a positive response, additional details on that symptom and associated symptoms, if any, should be obtained.
Thus, the methodology of collecting data in the open format using 'symptoms/signs checklist' is an interactive process, with the respondent taking the lead in providing the information, and the interviewer prompting where necessary for more details. The Field Interviewer gathers as much information as possible on the underlying cause of death from the respondent. It is imperative to get a logical and complete history of symptoms, signs, events, investigations and treatment, so that the medical reviewer gets sufficient information to assign a probable specific underlying cause of death.
Step 2
In the following two days, mock interviews were organized to illustrate techniques of probing a respondent to get the required information on cause of death as well as how to write the verbal autopsy report in local language (Tamil) in Appendix I in Additional file 1 as stated by the respondent.
Step 3
The third component of training included three days of hands on verbal autopsy training in the field. To limit distress over the terminal event, the field visit was carried out at least six months after death. Name of the deceased, father's name (if the deceased was a male) or spouse name (if the deceased was a female), age, gender, informant's name and address of the deceased at the time of death were given to field interviewers to locate the house of the deceased. The Field Interviewers carry Appendix I and II (symptoms/signs checklist) in Additional file 1 to the field. They were blind to the cause of death stated on the death certificate. The Field Interviewer located the house of the deceased based on the data given to him. He introduced himself to the respondent and began the interview. Each one completed twenty reports which were reviewed and feedback was provided two days after completion of field work to maximize quality of writing the verbal autopsy report.
Step 4
The final component of training was feed back session for 2 days. This session involved teaching them how to include essential information in report writing. The feedback session mainly focused discussion on reports which did not have a specified underlying cause of death and reports with minimal information to arrive at the probable underlying cause of death; for example, a report may say that a person had a stroke ten days ago but did not specify the type of onset (sudden or gradual, whether the person was conscious or unconscious, had difficulty in speaking or not, which parts of the body may have been affected etc.) or a report may say that the deceased had fever for ten days and died. It did not give details about the fever and other associated symptoms if any.
B: Verbal autopsy of 80 000 adult deaths (≥ 25 years at the time of death) in Tamilnadu, South India
This special verbal autopsy study was carried out in two areas in Tamilnadu. The Chennai city (urban) with a population of 4.2 million, and the Villupuram district (rural), with a population of 2.5 million were chosen for this study. We have successfully traced 48 000 adult deaths (≥ 25 years at the time of death) in urban area and 32 000 adult deaths in rural Villupuram district and reviewed 80 000 verbal autopsy reports to arrive at the probable underlying cause of death.
Mortality data in urban area (Chennai)
Information on deaths that occur in Chennai has been maintained manually by trained staff in Chennai Vital Statistics Department (VSD). The following data on deaths that occurred in Chennai during 1995 to 1997 were collected from the death registers in the Vital Statistics Department: deceased name, age, gender, marital status, father/spouse name, informant's name, occupation, place of death, address at the time of death, date of death and recorded cause of death (immediate, underlying and/or contributory). 72,000 deaths occurred during the study period of 1995–97. Of 72 000 found, 5000 deaths were attributed to external causes (unintentional injuries, suicide or homicide) in the death certificate, and were excluded. Of the remaining 67 000 deaths attributed in the VSD to medical causes, 48 000 of the households were successfully visited during 1998–99 to try to assign cause of death by verbal autopsy.
Mortality data in rural area (Villupuram district)
All formal and informal village records were to be sought to identify all deaths at any age during 1997–98. 41,000 such records were identified and 39 000 of the households were successfully visited during 1999–2000 to try to assign cause of death by verbal autopsy. Of 39 000 deaths, 7000 were before age 25.
Feed back sessions and re-interview
Feed back sessions were organized regularly throughout the study period to improve the quality of the verbal autopsy reports and 5% of the field visit reports were validated by re-interview one week after completion of the main interview, and blind to its results. This re-interviewing was done by one or other of two special interviewers because knowledge that a resurvey might well take place would ensure reliably motivated fieldwork at the initial survey, and also to check whether there were any systematic defects in the technique of any of the field workers: none were found. The underlying cause of death arrived based on re-interview data was not substantially different from the one arrived based on main interview data.
Arriving at underlying cause of death
All verbal autopsy reports were centrally reviewed by two physicians independently in order to arrive at "probable underlying cause of death". Each made a diagnosis based on signs, symptoms and sequence of events prior to death given in the verbal autopsy report, which were then coded according to the 9th International Classification of Diseases, Injuries and Causes of Death [8]. The same 2 physicians reviewed all the 80,000 verbal autopsy reports. The discrepancies in the underlying causes of death were noted in 5% of verbal autopsy reports. These were discussed and resolved. The disagreement between 2 physicians in arriving at underlying cause of death was noted before classifying causes of death into broad groups. For example, 'Pneumonia' and 'Lower respiratory infection' were grouped under 'Infection'. According to one physician the underlying cause of death was pneumonia and for another physician it was lower respiratory infection.
Results
Urban study
In Chennai city, the study was done in 1998–99 and the verbal autopsy reports were reviewed for 27 726 male deaths and 20 631 female deaths. Table 1 shows about 1100 (M:683, F:456) deaths due to medical causes were reassigned to external causes based on verbal autopsy reports. Deaths from unspecified medical causes and unknown causes decreased from 54% to 23% (p < 0.0001).
Table 1 Cause of death from Vital Statistics Department* and based on Verbal Autopsy of 48 000 adult deaths (aged ≥ 25) in Chennai (urban), south India: 1995–97
Causes of death (ICD9 codes) Cause of death in VSD Cause of death based on Verbal Autopsy
M (%) F (%) M (%) F (%)
Vascular disease (390–415, 418–459) 8319 (30) 5168 (25) 11056 (41) 7435 (37)
Respiratory tuberculosis (TB) (011, 012, 018) 1399 (5) 372 (2) 2231 (8) 575 (3)
Other respiratory diseases (416, 417, 460–519) 1088 (4) 596 (3) 1597 (6) 855 (4)
Neoplasm (140–239) 1163 (4) 1002 (5) 2344 (9) 1999 (10)
Infection except respiratory & TB (rest of 1–139, 279.8 [HIV], 320-6, 590, 680-6) 584 (2) 303 (2) 1034 (4) 618 (3)
Unspecified medical causes (780-9, 797-9) 12291 (44) 115 11 (56) 4367 (16) 5889 (29)
Other specified medical causes 1899 (7) 1045 (5) 4414 (16) 2804 (14)
No cause given in VSD (hence probably medical) 983 (4) 634 (3) Nil Nil
Total deaths – medical 27 726 20 631 27 043 20 175
Re-assigned by VA to external causes *Excluded from the study 683 456
Total deaths (medical causes+external causes) 27 726 20 631 27 726 20 631
*Deaths(M: 3644; F:1644) that were assigned by the Vital Statistics Department(VSD) to non-medical causes were excluded from the study
Rural study
In Villupuram district, verbal autopsy report was written for all deaths i.e., deaths due to medical and external causes. So verbal autopsy reports of 19 294 male deaths and 12 494 female deaths were reviewed. Deaths from unspecified medical causes and unknown causes decreased from 41% to 26% (p < 0.0001) (Table 2).
Table 2 Cause of death from various local records in Villupuram district and based on Verbal Autopsy of 32 000 adult deaths (aged ≥ 25) in Villupuram (rural), south India: 1997–98
Causes of death (ICD9 codes) Cause of death in local records Cause of death based on Verbal Autopsy
M (%) F (%) M (%) F (%)
Vascular disease (390–415, 418–459) 3351 (20.3) 1614 (14.4) 3928 (24.6) 2404 (22.0)
Respiratory tuberculosis (TB) (011, 012, 018) 1659 (10.1) 686 (6.1) 1841 (11.5) 671 (6.1)
Other respiratory diseases (416, 417, 460–519) 717 (4.4) 471 (4.2) 1044 (6.5) 728 (6.6)
Neoplasm (140–239) 415 (2.5) 594 (5.3) 488 (3.1) 664 (6.1)
Infection except respiratory & TB (rest of 1–139, 279.8 [HIV], 320-6, 590, 680-6) 1818 (11.0) 1584 (14.1) 1954 (12.2) 1411 (12.9)
Unspecified medical causes (780-9, 797-9) 5829 (35.4) 4565 (40.7) 4173 (26.1) 2737 (25.0)
Other specified medical causes 2237 (13.6) 1346 (12.0) 2570 (16.1) 2334 (21.3)
No cause given (hence probably medical) 451 (2.7) 343 (3.1) Nil Nil
Total deaths – medical 16 477 11 203 15 998 10 949
External causes 2817 1291 3296 1545
Total deaths (medical causes+external causes) 19 294 12 494 19 294 12 494
Validity of verbal autopsy tool
The cause of death stated on the death certificate is often inaccurate. Studies, which have been undertaken around the world, show substantial difference (10–40%) between the clinical diagnosis or clinical cause of death and postmortem findings [9-12] and many of the completed death certificates failed to provide relevant information to allow adequate ICD-10 coding [13]. In India, individuals whose deaths might have been due to external causes are often subjected to postmortem examination, but others are not. So it is not possible to compare (clinical diagnosis of) medical causes of death against postmortem findings in India. Gajalakshmi et al [1] had done a study in Chennai to determine the sensitivity of the death certificate to identify cancer by comparing the cause of death stated on the death certificate with the morbidity date base of Chennai population-based cancer registry. It was found that the sensitivity of the death certificate to identify cancer as the underlying cause of death was 57%. In Chennai, about 75–80% of cancer patients attend health care facilities at late stage of the disease; about half of those who died at home soon after the diagnosis of cancer (and whose deaths were therefore, in almost all cases, likely to have been caused by their cancer) did not have cancer mentioned on their death certificates. Hence verbal autopsy tool was developed to determine specific cause of death, to compute cause specific death rates.
Where a cause recorded on the death certificate in the VSD differed from the underlying cause assigned by the VA, there was often no absolute way of knowing which was correct (where the assigned cause by a medical doctor on the death certificate lacked detail, the VA may well be more reliable, and vice-versa) except for cancer deaths which could be verified with the Chennai cancer registry records. Hence, the validity of VA diagnosis was assessed only for cancer deaths(ICD 9:140–208) by comparing with the stated cause of death in the VSD records and verifying with the Chennai cancer registry records and hospital medical records (only for cancer diagnosis). Chennai Population-Based Cancer Registry is a demographic registry in the network of the Indian Council of Medical Research, Govt. of India and has been functioning since 1982 at the Cancer Institute(WIA), Chennai. Cancer is not a notifiable disease in India. Hence registration has been done by active method. Cancer patients attending the Govt. hospitals are interviewed to collect data on age, sex, address, duration of stay in Chennai city, marital status, mother tongue and educational level. Interviews are done at the houses for those who have been missed by the registry staff during their (Govt.) hospital visit. The clinical data, such as date of cancer diagnosis, method of diagnosis, site of cancer, any spread of the disease, histology, treatment details and status (alive or dead, if dead at the hospital, then, date and cause of death) for all registered patients are abstracted from the hospital medical records. All data on cancer patients attending the private hospitals are abstracted from the hospital records. The mortality data available at the Vital Statistics Department are linked with the Chennai Cancer Registry data base. Therefore the cause of death arrived based on verbal autopsy report was compared with the hospital data on cancer patients available in the Chennai Cancer Registry data base.
Table 3 shows that 3053 deaths were identified as being due to cancer by VA. Review of VSD records revealed that 1435 of 3053 deaths as being due to non-cancer causes (majority of deaths were attributed to ill-defined/unknown followed by vascular causes) and 1618 (of 3053 deaths) as being due to cancer. Since the sensitivity of death certificate to identify cancer as underlying cause of death is only 57% in Chennai [1], all deaths at ages 25–69 included in the present study were verified with Chennai population-based cancer registry records. Table 4 shows that out of 3053 deaths identified by VA as cancer underlying cause of death, 2765 deaths matched with Chennai population-based cancer registry data base and 288 deaths did not. The cancer deaths identified by VSD records and not by VA (n = 107) (Table 3) matched with Chennai population-based cancer registry data base. Thus 288 cancer deaths, identified by VA, were not registered in the Chennai population-based cancer registry [14,15]. These were missed by the Chennai population-based cancer registry, both in the routine morbidity and mortality data registration process. We were successful in identifying all 288 cancer deaths, not available in the Chennai population-based cancer registry, in the medical records of the hospitals located in Chennai city. Thus all 3053 cancer deaths identified by VA were confirmed by linking with Chennai Cancer Registry records and hospital medical records. So there were no false positive cancer deaths recorded by VA. The sensitivity of VA to identify cancer was 94% (1618/1725) compared to VSD records and 96% (2765/2872) compared to Chennai population-based cancer registry in the age group 25–69 [15,16] and the Chennai population-based cancer registry missed 9% (288/3160) of total cancer deaths in the early adult life and middle age during the study period of three years.
Table 3 Cancer (ICD 9: 140–208) deaths at ages 25–69 by Verbal autopsy (VA) and in Vital Statistics Department records (VSD) in Chennai (urban), South India
VSD
VA Cancer Noncancer Total
Cancer 1618 1435 3053
Noncancer 107 21941 22048
Total 1725 23376 25101
Table 4 Cancer (ICD 9: 140–208) deaths at ages 25–69 by Verbal autopsy (VA) and in Chennai population-based cancer registry in Chennai (urban), South India
Cancer Registry
VA Registered Not registered Total
Cancer 2765 288 3053
Noncancer 107 21941 22048
Total 2872 22229 25101
Discussion
The Tamilnadu study on verbal autopsy [15,16] used university graduates since it is very expensive to send professionally trained individuals to field visits, to write verbal autopsy reports. We have found it very difficult to get female graduates willing to do field work. Hence only males were recruited for the field work. Responders of female deaths were usually males who did not hesitate to reveal the circumstances/ symptoms etc prior to death. The participation rate was 100%. The informants were given full information about the objectives of the study and the participation in this study was entirely voluntary basis.
The verbal autopsy tool for adult deaths is an open narrative format uses the check list of symptoms and signs with filters to get more information on train of events or circumstances preceding the death. The sensitivity of this tool is 95% (94% compared to VSD records and 96% compared to Chennai population-based cancer registry) in the age group 25–69 during the study period. The validity of this verbal autopsy tool is influenced by the training given to the interviewers, on the immediate random checking of the 5% of interview data and reviewing of the field reports centrally by 2 physicians to arrive at the probable underlying cause of death which is better than that arrived by opinion-based algorithm [17]. There is little information or literature about validity of cause of death for adults by verbal autopsy in India.
As a result of using verbal autopsy method, adult deaths (≥ 25) from unspecified and unknown causes decreased from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu. Ten day training to write verbal autopsy reports followed by constant monitoring of the submitted reports resulted in arriving at the probable underlying cause of death for most of the deaths and to compute broad classification of the underlying causes of about 90% of deaths in early adult life or middle age: in old age, however, the proportion classifiable is substantially lower. The specific causes of death arrived based on verbal autopsy reports were used to estimate death rates for Chennai city [16] and to estimate the risk of death associated with smoking for broad groups of causes of death[18].
This methodology of arriving cause of death for adult deaths by verbal autopsy is now being adopted by the Registrar General of India, Govt. of India, for nationwide use in the Sample Registration System(SRS) that consists of 6671 units (4436 rural and 2235 urban) spread across the country covering 1.1 million households and about 6 million population. The SRS is a large demographic survey of vital events occurring in a national random sample of urban and rural areas in India by the Registrar General of India (RGI) to provide annual estimates of age-specific birth and death rates at the national and state levels. This exercise on verbal autopsy for adult deaths along with the verbal autopsy questionnaire developed by the SRS collaborators for deaths that occur at ages less than 15 years and for maternal deaths is expected to yield reliable cause-specific death rates for India.
Conclusion
A ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25–69 years) and less reliably for older ages (70+). Our experience shows that the open narrative, if well written, provides adequate information for assigning probable underlying cause of death for adult deaths.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
VG and RP participated in designing the study, analysis and preparation of the report. Development of verbal autopsy tool, training on verbal autopsy method, co-ordination of field work and data management were done by VG. Verbal autopsy review by VG and TS Kanaka, physician.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Appendix I – used to write the history of past illness and verbal autopsy report for adult deaths (25 years or older) and in Appendix II – Symptoms/signs checklist for adult deaths (≥ 25 years) is given
Click here for file
Acknowledgements
This study was funded by direct support from the UK Medical Research Council and Cancer Research UK to the Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford (R Collins & R Peto) and by the World Health Organisation (AD Lopez), Geneva. The field work was done when VG was with the Cancer Institute (WIA), Chennai.
We are thankful to the Indian Council of Medical research, Govt. of India for part funding of the Chennai Cancer Registry and to the Vital Statistics Department in Chennai and Villupuram district for providing mortality data. We thank the study participants, interviewers and G. Sheba for quality control of field work and data entry.
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| 15488138 | PMC535898 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Oct 15; 4:47 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-47 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-41550712510.1186/1743-422X-1-4ResearchDivergence of the mRNA targets for the Ssb proteins of bacteriophages T4 and RB69 Borjac-Natour Jamilah M [email protected] Vasiliy M [email protected] Jim D [email protected] Department of Biochemistry SL 43, Tulane University Health Sciences Center, 1430 Tulane Avenue, New Orleans, LA 70112, USA2 Lebanese American University, PO Box 13-5053, Mailbox S-37, Beirut, Lebanon2004 17 9 2004 1 4 4 12 7 2004 17 9 2004 Copyright © 2004 Borjac-Natour et al; licensee BioMed Central Ltd.2004Borjac-Natour 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.
The single-strand binding (Ssb) protein of phage T4 (T4 gp32, product of gene 32) is a mRNA-specific autogenous translational repressor, in addition to being a sequence-independent ssDNA-binding protein that participates in phage DNA replication, repair and recombination. It is not clear how this physiologically essential protein distinguishes between specific RNA and nonspecific nucleic acid targets. Here, we present phylogenetic evidence suggesting that ssDNA and specific RNA bind the same gp32 domain and that plasticity of this domain underlies its ability to configure certain RNA structures for specific binding. We have cloned and characterized gene 32 of phage RB69, a relative of T4 We observed that RB69 gp32 and T4 gp32 have nearly identical ssDNA binding domains, but diverge in their C-terminal domains. In T4 gp32, it is known that the C-terminal domain interacts with the ssDNA-binding domain and with other phage-induced proteins. In translation assays, we show that RB69 gp32 is, like T4 gp32, an autogenous translational repressor. We also show that the natural mRNA targets (translational operators) for the 2 proteins are diverged in sequence from each other and yet can be repressed by either gp32. Results of chemical and RNase sensitivity assays indicate that the gp32 mRNA targets from the 2 related phages have similar structures, but differ in their patterns of contact with the 2 repressors. These and other observations suggest that a range of gp32-RNA binding specificities may evolve in nature due to plasticity of the protein-nucleic acid interaction and its response to modulation by the C-terminal domain of this translational repressor.
Ssb protein, gp32, RNA-binding proteins, DNA-binding proteinstranslational control, DNA replication
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Introduction
T4 gp32, the single-strand binding (Ssb) protein of bacteriophage T4, is a well studied member of the Ssb protein family, and was the first such ssDNA-binding replication protein to be discovered [1]. The protein, product of T4 gene 32, is an essential component of the phage DNA replication complex and also plays essential roles in DNA repair and recombination [2,3]. Like other Ssb proteins, T4 gp32 facilitates transactions at the replication fork, especially along the lagging strand, through its binding to the unwound DNA template and its specific interactions with other protein components of the DNA replisome. T4 gp32 is known to stimulate the phage induced DNA polymerase (T4 gp43) and to play a role in the dynamics of primosome (T4 gp61-gp41 complex) recruitment by the primase-helicase assembly protein T4 gp59 [4-6]. In general, Ssb proteins lack specificity to the ssDNA sequence and this property allows them to perform their physiological roles at all genomic locations undergoing replication, repair or recombination. The presence of a Ssb protein in the right place at the right time may depend, in large measure, on specificity of its interactions with other proteins from the same biological source.
T4 gp32 has the interesting property of being able to control its own biosynthesis at the translational level in vivo. The protein binds to a specific target (translational operator) in the 5' leader segment of the mRNA from gene 32, and represses translation of this RNA [7]. Another Ssb protein, gp5 of the M13 ssDNA phage family, has also been shown to act as a mRNA-specific translational repressor, although in this case, the RNA target is located in the message for another essential M13 replication protein, gp2 (an endonuclease) [8,9]. It is not known if other Ssb proteins, especially those for cellular DNA replication and maintenance, also possess RNA binding functions that regulate specific translation or other physiologically important RNA-dependent processes. In T4, the physiological link between the sequence-independent ssDNA and specific RNA binding functions of gp32 has been explained by a model based on in vitro measurements of the protein's binding affinities to different nucleic acid ligands. It has been observed that ssDNA is favored over translational operator RNA as a ligand for T4 gp32 and that RNA of nonspecific sequence is the least preferred nucleic-acid ligand for this Ssb protein [10-12]. In vivo, T4 encoded mRNA for gp32 is intrinsically more metabolically stable than the typical prokaryotic mRNA and is thought to have opportunities to undergo many cycles of gp32-mediated repression and depression during the replication and other processing of phage DNA. The potential for translation of this mRNA in the T4 infected E coli host is thought to be determined by availability of ssDNA in the metabolic pool [10,13,14]. DNA damage or unwinding transactions are thought to draw gp32 away from its mRNA target to the exposed ssDNA, thus causing derepression of translation and upward adjustments in gp32. Repression of the mRNA would then be reestablished if the amount of gp32 exceeded the number of exposed ssDNA sites for the protein. This model is consistent with many in vivo observations relating to levels of T4 gp32 biosynthesis under conditions of DNA damage or abnormal accumulation of ssDNA in the phage infected bacterial host [7].
It is not clear how T4 gp32 distinguishes between specific RNA and the non-specific nucleic acid sequence of ssDNA or ssRNA ligands. It appears that single-strandedness of the nucleic acid is not the most important criterion used by the protein to selectively bind its own message in the phage-induced mRNA pool. The translational operator for T4 gp32 has been mapped by RNA footprinting assays and determined to consist of two contiguous components, a 5' terminal ~28-nucleotide component that forms a folded structure (RNA pseudoknot) and an adjacent, less structured, >40-nucleotide component that lies 3' to the pseudoknot [15,16]. The 3' terminal component includes several repeats of UUAAA or UAAA sequences, in addition to harboring typical prokaryotic nucleotide determinants for translation initiation by ribosomes [7,16,17]. The RNA pseudoknot and UUAAA/UAAA elements are both essential for autogenous repression of the mRNA by T4 gp32 [15,16,18]. In vitro studies suggest that the pseudoknot serves as the initial recognition (nucleation) site for the protein and that this gp32-RNA interaction leads to cooperative binding of additional gp32 monomers to the less structured downstream sequence containing the UUAAA/UAAA elements and ribosome-binding site (RBS) [16]. Cooperative binding to the mRNA is envisaged to be analogous to gp32-ssDNA interactions, except that the UUAAA/UAAA sequence elements probably contribute to specificity of the mRNA interaction to the protein.
The 3-dimensional structure of intact T4 gp32 has not been solved, although a number of biochemical and physiological observations have provided clues that the protein is modularly organized into 3 distinct domains [19]. In particular, studies with proteolytic fragments of purified T4 gp32, including the analysis of a crystal structure for one of these fragments [20], have assigned the ssDNA binding function to a module formed by an internal segment of the 301-residue protein. It is presumed that this domain is responsible for binding specific RNA as well, although no direct evidence exists for this notion. In the studies described here, we show that the ssDNA-binding domain is highly conserved between T4 gp32 and the phylogenetic variant of this protein from the T4-like phage RB69. Yet, we also show that sequences of the mRNA targets for the two Ssb proteins are different and that the two repressors differ in their patterns of interaction with these targets. We present results suggesting that specificity of gp32 to RNA has co-evolved with specificity of this Ssb protein to other phage induced proteins of DNA metabolism that interact with gp32's C-terminal domain. Our studies suggest that the ability of a diverging regulatory RNA to make alternate contacts with a mutually plastic, but highly conserved, RNA-binding protein site may allow the RNA to tolerate mutational changes without loss of the regulatory function. Such plasticity of the interacting partners could allow for the evolution of a broad spectrum of gp32-RNA binding specificities despite selective pressures that conserve the amino acid sequence of the protein's nucleic acid-binding domain.
Methods
Bacterial and phage strains used
The E coli K-12 strain K802 (hsdR, hsdM+, gal, met, supE) was used as host in cloning experiments and the E coli B strain NapIV (hsdRk+, hsdMk+, hsdSk+, thi, supo) was the host for plasmid-mediated gene expression studies that utilized lambda pL control. E coli B strain BL21(DE3), which harbors a T7 RNA polymerase gene under cellular lac promoter control [21], was used as the host for T7 Φ10-promoter plasmids in pilot experiments that assessed toxicity of cloned RB69 gene 32 to bacterial cells.
Cloning and nucleotide sequence determination of RB69 gene 32
In preliminary experiments, we used Southern blot analysis of AseI-digested RB69 genomic DNA to identify and retrieve an ~35-kb DNA fragment that hybridized to a T4 gene 32-specific riboprobe under stringent conditions. The riboprobe was prepared by methods described previously [22,23] using the T4 gene 32 clone pYS69 [15], which was generously provided by Y Shamoo. We were unable to clone this AseI fragment in AseI-compatible Eco R1-generated ends of plasmid vectors. However, further digestion of the AseI fragment with ApoI (which generates Nde1-compatible ends) yielded a shorter, ~15-kb, fragment that could be cloned in the NdeI-EcoRI interval of vector pNEB193 (cat# N3051S, New England Biolabs, Beverly, MA). The cloned fragment was sequenced and found to be very similar to the T4 genetic segment extending from gene 59 through the 5' terminal ~2/3 of gene 32, except that the RB69-derived DNA appeared to lack a homologue of the T4 ORF 32.1 (see below). Comparisons between the T4 and RB69 gene 59-32 regions are diagrammed in Fig 1. We retrieved the remainder (3' terminal segment) of RB69 gene 32 from RB69 genomic DNA, through PCR amplification using Taq DNA polymerase. For this purpose, we utilized two primers, one perfectly matching a sequence in the cloned AseI-ApoI RB69 fragment (ie, upstream primer: 5'GCTGCTAAGAAATTGTTCATAG3') and the other (the downstream primer), an 18-mer bearing the sequence 5'CAGCAGCAGTGAAACCTTTA3', was chosen from a PCR screen of an RB69 primer library. DNA amplification was carried out under low-stringency conditions for primer annealing (30 sec at 25°C), which allowed activity from the imperfectly matched downstream primer. We obtained several products that we resolved by agarose gel electrophoresis Only one of these products, an ~35-kb DNA fragment, hybridized, although poorly, to the T4 gene 32-specific riboprobe initially used for the Southern blot analysis of Ase1-digested RB69 genomic DNA. This fragment was sequenced, using the PCR, and found to contain the 3' terminal segment of RB69 gene 32 as well as some of the region distal to RB69 gene 32 (relative to the T4 genetic map). Collectively, sequence analysis of the cloned and amplified RB69 genomic segments yielded sufficient information for designing new primers to amplify, from genomic DNA, the entire wild-type RB69 gene 32, as well as shorter segments of this gene and its putative control region in the untranslated RB69 IC59-32 region (Fig 1). DNA sequence information obtained from these analyses was also used for another study, which was aimed at determining the sequence of the entire RB69 genome (GenBank NC_004928).
Figure 1 A comparison between the genetic maps of the Ssb protein (gp32) encoding regions of phages T4 and RB69. Note the presence of an open-reading frame (ORF) for a homing endonuclease (SegG protein; [45]) between T4 genes 59 (gp59; primase-helicase loader) and 32 (gp32; Ssb protein). The restriction sites we used for cloning RB69 gene 32 are marked, and compared to the locations of analogous sites in T4. GenBank Accession numbers for the genetic regions of interest are also noted.
Assays for plasmid directed gene 32 expression
We used the lambda pL plasmid vector pLY965 [24] to clone RB69 gene 32 sequences that were designated for in vivo expression studies. This vector expresses cloned DNA under control of the heat-inducible λcI857pL element, which produces sufficient cI857 repressor under uninduced conditions (≤30°C) as to maintain pL-mediated expression at undetectable levels. Minimizing plasmid-driven transcription from pL contributed to stable maintenance of the cloned wild-type RB69 gene 32, the product of which is highly toxic to bacterial cells. RB69 gene 32 mutants still emerged when such clones were grown at ≤30°C. Some of these mutants were archived for use as controls in certain studies (eg, PL2 and PL8, Fig 4). With the T7 Φ10-promoter expression vector pSP72 (Promega) as the cloning vehicle, clones containing the wild-type RB69 gene 32 were not viable when introduced into E coli BL21(DE3), probably because of residual (constitutive) lac-promoter activity in this bacterial host. To circumvent potential toxicity, pSP72-based recombinants were propagated in hosts lacking a T7 RNA polymerase gene. The purified plasmid DNA from these hosts was used for in vitro transcription and translation assays. Methods for the radiolabeling of plasmid encoded proteins and their subsequent analysis by SDS-PAGE have been described elsewhere [24,25], and conditions pertaining to specific experiments are given in figure legends.
Figure 4 Results of experiments showing that RB69 gp32 is an autogenous translational repressor. For Panel A, λCI857PLN-bearing plasmid clones of the diagrammed DNA segments were heat-induced (42°C) and assayed for gp32 synthesis as described in other work [24,27]. RBG32 is a DNA segment that carries the wild-type sequence from -120 through +900 relative to the first base of the initiator AUG of RB69 gene 32. RBG32Δop is a truncated derivative of RBG32 that lacks elements of the putative RNA pseudoknot of RB69 gene 32 (Figs 3 & 6). PL8 is identical to RBG32 except that it carries a single-base substitution (marked with an asterisk) in codon 173, leading to a F173S substitution in RB69 gp32. PL2 is similar to RBG32 and PL8, except that it carries several point mutations (map positions marked with asterisks). Panel B shows results of an experiment in which purified RB69 gp32 was shown to inhibit in vitro translation of purified mRNA from the cloned RBG32 fragment, as well as mRNA from in vitro expressed plasmid clone (coupled transcription/translation). Conditions for these assays are described in METHODS.
Purification of gp32 from clones of the structural gene
RB69 gp32 and T4 gp32 were purified from the overproducing clones pRBg32Δop (RB69 gp32) and pYS69 (T4 gp32), respectively. We used the gp32 purification protocol outlined by Bittner et al, [26] with minor modifications. The preparation of crude extracts, from 6-liter batches of heat-inducible E coli NapIV clones of phage genes, was as described previously for T4 RegA protein [27]. Anionic-exchange chromatography (using Q-Sepharose; Cat# 17-0510-01; Pharmacia) was as described for purification of plasmid-generated RB69 gp43 [28]. Under the conditions used, gp32 eluted at 0.3–0.4 M NaCl. In the subsequent chromatographic step, utilizing Phenyl-Sepharose (Cat#17-0965-05; Pharmacia), we tested column fractions for nuclease contamination by incubating 4 μl samples with plasmid DNA (~1 μg) overnight at room temperature and then analyzing the mixtures by agarose gel electrophoresis. The gp32-containing fractions that exhibited no hydrolysis of the plasmid DNA were pooled and the protein was purified further by chromatography on ssDNA-agarose (Cat #15906-019; Invitrogen). Pooled fractions from the ssDNA chromatography were dialyzed against a gp32 storage buffer containing 0.1 M NaCl, 20 mM Tris-HCl pH 8.0, 1 mM EDTA, 0.5 mM DTT and 50% glycerol Protein stocks (at 4–8 mg gp32/ml) were stored at -20°C until used.
Preparation of RNA for in vitro studies
RNA preparations used for footprinting and other in vitro studies originated from in vitro transcription of pSP72 clones of the desired gene 32 sequences Methods have been described elsewhere [29]. Phage-specific RNA sequences of the purified transcription products used for footprinting included nucleotide positions -102 to +161 (relative to the initiator AUG) in case of the RB69 gene 32 transcripts and positions -96 to +161 in case of the T4 gene 32 transcripts. These products also included a 10-nt sequence from the plasmid's T7 promoter region RNA sequencing was carried out by using the RVT-catalyzed primer-extension (cDNA synthesis) method described elsewhere [23,29]. Sequencing primers were annealed to codons 12 to 20 of the transcripts and the sequenced segments of the RNA spanned nucleotide positions +36 through about -100 relative to the initiator AUG. For in vitro translation assays, the RNA preparations included full length and truncated versions of the gene 32 open-reading frame from each of the 2 phage sources.
Assays for gp32-mediated in vitro translational repression
We used E coli S30 cell-free extracts (Cat#L1020; Promega) with purified pSP72-based gene 32 recombinant DNA (coupled transcription-translation assays) or purified RNA (DNA-free translation assays) to assess repressor activities of purified RB69 gp32 and T4 gp32. With plasmid-directed gene 32 expression, it was possible to use expression of the plasmid borne bla gene (β-lactamase) as an internal control. Each 50 μl in vitro assay reaction mixture (placed in a 15-ml conical tube) contained 1 μg of plasmid DNA template or 4 μg RNA, 5 μl of a mixture of all amino acids (1 mM each) except L-methionine, 1 μl of an S30-premix cocktail (containing rNTPs, tRNAs, an ATP generating system and required salts), 15 μl S30 extract and the balance of volume in nuclease-free water Reaction mixtures, including any added gp32, were constituted in an ice bath before transferring to 37°C for incubations (30 or 60 min). Reactions were stopped by rechilling in the ice bath. Proteins from 5 μl samples were precipitated with 20 μl acetone, collected by centrifugation, dried and suspended in SDS extraction buffer for analysis by SDS-PAGE and autoradiography. Analysis of plasmid encoded (N-terminal) gp32 fragments was carried out in SDS-PAGE (10% gels) using Tricine as the electrophoresis buffer. This buffer system allows for effective resolution of small polypeptides [30]. When used, purified gp32 was added at concentrations ranging between 5 and 20 μM.
Treatments of RNA with RNases and chemical agents
The RNA-modifying chemical reagents Dimethylsulfate (DMS; Cat# D18,630-9; Aldrich) and Diethylpyrocarbonate (DEPC; Cat# D5758; Sigma) and the ribonucleases (RNases A1, T1 and V1 respectively) were used to probe RB69- and T4-derived operator RNAs for intrinsically structured regions. The RNases were also used for RNA footprinting (protection by gp32) studies.
DMS was diluted in absolute ethanol at ratios of 1:2, 1:4, and 1:5 ratio v/v and its effects were analyzed at the three concentrations. The reaction buffer contained 30 mM HEPES pH 7.5, 10 mM MgCl2. Reactions were stopped in 0.5 M β-mercaptoethanol and 0.75 M sodium acetate. The protocol for DEPC treatment was identical to that for DMS, except that we used 1 μl of DEPC per 100 μl of reaction mix and incubated the reactions at room temperature for 10 min.
For the RNase-sensitivity assays, including gp32-mediated RNA footprinting, digestions with RNases A1 and T1 were carried out in 30 μl buffer containing 60 mM NH4Cl, 10 mM Mg acetate, 10 mM Tris-HCl pH 7.4, and 6 mM β-Mercaptoethanol. The buffer for digestions with RNase V1 contained 25 mM Tris-HCl pH 7.2, 10 mM MgCl2, and 0.2 M NaCl Incubations were at 37°C in 30 μl buffer in all cases. RNase treatments were halted with an equal volume of buffer containing 0.4 M Na acetate pH 5.2, 20 mM EDTA, and 30 μg E coli tRNA. When used for RNA footprinting, RB69 gp32 or T4 gp32 was added at concentrations in the range between 1 μM and 5 μM.
Results
A sequence comparison between T4 gp32 and RB69 gp32
The amino acid sequence of RB69 gp32 was deduced from the determined nucleotide sequence of the gene. An alignment between the predicted primary structures of this protein and its T4 homologue is shown in Fig 2, which also highlights the main differences between the 2 proteins and points out certain functionally important landmarks on the T4 gp32 sequence. The two proteins are identical at ~85% of amino-acid positions (92% overall similarity), with most of the differences being clustered in 2 short blocks of amino-acid sequence in the highly charged C-terminal segment of the protein, D264(RB69)/A264(T4) to L299(RB69)/L301(T4). Both C-terminal segments are rich in serines and aspartates; however, they differ in their arrangements of these residues and the serine-rich cluster is 5 residues longer in T4 gp32 (S282-S286). In contrast to their conspicuous differences in the C-terminal domain, T4 gp32 and RB69 gp32 are closely similar in segments that, in T4 gp32, have been implicated in cooperative gp32-gp32 interactions (95% identity/100% similarity for the N-terminal 21 residues) and ssDNA binding (residues 21 to 254; ~92% identity/~95% similarity). We note that all T4 gp32 residues that have been implicated in ssDNA binding are conserved in RB69 gp32 (Fig 2). However, interestingly, codon sequences for the two aligned N-terminal gp32 segments differ at many third nucleotide positions between T4 and RB69, suggesting that there has been natural selection for amino acid identity (and not merely chemical or side-chain similarity) in the N-terminal two-thirds of the phage Ssb protein. We also note that both proteins contain 2 "LAST" (3KRKST7 or 110KRKTS114) sequence motifs, which in the T4 system have been implicated in interactions with the negatively charged surfaces of DNA as well as with the C-terminal domain of gp32 [31]. One of these motifs (K3-T7) lies near the extreme N-terminus of the protein and the second (K110-S114) is adjacent to a short sequence (residues 102–108) that diverges between T4 and RB69 (~50% similarity), but that also contains 3 conserved charged residues including the DNA-binding tyrosine Y106 of T4 gp32 [20].
Figure 2 Amino-acid sequence alignments between the Ssb proteins (gp32s) of T4 and RB69. Residues and segments of the T4 gp32 sequence that have been implicated in specific biological functions of the protein are marked as follows: Db [DNA binding residue]; Zb (residues that coordinate Zn++ in the zinc-binding domain; [20,46]); gp32-gp32 [residues involved in cooperative gp32 binding to ssDNA]; XLgp59 (residue that cross-links to gp59; [42]); LAST (sequence motifs, (Lys/Arg)3 (Ser/Thr)2, that have been proposed to directly bind nucleic-acids or mediate gp32-gp32 interactions [31]). The shaded C-terminal portion of T4 gp32 has been implicated in interactions with other phage induced proteins [38]. The small deletion (Δ32PR201) alters specificity of T4 gp32 in phage replication without affecting autogenous translational repression [39]. The largest vertical arrows denote trypsin-hypersensitive sites (19) The G-to-A mutation marked "(ts)" was isolated in this laboratory as a missense (temperature-sensitive) suppressor of a defective gp43 function (unpublished). In the RB69 gp32 sequence, residues whose codons differ from their conserved T4 counterpart at the third nucleotide are underscored with a single dot; those differing by 2 nucleotides are marked by 2 dots.
The RB69 IC59-32 region
Figure 3 shows an alignment of the RB69 IC59-32 region with its counterpart (the IC32.1-32 region) from T4 The T4 region (GenBank NC_00866) has been experimentally documented to harbor the translational operator for gene 32 expression [6]. The RB69 counterpart (GenBank NC_004928) is 7 nucleotides longer and ~70% identical in sequence. By comparison, the gp32 encoding portions of the T4 and RB69 genes are ~80% identical in the overall nucleotide sequence (see Fig 1 for GenBank accession numbers) and their predicted protein products are >90% similar in amino acid sequence. There is an additional 40-nt untranslated sequence in the RB69 IC59-32 region that appears to have no T4 counterpart (Fig 3), and ORF321 is missing altogether in RB69 (Fig 1). So, it appears that the regions between genes 59 and 32 of T4 and RB69 have undergone more evolutionary divergence from each other than their gp32-encoding regions. However, despite their differences in nucleotide sequence, the translational operator sequence of T4 gene 32 and its putative RB69 counterpart are predicted, by computer programs, to form similar structures. We address this prediction below and present experimental evidence for the RNA structure and its role in translational control of RB69 gp32 synthesis.
Figure 3 A comparison between the nucleotide sequences of the T4 IC32.1-32 and RB69 IC59-32 regions. These 2 regions contain determinants for translation initiation of the respective phage-induced mRNAs for gp32. The chart emphasizes sequence differences (entered as lettered residues in the RB69 sequence) between the 2 regions. The dashes indicate identity between RB69 and T4 residues. Sequence elements contributing to RNA pseudoknot formation in the T4 gene 32-specific mRNA are marked by horizontal arrows. Note the sequence overlap between elements of the pseudoknot and ORF32.1 (segG) of the T4 sequence. Also, see Fig 6 for a summary of properties of the RB69 sequence.
RB69 gp32 and T4 gp32 are functionally similar
Figure 4 shows results from experiments that measured the effects of RB69 gp32 on its own synthesis in vivo (Fig 4A) and in vitro (Fig 4B). The in vivo experiments measured plasmid-directed RB69 gene 32 expression by E coli clones carrying wild-type and mutant versions of the RB69 gene. As shown in Fig 4A, induced expression of the gene was lower (by ~4-fold) with the wild-type construct than with deletion mutants of the untranslated 5' leader of the mRNA (RBG32Δop, Fig 4A) or missense mutants in the structural gene from this phage (PL2 and PL8 constructs; Fig 4A). These observations are consistent with the explanation that RB69 gp32, like T4 gp32, is able to bind and repress its own mRNA. The results shown in Fig 4B confirm that purified RB69 gp32 is a potent repressor of translation of purified mRNA for this protein.
We have used similar experiments to those for Fig 4 to compare repressor activities of T4 gp32 and RB69 gp32 on identical RNA targets, and observed that either protein can repress gene 32-specific mRNA from either source (results not shown). However, such experiments, which require 10–30 μM purified protein to demonstrate repression (Fig 4B), did not unambiguously distinguish between the RNA-binding specificities of the 2 proteins. Also, in phage-plasmid complementation assays, we observed that the cloned RB69 wild-type gene 32 supported efficient growth of T4 gene 32 mutants (bursts of ~100) By these criteria, the T4 and RB69 proteins appeared to be similarly functional in each other's physiological systems. Yet, the natural targets for the 2 proteins are clearly different from each other in topography (Fig 3) and as we describe later, RNA-binding specificity differences between the 2 proteins could be detected through in vitro RNA-footprinting assays, which utilized lower concentrations of gp32 than is usually required to detect gp32-mediated repression by in vitro translational assays.
RNA structure in the RB69 gene 32 translational initiation region (TIR)
As discussed above for Fig 3, computer-assisted and visual examinations of the RB69 IC59-32 nucleotide sequence predicted an RNA topology that was similar to the T4 gene 32 translational operator, particularly with regards to presence of a putative RNA pseudoknot structure to the 5' side of the Shine-Dalgarno and UUAAA/UUAA sequence elements of the mRNA. We used 3 RNA modifying agents to test directly for intrinsic secondary or higher-order structure in the RB69-derived RNA: DMS, DEPC and RNase V1, respectively. Results are shown in Fig 5. We observed that the RB69-derived sequence from nucleotide position A(-1) through A(-45), relative to the initiator AUG, was hypersensitive to cleavage following DMS or DEPC treatment (Fig 5A) and relatively insensitive to cleavage by the dsRNA-specific RNase V1 (Fig 5B). These observations, which are summarized in Fig 6A, are consistent with the prediction that the A(-1) to A(-45) segment of the RB69 IC59-32 RNA region is intrinsically unstructured. In contrast, the segment of this RNA corresponding to the putative pseudoknot structure can accommodate a range of /RNA sequences. The interaction may also be subject to is hypersensitive to RNase V1 (Fig 5B) and less sensitive than the A(-1) to A(-45) segment to the 2 chemical agents used (Fig 5A). There was one unexpected observation in these experiments RB69 nucleotide position U(-20), which is located in the putatively unstructured portion of the RNA target (Fig 6A), appeared to be insensitive to DEPC modification (Fig 5A). Below, we show that another position in this segment, G(-10), is relatively insensitive to the ssRNA-specific RNase T1. Possibly, cleavage at U(-20) and G(-10) by RNA modifying agents is affected by RNA hairpin formation in the U(-8) to A(-21) sequence. The location of this putative hairpin, which is not predicted in the T4 RNA counterpart, is diagrammed in Fig 6A. In summary, the T4 gene 32 translational operator region and its putative counterpart from RB69 exhibit several topographical differences from each other, including an additional 6-nt sequence in RB69 that may contribute to RNA secondary structure formation in the RBS. Below, we show that the 2 regions also differ in their interactions with translational repressors.
Figure 5 Portions of autoradiograms from RNA sequencing gels showing sites of cleavage in RB69 gene 32-derived RNA following treatments with DMS and DEPC (Panel A) and RNase V1 (Panel B). These experiments probed the RB69 RNA for secondary and higher-order structure. The lanes marked "RNA seq" show results from sequencing untreated RNA by the RVT-catalyzed chain termination method [23,35]. In Panel A the lane marked with a "minus" sign shows the positions of RVT chain termination caused by RNA structure in the untreated RNA. The DMS and DEPC lanes show sites of hypersensitivity (cleavage) of the same RNA to treatment with these chemical agents. In Panel B, the V1 lanes denote the amount of RNase V1 (×10-5 units) used to digest the RNA substrate.
Figure 6 Summaries of results from the chemical and RNase sensitivity and RNA footprinting studies reported here. Panel A shows our interpretation of experiments that probed the existence of RNA structure in RB69 gene 32-specific RNA (Fig 5). The T4-derived RNA counterpart is shown for comparison The "caret" symbol denotes sensitivity to cleavage after DMS treatment; asterisks denote sensitivity to cleavage after DEPC treatment. The darker symbols denote greater sensitivity. Positions that are not marked by any symbols were resistant to the modifying agents under the conditions used. Vertical arrows mark positions that were sensitive to RNase V1. Panel B shows our interpretation of the RNA footprinting studies described in Figs 7 and 8. Positions of protection from RNaseA1 by gp32 are marked by the triangles and protection from RNase T1 by the pentagonal symbols. The darker symbols denote stronger protection. Unmarked positions were not protected by either gp32 from phage source under the experimental conditions used.
The footprints of T4 gp32 and RB69 gp32 on gene 32-specific RNA targets from T4 and RB69
We used the ssRNA-specific RNases A1 and T1 to determine the abilities of gp32 from the 2 phage systems to protect RNA targets from cleavage with these enzymes. These RNA footprinting studies also extended the information we obtained from treatments with DMS and DEPC about intrinsic structure of the RNA targets. Results are shown in Fig 7 for the RB69-derived RNA target and Fig 8 for the T4-derived target. Also, a summary of our observations from these experiments is presented on the RNA sequence charts in Fig 6B In the aggregate, our studies showed that T4 gp32 and RB69 gp32 contact RNA targets differently from each other, although the two proteins overlap in their RNA-binding properties. We highlight the following specific observations.
Figure 7 In vitro footprinting of RB69 gene 32-specific RNA with purified RB69 gp32 (Panels A and B) and T4 gp32 (Panels C and D). Preparation of RNA and proteins and experimental conditions for footprinting are described in METHODS. Horizontal arrows mark nucleotide positions (Fig 6B) that exhibited gp32-mediated protection from RNaseA (panels A and C) and RNase T1 (panels B and D). Darker arrows denote stronger protection. The results are summarized in Fig 6B.
Figure 8 In vitro footprinting of T4 gene32-specific RNA with purified RB69 gp32 (Panels A and C; RNase A) and T4 gp32 (Panels B and D; RNase T1). Conditions for these experiments were identical to those described in Fig 7, except that the RNA substrate used for footprinting was derived from clones of T4 gene 32 rather than RB69 gene 32. See also Fig 6C for a summary.
1. At the protein concentrations used (1–5 μM), the RB69 gp32 footprint on the RNA target from RB69 was 5 residues longer than the footprint of this protein on the T4-derived RNA target; however, the positions of the 2 footprints relative to the respective initiator AUG and 5' terminal boundary of the pseudoknot structure appeared to be identical (Fig 6)
2. As can be seen in Figs 7A and 7B, RB69 gp32 protected its own mRNA target strongly within the nucleotide segment between U(-14) and G(-61), and weakly in the segment from U(-2) to G(-9) In contrast, as seen in Figs 7C and 7D, T4 gp32 protected this RNA strongly only in the segment from C(-42) to G(-61)
3. As can be seen in Fig 8C and 8D, T4 gp32 protected the T4-derived RNA strongly in the G(+3) to U(-70) segment In contrast, RB69 gp32 protected this RNA target best in the U(-16) to U(-70) segment (Fig 7A and 7B)
It should be noted that the gp32 footprint sizes reported here are shorter than has been reported in studies that utilized higher concentrations of T4 gp32 with T4-specific RNA targets [16,32]. As stated earlier in this report (Fig 4), the higher gp32 concentrations (>5 μM) mask specificity differences between the T4 and RB69 proteins.
Discussion
Phages T4 and RB69 are phylogenetically related to each other and encode homologous sets of DNA replication proteins that exhibit a significant degree of compatibility with each other's biological systems [22,24]. Despite such overlaps in function, we have commonly observed specificity differences between protein homologues from the 2 phage systems. For example, in plasmid-phage complementation assays, RB69 DNA polymerase (gp43) was observed to be just as effective as T4 gp43 in T4 DNA replication in vivo, whereas the T4 enzyme was less effective than its RB69 counterpart for RB69 DNA replication [22,33]. Also, the 2 DNA polymerases, like the 2 Ssb proteins compared here, are RNA-binding autogenous translational repressors that differ in RNA binding specificity and RNA target sequence. Studies with the T4 versions of gp43 and gp32 clearly show that the binding of these proteins to specific RNA is mutually exclusive with their binding to DNA [7,34]. So, conservation of the translational functions of these proteins may be related to conservation of their replication functions. Based on previous studies with RB69 gp43 [35], as well as the current study with RB69 gp32, we surmise that neither of these translational repressors possesses a domain that binds RNA exclusively. Rather, in both cases, the RNA binding site seems to be contained within the region of the protein that binds DNA. Thus, it is possible that in phage infected cells, specific RNA serves as a regulator of both the biosynthesis and replicative activities of these proteins.
In the purified system we have used to compare RNA footprints for gp32 from T4 and RB69 (Figs 6, 7, 8), we observed that the same RNA target could exhibit different patterns of protection depending on source of the Ssb protein. This observation suggests that the RNA-protein interaction is intrinsically flexible and can accommodate a range of RNA sequences as long as these sequences can be made to assume a certain configuration. In addition, the interaction could be subject to modulation by intra- and intermolecular protein-protein interactions of the repressor. In this regard, it is known that the extreme N-terminal segment (~20 residues) and C-terminal segment (~100 residues) of T4 gp32 have profound effects on the ssDNA binding activity, which is housed in the region bracketed by these 2 segments of the protein [19,36,37]. The N-terminal segment determines cooperative binding to ssDNA (through gp32-gp32 interactions) and the C-terminal segment has been implicated in interactions of gp32 with other phage induced proteins [38]. Possibly, the observed sequence divergence between the C-terminal domains of T4 gp32 and RB69 gp32 (Fig 2) was in part coupled to divergence of the mRNA targets during evolution of the 2 related translational repressors.
Although we cannot rule out the possibility that the C-terminal domain of gp32 influences specificity to RNA by interacting directly with this ligand, there are indications that this negatively charged segment of gp32 is a modulator of gp32 interactions with nucleic acids rather than a carrier of nucleic acid binding determinants. In particular, a small deletion that maps within this protein segment (Δ32PR201; Fig 2) exhibits altered specificity to other proteins but has no effects on autogenous control of gp32 synthesis in vivo [39]. Also, recent studies with purified RB69 gp32 implicated the C-terminal domain of this protein in regulating access of gp43 from the same phage to binding sites in the ssDNA-binding module of the Ssb protein [40]. It has also been shown that in T4, the ssDNA-binding module of gp32 forms specific crosslinks to gp59, the phage-induced primase-helicase loading protein [41,42]. Such observations suggest that the 2 nucleic-acid binding functions of gp32 may be subject to regulation by a combination of intra- and intermolecular protein-protein interactions involving the divergence-prone C-terminal domain. It would be particularly interesting to find out if the gp32 sequence divergence near the DNA binding residue Y106 (Fig 2) is important for RNA recognition. X-ray crystallographic studies [20] suggest that T4 gp32 residues T101-K110 constitute part of the ssDNA-binding surface of the protein, which includes Y84, Y99, Y106 and the nearby "LAST" motif (residues 110–114; 31). Also, as suggested by the 3D structure, these residues are located within or very close to the Zn-binding domain of the protein; ie, the putative "zinc-finger" sequence Cys77-X3-His-X5-Cys-X2-Cys90, which has counterparts in a number of RNA-binding proteins [40]. The construction and analysis of RB69-T4 gp32 chimeras could help to establish if the divergence near Y106 is responsible for the observed differences in RNA footprints between T4gp32 and RB69 gp32 (Figs 6, 7, 8).
In summary, we envisage that as a mediator of gp32's interactions with other phage induced proteins, the C-terminal domain of gp32 may co-diverge with its protein targets to maintain mutual recognition, and that structural plasticity of a conserved ssDNA-binding domain may allow an also diverging RNA target to establish rearranged contacts within a relatively conserved protein pocket. It is unclear if the 2 sets of divergence are interconnected, but together, they could facilitate the evolution of a high degree of diversity in how the synthesis and/or replication activity of this Ssb protein is regulated among phylogenetic relatives of T4. It will be important to find out if this diversity includes RNA ligands for gp32 that control the DNA-binding activity but not synthesis of gp32, or if autogenous translational repression has been replaced by other mechanisms for control of gene 32 in some T4 relatives. There is at least one reported example where evolution resulted in lack of RNA binding function in the Ssb protein of an M13-like phage [43]. Also, a scan of available genomic sequences for T4-like phages reveals a high degree of sequence divergence in the putative translational operator regions of the corresponding gene 32 regions. In one case, phage RB49 (GenBank NC_005066), it has been reported that there are no indications that an RNA pseudoknot structure exists in the putative TIR for gene 32, although the UUAA/UUAAA sequence units are conserved in the RB49 IC59-32 region [44]. It remains to be seen if gp32 from this and other T4 like phages that appear to lack the RNA pseudoknot do bind their respective TIR regions or repress their own translation.
Finally, we should comment about ORF32.1 (Fig 1) and its possible relevance to evolution of the mRNA target for gp32. This ORF is present in some T4-like genomes (eg T4 and GenBank Ac No AF033323) and absent in others (eg, RB69 and GenBank Ac No AY310907). Recently, it was shown that T4 ORF 32.1 encodes a Seg-type (G1Y-YIG family) homing endonuclease (now named SegG) that mediates its own transfer, along with T4 gene 32, to the ORF32.1-less genome of phage T2 in T4 × T2 genetic crosses. We note that the 5' terminal sequence of the RNA pseudoknot for T4 gp32 translational control overlaps the reading frame of the segG gene, in addition to being very similar (~83% identity) to the corresponding segment of the pseudoknot sequence of RB69, which lacks a segG gene (Fig 3). Possibly, this portion of the RNA pseudoknot preexisted the entry of an ORF32.1-like sequence element into the gene 59-32 intercistronic region of a T4 progenitor and that the modern day segG gene (ORF32.1) may be a chimera consisting of an extension of the parental segG reading frame into the recipient genome's pseudoknot sequence. Such lateral transfer events and subsequent mutation may have profound influences on evolution of the RNA binding functions of proteins that have relaxed sequence but stringent structural requirements for their RNA target.
Competing interests
None declared.
Authors' contributions
Jamilah Borjac-Natour: Conducted most of the experimental work and initial data analysis and prepared summaries; wrote the first draft and participated in subsequent revisions of the manuscript. Vasiliy Petrov: Conducted independent analysis of data and generated summaries and composite figures for presentation in the manuscript. Participated in revision of the manuscript during later stages of preparation. Jim Karam: Directed the study, evaluated results on an ongoing basis, worked closely with the coauthors during preparation of Figures, played a major role during revision of manuscript drafts and communicated the manuscript to the journal.
Acknowledgment
All the experimental work reported here was carried out by the first author in partial completion of the requirements for the PhD in Biochemistry. This author's work was supported by NIH grant GM54627 and a predoctoral stipend from Tulane University School of Medicine. The second and corresponding authors were also supported by NSF grant 038236 during preparation of this manuscript. We thank our colleagues James Nolan and Henry Krisch for stimulating discussions about phage evolutionary biology and Jill Barbay for extensive help with manuscript preparation.
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| 15507125 | PMC535899 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Sep 17; 1:4 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-4 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-561557163510.1186/1471-2458-4-56Research ArticleMineral water intake reduces blood pressure among subjects with low urinary magnesium and calcium levels Rylander Ragnar [email protected] Maurice J [email protected] Department of Environmental Medicine, Sahlgrenska Academy at Göteborg University, Gothenburg, Sweden2 Nestlé Water Institute and Nestlé Ltd, Vevey, Switzerland2004 30 11 2004 4 56 56 21 6 2004 30 11 2004 Copyright © 2004 Rylander and Arnaud; licensee BioMed Central Ltd.2004Rylander and Arnaud; 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
Several previous epidemiological studies have shown a relation between drinking water quality and death in cardiovascular disease whereas others have not found such a relationship. An intervention study was undertaken to evaluate the effect of water with added magnesium and natural mineral water on blood pressure.
Methods
A group of 70 subjects with borderline hypertension was recruited and consumed 1) a water low in minerals, 2) magnesium enriched water or 3) natural mineral water, in a random, double blind fashion during four weeks.
Results
Among persons with an initial low excretion of magnesium or calcium in the urine, the urinary excretion of magnesium was increased in the groups consuming the two waters containing magnesium after 4 weeks. A significant decrease in blood pressure was found in the group consuming mineral water at 2 and 4 weeks.
Conclusion
The results suggest that minerals taken in water are significant for the body burden and that an intake of mineral water among persons with a low urinary excretion of magnesium or calcium may decrease the blood pressure. Further studies should investigate the extent of mineral deficiency in different populations and the efficiency of different vehicles for supplying minerals, particularly magnesium and calcium.
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Background
A relation between mortality from ischaemic heart disease (IHD) and drinking water characteristics was first shown in Japan in 1957 [1]. Since then, several studies have demonstrated the same relationships, one of the last being a study from Finland in 2004 [2] and reviews have been presented [3,4]. Other studies have, however, not found such relationships or only weak associations between mineral intake and risk for cardiovascular disease [5,6]. This discrepancy may be due to an absence of causality or to variations in the populations studied regarding intake of minerals.
In a case-control study, an inverse relation was found between the amount of magnesium in drinking water and death from acute myocardial infarction and for females also between the amount of calcium and death [7]. Diets rich in vegetables and fruit, which contain high amounts of minerals, had a protective effect on cardiovascular disease [8,9]. This suggests that the mineral balance in individuals depends on different types of intakes which may vary depending on geographical and socio-economical conditions.
Regarding individual minerals, several studies have been reported where hypertensive subjects were treated orally with nutritional doses of magnesium [10]. The results suggested a dose-dependent reduction in blood pressure from the magnesium intervention but it was concluded that the relationship must be confirmed in larger studies, using higher doses of magnesium. A similar meta-analysis reported a very small effect of calcium supplementation [11]. A meta-analysis of 33 studies on potassium intervention concluded that there might be a beneficial effect on blood pressure [12]. Many of the studies reviewed were, however, dietary intervention studies, and the intervention thus comprised several minerals and other agents rather than potassium alone.
Epidemiological studies on cardiovascular disease suggest that drinking water is an important vehicle for the supply of minerals [7]. This is supported by data from short-term intervention studies using mineral water, as well as an epidemiological study [13-15].
The present intervention study was undertaken to determine the effect of minerals in water on one of the major risk factors for cardiovascular disease – blood pressure. Subjects with slightly elevated blood pressure consumed water with different levels of minerals. Serum and urinary levels of minerals were measured as a marker of intervention and blood pressure was measured before and after the intervention.
Methods
Subjects
Female and male subjects, aged 45 – 64 years (n = 70) were recruited by advertising in local newspapers. Inclusion criteria were living in an area with low magnesium content in the drinking water, systolic pressure 15 mm above normal values for their age, diastolic pressure above 90 mm Hg, and within 20% of ideal body weight. Exclusion criteria were hypertension target organ damage, chronic diseases (heart, liver, kidney, diabetes mellitus), pregnancy, and taking oral contraceptives or regular intake of mineral supplements. Subjects with a diastolic pressure above 100 mm Hg were advised to consult a physician for treatment. A few persons decided not to seek a physician's advice and choose to participate anyway. The Ethical committee at the Medical faculty, University of Gothenburg, approved the study.
Blood pressure
Blood pressure was measured using standardized techniques before the intervention, at 2 weeks and at the end at 4 weeks. Two separate recordings were made (diastolic pressure as Korothoff phase 5) after 5 minutes of supine rest. The blood pressure is reported as the average of these recordings.
Blood and urine samples
Blood samples were taken before and after the intervention to determine the serum concentration of magnesium, calcium, sodium, creatinine and potassium (analysis performed at the accredited laboratory for Clinical Chemistry, Sahlgren's Hospital, Gothenburg). Before and after the intervention period, 24 hours urine samples were collected and the amounts of magnesium, calcium, and creatinine were determined (idem). Magnesium and calcium levels in urine were expressed as the creatinine ratio.
Intervention
The participants were randomly allotted into three groups to which the three waters were supplied in similar bottles marked A, B and C. The composition of the waters (see Table 1) was unknown to the persons engaged in the intervention study. The subjects were asked to consume at least one liter of water/day. When preparing coffee and tea, ordinary tap water could be used. There were no difficulties in consuming the allotted quantity and spot checks were made to control for the proper consumption. The intervention lasted 4 weeks. None of the subjects changed their normal dietary habits during the trial.
Table 1 Composition of the three waters used in the intervention study. (1)Valvert®; (2)Distilled water+MgSO4; (3)Contrex®.
Waters Minerals/mg L A1 B2 C3
Calcium (Ca2+) 67.6 4 486
Magnesium (Mg2+) 2 82.3 84
Sodium (Na+) 1.9 2.4 9.1
Potassium (K+) 0.2 0.1 3.2
Sulphate (SO42-) 18 326 1187
Bicarbonate (HCO3-) 204 12 403
Chloride (Cl-) 4 0.7 8.6
Fluoride (F-)0 <0.05 0 0.32
Silica (SiO2) 5.7 0 8
Statistical evaluation
The three groups were compared using the Student's t-test for paired samples and p < 0.05 was considered statistically significant. For a smaller subgroup of 6 individuals, comparisons were made using the Wilcoxon test for pairs.
Results
In the analysis of the whole group, no differences in any parameters were found between persons consuming the different types of water. For the subsequent analysis, subjects with serum or urine values in excess of the 75 percentile were excluded on the ground that these represented a group with a sufficient body burden of the minerals and would not be influenced by the intervention. For magnesium in urine, this value was 0.39 mmol/l, and for calcium 0.50 mmol/L. For magnesium, calcium, potassium and sodium in blood the values were 0.9, 2.4, 4.4 and 141 mmol/L, respectively.
There was a close relation between the amount of calcium/creatinine and magnesium/creatinine in the urine before the intervention (p = 0.001). Table 2 shows the amounts of calcium and magnesium in urine before and after the intervention with different kinds of waters. It is seen that persons consuming waters B and C had significantly higher amounts of magnesium in the urine after the intervention. No significant effects of the waters on serum levels of magnesium could be detected (data not shown).
Table 2 Magnesium/creatinine and calcium/creatinine in urine (mmol/L) among subjects with an initial level less than 0.40 mmol/L magnesium before and after intervention with different waters.
Water n Before After p
Magnesium
A 18 0.25 (0.08) 0.26 (0.07)
B 18 0.28 (0.06) 0.34 (0.09) 0.009
C 19 0.30 (0.07) 0.35 (0.09) 0.019
Calcium
A 18 0.40 (0.22) 0.35 (0.40)
B 18 0.33 (0.12) 0.35 (0.40)
C 19 0.34 (0.13) 0.38 (0.16)
Table 3 shows the blood pressure before and after intervention. Among persons consuming water C, both the systolic and diastolic blood pressures decreased significantly at 2 and 4 weeks. A similar result was obtained when the group with an initial low level of calcium in the urine was evaluated (data not shown).
Table 3 Blood pressure among subjects with an initial magnesium level in urine less than 0.40 mmol/L before and after intervention with different waters. * denotes group with initial systolic values below 170 mm (see text).
Water n Before 2 weeks 4 weeks
Systolic
A 18 151.9 (9.8) 154.2 (15.9) 148.3 (12.4)
B 18 148.3 (10.5) 146.8 (13.6) 147.9 (11.5)
C 19 156.8 (15.9) 150.1(16.1)p = 0.034 150.4(15.5)p = 0.017
C* 13 148.8 (13.3) 142.5(10.1)p = 0.028 144.3(14.4)p = 0.047
Diastolic
A 18 90.1 (4.4) 91.2 (6.1) 89.8 (5.0)
B 18 90.4 (4.2) 89.3 (6.0) 90.9 (6.6)
C 19 91.7 (6.3) 88.0 (7.6) p = 0.014 89.1 (8.0) p = 0.020
C* 13 91.3 (6.4) 87.3 (6.3) p = 0.004 88.1 (8.6) p = 0.012
In spite of the random allocation to the different waters, it was found that the group consuming water C comprised a larger number of persons with a high initial systolic pressure. In the groups receiving waters A and B, none of the subjects had systolic blood pressures above 170 before the intervention. The subjects drinking water C were divided into those with an initial systolic pressure above and below 170 mm. For the group with the higher pressure (n = 6), there was a decrease in the systolic pressure before and at 4 weeks (p = 0.023) but no difference at 2 weeks or for diastolic pressure. The results from the remainder of the group are also shown in Table 3. It is seen that significant reductions were observed both for systolic and diastolic blood pressure after 2 and 4 weeks.
Discussion
The study is of exploratory character, based on a relatively small number of subjects and should be interpreted with care. There is also a lack of some data that retrospectively would have been of interest such as sodium in the urine and the effect of water with only calcium added. We do not think, however, that this has any influence on the major conclusions from the study.
The intervention with the two waters with added magnesium influenced the body burden in terms of an increased excretion of magnesium in urine. This is consistent with findings from previous intervention studies [16,17] although the dose of magnesium used here was rather low in comparison to several previous studies [10]. It could have been of interest to study the effect of different doses of magnesium only, but in view of the conclusions regarding the better effect of total mineral water on blood pressure, this does not have a high priority. The absence of an effect on serum was expected; it has previously been shown that serum magnesium is a poor indicator of the body burden or the intracellular content [18].
The intervention with water containing high amounts of several minerals decreased the blood pressure significantly in contrast to water with magnesium only where no significant effect was detected. This does not exclude that an effect could have been found with the latter water, had the intervention time been longer. On the other hand, the finding supports the concept that interventions should be performed under conditions similar to the ones present in normal environments, rather than with one specific agent. This could also explain the lack of an effect in previous studies where single minerals have been given as reviewed in the introduction.
Conclusion
In summary, the results suggest that waterborne minerals constitute a supply for the body burden, that the urinary excretion can be used as a physiologically relevant indicator of the body burden of magnesium and calcium, and that the supplementation of magnesium together with other minerals may reduce blood pressure among persons with a low body burden of magnesium and calcium, either due to an insufficient intake through food or water, or through some metabolic/clinical disturbance. Additional studies are needed to explore this further.
Competing interests
The study was supported by an unconditional grant to Gothenburg University from the Nestlé Water Institute, Vittel, France. The authors had full freedom for data analysis, manuscript preparation and submittance to a journal. RR has not received any fees or salaries for this work, including article processing charges, nor does he own any shares or has any other financial interest in the company. MA was an employee at the Water Institute at the time of the study.
Authors' contribution
RR and MA jointly developed the research plan. RR conducted the field study. RR and MA jointly analyzed the data and wrote the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors are grateful for the valuable contributions to the research plan given by Dr Eva Rubenowitz and Professor Ove Andersson and thank Erika Kerekes, Ingela Sahlberg and Gunilla Arvidsson for excellent technical assistance and Dr Florence Constant for the preparation of the different waters.
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| 15571635 | PMC535900 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Nov 30; 4:56 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-56 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-251555506610.1186/1472-6920-4-25Research ArticleEvaluation of an inter-professional workshop to develop a psychosocial assessment and child-centred communication training programme for paediatricians in training Nestel Debra [email protected] Sharon [email protected] Quentin [email protected] Department of Psychological Medicine, Imperial College London, United Kingdom2 Imperial College, St Mary's Higher Training Scheme, United Kingdom3 Chichester & St George's Hospital Medical School, United Kingdom2004 21 11 2004 4 25 25 16 6 2004 21 11 2004 Copyright © 2004 Nestel et al; licensee BioMed Central Ltd.2004Nestel 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
The quality of psychosocial assessment of children in consultations varies widely. One reason for this difference is the variability in effective mental health and communication training at undergraduate and post-qualification levels. In recognition of this problem, the Royal College of Paediatrics and Child Health in the United Kingdom have developed the Child in Mind Project that aims to meet this deficit in medical training. This paper describes the evaluation of a workshop that explored the experiences and expectations of health care professionals in the development of a training programme for doctors.
Methods
The one-day inter-professional workshop was attended by 63 participants who were invited to complete evaluation forms before and immediately after the workshop.
Results
The results showed that the workshop was partially successful in providing an opportunity for an inter-professional group to exchange ideas and influence the development of a significant project. Exploring the content and process of the proposed training programme and the opportunity for participants to share experiences of effective practice were valued. Participants identified that the current culture within many health care settings would be an obstacle to successful implementation of a training programme. Working within existing training structures will be essential. Areas for improvement in the workshop included clearer statement of goals at the outset and a more suitable environment for the numbers of participants.
Conclusions
The participants made a valuable contribution to the development of the training programme identifying specific challenges. Inter-professional collaborations are likely to result in more deliverable and relevant training programmes. Continued consultation with potential users of the programme – both trainers and trainees will be essential.
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Background
Childhood mental health disorders are common. A Department of Health survey of 5 to 15 year-olds in England and Wales, 5% had conduct disorder, 4% had emotional disorders and 1% rated as hyperactive [1]. However, during consultations the psychosocial assessment of children is sometimes compromised. The reasons are varied and most often reflect deficits in relevant knowledge, attitudes and skills of health care professionals. Children are often not placed at the centre of the consultation [2-7]. Further, health care professionals have been found to lack knowledge in pain management of children [8,9] and are poor at giving information to children and adolescents with cancer [10] which may lead to poor adjustment to illness and emotional problems. The consequences for children and their families can be profound.
Knowledge of psychosocial and mental health problems is only part of the patient assessment process. The ability to communicate effectively with the patient is pivotal for accurate assessment [11-13]. A unique feature of the paediatric interview is its triadic structure. That is, consultations often involve a child, their parent and a doctor. However, research in paediatric interviewing usually deals with one dyad (parent-doctor), sometimes two dyads (parent-doctor and child-doctor) or even three dyads (parent-doctor and child-doctor and parent-child) rather than a triad of parent-doctor-child [14].
New graduates in the United Kingdom are expected to be able to "communicate clearly, sensitively and effectively with patients and their relatives" [15]. Teaching and learning about medical interviewing is now part of mainstream undergraduate medical education [16]. Acquisition of medical interviewing skills should not stop at graduation. Continuing professional development supports the maintenance and extension of patient-centred interviewing skills. Although summative assessments of interviewing skills are well established in some Royal Colleges [17], they are being incorporated in others [18]. Training programmes to support doctors in these summative assessments are developing simultaneously. In the United Kingdom there is scant evidence that medical interviewing programmes at any level consider the specialised skills required for triadic interviewing associated with paediatric consultations. However, studies from Europe and North America report educational interventions that incorporate triadic interviewing [19-21] while others focus on the dyad (e.g. doctor-parent; doctor-child; doctor-adolescent) [22,23].
In order to address these two issues, the Child in Mind Project at the Royal College of Paediatrics and Child Health (RCPCH) aims to develop psychosocial awareness and interviewing skills of paediatricians (in training). This will improve the assessment and management of psychosocial issues that effect children. To achieve this goal, a modular training programme in child and adolescent mental health is proposed. The programme will be piloted with senior house officers (SHOs) on paediatric rotations. Building additional modules into existing SHO training programmes is problematic since clinical and other commitments already consume a shrinking working week. Therefore, a key consideration in developing the training modules is to work within existing SHO training by maximising both planned and opportunistic teaching and learning in psychosocial care and interviewing skills appropriate for working with children, adolescents and their families.
At an early stage in the project, the team thought it appropriate to elicit ideas and experiences of interested professionals as well as recruit individuals to help with developing the project. An open invitation to attend a one-day workshop held at the RCPCH was advertised in relevant professional newsletters for paediatricians, child psychiatrists, and child psychologists and by word of mouth in other disciplines. The invitation stated that the workshop would elicit the views of an inter-professional group interested in developing a training programme to improve psychosocial assessment and child-centred communication skills of doctors. Participants were chosen from twice the number of applicants to represent a balance of disciplines and geographical spread. Selection within these criteria was made on the order of receipt of applications. Numbers were limited by the capacity of the College facilities.
This paper describes the evaluation of the inter-professional workshop in the development of the training programme.
Description of workshop
The opening plenary session introduced the Child in Mind Project together with the aims of the workshop (Table 1). These aims reflected the preliminary work the Project team had undertaken as well as their areas of content expertise. Four parallel group sessions then focused on core content and process issues for paediatric trainees: communication and interviewing skills, management of children and adolescents with recurrent aches and pains and intentional overdose. Topic experts were invited by the RCPCH to facilitate each group. The structure and methods used in the groups varied. Participants were allocated to specific sessions so that there were approximately equal numbers representing all disciplines present in each group.
Table 1 Participants' ratings of the helpfulness of the sessions in meeting the aims of the workshop (n = 28)
Not at all helpful Partially helpful Very helpful
Plenary Session 1 14 11
Introduction, background, aims
Group Sessions – Content
1. How can we teach communication & interview skills? 3
2. How can we teach communication & interview skills? 1 3 3
3. How can we teach the management of recurrent aches and pains? 2 7
4. How can we teach the management of intentional overdose? 4 2
Plenary Session 2 2 17 6
Feedback from morning sessions, planning for afternoon
Group Sessions – Process
1. How can we get trainees to role-play? 4 2
2. How can we combine traditional teaching and learning methods with new technology? 2 1 3
3. How can we integrate child mental health into existing training programmes? 7 3
4. How can we assess paediatric trainees in child mental health? 1 5
Plenary Session 3 11 6
Feedback from afternoon sessions, conclusions, action
Immediately after lunch, a plenary session was held in which the key issues from the morning sessions were shared with the entire group. The four parallel sessions that followed focused on core process issues in training: promoting role-play, introducing technology, integrating new with existing programmes, and assessment. The final plenary session provided an opportunity for groups to share their experiences and then a wider discussion considered key issues from both morning and afternoon sessions. An action plan was devised based on this discussion and was shared with participants providing an opportunity for participants to continue to be involved in the project.
Methods
All participants were invited to complete evaluation forms immediately before and after the workshop. The pre workshop form explored participants' reasons for attending, their expectations, their most important issue in relation to the workshop, their experiences in learning about communication and education, their current role, age and sex (Figure 1). The post workshop form was divided into two parts (Figure 2). The first part asked participants' about their experiences of the workshop, the most important issue that was covered, the degree to which their expectations were met, the aspects of the workshop that went well and those that could have been improved. The second part asked them to rate each session in relation to whether it was helpful in meeting the aims of the workshop. All responses were anonymous.
Figure 1 Pre-Workshop Evaluation Form
Figure 2 Post-Workshop Evaluation Form
Results
Sixty-three participants attended the workshop of whom 23 were clinical psychologists (36.5%), 18 paediatricians (28.6%), 9 psychiatrists (14.3%), 9 nurses (14.3%) and one representative from each of the following professions: social work, education, play therapy and occupational therapy (6.4%). Forty-one participants were female (65.1%) and 22 were male (34.9%). Approximately twenty participants (31.8%) left the workshop immediately prior to the closing plenary session. This was unexpected and apparently not triggered by anything more than a need to catch commuter and intercity trains. That is, the exodus seemed unrelated to the quality of the meeting. Twenty-two participants (34.9%) completed the pre workshop form and 28 completed the post workshop form (44.4%).
Pre workshop evaluation
Of the 22 respondents completing the pre workshop form, 17 were female (77.3%) and 5 were male (22.7%) with an age range of 34 to 58 years and mean age of 45. Although the group were inter-professional, the respondents were predominantly medical with 10 paediatricians (45.5%), 5 child and adolescent psychiatrists (22.7%), 4 clinical psychologists (18.2%), one occupational therapist (4.5%) and one educator (4.5%).
Nineteen (86.4%) participants reported previous formal training in communication as part of undergraduate, post-graduate and continuing professional development. Training included theoretical and skills practice within and outside of paediatrics at fundamental (e.g. presentation skills, psychology training) and advanced levels (e.g. Balint groups, psychology training, bereavement). Eighteen participants (81.1%) reported at least some previous formal training in education.
Reasons for attending the workshop
Participants' reasons for attending the workshop were diverse and included a strong interest and or experience in the major themes of the workshop – assessment and management of psychosocial issues in paediatrics, paediatric interviewing and training.
To enhance the "voice of the child" in paediatrics. (4)
I have a long standing interest and involvement in the teaching of junior paediatricians the skills of communication, family therapy and management of behavioural and emotional issues. I have been trying to find ways of formalizing mental health training for paediatricians. (1)
Because I have a very real interest in improving the awareness and training of paediatricians in the psychosocial aspects of paediatrics. (7)
To participate in the development of paediatric psychological training. (17)
To help develop teaching of mental health issues in childhood. (18)
Having worked in the area of paediatric psychology for some years, I am particularly interested in developing the awareness of paediatricians re psychological issues. (19)
To contribute to the planning of teaching paediatricians how to tackle social and emotional issues. (20)
To better understand needs of paediatricians for training in psychological needs of children and families. (11)
Some participants wanted to develop existing local programmes.
To feedback ideas to our paediatric College/Clinical Tutor who did not get a place at the course. (12)
To try and improve our in-house teaching of psychological factors in paediatrics. (13)
One participant acknowledged a deficit in current training.
I realize we are generally very poor at integrating psychological aspects of child and family health into the busy acute training programme. (2)
Expectations of the workshop
The second question asked participants what they were expecting from the workshop. Various themes emerged and included the generation of ideas for the Child in Mind project generally and specifically in the development of training materials. Participants expected to be able to exchange ideas on what and how to change existing training and there was an expressed desire not only to influence these developments but to ensure they are deliverable.
To meet, share and hopefully influence colleagues. (17)
To participate in putting together relevant training modules and to have a voice in the future training of paediatricians. (19)
To ensure that programmes will be acceptable. To broaden ideas around what to include in communication programme. (5)
To offer my experience in direct work with children, adolescents and families. (9)
An understanding of where the project is so far – aims, methods, plans. A chance to contribute. (14)
A second theme related to expectations of inter-professional collaboration both in the development and delivery of the training module and the third and overlapping theme focussed on the opportunity for networking.
Decrease inter-professional tension and enhance collaboration. (4)
Participants' perceptions of the most important issue to be addressed in the workshop reflected their different expectations. Training issues were dominant and focused on both the content and process. Content issues included thinking about ways of raising the significance of the assessment and management of psychological problems in children and adolescents together with the need to identify the child and adolescent's perspective separately to their family's and health care professionals.
Developing a culture of respect for child and family. Accessing children's thoughts and feelings independently of their parents or other professionals. (4)
Process issues included identifying ways to maximise existing expertise, to use limited resources efficiently, to encourage participation from paediatric trainers and trainees and to consider assessment and evaluation as integral to the training programme.
Post workshop evaluation
Twenty-eight (44.4%) participants completed the post workshop form. Demographic data was not collected. The "did not attend" option on the evaluation form is not included in Table 1 because participants did not use it. Instead, they indicated the parallel session that they attended by rating it. Ratings were consistent with the numbers of forms received. That is, 25 (89.2%) for plenary sessions 1 & 2 and the morning group sessions while 28 (100%) rated the afternoon group sessions and 17 (60.7%) rated plenary session 3.
Using a 3-point scale from not at all, partially to completely, participants rated the helpfulness of the sessions in meeting the objectives of the workshop (Table 1). The majority of participants rated the sessions as at least partially helpful. The qualitative data provided insight into participants' ratings.
Most important issues
Respondents were asked to identify the most important issues that they thought had been addressed in the workshop. Several participants wrote of the need to change the existing culture to one in which psychosocial assessment and communication skills are valued. There was also acceptance of diversity in workplaces and the training offered therein. To ensure that the new training programme is deliverable it must be sufficiently flexible to fit within these diverse settings and that it must be evaluated. The need for training both supervisors and trainers was considered requisite for implementing any programme.
Learning about the hospital paediatric culture and previous difficulties of teaching SHOs and getting the culture right. (20)
The importance of mental health teaching/learning for all doctors caring for children/families. (2)
Delivering training for trainers and that the child mental health programme needs to be integrated into existing paediatric training. (3)
Need to address appropriate training and supervision of SHOs and for consultants to be trained first themselves. (5)
The importance of introducing a general shift. The extreme inflexibility of the system as a whole. (9)
The realities of teaching busy SHOs who are preoccupied with passing exams. (14)
Changing culture of consultants to understand importance of training for mental health and communication skills. (24)
Some participants valued the opportunity to learn about existing effective practices while others gave consideration to who should teach, how and that whatever is taught must be relevant.
The importance of taking a full history and empowering SHOs to ask difficult questions, to reflect on their practice and to have supervision in order to understand what to do with the information they have gathered. (17)
Introduction of video review of consultations/interactions with children and parents to paediatrics. (22)
Meeting expectations
Participants used a 3-point scale from not at all, partially to completely to rate the degree to which they met their expectations. Eight participants completely met their expectations (28.6%) while twenty participants (71.4%) partially met their expectations.
What worked well
In response to being asked what worked well in the workshop, participants identified the opportunity to exchange ideas with colleagues with different levels of experience, who work in different settings and have different professional backgrounds.
The group sizes for sessions were valued since they were sufficiently small to enable several participants to express their views and large enough for diverse experiences to be shared. The plenary sessions were helpful in summarising group sessions and consolidating broad ranging issues.
The enthusiasm of delegates was thought to contribute to the success of the workshop together with the relaxed atmosphere and the genuine desire of participants and organisers to change existing practices.
Improvements to workshop
Most participants recorded at least one response to being asked how the workshop could be improved. The single most frequently cited issue related to the venue. Groups were too large for their rooms and for two groups, their presence in the same large room impeded discussion.
Other improvements included stronger facilitation in some groups to ensure all views were heard and that the discussion stayed focused. Providing delegates with basic information prior to the workshop on the aims, objectives and content of group sessions could have improved the quality of the discussions. Participants expressed a desire to attend the group sessions of their choice. One participant thought that the workshop was too rigidly organised between content and process and that this limited creativity in thinking about training. Two participants suggested including SHOs for whom the training will initially be delivered.
I felt unable to contribute much of my experience and knowledge with the tight preset agenda. I was particularly wanting to discuss raising awareness of child protection issues, and working with children in complex and or chaotic home situations. Also were there many current paediatric SHOs here today. If not, there should have been. If so, could they have contributed more? (17)
Sign up for preferred workshops on arrival – I don't remember what preferences I indicated but they certainly weren't the ones I was allocated. More general paediatricians – meeting seemed to be dominated by psychiatrists/psychologists (23)
The workshop was planned with definite areas for discussion and a very strong split between content and process. This defined structure led to cramping of ideas (27)
Maybe including some real SHOs just for the occasional reality check (14)
Discussion
The workshop was valuable in contributing to the development of the Child in Mind Project training programme. The content and process of the programme were explored and several issues emerged that will need serious consideration by the Child in Mind project team. These include the strongly expressed need for a change in culture within the health care system that will embrace child-centred mental health care. The magnitude of change required is uncertain but may well be extensive given evidence that a study based in general practice in The Netherlands reported that the inclusion of the child in all phases of the consultation was "limited" with parents frequently speaking for the child, the child not questioning the parent, and the GP supporting this behaviour by minimal exploration of meta-communicative behaviours. The authors described this process resulting in a dyadic emphasis as being "institutionally co-constructed" [24].
Ways to change the health care culture in the United Kingdom were not explicitly identified. However, the project teams' desire to implement the training programme in a few centres that were already enthusiastic suggests that creating centres of best practice is inherent in their approach for change. This supports theoretical approaches for effective institutional change [25,26]. That is, implementation commences in sites receptive to change before introducing change on a wider scale having already demonstrated positive outcomes. One outcome of the workshop was the identification of individuals willing to trial the new programme with their trainees. These individuals work in centres with different structures and functions in the health care system so will prove valuable in evaluating how deliverable the programme is in different types of settings.
The inter-professional nature of the workshop was beneficial in exchanging views from different perspectives. This supports the findings of the few studies in medical curriculum development that reports this approach [27]. Most participants acknowledged the importance of continuing the consultation process although there was no attempt to agree on format. The importance of regular consultation with the principal users of the training programme – the senior house officers – will be essential to ensure that the programme is deliverable within the diverse settings in which they learn and work.
Although consultation with other stakeholders (children, adolescents and their families) was not identified by this group, it is important that they are also included in the development and evaluation of the training programme. Community participation – especially of key stakeholders, is often lacking in all phases of professional education (development, implementation and evaluation). In order that the training can best meet the needs of its intended targets their voices should be considered. The medical education literature strongly supports inclusion of patient voices in all aspects of curricula development [28-30].
The importance of training the trainers of the programme was identified as key to success of implementation. Although agreement was not sought, there was a powerful sentiment that trainers should be inter-professional. This notion may also address cultural barriers that relate to doctors' lack of understanding of other health care professional roles by exposing them to trainers who have mental health assessment and/or communication skills expertise. The nature of support provided to trainers may vary reflecting the diverse settings in which the training programme will eventually be implemented.
There appeared to be agreement that the workshop was not an appropriate forum for identifying the details of content and process of the training programme. Rather core issues were identified in psychosocial assessment, mental health and communication. Effective approaches to learning patient-centred communication skills are labour-intensive (videotaped interviews with feedback) [31,32] so maximising the benefits of such activities will be essential. The literature reports examples of communication skills programmes for trainee paediatricians [19,21] as well as other doctors and health care professionals who work with children [20,22,23,33] that address diverse issues. Common to many of these programmes is the use of simulated patients and parents incorporating critiquing of videotapes. This may provide valuable guidance in selecting educational methods that are effective and can be delivered in different settings. Ensuring that the training programme incorporates principles of work-based and other adult-learning approaches are essential [34-37].
The purpose of eliciting participants' reasons for attending and their expectations of the workshop is to help make sense of their satisfaction afterwards. Although the invitation outlined the purpose of the workshop, participants came with varied views that to some extent reflected their level of experience, their unique professional perspective and their interpretation of the information provided in the invitation. However, there was an overarching expectation that each would contribute to the development of a training programme. It is important to reflect on the reasons that only 28.6% of the participants reported that their expectations were completely met.
The suggestions given for improvements offer insight into why more participants did not meet their expectations. Restating the project team's aims at the commencement of the workshop may have been helpful. Although some participants felt able to express their views others were unable to do so because of the structure of sessions, the way in which they were facilitated and the settings in which the discussions took place. Providing a more open forum for discussion may have generated different ideas. The breadth and depth of the "culture change" some participants consider essential for implementation of the training programme is extensive and is likely to have influenced their judgement as to what could be realistically achieved both in the workshop and the training programme.
The physical limitations of the workshop impeded discussion in some groups.
Although group sizes were thought appropriate, providing spaces in which they could work will need to be considered in future workshops.
Limitations of the evaluation
There are several limitations with this evaluation project some of which were beyond the control of the evaluator (DN).
• Higher response rates may have improved the quality of the evaluation. It is possible that respondents differed to non-respondents which may influence the results in someway although it is difficult speculate how.
• Scheduling the evaluation forms as part of the workshop may have increased response rates and may also help participants to focus on their expectations immediately before the meeting and then afterwards in considering what they achieved.
• The low response rate in relation to the final plenary session may be explained by the request to complete the forms immediately after the workshop. It is possible that some participants wanted more time to reflect on their experiences. It may have been more helpful to contact participants after the workshop.
• Further, the responses may not represent the diversity of opinions expressed during the workshop nor were the professional groups equally represented in the evaluation forms. For example, no nurses completed the pre workshop evaluation form. It is unclear why this was the case as all respondents were equally encouraged to complete the forms.
Future evaluations of workshops attended by disparate groups may consider:
• Scheduling the completion of evaluation forms into the workshop timetable
• Using identifiers to link pre and post workshop evaluation forms
• Following-up participants some time after the workshop to elicit their considered views
Despite these methodological weaknesses, the evaluation offers useful insights to the management of an inter-professional workshop for curricula development.
Conclusions
The workshop provided the Child in Mind project team with valuable insight relevant to the development of a deliverable training programme in mental health and communication. This was an adequate forum in which the ideas and experiences of an interested inter-professional group could contribute although there were several ways in which this could have been improved. The diversity of the settings in which the programme will be delivered was highlighted as was the need for cultural change and support not only for trainees but the trainers themselves. Continued consultation with this inter-professional group together with broadening the consultation process to include other stakeholders may lead to the development of an effective training programme. Commencing the programme in sites with clinicians who are receptive to change of this nature is likely to influence its' success. Evaluation will continue to be essential to monitor the process. The enthusiasm of the participants needs to be harnessed to ensure that the long-term goals of the project team will be met.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors contributed to each phase of the project although DN took a lead role in writing the paper. DN was responsible for the evaluation while ST and QS were instrumental in the development of the workshop.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Thanks to the participants of the workshop.
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| 15555066 | PMC535901 | CC BY | 2021-01-04 16:30:53 | no | BMC Med Educ. 2004 Nov 21; 4:25 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-25 | oa_comm |
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BMC BiochemBMC Biochemistry1471-2091BioMed Central London 1471-2091-5-171555508210.1186/1471-2091-5-17Research ArticleRhodobacter capsulatus porphobilinogen synthase, a high activity metal ion independent hexamer Bollivar David W [email protected] Cheryl [email protected] Rachel [email protected] Siiri [email protected] Bashkim [email protected] Robert [email protected] Lenka [email protected] Eileen K [email protected] Department of Biology, Illinois Wesleyan University, P.O. Box 2900, Bloomington, IL 61702-2900, USA2 Biology Department, Haverford College, Haverford, PA 19041, USA3 Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA2004 22 11 2004 5 17 17 13 8 2004 22 11 2004 Copyright © 2004 Bollivar et al; licensee BioMed Central Ltd.2004Bollivar 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
The enzyme porphobilinogen synthase (PBGS), which is central to the biosynthesis of heme, chlorophyll and cobalamins, has long been known to use a variety of metal ions and has recently been shown able to exist in two very different quaternary forms that are related to metal ion usage. This paper reports new information on the metal ion independence and quaternary structure of PBGS from the photosynthetic bacterium Rhodobacter capsulatus.
Results
The gene for R. capsulatus PBGS was amplified from genomic DNA and sequencing revealed errors in the sequence database. R. capsulatus PBGS was heterologously expressed in E. coli and purified to homogeneity. Analysis of an unusual phylogenetic variation in metal ion usage by PBGS enzymes predicts that R. capsulatus PBGS does not utilize metal ions such as Zn2+, or Mg2+, which have been shown to act in other PBGS at either catalytic or allosteric sites. Studies with these ions and chelators confirm the predictions. A broad pH optimum was determined to be independent of monovalent cations, approximately 8.5, and the Km value shows an acidic pKa of ~6. Because the metal ions of other PBGS affect the quaternary structure, gel permeation chromatography and analytical ultracentrifugation experiments were performed to examine the quaternary structure of metal ion independent R. capsulatus PBGS. The enzyme was found to be predominantly hexameric, in contrast with most other PBGS, which are octameric. A protein concentration dependence to the specific activity suggests that the hexameric R. capsulatus PBGS is very active and can dissociate to smaller, less active, species. A homology model of hexameric R. capsulatus PBGS is presented and discussed.
Conclusion
The evidence presented in this paper supports the unusual position of the R. capsulatus PBGS as not requiring any metal ions for function. Unlike other wild-type PBGS, the R. capsulatus protein is a hexamer with an unusually high specific activity when compared to other octameric PBGS proteins.
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Background
The enzyme porphobilinogen synthase (PBGS, EC 4.2.1.24) catalyzes the first common step in the biosynthesis of the tetrapyrrole pigments such as heme, chlorophyll, and cobalamin [1]. PBGS is very highly conserved in sequence and structure but contains a remarkable phylogenetic variation in metal ion usage for catalytic and allosteric functions [2,3]. As of 2003, approximately one-half of the ~130 PBGS sequences available contained the binding determinants for a catalytic zinc ion, and about one-half did not [2]. On the other hand, approximately 90% of the known PBGS sequences contain the binding determinants for an allosteric magnesium. The only known PBGS sequences that lack the binding determinants for both the catalytic zinc and the allosteric magnesium are in the bacterial genus Rhodobacter [2]. These atypical PBGS expressed by Rhodobacter sphaeroides and Rhodobacter capsulatus were two of the earliest PBGS enzymes to be characterized in the pioneering work of Shemin and coworkers and were erroneously chosen as representative of PBGS from all photosynthetic organisms [4,5]. However, one distinct difference between the ∝-proteobacteria, of which R. capsulatus is an example, and other photosynthetic organisms is the biosynthetic pathway used to produce the PBGS substrate, 5-aminolevulinic acid (ALA). The ∝-proteobacteria synthesize ALA from succinyl-CoA and glycine while other photosynthetic organisms use glutamic acid to make ALA [6].
In light of the vast information now available on phylogenetic variations in tetrapyrrole biosynthesis and on the PBGS that require a catalytic zinc and/or that utilize an allosteric magnesium, the current study revisits the PBGS of Rhodobacter capsulatus with emphasis on understanding the enzyme's unique characteristics. Since other PBGS have been shown to absolutely require divalent cations for catalytic activity, and in light of the enhanced purity of modern reagents, it is important to revisit the metal ion requirements of R. capsulatus PBGS to test the predictions of the sequence analysis that suggests the absence of any metal binding determinants. Herein we present evidence that there is absolutely no effect of Zn2+ or Mg2+ on the activity of the enzyme and no other metal ions appear to be required for enzyme function. Prior studies have also shown that some PBGS enzymes exhibit a pH rate profile whose pKa value is altered by the presence of monovalent cations [7,8]. Hence, we include an analysis of enzyme activity in relation to pH and monovalent cations.
The native holoenzyme quaternary structure for PBGS from most species is a homo-octamer as supported by 18 deposited PBGS crystal structures from yeast, human, E. coli, and Pseudomonas aeruginosa, and noncrystallographic cross-linking data on PBGS from the green plant pea [9-14]. However, an alternative hexameric structural variant was revealed by the crystal structure of a rare allele of human PBGS [15]. The hexameric structure suggests a functional relationship between binding of the allosteric magnesium of most PBGS and a putative hexamer-octamer distribution that serves as the structural basis for allosteric regulation of enzyme function [15]. In the absence of the binding sites for either catalytic or allosteric metal ions we investigated the oligomeric structure of R. capsulatus PBGS, and results suggest that the protein is a homo-hexamer. We present a homology model of the hexameric R. capsulatus PBGS structure.
Results
Cloning and sequencing
The cloning of the hemB gene (which encodes PBGS) from R. capsulatus was accomplished by PCR using primers based upon the sequence of Indest and Biel [[16], GenBank accession U14593]. The sequence of the cloned gene differed from that published previously. Figure 1 presents an alignment of the predicted polypeptides based on the published R. capsulatus and R. sphaeroides sequences (GenBank accession number AAL 26883) as well as the sequence determined from our PCR product (GenBank accession number AY618996). The changes in the predicted polypeptides are due to three differences that we observe when comparing the nucleotide sequence determined herein with the published sequence: a deletion of G at position 425, an insertion of an A at position 468, and reversal of the AC at positions 643–644 to CA. The first two sequence differences result in alteration of the predicted amino acid sequence from amino acids 72 to 86. The third sequence difference alters the amino acid at position 145 from leucine to isoleucine. Based on the aligned polypeptide sequence, the newly determined sequence appears more homologous to R. sphaeroides PBGS, and hence is deduced to be the correct sequence.
Figure 1 PBGS Protein Sequence Alignment. Amino acid sequences as predicted from published DNA sequences for R. capsulatus PBGS and R. sphaeroides PBGS as well as the PCR product sequences from our genomic R. capsulatus DNA (labeled as revised sequence). Sequence identity between all three sequences is boxed. The regions where the published R. capsulatus sequence and the sequence we determined differ are indicated with an asterisk below those amino acid positions. In these regions, a black background indicates where sequence matches occur between the revised sequence for R. capsulatus PBGS and R. sphaeroides PBGS, but not for the previously published R. capsulatus PBGS sequence.
Expression and purification
As demonstrated in Figure 2, we were able to express and achieve substantial purification of R. capsulatus PBGS. The specific activities of the ultracentrifuge supernatant, redissolved ammonium sulfate pellet, phenyl-sepharose pool, DEAE pool, and concentrated S-300 pool were 130, 250, 212, 176, and 364, μmol h-1 mg -1 respectively. We suspect that some of this variation is due to a protein concentration dependence to the specific activity (see below). The enzyme has an apparent molecular weight that is in agreement with the predicted mass of 35.8 kDa (Figure 2). Based on SDS-PAGE and silver staining, the protein appears to have been purified to homogeneity.
Figure 2 SDS-PAGE (4–20%) of PBGS Purification Steps. Lane M is marker; lane 1 is Crude Extract; lane 2 is Ultracentrifuge supernatant; lane 3 is 25% ammonium sulfate pellet; lane 4 is pooled Phenyl-Sepharose fractions; lane 5 is pooled DEAE fractions; and lane 6 is pooled S-300 fractions.
Protein concentration dependence of the specific activity
Other PBGS that lack the catalytic zinc ion binding site have been shown to exhibit a protein concentration dependence to the specific activity [7-9] This unusual phenomenon indicates that a maximally active oligomer can dissociate into less active smaller units. A protein concentration dependent specific activity is illustrated for R. capsulatus PBGS in Figure 3. The maximal R. capsulatus PBGS activity, ~450 μmol h-1 mg -1, is the highest ever seen for a purified PBGS, and the lowest R. capsulatus PBGS activity, ~150 μmol h-1 mg-1, shows that the smallest oligomeric structure retains significant activity. As shown in Figure 3, the activity of the smaller unit is significantly different from what is documented for PBGS that lack the catalytic zinc but contain the allosteric magnesium binding site; in those instances, the smallest oligomers are inactive [7,9]. Kinetic characteristics described below use protein concentrations varying from 0.15–15 μg ml-1 and this variation does not appear to effect the pKa values apparent from the pH rate profiles, the Km values, or the effects of metal ions or metal ion chelators, which suggest that these properties are more or less the same for the largest and smallest oligomer.
Figure 3 Protein Concentration Dependence to R. capsulatus PBGS Specific Activity. Protein was varied from 0.01 μg ml-1 to 40 μg ml-1 and assay times used were 5 min (○) and 1 h (□) with the purified R. capsulatus enzyme. The solid line represents a hyperbolic fit to the activity data presented for the combined 5 min and 1 h assays. The final pH for the R. capsulatus assays was 8.3 in BTP-HCI. For comparison, protein concentration curves are presented from previous experiments for B. japonicum PBGS (+) [7] and P. sativum PBGS (Δ,) [9] and the hyperbolic fits are presented as dotted and dashed lines respectively.
Kinetic characteristics
Metal ion requirements of purified R. capsulatus PBGS were determined and the results confirm that the Rhodobacter enzyme is different in its response to a variety of cations compared to most known PBGS enzymes. At a protein concentration of ~1 μg ml-1 there is no significant stimulation or inhibition of R. capsulatus PBGS by the addition of Zn2+ or Mg2+ ions. The presence of Zn2+, up to 100 μM, caused no change in activity and inclusion of 10 mM Mg2+ resulted in 86% activity. There is also no apparent inhibition by the addition of 10 mM EDTA or by pretreatment with Chelex resin. Inclusion of 1,10-phenanthroline from concentrations of 10 μM – 10 mM had no effect on R. capsulatus PBGS activity. The purified enzyme was tested for the presence of zinc and magnesium by atomic absorption spectroscopy; under conditions where it would have been possible to detect as little as 0.05 metal ion per subunit, none were detected.
Previous experiments with a wide variety of PBGS enzymes suggest that monovalent cations can affect the activity of the enzyme. Such cations were clearly shown to shift the pH rate profile for some PBGS [7,8]. Results presented in Table I (data obtained at ~1 μg protein ml-1) suggest that there is little if any effect of the monovalent cations K+, Na+ or NH4+ over a wide range of concentrations (0–100 mM). Figure 4 shows that inclusion of 0.1 M KCl has no significant effect on the pH rate profile at 15 μg ml-1 protein concentration.
Table 1 Monovalent cation effects. Samples were pre-incubated with various concentrations of chloride salts of the monovalent cations and then assayed using the standard procedure. Reported values and standard errors are triplicate absorbances at 555 nm.
Concentration of Salt (mM)
0 1 5 10 50 100
KCl 0.270 ± 0.019 0.268 ± 0.006 0.271 ± 0.002 0.261 ± 0.007 0.269 ± 0.003 0.310 ± 0.014
NaCl 0.302 ± 0.006 0.294 ± 0.017 0.292 ± 0.002 0.276 ± 0.012 0.262 ± 0.003 0.268 ± 0.003
NH4Cl 0.297 ± 0.022 0.278 ± 0.004 0.279 ± 0.009 0.225 ± 0.001 0.224 ± 0.009 0.220 ± 0.005
Figure 4 pH Rate Profile. The pH rate profile is presented at 10 mM ALA in BTP-HCl in the presence (○) and absence (□) of 0.1 M KCl. Protein was 15 μg ml-1. The Km (filled tringle) and Vmax (filled circle) values as a function of pH at (0.15 μg ml-1) are given in units of mM and A555, respectively.
To determine the optimal pH for the enzyme we determined the Vmax and Km values at ~1 μg ml-1 protein at a variety of pH values as presented in Figure 4. The results demonstrate that maximal activity was observed around pH 8.0, but the enzyme is still very active over a wide range of pH values. It is also clear that the Km value drastically increases at lower pH values, which is similar to what has been observed for other PBGS enzymes e.g., human PBGS [17], E. coli PBGS [18], or B. japonicum PBGS [7]. Based on the references cited, the rise in Km at low pH appears to be independent of the metal ion requirements for PBGS.
Quaternary structure
A recent paper described an alternative quaternary structure for a rare allele of human PBGS. The unprecedented structural change was shown to have significant effects on the enzyme activity [15]. In contrast to the active octameric human PBGS, a hexameric form observed with the rare human allele was relatively inactive. The interconversion of these two oligomeric structures was related to the allosteric regulation of some non-human PBGS by magnesium. To assess the different oligomerization states possible for R. capsulatus PBGS, a native gel analysis was performed and the gel was stained for enzyme activity followed by Coomassie staining for protein (see Figure 5). Previous studies have shown that native gel electrophoresis can give good separation of the quaternary structure forms of PBGS (see also ref [19]). Four different sized complexes are observed whose mobility fit well to an mixture of dimer, tetramer, hexamer, and octamer (note constant charge/mass ratio). As had been seen before for E. coli PBGS [19], the distribution of these oligomeric forms is altered by substrate. By comparing samples preincubated without substrate (Figure 5A, lanes 1 and 4) with samples preincubated in the presence of substrate (Figure 5A, lanes 2 and 3) it can be observed that the complex that runs as the smallest form (putative dimer) becomes less prominent and the largest molecular weight complex (putative octamer) becomes visible. It is also interesting to note that no activity is observed for the two smallest complexes (Figure 5B).
Figure 5 Native gel electrophoresis. Part A presents the Coomassie stained gel, part B presents the same gel stained with an activity stain based on the pink color formed by complex of porphobilinogen with Ehrlich's reagent. Lanes 2 and 3 contain enzyme preincubated with ALA, Lanes 1 and 4 do not include ALA in the preincubation buffer. Each lane was loaded with 2.25 μg of protein.
Based upon a combination of size-exclusion chromatography and analytical ultracentrifugation, the size of the major complex was determined. The major component in size exclusion chromatography ran at an approximate molecular weight of 220,000 Daltons. The predicted molecular weight for a monomer is 35,856 Daltons so it would appear that R. capsulatus PBGS is predominantly a hexamer.
Ultracentrifugation data for samples collected at several speeds were fit well using a single species model and show that the R. capsulatus PBGS is largely a hexamer (Table II). The molecular weight obtained from a global fit to data collected at 8,000, 10,000, 12,000 and 14,000 rpm is 215,700 ± 9,700 and was largely independent of speed, indicating strong evidence for ideal single species behaviour. Since we do observe a slight trend in decreasing molecular weight with increasing speed, we further analyzed the data by considering more complex two state models (Table III). The lowest apparent molecular weight from Table II was no smaller than that expected for a hexamer so we mainly considered models with the hexamer and some larger second state. A lack of lower molecular weight species is not surprising since the protein concentration dependence of the activity plateaus at about 1 μM and the loading concentration in the AU is greater than ten-fold higher. As judged by the square roots of variance for the fits of the various models to the data, collected either in dithiothreitol (DTT) or 2-mercaptoethanol (βME), we saw no appreciable improvement in the fits relative to the pure hexamer model. In the one instance where a slight improvement was noted (the hexamer-octamer model for the βME sample), less than 5% of the total absorbance could be ascribed to the octamer, suggesting very little higher order self-association. Prolonged dialysis results in some non-specific aggregation, presumably due to a build-up of oxidized DTT or βME, and may provide an explanation for problems with unwanted aggregation. Finally, we also tested the possibility that the apparent molecular weight from single species analysis might instead reflect a proportionation between an octamer and some smaller species. We were able to rule this out as such models resulted in poorer fits to the data (e.g. a fit to a tetramer-octamer model is shown in Table III).
Table 2 Molecular weight analysis of R. capsulatus PBGS as measured by equilibrium sedimentation. Data were collected at 4°C. The column headings refer to RPM values. All results are in Da. The monomer molecular weight is 35,857 Da.
Sample 8000 10000 12000 14000 Overall
12.3 μM (.1 mM DTT) 221,698 222,291 213,584 206,743 215,700 ± 9,700
12.3 μM (1 mM βME) 227,785 230,906 224,189 205,239 220,200 ± 9,100
Table 3 Sedimentation equilibrium model analysis of R. capsulatus PBGS. All numbers reported are the square root of variance (×10-3) from the fits of the various models to the data. The data for all speeds were fit globally to individual models
Model (.1 mM DTT) (1 mM BME)
single species 10.50 8.20
hexamer 10.49 8.38
6----->8 10.51 8.27
6----->10 10.52 8.32
6----->12 10.54 8.36
4----->8 12.98 9.82
R. capsulatus PBGS hexamer homology model
Figure 6 is an illustration of the homology model of hexameric R. capsulatus PBGS based on a model of hexameric P. aeruginosa PBGS (see below). Regions of highest uncertainty are places where alignment of R. capsulatus PBGS and P. aeruginosa PBGS contain insertions and deletions. These regions are illustrated in the Figure; they are all on the solvent exposed surface of the oligomer and thus unlikely to affect subunit interactions. In an attempt to understand why, unlike other PBGS, R. capsulatus PBGS does not predominate as an octamer, we analyzed points of subunit contact in the octamer of the highly homologous P. aeruginosa PBGS. Each P. aeruginosa PBGS monomer contains thirty-six residues that are within 3.2 Å of an adjacent subunit of the octamer. Twenty-five of these residues are identical in R. capsulatus PBGS; eleven are different. The majority of these differences result in a reduced ability in R capsulatus PBGS to form an ion pair or hydrogen bonding interaction at the subunit interface. The differences are tyrosine to phenylalanine or isoleucine, arginine to glutamine or glycine, valine to isoleucine, aspartic acid to asparagine or alanine, histidine to arginine or leucine, and glutamic acid to alanine or asparagine. This analysis is consistent with the observation that the interactions between PBGS subunits are largely hydrophilic in character with ordered water molecules at the subunit interfaces (see Discussion).
Figure 6 A homology model for hexameric R. capsulatus PBGS. A) The R. capsulatus PBGS detached dimer model, shown in stereo view, looking down the αβ-barrel domain of subunit A (red), directly into the active site. The two active site lysine residues, implicated in covalent catalysis [13], are shown ball-and-stick. The N and C termini are labeled for subunits B and A respectively. B) The hexamer is composed of three detached dimers, which are shown as ribbon representations and colored shades of red, blue, and green. For the dimer view only, side chains in regions where the model must accommodate deletions and/or insertions are shown as sticks colored as the subunit; amino acids homologous to, but different from those at the hugging dimer interface of P. aeruginosa PBGS are shown in space fill representation. In this representation, the N and C termini are visible for subunits A and B and are labeled in the dimer representation.
Discussion
Prior to the current work, it appeared that there was a considerable sequence diversion in a 13 amino acid segment between R. capsulatus and R. sphaeroides PBGS. The revised sequence for R. capsulatus PBGS presented shows close homology between the PBGS of the two Rhodobacter species. This is interesting in light of the apparent differences between these enzymes regarding monovalent cation usage. The R. sphaeroides enzyme is reported to be stimulated by monovalent cations [5], while the R. capsulatus is not affected by monovalent cations. While it was previously tempting to ascribe the difference in response between the enzymes to this region, clearly this is not the case.
To further analyze the enzymatic characteristics of the R. capsulatus PBGS, the enzyme was purified after being heterologously expressed in E. coli. The purification has allowed us to carry out definitive experiments on the requirements for R. capsulatus PBGS function.
Based on sequence comparisons and known crystal structures for some PBGS, the Rhodobacter PBGS appear to constitute a unique form of the enzyme that does not require metal ions for structure, activity, or allosteric regulation [2]. Although the original description of the Rhodobacter PBGS enzymes [4,5] did not demonstrate a requirement for metal ions, the reagents of that time period are now known to be contaminated with metal ions, particularly zinc. The current reagents are of better quality, thus allowing us to confirm the metal requirements for R. capsulatus PBGS. R. capsulatus PBGS activity is independent of all metal ions tested.
It has been proposed that chloroplast containing photosynthetic organisms use the allosteric regulation of PBGS by magnesium as part of a complex control of the biosynthesis of chlorophyll [15]. Although Rhodobacter capsulatus makes a similar pigment, bacteriochlorophyll, the PBGS enzyme from this organism does not exhibit any regulation by divalent cations. In the absence of the allosteric magnesium, Rhodobacter must use alternative mechanisms to prevent the accumulation of photoreactive intermediates in the biosynthesis of its physiologically relevant tetrapyrroles.
The preferred pH for enzyme function was determined by measuring Vmax and Km in a systematic fashion. Based upon these analyses, the apparent pH optimum is approximately pH 8.0, but the enzyme demonstrates significant activity from pH 7–9. The Km value at optimal pH is still high (0.5 mM) relative to other PBGS at their optimal pH in the presence of their required metal ions (~150 μM). This suggests that an unknown factor may be required in vivo. Because there have been reports for stimulation of PBGS enzymes from other organisms by the addition of monovalent cations, several monovalent cations were tested for their ability to stimulate enzyme function at the pH optimum. At the pH optimum no stimulation by monovalent cations was observed (see Figure 4).
The rare human allele for PBGS encoding F12L revealed the possibility for alternative quaternary structures of PBGS that have been proposed to form the basis for allosteric regulation of PBGS that contain an allosteric magnesium binding site [15]. These types of PBGS are found in all plants, all archaea, and all bacteria except those of the genus Rhodobacter [2]. Evidence for alternate quaternary forms of PBGS is particularly apparent for those PBGS that contain the allosteric magnesium but do not contain the catalytic zinc, because these forms exhibit a protein concentration dependence to their specific activity as illustrated in Figure 3[7,9,15]. The protein concentration dependence has been proposed to be due to an equilibrium between a fully active octamer and an inactive hexamer [15], which is consistent with the data for pea and B japonicum PBGS presented in Figure 3. The data for R. capsulatus PBGS suggests however that the active form of this protein is a hexamer of specific activity ~400 μmol h-1 mg-1, and that this active hexamer can dissociate into a smaller form that is less active, but not inactive, with a specific activity of ~150 μmol h-1 mg-1. These observations lead to three interrelated questions. Why does R. capsulatus PBGS associate into a hexamer rather than an octamer; why is the R. capsulatus PBGS hexamer of high activity rather than of low activity; and what is the less active quaternary structure that is in equilibrium with the R. capsulatus PBGS hexamer?
To answer these questions, we need to address the factors that govern the interconversion of PBGS quaternary forms and the structural basis for the different activities associated with these different quaternary forms. Crystal structures reveal that those PBGS that readily interconvert between quaternary forms contain subunit-subunit interfaces that are hydrophilic in character. For instance, in the P. aeruginosa PBGS structure, the interaction of the barrel of subunit A and the N-terminal arm of subunit B, which form a hugging interaction, is dominated by hydrogen bonds and buried water molecules. One could argue that this type of subunit-subunit interface can readily dissociate because the protein-protein interactions are similar in energy to aqueous solvation of the protein surfaces [20,21]. A sequence comparison between P. aeruginosa PBGS and R. capsulatus PBGS shows that the amino acids that lie at the hugging dimer interface of the former are of lower hydrogen bonding and ion pairing capacity in the latter, which might explain why R. capsulatus PBGS is not an octamer. As for the functional difference between human octameric and hexameric PBGS, this has been ascribed to the mobility/stability of an active site lid that serves to gate access to solvent [15]. In that case, destabilization of the lid causes the pH rate optimum for the reaction to shift dramatically toward basic pH and causes a dramatic decrease in affinity for the Km determining substrate molecule since the lid residues form part of the binding site for this substrate [15]. Consistent with the fact that R. capsulatus PBGS appears to be a hexamer, the pH optimum is basic and the Km is 2 – 3 fold higher than most other PBGS species under their optimal assay conditions. Crystallization trials for R. capsulatus PBGS are currently underway to provide insight into these fascinating questions. Finally, based on the native gel analysis, we propose that the active R. capsulatus PBGS hexamer can dissociate into a less active dimer. Preliminary unpublished results suggest that one can produce an active dimeric species of human PBGS by destabilizing the dimer-dimer interactions that are essential for oligomerization of PBGS into either the hexamer or the octamer.
Conclusions
The evidence presented in this paper supports the unusual position of the R. capsulatus PBGS as not requiring any metal ions for catalytic function, which may be characteristic of the Rhodobacter genus. Unlike other wild-type PBGS, the R. capsulatus protein is a hexamer. What remains to be determined is how the reaction mechanism for this enzyme might differ from those PBGS that show both an absolute requirement for metal ions and an octameric quaternary structure.
Methods
Materials
Chemicals and buffers were obtained from Fisher or Sigma, in the purest possible form, except where noted below. Ultrafiltration devices used for concentrating protein were obtained from Fisher as were Slide-A-Lyzer dialysis cassettes.
Construction of the expression plasmid
The DNA encoding PBGS was amplified from R. capsulatus genomic DNA by PCR using oligonucleotide primers (Integrated DNA Technologies) directed to the 5' and 3' ends of the coding region based on published sequence [16]. The forward primer PBGS 5' (5'-GCATATGACCCTGATCACCCCCCCC-3') introduced an NdeI site and the reverse primer PBGS 3' (5'-CGGATCCGCGGTCAGGCGCCGATCAGC-3') introduced a BamHI site. The PCR reaction was performed using a thermocycler from MWG Scientific and Pfu polymerase (Stratagene) with the following program: 45 sec at 95°C, 45 sec at 48°C, 1 minute at 72°C. The resulting PCR fragment was cloned into vector pPCRScript Amp (Stratagene) creating plasmid pPBGS1. The PCR fragment was removed from the vector by digestion with NdeI and BamHI and ligated into the vector pET11a (Novagen) digested with the same restriction enzymes. The resultant plasmid pPBGS4 was sequenced in the FCCC DNA Sequencing Facility using external and internal primers to confirm the sequence in both directions. For expression, the recombinant plasmid pPBGS4 was transformed into strain BLR (DE3).
Enzyme expression and purification
A 1 L culture of LB broth with 0.4% glucose was inoculated with a single colony from a fresh transformation and grown for 16 hours at 37°C. The cells were harvested by centrifugation (10 min at 10,800 × g) and resuspended in fresh LB medium containing no glucose, but with 100 μM isopropyl-1-thio-β-D-galactopyranoside (Research Organics) and grown for 45 hr at 15°C. From this point all steps were performed on ice or at 4°C. Cells were harvested by centrifugation for 10 min at 10,800 × g with a yield of 5.74 g wet weight. The cells were washed with 0.1 M BisTris Propane (BTP, Research Organics) pH 8.5 and then resuspended in 15 ml of 0.1 M BTP pH 8.5 and lysed by two passes through a French Press in the presence of Benzonase™ (Novagen) nuclease and 1 mM AEBSF (Calbiochem). Unbroken cells, inclusion bodies, and debris were removed by centrifugation for 15 min at 21,500 × g. The sample was then ultracentrifuged for 1 hour at 141,000 × g to remove membranes. The enzyme was precipitated from solution by treatment with 25% ammonium sulfate and collected by centrifugation for 20 min at 31,000 × g. The pellet was resuspended in 0.1 M BTP pH 7.0 and loaded onto a 100 ml Phenyl-Sepharose (Amersham Biosciences) column pre-equilibrated in 20% ammonium sulfate, 0.1 M BTP pH 7.0 buffer. The enzyme was eluted from the column with a two column volume gradient running from 20% to 0% ammonium sulfate in 30 mM BTP pH 7.0. The PBGS eluted from the column very close to the end of the gradient. Fractions with specific activity greater than 100 μmol h-1 mg-1 were then pooled and loaded to a 100 mL DEAE-Sepharose column equilibrated in 30 mM BTP pH 7.5 and eluted with a two column volume gradient from 0 to 0.4 M KCl in 30 mM BTP pH 7.0. The fractions with a specific activity greater than 300 μmol h-1 mg-1 eluted near the end of the gradient and were pooled and concentrated using centrifugal concentrators. The concentrated DEAE fraction (0.9 mg ml-1) was then loaded on to a 300 mL S-300 column (2.6 cm × 60 cm) (Amersham Biosciences) pre-equilibrated with 0.1 M BTP pH 7.0, and eluted with the same buffer. The fractions with specific activity greater than 185 μmol h-1 mg-1 were pooled and concentrated. The enzyme appeared to be greater than 95% pure based on SDS-PAGE analysis.
Enzyme activity assays
Enzyme was pre-incubated in 0.1 M BTP pH 8.6 in the presence or absence of various metal ions and other reagents for 10 min at 37°C. Assays were initiated by the addition of ALA-HCl (Aldrich) to a final concentration of 10 mM and were allowed to run for 5 minutes in a final volume of 1 ml. Assays were terminated by the addition of 0.5 ml of 20% TCA. The product was then quantified by reaction of the stopped assay mixture with an equal volume of modified Ehrlich's reagent and measurement of absorbance at 555 nm approximately 8–10 minutes later. All assays were performed in duplicate or triplicate. If the amount of product resulted in an absorbance above 1.0 OD, the stopped assay mixture was diluted prior to the addition of Ehrlich's reagent. Inhibition by 1,10-phenanthroline was carried out as described previously [22]. Inhibition by Chelex 100 resin (BioRad) was assayed by incubating PBGS in 0.1 M BTP pH 8.5 at 0.05 g resin per mg enzyme, on ice for 4 hours with occasional stirring, followed by centrifugation at 13,000 × g for 5 min to pellet the resin. The resultant enzyme was then assayed. For determination of monovalent cation effects at pH 8.3, the enzyme was first dialyzed against 100 mM BTP pH 8.6. Monovalent cations were added as the chloride salts.
Effect of pH
For assays performed as a function of pH, the buffer was 0.1 M BTP (initial pH 6–9). For determination of Km and Vmax as a function of pH, the enzyme was at 0.13 μg ml-1. Since the substrate is an acid, the actual pH values for assays were determined by running a mock assay without enzyme and measuring the actual pH of the combined reaction. When ALA concentration was varied for determination of Vmax, the pH of the assay mixture was adjusted with 0.5 M HCl to control the final pH. For determination of the pH rate profile with and without 0.1 M KC1, the enzyme was at 15 μg ml-1.
Gel electrophoresis
SDS-PAGE was performed using the Laemmli system and precast 4–20% gradient gels from Cambrex (Rockland, ME). Gels were stained using a silver stain kit from Pierce Chemical (Rockford, IL). Native gels were performed using the same gel system but omitting SDS from all buffers. Activity staining of the gel was performed as described previously [19]. Following a wash with 20% TCA, the activity stained gel was then stained with Coomassie blue to visualize protein bands. Samples for the native gels were preincubated at a final concentration of 0.15 mg ml-1 for 10 minutes in 0.3 M Tris pH 6.8 with additions of 100 μM Zn, 13 mM ALA or both prior to loading. 15 μl of the preincubated sample was then loaded to each well.
Holoenzyme size determination
The size of the enzyme was determined both by size exclusion chromatography and by analytical ultracentrifugation (AU). Size exclusion chromatography was performed using a Waters 600 system equipped with a Waters 996 photodiode array for detection of protein elution at 280 nm. The column used was a Superose 6 (10 × 300 mm, Amersham Pharmacia) and was run at 0.5 ml/min with 100 mM BTP pH 8.6 with 100 μL of a 200 μg ml-1 solution. The column was calibrated with standards obtained from the manufacturer. For AU experiments, protein samples were dialyzed against 10 mM Tris-HCl pH 7.7 with either 0.1 mM DTT or 1 mM βME as reducing agent. Although TCEP (Tri(2-carboxyethyl)phosphine) is the preferred reducing agent for such biophysical experiments, we discovered that the oligomeric state was destabilized in the presence of this reagent with other PBGS; weak chelation of divalent cations has been observed for this reagent suggesting a mechanism for this destabilization. Protein loading concentrations were 12.3 μM in monomer (440 μg ml-1). Concentrations were determined by UV absorption at 280 nm. The extinction coefficient used for the protein was 29,870 L mol-1 cm-1 and represents the sum of individual tyrosine and tryptophan absorbance coefficients. Sedimentation equilibrium (SE) experiments were carried out at 4°C using a Beckman Optima XL-A analytical ultracentrifuge equipped with an An60 Ti four-hole rotor. Samples were loaded into six-channel charcoal-filled Epon centerpieces. Temperature-corrected partial specific volumes and solution densities were calculated using the Sednterp program [23]; the solution density was 1.00028 g ml-1 and partial specific volume was 0.7304 ml g-1. Data analysis was performed using the WinNonlin V.1.060 nonlinear least squares fitting program obtained from the National Analytical Ultracentrifugation Facility at the University of Connecticut.
Homology Model Building
The only existing crystal structure on which one can base a model of hexameric R capsulatus PBGS is that of hexameric human PBGS clinical variant F12L, PDB code 1PV8 [15]. Unfortunately, the crystal structure of F12L shows significant disorder, which limits its use as the sole foundation for homology model building. However, comparison of human PBGS octameric and hexameric structures (PDB codes 1E51 and 1PV8) show near identity for the amino acids that comprise a TIM-like αβ-barrel domain. The differences between the octamer and the hexamer are in the 24 N-terminal amino acids and in the disordered regions [15]. Hence, one can use a higher quality crystal structure of a PBGS octamer for homology model building the αβ-barrel domain of R. capsulatus PBGS. The chosen structure is PDB code 1GZG [13], which is a highly ordered, high resolution crystal structure of Pseudomonas aeruginosa PBGS, and 56% sequence identical to R. capsulatus PBGS. The model building procedure was a two step process. The first step was construction of a model of a hexameric form of P. aeruginosa PBGS; the second step was to use that hexamer to build the R. capsulatus PBGS hexamer.
P. aeruginosa PBGS hexamer preparation used various capacities of the program Swiss-PDB Viewer [24] and some in-house programs. First, the N-terminal arms (amino acid numbers <32) were removed from the structure file for the 1GZG dimer. The resulting αβ-barrel domains were successively overlaid upon the three dimers of hexameric 1PV8 to create a hexameric assembly of P. aeruginosa PBGS αβ-barrels. There is no significant sequence identity between the N-terminal arms of human and P. aeruginosa PBGS, hence there is an alignment ambiguity when trying to build the outstretched arms of the P. aeruginosa PBGS hexamer. However, there is a region of the arm that is α-helical in both the human octamer and the human hexamer. Hence, a structure alignment of octameric forms of human PBGS and P. aeruginosa PBGS was used to determine the proper sequence alignment for this α-helical segment. This information was used to spatially position the amino acids 22 – 29 of P. aeruginosa PBGS in the hexamer according to the position of this helix in the hexamer of human PBGS. Loop and side-chain prediction was performed in a graphical user environment, developed in the FCCC Molecular Modeling Facility, that integrates the functions of the programs Loopy [25], and SCWRL [26]. Within this environment, the program Loopy [25] was used to model the backbone of amino acids 29 – 32, so as to connect the N-terminal α-helix to the αβ-barrel domain of each subunit. The most N-terminal amino acids were built onto the structure within the Swiss-PDB viewer software using phi, psi, and omega angle information for the corresponding amino acids of hexameric human PBGS. Finally, the program SCWRL [26] was used to position the side chains for the N-terminal arm segments resulting in a model for hexameric P. aeruginosa PBGS, which could then be used for preparing hexameric models of other PBGS as we have done before for the octameric forms of pea and D. melanogaster PBGS [7,9]. The model for hexameric R. capsulatus PBGS was built using the same integrated graphical environment developed in house. This software integrates sequence alignment, threading, loop model building to accommodate insertions and deletions, and side chain optimization similar to that used for our previously published models [7,9].
Authors' contributions
DWB was responsible for the initial cloning by PCR, purification of the enzyme, initial assays of the enzyme, and preparation of Figs. 1, 2, and 5. CC created the expression vector and helped with initial purification and assays. RL was involved in determining the pH dependence of the enzyme. SF performed the native gel electrophoresis experiments and biochemical characterizations. BK and RF performed the analytical ultracentrifugation experiments. LK and EKJ were involved in creating the homology model. EKJ performed much of the writing, supervised some of the biochemical characterization and prepared Figs. 3, 4, and 6.
Acknowledgements
The authors acknowledge Dr. Adrian Canutescu of the FCCC molecular modeling facility for generously allowing us to use software under development. Ms. Linda Stith is acknowledged for technical assistance. This work was supported by Grant MCB-0109909 (to D.W.B.) from the National Science Foundation, by Grant ES03654 (to E.K.J) from the National Institutes of Environmental Health Sciences, by grant CA006927 (to FCCC) from the National Cancer Institute, and by Grant MCB-0211754 (to R.F.) from the National Science Foundation.
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1431546960810.1186/1471-2105-5-143SoftwareIdentitag, a relational database for SAGE tag identification and interspecies comparison of SAGE libraries Keime Céline [email protected] Francesca [email protected] Dominique [email protected] Laurent [email protected] Olivier [email protected] Équipe Signalisation et identités cellulaires, Centre de Génétique Moléculaire et Cellulaire CNRS UMR 5534, Université Claude Bernard Lyon 1, bâtiment Gregor Mendel, 16 rue Raphaël Dubois 69622 Villeurbanne cedex France2 Équipe Bioinformatique et génomique évolutive, Laboratoire Biométrie et Biologie Évolutive CNRS UMR 5558, Université Claude Bernard Lyon 1, bâtiment Gregor Mendel, 16 rue Raphaël Dubois, 69622 Villeurbanne cedex France2004 6 10 2004 5 143 143 26 5 2004 6 10 2004 Copyright © 2004 Keime 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
Serial Analysis of Gene Expression (SAGE) is a method of large-scale gene expression analysis that has the potential to generate the full list of mRNAs present within a cell population at a given time and their frequency. An essential step in SAGE library analysis is the unambiguous assignment of each 14 bp tag to the transcript from which it was derived. This process, called tag-to-gene mapping, represents a step that has to be improved in the analysis of SAGE libraries. Indeed, the existing web sites providing correspondence between tags and transcripts do not concern all species for which numerous EST and cDNA have already been sequenced.
Results
This is the reason why we designed and implemented a freely available tool called Identitag for tag identification that can be used in any species for which transcript sequences are available. Identitag is based on a relational database structure in order to allow rapid and easy storage and updating of data and, most importantly, in order to be able to precisely define identification parameters. This structure can be seen like three interconnected modules : the first one stores virtual tags extracted from a given list of transcript sequences, the second stores experimental tags observed in SAGE experiments, and the third allows the annotation of the transcript sequences used for virtual tag extraction. It therefore connects an observed tag to a virtual tag and to the sequence it comes from, and then to its functional annotation when available. Databases made from different species can be connected according to orthology relationship thus allowing the comparison of SAGE libraries between species. We successfully used Identitag to identify tags from our chicken SAGE libraries and for chicken to human SAGE tags interspecies comparison. Identitag sources are freely available on web site.
Conclusions
Identitag is a flexible and powerful tool for tag identification in any single species and for interspecies comparison of SAGE libraries. It opens the way to comparative transcriptomic analysis, an emerging branch of biology.
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Background
In order to characterize the molecular basis underlying self-renewal versus differentiation decision-making process we investigated the transcriptomic changes of various states related to this process, in two model systems : one derived from chicken and the other from human cells. We decided to use Serial Analysis of Gene Expression (SAGE) [1] to attain this aim, for a number of reasons including the absence of available pan-genomic DNA arrays in the chicken and the ability to compare SAGE libraries across different experiments. We therefore had to resolve two problems : tag-to-gene mapping in chicken and comparing SAGE libraries from two different species (here chicken and man).
Serial Analysis of Gene Expression is a comprehensive method for analyzing transcriptomes (i.e. the complete set of mRNAs expressed in one given biological situation at one given time point) without any a priori regarding the genes to be studied. It can be used with mRNAs derived from cells of any eukaryotic species. SAGE is based on the isolation of a unique sequence tag from each individual transcript and on serial concatenation of several tags into long DNA molecules. Sequencing of concatemer clones reveals individual tags and allows quantification and identification of transcripts. Tag counts are digitally archived and statistically significant comparisons of expression levels can be made between tag counts derived from different populations of cells.
An essential step in SAGE library analysis is the unambiguous assignment of each 14 bp tag to the transcript from which it was derived. This process, called tag-to-gene mapping, represents a step that has yet to be completed in the analysis of SAGE libraries. The automated version of this process mostly involves extracting "virtual tags" from sequence databanks : these virtual tags are predictions of the 14 bp sequences that might be produced by a SAGE experiment. The quality of the databanks from which the virtual tags are extracted represents a limiting step in this process. Ideally, the databanks should represent the complete collection of each and every transcript, fully sequenced and annotated. This clearly has yet to be achieved for most species, and therefore one must use the available information that comes mainly from large EST (Expressed Sequence Tags) projects.
Different resources have already been described for tag identification in human and mouse, including the SAGE Map [2], the SAGE Genie [3], the Melbourne Brain Genome Project [4], the Mouse SAGE site [5] and the Human Transcriptome Map [6] web sites, but fewer resources are available for tag-to-gene mapping in other species. Nevertheless, a very large number of species have been subjected to a SAGE analysis (for an up to date bibliography, see the SAGEnet web site [7]) and actually the SAGE Map web site hosts a SAGE tag to UniGene mapping for 11 species (Arabidopsis thaliana, Bos taurus, Homo sapiens, Medicago truncatula, Meleagris gallopavo, Mus musculus, Pinus taeda, Rattus norvegicus, Sus scrofa, Triticum aestivum and Vitis vinifera). However, this site doesn't include tag to UniGene mapping for several other species for which numerous EST and cDNA have already been sequenced. This is the reason why we designed and implemented a freely available tool for tag identification that can be used in any species for which transcript sequences are available. It can include both complete cDNAs and EST cluster sequences and allow to interrogate the database according to the source of data, to assess the quality of virtual tags derived from different transcript sequences. In this paper we describe the use of this tool for the chicken (Gallus gallus) where a large EST sequencing effort was completed [8].
In order to allow rapid and easy storage and updating of data and, most importantly, in order to be able to query the results using sophisticated combinations of criteria, we have designed a relational database structure. We implemented this relational database called Identitag using the freely available MySQL database management system (DBMS) [9].
One important function of Identitag is the possibility to compare the tags obtained in a given species to their counterpart present in another species. This allows a direct comparison of SAGE transcription profiles obtained from different species. To the best of our knowledge, this is the first tool that allows SAGE libraries interspecies comparison : this open the way to comparative transcriptomic analysis.
Here we describe the use of Identitag for chicken SAGE tag identification as well as for chicken to human interspecies comparison.
Implementation
Database for tag identification : Identitag
Database organization
The Identitag relational schema is presented in Figure 1, and a complete data dictionary of the database is available on the Identitag web site . We implemented this database using the freely available and cross-platform Mysql DBMS. A perl script which generates the SQL script creating Identitag tables according to the name of the species considered, is also available on Identitag web site.
Completing the database
Various sources of data presented in Figure 1 are needed to complete Identitag. Transcript sequences in Fasta format and a file resulting from their comparison with protein from databanks using the BLASTX algorithm [10] are needed for completing the first and third Identitag modules. For chicken Identitag we used various transcript sequences : 3425 chicken mRNA from Genbank (extracted using query software [11]) and 88504 SIGENAE chicken EST cluster consensus sequences (INRA, M. Douaire, P. Deshais and C. Klopp, personal communication). As consensus sequence orientation is not always known, we used both sequences and their reverse complementary. Then we could assess the correct orientation of the sequences using various sources of information stored in Identitag database (see results section). For completing the second module, a flat file with tag sequences and their relative frequency is required, for each library. So far in chicken Identitag we have stored four different libraries generated from normal chicken immature erythroid progenitors called T2ECs [12]. The first two libraries were generated from self-renewing T2EC cells and from T2EC cells induced to differentiate for 24 hours respectively [13]. The last two libraries have been generated from T2EC cells treated with two inhibitors of the MEK-1 signaling pathway which is important for maintaining self-renewal (S. Dazy et al, in preparation). For these four libraries, we used SAGE 2000 software [14] in order to extract tags from concatemer sequences : we generated 4 files for the 4 SAGE libraries considered, with 17853, 19736, 16631 and 11669 tags respectively. 6440 different tags appear more than once in these 4 libraries.
Several programs were then used for loading these data into Identitag database. They are written in Perl and Shell and are available on Identitag web site. A Shell script that allows to launch all these programs is also available on this web site : it asks for all information required by these different programs, then launches all programs with adapted arguments and loads the files generated by these programs in the corresponding Identitag tables. The creation and completion of Identitag tables with these programs was successfully tested on SUN, Linux and Mac OS X operating systems.
Querying the database
The completed database can be interrogated using SQL (Structured Query Language) and allows a number of tag identification procedures to be launched (see for example the procedure described in results section).
Redundancy reduction
When several transcripts identify a same tag, these transcripts are compared with each other using Blastclust [15] to determine whether they correspond to redundant sequences of the same transcript or to different transcripts. We consider that two sequences are redundant if they share more than 95% similarity over more than 100 bp.
Database for interspecies comparison of SAGE libraries
We connected two Identitag databases from two different species by using orthology relationships between transcripts that identify SAGE tags (Figure 2A).
Design of the orthology relationship
We designed a method for identifying transcript sequence pairs that are putatively orthologous between the two species considered. This method (described in figure 3A and in text below) is an approximation of the search for reciprocal best BLAST hits for two datasets with redundancy and that do not represent the entire transcriptome of the two species considered.
The first step of this method (Figure 3A, step 1) consists of two reciprocal TBLASTX. First, we compare each species A transcript with a databank containing species B transcripts, using the TBLASTX algorithm [10] (Figure 3A, TBLASTX(1)). We store all the best hits for which the corresponding E-value is less than 0.001 : all these sequences form a subset of species B transcripts. Second, we compare each sequence from this subset with a databank containing species A transcripts, using the TBLASTX algorithm (Figure 3A, TBLASTX(2)). We further consider corresponding best hits harboring an E-value lower than 0.001, they form a subset of species A transcripts that are considered for further analysis.
The second step (Figure 3A, step 2) consists in staring only pairs of transcript sequences sufficiently similar between TBLASTX(1) and TBLASTX(2). For example, transcript sequence A1 is similar to transcript sequence B1 (result provided by TBLASTX(1)), and transcript sequence B1 is similar to transcript sequence AX (result provided by TBLASTX(2)). If the two transcript datasets were complete and non-redundant, X should be equal to 1 : when that is the case, A1 is paired with B1. If not, we search if AX transcript sequence is redundant with A1, with the same criteria as described above to asses if two sequences are redundant. If AX is similar to A1, we further consider the pair A1-B1. If not, the A1-B1 pair is discarded from further analysis. We use the same method for each transcript pair obtained in first step.
If the set of transcripts from species A and B are not complete, the best reciprocal hits might correspond to paralogs (see figure 3B). To limit this risk of erroneous orthology assignment we consider the pairs stored in previous step: we compare the species A transcript sequence from each pair with species B proteins from SwissProt and TrEMBL databanks, using the BLASTX algorithm (Figure 3A, step 3). We compare the best resulting hit (a protein from species B) with the species B transcript putatively orthologous to species A transcript : if these two sequences are similar (i.e. they share more than 95% similarity over more than 100 bp), we consider the pair of the species A transcript and the species B transcript as a pair of orthologous sequences. If these two sequences are not similar, it means that protein databanks contain a species B sequence that is more similar to species A transcript than the species B sequence found using best reciprocal hit. Thus the pair of the species A transcript and the species B transcript might correspond to a pair of paralogous sequences.
These three steps allow us to obtain pairs of transcripts which are probably orthologous, by trying to eliminate erroneous assignments of orthology for paralogous sequences instead of orthologous ones. However a limiting aspect of this method is the identification of only 1-1 orthology relationships : if one transcript sequence from species A has several orthologous sequences in species B this method will only identify one of the pairs of orthologous sequences. The scripts that implement this method are available on Identitag web site.
We applied this method to chicken and human. For this we used chicken transcript sequences from Genbank, chicken SIGENAE EST cluster consensus sequences and human transcript sequences from Refseq release 2 (19902 mRNA sequences, with the accession prefix "NM_").
Connecting two Identitag databases by using this relationship
The database for interspecies comparisons of SAGE libraries is composed of two Identitag databases for tag identification in two different species, and connected through the SpeciesA_SpeciesB_Transcript table (see for example the organization of the database for chicken-human interspecies comparison in Figure 2B). A shell script available on Identitag web site asks for all information required. Then it searches for putative orthologous sequences using the method previously described, creates the table SpeciesA_SpeciesB_Transcript, and then loads corresponding data in this table.
Results
The use of Identitag for identification purpose
Database organization
Identitag can be depicted as three interconnected modules, as presented in Figure 1.
The first module stores data concerning transcript sequences and virtual tags extracted from these sequences. The Species_Transcript table contains transcript identification (identification number and the databank from which they originate), together with information that can then be used for assessing the quality of the "virtual" SAGE. The quality of virtual tags depends on the source of the transcript sequence from which it was derived (e.g. the sequence quality of mRNA is higher than that of EST clusters), this is why this information is stored in the Species_Transcript table. Other information can also be used for assessing the quality of the "virtual" SAGE. This information allows one to assess if a transcript sequence is complete and if its orientation correct. This includes the presence or absence of different polyA signals (AATAAA and its most common variant ATTAAA) as well as their localization along the sequence and the length of the possible poly A tail. This length corresponds to the longest poly A stretch among the 50 last base pairs of the transcript sequence : this calculation allows us to take into account the polyA tail even if there are some bases belonging to a cloning vector or sequencing errors at the end of the corresponding transcript sequence. Some of the EST we used were labeled as constructed and sequenced from the 3' region. When available this information was stored in the database. The Species_Transcript table is linked with an NN relationship to the Species_Virtual_Tag table. This table contains virtual tags extracted from the transcript sequences and information about how the tags were extracted. This includes the anchoring enzyme considered (e.g. NlaIII) and the position of its recognition site in the transcript sequence : the Species_Virtual_Tag table stores both the 10 bp sequence immediately downstream of the most 3' anchoring enzyme recognition site and 10 bp sequence downstream of the next-to-last anchoring enzyme recognition site. Indeed, the cutting enzyme may on rare occasions (0,1 %, [16]) cut not its most 3' but its recognition site that is just 5' from the last one (called next-to-last). Both conventional 10 bp tag sequences as well as virtual long-SAGE 17 bp tag sequences [17] are stored in the Species_Virtual_Tag table.
The second Identitag module allows storage and retrieval of the experimental part of the SAGE experiments. It consists of a table containing information about the construction of SAGE libraries (Species_SAGE_Library table), connected with a table that stores tag sequences from that SAGE library and their corresponding count (Species_Observed_Tag table).
The connection between modules 1 and 2 leads to a direct comparison between observed and virtual tags : it is the key to the tag identification procedure. Only perfect matches are allowed at that stage.
The third Identitag module allows annotation of transcript sequences from which virtual tags are extracted, via their similarity to known proteins. For this purpose we compare each transcript sequence with the protein sequences from Swissprot and TrEMBL databanks, using the BLASTX algorithm [10]. For this sequence comparison we only consider the same transcript sequence orientation as the orientation that we used for tag extraction. When available, the best BLASTX hits (harboring an E-value < 0.001) are stored in Identitag (in the Protein table), together with different BLAST statistics (stored in Species_Transcript_Protein table). This information can then be used for assessing the quality of the annotation.
Identitag web site provides all scripts necessary to build a such Identitag database and to load all data into this database, for any species from which transcript sequences are available. To do this one need data represented in figure 1. After running the main script one just have to answer all questions asked by the script and then all tables of the database are created and completed, for the species considered.
Various identification situations
Identitag can be interrogated in various ways, using different identification criteria concerning the quality of the virtual SAGE and the annotation provided. For example, the interrogator can use the following step-by-step procedure (Figure 4), for each observed tag from the SAGE experiment considered (an example of each identification situation is provided in table 1) :
1. If the observed tag matched to no virtual tag, then it is declared unidentified and the identification process is aborted : approximately 25% of the tags belonging to the four chicken SAGE libraries we have studied are unidentified. If this is not the case it means that the observed tag corresponds to a virtual tag extracted from one or more transcript sequence(s) ; the identification process proceeds to the next step.
2. If these transcript sequences are not annotated (i.e. they do not have any BLASTX hit with an E-value < 0.001), the observed tag is identified as matching non-annotated EST cluster(s) : 37% of the tags belonging to the four chicken SAGE libraries we studied correspond to non-annotated EST cluster(s). Then we distinguish tags identified as matching one EST cluster (57% of these tags) or more than one EST clusters. For tags corresponding to more than one EST clusters, we compare these sequences to determine whether they are redundant or not. Indeed, there could be redundancy in transcript databanks based on EST clusters due to the threshold for sequence similarity being to high to assign different EST to the same cluster. Therefore, there could be different EST clusters corresponding to the same transcript. This is the reason why we use the procedure described in implementation section to identify sequence redundancy. Among the tag identifications corresponding to more than one EST clusters, 19% correspond to redundant sequences. Finally, 65% of identifications corresponding to non-annotated EST cluster(s) are unambiguous (one corresponding EST cluster or more than one but redundant corresponding EST clusters). If the observed tag does not correspond to previous identification cases, it means that the observed tag corresponds to a virtual tag extracted from one or more annotated transcript sequence(s) and the identification process proceeds to the next step.
3. If there is only one annotated transcript sequence from which this virtual tag has been extracted, then the protein name (i.e. the description field corresponding to its Swissprot or TrEMBL accession number) is used to identify this tag and the identification process is stopped : 26% of the tags belonging to the four chicken libraries we studied correspond to this identification case. We call these tags "annotated tags". When this is not the case it means that the observed tag corresponds to a virtual tag extracted from different annotated transcript sequences ; the identification process proceeds to the next step.
4. If the different transcript sequences have the same annotation (i.e. the same best BLASTX hit), then the protein name is used as the identification and the identification process halts. This case is mainly due to transcript databank redundancy. Thus by using annotation to identify SAGE tags, we reduce the number of multiple matches. As in previous identification situation we designate the corresponding tags as "annotated tags" because their annotation is not ambiguous : 4% of the tags belonging to the four chicken libraries we studied correspond to this identification case. When this is not the case it means that the observed tag corresponds to a virtual tag extracted from several transcript sequences differently annotated ; the identification process proceeds to the next step.
5. The last case corresponds to an observed tag matching to more than one transcript sequences with more than one different annotations. This corresponds to 8% of the tags belonging to the four chicken SAGE libraries we studied. By using annotation to identify SAGE tags, we reduce the number of multiple matches that may occur because of redundancy in transcript databanks. Nevertheless, some of these multiple matches remain. This may occur because there is redundancy in protein databank, thus the redundant transcripts can be differently annotated : this leads to a multiple match. It also appear when redundant transcripts match to the same protein but in different species. Indeed the annotation is provided by a BLASTX against Swissprot and TrEMBL databanks (with all species considered) : thus we could annotate transcripts for which we don't already know the corresponding protein in the species considered, but which is identified in another species. However, this method presents the drawback of causing multiple matches when redundant transcripts match to the same protein in different species. This case is considered as a multiple match because the best BLASTX hits of transcripts identifying the same tag are different. To reduce these two cases of ambiguity, we align the different transcript sequences identifying the same tag (see implementation section) : according to the sequence similarity between them we could avoid these cases of multiple matches. Among the 521 identifications corresponding to more than one transcript sequences with more than one different annotations in our four chicken libraries, 28% could be discarded by this method. The cases of multiple matches remaining occur presumably mainly due to different transcripts that really have the same tag.
We provide an example illustrating the repartition of these different situations regarding tag identification in the chicken libraries we studied (figure 5). When we don't consider the next-to-last tag during the identification procedure, we reduce the number of multiple matches, but increase the number unidentified tags.
This process could be pursued further by discriminating between multiple matches, using criteria concerning the quality of the virtual SAGE, e.g. the quality of the transcript sequences (cDNA or EST), the tag position in the transcript sequence (last or next-to-last) and/or the availability of the transcript sequence end.
One can also consider a process for tag identification based only on high quality tag identifications. For example, one could obtain an identification based solely on cDNA sequences, or only using tags appearing at the last position. One can also use any logical combination of the above criteria.
The use of Identitag for interspecies comparison of SAGE libraries
In order to directly compare SAGE libraries performed in chicken (4 libraries performed in our lab until now) with those performed in human (273 libraries available as of 08/03/04 on SAGE Genie web site : [3]), one first needs to associate each chicken tag with its human counterpart. For this we decided to connect the chicken Identitag to its human counterpart. This connection relies upon the concept of orthology (Figure 2A) [18]. Two genes in two different species are said to be orthologous if they diverged after a speciation event. It is important to note that conservation of function is not part of the definition of orthology, but rather its consequence. It can also be envisaged that after the speciation event, the function of the resulting genes diverged in the two species. A tool for interspecies comparison of expression data would be very helpful for investigating such questions, and notably the level of conservation in expression patterns of orthologous genes, a task that has only begun using DNA arrays ([19,20]).
The structure of Identitag was originally designed with this purpose in mind and therefore chicken and human Identitag could easily be connected together (Figure 2B). For example, this procedure will allow the chicken GATA-1 tag (GGGGACCCCG) to be associated with its human counterpart (GCCTCCAGAG) via the chicken transcript GATA-1 <-> human transcript GATA-1 orthology. We designed a method for identifying transcript sequence pairs that are putatively orthologous between the two species considered. This method (described in figure 3A) is an approximation of the search for reciprocal best BLAST hits for two datasets with redundancy and that do not represent the entire transcriptome of the two species considered. We first perform two reciprocal TBLASTX between species A and species B transcript sequences (TBLASTX(1) and TBLASTX(2)). We then conserve only the pairs of transcript sequences originating consistent TBLASTX(1) and TBLASTX(2) results. We finally consider previously obtained pairs in order to limit erroneous assignment of orthologous pairs for paralogous ones. For a more precise description of the design of this design of orthology relationship see implementation section.
Among the 3500 transcripts corresponding to unambiguously identified tags from our 4 chicken libraries (either annotated or unambiguously identified by EST cluster(s)), 1190 have a human orthologue as previously defined. Therefore the corresponding tags can now be translated into their human counterpart and thus SAGE libraries from two different species can be compared.
Discussion
We have designed and implemented a tool allowing the identification of SAGE tags, based on a relational database. This structure allows to use Identitag in two different ways.
First one can precisely choose identification criteria and obtain only the tag identifications provided by using these criteria. One can specify different criteria and thus determine the quality of the identification : e.g. identification generated from EST clusters or mRNA sequences, from last or next-to-last tag, presence of the 3' end of the transcript sequence that could be inferred through the presence of a 3' label or a poly A tail and/or signal. One can also specify different criteria allowing the quality of the annotation to be controlled, e.g. quality of the similarity between transcript and protein sequences through BLAST parameters, quality of the protein sequence used to annotate the transcript via the databank from which the protein originated (Swissprot or TrEMBL) and its species (an annotation with a protein from the same species than that we consider is more accurate than with a protein from another species). All these criteria can be combined allowing the investigator to perform sophisticated interrogations of the database. To the best of our knowledge it is the first tool that allows the user to precisely adjust the identification parameters depending upon its needs.
The second way to use Identitag is to ask for all identifications available, for example for a tag of interest, and how these identifications have been generated : it is then possible to consider all these identifications and to further choose among the different identifications if necessary.
These two different ways of using Identitag can be used for any species for which transcript sequences are available. Identitag is an open source tool, the programs necessary to build and run the database are available on the Identitag web site . Identitag can therefore be used to build a tag-to-gene mapping procedure in any species, using a flat file containing transcript sequences and a BLASTX file results as input of these programs.
Identitag was successfully used for tag-to-gene mapping in chicken. It played a key role for allowing biological interpretation of the SAGE libraries obtained from normal chicken erythroid progenitor cells and allowed us to better understand the changes underlying the self-renewal versus differentiation-making process in these cells [13]. Among the identifications provided by Identitag, a few were investigated further and the vast majority of these identifications were subsequently confirmed by real-time PCR [13]. Identitag has also been successfully used for tag-to-gene mapping in Bombyx mori (J. Briolay et al, in preparation).Identitag is currently in use to identify human tags from SAGE libraries generated in order to investigate the molecular basis underlying the self-renewal versus differentiation decision-making process in human cells.
The next step will be to compare gene expression patterns between our chicken and human model systems, in order to study the possible conservation of the molecular basis of self-renewal during evolution. Comparisons of gene expression between two organisms have recently been initiated with DNA arrays ([19,20]). But it is one on the main limitations of DNA arrays that comparisons between experiments done in different laboratories (not to mention on different species) are at best approximate. It is one of the main advantage of the SAGE technique for which results can be compared without the need for sophisticated and approximative normalization procedures. The SAGE technique is therefore ideally suited for quantitative comparisons to be performed between different libraries made from different cell types in different laboratories. We therefore expect that Identitag will become a standard tool for comparative transcriptomic analysis using SAGE data, an emerging branch of biology consisting in the comparison of large scale transcriptomes obtained from various cell types belonging to different species.
Conclusions
Identitag is a flexible and powerful tool for tag identification in any single species and for interspecies comparison of SAGE libraries. It opens the way to comparative transcriptomic analysis, an emerging branch of biology.
Availability and requirements
• Project name: Identitag
• Project home page:
• Operating system(s): SUN, Linux, Mac OS X
• Programming languages: Perl, Bourne Shell, MySQL
• License: GNU GPL
Authors' contributions
CK participated to the design of Identitag, implemented Identitag, and participated to the biological validation of Identitag with SAGE libraries. FD constructed the first two SAGE libraries with which Identitag was tested and participated to the biological validation of Identitag with these data. LD and DM brought their expertise in the orthology area, in order to design the orthology relationship. OG supervised this work. All authors participated in the writing of the manuscript, read and approved the final manuscript.
Acknowledgements
We wish to thank the SIGENAE group (and especially Patrice Deshais, Christophe Klopp and Madeleine Douaire) for sharing chicken EST cluster sequences. We thank IN2P3 and especially Pascal Calvat for their computing resources. Of the four libraries analyzed, two were constructed by Sébastien Dazy and Claudine Faure (S. Dazy et al., in preparation). The work in our laboratory is supported by CNRS, UCBL, INRA (Program "AGENA"), Région Rhône-Alpes (Program "Cellules souches"), Fondation de France, Association pour la Recherche contre le Cancer and a joint INSERM/AFM program (Program "Cellules souches"). Sequencing was partially funded through an InterEPST program ("Séquençage à grande échelle", call 2002). FD is a fellow from the EU RTN program "Hematopoiesis" (contract HPRN-CT-2000-00083). We wish to thank Edmund Derrington for manuscript corrections.
Figures and Tables
Figure 1 Identitag relational schema. This figure provides a schematic view of the Identitag tables and their relationships. Identitag can be depicted as three interconnected modules represented in this figure. For a more precise description see data dictionary (available on Identitag web site). The term "Species" could be replaced by any specific species for which transcript sequences are available. The different sources of information needed for completing Identitag are also shown. The minimum number of files consist of : one file containing tag sequences (extracting from ditag concatemers with a software like SAGE 2000), a Fasta file containing transcript sequences from the species considered and a file containing results of their comparison with protein databanks (using BLASTX).
Figure 2 Identitag for interspecies comparison of SAGE libraries. A : General structure behind the process of interspecies comparison of SAGE libraries. B : Detail of the connection between two Identitag databases for generating a tool for SAGE libraries interspecies comparison (example provided for a chicken to human comparison).
Figure 3 The orthology relationship. A. Design of the orthology relationship. Step 1 : Two reciprocal TBLASTX for comparing species A and species B transcript sequences. Step 2 : We conserve only the pairs of transcript sequences originating consistent TBLASTX(1) and TBLASTX(2) results. Step 3 : We consider previously obtained pairs in order to limit erroneous assignment of orthologous pairs for paralogous ones. B. The best reciprocal TBLASTX hits might correspond to paralogs. This figure provides an example of a phylogenetic tree where the best reciprocal TBLASTX hits correspond to paralogs because several transcript sequences are unknown (represented with dotted lines). To avoid such erroneous assignment of orthologous pairs we followed reciprocal best BLAST by another step (figure 3A, step 3) considering that even if the transcript sequences A1 and B2 are unknown, one of their corresponding proteins might be in a protein databank.
Figure 4 Identitag as a tag identifier. A : An example of identification process using Identitag. This process was used to identify SAGE tags from four chicken libraries ([13]; S. Dazy et al, in preparation).
Figure 5 Repartition of the different identification situations. Repartition of the different situations exemplified in table 1 on 6440 different tags obtained in the total of four chicken SAGE libraries ([13]; S. Dazy et al., in preparation).
Table 1 Identitag as a tag identifier. This table provides examples illustrating various situations regarding tag identification using Identitag.
Tag sequence Transcript identification number Protein identification number Protein description
1. Unidentified CATGACAGCACGGG
one EST cluster CATGAAAATTCTAA BBSRC603768552F1.1.3.1
2. EST cluster(s) redundant EST clusters CATGAGAATAATCT BBSRC602906088F1.1.3.4
BBSRC602906088F1.1.3.5
AJ508717
more than one EST clusters
non redundant EST clusters CATGAAAGACTTCT 12595754.1.3.1
BBSRC603772421F1.1.3.1
AB038230.1.3.477
3. Annotated (one annotated transcript sequence) CATGAAAGCCAAGA J02828 P09207 Tubulin beta-6 chain
4. Annotated (different annotated transcript sequences with the same annotation) CATGAACTAAAACC BBSRC603002454F1.1.3.1
BSRC603002454F1.1.3.10 P18937
P18937 NADH-ubiquinone oxidoreductase chain 2
NADH-ubiquinone oxidoreductase chain 2
5. More than one different annotations redundant annotations CATGCACTTTGTAT BBSRC603008189F1.1.3.1
BBSRC603122686F1.1.3.2
AF251344 Q8QG60
Q9NP50
Q8QG52 Cryptochrome 2
Hypothetical protein
Cryptochrome 2 (Fragment)
non redundant : multiple match CATGGTGGTGTGGT AB038230.1.3.135
gcag0018c.a.01_5.1.3.1 P16039
Q8R4X1 Nucleophosmin
Alkaline ceramidase
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| 15469608 | PMC535903 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 6; 5:143 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-143 | oa_comm |
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BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-221557420110.1186/1471-2261-4-22Research ArticleSurvival of patients treated with intra-aortic balloon counterpulsation at a tertiary care center in Pakistan – patient characteristics and predictors of in-hospital mortality Jafary Fahim H [email protected] Sohail A [email protected] Haresh [email protected] Numaan F [email protected] Khawar A [email protected] Sajid [email protected] Azam [email protected] Aamir [email protected] Javed [email protected] Najaf [email protected] Department of Medicine, Section of Cardiology, Aga Khan University Hospital, Karachi 74800, Pakistan2 Department of Cardiology, National Institute of Cardiovascular Disease, Karachi, Pakistan2004 1 12 2004 4 22 22 2 9 2004 1 12 2004 Copyright © 2004 Jafary et al; licensee BioMed Central Ltd.2004Jafary 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
Intra-aortic balloon counterpulsation (IABC) has an established role in the treatment of patients presenting with critical cardiac illnesses, including cardiogenic shock, refractory ischemia and for prophylaxis and treatment of complications of percutaneous coronary interventions (PCI). Patients requiring IABC represent a high-risk subset with an expected high mortality. There are virtually no data on usage patterns as well as outcomes of patients in the Indo-Pakistan subcontinent who require IABC. This is the first report on a sizeable experience with IABC from Pakistan.
Methods
Hospital charts of 95 patients (mean age 58.8 (± 10.4) years; 78.9% male) undergoing IABC between 2000–2002 were reviewed. Logistic regression was used to determine univariate and multivariate predictors of in-hospital mortality.
Results
The most frequent indications for IABC were cardiogenic shock (48.4%) and refractory ischemia (24.2%). Revascularization (surgical or PCI) was performed in 74 patients (77.9%). The overall in-hospital mortality rate was 34.7%. Univariate predictors of in-hospital mortality included (odds ratio [95% CI]) age (OR 1.06 [1.01–1.11] for every year increase in age); diabetes (OR 3.68 [1.51–8.92]) and cardiogenic shock at presentation (OR 4.85 [1.92–12.2]). Furthermore, prior CABG (OR 0.12 [0.04–0.34]), and in-hospital revascularization (OR 0.05 [0.01–0.189]) was protective against mortality. In the multivariate analysis, independent predictors of in-hospital mortality were age (OR 1.13 [1.05–1.22] for every year increase in age); diabetes (OR 6.35 [1.61–24.97]) and cardiogenic shock at presentation (OR 10.0 [2.33–42.95]). Again, revascularization during hospitalization (OR 0.02 [0.003–0.12]) conferred a protective effect. The overall complication rate was low (8.5%).
Conclusions
Patients requiring IABC represent a high-risk group with substantial in-hospital mortality. Despite this high mortality, over two-thirds of patients do leave the hospital alive, suggesting that IABC is a feasible therapeutic device, even in a developing country.
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Background
Intra-aortic balloon counterpulsation (IABC) has an established role in the treatment of patients presenting with cardiogenic shock [1-3], refractory heart failure [4,5], ischemia [6] and arrhythmias [7] as well as for prophylaxis [8,9] and treatment of complications of percutaneous coronary intervention (PCI). Patients requiring IABC represent a high-risk subset with an expected high mortality [10]. In an international registry of over 16,000 cases selected from primarily developed nations [11], the overall adjusted in-hospital mortality was 21.2%. However, there were geographic differences with lower mortality rates in U.S. patients compared to their non-US counterparts (20.1% vs. 28.7%; p < 0.001) [12]. Major predictors of mortality in these patients include age, gender, and presentation with cardiogenic shock. There is paucity of data on the usage patterns as well as outcomes of patients undergoing IABC in the Indo-Pakistan region. This is partly due to the limited availability and capacity to implant the device as only a few centers in Pakistan have the required logistical as well as technical expertise. Our institution has previously reported on our initial experience of 15 patients undergoing IABC prior to coronary artery bypass graft (CABG) surgery [13]. We now report on an extended experience with intra-aortic balloon counterpulsation and describe the patterns of usage as well as the independent predictors of in-hospital mortality in patients undergoing IABC.
Methods
Patient population
We reviewed the charts of 95 patients undergoing IABC at the Aga Khan University Hospital (AKUH), Karachi, Pakistan between January 2000 and December 2002. Patients requiring IABC in the operating room immediately following CABG to assist weaning off cardiopulmonary bypass were excluded from this study. However, those patients who underwent IABP implantation prior to surgery were included. The AKUH is a tertiary care hospital located in the metropolitan city of Karachi that receives a mixture of affluent as well as low and middle income patients and serves the entire city as a referral center for patients requiring high-intensity tertiary care. Variables collected included age, gender, indication for IABC (shock or non-shock), history of diabetes, hypertension, smoking, prior PCI or CABG, left ventricular function, refractory ischemia and treatment (revascularization vs. no revascularization). Cardiogenic shock was defined as a systolic blood pressure (SBP) of < 90 mm Hg for at least 30 minutes (or requirement of inotropes to maintain a SBP > 90 mm Hg) associated with hypoperfusion (decreased urine output or cool extremities) and a heart rate of ≥ 60 beats per minute. Left ventricular (LV) ejection fraction (EF) was assessed by visual estimation. LV function was recorded as normal for an EF of ≥ 55%, mildly impaired for an EF 40–54%, moderately impaired for an EF 26–39% and severely impaired if the EF was ≤ 25%. Heart failure was diagnosed using clinical signs as defined by the Framingham criteria [14]. Refractory heart failure was defined as heart failure failing to respond to therapy including inotropic support. Refractory ischemia was defined as on-going ischemic chest pain and/or dynamic ECG changes (ST depression or ST elevation ≥ 1 mm in two or more contiguous leads) despite adequate medical therapy including antiplatelet drugs, beta-blockers and heparin. The outcome of interest was in-hospital mortality.
Statistical methods
All variables were entered into Statistical Package for Social Sciences (SPSS) version 10. Means and standard deviations were calculated for continuous variables and frequencies for categorical variables. Variables were analyzed by simple logistic regression to calculate the unadjusted odds ratios for factors associated with in-hospital mortality. Those variables with a p value of ≤ 0.25 on univariate analysis were entered into the multivariable model and adjusted odds ratios for factors associated with in-hospital mortality were calculated. Finally, the model fit was assessed using the Hosmer-Lameshow test. A p value of < 0.05 was considered significant.
Results
Table 1 summarizes the patient characteristics. The mean age of the study group was 58.8 (± 10.4) years. The majority of subjects were male (78.9%) and a high proportion had hypertension (55.8%), diabetes (43.2%), a smoking history (37.9%), previous PCI (30.5%) or CABG (48.4%). About half (48.4%) of the patients presented with cardiogenic shock and a similar number (52.6%) had moderate or severe depression of left ventricular function at presentation. All except two patients underwent coronary angiography and over two-thirds had three-vessel coronary artery disease. A revascularization procedure (either surgical or PCI) was performed in 74 patients (77.9%). In the remaining 21 patients, the main reasons for not performing revascularization were as follows: diffuse disease not amenable to PCI or CABG (5 patients), CABG felt to be too high-risk on account of comorbid conditions (6 patients), death in the catheterization laboratory prior to revascularization (6 patients), failed PCI (1 patient) and no need for revascularization (3 patients). The overall in-hospital mortality rate in this study group was 34.7% with six patients (6.3%) dying in the laboratory while the remaining 27 (28.4%) died during the hospital stay. Sixty-five patients (65.3%) left the hospital alive.
Table 1 Patient Characteristics.
Characteristic N (%)*
Age (mean/SD) 58.8 (10.4)
Males 75 (78.9)
Female 20 (21.1)
Diabetes 41 (43.2)
Hypertension 53 (55.8)
Smoking 36 (37.9)
Previous PCI 29 (30.5)
Previous CABG 46 (48.4)
Coronary Anatomy
Single vessel disease 6 (6.3)
2-vessel disease 15 (15.8)
3-vessel disease 72 (75.8)
LV function – normal or mildly impaired 45 (47.4)
LV function – moderate or severely impaired 50 (52.6)
Cardiogenic shock 46 (48.4)
Underwent revascularization 74 (77.9)
Percutaneous 26 (27.4)
Surgical 48 (50.5)
* mean/Standard Deviation for age; (%) for others
LV = left ventricular; PCI = percutaneous coronary intervention; CABG = coronary artery bypass grafting
Table 2 shows the indications for the implantation of an intra-aortic balloon pump (IABP). Almost half were inserted for cardiogenic shock. In the univariate analysis (Table 3), variables associated with in-hospital mortality included (odds ratio [95% CI]) age (OR 1.06 [1.01–1.11] for every year increase in age; diabetes (OR 3.68 [1.51–8.92]) and cardiogenic shock at presentation (OR 4.85 [1.92–12.2]); left ventricular dysfunction, hypertension and 3-vessel (versus no 3-vessel) coronary artery disease were not significantly associated with in-hospital mortality in these patients. A significant protective effect of a prior history of CABG surgery (OR 0.12 [0.04–0.34]) and in-hospital revascularization, either surgical or percutaneous, (OR 0.05 [0.01–0.189]) was noted in this study.
Table 2 Indications for Intra-aortic balloon counterpulsation
Indication N (%)
Cardiogenic shock 22 (23.2)
Cardiogenic shock with mechanical complication 24 (25.3)
Left Main disease, no chest pain 9 (9.5)
Left Main disease, chest pain in laboratory 6 (6.3)
Refractory heart failure 8 (8.4)
Refractory Ischemia 23 (24.2)
Complication during PCI 2 (2.1)
PCI = percutaneous coronary intervention (includes abrupt closure, severe "no-reflow")
Table 3 Univariate Predictors of In-Hospital Mortality
Survived (%) (n = 62) Died (%) (n = 33) Unadjusted OR (95% CI) p value
Age (SD) 56.9 (10.1) 62.5 (10.3) 1.06 (1.01–1.11) * 0.016
Male Gender 51 (82.3) 24 (72.7) 0.58 (0.21–1.57) 0.281
Diabetes 20 (32.3) 21 (63.6) 3.68 (1.51–8.92) 0.004
Hypertension 32 (51.6) 21 (63.6) 1.64 (0.69–3.93) 0.263
Smoking 25 (40.3) 11 (33.3) 1.35 (0.56–3.27) 0.504
Previous PCI 20 (32.3) 9 (27.3) 0.79 (0.31–2.0) 0.616
Previous CABG 40 (64.5) 6 (18.2) 0.12 (0.04–0.34) <0.001
Cardiogenic Shock 22 (35.5) 24 (72.7) 4.85 (1.92–12.2) 0.001
3-vessel disease** 44 (72.1) 28 (87.5) 2.70 (0.83–8.89) 0.101
LV dysfunction *** 30 (48.4) 20 (60.6) 1.64 (0.70–3.87) 0.258
Revascularized 58 (95.1) 16 (48.5) 0.05 (0.01–0.19) < 0.001
SD = standard deviation. PCI = percutaneous coronary intervention. CABG = coronary artery bypass graft. LV = left ventricular
* for every 1 year increase in age
** vs. no 3-vessel disease
*** moderate/severely impaired LV function vs. normal/mildly impaired
In the multivariate analysis (Table 4), the significant independent predictors of in-hospital mortality were age (OR 1.13 [1.05–1.22] for every year increase in age); diabetes (OR 6.35 [1.61–24.97]) and cardiogenic shock at presentation (OR 10.0 [2.33–42.95]). Revascularization during hospitalization remained a significant protective factor against mortality (OR 0.02 [0.003–0.12]) The Hosmer-Lemeshow test indicated a good fit for the model (χ2 6.09; p = 0.637). In the adjusted analysis, a prior history of CABG did not remain a significant predictor of survival primarily because forty-five out of 46 patients underwent revascularization.
Table 4 Multivariate Predictors of In-hospital Mortality*
Survived (%) (n = 62) Died (%) (n = 33) Adjusted OR (95% CI) p value
Age (SD) 56.9 (10.1) 62.5 (10.3) 1.13 (1.05–1.22) * 0.001
Diabetes 20 (32.3) 21 (63.6) 6.35 (1.61–24.97) 0.008
Cardiogenic Shock 22 (35.5) 24 (72.7) 10.0 (2.33–42.95) 0.002
Revascularized 58 (95.1) 16 (48.5) 0.02 (0.003–0.12) < 0.001
* adjusted for gender, previous CABG, hypertension and LV dysfunction (none/mild vs. moderate/severe). Hosmer-Lemeshow χ2 6.09; p = 0.637
When age as a risk factor was further analyzed by plotting an ROC curve, an age cut-off of 66.5 years had a high specificity for the outcome of in-hospital mortality (specificity 83.9%; area under ROC-curve 0.66; p = 0.01). Thus older patients requiring IABC suffer worse outcomes than younger subjects.
The overall complication rate related to the device implantation was low. Eight patients (8.5%) developed limb ischemia necessitating removal of the IABP; however, only one of these eight required surgery. There were no significant bleeding complications although one patient developed a hematoma following removal of the device; however this patient did not require blood transfusion or surgical repair of the arteriotomy site.
Discussion
Patients requiring IABC are at high risk for death on account of their critical underlying conditions. Despite this, several data have suggested that IABC can improve morbidity and mortality in specific subsets of patients including those presenting with cardiogenic shock. The use of IABC in developing countries is limited on account of lack of equipment as well as skilled personnel who can insert and manage the device. Ours is the first report on a sizeable experience with IABC from the Indo-Pakistan subcontinent. Our experience is similar to that of other centers in the West. We report a high in-hospital mortality rate in patients undergoing IABC (almost 35%). However, given that nearly half of the subjects had cardiogenic shock at presentation, this mortality rate is reasonably acceptable. Advanced age (over 66.5 years), diabetes and cardiogenic shock at presentation were strong independent predictors of in-hospital mortality, while revascularization (either surgical or PCI) was associated with high odds of survival. The latter finding is consistent with recently reported data from the IABP Benchmark Registry [15]. Of particular interest is the finding that patients with a prior history of CABG were more likely to survive, a finding driven by the fact that the majority underwent revascularization. This suggests that repeat revascularization of patients with a prior history of bypass surgery (a clearly high-risk subset) is not only feasible but also effective in a developing country setting. Our complication rates were acceptably low, supporting the feasibility of using IABC in our setting.
Several limitations of this study should be acknowledged. First, the sample size is fairly small and this is reflected in the relatively wide confidence intervals for the odds ratios. Due to a small sample size, it is difficult to make a comparison of correlates of mortality between subgroups, for example those presenting with cardiogenic shock versus those who did not and those undergoing surgical versus percutaneous revascularization. However, as expected, patients presenting with shock had a significantly higher mortality (72.7% vs. 27.3%; p = 0.001). Second, the patient group selected may not be representative of other centers in Pakistan given that our institution is a unique tertiary care hospital in the country. Third, our cohort did not contain patients undergoing prophylactic IABC prior to high-risk PCI for indications other than cardiogenic shock. This probably represents practice patterns at our institution whereby, largely due to cost constrains, very high-risk patients (for example those with multivessel disease and/or severe impairment of LV function) are preferentially send for surgery. The cumulative cost of IABC with multivessel stenting far exceeds that of a bypass operation. Only two patients required emergent IABC during PCI in the study period. This may reflect a selection of lower risk patients for PCI at our institution. Fourth, while the survival rate following IABC is nearly 65%, no analysis has been made of the cost effectiveness of this therapy.
Conclusions
In conclusion, cardiogenic shock and refractory ischemia are common indications for IABC in a Pakistani setting. Patients requiring an IABP represent a high-risk group with substantial in-hospital mortality. This is consistent with the nature of the presenting illnesses in these patients and is similar to western data. Despite this high mortality, over two-thirds of patients do leave the hospital alive, suggesting that IABC is a feasible therapeutic device, even in a developing country. Age (particularly over 66.5 years), diabetes and cardiogenic shock at presentation are significant predictors of mortality in this group of patients. Revascularization is a significant predictor of survival and complication rates are acceptably low. Larger studies are needed to evaluate which subsets of patients benefit the most from this device and further cost effectiveness analyses are warranted.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
FHJ, SAK conceptualized this study and participated in the study design. FHJ performed the statistical analysis. HK, NFM collected the data. KAK, SD, AS were involved in manuscript review. AH, JT and NN participated in manuscript drafting and review. All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15574201 | PMC535904 | CC BY | 2021-01-04 16:30:02 | no | BMC Cardiovasc Disord. 2004 Dec 1; 4:22 | utf-8 | BMC Cardiovasc Disord | 2,004 | 10.1186/1471-2261-4-22 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-461555507410.1186/1471-2202-5-46Research ArticleNeuroanatomical organization of gonadotropin-releasing hormone neurons during the oestrus cycle in the ewe Batailler Martine [email protected] Alain [email protected] Benoît [email protected] Yves [email protected] Neurobiologie et Maîtrise des Fonctions Saisonnières, UMR 6175 INRA-CNRS-Université de Tours-Haras Nationaux, IFR 135, INRA Centre de Tours, 37380 Nouzilly, France2 Contrôle Central de l'Ovulation, UMR 6175 INRA-CNRS-Université de Tours-Haras Nationaux, IFR 135, INRA Centre de Tours, 37380 Nouzilly, France2004 22 11 2004 5 46 46 11 6 2004 22 11 2004 Copyright © 2004 Batailler 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
During the preovulatory surge of gonadotropin-releasing hormone (GnRH), a very large amount of the peptide is released in the hypothalamo-hypophyseal portal blood for 24-36H00. To study whether this release is linked to a modification of the morphological organization of the GnRH-containing neurons, i.e. morphological plasticity, we conducted experiments in intact ewes at 4 different times of the oestrous cycle (before the expected LH surge, during the LH surge, and on day 8 and day 15 of the subsequent luteal phase). The cycle stage was verified by determination of progesterone and LH concentrations in the peripheral blood samples collected prior to euthanasia.
Results
The distribution of GnRH-containing neurons throughout the preoptic area around the vascular organ of the lamina terminalis was studied following visualisation using immunohistochemistry. No difference was observed in the staining intensity for GnRH between the different groups. Clusters of GnRH-containing neurons (defined as 2 or more neurons being observed in close contact) were more numerous during the late follicular phase (43 ± 7) than during the luteal phase (25 ± 6), and the percentage of clusters was higher during the beginning of the follicular phase than during the luteal phase. There was no difference in the number of labelled neurons in each group.
Conclusions
These results indicate that the morphological organization of the GnRH-containing neurons in ewes is modified during the follicular phase. This transitory re-organization may contribute to the putative synchronization of these neurons during the surge. The molecular signal inducing this plasticity has not yet been identified, but oestradiol might play an important role, since in sheep it is the only signal which initiates the GnRH preovulatory surge.
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Background
Gonadotropin-releasing hormone (GnRH), the peptide responsible for the regulation of secretory activity of the pituitary gonadotropes, is found in a diffuse neuronal system situated in the preoptic area and anterior hypothalamic area [1]. The neurons project their terminals to the median eminence where the peptide is released into the hypothalamo-hypophyseal portal vessels. The secretion of GnRH is normally pulsatile, as demonstrated by the measurement of GnRH following the direct sampling of hypothalamo-pituitary portal blood in various species such as sheep [2,3]. The frequency of this pulsatility is the main characteristic of this secretion and it encodes each part of the sexual cycle in the female [4]. The highest circulating concentration of GnRH is induced during the follicular phase in the female by the sequential action of the two main ovarian steroids (progesterone and oestradiol). These act within the brain to trigger a large and sustained period of GnRH release, the GnRH surge, which stimulates the preovulatory LH surge, and subsequently ovulation.
The pulsatile nature of GnRH secretion, as well as the large amount of peptide released during the preovulatory surge, indicate that the activity of GnRH-containing neurons can be synchronized, and it has been demonstrated that the GnRH neurons of the preoptic area receive synaptic contacts from GnRH neurons [5-7]. Oestradiol, which initiates the preovulatory surge of GnRH, has been shown to be involved in the synaptic plasticity demonstrated in various neuronal populations, including those in the arcuate nucleus [8,9]. Moreover, in rats, an increase in the expression of the molecular markers of synaptic remodelling is observed at the time of the surge in the preoptic area where most of the GnRH neuron cell bodies are found [10]. In sheep, changes in the expression of the polysialylated form of the neural cell adhesion molecule around the periphery of the GnRH cell bodies, associated with the seasonal changes in GnRH secretion, are indicative of potential plastic changes in this neural system linked with changes in secretory activity [11]. In addition, oestradiol and progesterone have been shown to modify the morphology and staining intensity of GnRH neurons within the ovine hypothalamus [12], suggesting that ovarian steroids could elicit plastic changes in GnRH neurons during the oestrous cycle. However, very little work has been done regarding neuronal organization at the level of the GnRH cell bodies during the oestrous cycle in the ewe.
The aim of this work was to study the number, distribution and potential contacts present between GnRH-containing cells in intact ewes under natural conditions, focusing particularly on the neuroanatomical organization of these neurons around the preovulatory surge when a high level of oestradiol is released by the growing follicles.
Results
Plasma hormones
Because there are some individual variations in the response to the induction of ovulation treatment (oestrus onset, preovulatory LH surge onset, etc.), an analysis of plasma hormone concentration was carried out to confirm the allocation of animals to experimental groups according to the characteristic hormonal level previously described [13]. In group 1, as illustrated in figure 1, progesterone and LH concentrations were low, characteristic of the late follicular phase. In group 2, progesterone remained low (less than 0.05 ng/ml) and a surge of LH was observed for 4 out 5 animals. Analysis of the LH profile concentrations showed that these animals were killed either during the ascending (n = 2) or descending (n = 2) part of the LH surge; for the remaining animal only a slight increase in LH secretion was observed at the end of the sampling period. In groups 3 and 4 a preovulatory surge of LH was clearly identified in all animals. These LH surges were followed by a clear increase in progesterone concentrations and the progesterone level was in the range of 2.5 to 4.5 at the end of the sampling period (except for one animal 1.9 ng/ml in group 4). Global analysis of plasma hormone concentrations at the last sampling time before animals were killed revealed significant differences between groups for LH (p < 0.01) and progesterone (p < 0.05). As shown in figure 1, the LH level was higher in group 2 than for any other group (p < 0.01), and progesterone concentrations were lower in groups 1 and 2 than in groups 3 and 4 (p < 0.01). For groups 1, 2, 3 and 4, mean levels ± SEM were respectively 1.07 ± 0.12, 28.20 ± 7.08, 0.56 ± 0.03, 0.65 ± 0.06 for LH and 0.05 ± 0.0, 0.05 ± 0.0, 2.74 ± 0.10, 3.62 ± 0.26 for progesterone. However, the ewes showing no clear LH surge (group 2) or a moderate increase in progesterone concentration (group 4) were considered as slightly different from any other animal of the same group and were not used for determining the number or relative clustering of GnRH neurons.
Four of the eight animals in experiment 2 were killed during the LH surge (2 during the ascending and 2 during the descending part). The 4 remaining animals were killed 4–6 hours after the end of the LH surge and were discarded from the study.
GnRH-immunoreactive neurons
Experiment 1
GnRH-containing neurons were distributed in the preoptic area (fig 2) as previously described [14]. There was some variation in the staining intensity in the same section, but it was usually strong. The black DAB/Nickel ammonium sulfate precipitate was distributed throughout the cytoplasm in the cell bodies and processes. The nucleus was never stained. GnRH-ir fibres were also observed around perikarya, presenting large varicosities. The distribution of GnRH cell bodies was homogenous between animals. GnRH-ir neurons were counted when the nucleus was clearly visible.
There was no difference in the total number of GnRH-ir neurons in the experimental groups (group 1: 291 ± 38, n = 5, group 2: 347 ± 41 n = 4, group 3: 345 ± 38 n = 5, group 4: 241 ± 75 n = 4) (fig 3A). However, the organization of the labelled neurons differed in the different experimental groups. Clusters of two or more GnRH-ir neurons in which there appeared to be close contacts between neuronal cell bodies (figs 4a,4b) were found in the preoptic area of all groups. However, the number of clusters and the proportion of identified neurons involved in clusters varied significantly between the experimental groups. These clusters (mainly pairs of neurons) were more numerous in groups 1 (43 ± 7) and 2 (41 ± 8) at the moment of the preovulatory surge than in groups 3 (28 ± 2) and 4 (25 ± 6) in the luteal phase (p < 0.05) (fig 3B).
The percentage of clusters was higher in group 1 (15.1 ± 1.2) than in groups 2 (11.8 ± 1.0), 3 (8.3 ± 0.4) and 4 (10.7 ± 0.9) (fig 3C).
Experiment 2
In the second experiment, four additional animals were studied during the GnRH surge, and the data compared with those of the group 2 ewes in the first experiment. As shown in table 1, there was no difference in the proportion of clusters of GnRH-ir neurons in the four animals sampled during the ascending phase of GnRH surge and the four sampled during the descending phase of the surge.
Discussion
Our observations indicate that the organization of GnRH-ir neurons was modified during the oestrous cycle, since more clusters were observed during the follicular phase. However, we did not observe any difference between the ascending and descending phases of LH release in group 2. Therefore, our data support the hypothesis that there are changes in communication between GnRH cell bodies prior to the initiation of the preovulatory GnRH surge.
The increase in the number of clusters was not mirrored by a parallel change in the total number of GnRH cell bodies. However, as the number of clusters was relatively small compared to the GnRH cell population (constituting 10% to 15%), the putative increase in the number of GnRH-ir neurons could have been masked due to the large variations in the number of labelled neurons between animals. An alternative hypothesis is that the number of GnRH neurons remains constant, and that the variations in the number of clusters is linked to variations in the peptide level, which are consistent with the variations in the staining intensity observed in all animals. In this case, the level of GnRH would be higher in clustered neurons than in isolated neurons at the moment of the preovulatory surge, and therefore neuronal communication would be more efficient in clustered neurons. The percentage of clusters increased only in group 1, while the difference between the number of clusters in groups 2, 3 and 4 was not statistically significant. This special morphological organization may constitute an anatomical support for the synchronization of GnRH neuronal activity needed to induce the GnRH preovulatory surge.
Clusters of GnRH-ir cell bodies have been previously described in sheep [15] (Lehman et al, 1986), rats and monkeys [16]. In rats they represented only 2–7% of the GnRH neuronal population [16]. In our study, the proportion of clusters was similar (10–15%) to that observed in monkeys (3–15%). However, contrary to our observation, the numbers did not change with the different hormonal conditions in the monkey [16]. This might be related to the distribution of GnRH cell bodies which differs between monkeys and sheep [17], and it is possible that the increase of clusters observed in our experiments was related to a specific GnRH neuronal population involved in the GnRH surge secretion. Indeed, Fos expression at the time of the preovulatory LH surge has been shown to be expressed in a subset of GnRH neurons in rats [18].
A neuronal re-organization of GnRH-containing neurons has also been previously described in anoestrous ewes where the neurons are ensheathed by glial processes which decrease the number of axo-somatic synaptic contacts [19]. In this latter situation, photoperiod would be the major signal to induce such a modification. Another example of the relationship between synaptic afferents and GnRH secretion has been described in the monkey where the increase of GnRH activity at puberty correlates with the decrease in synaptic afferents on GnRH-containing neurons [20]. This morphological regulation of GnRH-containing neurons, through synaptic contacts or perikaryal apposition, involves glial processes, since in both sheep and monkeys these neurons are ensheathed by numerous glial processes [19,21].
Steroids, mainly oestradiol, are the most potent regulators in the control of GnRH neuronal activity. Therefore, we may hypothesize that this "plasticity" results from an oestradiol effect, as has been shown in the monkey where oestradiol modulates the number of synaptic contacts on GnRH neurons [22]. In rats and monkeys, numerous studies have demonstrated that oestrogens induce synaptic plasticity in the control of gonadotropin secretion (rat: [23]; monkeys: [24]). In our study, the highest number of clusters was found during the preovulatory surge which occurred about 24 hours after the expected oestradiol increase, this delay being consistent with an oestradiol effect.
There was no difference in the number of labelled neurons in the experimental groups, and this observation may indicate that the level of GnRH-immunoreactivity was stable in the neurons of the preoptic area. It has previously been demonstrated in the ewe that GnRH messenger ribonucleic acid expression changes before the onset of the oestradiol-induced luteinizing hormone surge [25], and that the staining intensity of GnRH-ir perikarya increases after oestradiol treatment, but that the number of labelled neurons does not change [12,26]. In addition, the variations of LH secretion induced by progesterone withdrawal are not linked to a variation in GnRH mRNA [27]. Unlike the apparent stability of the GnRH level in the perikarya, GnRH-immunoreactivity decreases in the median eminence after ovulation [28]. This information could indicate that variations in GnRH secretion arise from its release from the median eminence terminals without variations in peptides in the perikarya.
Conclusions
In conclusion, we have demonstrated that the morphological organization of the GnRH neurons of the preoptic area is modified during the oestrous cycle, although the overall number of GnRH-ir neurons does not change. During the preovulatory surge, the GnRH neurons are more frequently found in clusters, and the percentage of clusters is significantly higher immediately prior to the preovulatory LH surge. Actual contacts between GnRH-ir neurons cannot be determined using light microscopy, but can only be demonstrated using electron microscopy. Because oestradiol is the most powerful regulator of GnRH activity during the oestrous cycle, we can hypothesize that this plasticity may be induced by steroids. The role of this plasticity remains to be demonstrated, but it could increase interneuronal communication during the preovulatory surge.
Methods
All experimental procedures were carried out in accordance with authorisation A37801 of the French Ministry of Agriculture.
Animals
Intact adult Ile de France ewes (n = 28) from the laboratory flock, maintained under natural photoperiod were used. Two experiments were performed (one year apart) during the breeding season (between November 15th and December 15th). Animals were fed daily with hay, straw and corn, and water was available ad libitum. All of the ewes had lambed at least once.
Experimental design
Experiment N° 1
Animals (n = 20) were killed at specific time points in the oestrous cycle. In order to synchronize oestrous cycles, animals were treated with an intravaginal progesterone-releasing device (CIDR InterAg, Hamilton, New Zealand) for 14 days. Following removal of the progesterone device, animals were treated (i.m.) with 500 UI of Pregnant Mare Serum Gonadotropin (PMSG), a treatment known to induce the LH surge and ovulation in sheep (for review see [29]). It has been shown that oestrous behaviour in this breed precedes the LH surge by 7.0 ± 1.6 hours [30]. Therefore, oestrous behaviour was recorded for each ewe every 6 hours, from 18 hours after PMSG administration until the animal was observed to be in oestrus using a ram wearing an apron. The animals were then allocated to four groups:
Group 1: Animals killed 1–2 hours after oestrus, i.e. before the LH surge onset, (n = 5)
Group 2: Animals killed 8–12 hours after oestrus, i.e. during the preovulatory LH surge, (n = 5)
Group 3: Animals killed 8 days after the preovulatory LH surge, (n = 5)
Group 4: Animals killed 15 days after the preovulatory LH surge, (n = 5)
Experiment N° 2
Analysis of LH secretion (see paragraph below) revealed that animals in group 2 in experiment 1 were killed either in the ascending (n = 2) or descending (n = 2) phase of the LH surge. Therefore, in order to increase the N for each sub-group, 8 animals were synchronised as previously described (one year later, during the breeding season) and killed 12 and 20 hours (n = 4) after oestrus.
For both experiments, blood samples were collected by venepuncture every 2 hours from 18 hours after PMSG administration until sacrifice (groups 1 and 2) and daily thereafter (groups 3 and 4).
Hormone assays
Plasma samples were assayed in duplicate for LH with 100 μl aliquots of plasma using a previously described RIA method [31]. All samples from one experiment were run in a single assay. Intra-assay coefficient of variation averaged 9% and assay sensitivity was 0.16 ± 0.05 ng/ml (4 assays) of standard 1051-CY-LH (i.e. 0.31 ng/ml of NIH-LH-S1). Progesterone was determined in a single assay after hexane extraction of 100 μl of plasma [32]. The sensitivity was 5 pg/tube and the intra-assay CV 10%.
Immunohistochemistry
A solution of heparin (25,000 units) was injected iv 10 min before decapitation. Both carotids were catheterised and the head was perfused with 2 litres of 1% sodium nitrite followed by 4 litres of 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4). After perfusion, the brain was quickly removed and a block containing the whole preoptic area was isolated and post-fixed for 2 hours at +4°C in the same fixative. The blocks were immersed in 30% sucrose containing 0.1% sodium azide at +4°C for 5 days.
Serial 40 μm coronal sections were obtained using a freezing microtome and stored at +4°C in PBS containing 0.1% sodium azide. Every fifth section was stained for GnRH immunoreactivity.
Sections were incubated for 2 hours at +4°C in PBS containing 0.3% triton X100 and 1% H2O2 and then preincubated for 30 min at room temperature in blocking solution (PBS, 6% normal sheep serum, 0.3% triton, 0.1% sodium azide). Sections were exposed to GnRH primary antiserum at a dilution of 1/10,000 in PBS containing 0.3% triton, 0.2% HSA, 0.1% sodium azide for 4 days at +4°C. After washing in PBS, sections were incubated for 3 hours at room temperature in the secondary antibody (1/500 with PBS containing 0.2% BSA). After washing in PBS, sections were incubated in rabbit peroxidase-antiperoxidase complex diluted 1/10,000 in PBS containing 0.2% BSA overnight at +4°C. After washing twice in PBS and in Tris buffer (0.05 M pH 7.6), the sections were reacted with 3,3-diaminobenzidine tetrahydrochloride (0.02%) and nickel ammonium sulfate (0.25%) in the same Tris buffer containing 0.0025% H2O2 for 20 min. The reactions were stopped by rinsing the sections with Tris buffer. Sections were mounted onto gelatine-coated slides, dried, counterstained with a solution of neutral red for 5 min and mounted with a cover-slide using Depex.
The characteristics and the specificity of the GnRH antibody were as previously described [14].
Data analysis
The LH surge was assumed to start when LH concentration exceeded 6 ng/ml of plasma, i.e. about twice the maximum value of a pulse during the follicular phase for this breed and for this LH assay. For hormone values, a global comparison was carried out using a non-parametric ANOVA with exact general score test, and comparison between groups was carried out using the non-parametric exact permutation test for independent samples (Stat Exact Software, Cytel Software Corporation, Cambridge, MA, USA).
The first observation of the sections allowed the immunoreactive area surrounding the OVLT to be determined (fig. 2). We selected two sections where the size of the OVLT was maximum in the third ventricle, and the two rostral and caudal sections were studied. A total of six sections at 160 μm intervals were studied for each ewe. In each section, all immunoreactive neurons for GnRH were counted using a light microscope, and groups of closely-associated neurons were noted. Total number of cells and number of clusters were analyzed by a 2-way (group and section number) ANOVA (SuperANOVA, Abacus concept, California) followed by a student-Newman-keuls post-hoc test for two by two comparisons. The percentage of cells forming clusters was calculated for each animal. It was analyzed by a one-way (group) ANOVA after arcsin transformation followed by a student-Newman-keuls post-hoc test for two by two comparisons. The results are expressed as means ± standard error of the means.
Authors' contribution
MB performed the immunohistochemical reaction and counted the neurones, AC and YT conceived the study, and participated in its design, coordination and in the collection of biological samples. BM participated in the final statistical analysis of results and corrected the manuscript.
Acknowledgements
The authors wish to thank Francis Dupont and the shepherds for animal care, and the staff of the RIA laboratory in Nouzilly for performing the LH and progesterone assays.
Figures and Tables
Figure 1 Top: Plasmatic hormone concentrations (Mean ± SEM) for LH (black bars) or progesterone (open bar) at the last sampling time before animals were sacrificed. Different letters indicate differences between groups (P < 0.05). Bottom: Individual plasmatic levels of LH (black circles) and progesterone (open squares) in ewes representative of each experimental group.
Figure 2 GnRH-immunoreactive neurons in the preoptic area where the highest number of GnRH-immunoreactive neurons was found. Scale bar: 400 μm
Figure 3 Mean number (±SEM) of single (A), clustered (B) and percentage of clusters (C) of GnRH-immunoreactive neurons counted in the preoptic area of ewes in the four experimental groups of the first experiment. Different letters indicate significant differences (p < 0.05). On histogram B, groups 1 and 2, like groups 3 and 4, are not statistically different, but groups 1 and 2, and groups 3 and 4, are statistically different; on histogram C group 1 is statistically higher than the other groups.
Figure 4 GnRH-immunoreactive neurons in the preoptic area of ewes. a and b represent two examples of clusters of GnRH-immunoractive neurons, white arrows indicate putative contacts between the neurons. Scale bar: a, b – 40 μm
Table 1 Mean number of isolated or clustered GnRH neurons counted during and after the preovulatory LH surge. Individual values are indicated for each animal during the first (Exp I) and second experiment (Exp II).
Ascending phase of the surge Descending phase of the surge
Exp I Exp II Exp I Exp II
Ewe identification n° 376 23 18 61 280 106 47 59
Single GnRH neurons 323 306 383 352 331 427 354 463
Clusters of GnRH neurons 34 47 79 85 34 48 67 103
% 10 15 20 23 10 11 19 22
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| 15555074 | PMC535905 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Nov 22; 5:46 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-46 | oa_comm |
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BMC Mol BiolBMC Molecular Biology1471-2199BioMed Central 1471-2199-5-181547390910.1186/1471-2199-5-18Research ArticleTranscriptional oscillation of canonical clock genes in mouse peripheral tissues Yamamoto Takuro [email protected] Yasukazu [email protected] Haruhiko [email protected] Makoto [email protected] Takayoshi [email protected] Toru [email protected] Osaka Bioscience Institute, Suita, Osaka 565-0874, Japan2 Life Science Laboratory, Materials Laboratories, Sony Corporation, Shinagawa, Tokyo 144-0001, Japan3 Graduate School of Medicine, Osaka City University, Osaka 545-8585, Japan2004 9 10 2004 5 18 18 16 6 2004 9 10 2004 Copyright © 2004 Yamamoto et al; licensee BioMed Central Ltd.2004Yamamoto 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
The circadian rhythm of about 24 hours is a fundamental physiological function observed in almost all organisms from prokaryotes to humans. Identification of clock genes has allowed us to study the molecular bases for circadian behaviors and temporal physiological processes such as hormonal secretion, and has prompted the idea that molecular clocks reside not only in a central pacemaker, the suprachiasmatic nuclei (SCN) of hypothalamus in mammals, but also in peripheral tissues, even in immortalized cells. Furthermore, previous molecular dissection revealed that the mechanism of circadian oscillation at a molecular level is based on transcriptional regulation of clock and clock-controlled genes.
Results
We systematically analyzed the mRNA expression of clock and clock-controlled genes in mouse peripheral tissues. Eight genes (mBmal1, mNpas2, mRev-erbα, mDbp, mRev-erbβ, mPer3, mPer1 and mPer2; given in the temporal order of the rhythm peak) showed robust circadian expressions of mRNAs in all tissues except testis, suggesting that these genes are core molecules of the molecular biological clock. The bioinformatics analysis revealed that these genes have one or a combination of 3 transcriptional elements (RORE, DBPE, and E-box), which are conserved among human, mouse, and rat genome sequences, and indicated that these 3 elements may be responsible for the biological timing of expression of canonical clock genes.
Conclusions
The observation of oscillatory profiles of canonical clock genes is not only useful for physiological and pathological examination of the circadian clock in various organs but also important for systematic understanding of transcriptional regulation on a genome-wide basis. Our finding of the oscillatory expression of canonical clock genes with a temporal order provides us an interesting hypothesis, that cyclic timing of all clock and clock-controlled genes may be dependent on several transcriptional elements including 3 known elements, E-box, RORE, and DBPE.
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Background
The circadian rhythm of about 24 hours is a fundamental physiological function observed in almost all organisms from prokaryotes to humans. Circadian rhythms have been known to be generated in pacemaker cells, the suprachiasmatic nuclei (SCN) of hypothalamus in mammals, and entrained by environmental cues, such as light, temperature, noise, feeding or social cues, whereas a recent analysis using mPer2luciferase knockin mice has demonstrated that peripheral tissues express self-sustained circadian oscillations [1]. The output of circadian oscillation appears as locomotive activity, hormonal secretion, the sleep-wake cycle, and many other physiological functions. Disruption of the circadian rhythms has been associated with various kinds of diseases, such as cardiovascular diseases, psychiatric diseases and cancer in humans [2-6]. Identification of clock genes has allowed study of the molecular bases for circadian behaviors and temporal physiological processes and has prompted the idea that molecular clocks reside not only in a central pacemaker, but also in peripheral tissues, even in immortalized cells [2,3,6]. Furthermore, previous molecular dissection revealed that the mechanism of circadian oscillation at a molecular level is based on transcriptional regulation of clock and clock-controlled genes, which consists of interwoven positive and negative feedback loops [2,7-10].
There is a distinct connection between genes and behaviors in circadian rhythms, which is conserved from fly or other lower organisms to humans [6,8]. The Drosophila period mutants, originally identified as a circadian mutant brought us the first clock gene, period [8,11], while a point mutation of hPer2 was recently shown to cause a familial advanced sleep phase syndrome [12]. As described above, circadian rhythms rely on a negative feedback loop in gene expression that involves a limited number of clock genes. Recent molecular dissection has increased our understanding of the molecular nature of the transcriptional regulation of some clock genes. The circadian phenotypes at the cellular level may be represented as temporal mRNA expression. Global gene expression profiling using microarrays has led to the discovery of many circadian-regulated genes, but there is only a minor overlap of cycling transcripts between tissues [10,13,14]. Thus, circadian rhythms are an appropriate study target for systems biology.
In this study, we systematically examined the mRNA expression of common circadian-regulated genes in several mouse peripheral tissues and made oscillatory profiles of canonical clock genes. Moreover, by bioinformatics, we identified 3 clock elements for circadian transcription (E-box, RORE, DBPE). These 3 elements and their combination would suffice to explain the biological timing of expression of these clock and clock-controlled genes.
Results and discussion
To examine the circadian expression of mouse clock and clock-related genes in peripheral tissues, we performed the quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR) method on mRNAs from 7 different mouse peripheral tissues (heart, lung, liver, stomach, spleen, kidney, and testis; Fig. 1). After entrainment of housed mice for 2 weeks under a light-dark (LD) cycle, samples were collected every 4 hr starting at circadian time (CT) 0 (n = 3 at each time point) in the third dark-dark (DD) cycle. Out of 14 mouse genes examined, 8 genes (mBmal1, mNpas2, mRev-erbα, mDbp, mRev-erbβ, mPer3, mPer1 and mPer2; given in the temporal order of the rhythm peak; see also Fig. 2) showed robust circadian of mRNA expression in all tissues except the testis. These genes should thus be considered to be core molecules of the circadian clock. Circadian mRNA expression patterns were similar in each tissue with the exception of testis, where weak or no rhythm was observed. This common pattern of the rhythm throughout many peripheral tissues implies that there may exist a universal mechanism for resetting the peripheral clock. The peak transcript level of each circadian rhythm was as follows: mBmal1 and mNpas2, in subjective night at CT20-CT0; mRev-erbα, in subjective day at CT4-8; mDbp and mRev-erbβ, at CT8; mPer3, at CT8-12; mPer1, at CT12; and mPer2, at CT12-16 (Figs. 1, 2, and 4). In the peripheral tissues mRNA peaks occurred approximately 4 hr later than those in the central pacemaker, SCN [15-20]. In the testis, 4 genes (mBmal1, mNpas2, mRev-erbα, and mDbp) showed weak rhythms of their mRNA expression, while no other genes, including the mPer family, showed any clear oscillation. Surprisingly, the expression of mPer1 transcripts in the testis, which did not show a circadian rhythm, was substantially higher than other tissues, and exceeded RNA expression of other genes studied in the testis. This is consistent with data recently reported [21-23]; although a previous report indicated circadian rhythm of mPer1 in the testis [20]. These findings suggest that mPer1 may play an alternative role in the testis, including developmental regulation during spermatogenesis. The rhythm of mCry1 mRNA expression was obviously circadian (peaking at CT16-20, with a trough at CT4-8) except in the testis, but the peak-trough amplitude was relatively smaller than that of the above genes. mCry2 and mClock RNA levels seemed to be rhythmic except in the testis, but the rhythm was rather weak and not clearly circadian. No circadian rhythms were observed in the remaining 3 genes examined, i.e. mCKI-δ, mCKI-ε, and mTim. Despite a central role in Drosophila for timeless (dTim), the mammalian homologue (mTim) was originally found to show weak or no rhythm in the SCN [24-26]; and its functional role in mammalian clocks remains controversial [27,28].
Figure 1 Temporal mRNA expression of clock and clock-related genes in mouse peripheral tissues Abscissa presents time at CT (circadian time); and ordinate, mRNA amounts. The relative levels of each RNA were normalized to the corresponding G3-PDH RNA levels. The maximum RNA amount was set to 100. Data are presented as the mean ± SE of triplicate samples.
Figure 2 Circadian mRNA expression of canonical clock genes in mouse peripheral tissues The maximum RNA amount in a 24-h period is indicated by the darkest red (100), while no RNA (0) shown by white. The depth of the color corresponds to the RNA amount.
Figure 4 Diagram representing the relationship between 3 transcriptional elements and the peak mRNA expression of canonical clock genes The peak of Per3 mRNA expression is at CT8-12, and its regulatory region includes only conserved DBPEs (yellow) among the 3 elements. The peak of Per2 mRNA is at CT12-16, and its promoter region includes only an E-box (green). The peaks of Bmal1 and Npas2 mRNA are at CT20-0, and their regulatory regions contain only ROREs (blue). The peaks for the other clock genes, which contain 2 or 3 elements, are placed as indicated. The bar at the top represents light (gray) and dark (black) cycles.
As described above, the expression of 8 genes fluctuated in an overt circadian fashion; and so we aligned them in the order of the peak of their oscillatory phase (Fig. 2). Among them, the transcriptional oscillation of 2 representative clock genes, Bmal1 and Per2, was examined by using the real-time luciferase reporter assay. NIH3T3 cells were transfected with the hBmal1-Luc or mPer2-Luc construct and then stimulated with a high concentration of serum. After the serum shock, in the presence of luciferin, light emission was measured and integrated for 1 min at intervals of 15 min. Both promoters fused to luciferase showed circadian rhythms (Fig. 3). The phase of Bmal1 oscillation in cultured cells was almost the opposite of that of Per2, which is consistent with data of mRNA expression in mouse peripheral tissues obtained by real-time RT-PCR (Fig. 2). This result indicates that the promoter regions used in the real-time luciferase reporter assay are sufficient for producing circadian transcriptional oscillation. The promoter analyses of several clock genes have been reported, as described below.
Figure 3 Transcriptional oscillation of Bmal1 and Per2 Transcriptional oscillation of Bmal1 (red) and Per2 (blue) was monitored by using a cell culture-based luminescence reporter assay. NIH3T3 cells were transfected with the hBmal1 -Luc or mPer2 -Luc constructs and then stimulated with a high concentration of serum. After the serum shock, in the presence of luciferin, light emission was measured and integrated for 1 min at intervals of 15 min. Ordinate and abscissa represent relative counts and time after serum shock, respectively. The peak of the count was set to 1. 1440 minutes = 1 day.
Figure 5 Table 1. Three transcriptional elements (RORE, E-box, and DBPE) conserved among the sequences of 3 species (human, mouse, and rat) Numbers in parenthesis indicate the number from the transcriptional start site. The sequences of the binding elements are in bold type, and the sequences matching the consensus sequence are underlined. H: human, M: mouse, R: rat.
A molecular mechanism of canonical clock genes is based on transcriptional regulation via interlocked feedback and/or feed-forward loops [2,4,7,8]. One example of the regulation of a known characterized gene in mammals is that of Per1. The transcription of Per1 is activated by binding of the CLOCK/BMAL1 hetero-complex, both members of which are bHLH-PAS (basic helix-loop-helix-Per-Arnt-Sim) proteins, to the E-boxes in the promoter region of Per1 [29]. The translated PER1 is posttranslationally modified by CKI-ε [30] and, together with other clock proteins such as CRYs [31], is returned to the nucleus to suppress its own transactivation, resulting in closure of the PER1 loop. E-box elements are also known to be essential for transcriptional regulation of many clock-controlled output genes including the vasopression genes [32]. On the other hand, one of the positive elements, BMAL1, whose mRNA expression is cycled antiphase to Pers, as described above, forms another loop [33,34]. The orphan nuclear receptors RORα and REV-ERBα regulate circadian transcription positively and negatively, respectively, through ROR/REV-ERB elements (ROREs) in the promoter region of Bmal1 [16,35]. Moreover, an in silico search identified these 2 ROREs in the promoter region of Npas2 (Table 1, see figure 5), the expression pattern of which was very similar to that of Bmal1. These findings gave us the idea that the circadian pattern of RNA expression might be dependent on transcriptional regulation by specific transcription factors.
Using the NCBI database and Celera Database System, we systematically searched for the above 2 elements and another clock element, a DBP-binding element (DBPE), described below. These elements are conserved among human, mouse, and rat genome sequences in the regions 9-kb upstream and 5-kb downstream of the transcription start site (Table 1, see figure 5). Intriguingly, the transcriptional elements corresponded to the cyclic pattern of the clock genes shown in Figure 4. Bmal1 and Npas2, the circadian peaks of which were both in subjective night at CT20-24, included ROREs in their promoters, as described above. In the promoter of Per1 and Per2, the peaks of which were in subjective day at CT12-16, E-boxes were found. In fact, the conserved E-box in Per2 is not a typical E-box of the molecular clock, CACGTG, but an atypical element, CACGTT. Compared with Per1, which contains 5 conserved E-boxes, Per2 may have other unknown factors and elements responsible for its robust transcriptional oscillation. Per1, whose peak was a bit earlier than that of Per2, has another element, a DBPE, in its promoter region, in addition to the 5 E-boxes well studied in vitro [36]. Per3, the peak of which was even earlier at CT8-12, did not have conserved E-boxes but instead contained DBPEs in its gene. Three genes, Rev-erbα, Dbp, and Rev-erbβ, the peaks of which were between those of Bmal1 and Per, had a mixed combination of the elements. The Rev-erbα genome sequence included 1 RORE, 1 DBPE, and 5 E-boxes, whereas Rev-erbβ included all 3 elements only in the mouse genome sequence. Dbp contained 2 ROREs and 2 E-boxes in each genome. Among the elements described above, some of them in (Bmal1, Dbp, and Per1) were experimentally studied and confirmed [16,29,35-38].
Transcripts of 8 genes (mBmal1, mNpas2, mRev-erbα, mDbp, mRev-erbβ, mPer3, mPer1, and mPer2) showed a robust circadian rhythm in different peripheral tissues (see Fig. 1). The amount of mRNA in the trough was nearly zero and the peak-trough amplitude of these genes was clearly higher than that of the others examined. Thus, in terms of mRNA expression among the canonical clock genes examined, these 8 genes likely constitute the core molecules of a molecular circadian clock. The expression timing in a 24-h period appears to be conveyed through 3 kinds of sequence elements bound by specific transcription factors (see Fig. 4). Recent genome-wide analyses using microarrays revealed that many genes (about 10 % of the total number of genes studied) oscillated but only several tens of common genes overlapped between two tissues examined [13,14]. Among the core candidate genes with similar circadian regulation in those 2 tissues, we examined the circadian transcription of 15 candidate genes besides the known clock genes, but could not find genes with oscillatory behavior in different peripheral tissues comparable to that in the 8 genes described above. The 8 genes studied here may approximate the entirety of the core oscillatory genes in the genome. If so, the 3 elements described here may be sufficient for explaining the biological timing of mRNA expression of clock genes. However, our preliminary results showed that an atypical E-box in Per2 promoter may be insufficient for full transcriptional oscillation (Akashi and Takumi, unpublished data). Further detailed studies of each promoter, combined with systematic analyses using microarrays and real-time RT-PCR, will give us a more detailed comprehension of the intertwined positive and negative regulatory loops of molecular biological clocks.
Conclusions
The current study has clarified the detailed circadian expression of mRNAs for clock and clock-related genes in different peripheral tissues of the mouse. The observation of oscillatory profiles of canonical clock genes is not only useful for physiological and pathological examination of the circadian clock in various organs but also important for systematic understanding of transcriptional regulation on a genome-wide basis. Our finding of the oscillatory expression of canonical clock genes in a temporal order provides us an interesting hypothesis, that cyclic timing of all clock and clock-controlled genes may be dependent on several transcriptional elements including 3 known elements, E-box, RORE, and DBPE.
Methods
Animals
Male Balb/c mice purchased 5 weeks postpartum from Japan SLC (Hamamatsu, Japan), were exposed to 2 weeks of light-dark (LD) cycles and then kept in complete darkness as a continuation of the dark phase of the last LD cycle. mRNA expression was examined in the third dark-dark (DD) cycle. All protocols of experiments using animals in this study were approved by the OBI (Osaka Bioscience Institute) Animal Research Committee.
Quantitative RT-PCR
Real-time quantitative RT-PCR was performed by using an ABI PRISM 7000 (Applied Biosystems). The PCR primers were designed with Primer Express software (Applied Biosystems), and the sequences of the forward and reverse primers were as follow: mPer1 FW: CAG GCT AAC CAG GAA TAT TAC CAG C, mPer1 RV: CAC AGC CAC AGA GAA GGT GTC CTG G; mPer2 FW: GGC TTC ACC ATG CCT GTT GT, mPer2 RV: GGA GTT ATT TCG GAG GCA AGT GT; mPer3 FW: CTG CTC CAA CTC AGC TTC CTT T, mPer3 RV: TTA GAC AGC AAG GCT CTG GTT CT; mNpas2 FW: GTA TGC ACA GAG CCA AGT GAT GTT, mNpas2 RV: TGC TCA CTG TGC AGA GAT GTT G; mDbp FW: AAT GAC CTT TGA ACC TGA TCC CGC T, mDbp RV: GCT CCA GTA CTT CTC ATC CTT CTG T; mBmal1 FW: GCA GTG CCA CTG ACT ACC AAG A, mBmal1 RV: TCC TGG ACA TTG CAT TGC AT; mRev-erbα FW: CGT TCG CAT CAA TCG CAA CC, mRev-erbα RV: GAT GTG GAG TAG GTG AGG TC; mRev-erbβ FW: ACG GAT TCC CAG GAA CAT GG, mRev-erbβ RV: CCT CCA GTG TTG CAC AGG TA; G3-PDH FW: ACG GGA AGC TCA CTG GCA TGG CCT T, G3-PDH RV: CAT GAG GTC CAC CAC CCT GTT GCT G; mCry1 FW: CCC AGG CTT TTC AAG GAA TGG AAC A, mCry1 RV: TCT CAT CAT GGT CAT CAG ACA GAG G; mCry2 FW: GGG ACT CTG TCT ATT GGC ATC TG, mCry2 RV: GTC ACT CTA GCC CGC TTG GT; mCKIε FW: GGA TGT GAA GCC CGA CAA CTT, mCKIε RV: TCT CGA CGG CTT TGC TCA AT; mCKIδ FW: CCA GCC TGG AAG ACC TGT TC, mCKIδ RV: TGG CCA GCC CAA AGT CAA; mClock FW: CCT ATC CTA CCT TGG CCA CAC A, mClock RV: TCC CGT GGA GCA ACC TAG AT; mTim FW: ACA TGT GGG CAA TGG CTT, mTim RV: CTG CTC CAC AAA GTG AAA GGT. Specificity of gene amplification was confirmed by measuring the size and purity of the PCR product by gel electrophoresis, and by analyzing the dissociation curve with ABI PRISM 7000 SDS software (Applied Biosystems). For a 25-μl PCR reaction, 50 ng cDNA template was mixed with the forward and reverse primers to a final concentration of 300 nM each and 12.5 μl of 2x SYBR Green PCR Master Mix (Applied Biosystems). The reaction was first incubated at 50°C for 2 min, then at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min. Each gene-specific PCR was performed in triplicate. G3-PDH primers were used as the control.
Real-time luciferase reporter assay
NIH3T3 cells were cultured, transfected with hBmal1 -Luc or mPer2 -Luc, and incubated for 24 hours. The medium was then exchanged for serum-rich medium (DMEM, supplemented with 50 % serum). Two hours later this medium was replaced with normal culture medium. In the presence of 0.1 mM luciferin, light emission was measured and integrated for 1 min at intervals of 15 min, with a photomultiplier tube (Hamamatsu Photonics).
In silico search
The sequences were downloaded from the Celera Database System and the NCBI Gene database. Each gene sequence spanning from 9 kb upstream to 4 kb downstream of the transcription start site was examined in each database. Multiple sequence alignments of these sequences for each gene were obtained by Clustalw version 1.83 with default parameters. The binding elements were then searched from these alignments using a pattern finding tool, fuzznuc, with the following consensus sequences allowing for a 1-base mismatch:
DBPE: [GA]T[GT]A[TC]GTAA[TC]
E-Box: CACGTG
RORE: [AT]A[AT]NT[AG]GGTCA
The accession numbers used and the sequence numbers analyzed are as follow: Bmal1; Human, NT_009237.16, 12054318–12069318, Mouse, NT_081129.1, 107781–122781, Rat, NW_047562.1, 13774073–13789073, Npas2; Human, hCG27614, 95632226–65646226, Mouse, mCG8437, 35980102–35994102, Rat, rCT22431, 39204499–39218499, Rev-erbα; Human, hCG93862, 34926094–34912094, Mouse, mCG15360, 105438925–105424925, Rat, rCG33292, 82492796–82478796, Dbp; Human, NT_011109.15, c21417778–21402778, Mouse, NT_078442.1, 59711–74711, Rat, NW_047558.1, 5120734–5135734, Per3; Human, NT_021937.16, 1962822–1977822, Mouse, NT_039268.2, c4331528–4316528, Rat, NW_047727.1, c8016956–8001956, Per1; Human, NT_010718.14, c6905708–6890708, Moues, NT_039515.2, 65661216–65676216, Rat, rCG34390, 52960430–52974430, Per2; Human, NT_005120.14, c5136562–5121562, Mouse, NT_039173.2, c5833757–5818757, Rat, NW_047817.1, c6827703–6812703.
List of Abbreviations used
SCN, suprachiasmatic nuclei; RT-PCR, reverse transcription-polymerase chain reaction; CT, circadian time; LD, light-dark; DD, dark-dark; bHLH-PAS, basic helix-loop-helix-Per-Arnt-Sim; RORE, ROR/REV-ERB element; DBPE, DBP-binding element; NCBI, the National Center for Biotechnology Information.
Authors' contributions
TY and YN carried out the molecular biology studies. HS carried out the in silico study. MA carried out the real-time luciferase reporter assay. TM participated in the coordination, and provided financial support. TT conceived of the study, participated in its design and coordination, and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Setsuko Tsuboi, Chiaki Matsubara, Yoko Sakakida, and Dan Trcka for their technical assistance, Paul Burke for his review of the manuscript, and acknowledge Yasuhiro Sakamoto for his administrative assistance. This work was supported in part by a research grant from MEXT. The support of fellowships from the Japan Society for the Promotion of Science (M.A.) is also acknowledged.
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| 15473909 | PMC535906 | CC BY | 2021-01-04 16:48:01 | no | BMC Mol Biol. 2004 Oct 9; 5:18 | utf-8 | BMC Mol Biol | 2,004 | 10.1186/1471-2199-5-18 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-521557163110.1186/1471-2148-4-52Research ArticleHost resistance does not explain variation in incidence of male-killing bacteria in Drosophila bifasciata Veneti Zoe [email protected] Masanori J [email protected] Gregory DD [email protected] Department of Biology, University College London, 4 Stephenson Way, London NW1 2HE, UK2 Institute of Low Temperature Science, Hokkaido University, N19 W8, Kita-ku, Sapporo 060-0819, Japan2004 30 11 2004 4 52 52 14 9 2004 30 11 2004 Copyright © 2004 Veneti et al; licensee BioMed Central Ltd.2004Veneti 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
Selfish genetic elements that distort the sex ratio are found widely. Notwithstanding the number of records of sex ratio distorters, their incidence is poorly understood. Two factors can prevent a sex ratio distorter from invading: inability of the sex ratio distorter to function (failure of mechanism or transmission), and lack of drive if they do function (inappropriate ecology for invasion). There has been no test to date on factors causing variation in the incidence of sex ratio distorting cytoplasmic bacteria. We therefore examined whether absence of the male-killing Wolbachia infection in D. bifasciata in Hokkaido island of Japan, in contrast to the presence of infection on the proximal island of Honshu, was associated with failure of the infection to function properly on the Hokkaido genetic background.
Results
The male-killer both transmitted and functioned well following introgression to each of 24 independent isofemale inbred lines carrying Hokkaido genetic backgrounds. This was maintained even under stringent conditions of temperature. We therefore reject the hypothesis that absence of infection is due to its inability to kill males and transmit on the Hokkaido genetic background. Further trap data indicates that D. bifasciata may occur at different densities in Hokkaido and Honshu populations, giving some credence to the idea that ecological differentiation could be important.
Conclusions
The absence of the infection from the Hokkaido population is not caused by failure of the male-killer to function on the Hokkaido genetic background.
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Background
Selfish genetic elements that distort the sex ratio of their host are known widely in arthropods [1]. Despite over 70 years of research, we still do not fully understand the factors that dictate their presence or absence in different species in the field, nor the correlated question as to the factors causing variation in their frequency over geographical space within a species. A good approach to this problem is to examine the causes of variation within species, and in particular to identify the factors contributing to absence of elements from some populations in species known to bear the element in other areas.
Factors causing variation in prevalence/incidence over space may be either ecological or associated with differences in host genetic constitution. For instance, in the case of X chromosome meiotic drive in Drosophila pseudoobscura, frequency declines at both high latitude and high altitude. This is not associated with resistance to the action of the driver (no resistance is known), and variation in rates of multiple mating is suspected as a cause [2]. In contrast, in the case of X chromosome drive in D. subobscura, X drive is present in North Africa and absent in Europe. Here, absence of X drive is associated with the less efficient function of X drive on the European genetic background [3].
This study pertains to the factors affecting the incidence of male-killing bacteria. These bacteria pass from a female to her progeny, and kill any males they enter. Male-killers are common in insects, but an appreciation of the factors underlying their incidence is lacking [4]. In the first place, host genetic factors may affect the ability of a male-killer to transmit or function. Within Drosophila prosaltans, for instance, there is intra-population host genetic variation in refractoriness to male-killer action/transmission [5]. Thus, it is logical to conjecture that a male-killer can be absent from a population because the host has evolved resistance to its action or transmission. In the second place, presence/absence of infection can be determined by ecological, environmental or genetic variation that influences the benefit of male-killing to the bacterium [6]. For instance, laboratory studies by Jaenike et al. [7] have demonstrated that the number of females ovipositing within a patch is a key determinant of invasion success.
In this paper, we examine the factors that could cause incidence variation for the male-killing Wolbachia in Drosophila bifasciata in Japan. Drosophila bifasciata feeds on sap fluxes in deciduous forests, and sampling across 10 populations within Honshu island in Japan revealed a relatively constant frequency of infection, with between 5 and 7% of females infected with the male-killer [8]. The male-killer is a strain of Wolbachia still present on Honshu to this day [9].
In contrast to collections from Honshu, past surveys across Hokkaido, the North island of Japan, indicated flies in this area are not infected with the male-killer, despite the relative proximity of the sites to the infected populations in the Northern most sites in Honshu. In total, 559 flies from six locations within Hokkaido were tested, with no evidence of sex ratio distortion in any case [8]. We can be almost certain that the absence of infection is not due to the infection never arriving on this island. First, infection otherwise has a worldwide distribution, being found in Italian D. bifasciata populations [10]. Second, the 5 km wide Tsugara Straits between Honshu and Hokkaido may limit gene flow (and hence support differentiation), but are very unlikely to have been an absolute bar to the arrival of the infection.
We tested whether the absence of infection in Hokkaido was associated with an effect of host genotype on the efficiency of male-killer transmission or strength of male-killing ability. Beyond this, we examined whether trap collection data were consistent with difference in D. bifasciata ecology between Hokkaido and Honshu islands. Our results indicated that the Hokkaido genetic background supported the transmission and action of the male-killer even under stringent conditions, ruling out genetic differentiation as a cause of the absence of the male-killer from Hokkaido. We did observe higher capture rates of D. bifasciata in the island of Hokkaido, and future work should therefore be focussed on the degree to which ecological differences affecting the drive of the infection dictates incidence in this species.
Results
Sex ratio of Hokkaido flies
Twenty-eight female flies were collected from the field in Hokkaido. Of these, 4 failed to produce progeny. The remaining 24 all produced a normal sex ratio, consistent with continued absence of the male-killing trait in Hokkaido.
Intensity of male-killing on the Hokkaido genetic background
We tested whether the Hokkaido genetic background supported the male-killing Wolbachia from Honshu by introgression of the infection onto the Hokkaido genetic background. The progeny from the 24 Hokkaido females were maintained as isofemale inbred lines for three generations to capture genetic variation within them. The male-killing Wolbachia from Honshu was then placed onto each inbred background via crossing infected females from Honshu to males from each of the Hokkaido lines, with subsequent generations being maintained through further crossing to males from the appropriate Hokkaido line.
The sex ratio produced following introgression of the infection to the Hokkaido genetic background was female biased and penetrance of the male-killing phenotype was perfect in the first generation. A few males appeared sporadically in six of the 24 lines in one or more subsequent generations, with highest frequency in lines 15 and 25 (Table 1). However, no males were produced in the F4 in any case, indicating no loss of infection or repeatable resistance to male-killer action. We particularly maintained observation over lines 15 and 25 over four subsequent generations, and adult males were not observed in the culture over this period (data not shown). These data are broadly comparable with data from Honshu control lines, where 4 of 27 lines showed sporadic male production.
Table 1 The sex ratio produced during introgression of the male-killing infection to the Hokkaido genetic background. 'All-female' classification represent cases where both replicates produced all female broods. Where males were produced within a female-biased sex ratio, data is given separately for each replicate of the isofemale line.
Generation Sex ratio No. of lines Line-replicate: male
n progeny/total
F1 All female 24
Female biased 0
F2 All female 21
Female biased 3 15-1: no males. 15-2: 2/74
21-1: 1/32. 21-2: no males
25-1 no males. 25-2: 1/10
F3 All female 19
Female biased 5 12-1 no males. 12-2: 1/18
15-1: 2/27 15-2: 2/52
17-1: 2/20 17-2: 1/46
20-1: 1/10 20-2: no males
25-1: 1/40 25-2: no males
F4 All female 24
Female biased 0
Effect of stringent temperatures
The lines above were moved to 23.5°C, the upper temperate before thermal induced loss of infection occurs in Honshu flies [11], and we maintained the lines at this temperature by backcrossing to males from the source uninfected Hokkaido line as before, for four further generations. No effect of elevated temperature on the penetrance or transmission of the male-killing trait was observed on the Hokkaido genetic background (Table 2). Sporadic males were observed in 4 of 20 lines over the four generations. However, males were never observed in both replicates within a line, nor were they ever observed in more than one generation within a line. Notably, none were produced in the final generation, indicating the infection was perfectly transmitted during the experiment. Control lines from Honshu maintained production of all female broods, in agreement with past observations.
Table 2 The sex ratio in male-killer infected isofemale lines from Hokkaido following transfer of the introgressed infected lines to 23.5°. 'All-female' classification represents cases where both replicates produced all female broods. Where males were produced within a female-biased sex ratio, data is given separately for each replicate of the isofemale line.
Generation Sex ratio No. of lines Line-replicate: male
n progeny/total
F1 All female 20
Female biased 0
F2 All female 20
Female biased 0
F3 All female 16
Female biased 4 11-1 no males 11-2: 1/21
19-1: 1/28 19-2: no males
23-1: 1/37 23-2: no males
25-1: 1/11 25-1: no males
F4 All female 20
Female biased 0
Collection rates of D. bifasciata in traps
We captured flies in the field and scored the samples for both absolute capture rate of D. bifasciata, and capture rate relative to other species of Drosophila. The capture rate of D. bifasciata was substantially higher in all four samples taken in Hokkaido province (Misumai, Koryukozan, Tomakomai, Matsumae) than in the two samples from the Northern Honshu populations (Mimmaya, Morioka) and the population from Mid Honshu (Kiyosumi). Increased capture rate in Hokkaido was also reflected in an increase in the proportion of all drosophilids sampled that were bifasciata (Table 3). This is consistent with the idea that the ecology of D. bifasciata varies between Honshu and Hokkaido, and that this may cause the presence of the infection in one island and absence in the other.
Table 3 Catch rate of D. bifasciata in seven locations within Japan during early-mid October between 1973 and 1984. Catch rate is given as mean per trap per day, with number of traps and number of days trapped in parentheses. Proportion of catch that was bifasciata is given across all traps and days, with total Drosophila catch in parentheses.
Island Location bifasciata caught per day/ per trap (traps, days) bifasciata as proportion of catch (n)
Hokkaido Misumai 42°57' N 141°16' E 4.67 (5, 14) 13.6% (2407)
Koryukozan 42°51' N 141°17' E 8.38 (5, 17) 15.94% (4458)
Tomakomai 42°43' N 141°36' E 6.48 (6, 14) 2.01% (27033)
Matsumae 41°26' N 140°08' E 2.54 (8, 7) 2.48% (5726)
Honshu Mimmaya 41°10' N 140°24' E 0.21 (8, 7) 0.51% (2342)
Morioka 39°15' N 141°10' E 0.79 (4, 7) 0.82% (2683)
Kiyosumi 35°10' N 140°10' E 0.00 (4, 7) 0 (903)
Discussion
Previous study has shown that male-killing Wolbachia are absent from D. bifasciata in Hokkaido province of Japan, despite the infection being present in neighbouring Honshu. In this study, we have demonstrated that the absence of male-killing Wolbachia in the Hokkaido population is not caused by the inability of the male-killing Wolbachia to operate on the Hokkaido host genetic background. In contrast, the male-killer was perfectly transmitted in all 24 lines tested (after 4 generations of introgression, no males were produced in any of the lines). This high efficiency was maintained even at the threshold for complete male-killing in Honshu, 23.5 C (after a further 4 generations, all 20 lines still had all female broods). Thus, the male-killer is proficient at being transmitted and killing males on the Hokkaido background even under relatively stressful environmental conditions. Further, we continue to maintain the infection on the Hokkaido background (we are now at generation 15) without any loss of the infection and without appearance of males. Thus, it is not tenable to argue that the hosts themselves are not genetically suitable for the function of male-killer, at least on an ecological timescale. This situation contrasts with the case of meiotic drive in the related fly D. subobscura, where absence of drive in Europe was associated with refractoriness to the action of the sex ratio distorting element.
In the absence of variation in the ability of the male-killer to function on the different genetic backgrounds, the question arises as to the features that cause infection to be absent from the Hokkaido population. Our study did reveal differences in trap collection rates between the populations of Hokkaido and those of Northern Honshu, with bifasciata captured at lower rate in the populations from Honshu than in Hokkaido. Thus, ecological heterogeneity is a possible source of the incidence pattern. There are three parameters in male-killer dynamics that may be environmentally influenced. First, the advantage to male-killing may not be as strong in Hokkaido populations. Second, the cost of infection to female flies may be higher in Hokkaido than in Honshu. Third, the transmission efficiency may alter, mediated via elevated temperature, or possibly by reduced overwinter temperature.
In our view, the latter factor is unlikely to be driving the observed pattern. It is notable that the survey of Ikeda revealed the infection to be present in Northern Honshu, but not in Southern Hokkaido. Given these two areas are geographically and climatically very close, temperature differences have poor explanatory power. Explanations based on temperature are also weak because this species exhibits a degree of homeostasis in temperature, moving to elevated altitudes to avoid excess temperature.
This leaves us with two hypotheses to explain absence of infection in Hokkaido. The first is that there is a weaker advantage to male-killing in Hokkaido than on Honshu, such that there is insufficient drive to maintain the bacterium or permit its spread. The advantage of male-killing to the bacterium depends upon the number of females ovipositing in a single patch [7]. If the high density of bifasciata observed in Hokkaido translates into many females laying eggs in a single sap flux, male death will not greatly increase the survival of infected females over uninfected, and infection will decline in frequency. The second factor that may cause infection to decline in frequency is if costs of infection are higher in Hokkaido than in Honshu. This factor can be ecologically contingent. Ikeda demonstrated that the relative fitness of infected flies compared to uninfected flies was lower under high densities in the laboratory [8]. Thus, if the high density of the adult fly we observed corresponds to a high density of larvae within a single sap flux, the direct cost of infection would be higher, and the infection would be expected to be less common or absent.
Aside from these possibilities, which we consider most likely, other ecological discontinuities between Hokkaido and Honshu deserve investigation. Some symbionts, for instance, give the host protection against parasitoids [12], and thus differences in parasitoid infection rates could affect the frequency of a symbiont. Co-existing heterospecific competitors may also diminish the benefit of male-killing; if there are many species ovipositing within a patch, the advantage to male-killing may decline. Thus, the intensity of inter-specific competition also deserves investigation. Finally, the existence of other 'competing' inherited microorganisms should be excluded as a reason for the absence of the male-killing Wolbachia from Hokkaido.
Conclusions
It is not variation in the ability of Wolbachia to function on different host genetic backgrounds that drives presence or absence of infection in the D. bifasciata- male-killing Wolbachia system. We have demonstrated that despite being absent from Hokkaido, Wolbachia can both be maintained and express male-killing on the Hokkaido host genetic background. We observe that the two populations show differences in trap capture rates, and argue that either ecologically contingent benefits or ecologically contingent costs of infection may explain presence and absence of infection in this species, and that future research be focussed at this level.
Methods
Source of wild flies for introgression
Twenty eight wild female D. bifasciata were collected on the campus of Hokkaido University, Sapporo (43°4'56"N, 141°20'21"E), Japan, in May 2003. These were then brought into the laboratory, where they were maintained individually in vials at 21°C on a modified cornmeal-agar diet (70 g sucrose, 60 g maize meal, 15 g yeast extract, 10 g agar, 2.5 g nipagin in a total volume of 1 liter). These female were checked for the presence of the male-killing trait through scoring of the sex ratio, and maintained by sib-sib inbreeding (2 males, 2 females) for three generations to make inbred isofemale lines. In total, 24 isofemale inbred lines were created (4 lines went extinct), which were maintained thenceforth by simple tossing every three weeks.
Introgression of the infection onto the Hokkaido genetic background
The genetic background of each of the 24 uninfected Hokkaido lines was then independently crossed onto the male-killer infected cytotype over four generations. To this end, 4 males were taken from each of the 24 Hokkaido lines, and mated to 4 Wolbachia infected virgin females extracted from a culture derived from Honshu island, Japan (established in [9]). This procedure was performed twice for each Hokkaido inbred line to give 24 introgression lines, each replicated with two replicates. Following this initial cross, introgression of the appropriate Hokkaido nuclear background continued for four generations, on each occasion four female offspring of each line being backcrossed to males from the respective Hokkaido uninfected inbred line. Flies were kept at 21°C throughout the experiment, and the sex of each line scored at each generation (n>10 individuals in every case). As a control against spontaneous loss of infection not associated with genetic differentiation, the male-killer was concurrently maintained on the Honshu genetic background, in lines maintained by backcrossing to individual isofemale lines (as established in [11]) with likewise monitoring of sex ratio.
Temperature effect
Temperature is known to affect the stability of the male-killing trait, and previous study demonstrated that an upper threshold of 23.5 C existed for stable maintenance of the infection on the Honshu genetic background [11]. We tested whether the infection remained stable at this stringent temperature on the Hokkaido genetic background. To this end, 20 of the above introgressed fly lines were transferred from 21° to the 23.5°, and the sex ratio of the offspring recorded for four further generations, with the lines maintained by backcrossing to males from the appropriater parental Hokkaido uninfected line as before. As a control, four Honshu isofemale lines were concurrently maintained at 23.5°.
Collection rates of D. bifasciata in traps
Evidence of differences in fly density can be derived from sampling the Drosophila communities. Drosophila communities were sampled in 4 deciduous forests in Southern Hokkaido, 2 sites in Northern Honshu, and one site in Mid Honshu, in early-mid October over a number of years between 1973 and 1984 (Figure 1). Collections were carried out using traps baited with fermented banana suspended from the canopy during early-mid October. The traps were especially designed for retaining trapped insects in a bottle of fixative solution and set vertically at different heights from the floor [13,14]. Since D. bifasciata is a typical forest-canopy dweller, sampling from the forest canopy is essential for estimating its population density. These traps were cleared 7 or 10 days after setting, and the capture rate of bifasciata per trap per day calculated at each locality to represent the density of this fly in this region. All drosophilid flies were identified, and the proportion of flies caught that were bifasciata recorded.
Figure 1 Collection sites for D. bifasciata in Japan
Authors' contributions
ZV helped with the design of the crossing scheme, conducted the crosses involved, and performed the analysis of these crosses. MT designed and conducted the field sampling and scored trap collections. GH conceived the project, organised collection, helped with design of the crossing scheme, and wrote the paper. All authors read and commented on drafts of the manuscript, and approved the final manuscript.
Acknowledgements
We wish to thank the BBSRC for funding for this project. We wish to thank Horacio Montenegro, Sylvain Charlat and Max Reuter for reading the manuscript, and two anonymous reviewers for helpful comments.
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| 15571631 | PMC535909 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 30; 4:52 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-52 | oa_comm |
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BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-211557419610.1186/1471-2261-4-21Research ArticleProgression of coronary calcification in healthy postmenopausal women Hsia Judith [email protected] Afifa [email protected] Anjana [email protected] Jeremy [email protected] Lucile L [email protected] Barbara V [email protected] Department of Medicine, The George Washington University, Washington, DC 20037, USA2 Biostatistics Center Medical Center Unit, The George Washington University, Washington, DC 20037, USA3 Howard University Cancer Center, Washington, DC 20060, USA4 Medstar Research Institute, Hyattsville, MD 20783 USA2004 1 12 2004 4 21 21 5 8 2004 1 12 2004 Copyright © 2004 Hsia et al; licensee BioMed Central Ltd.2004Hsia 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
Coronary artery calcium score incrementally improves coronary risk prediction beyond that provided by conventional risk factors. Limited information is available regarding rates of progression of coronary calcification in women, particularly those with baseline scores above zero. Further, determinants of progression of coronary artery calcification in women are not well understood. This study prospectively evaluated rates and determinants of progression of coronary artery calcium score in a group of healthy postmenopausal women.
Methods
We determined coronary calcium score by computed tomography and recorded demographic, lifestyle and health characteristics of 914 postmenopausal women, a subset of those enrolled in the Women's Health Initiative Observational Study. The 305 women with calcium score ≥10 Agatston units at baseline were invited for repeat scan. This analysis includes the 94 women who underwent second scans.
Results
Mean age of study participants was 65 ± 9 years (mean ± SD), body mass index was 26.1 ± 6.1 kg/m2, and baseline calcium score was 162 ± 220 Agatston units. Mean interval between scans was 3.3 ± 0.7 years. A wide range of changes in coronary calcium score was observed, from -53 to +452 Agatston units/year. Women with lower scores at baseline had smaller annual increases in absolute calcium score. Coronary calcium scores increased 11, 31 and 79 Agatston units/year among women with baseline calcium score in the lowest, middle and highest tertiles. In multivariate analysis, age was not an independent predictor of absolute change in coronary calcium score. Hydroxymethylglutaryl coenzyme A reductase inhibitor (statin) use at baseline was a negative predictor (p = 0.015), whereas baseline calcium score was a strong, positive predictor (p < 0.0001) of progression of coronary calcification.
Conclusion
Among postmenopausal women with coronary calcium score ≥ 10 Agatston units, rates of change of coronary calcium score varied widely. In multivariate analysis, statin use was a negative independent determinant, whereas baseline calcium score was a strong positive predictor of annual change in coronary calcium score.
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Background
Coronary calcium, assessed by computed tomography, strongly and independently predicts coronary risk [1-3]. Age is by far the most potent determinant of calcium score [4], although conventional risk factors also been associated with the extent of coronary calcification [4,5].
The rate of progression of coronary calcification appears to further stratify risk [6,7], but reports have been limited by sample size [8], retrospective design [6,8,9], inclusion of individuals with baseline calcium scores of zero [9,10] and limited interval between tomographic scans [8,10,11]. Further, not all studies adjusted for use of hydroxymethylglutaryl coenzyme A reductase inhibitors (statins), which have been reported to attenuate progression [9,12,13].
Calcium scores differ in men and women [4], but progression of coronary calcification has not been reported by gender, except for the Healthy Women Study, which only included women [10]. Of the 80 women in that cohort, 52 (65%) had calcium scores of zero at baseline. After mean follow up of 18 months, 47 of the 52 (90%) had no coronary calcium on repeat scan. Mean change for the 52 women was 0.4 Agatston units and median change was 0. Among the 28 women with measurable coronary calcium at baseline, mean change was 11 Agatston units for women with baseline calcium score 1–99, and 72 Agatston units for the 9 women with baseline calcium score ≥100.
In this study, we prospectively assessed the rate of progression of coronary calcification in an ethnically diverse group of healthy women with coronary calcium scores of at least 10 Agatston units at baseline, and identified independent predictors of progression.
Methods
Patient population
Study participants were a subset of women enrolled in the Women's Health Initiative Observational Study [14] at the George Washington University and Howard University/Medstar clinical sites between February 1995, and December, 1998. Women who joined this ancillary study provided informed consent in a form approved by the respective institutional review boards.
The entire Observational Study cohort comprises 93,676 women at 40 clinical sites. For this ancillary study, participants at the George Washington and Howard/Medstar clinics (n = 4435) were invited for computed tomography. Baseline scans were performed on the 914 women who responded to the invitation. Of these, 528 had no coronary calcium detected and 81 had calcium scores of 1 – 9 Agatston units. The remaining 305 women with calcium score ≥10 Agatston units were mailed a letter inviting them to have a second scan; African-American women received two mailings because of a historically lower response rate. This analysis includes the 94 women with serial scans, which were performed a mean of 3.3 ± 0.7 years after the baseline study.
Variables
Participants provided data on a wide range of health variables including dietary habits, medical history and anthropometric measures. Questionnaire measures assessed self-reported hypertension, diabetes mellitus (excluding gestational diabetes), current smoking, high cholesterol requiring pills, postmenopausal hormone therapy, and family history of premature coronary disease (father with myocardial infarction at age 55 years or younger, or mother with myocardial infarction at age 65 or younger). Statin use at baseline was assessed by medication inventory.
Dietary fat consumption was assessed using a food frequency questionnaire based on instruments used in the Women's Health Trial [15]. Nutrient estimates from the food frequency questionnaire were similar to those from short-term dietary recall and from four-day food records [16].
Physical activity was assessed by questions on a frequency and duration scale of four walking speeds and three other types of activity classified by intensity (strenuous, moderate or light)[17]. For this analysis, we categorized women by the number of weekly episodes, at least 20 minutes in duration, of moderate or strenuous activity.
Plasma lipids were only measured in a 1% random subsample of Observational Study participants, so were not included as variables in these analyses.
CT image acquisition and analysis
Images were acquired using an Imatron C – 150 scanner. Thirty contiguous 3-mm slides (100 ms/slice) were acquired during a single breathhold beginning 1 cm caudad to the carina. Each level was triggered by ECG in end-diastole (80% of R-R interval). Images were obtained with a 30-cm2 field of view (pixel size, 0.586 mm). Images were analyzed by the Agatson method [18].
Analysis
Descriptive statistics such as frequencies, percentages, means and standard deviations (SD) were used to describe the study population and to explore the relationships between coronary calcium score and several explanatory variables. Group comparisons were made by t test, chi square and, where appropriate, the Mantel-Hanzel chi square test. In Table 2, the p value is based on ranked scores because of variance heterogeneity. Annual change in coronary calcium score in Table 2 is adjusted for age using ANCOVA's least squares means.
Table 2 Annual change in calcium score
Baseline calcium score Annual change in calcium score
Unadjusted Age-adjusted % change
(Agatston units) (Agatston units/y)
mean (SD) range mean (SD) range
1 (n = 32) 32 (11) 13 to 51 11 (16) -7 to 38 11 33%
2 (n = 30) 94 (39) 52 to 189 31 (31) 3 to 148 34 33%
3 (n = 32) 559 (292) 194 to 1236 79 (102) -53 to 452 77 14%
p across tertiles 0.0001
Determinants of annual change in calcium score were evaluated in a multiple linear regression model which included age, baseline calcium score, and statin use at baseline as independent variables (Table 4). In a separate model, hypertension was added. Age and statin use were selected as independent variables because they have consistently been identified as determinants of coronary calcification [12,13,19]. Baseline calcium score and hypertension were included because of their relationship to change in calcium score in Table 2 and 3, respectively. Analyses were carried out using SAS System for Windows v8.02.
Table 3 Risk factors by tertile of annual change in calcium score
Tertile p value
1 2 3
n 32 30 32
mean (SD)
Age, y 64 (7) 65 (7) 67 (12) 0.55
Body mass index 25.1 (4.7) 26 (7.3) 27.1 (6.04) 0.45
% calories from fat 27 (6) 27 (7) 27 (10) 1.00
# days/week moderate-vigorous exercise 1.8 (2.2) 2.7 (2.6) 2.2 (2) 0.33
%
Hypertension 33 39 59 0.08
Diabetes 7 0 9 0.60*
High cholesterol 16 26 22 0.65
Current smoking 10 10 6 0.63*
Family history premature CHD 13 6 19 0.48*
Hormone use at baseline 48 42 44 0.87
Statin use at baseline 13 10 9 0.65*
* Mantel-Hanzel Chi Square used because of small cell sizes
Table 4 Multivariate analysis: Determinants of annual change in calcium score
Parameter estimate 95% CI p value
Age 0.4 -0.83, 1.63 0.52
Baseline calcium score 0.15 0.11, 0.20 <0.0001
Statin use at baseline -43.95 -79.00, -8.88 0.015
Results
Baseline demographic and health characteristics of the 94 women with serial scans are shown (Table 1). Among the 305 women with calcium score ≥10 Agatson units, characteristics of the 94 women with a second scan were similar to the 211 women without a second scan (data not shown), except that the former group was enriched for African-American women, 31/94 (31%) vs 11/211 (5%)(p < 0.0001 across ethnic groups).
Table 1 Comparison of women with serial scans vs women with calcium score <10
Group A Group B
mean (SD) p value
N 94 609
Age, y 65 (9) 61 (8) <0.0001
Body mass index, kg/m2 26.1 (6.1) 25.9 (5.6) NS
Baseline calcium score, Agatston units 162 (220) 0.5 (1.6) <0.0001
% dietary calories from fat 27 (8) 26.5 (7.4) NS
BP, mm Hg
Systolic 126 (19) 119 (17) 0.001
Diastolic 75 (10) 73 (13) NS
No. (%)
Ethnicity <0.0001
White 58 (62%) 490 (80%)
Black 31 (33%) 88 (14%)
Asian/Pacific islander 3 (3%) 12 (2%)
Hispanic 1 (1%) 11 (2%)
Unknown 1 (1%) 8 (1%)
Hypertension* 41 (44%) 146 (24%) <0.0001
Diabetes mellitus 5 (5%) 7 (1%) 0.004
Current smoking 8 (9%) 24 (4%) <0.05
Self-reported high cholesterol requiring pills 20 (21%) 76 (12%) 0.02
Hormone use at baseline 42 (45%) 366 (60%) 0.005
Statin use at baseline 9 (10%) 51 (8%) <0.05
# days/week moderate/vigorous exercise 2.2 (2.3) 2.5 (2.4) NS
Group A, women with baseline calcium score ≥10 Agatston units and serial scans;
Group B, women with baseline calcium score <10
* Self-reported hypertension or measured BP systolic >140 or diastolic >90
Table 1 compares the 94 women with serial scans, all with baseline calcium scores ≥10 Agatston units (Group A), with women whose baseline calcium scores were <10 Agatston units (Group B). Women in Group A were older, and more likely to report hypertension, diabetes, current smoking, and high cholesterol requiring pharmacologic therapy. Statin use was slightly more prevalent and postmenopausal estrogen use slightly less prevalent among women in Group A. Baseline calcium scores for white and African-American women were 278 ± 330 and 163 ± 214 Agatston units, respectively (p = 0.05).
Annual change in calcium score and age-adjusted change in calcium score are shown by tertile of calcium score at baseline (Table 2). Women with higher baseline calcium scores had greater absolute annual increase in both unadjusted (79 vs 11 Agatston units/year for women in the highest vs lowest tertile, p < 0.0001 across tertiles) and age-adjusted calcium score. Changes in calcium scores ranged from -53 to +452 Agatston units/year. Annual change in calcium score did not differ between white and African-American women (data not shown).
Coronary risk factors are shown by tertile of annual change in calcium score (Table 3). Body mass index, dietary fat consumption and physical activity were not different in women with more or less rapid progression of coronary calcification. Conventional risk factors, including age, cigarette smoking and diabetes, also demonstrated no significant trend across tertiles of progression in calcium score. Hypertension was reported somewhat more frequently by women with greater progression of coronary calcification (33% vs 59% in lowest vs highest tertile, p = 0.08).
In multiple linear regression analysis, age was not independently associated with annual change in calcium score (Table 4). Statin use was a weak negative predictor of progression (p = 0.015), whereas calcium score at baseline was a strong positive predictor of annual change in calcium score (p < 0.0001). Results were similar when hypertension, which had shown a non-significant trend across tertiles of change in coronary calcium score, was added to the model. Hypertension itself was not an independent determinant of progression (data not shown).
Discussion
In this ethnically diverse group of Women's Health Initiative observational study participants, the rate of progression of coronary calcification was 33%/year among women with calcium scores between 10 and 190 Agatston units at baseline, and 14%/year for women with higher calcium scores. The rate of change in calcium score ranged widely in individual women, from -53 to +452 Agatston units/year. In multivariate analysis, statin use was negatively associated with progression of coronary calcification, whereas baseline calcium score was a strong and independent positive predictor of progression.
One strength of this analysis is the inclusion of a wide range of variables affecting atherosclerotic risk, including body mass index, physical activity, dietary fat consumption, postmenopausal hormone and statin use in addition to conventional risk factors. Another strength is the relatively long interval between scans, 3.3 years (mean), enhancing accuracy of the estimated rate of progression. This study includes only women, a group traditionally underrepresented in studies of coronary computed tomography [6,8,9,11], only those with measurable coronary calcium at baseline, and 37% non-white participants. Limitations include the sample size and absence of laboratory measures, such as lipids and glucose. These were performed only in a random 1% subsample of Observational Study participants, and consequently are not available for inclusion in this analysis.
The rate of progression of coronary calcification observed in this analysis is within the range reported by others [6,9,11]. Similarly, the lack of relationship between postmenopausal hormone use and progression of coronary calcification is consistent with prior reports [10,20], as is the observed inverse association of statin use with progression of coronary calcification [12,13].
Efforts to identify independent predictors of progression of coronary calcification have been limited, particularly in women. A retrospective study of 55 high-risk men identified baseline calcium score and Lp(a) as independent predictors of progression in a multivariate model which did not include statin use [21]. A prospective study of 87 men and 24 women identified baseline calcium score as a weak independent predictor (p < 0.05) of change in calcium score [11]. Smoking, hypertension, diabetes, age, plasma lipid levels, body mass index, prevalent coronary heart disease and use of lipid-lowering medications, aspirin, beta-blockers, angiotensin converting enzyme inhibitors, calcium antagonists or nitrates were not independent predictors of progression, although the ability to detect such relationships was limited in view of the sample size, which presents a similar limitation in this analysis.
In contrast, smoking was found to be an independent determinant of progression in a larger study of 311 men and 184 women, which also identified baseline calcium score as a potent predictor of progression [7].
Another limitation of this study was use of Agatston score, rather than calcium volume score. The latter provides better reproducibility [22], but was not in widespread use at the time baseline scans were acquired and was not available for the baseline scans in this study. The difference between Agatston and volumetric scores increases with the coronary artery calcium score. For example, among women aged 60–64 years, the 75th percentile has been reported as 59 Agatston units or 42 volumetric units, and the 90th percentile as 202 Agatston units or 163 volumetric units [23]. We cannot exclude the possibility that areas of coronary calcification may have consolidated through retraction during follow up, a phenomenon which may not have been accurately assessed using the Agatston score.
Calcium scores differ in men and women [24]; for example the 75th percentile scores for 50–54 year old men and women are 99 and 3, respectively [4]. For 60–64 year old men and women, the 75th percentile scores are 247 and 49, respectively. If baseline score is the major determinant of progression, a gender difference in rates of progression would be expected on this basis alone. In fact, male gender has been reported as an independent determinant of progression [7]. Whether rates of progression differ between men and women after adjustment for baseline score is uncertain.
Our observations raise several issues with regard to the potential use of coronary calcium score progression as either a clinical tool or an outcome for atherosclerotic intervention trials. First, the variance for this variable is high, both in our study and in others [7,9,12], limiting, at least to some extent, its value as a clinical predictor in individual patients and its appeal as an intermediate outcome. Second, stratification by statin use should be considered. Third progression should be adjusted for baseline calcium score.
Conclusions
In an ethnically diverse cohort of postmenopausal women with coronary calcium score ≥10 Agatston units, rates of progression or coronary calcification vary widely. In multivariate analysis, statin use was inversely associated with progression, whereas baseline calcium score was a strong positive predictor of progression.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JH study concept, design, data analysis, initial and final manuscript preparation. AK data analysis. AP and JB data acquisition and management, manuscript revision. LLA-C and BVH manuscript revision. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Supported by NIH contracts NO1-WH-4-2108 and WH-4-2123. The Women's Health Initiative is funded by the National Heart, Lung, and Blood Institute, US Department of Health and Human Services. Computed tomographic scans provided at no cost by HeartScan (now HeartCheck) Washington.
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| 15574196 | PMC535923 | CC BY | 2021-01-04 16:30:02 | no | BMC Cardiovasc Disord. 2004 Dec 1; 4:21 | utf-8 | BMC Cardiovasc Disord | 2,004 | 10.1186/1471-2261-4-21 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1551549407810.1186/1471-2105-5-155Research ArticlePASBio: predicate-argument structures for event extraction in molecular biology Wattarujeekrit Tuangthong [email protected] Parantu K [email protected] Nigel [email protected] National Institute of Informatics (NII), National Center of Sciences, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8430, Japan2 Structural and Computational Biology Program, European Molecular Biology Laboratory, Heidelberg, Germany3 Max Delbruck Center for Molecular Medicine, Berlin-Buch, Germany2004 19 10 2004 5 155 155 10 3 2004 19 10 2004 Copyright © 2004 Wattarujeekrit 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
The exploitation of information extraction (IE), a technology aiming to provide instances of structured representations from free-form text, has been rapidly growing within the molecular biology (MB) research community to keep track of the latest results reported in literature. IE systems have traditionally used shallow syntactic patterns for matching facts in sentences but such approaches appear inadequate to achieve high accuracy in MB event extraction due to complex sentence structure. A consensus in the IE community is emerging on the necessity for exploiting deeper knowledge structures such as through the relations between a verb and its arguments shown by predicate-argument structure (PAS). PAS is of interest as structures typically correspond to events of interest and their participating entities. For this to be realized within IE a key knowledge component is the definition of PAS frames. PAS frames for non-technical domains such as newswire are already being constructed in several projects such as PropBank, VerbNet, and FrameNet. Knowledge from PAS should enable more accurate applications in several areas where sentence understanding is required like machine translation and text summarization. In this article, we explore the need to adapt PAS for the MB domain and specify PAS frames to support IE, as well as outlining the major issues that require consideration in their construction.
Results
We introduce PASBio by extending a model based on PropBank to the MB domain. The hypothesis we explore is that PAS holds the key for understanding relationships describing the roles of genes and gene products in mediating their biological functions. We chose predicates describing gene expression, molecular interactions and signal transduction events with the aim of covering a number of research areas in MB. Analysis was performed on sentences containing a set of verbal predicates from MEDLINE and full text journals. Results confirm the necessity to analyze PAS specifically for MB domain.
Conclusions
At present PASBio contains the analyzed PAS of over 30 verbs, publicly available on the Internet for use in advanced applications. In the future we aim to expand the knowledge base to cover more verbs and the nominal form of each predicate.
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Background
We are now in an era where full genomes, data from high throughput experimental methods (e.g. micro-arrays) and electronic versions of scientific literature are easily available to every researcher over the Internet. These advances have made it possible to work on more than one gene at a time, ask complex questions and increase the pace of biological discovery. However, the progress made in scientific research until now has been recorded in the form of free-text articles readable only by humans and accessible by machine mostly through shallow keyword-based search engines. For improved methods of information access and knowledge discovery it is necessary to automatically map from the unstructured text representation into partially structured forms that provide discovered facts to databases.
The large-scale data generated from the experiments in molecular biology needs to be assessed and integrated into the scientific communities' knowledge stores. This has created a need for various kinds of specialized databases. While some existing databases contain only molecular level information (e.g. PDB [1], SCOP [2]) others (e.g. BIND [3], SWISS-PROT [4], MINT [5]) contain literature associated with molecular entities. These literature databases contain a higher level of relationships (e.g. functional modules, interaction networks, gene products and disease phenotypes), are more informative and can be mined for further knowledge discovery (e.g. G2D [6]). At the same time hand curation of these databases is limiting their growth and reducing the accuracy of the information provided. This is where information extraction (IE) has an important role to play.
Previous research in IE for biology has focused intensively on the recognition of named entities (NE) from scientific texts [7-9], i.e. the identification and classification of technical terms such as proteins, genes, drugs or cell types. Recently, the focus of research has been moving to higher levels of IE such as co-reference resolution and event extraction [10-18] which involves the filling of an event template that makes use of the results from NE recognition. However, significant challenges remain at all levels of biology IE due to the complexity of biological terminology and sentence structure. From the early days of research into computational linguistics it has been known that scientific sublanguages have special properties that make them different from general language [19]. These differences are notable at the level of vocabulary, semantic relationships and sometimes even syntax [20] and often require specialized knowledge sources to aid in analysis. In this article we focus on differences at the semantic and syntactic levels and we will provide motivating examples throughout the following discussion.
Predicate-argument structure (PAS) analysis seeks to formally describe 'frames' for predicates (usually verbs) and the roles of their arguments (parts of the sentence surrounding it). Such roles usually need to be specified according to several factors including meaning and obligation. Meaning can be determined in several ways such as a domain or predicate-specific fashion such as catalyst and reaction being catalyzed in the case of the first and second arguments to the predicate catalyze. Alternatively, functional roles can be employed such as thematic relations that try to express some linguistically motivated aspect of the argument's behavior such as agent, location or experiencer.
Traditional IE systems that use regular expressions based on shallow chunking at the phrase level (e.g. noun phrase, verb phrase, preposition phrase) capture weak notions of 'argument' for event predicates and their linear precedence. Such approaches seem to be inadequate to the goal of achieving high completeness and accuracy in event extraction. In recognition of this several major projects [21-24] have now begun based on newswire and balanced text collections which examine the relations that exist between the constituents in a sentence with the key assumption that those arguments correspond to major objects in events of interest. Although PAS frames seem to be expensive to construct by hand in terms of time and effort, particularly where this requires insights from domain specialists, we believe that this is justified as they provide a systematic reference guide for improving performance compared to ad-hoc pattern-building approaches.
For PAS to be practically realized within IE three major knowledge components will be required: (1) a hierarchy of concept categories for objects of interest; (2) a definition of predicate-argument frames and the semantic labels of their arguments; and (3) the mapping rules that define how to transform the relevant parts of a surface sentence to the arguments in the PAS frame. Currently (1) is already quite advanced with several controlled vocabularies such as MeSH [25] or Gene Ontology [26] now in wide-scale use. At a more modest level core domain specific ontologies for individual annotation schemes such as the GENIA project [27] have also been proposed. To the best of our knowledge, however, nobody has yet made a proposal for (2) which will then serve as the basis on which to develop annotated resources for machine learning approaches to (3). This is the approach we intend to follow and this paper focuses on (2). It is of course possible to approach the task of PAS definition from a machine learning approach, and also to follow a path of hand-built heuristic mapping rules but we believe that both of these approaches may prove to be more costly in terms of time than the one we advocate here.
In this work we introduce the concept of semantic analysis of argument roles in biological texts and propose the construction of PAS for molecular biology (PASBio). We have analyzed and annotated sentences from MEDLINE abstracts and full-text journal articles for building PASBio. The working scheme is similar to the PropBank project [22,23]. Results of our analysis are available online as a knowledge base of predicates and their respective argument sets at PASBio's web page [28]. By specifying PASBio we hope to enhance the event extraction system for accuracy (i.e. the ability to extract only relevant facts) by means of corpus-based semantic interpretation. To achieve this the intended IE system consists of 4 steps: (1) creation of a semantic lexicon (PASBio); (2) semantic annotation of texts using PASBio as a reference resource; (3) building an automatic semantic interpretation model using the annotated texts as a machine learning training corpus; (4) embedding this automatic semantic interpretation module into an IE system. This paper focuses on the key PASBio creation step by discussing the influential processes and choice points and a comparison to other schemes. The annotation task has been done on more than 300 sentences as the result of a preliminary analysis to support in defining PAS frames. This amount of annotation is unlikely to be sufficient for machine learning purposes, so further corpus annotation as well as the machine learning task needs to be carried out in order to reach the final step. It should be noted that other event extraction approaches [14,17,18] and also other text analysis applications (e.g. machine translation (MT), NE recognition tasks, text summarization [29,30]), requiring the use of semantic relations between a verb and its argument in their processing, would be able to take advantages of PASBio.
In this article we first give a short introduction to IE and PAS. Next, we describe the approach taken in the PropBank project. Then, we discuss and exemplify how the specification of predicate-argument frames needs to be extended to meet the requirements for extracting molecular events. The second half of the paper is devoted to explaining the methodology used to define the PAS and discussing results of our analysis and its comparison with those of PropBank. Finally, we describe how the PAS frames can be exploited by showing their place in the IE system for molecular biology and discussing existing IE systems used for event extraction in molecular biology.
Results and discussion
Information extraction
IE systems aim to provide instances of structured knowledge representations from unstructured free-form text. IE, based on the Message Understanding Conference (MUC) tradition of task segmentation [31] works fundamentally by using predefined frames and slots in agreement with a specific scenario describing user requirements. Such systems typically use regular expressions to match facts for the event to be extracted in each sentence. Each logical form is founded upon the syntactic relationship between components in each sentence. To take an example from the newswire domain: if we wanted to extract facts relating to a scenario (company outlook) then patterns such as "np (stock index) + vp (driven up) + integer (number %)" and "np (company) + vp (bid) + np (stock)" could be developed as a template. Sentences in documents which (1) contain a noun phrase (np) describing stock index, together with a verb phrase (vp) driven up, and followed by a number; or (2) contain a noun phrase representing a company name, followed by a verb phrase with bid, plus a noun phrase of stock index should be extracted. The difficulties are compounded because a single event can nearly always be written in a variety of syntactic forms due to linguistic processes such as passive voice, (pro-) nominalization, raising, etc.
The following simple example involves a linguistic phenomenon sometimes called locative alternation or spray alternation by Levin [32]. The verb spray may express its arguments in at least two different ways, i.e. (a) "Peter sprayed water on his flowers." and (b) "Peter sprayed his flowers with water." Thus, two syntax-based regular expressions plus some information about NE as "np (people) + vp (spray) + np (object1) + pp (on) + np (object2)" and "np (people) + vp (spray) + np (object2) + pp (on) + np (object1)" are required.
Surface level extraction patterns can be hand built [33] or based on machine learning (ML) from a sample of annotated text (a corpus) [10] or from a few patterns which are known to be good indicators of the topic of interest (seed patterns) [34,35] to reduce the cost and time in constructing patterns manually. However, to extract the relations between objects in the complex sentences that frequently occur in technical and scientific texts requires deeper semantic knowledge. Reported systems [15-18] generally use a set of rules relevant to syntactic roles (e.g. subject, object, and modifier) obtained from parsers together with surface level patterns to extract the interactions between genes or gene products from the biological literature. Although extending the systems with syntactic roles or syntactic functions can achieve better performance compared to the pure pattern-matching approach, some errors resulting from a lack of semantic understanding still remain. For example, [15] mentions that their system will incorrectly extract a protein interaction between "Msp1p" and "Dec1p" from a sentence "These findings suggest that Msp1p is a component of the secretary vesicle docking complex whose function is closely associated with that of Dec1p.", because it conforms to the pattern "A associate with B" predefined within the system. In this respect we consider that deeper knowledge, describing the semantic relationship between verbs and their arguments, encoded in PAS are needed.
Predicate-argument structures
An event is described in a sentence by a composition of a verb and its arguments. A verb, which indicates a particular type of event conveyed by a sentence, can exist in its verbal form, its participial modifier format or its nominal form. For example, the normal form of a verb used to describe the event "making something active" would be activate, its participial modifier format would be activating or activated, and its nominal format would be activation. Beyond a verb, sentence constituents holding semantic roles to complete the meaning of an event indicated by the verb are called arguments. The semantic roles played by the set of arguments with respect to the particular verb are represented in the PAS frame of that verb.
Recently several major projects have been proposed that provide resources in the form of an English predicate-argument lexicon. These projects include VerbNet [24], FrameNet [21], and PropBank [22,23]. There are significant differences in approach among these 3 projects. For example, PAS of verbs sell and rent are proposed as two distinct structures in the case of PropBank and only a single structure for both verbs in the case of VerbNet and FrameNet (Figure 1). VerbNet defines general PAS for a group of verbs that share similar syntactic behavior, underlying Levin's alternations theory [32]. VerbNet's PAS for give contains sell and rent as members. Argument roles for all of the give verb members are assigned for agent, theme, and recipient illustrated by example sentences 1 and 2. In the case of FrameNet, PAS is defined based on the underlying principal of what users or applications expect to see for a specific event definition. FrameNet's PAS for event Commerce_sell shown in Figure 1 expects only argument seller and goods from the event driven by any verb in a set of verb members. Considering the annotation on sentence 1 in these 3 projects, "All Brownstein" is annotated as seller, agent, and seller in PropBank, VerbNet, and FrameNet respectively. Similarly, there is also an argument to support the annotation of "it" in all projects. But, only the PropBank scheme has an argument labeled price paid to support element "$60 a bottle" of sentence 1 which is likely to be an important participant of the event describing a selling activity. Moreover, a constituent "a week" in sentence 2 is considered to be an argument labeled as term only by the PropBank scheme. We consider that arguments like price paid for the events involving the verb sell, and an argument term for events involving the verb rent, are important for down-stream user applications. In contrast to VerbNet and FrameNet, PropBank defines individual verb-specific PAS frames which are likely to contain more detailed specifications of arguments than are possible for verb groupings. Moreover, PAS construction in a more verb-specific manner than either VerbNet or FrameNet would assist explicitly in discovering rules for mapping from surface syntactic structures to underlying semantic propositions.
Hence, we utilize PropBank's scheme as a basic starting point and examined sentences containing interesting verbs from a variety of molecular biology journal articles such as MEDLINE abstract [36] and full-text journal articles as EMBO [37], PNAS [38], NAR [39] and JV [40]. The verbs were analyzed and compared to frames proposed by PropBank, which were created based on an analysis of the Wall Street Journal corpus. At least one PAS frame per verb was defined. The verbs were chosen based on both their frequency in the articles and also based on their importance in a number of major event types such as gene expression, molecular interactions and signal transduction.
In PropBank a verb may get more than one PAS frame if the verb sense and its argument set differ, reflecting the fundamental assumption that syntactic frames are directly related to the underlying semantics. For example, PropBank defines three distinctive PAS frames (Figure 2) for the verb run on account of sense variation. Each structure contains its own set of arguments labeled with semantic roles. A semantic role of an argument represents a semantic relationship between the argument and its related verb. It is possible that in any particular sentence a complete set of semantic roles or a set of arguments for each sense will not all occur together. The example sentence in Figure 2(a) illustrates this point i.e. only Arg0 and Arg1 occur in this sentence without the occurrence of Arg2, Arg3, and Arg4 though all arguments are defined as core arguments of the PAS. In each PAS, arguments are labeled ranging from Arg0 up to Arg5 with a mnemonic label indicating its predicate-dependent role.
Besides these core arguments defined in PAS are adjuncts which are traditionally not defined in PAS because they can potentially take multiple values and not required to minimally define the event. PropBank does consider adjuncts when annotating sentences, and provides labels such as ArgM plus tags such as TMP for temporal information, LOC for locative information, PRP for a reason or motivation, etc. Covering the full working details of PropBank is out of the scope of this paper and we refer interested readers to [22,23] for more information. After manually defining PAS, PropBank has annotated the Penn TreeBank II Wall Street Journal corpus, which contains constituency and dependency information from the TreeBank project [41].
Events in molecular biology
According to the Gene Ontology (GO) [42], the term biological process refers to a broad category of biological tasks accomplished via one or more ordered assemblies of molecular entities (gene products). It often involves transformation, in the sense that something goes into a process and something different comes out of it. Examples of biological processes are cell growth and maintenance, signal transduction, metabolism and biosynthesis etc.
A biological process can be subdivided into temporal and spatial molecular events. Each molecular event is carried out by a gene product or well-defined assemblies of them. For example, phosphorylation of a protein molecule by a protein kinase is a molecular event, which is a part of the cellular signalling process or transcription of a gene by a polymerase is a part of the gene expression process. Hence, by definition a molecular event or a disruption of it will have a local effect in terms of the process that it is a part of and an observable or phenotypic effect in terms of overall effect of disruption of the entire process. For example, a mutation in the coding region of a gene that introduces a stop codon into the open reading frame would lead to a pre-mature termination of transcription considered as the local effect and may be responsible for a disease state of an organism due to deficiency of that protein as the phenotypic effect. Different events are described by different verbs (Figure 3) using its associated sets of arguments.
Need for semantic relationships in molecular event extraction
As we exemplified previously for the newswire domain, similar issues of syntactic variants will inevitably be encountered in scientific domains. The following examples from our analysis (Figure 4) illustrate these points.
The sentences (1)–(3) in Figure 4 show some different instances of the event eliminate taken from our corpus of MEDLINE [36] and EMBO [37] Journal articles. Here, we consider 3 different pieces of information to be extracted, i.e. A – causal agent of the event, B – the entity being removed, C – location at molecular (sequence) or cellular level where the entity is being removed. In Figure 4, sentence (1) shows simple indicative form of which the syntactic-based extraction pattern would be "A eliminates B in C" (where A = One mutation, B = the BamHI site and C = exon7); sentence (2) shows the passive form, without mention of A and C, for which a syntactic-based extraction pattern would be "B are eliminated" (where B = all three sites); sentence (3) shows a form, using a different preposition compared to sentence (1) in order to mention C, for which the syntactic-based extraction pattern would be "A would eliminate B within C" (where A = a 3-bp in-frame deletion, B = an asparagines residue and C = a kinase domain of the product).
Examples of sentences describing the event express are shown as sentences (4)–(6). Information slots consist of A – entity expressed, B – physical property of the expressed entity, and C – location referring to organelle, cell or tissue. In sentence (4), (where A = the enzyme, B = two mRNA isoforms of 2.4 and 4.0 kb, C = brain) the information needed to describe the event with respect to slot B is marked by using a prepositional phrase, but in sentence (5), (where A = two equally abundant mRNAs for il8ra, B = 2.0 and 2.4 kilobases in length, C = neutrophils) using an appositive form, seemingly not playing an important role in the description of the event in which it participates. Sentence (6), (where A = RNA and protein for all four transgenic TCR proteins and C = T cells, without mentioning B) shows a different kind of problem that arises because biologists generally would not think of "T cells" as an agent in this context, perceiving it as information about location. On the other hand, without deep domain knowledge one may understand "T cells" as an agent of the express event instead of its intended role as a cell or tissue.
These examples show that using regular expressions around syntactic information of the surface texts would not be adequate for IE to make sense of the complex surface structure. PAS represents information describing verb arguments and the semantic roles these arguments play in conveying a certain event. Different surface forms describing the same event can be mapped into the same PAS.
To illustrate this point we return to the example mentioned earlier, (a) "Peter sprayed water on his flowers." and (b) "Peter sprayed his flowers with water." Both sentences can be mapped into the PAS of a verb spray, which indicates the particular event "apply thin liquid to surface" with 3 required arguments (i.e. agent, liquid, surface). The sentence's constituent "Peter" in both sentences is perceived from its verb-specific semantic role to be an agent that does the action. "water", when it is either a direct object as in sentence (a) or an object of a preposition as in (b), is perceived as the liquid used in the event, and "his flowers" is perceived as the surface getting wet. Similarly, a surface text from molecular biological corpus such as "One exon is spliced out of the MLC3 nm transcript in smooth muscle to give an alternative product." could be conceptualized into PAS relationship as shown at the topmost level in Figure 5.
Figure 5 illustrates understanding a sentence from the surface text level up to the PAS level. The sentence's constituents "One exon", "is spliced out", "of the MLC3 nm transcript", "in smooth muscle", and "to give alternative product" have their syntactic categories as noun phrase, verb, prepositional phrase, prepositional phrase, and verb phrase respectively. At the syntactic relations level, "One exon" shows its role as the surface subject of the passive form verb "is spliced out" and other constituents play the role of complements.
Beyond the syntactic level of description, there are semantic levels including argument categories level and predicate-argument relations level. At the argument categories level "One exon", "the MLC3 nm transcript", "smooth muscle" and "alternative product" constituents pertain to the domain concept classes of a gene product (RNA), tissue and alternative mRNA respectively. At the highest level of our scheme the representation contains the most abstract information. Semantic roles played by other constituents to the verb indicating the event are represented at this level. Thus, the process of removal of an exon from mRNA (alternative splicing) is indicated by the verb splice out. Here, the verb arguments play the semantic roles of lost component ("One exon"), entity getting spliced ("the MLC3 nm transcript"), location referring to tissue ("smooth muscle"), and secondary predication – showing purpose or reason in this example ("to give an alternative product"). The semantic role secondary predication is assigned to the argument "to give an alternative product" because this by itself is capable of instantiating a PAS frame and is considered in our scheme to possibly be a core argument.
The semantics of a sentence relate in complex ways to the syntax of the sentence, as we can see from the illustration of semantic and syntactic levels in Figure 5. Using this layered approach different surface forms describing the same event can be mapped into the same PAS. Thus, PAS could be helpful for IE to overcome the syntactic variation problem. After we describe the PAS frames constructed for molecular biology (PASBio), we provide an explanation about how to apply this knowledge in PASBio for event extraction.
Defining predicate-argument structures for molecular biology
In molecular biology, a gene and its products are at the center of the study, as a set of these molecular entities dictate, and their products carry out, different functions at the cellular level and the combined effects can be seen at the organism level. Hence, in the literature a gene or a gene product is possibly described as an agent participating in some events, with the help of various appropriate verbs indicating the specific events. Different molecular-level or phenotypic effects are described as the other arguments of such events. As described above, PAS is a representation of semantic relationships between arguments with specified roles and a verb relating to a particular event narrated in a sentence. Thus, PAS would be a natural choice for IE, especially event extraction in molecular biology.
Guidelines to define PAS
We use PropBank's scheme (with necessary adaptations) to define PAS for the molecular biology domain. To define PAS for any verb, a survey about the usages of the verb from a set of sample sentences in a representative corpus is made. Examining the usage of an individual verb will indicate if it needs to be divided into several senses. In PASBio, these senses are divided with the aim of obtaining fine-grained semantic senses using the WordNet [43] lexical database. Each of PASBio's PAS contains a set of core arguments. A core argument is an argument shown by its usage to be important to complete the meaning of the event. Nevertheless, if an argument is considered important but there is no evidence to show that the argument exists together with the predicate in at least 20% of our selected sentences, this predicate may not be assigned as a core argument. There are two different types of core argument: the first type plays a role during the main event denoted by the predicate while the second type plays a role after the main event and aims to express results or consequences of the main event. Further details are given in the next section (Figure 6-Frame 1) illustrated with the PAS for mutate. Arg X (with X, a cardinal number, starting from 0 and then incremented for each additional argument) is used for labeling the first type of core argument and ArgR is used for the second type. A mnemonic label is added after Arg X and ArgR in order to give a short description of the semantic role played by the argument. Biological function and usage of the argument are used to describe the semantic role in PAS. No attempt is made to ensure the consistency of mapping between argument labels (argument name) and the roles (the mnemonic labels) played by the arguments across verb frames, except Arg0. Arg0 is reserved for only the argument playing the semantic role of agent. In some cases, this agent argument is not found in the usage of some verbs. Thus, PAS frames of such verbs will contain the core argument from Arg1. See PAS frames for mutate (Figure 6-Frame 1), express (Figure 9) and transform.02 (Figure 10-Frame 9) as examples.
In addition to annotating a sentence's constituents corresponding to core-arguments with the tag Arg X or ArgR, the sentence's constituents which do not play the role of core arguments but fall into three types, i.e. adverbial, negation and modality, are annotated with the tag ADV or MAN in the case of an adverbial, NEG in the case of negation, and MOD in the case of modality. At the current stage of this project, only adverbials in terms of adverbs are considered to be annotated as MAN (for a manner adverb) or ADV (for other types of adverbs). If any adverbials in terms of phrases or clauses are mandatory for expressing events indicated by particular predicates, these adverbials will be defined as core arguments within PAS frames. For example, an adverbial phrase playing the role of locative modifier is included in the set of core arguments in the frame for predicate initiate. (Refer to example sentence "Apparently HeLa cells either initiate transcription at multiple sites within RPS14 exon 1."). Moreover, we are interested in distinguishing only the adverb playing the roles of manner modifiers (e.g. normally, genetically, etc.) from other adverbs. A manner adverb deserves special distinction from other adverb types because it shows how a certain action is performed which is very important to understand facts in a biological sentence. For example, "normally" in the sentence "Mice have previously been shown to develop normally" is necessary for IE in order to understand that there is no problem in the development of the mice. Other types of adverbs for example play the roles of aspectual modifiers that give information about whether some event or state of affairs is completed or is still going on, and so forth (e.g. "still" in the sentence "Wanda still would like to talk about the music festival."), adverbs playing roles as frequency modifiers that indicate the frequency of a certain type of event (e.g. "always" in the sentence "One always hears rumors."), adverbs playing roles as focusing modifiers that consist of the four words even, only, also, and too (e.g. "The transcription is initiated only in female blastoderm embryos."), and so on will be all tagged as ADV. In case of negation and modality, NEG and MOD are given directly to a negator word (i.e. not or n't) and a modal verb (i.e. will, may, can, shall, must, might, should, could and would) respectively. Though negations (operating at the sentence level) and modality (operating at various levels) are not defined as core arguments (mandatory arguments) within any PASBio's PAS frames because linguistically both of them cannot even be considered as any types of predicate's arguments, they are all worth annotating from an IE perspective if they exist in the same clause where a focused predicate exists. Similarly, adverbials which are not mandatory enough to be core arguments are also considered worthy of being annotated when found in the text. We consider that they should not be ignored because they can significantly alter or even reverse the meaning of the sentence.
Examples of defined PAS
In this subsection, we show some examples of PASBio's PAS frames and discuss how each frame is defined by examples of sentences relevant to it. There are three important cases that we examine in comparison to PropBank: (1) verbs that are rarely used in general language (e.g. splice) or have a unique biological interpretation (e.g. express, translate, etc.), (2) verbs that have a similar meaning used in the newswire domain and biology domain but show different patterns of usage (e.g. alter, initiate, etc.), and (3) verbs that are used with the same meaning and usage style in both domains (e.g. abolish, delete, etc.). The usage of different verbs in biology influence PAS for biological domain falls into four groups: A – same sense, more arguments; B – same sense, fewer arguments; C – same sense, same structure; D – different sense or does not occur. Table 1 shows some verbs for each group. We give PAS of two verbs as examples of each group.
Group A
Verbs in this group have been used in biology documents with the same semantic sense as in PropBank, but they required more core arguments in their structures.
Consider the event of mutation, one of the most important biological events and a general cause behind genetic diseases. The verb mutate is used to describe the changes in an entity (gene or gene product) and mutations can be natural or engineered. PropBank defines two arguments for this verb which are Arg0: agent and Arg1: entity undergoing mutation, but from analysis we propose four arguments for the PAS frame of the verb mutate. As mentioned in the Guidelines section, Arg0 is reserved only for the argument playing the semantic role of agent. From all the examples we observed, passive forms are used to describe mutate events which mean that the agent does exist in the event but it is unnecessary to be explicitly stated because it is commonly known by the domain experts. This results in PASBio's core arguments for mutate starting from Arg1 and we leave a position for agent which possibly could be mentioned in other biological sub-domains. The PASBio's Arg2 describing event participating entities (referred to as 'Name Entities') is analogous to PropBank's Arg1. Thus PASBio's Arg1, Arg3, and ArgR are extra arguments compared to PropBank. The arguments Arg1 and Arg3 are captured conforming to linguistic criterion [44] which considers that a sentence element which plays a particular role to a predicate will be considered to be a core argument in a PAS frame even though it does not exist in every sentence in which the predicate appears. In sentences where such an element is omitted we infer that it is implied by the existence of the predicate. For example, in the sentence "John is eating" we infer the existence of a core argument of eat which denotes a type of food. Similarly, Figure 6-Frame 1 shows that Arg1 and Arg3 do not exist in all sentences 1.1 to 1.3, but are assigned as core arguments by their intuitive presence in the domain models of biologists. Noticeably, consequences of the event driven by verb mutate are often seen in examples. Apart from "changes at molecular level" assigned as Arg3, the consequence, "changes at phenotype level" is suggested as ArgR (explained below). Sentence 1.1, 1.2, and 1.3 support this explanation.
The argument ArgR:results/consequences is an argument giving information about consequences after the event denoted by the predicate occurs. For mutate, most of the example sentences describing this event contain an ArgR argument, revealing the necessity of it. The requirement of this argument from an observation perspective coincides with biologist's viewpoint, thus we consider this as a core argument (more precisely an IE core argument) and named as ArgR instead of Arg X (a core argument from a purely linguistic perspective). We make this distinction under the rule that Arg X has to play a role during the event but not after the event. This condition is depicted by a formula like "mutation event = (Arg X + mutation + Arg X) + ArgR". Empirically, we find that this result argument (ArgR) is used with verbs relating to an abnormal biological phenomenon. Examples of other verbs that need this argument are skip, delete, etc.
Verb initiate also takes additional arguments as core arguments. As shown in Figure 6-Frame 2, Arg2 (sentences 2.1 and 2.2) describes the point of transcription initiation and Arg3 provides information about the tissue/cell where the gene (or product) is expressed. In PropBank, the sentence's segments defined by the parser with functional tag as LOC (location) will be considered as non-required elements. However, the extraction of spatial information is very important from the perspective of biological description. Furthermore, another interesting point that can be seen from the examples in Figure 6-Frame 2 is that authors in biology not only put the agent but also various other kinds of semantic roles in the subject position. In Sentence 2.1 "HeLa cells" is syntactically the subject which seems to be the agent of an initiate event, but domain knowledge suggests that the agent can be only a protein (usually polymerases bound to the gene being transcribed) in this case. "HeLa cells" is annotated as Arg3:location as tissue or cell instead of Arg0:agent. In sentence 2.2, "I kappa B-epsilon translation" is also a subject as in the previous example, but it is "entity created" assigned as Arg1. Only in Sentence 2.3 (describing initiation of signaling event), the subject of the sentence fills the semantic role "agent", so a subject "RTKs" can be annotated as Arg0. Additionally, the point to note is "the entity created" in sentence 2.3 is different from sentence 2.1 and 2.2 as it is a signaling event that is initiated, but not a transcription or translation event.
Group B
Verbs in this group have been used in biological texts with the same semantic sense as in PropBank, but they required fewer arguments in their structures in our PAS
Verb block both in biomedical texts and in business news texts has very similar semantics. However, an event described by verb block in the biomedical domain may not mention information about secondary predication and instrument most of the time. The semantic role secondary predication is assigned to the argument that is in itself capable of instantiating another PAS frame. The sentence " [JohnArg0] blocked [MaryArg1] from [completing her dissertationArg2] with [his constant pesteringArg3]." is annotated by PropBank's PAS frame. An argument Arg2-secondary predication is annotated for "completing her dissertation" because this contains in itself the PAS of the verb complete. From this PropBank example, the meaning of the event denoted by block cannot be completely understood if the sentence just states as " [JohnArg0] blocked [MaryArg1]." as it is necessary to mention the action being stopped. In contrast in the biology domain, by mentioning only the entity being stopped (Sentence 3.1–3.3), the expert reader can understand that the event which applies to that entity is being stopped without providing an explanation of the action being stopped at the position of secondary predication. Similarly, an instrument used to block is encoded in the nature of an agent or causer. The structure of block and its examples are given in Figure 7-Frame 3. Only core arguments as defined in the structure exist in Sentence 3.1 and 3.2 (the agent is not mentioned). In sentence 3.3, MAN is used to label "specifically" as this adverb plays the role of a manner modifier.
In Figure 7-Frame 4 the PAS frame of generate is similar to that of block. Only Arg0-agent and Arg1-entity created are expressed in all observed sentences from our biology corpus.
Group C
Verbs in this group have been used in biological documents with the same semantic sense as in PropBank. Moreover, their usage in both the biology corpus and PropBank indicates that their PAS frames are identical. Specialization of domain does not seem to affect verbs in this group.
In Figure 8, Frame 5 and Frame 6 show PAS for confer and lead. In both biology and newswire corpora, confer is used with semantic "to give (as a property or characteristic) to someone or something" and lead to is used in the sense of "to tend toward or have a result".
Group D
Verbs in this group have been used in biology documents with a different semantic sense compared to PropBank, or PAS frames for them are not found in PropBank. More than one semantic sense is found in our corpus for some verbs. PAS frames for express and transform are presented in Figures 9, 10, respectively to illustrate predicate-argument structures for this group.
The verb express is used in the biology domain with the meaning "to manifest the existence of a gene or gene product" (or detection of the same by the experimenter) unlike its normal usage with the meaning of "give an opinion or send quickly". The PAS of express is given as Figure 9.
In the case of transform, two senses are used in biology papers: "to cause (a cell) to undergo genetic (or neoplasmic) transformation" as shown in Figure 10-Frame 8 and "to transfer a gene from source organism into target organism" as shown in Figure 10-Frame 9. Even though the first meaning of transform found in our corpus is similar to the sense of "change" found by PropBank, there is still a huge gap between them. In the biological literature, illustrated by examples in sentences 8.1–8.3, this genetic transformation mentions only the agent or causer, what entity is getting transformed, and what will be the effect after transformation. It will not mention the start state of the entity undergoing transformation because it is known from the expert reader's domain 'common sense' knowledge that the start state refers to a normal condition of that entity. Transform in the second sense always occurs in a sentence connected by preposition into, and in the passive voice form in which no mention is made with regard to the agent.
Complexities in biology texts
In the discussion so far we have assumed that the predicate is the center of semantic information. Here we intend to show that the argument contents can change the event description specified by the predicate, by examining sentences that describe an 'alternative splicing' event. Alternative splicing is used to generate multiple transcripts from a single gene and hence is a helpful event for increasing the functional complexity of eukaryotic systems.
Consider the following example of a set of sentences that talk about the 'expression' of a single type of mature mRNA generated from 'splicing' of pre-mRNA and generation (and expression) of multiple mature mRNA transcripts with different properties from the single type of pre-mRNA. Sentences annotated follow PASBio's frame for express: (a) "Northern blot analysis with mRNA from eight different human tissues demonstrated that [the enzymeArg1] was expressed exclusively [in brainArg3], [with two mRNA isoforms of 2.4 and 4.0 kbArg2]." and (b) "[A complementary DNA cloneArg1] encoding the large subunit of the essential mammalian pre-messenger RNA splicing component 2 snRNP auxiliary factor(U2AF65) has been isolated and expressed [in vitroArg3]." Sentence (a) is considered as a sentence denoting the alternative splicing event but sentence (b) is considered as a negative (not describing alternative splicing) sentence, which talks about expression of an mRNA of a splicing factor.
It would be difficult, based on word contents or regular expression methods, to put these two examples into different 'bins' for alternative splicing events. But the discussion about the length of the two different transcripts in Arg2 (with two mRNA isoforms of 2.4 and 4.0 kb) in the first sentence can be helpful to understand it as a sentence discussing about alternative splicing. On the other hand, the later sentence contains all the interesting words (e.g., mRNA, express and splicing) but misses Arg2, hence describes just an expression event.
Utilization of PASBio
Construction of PAS frames by expert introspection may be considered as a time-consuming process, however domain-specific PAS frame definitions have valuable uses in several applications as discussed below.
Each PAS frame in PASBio provides a set of semantic relationships between arguments participating in an event and a verb conveying the event. Although we focus on applying PASBio for event extraction in the molecular biology domain, information processing applications that require semantic understanding of a sentence will be able to take advantage of this knowledge. For example, machine translation (MT) requires encoding a surface sentence of a source language into a language independent logical form of clause meaning, and then generating from this logical representation a surface sentence in a target language. PAS would be one practical choice to be used as such a logical representation in MT [29,30]. In the case of a text summarization application, PAS frames could naturally be employed as the basic unit of a discourse representation, before being summarized [45]. PASBio is available online for the wider research community in the molecular biology domain for exploitation in such applications.
With respect to our molecular event extraction system, as we discussed in the introduction, PASBio takes on the role of a reference source in the stage of corpus annotation for creating training examples for machine learning. The planned IE system is composed of 4 activities: (1) construction of PASBio semantic lexicon; (2) annotation of full-text journal in terms of semantic represented in PASBio's frames; (3) construction of the module for automatically transforming an unseen sentence into a logical form of semantic relationships drawn within each particular PASBio frame; (4) integration of the resultant automatic semantic interpretation module within the event extraction system. So far, manual annotation and machine learning have not been completed yet and will be reported elsewhere. For a description of an IE system that can make use of such an annotated corpus we refer readers to the work of for example Surdeanu et al. [46] who uses PAS defined for the newswire domain to extract market change events.
Apart from our corpus-based semantic interpretation approach, several other research groups have proposed systems for event extraction from the biological literature, especially about the interaction information between genes and genes product. Related work so far can be summarized into two sets. The first set of methods use regular expressions and rely on syntactic patterns. These methods may use statistical models of the surface words [12,13], rules of the sentence elements' precedence order [11], shallow knowledge like part of speech tags, syntactic roles of constituents [15,16], gene/protein name dictionaries and domain knowledge (e.g. a template slots for the particular event) about the events they intend to extract [17,18]. A template used in this research group consists of only a simple set of slots for a simple predicate (i.e. the predicate relating only two arguments: subject and object) and only a shallow notion of the predicate-argument structure has been considered (i.e. consider one argument as subject and another as object, but not consider as arguments' semantic roles).
The only work in the second set, that has taken into account a large number of linguistic and deeper semantic aspects is, that of Novichkova et al. [14]. The approach described in Novichkova et al., is to construct a biology IE system (MedScan) containing two components: an NLP engine deducing the semantic structure of a sentence, and a configurable information extraction component to validate and interpret results produced by the NLP engine, in order to achieve a flexible and efficient IE system. In one of its steps, the authors propose to transform the syntactic tree of a whole sentence into a normalized semantic tree, which represents the logical relationships between the words in a sentence. To carry out the transformation, a set of semantic frames describing predicate-argument structures, are required. However, the MedScan system's semantic interpretation process is still under development and not precisely specified.
As mentioned above, most of the approaches, whether a deep notion of predicate-argument relations is taken [14] or a shallow notion [17,18], do require a reference resource of PAS frame for each predicate. In this respect, we believe that PASBio's description of PAS frame for each predicate would make a useful complement to other approaches.
Recently, another research group [47] reported the aim of annotating a biological corpus with semantic knowledge in the form of PAS. While this work appears to be at an early stage it again shows the importance of the definition of predicate-argument frames and the semantics of their arguments as a key knowledge for IE in the molecular biology domain.
Conclusions
With the explosion of molecular data, tools developed by computer scientists are gradually being applied and integrated in the domain of biology to aid in information access and knowledge discovery. Text data appearing as reports about biological discoveries demands automated IE methods for faster knowledge discovery. Traditional IE systems that use regular expressions based on shallow chunking at the phrase level (e.g. noun phrase, verb phrase, preposition phrase etc.) capture weak notions of 'argument' for event predicates and their linear precedence. Such approaches seem to be inadequate to the goal of achieving high accuracy in event extraction in molecular biology. PAS which is used as a representation of the semantic relationship between a verb and its arguments participating in the event has the potential to support deep knowledge acquisition from a sentence within the extended system framework that is now being proposed within the IE community.
Due to the importance of PAS and the lack of a specific PAS frame resource for the domain of molecular biology, we have proposed the analysis of PAS for molecular biology in this article. We have analyzed sentences for 30 verbs (and different frames per senses of the verb) from MEDLINE abstracts and full-text journal articles where the sentences contain each verb in its verbal form and its participial modified form for building PASBio. Our analysis suggests in some cases a significant difference in the predicate frames compared to those obtained from analyzing news articles by the PropBank project. In addition to the significance of verb senses used in the molecular biology domain, syntactic constructions also differ markedly; such as the use of passives allowing the semantic subject to be omitted where they are part of the common-sense understanding in the domain. Human readers are required to have domain knowledge in order to understand the texts. Our result frames and examples are available to the wider research community as a knowledge base at PASBio's webpage.
In the future, we intend to utilize knowledge from the PASBio frames for annotating a corpus to be used as training examples to achieve automatic annotation of PAS semantics into sentences. Furthermore, we aim to complete analyzing PAS for more verbs related to molecular events and afterwards to extend our analysis to sentences containing the nominal forms of verbs.
Methods
Selection of verbs
The English language used in research articles of biological and biomedical sciences is a sublanguage of written natural language. While most of its vocabulary is similar to that of general English, some verbs are domain-specific in nature. Our main focus here is the verbs that are used for describing molecular events in biology. Various researchers have different areas of interest and new concepts are added in the literature continuously. However, the areas of cellular signaling, gene expression, regulation and disruption of expression events are very important for the larger community of investigators involved in basic biomedical research and those involved in high throughput analysis. They are discussed throughout different parts of papers as possible cause of normal and disease states of different organisms. Hence, ignoring the normal distribution (frequency) of different verbs in the literature we choose the verbs from those involved in the above-mentioned processes (events). Most of the verbs are shown in Figure 3.
Selection of example sentences
Information extraction work is still largely carried out using PubMed abstracts. Using abstracts is advantageous because they contain the highest density of keywords compared to other section of research articles but our intuition is that bio-text mining should scale-up to analyze full journal articles where the most detailed results are contained along with supporting evidence, comparisons to others work and background information, etc. [48] Recent investigations have shown that Introduction and Discussion sections apart from paper abstracts may be viewed as interesting sources of important biological information [49]. We thus define our PAS by analysis on sentences from MEDLINE [36] and from all other sections except the Method section on EMBO [37]. Furthermore, we inspect the usage of some verbs in other journals such as PNAS [38], NAR [39] and JV [40] in order to achieve usage agreement and good PAS. Sentences from the Method section are not used in this analysis as they are limited in terms of biomedical information, have generic written styles and verb sense usage tend to overlap with general language.
Sentences were carefully chosen to cover a broad usage of each verb under study from the MEDLINE and full text journal corpora as described before. We tried to choose equal numbers of sentences containing a particular verb in its verbal format and its participial modifier format. Before starting an analysis on each sentence, a sentence was parsed using Connexor Parser [50] that uses Functional dependency Grammar (FDG), to give parse tree, word, lemma, syntactic function and dependency links between words in order to help in determining the boundary of each argument exists in a sentence. This parse tree served as a useful guide in hand analysis, but was not considered by any means as a gold standard. At least 10 sentences were selected to determine PAS of the verb under study. The use of the parser considerably reduces the manual labors involved in defining arguments.
Authors' contributions
This work was directed by NC. TW carried out the analysis of the predicate-argument structures with technical support from NC and biological knowledge from PKS. PKS chose the predicates and the sentences analyzed from the MedLine corpus. Sentences from other corpuses were complemented by TW. TW prepared the figures (except fig 3 by PKS). All authors contributed during the whole length of the project and writing of the paper. All authors read and approved the final manuscript.
Acknowledgements
We gratefully acknowledge the kind support of Yoko Mizuta, Ai Kawazoe and Tony Mullen (NII) for useful discussions on the linguistic aspects of the examples discussed in this paper. We would also like to express our gratitude for the many helpful comments provided by the anonymous reviewers. Part of the work has been funded by Dr. Peer Bork (EMBL) for Parantu K Shah's travel to Tokyo. Partial funding came from the Japanese Ministry of Education and Science (grant no. 14701020).
Figures and Tables
Figure 1 Predicate-argument structures of PropBank, VerbNet and FrameNet. The scheme to assign predicate-argument structures can be varied among different projects due to their different focused applications. This figure shows the differences of predicate-argument structures defined from these three projects: PropBank [22, 23], VerbNet [24] and FrameNet [21]. Similar scheme as PropBank is applied to our PASBio project. Discussion about the reason why we are interested in PropBank scheme is discussed in the main text.
Figure 2 PropBank's three distinct predicate-argument structures of run. The figure shows examples of predicate-argument structures defined in PropBank [22, 23] project. PropBank defines different predicate-argument structures on account of verb sense variation. Three distinctive predicate-argument structures are defined for the verb run. A predicate-argument structure for each sense contains its own set of arguments labeled with semantic roles as shown in the figure.
Figure 3 Molecular events shown by associated predicates. The figure shows a hypothetical signal transduction pathway of an idealized cell. The signal is triggered at the outer membrane ligand-binding to receptor dimers. This signal is mediated (by various proteins) to the nucleus of the cell using various events (protein-protein interactions, phosphorylation etc.) and initiates transcription of a gene. The protein product (after splicing, translation and synthesis) of the gene inhibits receptor signaling. Thus, it regulates its own expression levels via a negative feedback loop. The direction of information flow is shown with arrows. Cell compartments, molecular entities and predicates describing various events are shown. The predicates analyzed in this work aim to cover events in gene expression, regulation and signaling processes.
Figure 4 Example of different forms of eliminate and express. Sentences (1)–(3), three different sentences using predicate eliminate taken from MEDLINE [36] and EMBO [37] Journal articles, are given as examples to illustrate the variation of the language usage in biological articles. To convey the information marked as [...A] or [...B] or [...C] can be written in various forms as discussed in the main text. Similarly, the variation of surface linguistic expressions can also be seen from sentences (4)–(6) conveying event express. Sentence (6) is an example to show that the domain knowledge is really necessary for correct understanding.
Figure 5 Syntactic and semantic level description of the surface text. The understanding makes on the surface text can be shown in different levels. Syntactic categories level gives a syntactical class for each constituent of the sentence. Syntactic relations level describes syntactical function of each constituent of the sentence to predicate of the sentence. Argument categories level offers the semantic concept for each constituent of the sentence. Predicate-argument relation level helps in understanding the semantic role played by each constituent or argument related to its predicate.
Figure 6 Examples of predicate-argument structures for group A As shown by Frame 1, PAS of mutate provided in PASBio contains more arguments than as suggested by PropBank [22, 23]. Extra arguments responsible for consequences of the event mutate are proposed to be core arguments as they are often seen in sentences from biomedical documents. WordNet [43] sense 1 – undergo mutation is correspond to biological sense we found for mutate. Three sentences are given to illustrate how surface sentences are mapped into PASBio's predicate-argument structure. Frame 2 shows predicate-argument structure of initiate which also belongs to group A – same sense, more arguments as same as predicate mutate. Extra arguments responsible for spatial information of the event intitate are proposed to be core arguments in PASBio, because of their importance from the perspective of biology as discussed in the main text.
Figure 7 Examples of predicate-argument structures for group B Predicate-argument structure for block, belonging to group B – same sense, fewer arguments, is shown as Frame 3. Though block is used to mean stop in both biological corpus and business news corpus, set of arguments are not the same. Use of MAN is illustrated here. Similar to predicate block, PASBio's predicate-argument structure of generate has less arguments than in PropBank [22, 23] as shown in Frame 4.
Figure 8 Examples of predicate-argument structures for group C Predicate confer and lead are assigned to group C – same sense, same structure, so their structures constructed in PASBio are as same as in PropBank [22, 23] as shown in Frame 5 and Frame 6, respectively.
Figure 9 Predicate-argument structure of express (a group D predicate) Predicate express is used in biological documents to mean as WordNet [43] sense 5 – manifest the effects of a gene or genetic trait which is totally different from the usage found in business news (i.e. say and send very quickly). Thus express is classified to group D – different sense or does not occur.
Figure 10 Predicate-argument structures of transform (a group D predicate) PASBio proposed two different structures for two different senses of transform found from the usage in molecular biology corpus. Predicate-argument structure as transform.01 is defined based on the usage with the meaning of WordNet [43] sense 2 – change or alter in form, appearance, or nature and transform.02 is in accordance with the WordNet sense 6 – change(bacteria cell) into a genetically distinct cell by the introduction of DNA from another cell of the same ore closely related species.
Table 1 Examples of predicates in each group
Group A : same sense, more arguments
alter, begin, develop, disrupt, inhibit, initiate, mutate, proliferate, skip
Group B : same sense, less arguments
generate, block, decrease, lose, modify
Group C : same sense, same structure
abolish, confer, eliminate, lead to, result, delete
Group D : different sense or not occur
splice, express, truncate, translate, encode, transform, catalyze, transcribe, recognize
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| 15494078 | PMC535924 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Oct 19; 5:155 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-155 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-501556656910.1186/1471-2148-4-50Research ArticleEvolutionary relationships of Fusobacterium nucleatum based on phylogenetic analysis and comparative genomics Mira Alex [email protected] Ravindra [email protected] Boris A [email protected] David [email protected]íguez-Valera Francisco [email protected] Evolutionary Genomics Group, División de Microbiología, Universidad Miguel Hernández, Apartado 18, San Juan 03550, Alicante, Spain2 UMR CNRS 8079, Ecologie, Systématique et Evolution, Université Paris-Sud, bâtiment 360, 91405 Orsay Cedex, France2004 26 11 2004 4 50 50 18 8 2004 26 11 2004 Copyright © 2004 Mira et al; licensee BioMed Central Ltd.2004Mira 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
The phylogenetic position and evolutionary relationships of Fusobacteria remain uncertain. Especially intriguing is their relatedness to low G+C Gram positive bacteria (Firmicutes) by ribosomal molecular phylogenies, but their possession of a typical gram negative outer membrane. Taking advantage of the recent completion of the Fusobacterium nucleatum genome sequence we have examined the evolutionary relationships of Fusobacterium genes by phylogenetic analysis and comparative genomics tools.
Results
The data indicate that Fusobacterium has a core genome of a very different nature to other bacterial lineages, and branches out at the base of Firmicutes. However, depending on the method used, 35–56% of Fusobacterium genes appear to have a xenologous origin from bacteroidetes, proteobacteria, spirochaetes and the Firmicutes themselves. A high number of hypothetical ORFs with unusual codon usage and short lengths were found and hypothesized to be remnants of transferred genes that were discarded. Some proteins and operons are also hypothesized to be of mixed ancestry. A large portion of the Gram-negative cell wall-related genes seems to have been transferred from proteobacteria.
Conclusions
Many instances of similarity to other inhabitants of the dental plaque that have been sequenced were found. This suggests that the close physical contact found in this environment might facilitate horizontal gene transfer, supporting the idea of niche-specific gene pools. We hypothesize that at a point in time, probably associated to the rise of mammals, a strong selective pressure might have existed for a cell with a Clostridia-like metabolic apparatus but with the adhesive and immune camouflage features of Proteobacteria.
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Background
The genus Fusobacterium, together with some close relatives such as Leptotrichia, forms an ecologically and physiologically coherent group [1]. They seem to be inhabitants of the mammal gastrointestinal tract probably specialized in the oral cavity. Specifically, they are components of the dental plaque, a highly complex habitat that has received considerable attention in recent years due to its involvement in dental pathology [2]. They are all fermentative anaerobes that use mostly peptides as their energy source (see, for example, [3]). The species Fusobacterium nucleatum has received particular attention being a key component of the human dental plaque that also has considerable pathogenic potential. In fact after Bacteroides, Fusobacterium is responsible for most human anaerobic infections, producing abscesses at different locations and aspiration pneumonia among other serious conditions [4,5].
Phylogenetically speaking the fusobacteria have become somewhat of a puzzle [6]. Originally classified with Bacteroides and other Gram negative anaerobes, their association became conflicting when, after the extensive gene sequencing carried out by the mid 80's, it became clear that Bacteroides showed a clear relationship to other aerobic Gram negatives such as Flavobacterium or Cytophaga [7-9] while on the grounds of the 16S rRNA sequence Fusobacterium appeared as a separate cluster only distantly associated to the low G+C Gram positives [10,11]. However, this association is methodology sensitive, and different algorithms or genes associate them with other groups such as the Proteobacteria, the Cyanobacteria, the Thermotogales, or within the Firmicutes (see for example [12-14]).
The publication of the Fusobacterium nucleatum genome [3] did not solve the problem since although most BLAST top-hits appeared as Clostridium species (low G+C Gram positives) genomic analysis showed also a strong proximity to Proteobacteria. Based on the ERGO chromosomal clustering tool, F. nucleatum had more "clusters" of genes with the same gene order in common with Escherichia coli than with Enterococcus or Staphylococcus, although less than with Clostridium or Bacillus [3]. As expected, most elements typical of a Gram negative cell wall were found in the genome including porins, outer membrane transport systems, lipid A synthesis pathways and LPS core compounds. It may be argued that the Gram negative cell wall is the ancestral situation and the Gram positives have lost the outer membrane. However, this scenario requires a remarkable stability in the components of the fusobacterial cell wall to remain so similar to other distant bacterial phyla [15]. On the other hand, there is the possibility that large portions of the fusobacterial genome could be the result of horizontal gene transfer (HGT). The oral cavity environment where F. nucleatum thrives is an ecosystem with a large bacterial biodiversity. In a recent survey using 16S rDNA sequences from sub gingival plaque samples, 347 species or phylogroups were identified, and the best estimate of the total species diversity in the oral cavity is approximately 500 species [16]. These 347 species belonged to 9 different bacterial taxa and F. nucleatum interacts with a great deal of them, because it plays a crucial "bridge" role between early and late colonizers of the tooth surface [17] and forms carbohydrate-mediated coaggregations with other species [18-21]. Because of the many species with which F. nucleatum interacts and aggregates (including spirochaetes, proteobacteria, bacteroidetes, firmicutes, and even fungi) there is a great potential for HGT.
We have reanalysed the fully sequenced genome of F. nucleatum, using a variety of bioinformatics tools, in an attempt to clarify the phylogenetic position of the Fusobacteria and the relative contributions of vertical descent and horizontal transfer in shaping the genome of this highly specialized organism. In addition, our study aims at providing material for further discussions on evolution of the gram-negative cell wall, and on the evolution of bacterial communities in micro-environments.
Results and Discussion
Phylogenetic position of core fusobacterial genes
It is generally assumed that in every genome there is, at least, a basic core of genes that are inherited vertically and may be used to infer relationships among prokaryotes [22]. Although most often the relationships obtained with the core genes are consistent with that of the 16S rRNA gene we have extended this type of analysis to include as many genes as possible. Firstly, a Bayesian tree using the combined 16S-23S rRNA sequences was constructed (Figure 1). A neighbor-joining tree based on the concatenated alignments from 44 ribosomal proteins gave a similar result [see additional file 1]. In both cases, the fusobacteria appear as a clearly defined and distinct group that branches out at the base of the Firmicutes but as an independent phylum. Finally, the 23 proteins conserved across all sequenced Bacteria were selected [23] and trees were constructed based on their sequences. Many of them gave results consistent with the previous two trees [see additional file 1]. However, some typical core genes hinted of a mixed ancestry. The ribosome-associated protein prlA, for example, produced a tree that associated Fusobacterium with the cyanobacteria and the elongation factor tufA with the proteobacteria. Other cases are also unclear. DNA pol III is a complex holoenzyme formed by 10 subunits in E. coli [24]. Interestingly, subunits α and β of the polymerase III seem related to Firmicutes while the gene for subunits γ and τ to Thermotoga and Aquifex. The RNA-directed DNA polymerase and a RNA helicase seem related to archaeal counterparts. In Clostridium, for example, to which most informational genes of Fusobacterium have a best match, all subunits of both polymerases cluster clearly into the Firmicutes (data not shown). Summarizing, the fusobacteria appear as an independent taxon with remote relatedness to the Firmicutes. Although the phylogenetic signal for many genes was considerably weak, the rRNA genes and the ribosomal proteins were very congruent and therefore their trees (Figure 1 and additional file 1) are likely to represent a reliable phylogenetic reconstruction of the core genome. Heretofore we will refer to this affiliation as ribosomal phylogeny and consider it as the reference for vertically inherited genes. We have assumed that genes showing a close relationship to other bacterial taxa (including the Firmicutes) are possible candidates for HGT origin, particularly when a close association has been proved by more than one approach.
Figure 1 Phylogenetic tree (Bayesian method) using the combined sequence of the 16S-23S rRNA of representative bacterial species. The Fusobacteria are a coherent and taxonomically independent group, that branches out at the base of the lineage leading to Firmicutes. Numbers represent bootstrap values. In the case of the branching of Fusobacteria/Firmicutes, the numbers represent the values obtained by four different methods: BA: Bayesian; NJ: Neighbor-joining; MP: Parsimony; ML: Maximum likelihood.
GC-skew plots
Figure 2 shows the GC-skew plot for F. nucleatum compared to Clostridium tetani (the sequenced species to which it shows the highest number of homologous genes) and Bacteroides thetaiotaomicron (the Gram-negative species to which it shows the highest number of homologs). Due to differences in mutational biases between the leading and lagging strands, it is common to find the GC skew value (G-C/G+C) with opposite signs on each replichore, the change in sign indicating the origin or terminus of DNA replication [25,26]. This skew is independent of GC content [27]. As the figure shows, both the Gram-positive and Gram-negative bacteria which appear to have the most similar gene content to F. nucleatum display a standard plot, with mainly positive values over the right replichore and negative values on the left replichore. Fusobacterium, however, does not show a clear pattern, with constant shifts in GC-skew values across the genome. This situation could be caused by horizontal gene transfer (HGT) incorporating xenologous sequences with a different GC-skew across the recipient chromosome, distorting a clear-cut plot. In favour of this, many GC skew oscillations coincide with clusters of putative xenologous origin (see thin arrows in Figure 2, top). It is interesting to note that an oscillating GC skew plot is also observed in other genomes that appear to have undergone massive HGT episodes (see, for example [28]).
Figure 2 (G-C)/(G+C) values (GC-skew) plotted every 5000 bp for Fusobacterium nucleatum, the low-GC Gram positive Clostridium tetani and the bacteroidete Bacteroides thetaiotaomicron. Red wide arrows represent replication origin (bottom) and terminus (top). Orange thin arrows indicate the 36 "clusters" of four or more contiguous genes that are potentially transferred from species outside the Firmicutes. Note that some of F. nucleatum shifts in GC skew coincide with putative HGT regions.
The observed GC-skew could also arise from chromosomal inversions (see, for example, the genome of Yersinia pestis -[29]). However, F. nucleatum should have undergone massive events of genomic scrambling to account for the effect, including numerous non-symmetric inversions around the replication origin and terminus, which are rarely observed [30,31] and are assumed to be detrimental [32]. Moreover, homologous genes present in the long DNA fragments sequenced in the close relative F. nucleatum subsp vincentii [33] show an almost perfect sinteny: In all 6 sequenced segments larger than 30 kb in vincentii, gene order was conserved without a single chromosomal inversion (data not shown). Although other related genomes are not available for comparison and the potential inversions could have happened prior to the split of both subspecies, the suggestion is that the oscillating GC-skew plot is not due to multiple inversions. Finally, the GC-skew plot of F. nucleatum could be partly due to multiple replication origins constantly shifting the values, but this situation has not been observed in any bacterial species.
Genome sequence similarity analysis
A sequence similarity search performed by BLASTP [34] against the whole available database reveals homology to over 150 bacterial and archaeal species. More than a quarter of the genes had no significant hit or a hit to a eukaryotic species. 64.6% of the hits went to Firmicutes species and 35.4% to other bacterial species (Table 1). These results seem congruent with the ribosomal phylogeny. However, from the hits to Firmicutes, 267 ORFs (representing 12.9% of the total genome) had a hit in only one genus within this bacterial group together with hits in another taxa, a feature suggestive of HGT to or from these bacteria, that are very numerous and diverse in the dental plaque [16].
Table 1 General function of F. nucleatum genes, divided by group of best BLAST hit1.
Function Category Archaea CFB group Low GC Gram pos α, β, γ Proteo δ, ε Proteo Other eubact Spiro-chaetes Eukarya/No hit Total
Aa biosynthesis 2 (6.5,3.9) 22 (71,2.3) 3 (9.7,2.0) 2 (6.5,1.9) 1 (3.2,1.4) 1 (3.2,0.2) 31
Cofactors and carriers biosynth. 1 (1.4,2.0) 59 (83,6.2) 4 (5.6,2.6) 2 (2.8,1.9) 2 (2.8,2.7) 2 (2.8,2.2) 1 (1.4,0.2) 71
Cell envelope 1 (0.6,2.0) 6 (3.6,11.3) 45 (27.1,4.7) 24 (14.5,16) 12 (7.2,11.4) 7 (4.2,9.5) 12 (7.2,13.3) 59 (35.5,10) 166
Cellular processes 2 (3.1,3.9) 2 (3.1,3.8) 35 (54.7,3.7) 4 (6.2,2.6) 2 (3.1,1.9) 2 (3.1,2.2) 17 (26.6,2.9) 64
Central intermed. metab. 3 (7.0,5.9) 3 (7.0,5.7) 24 (55.8,2.5) 6 (14.0,4.0) 2 (4.7,1.9) 3 (7.0,3.3) 2 (4.7,0.3) 43
DNA metab. 4 (5.5,7.8) 45 (61.6,4.7) 5 (6.8,3.3) 3 (4.1,2.9) 4 (5.5,5.4) 1 (1.4,1.1) 11 (15.1,1.9) 73
Energy metab. 1 (0.9,2.0) 9 (7.8,17.0) 82 (70.7,8.6) 6 (5.2,4.0) 6 (5.2,5.7) 4 (3.4,5.4) 3 (2.6,3.3) 5 (4.3,0.9) 116
Lipid metab. 21 (75.0,2.2) 3 (10.7,2.0) 3 (10.7,2.9) 1 (3.6,0.2) 28
Hypothetical prots. 3 (2.1,5.7) 43 (29.9,4.5) 4 (2.8,2.6) 2 (1.4,1.9) 3 (2.1,4.1) 9 (6.2,10.0) 80 (55.6,14) 144
Other categories 1 (5.3,1.9) 15 (79,1.6) 1 (5.3,0.7) 1 (5.3,1.0) 1 (5.3,0.2) 19
Protein fate 4 (7.0,7.5) 33 (58,3.5) 7 (12,4.6) 4 (7.0,3.8) 4 (7.0,5.4) 1 (1.8,1.1) 4 (7.0,0.7) 57
Protein synthesis 1 (0.9,2.0) 1 (0.9,1.9) 88 (78,9.2) 5 (4.4,3.3) 7 (6.2,6.7) 8 (7,10.8) 3 (2.7,0.5) 113
Nucleotides metab. 1 (2.9,2.0) 1 (2.9,1.9) 28 (80,2.9) 2 (5.7,1.3) 1 (2.9,1.0) 2 (5.7,2.7) 35
Regulat. functions 1 (2.0,2.0) 2 (3.9,3.8) 29 (57,3.0) 3 (5.9,2.0) 3 (5.9,2.9) 2 (3.9,2.7) 5 (9.8,5.6) 6 (11.8,1) 51
Signal transduction 3 (60.0,0.3) 2 (40,0.3) 5
Transcription 1 (5.0,2.0) 1 (5.0,1.9) 15 (75,1.6) 1 (5.0,1.4) 1 (5.0,1.1) 1 (5.0,0.2) 20
Transport/binding proteins 11 (5.7,21.6) 2 (1.0,3.8) 91 (47.4,9.5) 23 (12,15.2) 15 (7.8,14.3) 14 (7.3,18.9) 18 (9.4,20.0) 18 (9.4,3.1) 192
Unclassified 11 (7.0,21.6) 3 (1.9,5.7) 72 (45.6,7.5) 16 (10,10.6) 9 (5.7,8.6) 1 (0.6,1.4) 6 (3.8,6.7) 40 (25.3,6.8) 158
Unknown function 3 (3.4,5.9) 3 (3.4,5.7) 50 (57.5,5.2) 4 (4.6,2.6) 10 (11.5,9.5) 5 (5.7,6.8) 3 (3.4,3.3) 9 (10.3,1.5) 87
Hipothetical function 8 (1.3,15.7) 12 (2.0,22.6) 155 (26.1,16.2) 31 (5.2,20.5) 21 (3.5,20.0) 16 (2.7,21.6) 24 (4.0,26.7) 327 (55,55.6) 594
Total 51 53 955 151 105 74 90 588 2067
1 First number between brackets indicates the percentage of genes with a best match in a given taxon that have the function indicated on the row heading. Second number between brackets indicate the percentage of genes with a given function that have a best match in the taxon indicated on the column heading.
When the top hits to the different groups are plotted, the matches outside the Firmicutes are scattered along the Fusobacterium chromosome and, in many cases, they are clustered (Figure 3). There are 36 cases of clusters of 4 or more contiguous genes that have a best hit outside the Firmicutes, many of which still preserve some gene order compared to their phylogenetically unrelated counterparts. Five of these cases are shown in more detail in Figure 4. Gene arrangement conservation with distantly related groups is a strong indication of HGT events.
Figure 3 Gene-position plot with a reconstruction of vertical descent and potentially-transferred genes across F. nucleatum genome. Plus signs indicate genes whose phylogenetic affiliation to a certain group on the left is supported by BLAST analysis only. Crosses indicate genes whose phylogenetic origin is supported by BLAST and by one or two other methods (phylogenetic tree reconstruction and gene order conservation). Thirty-six clusters are indicated containing four or more consecutive genes that appear to have a xenologous origin (i.e. a phylogenetic affiliation outside the Firmicutes). Details of these clusters are explained in Table 2. Arrowheads at the top indicate the position of transposases. Arrowheads at the bottom indicate position of phage-related genes. Plus signs and crosses indicate potential transfers to/from Firmicutes (Firm), Spirochaetes (Sp), alpha-beta-gamma Proteobacteria (αβγ-Pr), delta-epsilon Proteobacteria (δε-Pr), Cytophaga-Flexibacter-Bacteroides Group (CFB), other bacterial groups (Others), Archaea, or genes that are consistent with the phylogeny (CWP) shown in Figure 1 and additional file 1.
Figure 4 Gene order conservation in some representative cases of potentially-transferred clusters. Homologous genes are shown with the same colour in F. nucleatum and the species with which it is compared. Small black boxes represent short orphan genes. Non-contiguous genes are separated by an interrupted line. Genes are not drawn to scale.
It is interesting to note that there are 40 transposase ORFs in the F. nucleatum genome and 73 assignments of possible IS elements [3]. Thirty-four of the transposase sequences are at the flanks of putative transferred genes, whereas 6 were between core genes (Figure 3). There are also two integrase genes, both at the edge of putative HGTs. In addition, Kapatral and collaborators [3] described that active and remnant IS-elements are flanking many genes with high similarity to proteobacteria. Among these there are outer membrane proteins, hemolysin precursors and activators, pyrophosphate synthesis genes and others. Another possibility for the insertion of xenologous sequences would be through the action of bacteriophages. In F. nucleatum, 31 genes were found to have homologs in phage regions of other bacteria (Figure 3) and 13 on plasmids. Small cryptic plasmids containing mobile elements are frequently found in F. nucleatum strains [35]. In addition, six phage contigs encoding 110 ORFs have been identified in its sister subspecies vincentii. In this bacterium, the phage genes have homology to Gram positive and Gram negative phages, with an average GC content of 28% and a similar codon usage to the chromosome [33]. Thus, it is possible that an old phage infection is partly responsible for the mosaic genome of F. nucleatum. For example, a region with 6 ORFs presents homology to the proteobacterial bacteriophage P2, a phage that has been shown to be responsible for HGT episodes in some E. coli strains [36].
Looking into more detail at the gene clusters with a best hit outside Firmicutes it was found that many genes were involved in typical gram-negative features, mainly membrane-associated functions (Table 2). For example, the segment of genes FN1893-FN1897 includes 3 homologs found in Salmonella typhimurium, coding for surface-exposed virulence proteins and a membrane-associated gene involved in D-ribose transport. Twenty-eight of the 36 clusters of 4 or more contiguous genes had a function related to the outer membrane, the periplasm or pathogenecity typical of proteobacteria, CFB group species or spirochaetes. Although this would have to be expected since those organisms posses also Gram negative cell walls, the similarity was always to the same groups and much higher than what would be expected based on a distant common origin of the corresponding Gram negative feature. The conservation of gene order and relatively high similarity to groups present in the dental plaque (see below) also hints at a secondary acquisition by HGT. Therefore, the interpretation of F. nucleatum as a gram-positive bacterium with gram-negative clothing [6] appears quite realistic, with the xenologous sequences being especially relevant in membrane-associated functions associated to a gram-negative cell wall.
Table 2 Clusters of 4 or more consecutive genes with a best match outside the Firmicutes5.
# Putative function Observations1 Sequence similarity2 Genes Sinteny3
1 Transposase + 4 hypothetical proteins of similar sequence Flanked by 3 short orphans4 One of proteins is a short ORF 24–32 FN1511 to FN1515
2 KDO (LPS core synthesis) + endonuclease and DNA pol III Includes a short orphan 31–58 FN1561 to FN1576
3 Peptide ABC transporter It includes two long (>1500 bp) hypothetical proteins 30–56 FN1650 to FN1656
4 sysnthesis of LPS (O chain) + phosphatidylcholine synthesis Split by a hypothetical protein and 3 short ORFs 37–61 FN1661 to FN1668
5 carbohydrate trasnport-pot operon (periplasmic binding prot dependent transport) Split by long spacer 22–55 FN1792 to FN1800 Thermotoga maritima
6 periplasmic binding protein dependent cation (Mn2+, Zn2+) transport posibly Co2+ Flanked by transposase and archaeal best-match ORF 24–56 FN1807 to FN1814 Pseudomonas putida
7 DNA pol III gamma and tau subunits and TonB OM export system Flanked by hypothetical orphans 25–36 FN1830 to FN1834 Helicobacter pylori
8 Periplasmic amilase and ribose ABC trasnporter Short orphan in the middle 23–32 FN1893 to FN1897
9 LPS synthesis and/or decoration and outer membarne stabilization Flanked by 3528 bp hypothet. protein with eukaryotic best-match followed by long spacer 25–77 FN1908 to FN1911 Geobacter sulfurreducens
10 capsule biosynthesis Includes 2 short ORFs (possible HIPA pseudogenes) 23–46 FN1997 to FN2003 Bordetella bronchiseptica Yersinia pestis
11 Slow porin homologous to OmpA (Bacteroides) or Opr (Pseudomonas) Split by a long spacer with some homology to membrane proteins. Includes 2 short ORF 23–49 FN2056 to FN2062
12 Hypothetical exported 24-amino acid repeat protein Includes 4 short ORFs (one of them with homology to subunit δ of DNA Polym. III) 34–45 FN2110 to FN2122
13 24 aa repeat protein like in cluster 23 Protein match to Helycobacter hepaticus 31–53 FN0023 to FN0028
14 Endonuclease + 3 genes implicated in porfirinic siderophore synthesis Flanked by short orphan 24–65 FN0185 to FN0188 Haemophilus influenzae
15 DNA helicase + peptide transporters High gene order conservation in an archaeal species 28–42 FN0191 to FN0197 Methanosarcina acetivorans
16 Sugar ABC transporter Short spacers/overlapping genes 31–48 FN0217 to FN0220 Escherichia coli
17 Large cluster of hemolysin/ hemagglutinin containing hemagglutinin FhaB Largest bacterial protein. Some degraded hemolysin copies found throughout genome 23–26 FN0290 to FN0293 Escherichia coli
18 ABC iron/haemin transporter with periplasmic binding protein Flanked by long spacer 27–47 FN0300 to FN0303 Methanosarcina acetivorans
19 Periplasmic binding protein dependent iron transport system Physically linked to other iron transport genes of Gram positive and Archaeal match 34–49 FN0309 to FN0312 Bordetella bronchiseptica
20 NA+/H+ antiporter + 3 genes of unknown function Split by a tRNA gene. Includes 2 short orphans 33–53 FN0350 to FN0354 Treponema denticola
21 Two clusters of genes implicated in drug efflux (detoxification) extrusion out of OM Flanked by two orphans of 402 and 618 bp 21–37 FN0515 to FN0519 Vibrio cholerae
22 Mixed functions cluster 30–44 FN0524 to FN0527
23 LPS synthesis and/or decoration and outer membarne stabilization Includes recA and recX proteins with best match to Caulobacter and Vibrio 29–100 FN0538 to FN0548 Haemophilus ducreyi
24 Structural lipoprotein with release and mureine anchoring components Flanked by short ORF 30–46 FN0579 to FN0582 Helicobacter hepaticus
25 Membrane-related functions + Fe-S oxidoreductase Includes a short hypothetical protein with biased codon use 32–55 FN0734 to FN0739
26 Haemin uptake with periplasmic binding protein iron acquisition Haemin genes tightly-linked, probable operon 24–59 FN0766 to FN0771 Campylobacter jejuni
27 Biotin biosynthesis Most spacers are short, possible cotranscription 31–55 FN0846 to FN0852 Campylobacter jejuni
28 Hydrolase + protease + aromatic compound synthesis Mixed function cluster 30–47 FN0869 to FN0873
29 Iron ABC transporter Flanked by a short orphan with biased codon usage 45–71 FN0879 to FN0882 Treponema denticola
30 Membrane proteins 1st and 2nd genes probably permeases 22–37 FN1030 to FN1033 Photorhabdus luminescens
31 Lipase B componet of type II secretion system + 24 aa repeat protein+ bacterioferritin All proteins of short length 26–34 FN1075 to FN1079
32 KDO (cetodeoxyoctulonic acid biosynthetic operon) KDO is a component of LPS core in Fusobacterium and many Gram negatives. 31–100 FN1221 to FN1224
33 Eps synthesis + EpsF (secretion of proteins/large biomolecules) Possible tandem duplication 30–47 FN1242 to FN1245 Ralstonia solanacearum
34 LOS choline decoration + Ton B (biopolymer transport through Outer Membrane) Includes a short ORF (a degraded copy of a biopolymer transporter) 29–40 FN1306 to FN1312 Pseudomonas aeruginosa
35 ABC transporter system Flanked by short orphan followed by a transposase 30–69 FN1346 to FN1355 Treponema denticola
36 ABC amino acid transport system liv G-M operon; biased and homogeneous codon usage 50–62 FN1428 to FN1431 Bifidobacterium longum
1 "Split" indicates a cluster separated by a long intergenic spacer, the two parts of the cluster generally coding for different functions.
2 Range of sequence similarity among the genes from each cluster compared to their BLAST top hits.
3 Representative species with similar gene order.
4 An "orphan" gene is defined as an ORF with an unknown function and no BLAST similarity in the current database. Short orphans (<500 bp) are likely to be pseudogene remnants or other non-functional regions (Mira et al. 2002, Davies et al. 2004).
5 Only protein coding genes are included in the analysis.
Phylogenetic, gene-order and compositional analyses
BLAST analysis has the advantage of giving a closest similarity match for almost every gene. It is however a crude method that can give as the top sequence similarity hit a species that is not the closest from a phylogenetic point of view [37] and it could also be much influenced by the undersampling of certain poorly-sequenced groups, such as the Fusobacteria. For example, when the BLAST top 10 best matches are considered, less than 70% of the hits fall on the same bacterial taxa as the top hit. In top hits to archaea, a domain from which fewer sequences are available, 58% of the top ten best matches hit groups other than Archaea. Since we used the top hit to designate potential phylogenetic origin, some degree of inaccuracy is expected. Thus, to complement the BLAST analysis we used a phylogenetic and a gene-order analysis, indicating in Figure 3 whether their results do or do not support the BLAST results. In the phylogenetic analysis, trees were constructed based on the sequence of each individual gene with sufficient homologs in the database (see experimental procedures section for details). Over 1200 trees were generated and analysed to detect a phylogeny either congruent with the ribosomal one, or suggestive of HGT. Almost two thirds of the trees could be resolved, and corroborated the high degree of gene transfer, with at least 25% of the genome being of xenologous origin (Table 3). Only 8.4% were consistent with the ribosomal phylogeny, and over 25% indicated a potential HGT to or from Firmicutes species. Part of the latter could also be due to multiple losses of the genes in most Firmicutes genera. However, it is not unreasonable to think that they could have been transferred between Fusobacteria and typical Firmicutes (particularly clostridiales and streptococci), which share the mouth and dental plaque ecosystem [16]. The phylogenetic analysis method, therefore, suggests a 25–50% of gene transfer. Although only trees with a bootstrap value over 500 were considered (see methods section), these numbers must be taken with caution. Given the distant relationship of F. nucleatum with most sequenced genomes, a weak phylogenetic signal may remain for many trees. The branching pattern in trees is also influenced by other variables like different rates of evolution for different genes, method of alignment or number of species included. Like in the sequence similarity method, the data presented are inferences based upon the data, and the limitations of each method should be kept in mind.
Table 3 Percentage of F. nucleatum ORFs classified by the taxa of potential origin.
Sequence similarity method (BLAST) Phylogenetic trees method Gene order conservation
Number of genes analyzed 2067 1236 738
Root of Firmicutes1 33.28 % 8.41 % 35.1 %
Inside Firmicutes2 12.92 % 25.8 % 15.45 %
CFB group 2.56 % 2.27 % 4.06 %
α, β, γ Proteobacteria 7.34 % 10.3 % 21.0 %
δ, ε Proteobacteria 5.07 % 3.4 % 5.7 %
Spirochaetes 4.35 % 4.32 6.37 %
Other eubacteria 3.58 % 4.53 4.2 %
Archaea 2.46 % 1.13 % 0.95 %
No hit, hit to eukaryotes, uncertain/unresolved 28.4 % 38.7 % 7.45 %
1 Genes consistent with the 16S-23S and ribosomal proteins phylogeny.
2 It indicates possible HGT to/from Firmicutes.
Another method used was based on the conservation of gene order among certain gene clusters, a character that can be used in phylogenetic reconstructions [38,39]. Only 738 F. nucleatum protein-coding genes belonged to clusters of 2 or more genes that had some order conservation in other bacteria. From these, 35% had the same order as most Firmicutes (Table 3), suggesting vertical inheritance. Over 15% of the genes belonged to clusters whose gene order was more consistent with HGT from this group (i.e. same order as only one of the Firmicutes genomes). The extent of HGT from Firmicutes could be overestimated if the genes are ancestral but subsequently lost in most Gram-positive lineages. This being the case, the addition of vertically-inherited genes and genes inside Firmicutes in Table 3 would indicate an upper limit of genes consistent with the ribosomal phylogeny. Even if HGT from Firmicutes is not considered, 42% of the genes were assigned as HGT from other bacterial taxa based on the gene-order method. These dramatic figures suggest again that the genome of F. nucleatum could be an amalgamation of genes from different groups, particularly those of species that inhabit mammalian hosts in general and the mouth niche in particular. A summary figure showing the outcome of the three methods is published as supplementary material [see additional file 1]. The discrepancies between the three methods can be partly influenced by the different sample sizes used (Table 3). In addition, it must be noted that most of the discrepancy appears in the Phylogenetic Trees method, where a very low percentage of vertical inheritance was detected. In this analysis, over 38% of the trees were unresolved, introducing an important degree of variation. It is therefore possible that many of the genes giving uncertain phylogenies are consistent with vertical inheritance, but the phylogenetic signal is too weak to give a clear-cut tree. The gene-order method could give higher numbers of horizontal transfers if operons are more likely to be transferred than single genes [40]. Thus, all three methods have its limitations, and although the importance of HGT is clear, the numbers obtained may be subject to certain bias imposed by the methodology [41].
Deviations from genomic GC content and codon usage have been used to infer potential gene transfers across bacteria [42,43]. However, only 40 genes with significantly extraneous DNA composition were found in F. nucleatum [44] suggesting that many transfers could come from low-GC species or that many of the transfers occurred long ago, allowing the xenologous genes to ameliorate and homogenize its characteristics with those of the recipient genome [45]. In addition, the extremely low GC content of F. nucleatum could make this method less discriminatory [46,47]. A few potential transfers were identified this way, including a cluster spanning two iron-sulphur binding proteins and two arsenic pump-driving ATPases. Another interesting case was a glutamate fermentation cluster with closest similarity and gene order conservation to the clostridial species Acidaminococcus fermentans. This represents a typical case of potential HGT from the Firmicutes that could be masked in a BLAST analysis as a vertically inherited cluster. As the tree and gene-order methods show, the amount of HGT from/to the Firmicutes species could be as high as 15–25%, assuming that the percentages are maintained among the genes that we could not analyse because the trees were unresolved or because they were not part of conserved-order clusters.
Chimeric enzymes and operons
To explore the possibility that the chimeric nature of Fusobacterium may apply not only to its genome but also to some of its metabolic pathways and enzymes, some specific cases were looked at in more detail. A potential example includes the RNA polymerase, where the β' subunit has a best BLAST hit to spirochaetes as well as the RNA polymerase sigma-E factor. This is confirmed by comparative analysis of domain architecture across bacteria [48]. An interesting instance is given by the phenylalanyl-tRNA synthetase, in which the α and β chains have a Clostridium and Geobacter (delta-proteobacteria) best sequence similarity match, respectively. The tree analysis confirms that the β chain is likely to have a proteobacterial origin. Interestingly, although the β chain is located in a proteobacterial cluster (at the edge of cluster 12), it is contiguous to the Firmicutes related α chain gene, separated by a very short spacer without a promoter. This exemplifies how selection may have put together two functionally related genes, presumably to ease cotranscription, even though their phylogenetic origin appears to be different.
Another example is given by an iron ABC transporter operon formed by a periplasmic binding protein followed by two iron permeases. A similar structure is repeated two other times in the subsequent genes (Figure 5a), forming a long iron transport system. Remarkably, the first operon is found in identical gene order in the archaeon Methanosarcina acetivorans, to which it presents the highest sequence similarity, whereas the second and third operon appear to be a blend of genes with relatedness to firmicutes, proteobacteria, spirochaetes and Thermotoga. These genes are present in many Gram negatives including Helicobacter and other Proteobacteria [49,50]. Thus, assuming that some of these genes have a xenologous origin, they must have been selected to occupy a precise gene order to maximise its function within the iron transport system. Another fascinating case of a potential chimeric gene system is that of transport of dipeptides (Figure 5b). There are as many as five dipeptide transport operons in F. nucleatum, this time dispersed along the genome. Although one of the sets has best matches to Firmicutes species, another one appears to be of spirochaete source (also present in the same gene order in Methanosarcina), whereas the other three are, according to sequence similarity, gene order and tree reconstruction a mixture of genes with archaeal, Firmicutes and proteobacterial origin. A third case can be seen in the three copies of a hemin transport system located away from each other and formed by a hemin receptor and the genes hmuT, hmuU and hmuV. Although the different taxonomical origin analysis methods are not always consistent, two of the hemin operons are probably of proteobacterial origin (Figure 5c). The other one has a closest gene order to the spirochaete Treponema denticola, also an inhabitant of the dental plaque, but is absent in its close relative T. pallidum, suggesting again that gene transfer is facilitated across the bacteria that occupy this specialised niche.
Figure 5 Chimeric operons (metabolic pathways of putatively mixed origin) in F. nucleatum. Arrowed boxes represent gene orientation, coloured by BLAST top hit. White boxes: top hit to Firmicutes; grey boxes: top hit to Archaeal species; black boxes: top hit in Gram negative species. Numbers below boxes indicate the percent of top ten hits that have matches in Firmicutes. Names above indicate gene names (I-BP: Iron binding protein; NIP: Nitrogenase iron protein; Oxdtase: Oxidoreductase; B, C, D, F: dipeptide permeases B, C, D, F; BP: dipeptide binding protein; Tr: ABC transporter; unk: unknown function gene; Rec: Hemin receptor). Best match taxa by the phylogenetic tree and gene order methods are also indicated (A: Archaea; Pr: Proteobacteria; Sp: Spirochaetes; CP: consistent with (ribosomal) phylogeny; O: other eubacteria; x: unresolved; --: not analysed. Plus signs indicate unusual DNA composition by the method of García-Vallvé et al. 2003.
Remnants of HGT
An indication of massive gene transfer events comes from looking at intergenic spacer regions of F. nucleatum. Although average spacer length in this species is 115 bp, there are many long spacers of 500 bp and higher scattered across the genome. It was found that 21 of these long spacers were located at positions flanked by a "core" gene (that with a low-GC Gram-positive best match) and a potential transferred gene, whereas only 8 appeared between core genes. Since intergenic spacer regions are known to increase in length as a result of genomic rearrangements and pseudogene formation [51], many of these long spacers might be signatures of ancient HGT events. In agreement with this view, another 17 long spacers were located inside gene clusters of a putative Gram negative or archaeal origin. We hypothesize that these long non-coding regions are remnants of transferred genes that were not selected for and have been mostly erased. When DNA sequence similarity searches are done with these long spacers located inside xenologous clusters, some significant matches are found to other regions of the genome. For example, the long spacers inside clusters 4, 8 and 11 all have some sequence similarity (more than 85% sequence identity over 125 bp or more using BLAST analysis, E-value <10-5) to one another and to other five long spacers scattered throughout the genome. In all cases except one, these long spacers are flanked by outer membrane proteins of Gram-negative origin, suggesting that they may represent remnants of old membrane-associated genes. A similar case is that of the long spacer located after the hemolysin activator protein precursor (FN1818), which shows high sequence similarity to a hemolysin activator located someplace else in the genome (and to another spacer and a short ORF with unknown function).
Another potential signature of ancient transfers subsequently erased is the high number of ORFs without a match in the complete, non-redundant NCBI database (including its closely related subspecies vincentii), spanning 450 sequences that represent 20% of the genome. On average, these orphan genes are extremely short (440 bp, versus 1040 bp for the rest of genes with a match on the DNA database, see Figure 6), suggesting that they do not represent real genes [52,31]. Overannotation of short ORFs that are not functional is more common on GC-rich genomes due to a lower probability of stop codons [53], but F. nucleatum is just 27% G+C. It is therefore possible that many of these short ORFs are eroded pseudogenes or remnants of fragmented genes, as it has been demonstrated in Rickettsia [54], where many genes appear to be under low selection coefficients in its intracellular environment. In Fusobacterium, it is likely that many transferred genes were not useful and got eliminated, a process known to happen very rapidly [55]. This would explain the high number of short ORFs without significant BLAST matches on the database, as small fragments of genes may have accumulated enough mutations to make them frequently unrecognisable by sequence similarity. For many of these small ORFs with no significant matches, some low sequence similarity is found to gram-negative outer membrane proteins (e.g. tolA), glycine permease, periplasmic-like proteins, etc.
Figure 6 Length frequency distributions for F. nucleatum proteins. Genes are divided by the group with closest sequence similarity match. ORFs without sequence similarity on the non-redundant NCBI database (orphan genes) are significantly shorter than the rest.
In addition, some short ORFs appear to be degraded fragments of bigger genes. For example, there are 3 sequences with similarity to HIPA proteins, one of which is less than half the length of the other two. As it also has a very biased codon usage, it is likely that it represents a degraded remnant of this protein. The 3 copies of integrases scattered across the genome show another case. Two of them are around 900 bp long and have a normal codon usage. The third copy (FN0402) is only 177 bp long, is flanked by a long spacer and has a very skewed codon usage. In general, the codon usage of these orphans is very biased (mean corrected χ2 values of 0.47 versus 0.22 for the rest of the genome). As it is unlikely that all these short ORFs are highly expressed, we believe that this biased codon utilization is reflecting very divergent pseudogene fragments. Thus, the picture that emerges is that of massive gene transfer leaving many non-coding segments that are remnants of unnecessary genes and genomic rearrangements.
Conclusions
The genome of F. nucleatum possesses a remarkable amount of patchiness with any kind of phylogenetic analyses used. This can be said to a certain degree of some other genomes (see for example [56]). One possible explanation for this kind of results is an undersampling of the group considered what gives only very distant and hence uncertain similarities to a variety of prokaryotic groups. This might be the case for part of the Fusobacterium genome that gives very weak and uncertain phylogenetic signal. However, the observation that certain genes and operons are shared by distantly related species that inhabit the dental plaque (for example, the spirochaete T. denticola, the proteobacteria Campylobacter and the CFB P. gingivalis) points to HGT as the most likely origin of these genes. Even less apparent, our work suggests multiple episodes of gene transfer to or from phylogenetically-related bacteria, like certain Firmicutes species (such as the cariogenic bacterium S. mutans or some Clostridia), that might be confounded with vertically inherited traits.
The origin of the Gram-negative cell wall found in Fusobacterium requires special consideration. Some type of Gram-negative cell wall seems to be the default phenotype in Bacteria (see, for example [57]), being found in most deeply branching groups. Moreover, even some deep branches of the Firmicutes contain organisms (such as Sporomusa and Desulfotomaculum) with Gram-negative cell wall structures [58,59]. On the other hand, it has also been proposed that the Gram-positive cell wall is the default structure [60]. It might be argued that Fusobacterium is a remnant of the ancestral cells predating the bacterial radiation that originated either Gram-positive or Gram-negative cell walls. This is supported by phylogenetic inferences based on conserved indels, which place Fusobacteria at an intermediate position between Gram-positive and Gram-negative taxa [61]. However, in light of our results this explanation does not seem likely. Fusobacterium does not show any primitive trait and its outer membrane and transport mechanisms show all the characteristics of any sophisticated Gram-negative cell wall. In addition, many of the outer membrane proteins are closest to specific taxa (mainly to proteobacterial species) and not equally dispersed among species with a Gram-negative cell wall. Thus, many of the genes involved in the construction of the Gram-negative outer membrane have probably been horizontally transferred. The extent of this transfer deserves further examination. If we assume that Fusobacterium evolved after the Gram-positive/negative divergence on the low-GC Gram-positive lineage, massive HGT is the most likely explanation for the formation of the outer membrane. On the other end of possible explanations, most genes of the outer membrane would already be present in the common ancestor of fusobacteria and Firmicutes, where a massive loss would be responsible for the differences observed today.
Recently, the idea of gene pools that are characteristic of certain environments has been advanced to explain the large number of common genes among groups of thermoacidophiles distantly related by ribosomal phylogeny [62]. The presence of a common pool of dental plaque genes is not unlikely in light of the results described here. However, the time scale of the adaptation to the latter habitat is much shorter that that of thermoacidophiles and can be probably estimated around the origin of mammals (about 120 million years). Even going backwards to the origin of the vertebrate's intestine it would put the selective pressure for these gene combinations to originate no earlier than 400 Myr ago. Former chimeric genomes have been explained as selected by strong environmental pressure. The case of Thermotoga is paradigmatic, a hyperthermophilic bacteria that is assumed to have recruited genes from the archaeal hyperthermophiles to reach its unusual (for bacteria) thermotolerance. Here (as in the case of Methanosarcina, a mesophilic anaerobe) there is not such an obvious explanation. F. nucleatum natural habitat seems to be the dental plaque of mammals, a rather unique and special environment that probably requires very special features to survive. Strong adhesion mechanisms, such as those found often in the Proteobacteria, probably represent an essential ability for survival in the early stages of plaque formation, particularly for non-motile cells. Also the mucose-associated immune system that prevails in the mouth of mammals could have acted as a strong selective pressure favoring the Gram-negative envelopes that are often less immunogenic and easier to disguise thanks to the LPS polysaccharide O chain [63]. Thus, it is not difficult to envisage that at a point in time, probably associated to the rise of mammals, a strong selective pressure might have existed for a cell with the metabolic apparatus of Clostridia for amino acid fermentation but with the adhesive and immune camouflage paraphernalia of the Proteobacteria. It is remarkable to note that many of the genes that determine the lifestyle of Fusobacterium and its interaction with the environment, such as peptide transport systems, cell adhesins and outer membrane components have probably been acquired by gene transfer. It is therefore not only the number of horizontal transfers but also their contribution to niche adaptation that makes the HGT mechanism of dramatic impact on genomes. It is interesting that some of these genes are shared by different organisms inhabiting the dental plaque. From an applied point of view, some of these highly transferred genes are likely to provide a critical advantage in the establishment and adaptation of the bacteria to their niche, and could be used as potential targets for antimicrobial agents.
Methods
Phylogenetic trees
rRNA and evolutionary conserved proteins trees
The different rRNA and conserved protein data sets were analyzed with Bayesian methods using the program MrBAYES 3 [64]. For the fusion of 16S+23S rRNA sequences, the GTR model with a Γ law (8 rate categories) and a proportion of invariant sites to take among-site rate variation into account was used. A similar procedure was used to construct the trees based on evolutionary conserved proteins (a mixed substitution model and a Γ law with 8 rate categories and a proportion of invariant sites were applied). The evolutionary conserved proteins were defined as those found in all sequenced species of Bacteria and assumed to form part of the minimal genome necessary for life [65,66]. The list was extracted from [23] but removing the genes for which paralogous ORFs were found. In all cases, the Markov chain Monte Carlo searches were run with 4 chains for 1,000,000 generations, with trees being sampled every 100 generations (the first 2,500 trees were discarded as "burnin").
Concatenated ribosomal proteins tree
The amino-acid sequences of ribosomal genes S1–S20 and L1–L35, excluding S1, S14, L24, L25, L30, L31, L32 and L33, were retrieved from the KEGG website from a total of 60 different bacteria. The bacteria chosen were all those represented in the KEGG ribosomal genes ortholog table [67], except Rickettsia prowazekii, Rickettsia conorii, Wigglesworthia brevipalpis, and Buchnera aphidicola, and with the addition of Bacteroides thetaiotaomicron and and Desulfovibrio vulgaris. An alignment was generated for each ribosomal gene, using the Clustalw software with default parameters [68]. When two or more paralogs were found in a species, the most divergent of the paralogs was removed from the alignment. A concatenated alignment including the species for which all of the selected ribosomal genes were present was generated. A neighbor-joining tree with 1000 bootstrap replicates was produced from the alignment using Clustalw [68], excluding positions with gaps, and correcting for multiple amino-acid substitutions (Kimura correction). The tree was visualized with NJPLOT [69]. Exclusion of ribosomal proteins was based on the following: S14 has been shown to be subject to horizontal transfer [70], L24 is truncated and split in Fusobacterium nucleatum, S1 is absent/truncated in the Mollicutes subgroup of the low-GC gram-positives, L25, L30, L31, L32, L33 contained a high number of paralogs and/or were absent in several key species.
Methods for detecting HGT
Blast method
The protein sequences of Fusobacterium nucleatum subsp. nucleatum ATCC 25586 were retrieved from . Peptide sequence database of all non-redundant GenBank CDS translations + PDB + SwissProt + PIR was retrieved from . We performed an all against all BLASTP [34] search of each protein in Fusobacterium nucleatum subsp. nucleatum ATCC 255586 against peptide sequence database. We then recorded the top hit for each protein sequence with an E-value of 10-5, filtering the hits whose sequence identity and length was lower than 30 and 50%, respectively. We categorized all the hits into 8 categories as belonging to the CFB group, Firmicutes bacteria, α,β,γ-Proteobacteria, δ,ε-Proteobacteria, Spirochaetes, other Bacteria, Archaea and Eukaryotes/no hit. Hits to Firmicutes (the group to which Fusobacterium appear to be more closely-related) were refined by further BlastP analysis between F. nucleatum and the 31 sequenced bacteria available from this group. If the gene had a homolog in only one genus from all the available low-GC gram-positive species, it was considered a HGT event from/to this group. If it was present in more than one genus it was considered vertically inherited and consistent with the ribosomal phylogeny. There were 61 cases of genes found in more than one genera from a single subgroup of this taxon (i.e. present only in the Clostridiales, the Bacillales, the Mollicutes or the Lactobacillales). These can be equally explained by HGT or by common descent and were conservatively assigned to the vertical inherited category.
Phylogenetic trees method
For each F. nucleatum gene, the protein sequences of up to 50 best blast hits with e-value lower than e-5 were retrieved (the hits were identified by the "Blast method" described above). All sequences were then automatically clustered with the Clustalw alignment tool with default parameters. A neighbor-joining tree with 1000 bootstrap replicates was generated from the resulting alignment, using Clustalw with default parameters. The trees were visualized with NJPLOT [69]. In all cases, the bootstrap values at the nodes chosen for a decision on taxonomical assignment had to be over 500. Assignment of the F. nucleatum genes to a taxonomic group was done using the following criteria:
Low-GC gram-positives
A F. nucleatum gene was determined to originate from the firmicutes if it was found in the tree most closely associated with at least 5 different species from that group, or with at least 3 species from 2 different subgroups (where the subgroups were: mollicutes, bacillales, lactobacillales, clostridiales). If the F. nucleatum gene branched at the base of the firmicutes, the gene was assigned as being consistent with phylogeny; otherwise, it was assigned as a potential horizontal gene transfer (HGT) from the firmicutes.
Proteobacteria
Same as described above (low-GC gram-positives), the subgroups in this case were:alpha-, beta-, gamma-, gamma-entero-, delta-, and epsilon-proteobacteria. In the case of the proteobacteria, all F. nucleatum genes with trees fulfilling this criteria were assigned as HGTs from proteobacteria. Note that a distinction was made between the grouping of the alpha-, beta-, and gamma- proteobacteria, and the grouping of the epsilon- and delta- proteobacteria whenever possible.
Archaea
To be assigned as originating from the archaeales, the F. nucleatum had to be closest to at least 3 species, and there had to be a clear association between the two groups, i.e. the branches were relatively short, and the tree topology did not resemble a "star phylogeny".
High-GC gram-positives, Cyanobacteria, Chlamydiales
To be assigned to these groups, the F. nucleatum gene had to be found closest in the tree to at least 3 different species, there had to be a clear association (see above in "Archaeales") and there should have been no obvious evidence of gene transfer from the Fusobacteria. Evidence of transfer from the Fusobacteria would be when, apart from the association to some species of the high-GC gram positives (or Cyanobacteriales, or Chlamydiales), the tree placed the F. nucleatum gene in agreement with the accepted species phylogeny (just outside of the firmicutes).
Aquifales, Deinococcales
The F. nucleatum gene had to be found closest to at least 1 species from that group (Aquifales, or Deinococcales). A clear association was necessary, as well as no evidence of transfer from Fusobacteria (see above).
Spirochaetes, CFB group
The F. nucleatum gene had to be closest to at least 2 species from that group, or 1 species with a clear association, and no evidence of transfer from Fusobacteria.
Unknown
If the tree contained less than four hits other than eukaryotes and other Fusobacteria, the gene was not considered for further analysis. In cases where it was not possible to clearly associate a taxonomic group to the F. nucleatum gene, it was then assigned as "unknown/not resolved".
Gene-order method
In order to identify clusters of at least two genes with conserved order between F. nucleatum and other genomes, all available amino-acid protein sequences sets for all replicons of all published bacterial and archaeal genomes at the time (may 1st 2004) were downloaded from the NCBI ftp website . All Orthologs of F. nucleatum genes (reciprocal best blast hit) were detected between the two replicons (F. nucleatum and replicon X). Clusters of consecutive orthologs were found (consecutive orthologs are determined in terms of the numbered position in both F. nucleatum (exactly consecutive) and replicon X (possible gap of 2 genes in between the orthologs), and for each cluster, a score was assigned as follows:
(1-(totalCost/numberOfGenesInCluster))*(1-(deletions/numberOfGenesInCluster)) *Mean(Identity%)*Mean(Length%)*numberOfGenesInCluster/10000
Where "totalCost" was determined by the program derange2 [71,72] with the following command-line: "derange2 -U -L $inputFile 5 1 1 1", i.e. the direction of the gene was ignored, and the cost for an inversion, a transition or translocation within the cluster was the same: 1."Deletions" is the number of "gene gaps" found in replicon X for that cluster, whatever their size, "Mean(Identity%)" is the mean of the %identity of all blast results for the orthologs of the cluster. Mean(Length%) is the mean of the length of all blast results for the orthologs of the cluster, where length is defined as the minimum of length of (blast hit/ length of query sequence) and (length of blast hit/length of subject sequence). Each ortholog in the cluster was assigned the same score, and following completion of the procedure for all replicons in the database, for each F. nucleatum gene the orthologs that were part of clusters were ordered by their score, and an excel table was generated for manual investigation. If the gene order of a given gene cluster was not preserved in Firmicutes species but maintained in another procaryotic group, the genes were assigned as HGT from/to the group with the highest score (highest gene-order conservation). If the order was preserved in at least one species from two or more groups of low-GC gram-positives (Clostridiales, Bacillales, Lactobacillales and Mollicutes) the cluster was assumed to be ancestral to the divergence of fusobacteria and Firmicutes, and consistent with the ribosomal phylogeny. If gene order was preserved in one or more species from only one of the low-GC gram-positive groups the cluster was classified as HGT from low-GC gram-positive bacteria.
GC-skew plots and gene classification
Classical GC-skew plots were done using the formula (G-C)/(G+C) in 5000 bp windows, following Lobry's methods [25,26]. The functional classification of F. nucleatum genes by function was based on the TIGR Gene Attribute Annotation [73].
Authors' contributions
AM carried out the gene-order analysis of gene transfer, and the study of chimeric operons and short ORFs. FRV conceived the study, and FRV and AM drafted the manuscript. RP and BAL did the bioinformatics work. RP and AM carried out the BLAST analysis of gene transfer and made the figures, except the phylogenetic trees. BAL developed the phylogenetic and gene-order method and did the ribosomal proteins tree. DM did all Bayesian trees. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Supplementary material published as additional information in the manuscript Mira et al. 2004. Evolutionary relationships of Fusobacterium nucleatum based on phylogenetic analysis and comparative genomics. The file contains three additional figures. It is available in pdf format and includes figure legends.
Click here for file
Acknowledgements
A.M. is the recipient of a 'Ramón y Cajal' research contract from the Spanish Ministry of Science and Technology (MCyT). Support from European Commission Project GEMINI (QLK3-CT-2002-02056) is also acknowledged.
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| 15566569 | PMC535925 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 26; 4:50 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-50 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1811555508110.1186/1471-2105-5-181Research ArticleDiscriminative topological features reveal biological network mechanisms Middendorf Manuel [email protected] Etay [email protected] Carter [email protected] Jen [email protected] Robin [email protected] Chaya [email protected] Gregory [email protected] Linda [email protected] Chris [email protected] Department of Physics, Columbia University, New York, USA2 College of Physicians and Surgeons, Columbia University, New York, USA3 Columbia College, Columbia University, New York, USA4 Fu Foundation School of Engineering and Applied Sciences, Columbia University, New York, USA5 Barnard College, Columbia University, New York, USA6 Department of Mathematics, Columbia University, New York, USA7 Department of Applied Physics and Applied Mathematics, Columbia University, New York, USA8 Center for Computational Biology and Bioinformatics, Columbia University, New York, USA2004 22 11 2004 5 181 181 24 7 2004 22 11 2004 Copyright © 2004 Middendorf 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
Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them.
Results
We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional) "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model.
Conclusions
Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.
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1 Background
The post-genomic revolution has ushered in an ensemble of novel crises and opportunities in rethinking molecular biology. The two principal directions in genomics, sequencing and transcriptome studies, have brought to light a number of new questions and forced the development of numerous computational and mathematical tools for their resolution. The sequencing of whole organisms, including homo sapiens, has shown that in fact there are roughly the same number of genes, for example, in mice and men. Moreover, much of the coding regions of the chromosomes (the subsequences which are directly translated into proteins) are highly homologous. The complexity comes then, not from a larger number of parts, or more complex parts, but rather through the complexity of their interactions and interconnections.
Coincident with this biological revolution – the massive and unprecedented volume of biological data – has blossomed a technological revolution with the popularization and resulting exponential growth of the computing networks. Researchers studying the topology of the Internet [1] and the World Wide Web [2] attempted to summarize these topologies via statistical quantities, primarily the distribution P(k) over nodes of given connectivity or degree k, which was found to be completely unlike that of a "random" or Erdös-Rényi graph. Instead, the distribution obeyed a power-law P(k)~k-γ. As a consequence many mathematicians concentrated on (i) measuring the degree distributions of many technological, sociological, and biological graphs (which generically, it turned out, obeyed such power-law distributions) and (ii) proposing various models of randomly-generated graph topologies which could reproduce these degree distributions (cf. [3] for a thorough review). The success of these latter efforts reveals a conundrum for mathematical modeling: a metric which is universal (rather than discriminative) cannot be used for choosing the model which best describes a network of interest. The question posed is one of classification, meaning the construction of an algorithm, based on training data from multiple classes, which can place data of interest within one of the classes with small test loss.
Systematic enumeration of substructures has so far been used to find statistically significant subgraphs or "motifs" [4-8] by comparing the network of interest to an assumed null model. Recently, the idea of clustering real networks into groups based on similarity in their "significance profiles" has been proposed [9]. We here use and extend these ideas to compare a given network of interest to a set of proposed network models. Rather than unsupervised clustering of real networks, we perform supervised classification of network models. In this paper, we present a natural mapping from a graph to an infinite-dimensional vector space using simple operations on the adjacency matrix. The coordinates (called "words", see Methods) reflect the number of various substructures in the network (see Figures 3 and 6). We then use support vector machines (SVMs) to build classifiers that are able to discriminate different network models. The performance of these classifiers is measured using the empirical test-loss on a hold-out set, thus estimating the probability of misclassifying an unseen test network. We selected 17 different mechanisms proposed in the literature to model various properties of naturally occurring networks. Among them are various biologically-inspired graph-generating algorithms which were put forward to model genetic or protein interaction networks. We are then able to classify naturally occurring networks into one of the proposed classes. We here classify data sets for the E. coli genetic network, the C. elegans neuronal network and the yeast S. cerevisiae protein interaction network. To interpret and understand our results further we define a measure of robustness to estimate the confidence of the resulting classification. Moreover, we calculate p-values using Gaussian kernel density estimation to find substructures that are characteristic of the network model or the real network of interest. We anticipate that this new approach will provide general tools of network analysis useful to a number of communities.
Results and Discussion
We apply our method to three different real data sets: the E. coli genetic network [10] (directed), the S. cerevisiae protein interaction network [11] (undirected), and the C. elegans neuronal network [12] (directed).
Each node in E. coli's genetic network represents an operon coding for a putative transcriptional factor. An edge exists from operon i to operon j if operon i directly regulates j by binding to its operator site. This gives a sparse adjacency matrix with a total of 423 nodes and 519 edges.
The S. cerevisiae protein interaction network has 2114 nodes and 2203 undirected edges. Its sparseness is therefore comparable to that of E. coli's genetic network.
The C. elegans data set represents the organism's fully mapped neuronal network. Here, each node is a neuron and each edge between two nodes represents a functional, directed connection between two neurons. The network consists of 306 neurons and 2359 edges, and is therefore about 7 times more dense than the other two networks. We create training data for undirected or directed models according to the real data set. All parameters other than the numbers of nodes and edges are drawn from a uniform distribution over their range. We sample 1000 examples per model for each real data set, train a pairwise multi-class SVM on 4/5 of the sampled data and test on the 1/5 hold-out set. We determine a prediction by counting votes for the different classes. Table 1 summarizes the main results. All three classifiers show very low test loss and two of them a very high robustness (see Subsection Robustness under Methods). The average number of support vectors is relatively small. Indeed, some pairwise classifiers have as few as three support vectors and more than half of them have zero test loss. All of this suggests the existence of a small subset of words which can distinguish among most of these models.
The predicted models Kumar [13], Middendorf-Ziv (MZ) [14], and Sole [15] are based on very similar mechanisms of iterated duplication and mutation. The model by Kumar et al. was originally meant to explain various properties of the WWW. It is based on a duplication mechanism, where at every iteration a prototype for the newly introduced node is chosen at random, and connected to the prototype's neighbors or other randomly chosen nodes with probability p. It is therefore built on an imperfect copying mechanism which can also be interpreted as duplication-mutation, often evoked when considering genetic and protein-interaction networks. Sole is based on a similar idea, but is an undirected model, and allows for two free parameters, a probability controlling the number of edges copied and a probability controlling the number of random edges created. MZ is essentially a directed version of Sole. Moreover, we observe that none of the biological networks were predicted to be generated by preferential attachment even though these networks exhibit power-law degree distributions. The duplication-mutation schemes arise as the most successful. However, it is interesting to note that every duplication-mutation model by construction gives rise to an effective preferential attachment [16]. Our classification results therefore do not dismiss the idea of preferential attachment, but merely the specific model which directly implements this idea.
Kumar and MZ were classified with almost perfect robustness (see Subsection Robustness under Methods) against 500-dimensional (out of 4680 dimensions) subspace sampling. With 26 different choices of subspaces, E. coli was always classified as Kumar. We therefore assess with high confidence that Kumar and MZ come closest to modeling E. coli and C. elegans, respectively. In the case of Sole and the S. cerevisiae protein network we observed fluctuations in the assignment to the best model. 3 out of 22 times S. cerevisiae was classified as Vazquez (duplication-mutation), other times as Barabasi (preferential attachment), Klemm (duplication-mutation), Kim (scale-free static), or Flammini (duplication-mutation) depending on the subset of words chosen. This clearly indicates that different features support different models. Therefore the confidence in classifying S. cerevisiae to be Sole is limited. The statistical significance of individual words in different models is investigated using kernel density estimation (see Methods) by finding words which maximize ηij ≡ pi(x0)/pj(x0) for two different models (i and j) at a word value of the real data set x0. Figure 1 shows training data for two different models used to classify the C. elegans network: the MZ model [14] which wins in the classification results, and the runner-up Grindrod model [17]. The histograms for the word nnz D(AU AD AT AU A) are shown along with their estimated densities, nnz D(AU AD AT AU A) extremely disfavors the winning model over its runner-up (minimizes ηij). The opposite case is shown in Figure 2 for E. coli, where the plotted word distribution supports the winning model (Kumar [13]) and disfavors (maximizes ηij) the runner-up Krapivsky-Bianconi model [18,14] (preferential attachment). More specifically we are able to verify that the likelihood to generate a network with E. coli's word values is highest for the Kumar model for most of the words. Indeed, out of 1897 words taking at least 2 integer values for all of the models, the estimated density at the E. coli word value was highest for Kumar in 1297 cases, for Krapivsky-Bianconi [18,14] in 535 cases and for Krapivsky [18] in only 65 cases.
Figure 2 shows the distributions for the word nnz D(AUT AUT AU AUT A) which had a maximum ratio of probability density of Kumar over that of Krapivsky-Bianconi at E. coli's word value. In fact, E. coli has a zero word count, meaning that none of the associated subgraphs shown in Figure 3 actually occur in E. coli. Four of those subgraphs have a mutual edge which is absent in the E. coli network and also impossible to generate in a Kumar graph. Krapivsky-Bianconi graphs allow for mutual edges which could be one of the reasons for a higher count in this word. Another source might be that the fifth subgraph showing a higher order feed-forward loop is more probable to be generated in a Krapivsky-Bianconi graph than in a Kumar graph. This subgraph also has to be absent in the E. coli network since it gives a zero word value, demonstrating that both the Kumar and Krapivsky-Bianconi models have a tendency to give rise to a topological structure that does not exist in E. coli. This analysis gives an example of how these findings are useful in refining network models and in deepening our understanding of real networks. For further discussions refer to our website. [14]
The SVM results suggest that one may only need a small subset of words to separate most of the models. The simplest approach to find such a subset is to look at every word for a given pair of models and compute the best split, then rank words by lowest loss. We find that among the most discriminative words some occur very often, such as, nnz (AA) or nnz (AT A), which count the pairs of edges attached to the same vertex and either pointing in the same direction or pointing away from each other, respectively. Other frequent words include nnz D(AA), nnz D(AT A) and ΣU(AT A). Figures 4 and 5 show scatter-plots of the training data using the most discriminative three words.
Conclusions
We proposed a method to discriminate different network topologies, and we showed how to us the resulting classifier to assess which model out of a set of given network models best describes a network of interest. Moreover, the systematic enumeration of countably infinite features of graphs can be successfully used to find new metrics which are highly efficient in separating various kinds of models. Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities.
Methods
Network models
We sample training data for undirected graphs from six growth models, one scale-free static model [19-21], a small-world model [22], and the Erdös-Rényi model [23]. Among the six growth models two are based on preferential attachment [24,25], three on a duplication-mutation mechanism [16,15], and one on purely random growth [26]. For directed graphs we similarly train on two preferential attachment models [18], two static models [17,27,20], three duplication-mutation models [13,28], and the directed Erdös-Rényi model [23]. More detailed descriptions and source code are available on our website [14].
For the (directed) E. coli transcriptional network and the (directed) C. elegans neuronal network we sample training data for all directed models; for the (undirected) S. cerevisiae protein interaction network we sample data for all undirected models. The set of undirected models includes two symmetrized versions of originally directed models [17,28]. One should note that properties of a directed model can differ significantly from its symmetrized version. In general, the more network classes allowed, the more completetely word space is explored, and therefore the more specific the classification can be.
In order to classify real data, we sample training examples of the given models with a fixed total number of nodes N0, and allow a small interval IM of 1–2% around the total number of edges M0 of the considered real data set. All additional model parameters are sampled uniformly over a given range (which is specified by the model's authors in most cases, and can otherwise be given reasonable bounds). Such a generated graph is accepted if the number of edges M falls within the specified interval IM around M0, thereby creating a distribution of graphs associated with each model which should best describe the real data set with given N0 and M0.
Some of the models can be described as a generalization of another model. Although a generalized model can overlap with a specific one in its support, word space is sufficiently high-dimensional that such confusing realizations are practically impossible. To build intuition, consider that the Erdös model itself includes all possible network topologies. Nonetheless there is extremely low test loss with any other models, indicating that it still defines a particular volume in this high-dimensional space. Similarly, very few real networks have non-negligible prediction scores for being classified as Erdös networks.
Words
The input space used for classifying graphs was introduced in our earlier work [6] as a technique for finding statistically significant features and subgraphs in naturally occurring biological and technological networks. Given the adjacency matrix A representing a graph (i.e., Aij = 1 iff there exists an edge from j to i), multiplications of the matrix count the number of walks from one node to another (i.e., [An]ij is the number of unique walks from j to i in n steps). Note that the adjacency matrix of an undirected graph is symmetric. The topological structure of a network is characterized by the number of open and closed walks of given length. Those can be found by calculating the diagonal or non-diagonal components of the matrix, respectively. For this we define the projection operation D such that
[D(A)]ij = Aijδij (1)
and its complement U = I - D. (Note that we do not use Einstein's summation convention. Indices i and j are not summed over.) We define the primitive alphabet {A; T, U, D} as the adjacency matrix A and the operations T, U, D with the transpose operation T(M) ≡ MT, for any matrix M . T(A) and A distinguish walks "up" the graph from walks "down" the graph. From the letters of this alphabet we can construct words (a series of operations) of arbitrary length. A number of redundancies and trivial cases can be eliminated (for example, the projection operations satisfy DU = UD = 0) leading to the operational alphabet {A, AT, AU, AD, AUT}. The resulting word is a matrix representing a set of possible walks, which can be enumerated. An example is shown in Figure 6.
Each word determines two relevant statistics of the network: the number of distinct walks and the number of distinct pairs of endpoints. These two statistics are determined by either summing the entries of the matrix (sum) or counting the number of nonzero elements (nnz) of the matrix, respectively. Thus the two operations sum and nnz map words to integers. This allows us to plot any graph in a high-dimensional data space: the coordinates are the integers resulting from these path-based functionals of the graph's adjacency matrix.
The coordinates of the infinite-dimensional data space are given by integer-valued functionals
F(L1L2...LnA) (2)
where each Li is a letter of the operational alphabet and F is an operator from the set {sum, sumD, sumU, nnz, nnz D, nnz U}. We found it necessary only to evaluate words with n ≤ 4 (counting all walks up to length 5) to construct low test-loss classifiers. Therefore, our word space is a 6 = 4680-dimensional vector space, but since the words are not linearly independent (e.g., sumU + sumD = sum), the dimensionality of the manifold explored is actually much smaller. However, we continue to use the full data space since a particular word, though it may be expressed as a linear combination of other words, may be a better discriminator than any of its summands.
In [6], we discuss several possible interpretations of words, motivated by algorithms for finding subgraphs. Previously studied metrics can sometimes be interpreted in the context of words. For example, the transitivity of a network can be defined as 3 times the number of 3-cycles divided by the number of pairs of edges that are incident on a common vertex. For a loopless graph (without self-interactions), this can also be calculated as a simple expression in word space: sum(D A A A)/sum(U AA). Note that this expression of transitivity as the quotient of two words implies separation in two dimensions rather than in one. However, there are limitations to word space. For example, a similar measure, the clustering coefficient, defined as the average over all vertices of the number of 3-cycles containing the vertex divided by the number of paths of length two centered at that vertex, cannot be easily expressed in word space because vertices must be considered individually to compute this quantity. Of course, the utility of word space is not that it encompasses previously studied metrics, but that it can elucidate new metrics in an unbiased, systematic way.
SVMs
A standard classification algorithm which has been used with great success in myriad fields is the support vector machine, or SVM [29]. This technique constructs a hyperplane in a high-dimensional feature space separating two classes from each other. Linear kernels are used for the analysis presented here; extensions to appropriate nonlinear kernels are possible.
We rely on a freely available C-implementation of SVM-Light [30], which uses a working set selection method to solve the convex programming problem with Lagrangian
with yi(w·xi + b) ≥ 1 - ξi; i = 1,..., m where f(x) = w·x + b is the equation of the hyperplane, xi are training examples and yi ∈ {-1, +1} their class labels. Here, C is a fixed parameter determining the trade-off between small errors ξi and a large margin 2/|w|. We set C to a default value . We observe that training and test losses have a negligible dependence on C since most test losses are near or equal to zero even in low-dimensional projections of the data space.
Robustness
Our objective is to determine which of a set of proposed models most accurately describes a given real data set. After constructing a classifier enjoying low test loss, we classify our given real data set to find a 'best' model. However, the real network may lie outside of any of the sampled distributions of the proposed models in word space. In this case we interpret our classification as a prediction of the least erroneous model.
We distinguish between the two cases by noting the following: Consider building a classifier for apples and oranges which is then faced with a grapefruit. The classifier may then decide that, based on the feature size the grapefruit is an apple. However, based on the feature taste the grapefruit is classified as an orange. That is, if we train our classifier on different subsets of words and always get the same prediction, the given real network must come closest to the predicted class based on any given choice of features we might look at. We therefore define a robust classifier as one which consistently classifies a test datum in the same class, irrespective of the subset of features chosen. And we measure robustness as the ratio of the number of consistent predictions over the total number of subspace-classifications. In this paper we consider robustness for a subspace dimensionality of 500, a significantly small fraction of the total number of dimensions 4680.
Kernel density estimation
A generative model, in which one estimates the distribution from which observations are drawn, allows a quantitative measure of model assignment: the probability of observing a given word-value given the model. For a robust classifier, in which assignment is not sensitively dependent on the set of features chosen, the conditional probabilities should consistently be greatest for one class.
To identify significant features we perform density estimations with Gaussian kernels for each individual word, allowing calculation of p(C = c|Xj = x), the probability of being assigned to class c given a particular value x of word j. By comparing ratios of likelihood values among the different models, it is therefore possible, for the case of non-robust classifiers, to determine which of the features of a grapefruit come closest to an apple and which features come closest to an orange.
We compute the estimated density at a word value x0 from the training data xi (i = 1,..., m) as
where we optimize the smoothing parameter λ by maximizing the average log-probability Q of a hold-out set using 5-fold cross-validation. More precisely, we partition the training examples into 5-folds , where {fi(j)}j is the set of indices associated with fold i (i = 1...5). We then maximize
as a function of λ. In all cases we found that Q(λ) had a well pronounced maximum as long as the data was not oversampled. Because words can only take integer values, too many training examples can lead to the situation that the data take exactly the same values with or without the hold-out set. In this case, maximizing Q(λ) corresponds to p(x, λ) having single peaks around the integer values, so that λ tends to zero. Therefore, we restrict the number of training examples to 4Nv, where Nv is the number of unique integer values taken by the training set. With this restriction Q(λ) showed a well-pronounced maximum at a non-zero λ for all words and models.
Word ranking
The simplest scheme to find new metrics which can distinguish among given models is to take a large number of training examples for a pair of network models and find the optimal split between both classes for every word separately. We then test every one-dimensional classifier on a hold-out set and rank words by lowest test loss.
Web supplement
Additional figures, more detailed description of the network models, and detailed results can be found at .
Source code
Source code was written in MATLAB and is downloadable from our our website .
Authors' contributions
MM, EZ, and CW had the original ideas for this paper. CW and LC guided the project. Most of the coding was done by MM and EZ. CA, JH, RK, CL, and GW coded most of the network generation agorithms. The final manuscript was mainly written by MM, EZ, CW, and LC.
Acknowledgments
It is a pleasure to acknowledge useful conversations with C. Leslie, D. Watts, and P. Ginsparg. We also acknowledge the generous support of NSF VIGRE grant DMS-98-10750, NSF ECS-03-32479, and the organizers of the LANL CNLS 2003 meeting and the COSIN midterm meeting 2003.
Figures and Tables
Figure 1 C. elegans: kernel density estimation for the word nnz D(AU AD AT AU A). Data for two different models are shown: the Middendorf-Ziv [14] model and the Grindrod [17] model. C. elegans is robustly classified as a Middendorf-Ziv network. The Grindrod model is the runner-up. We here show data for a word that especially disfavors the Middendorf-Ziv model over the Grindrod model. The histograms of the word over the training data are shown along with their associated densities calculated from the data by Gaussian kernel density estimation. The densities give the following log-p-values at the word value for the C. elegans network: log(pMZ) = -376, log(pGrindrod) = -6.23.
Figure 2 E. coli: kernel density estimation for the word nnz D(AUT AUT AU AUT A). Data for two different models are shown: the Kumar model [13] and the Krapivsky-Bianconi [18, 14] model. E. coli is robustly classified as a Kumar network. The Krapivsky-Bianconi model is the runner-up. We here show data for a word that especially favors the Kumar model over the Krapivsky-Bianconi model. The histograms of the word over the training data are shown along with their associated densities calculated from the data by Gaussian kernel density estimation. The densities give the following log-p-values at the word value for the E. coli network: log(pKumar) = -4.22, log(pKB) = -12.0.
Figure 3 Subgraphs associated with nnz D AUT AUT AU AUT A. Every word can be associated with a set of subgraphs. If the word has a non-zero value for a given network, at least one of these subgraphs must appear. The figure shows the subgraphs associated with the word nnz D AUT AUT AU AUT A.
Figure 4 Distributions of the E. coli training data in word space. The training data for E. coli for seven directed models is visualized in a 3-dimensional subspace of word space. The three chosen words were found to be most discriminative according to a word ranking method. Every color is associated with a different model. The point which is occupied by E. coli is also indicated. The axis correspond to words which can be associated with sets of subgraphs. If a network has a non-zero word value it must possess at least one of these subgraphs.
Figure 5 Distributions of the S. cerevisiae training data in word space. The training data for S. cerevisiae for seven undirected models is visualized in a 3-dimensional subspace of word space. The three chosen words were found to be most discriminative according to a word ranking method. Every color is associated with a different model. The point which is occupied by S. cerevisiae is also indicated. The axis correspond to words which can be associated with sets of subgraphs. If a network has a non-zero word value it must possess at least one of these subgraphs.
Figure 6 Example for a word and its associated subgraphs. Every word can be associated with a set of subgraphs. If the word has a non-zero value for a given network, at least one of these subgraphs must appear. The figure shows the subgraphs associated with the word nnz AT A. The elements of the matrix AT A count these two walks. T A corresponds to one step "up" the graph, the following A to one step "down". The last node could be either the same as the starting node as in the first subgraph (accounted for by the diagonal part D AT A) or a different node as in the second subgraph (accounted for by the non-diagonal part U AT A).
Table 1 Summary of classification results. Results of multi-class SVM: the empirical training loss <Ltr> averaged over all pairwise classifiers, the average empirical test loss <Ltst>, the average number of support vectors <Nsv>, and the winning model (with the highest number of votes from all pairwise classifiers). For the definition of robustness see Methods.
E. coli C. elegans S. cerevisiae
L
tr
1.6% 0.5% 2.1%
L
tst
1.6% 0.5% 1.8%
N
sv
109 51 106
Winner Kumar [13] MZ [14] Sole [15]
Robustness 1.0 0.97 0.64
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| 15555081 | PMC535926 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Nov 22; 5:181 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-181 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-451556084810.1186/1471-2121-5-45Research Article4-D single particle tracking of synthetic and proteinaceous microspheres reveals preferential movement of nuclear particles along chromatin – poor tracks Bacher Christian P [email protected] Michaela [email protected] Chaitanya [email protected] Harald [email protected] Roland [email protected] Division of Theoretical Bioinformatics, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany2 Division of Cell Biology, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany3 Complex Biosystems Modeling Laboratory, Massachusetts General Hospital, Harvard Medical School, Charlestown MA 02129, USA2004 23 11 2004 5 45 45 16 7 2004 23 11 2004 Copyright © 2004 Bacher et al; licensee BioMed Central Ltd.2004Bacher 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
The dynamics of nuclear organization, nuclear bodies and RNPs in particular has been the focus of many studies. To understand their function, knowledge of their spatial nuclear position and temporal translocation is essential. Typically, such studies generate a wealth of data that require novel methods in image analysis and computational tools to quantitatively track particle movement on the background of moving cells and shape changing nuclei.
Results
We developed a novel 4-D image processing platform (TIKAL) for the work with laser scanning and wide field microscopes. TIKAL provides a registration software for correcting global movements and local deformations of cells as well as 2-D and 3-D tracking software. With this new tool, we studied the dynamics of two different types of nuclear particles, namely nuclear bodies made from GFP-NLS-vimentin and microinjected 0.1 μm – wide polystyrene beads, by live cell time-lapse microscopy combined with single particle tracking and mobility analysis. We now provide a tool for the automatic 3-D analysis of particle movement in parallel with the acquisition of chromatin density data.
Conclusions
Kinetic analysis revealed 4 modes of movement: confined obstructed, normal diffusion and directed motion. Particle tracking on the background of stained chromatin revealed that particle movement is directly related to local reorganization of chromatin. Further a direct comparison of particle movement in the nucleoplasm and the cytoplasm exhibited an entirely different kinetic behaviour of vimentin particles in both compartments.
The kinetics of nuclear particles were slightly affected by depletion of ATP and significantly disturbed by disruption of actin and microtubule networks. Moreover, the hydration state of the nucleus had a strong impact on the mobility of nuclear bodies since both normal diffusion and directed motion were entirely abolished when cells were challenged with 0.6 M sorbitol. This effect correlated with the compaction of chromatin. We conclude that alteration in chromatin density directly influences the mobility of protein assemblies within the nucleus.
==== Body
Background
Interphase nuclei are structurally highly organized with chromosomes restricted to defined territories[1]. The movement of large complexes or nuclear bodies such as Cajal bodies or PML bodies in the nucleus has been described by various groups [2-4]. This type of organization of interphase chromosomes indicates that the resulting interchromatin compartment provides a so-called interchromosomal domain (ICD) space that differs significantly from that occupied by chromatin [5]. It was shown that nuclear bodies as well as specific RNA are excluded from the chromosome territories but reside in an interchromatin compartment [5-7]. Investigation of the diffusional accessibility of the nucleus for microinjected DNA and dextrans of varying molecular weight by fluorescent recovery after photobleaching (FRAP) methods revealed significant differences in mobility according to size. FITC-dextrans of molecular sizes up to 580 kDa were demonstrated to be fully mobile, whereas DNA fragments were nearly immobile [8]. Furthermore, a size- and electrical charge-dependent exclusion for macromolecules is encountered for chromatin regions [9]. In contrast, poly(A) RNA has been shown to move freely throughout the interchromatin space of the nucleus with properties characteristic of diffusion [10]. Moreover, the large ribosomal subunits seem to exhibit a random movement in a Gaussian manner without evidence for any direct path on their way from the nucleolus to the nuclear pores [11]. Recently, microinjection of fluorescent nanospheres has been used to track the movement of such particles under distinct experimental conditions [12]. The authors employed a silicon – intensifier target camera coupled to an epifluorescence microscope in combination with a 2-D particle – nanotracking routine implemented in the Metamorph / Metaview image processing system [13,14]. In particular, tracking of nanospheres within the nucleus revealed that the particles diffused freely in restricted "cages", eventually translocating into another "cage". These studies, however, did not reveal any information on the 3-D movement of particles in comparison with local chromatin density. Such a study requires recording of simultaneous time-lapse recording of three-dimensional image stacks of particles and chromatin using a confocal laser scanning microscope as described in the present study.
Kinetic analysis of nuclear bodies requires spatio-temporal microscopic imaging of live cells generating a huge amount of data that is only difficult or impossible to analyze in a standardized way without computational support. The present developments of an Open Microscopy Environment (OME) aims at providing a standardized informatics solution for the storage, management and analysis of light microscopic image data [15]. For quantitative analysis of complex data from live cell experiments a variety of systems have been developed (for review see [16]). An integrated image analysis solution should include tools for all steps in the image processing chain, i.e. image preprocessing and segmentation of objects, registration of moving and deforming cells, tracking of objects over time, and multi-dimensional visualization and kinetic analyzes of dynamic objects. Only with the availability of quantitative kinetic data it is possible to obtain insight into the underlying mechanisms of biological processes such as those involved in the functional and spatial organization of the cell nucleus.
In the present study we describe a combined computational and experimental approach to study the dynamic behaviour of nuclear body-like particles formed by GFP-NLS-vimentin [17] in response to different cellular inhibitors and, most importantly, in relation to the chromatin structure of the nucleus. This has been compared with the motion of polystyrene particles of similar size. Since both kinds of "bodies" display identical movement, the vimentin bodies can be regarded and hence employed as entities supposedly "biologically inert" for the nucleus. Using our novel image processing platform TIKAL we show that the kinetics of nuclear particles are influenced by various cellular inhibitors. Furthermore we show that the kinetics of nuclear bodies is directly influenced by local restructuring of chromatin domains.
Results
An experimental system for probing particle kinetics in the cell
We used fast 3-D time-lapse confocal laser scanning microscopy to analyze the mobility of Xenopus laevis GFP-NLS-vimentin and synthetic particles (polystyrene microspheres) within the nucleoplasm. GFP-NLS-vimentin is deposited at 37°C within the nucleus of stably transfected SW13 cells in multiple discrete bodies (8 – 40). On average the cells contain bodies that are nearly 1 μm in diameter as observed in the light microscope (Figure 1a). This corresponds to a particle diameter of about 200 – 500 nm in fixed cells as prepared for conventional electron microscopy (data not shown). From correlative light and electron microscopy studies we know that nuclear vimentin particles are excluded from dense chromatin regions (Richter et al., submitted). In contrast, transfection of human vimentin-free SW13 cells with an expression plasmid encoding GFP-vimentin without the engineered NLS sequence leads to the formation of many cytoplasmic particles (> 100) of very similar size (Figure 1c).
Figure 1 A) Nucleus of a SW13 cell stably transfected with an expression plasmid encoding for GFP-NLS-vimentin. B) SW13 cell nucleus containing microinjected 100 nm-microspheres. C) SW13 cell transfected with GFP-vimentin showing cytoplasmic particle formation. A', B', C'): Corresponding Hoechst 33342 chromatin stain, A", B", C") superimposed images.
To study the nucleoplasmic mobility of particles of a defined size we microinjected unloaded polystyrene beads into the nucleus of living cells. We used orange fluorescent 100 nm-beads that resemble in size authentic nuclear bodies such as PML- or Cajal bodies [3,18-20] on the light microscopic level. Thereby we attempted to find out how the mobility of the ectopically expressed nuclear vimentin particles related to polystyrene particles [17,21].
In the course of these studies we found that a system based on an ectopically expressed protein has several clear advantages compared to the microinjection of beads. First, the expression efficiency of the GFP-vimentin construct is very high. More than 50% of the cell nuclei show formation of nuclear vimentin bodies. Since the cells are stably transfected they reflect a normal physiological state. In contrast, for microinjection only approximately 10% – 20% of injected the cells survived over night culture (n = 300). Additionally, the artificial microspheres have to undergo tedious processing steps such as sonification and centrifugation prior to injection to avoid the formation of aggregates.
A computational system for tracking nuclear particles on the background of moving cells
For the analysis of complex data derived from spatio-temporal imaging of trafficking particles we developed a proprietary image processing platform, TIKAL (see Methods). The platform allows to directly and easily handle complex microscopic data and to dynamically interact with the data set throughout the whole quantitative data analysis steps. The image processing pipeline is initiated by image pre-processing steps including noise reduction followed by object segmentation (for details see Methods).
In many cases, cells move and also change their morphology during the observation period. Global movements include translocation and rotation, whereas morphological changes are either caused by global changes in size (affine transformation) or by local deformations. Since any of these transformations overlay the actual movement of nuclear particles within the cell, we corrected for them by rigid transformations (translocation and rotation), affine transformations (scaling) and by thin-plate spline models (local deformations; [22,23]; for details see Methods). These transformations allow a direct measurement of nuclear particle movements without any bias induced by external forces and cellular movements.
For quantitative evaluation of kinetics of moving particles we extended our single particle tracking approach formerly developed for two-dimensional time series [24] to automatically track objects in 3-D time series. The automatically computed 4-D tracks are visualized together with a surface rendered 3-D reconstruction of segmented nuclear particles in a multi-dimensional scene viewer (Figure 2). By interacting with the automatically computed trajectories the user is able to interactively control and correct for possible artefacts during the tracking procedure, e.g. deriving from noisy images. Applying TIKAL, we rapidly reconstructed, visualized and analyzed the trajectories of 1131 particles in more than 50 cells.
Figure 2 Screen shot of the image processing platform TIKAL. (top) Image shows a sample two-dimensional section through a nucleus with binarized nuclear particles (red) counterstained with Hoechst 33342 stain (green). Pull down menu exemplifies different tools for quantitative analysis integrated into TIKAL. Numbers indicate different nuclear particles reconstructed by 3D isosurface reconstruction (bottom). Computed tracks of nuclear bodies over time are displayed as spheres on a string in the multi-dimensional scene viewer.
In vivo observation of microspheres
We imaged the microinjected microspheres and the GFP-NLS-vimentin particles in SW13 cells over a time interval of 20 min (Figure 1a and 1b). After image processing a qualitative analysis of the trajectories of 154 microspheres visualized in 12 cells suggested the same kind of mobility for both the 100 nm-beads and the GFP-NLS-vimentin bodies (Figure 3a). For a more rigorous quantitative comparison the mean square displacement (MSD) was calculated for each individual particle as well as its anomalous diffusion coefficient α. Based on α, the analyzed particles were classified into four arbitrary groups of mobility using the theoretical framework from previous studies [25,26]: (i) confined diffusion (α < 0.1), (ii) obstructed diffusion (0.1 ≤ α < 0.9), (iii) simple diffusion (0.9 ≤ α < 1.1) and (iv) directed motion (α ≥ 1.1) (for sample trajectories see Figure 4). The comparison of the calculated anomalous diffusion coefficients of the GFP-NLS-vimentin bodies with those of the 100 nm-microspheres revealed no significant changes in the distribution (p = 0.126; compare Figure 5a and 5b. For statistical significance of interexperimental differences of distribution patterns for anomalous diffusion coefficients refer to Table 1).
Figure 3 4-D visualization of vimentin particle trajectories a) in the nucleus and b) the cytoplasm. Each color represents an individual vimentin body. The respective centers of masses are indicated as spheres. Tracks are symbolized by interconnecting lines. Arrows indicate the different kinds of diffusion (see Figure 4a). Major types of movement are indicated: I) confined diffusion; II) obstructed diffusion; III) normal diffusion; IV) directed motion.
Figure 4 Different classes of mean square displacement of tracked nuclear vimentin particles. a) Mean squared displacement (MSD) of four representative modes of particle mobility (x-axis: acquisition time in seconds; y-axis: MSD). Roman numbers: I) confined diffusion; II) obstructed diffusion; III) normal diffusion; IV) directed motion (The numeration refers to indicated trajectories in Figure 3a). b) The four different mobility classes are represented by particle trajectories. The trajectories correlate to the numbers indicated by arrows in Figure 3a.
Figure 5 Classification of particles into four groups of diffusional motion according to cellular localization and their calculated anomalous diffusion coefficient. a) nuclear vimentin particles, (b) microinjected nuclear 100 nm microspheres, (c) cytoplasmic vimentin particles.
Table 1 Significance analysis of interexperimental differences in distribution of anomalous diffusion coefficients by Kolmogorov – Smirnov test Statistical significance analysis for interexperimental differences in cumulative distribution of anomalous diffusion coefficients (for corresponding distribution plots see Figure 5 and Figure 9) was performed by the Kolmogorov – Smirnov test [45]. The different experiments are depicted as followed: control = vimentin particles in the nucleus; beads = sepharose microspheres (100 nm) in the nucleus; cytoplasm = vimentin particles in the cytoplasm; Vimentin particles in the nucleus with drug treatment: azide / deoxyglucose (10 mM / 50 mM); cytochalasin D (1 μg/ml); nocodazole (0.04 μg/ml); sorbitol (600 mM).
Experiment 1 Experiment 2 p-value
control beads 0.1264
control cytoplasm 1.11E-15
beads cytoplasm 5.22E-08
control azide / deoxyglucose
control cytochalasin D 0.01643
control nocodazole 9.70E-05
control sorbitol 1.78E-14
azide / deoxyglucose cytochalasin D 0.1095
azide / deoxyglucose nocodazole 0.004754
azide / deoxyglucose sorbitol 8.45E-11
cytochalasin D nocodazole 0.7306
cytochalasin D sorbitol 2.00E-09
nocodazole sorbitol 7.96E-09
Next, we were interested in the differences of the kinetic behaviour of the Xenopus GFP-vimentin particles in the nucleoplasm as compared to that in the cytoplasm. Transfected SW13 cells lack endogenous vimentin and therefore do not have intermediate filaments. Instead, only small spherical aggregates of the temperature-sensitive amphibian protein were deposited throughout the cytoplasm (Figure 1c; [27,28]). When directly comparing the particle trajectories of the nucleoplasmic vimentin bodies to the cytoplasmic vimentin bodies striking differences were found. Nuclear-targeted vimentin particles displayed a spatially restricted movement within distinct corrals. However, on occasion they were able to move spontaneously to an adjacent corral (Figure 3a). The maximum distance that a NLS-vimentin particle moved was 4 μm within the observation time of 20 min. This corresponds on average to a speed of 0.2 μm / min. Most strikingly, we never encountered crossing nuclear trajectories. In contrast, cytoplasmic vimentin particles moved along more extended trajectories and did hardly ever exhibit corralling events (Figure 3b). Moreover, the cytoplasmic bodies moved three times as fast, i.e. up to 12 μm in distance within 20 minutes.
A comparison of the overall kinetic characteristics of nuclear vimentin bodies versus sepharose beads revealed that in the nucleus obstructed diffusion is the major type of movement whereas in the cytoplasm directed motion is observed to a similar extent, both accounting for approximately 40 %. Notably, confined diffusion is very rarely found in the cytoplasm whereas in the nucleus 11.4 % of the movement can be accounted for it (Figure 5, Table 1).
Chromatin remodelling directly effects mobility of nuclear particles
In the next step we analyzed the influence of chromatin density on mobility of nuclear particles. Upon inspection of corralled versus highly mobile nuclear particles (Figure 6) we frequently observed a correlation between chromatin density in the neighbourhood of particles and their degree of motility. For a more rigorous quantitative analysis the mean grey value in a neighbourhood of 9 × 9 pixels was measured for each particle over an observation time of 20 min with a time-lapse of Δt = 10 seconds (Figure 7). Evidently, there is a strong correlation between chromatin density and particle velocity. Particles with high velocities were exclusively formed in areas of very low chromatin density. An increase in chromatin density directly led to a decrease of particle velocity (Figure 8a and 8b). A similar reverse effect was detected in cases where particles had very low velocities. After release of a body from a dense chromatin cluster a sudden increase of its mobility could be observed. In this case a high chromatin density was measured during the resting phase of the body, whereas a decrease of chromatin density was detected before the particle started to increase its velocity (Figure 8c). This phenomenon was prominently encountered with particles showing a high frequency of changes in corralled and more directed movement (Figure 8a,8b,8c). For particles with minimal changes in distance and velocities, a constant chromatin density with low fluctuation in measured grey values was observed (Figure 8d).
Figure 6 4-D – tracking of vimentin particles in the nucleus. (a, b) particles with restricted movement (confined diffusion) c) corralled particle and corresponding trajectories. (red = vimentin bodies; green = Hoechst 33342 staining).
Figure 7 Chromatin intensity analysis showing the preprocessing steps for the Hoechst 33342 images: a) unprocessed original image; b) Gaussian smoothing; c) image classified into 8 regions of grey values; d) localization of particles in the cell nucleus (red); e) measuring the mean grey value intensity around the center of mass for each individual vimentin particle (see Methods for a complete description).
Figure 8 Correlation plots between mean grey values (green line) of chromatin densities and particle velocity (blue line) over a time range of 20 minutes. Arrows indicate significant changes in intensity and particle velocity. Red: NLS-GFP-vimentin; green: Hoechst 33342 stain.
Influence of inhibitors on the mobility of nuclear vimentin bodies
In order to investigate the contribution of structural elements of the cytoplasm to nuclear body mobility in living cells, inhibitors of cellular energy as well as drugs that lead to the depolymerization of cytoskeletal systems were employed. In particular, we inhibited cellular ATP production and incubated cells with agents that depolymerize microtubules or microfilaments, both of which are tracks for molecular motors in the cytoplasm. Cells were imaged prior to addition of the inhibiting substance for 10 minutes with image stacks acquired every Δt = 10 seconds.
In a first step, the dependency of nuclear vimentin particles on energy-dependent mechanisms as investigated by depletion of ATP through incubation with 10 mM azide and 50 mM deoxyglucose followed by live cell imaging over a time interval of another 10 minutes. For more than 140 bodies in eight cells the diffusion coefficients were calculated. Compared to the control group (Figure 5a, 9a and Table 1) an absolute increase of 5.7 % for confined diffusion, an absolute increase of 0.6 % for obstructed diffusion, an absolute decrease of 1.7 % for simple diffusion and an absolute decrease of 4.6 % for directed motion were observed (Figure 9b, Table 1). Interestingly, after addition of azide / deoxyglucose a rapid condensation of chromatin was observed. Chromatin condensation was reversed after removal of the inhibitor as also reported recently [29], [46].
Figure 9 Mobility of nuclear vimentin bodies under the influence of inhibitors. Classification was performed according to the diffusion coefficient. a) control; b) azide / deoxyglucose (10 mM / 50 mM); c) cytochalasin D (1 μg/ml); d) nocodazole (0.04 μg/ml); c) sorbitol (600 mM).
Secondly, the impact of the presence of a functional actin cytoskeleton on vimentin body movement was tested using the actin polymerization inhibitor cytochalasin D at 1 μg/ml (Figure 9c, Table 1). Five cells were imaged after addition of cytochalasin D every Δt = 10 seconds for 10 minutes. A quantitative analysis of 121 bodies revealed an absolute increase of 5.3 % and 8.6 % for confined and obstructed diffusion, respectively, and an absolute decrease of 3.9 % for simple diffusion and a decrease of 10.0 % for directed motion compared to the control group.
To study the role for microtubule structures on particle mobility we used the microtubule polymerization inhibitor nocodazole (Figure 9d, Table 1). The effect of nocodazole treatment (0.04 μg/ml) was also imaged in 5 cells with image stacks every Δt = 10 seconds for 10 minutes. By analysing 119 bodies, an absolute increase of 8.7 % and 9.9 % was detected for confined and obstructed diffusion while an absolute decrease of 7.7 % and 10.9 % compared to the control groups was observed for simple diffusion and directed motion, respectively.
Particle movement in dehydrated cells
Finally the dependency of the GFP-NLS-vimentin mobility on availability of water in the nucleus was tested by treating the cells with sorbitol (600 mM) [30] (Figure 9e, Table 1). In contrast to the previous inhibitors, we observed a dramatic change in particle mobility. Calculation of diffusion coefficients for 101 particles in five cells revealed the total loss of simple diffusion and directed motion activities. Accordingly, 79.1 % of all particles were found in the confined diffusion and 20.9 % in the obstructed diffusion group.
In summary kinetic changes were most prominent for the directed motion mode. ATP depletion decreased directed motion about 30 % relative to the control group. Treatments with cytochalasin D and nocodazole even showed a 70 % decrease in directed motion relative to the control group. The most striking effect was encountered by treating the cells with sorbitol. Simple diffusion and directed motion were totally abolished whereas the number of particles exhibiting confined diffusion increased by a factor of 7 relative to the control group.
Discussion
In this study we developed comprehensive bioinformatics tools to analyze the kinetic behaviour of small particles in the cell nucleus. For this purpose, fast time-lapse confocal laser scanning microscopy was used to record fluorescent particles in their chromatin environment. Automated image processing algorithms such as image registration and single particle tracking were instrumental to analyze the resulting complex data sets in a most efficient way. Usually, sophisticated image processing methods are widely not accessible for cell biology laboratories working with multi-dimensional data sets. A qualitative, interactive analysis of complex processes in living cells can yield interesting results. However, a quantitative insight into the underlying mechanisms can only be achieved by a rigorous computational analysis. While computational systems have been provided for estimating diffusion and binding constants based on photobleaching experiments of populations of small proteins [23,31-33], integrated software packages for single particle tracking of nuclear bodies on the background of moving and shape changing objects have not been provided yet to the community. Here, our system TIKAL closes an important gap. For an automated analysis of even larger sets of spatio-temporal data as in this study any software system needs to be adapted to data storage systems that are devoted for handling such huge data sets [15]. At the same time image analysis workflows have to be deployed onto computing clusters or the GRID ([34], accepted). Both developments are underway in our laboratory.
We visualized the different kinetic behaviours of nucleus-injected 100 nm polystyrene microspheres. Furthermore, a stably transfected cell line expressing GFP-NLS-vimentin, which forms nuclear particles in the same size range as microspheres, was used. The majority of nuclear particles moved with obstructed diffusion within distinct corralled regions. This kind of movement was essentially found also for microinjected polystyrene beads. The obstructed diffusion behaviour supports the notion that these particles can diffuse within corrals restricted by dense chromatin regions. Upon chromatin remodelling distinct less dense chromatin regions are formed and enable the particle to move to an adjacent corralled region. We were able to quantitatively assess this phenomenon by measuring the chromatin intensity around an individual particle. Our data show that chromatin intensity decreases prior to a global velocity increase of the particle. Therefore we conclude that the particles do not actively push their way through the chromatin. Moreover, the chromatin itself is able to support or induce the movement of individual particles. The ability of the particle to move from one corral to the next is restricted and regulated by the surrounding chromatin remodelling activities. However, whether local chromatin regions can actively influence the destination of small nuclear particle movement has to be resolved in future investigations. With the present assay we cannot discern whether changes in the velocity of a body simply correlate with the entry of a body into a domain or whether the changes are caused by interaction between a body and the surrounding chromatin domain surfaces.
The addition of cellular inhibitors caused significant changes in the diffusional behaviour of nuclear particles. In all treatments a reduction of active transport processes were observed. This suggests that the coordination of nuclear processes such as chromatin remodelling is not solely dependent on single factors such as ATP, i.e. ATP consuming enzymes. Moreover, chromatin regions in interphase nuclei apparently move in a diffusional way [19], while other factors such as cytoplasmic microtubules and actin filaments attached to the nuclear periphery possibly account for large-scale spatial chromatin rearrangements [35].
The phenomenon of energy dependent nuclear body movement has been also described in other studies where an anomalous diffusion behaviour and an ATP- and transcription-dependent association of Cajal bodies with chromatin was reported [2]. Further, upon ATP depletion in BHK cells rapid and large-scale movement of PML bodies stopped, whereas small localized movements of PML bodies were still observed [3]. A recent examination of the dynamic behaviour of PML nuclear bodies showed their fission to microstructures after different physiological stresses, and their fusion upon recovery [36]. Moreover it has been shown that movements of PML and other nuclear bodies can be described by diffusion of the individual body within a chromatin corral and its translocation resulting from chromatin diffusion [46]. However, future systematic studies will help to reveal the influence of drug treatments and cellular inhibitors on the dynamic behaviour of those and other nuclear bodies.
A further interesting observation was the reversible formation of chromatin dense regions upon energy depletion. The general effect of this reorganization seems to influence the mobility distribution pattern of the particles only slightly. However, we could observe a significant decrease in directed motion of up to 30 % upon inhibition of energy-depended processes.
Furthermore, in order to evaluate the degree of nuclear particle movement with respect to cytoplasmic dynamics, we used the vimentin system to analyze cytoplasmic particle mobility. Mostly active transport processes were observed. Two possible explanations for this phenomenon are suitable. Vimentin, which belongs to the group of intermediate filaments, forms crossbridges to other cellular structures. Since the SW13 cells lack endogenous expression of intermediate filament proteins such as cytokeratins and vimentin, possible interactions with these cytoplasmic intermediate filaments can be omitted. Specific interactions with dynein have been described [37,38]. Hence, newly synthesized vimentin is subjected to active transport processes and "guided" to cellular locations for the establishment of vimentin networks. Another explanation could assume that the filaments do not bind any cytoplasmic structure. In this less likely scenario the active transport of vimentin particles would result from the densely packed cytoplasm and the resulting pushing and pulling of adjacent actively transported molecules.
From our data we conclude that the NLS-vimentin system is very suitable for further studies of nuclear architecture. Though we obtained the same results with microinjection assays, the GFP-NLS-vimentin system has significant advantages such as the higher expression efficiency and the fact that the experiments can be performed in a cell system with normal proliferation characteristics.
Conclusions
We presented a novel image analysis platform TIKAL that for the first time allows the 4-D tracking of nuclear particles on the background of moving and shape changing objects. TIKAL is complementary to other software systems designed for diffusion studies based on photobleaching experiments. Applying TIKAL we were able to analyze the dynamics of nuclear bodies under various different conditions and thus demonstrated that local chromatin remodelling accounts to a large extent for changes in the dynamics of individual nuclear bodies.
Methods
Expression plasmids and construction
The cloning of the Xenopus laevi GFP-NLS-vimentin expression plasmid has been described previously [17]. For the generation of the N-terminally tagged GFP-vimentin construct the vimentin cDNA was modified at the 5' – end to contain a BspE I – site followed by a Nde I – site containing the start methionine in frame subcloned into pBlueScript (Stratagene). The BspE I / BspE I fragment was then subcloned into pEGFP – C1 and the orientation verified by DNA sequencing.
Cell culture and transfection
SW13 lacking endogenous vimentin [27] were usually grown in DMEM (Invitrogen, Karlsruhe, Germany) supplemented with 10% fetal calf serum (Seromed, Berlin, Germany), 20 mM glutamine and 100 μg/ml penicillin/streptomycin (Invitrogen) at 37°C and 5% CO2. For live cell imaging purposes the cells were resuspended in complete DMEM without Phenol Red (Invitrogen) and grown in 2- or 4-well Lab-Tek® II chambers (Nalge Nunc International, Rochester, USA). Transient transfections were carried out using the FuGene 6 transfection reagent according to the manufacture's protocol (Roche, Mannheim, Germany).
Immediately before imaging, chromatin counter stain was obtained by incubating the cells with 1 μg/ml Hoechst 33342 in complete DMEM without Phenol Red for 20 min followed by three times washing with DMEM without Phenol Red. Cells were then kept in complete DMEM supplemented with 20 mM Hepes without Phenol Red. For tracking of nuclear particles we stably transfected SW13 cells with the expression plasmid encoding Xenopus laevis GFP-NLS-vimentin. To track vimentin particles in the cytoplasm, SW13 cells were transiently transfected with the Xenopus laevis GFP-vimentin cDNA construct described above.
Mircroinjection of fluorescent polystyrene microspheres
SW13 cells were cultured in P35G-1.5-7-C-Grid cell locate culture dishes (MatTek Corporation, Ashland, MA) in complete DMEM medium for 1 day after plating. Carboxylate-modified 0.1 μm microspheres (FluoSpheres, #F8800, Molecular Probes, Leiden, NL) were obtained as 2 % solids in solution and further diluted to 0.04 % solution in 1 M BSA in PBS. Before microinjection, the microspheres were sonificated for 30 seconds to avoid aggregation. The AIS 2 system (Cell Biology Trading, Hamburg, Germany) was used for microinjection. Injection needles were drawn from borosilicate glass capillaries GC120TF-10 (Harvard Apparatus, Edenbridge, UK) using a Flaming Brown micropipette puller P-97 (Sutter Instruments, Novato, CA). The injection pressure was adjusted to 20–250 kPa in the different experiments. For evaluation of cell viability microinjected cells were grown at 37°C with 5% CO2 overnight and examined the next day.
Imaging
Live cell imaging was carried out on a confocal laser scanning microscope TCS SP2 AOBS (Leica Microsystems, Wetzlar, Germany) using a 63x oil immersion objective with 1.4 optical apperture (HCX PL APO lbd.BL 63x / 1.4, #506192, Leica Microsystems). The microscope was further equipped with a 29 mm objective heater (#0280.010, Leica Microsystems) with temperature – controlled device (#0504.000, Leica Microsystems) and a temperature – controlled fan (ASI 400E, Nevtek, Burnsville, VA, USA). A diode laser (λ = 405 nm) was used for excitation of Hoechst 33342. An argon (λ = 488 nm) and a helium/neon laser (λ = 543 nm) was used for EGFP and fluorescent microsphere excitation, respectively. 3-D image stacks, each consisting of 17 2-D-images, of GFP-vimentin particles and Hoechst 33342 stained chromatin were acquired in parallel at the maximum scanning speed of 1400 Hz (i.e. scan lines per second) with a constant Δt = 10 seconds. Imaging format was set to 256 × 256 pixel, voxel sizes were generally between 0,093 μm × 0,093 μm × 0,325 μm and 0,093 μm × 0,093 μm × 0,450 μm. The laser intensity was adjusted to a minimum to avoid photo damage during imaging. For this purpose the acousto-optical beam splitter (AOBS) were set between 2 – 5% for both lasers with photo multiplier (PMT) settings of 715.7 Volt for the diode laser and 717.4 Volt for the argon and helium/neon lasers. All the image processing steps were carried out in TIKAL (see below).
Drug treatment
Before drug treatment, we acquired 3-D time series of cells with a time-lapse of Δt = 10 seconds for 10 minutes. Immediately afterwards the medium was exchanged for one of the following solutions: i) 600 mM sorbitol; ii) 20 mM azide / 50 mM deoxyglucose; iii) 0.04 μg/ml nocodazole; and iv) 1 μg/ml cytochalasin D – all in complete DMEM without Phenol Red except for ii), which was applied in PBS [39]. After change of medium, we immediately acquired further 3-D time series images for another 10 min. Thereafter, the drug containing medium was replaced by complete DMEM without Phenol Red. In order to document the recovery and viability of cells, images were acquired for another 10 minutes with the same microscope settings.
Image registration
4-D image registration [40,41] of consecutively captured three-dimensional images of cell nuclei counterstained with Hoechst 33342 was performed for correcting for global movement of cell nuclei. In order to reduce alignment artifacts due to acquisition noise all three-dimensional image stacks were preprocessed using a 3-D median filter and a 3-D automatic gamma correction for maximum gray value range. Image registration was then performed using an implementation of an automated image registration algorithm [40] running on a high-performance computing cluster. In this study, we only applied rigid and affine transformation since we did not observe drastic local deformations in cellular shape which would require correction by our non-rigid transformation method [23]. Rigid and affine transformation matrices were computed in a two-step process for optimal 4-D object alignment. First, objects at time point t + 1 were consecutively aligned with 3-D objects at time point t providing a pre-registered image stack at each time point. Secondly, each pre-registered image stack was aligned with respect to the initial image stack at time point t = 0. Transformation matrices calculated for the chromatin stained images were applied to all corresponding image stacks in the other color channels.
Tracking beads and vimentin particles
Image processing was carried out using our in-house developed image analysis platform. The analysis chain consisted of three major modules: image preprocessing and segmentation, 4-D tracking and quantification of dynamics, 4-D visualization and user interaction with 4-D data sets. To reduce noise in the vimentin channel, images were subjected to 3-D diffusion filtering [24] followed by segmentation with a pyramid linking algorithm [42]. Particle tracking was performed by extending our already implemented single particle tracking algorithm from 2-D + time to 3-D + time [43,44]. The tracking algorithm uses parameters such as the individual center of masses, volumes, total grey value intensities, velocities and accelerations. To control possible tracking and segmentation artifacts we visualized 4-D tracks of beads and vimentin particles, respectively, together with their isosurface reconstruction at each time point.
Correlation analysis of chromatin density and tracked nuclear particle
Chromatin images were preprocessed by a 2-D Gaussian smoothing filter to reduce noise. Additionally a gamma filter was applied to use the full grey value range of 8 bits. For additional noise reduction the 256 different grey values were divided into eight grey value classes ranging from 0 – 32, 33 – 65, etc. The centre of mass for each individual polystyrene bead or nuclear vimentin body was calculated and tracked in 3-D over time. For each time point the corresponding binned mean grey value intensities of a 9 × 9 pixel area around the centre of mass was determined.
Calculation of mean squared displacements and anomalous diffusion coefficients
The mean square displacement (MSD) was calculated for each particle and time point of the data set according to <Δd2> = < [d(t) - d(t + Δt)] 2> and plotted as <d2> (μm2) versus Δt (s) using Matlab (The MathWorks, Inc., Novi, MI). Further evaluation of anomalous diffusion (α) [25,26] was determined by using the regression curve fitting functions implemented in Matlab.
TIKAL image processing platform
TIKAL is an in-house developed platform for multi – purpose image processing (executable code can be requested at: ). The program consists of a graphical user interface for easy access of the underlying algorithms. The software is written in C/C++ and can be deployed both on Linux and Windows systems. The main components of the program are separated into several main modules, namely a filter, registration, tracking, visualization and data handling module. The filter module contains 2-D and 3-D image processing algorithms such as gamma correction, median, gaussian, anisotropic diffusion filters [24]. Segmentation filters range from simple threshold operations to complex pyramid linking segmentation [42]. The registration module is capable of handling single 3-D image stacks as well as 4-D time-lapse series of image stacks. Image stacks can be corrected by a combination of rigid and affine transformations as well as non-linear correction of local deformations by registration based on a thin plate spline model [22,23]. The tracking [44] and visualization part can be combined to allow the user a visual inspection of the tracking result and enable a correction of falsely assigned trajectories. The tracking and visualization module contains functions for calculation of quantitative parameters such as distances, velocities and mean squared displacement (MSD).
The software is capable to import both the proprietary Leica and the commonly used Tiff file format. Currently we are working on an implementation to integrate and combine our software with the Open Microscopy Environment, OME [15]. This is of particular importance when working with huge data sets, when either computer memory or disk space become limiting factors in the analysis stream.
List of abbreviations
Expression plasmids and construction
NLS – Nuclear localization sequence
GFP – green fluorescent protein
Authors' contributions
CPB wrote the program for 4-D data analysis (TIKAL). With this platform he was able to perform data analysis and data evaluation. Further, microinjection of cells and nuclei and their respective imaging was carried out by CPB. In additions he assisted MR with imaging and cell culture of the vimentin transfected SW13 cells.
MR performed the transfection, culturing, imaging and inhibitor treatment of SW13 cells. Additionally she contributed to the interactive 4-D tracking analysis. CA was involved in planning of this study at an earlier stage. HH supervised the experimental part, whereas RE was responsible for the entire setup and planning of the computational part of this study.
Acknowledgements
We are grateful to P. Lichter for his helpful discussions. We thank S. Goerisch for help in the experimental setups for microinjection. Further we thank J. Moore and M. Gebhard for additional support concerning the development of TIKAL. RE was supported by a BioFuture grant from the German Ministry for Education and Research (0311880/0). RE acknowledges support from the Human Frontiers Science Programme (RGP0019/2003).
==== Refs
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